Valuation and Sustainability: A Guide to Include Environmental, Social, and Governance Data in Business Valuation 3031305329, 9783031305320

Table of contents :
Preface
Acknowledgments
Contents
Abbreviations and Acronyms
Value and Externalities in Economics and Finance
1 Value Theories in Economics
1.1 Definitions of Value in Economics
1.2 Intrinsic Theory of Value
1.3 Labor Theory of Value
1.4 Subjective Theory of Value
1.5 Monetary Theory of Value
2 What Are Externalities, and Why Should We Factor in Environmental, Social, and Governance Factors in Valuation?
2.1 Market Equilibrium
2.2 What Are Externalities?
2.3 Climate Change as an Example of Externality
2.4 Solutions to the Externalities Issue
2.4.1 Carbon Taxes
2.4.2 Regulations
2.4.3 Carbon Markets
3 Conclusion
References
Reminder on Common Valuation Techniques
1 Introduction and Purpose
2 Define the Value of a Stock or an Asset
2.1 Introduction to the Value of an Asset
2.2 Sharing Value Between the Stakeholders: Enterprise and Equity Value
2.3 Direct and Indirect Valuation Approach
3 Generating Value and Cash Flows
3.1 Accounting for Valuation
3.2 Capital Employed and Financial Resources
3.3 Creating Value: Return on Investment and Return on Equity
3.4 Generating Cash Flows
4 The Cost of Capital
4.1 Understanding the Cost of Capital
4.2 The Perspective of the Investor
4.3 The Perspective of the Company
4.4 Matching Both Perceptions
4.5 Measuring Risk and Portfolio Theory
4.6 Introduction to the Capital Asset Pricing Model
4.7 Computing the Cost of Capital
4.7.1 Cost of Equity
4.7.1.1 The Risk-Free Rate
4.7.1.2 Equity Risk Premium (ERP)
4.7.1.3 A Brief Remark on Inflation
4.7.1.4 Beta
4.7.2 Cost of Debt
4.7.3 Weighted Average Cost of Capital
5 DCF Valuation
5.1 Estimating Cash Flows in the First Phase (the Business Plan)
5.2 Terminal Value
6 Relative Valuation (Multiples)
6.1 Choosing a Relevant Variable
6.2 Indirect and Direct Relative Valuation Approach
6.3 Relevant Observation Period
6.4 Peer Group
6.5 Application
6.6 Going Further with Multiples
6.6.1 EBITDA Adjustments
6.6.2 Net Debt Adjustments
7 Conclusion
References
ESG Data and Scores
1 Definition and Use Cases
1.1 An Aggregation of ESG Indicators
1.2 A Wide Diversity of Rating Methodologies
1.2.1 Risk-Based Approaches
1.2.1.1 Target and Context
1.2.1.2 Interconnectivity of ESG Risks
1.2.1.3 Assessment of ESG Risks
1.2.1.4 Reputational Risks
1.2.2 Impact-Based Approaches
1.2.2.1 Motivations for Seeking Impact
1.2.2.2 Impact Is Still Difficult to Define
1.2.2.3 Positioning Impact Initiatives
1.2.2.4 Choosing the Right Impact Strategy
1.2.2.5 Implementing Impact Strategies
2 The ESG Rating Landscape
2.1 Data Providers and Rating Agencies
2.2 Main Corporate Reporting Standards
2.2.1 Global Reporting Initiative
2.2.2 Sustainability Accounting Standard Board
2.2.3 Task Force on Climate-Related Financial Disclosure
2.2.4 CDP
2.3 Fintech and Innovation
2.3.1 Artificial Intelligence and Machine Learning
2.3.2 Computer Vision and Satellite Imagery
2.3.3 Blockchain and Distributed Ledger Technology (DLT)
3 Legal Framework
3.1 The EU Framework
3.1.1 A Comprehensive Framework
3.1.2 The European Taxonomy
3.1.2.1 What Is the Taxonomy?
3.1.2.2 How Does It Work?
3.1.2.3 How to Apply It?
3.1.3 SFDR, CSRD, and Benchmarks
3.1.3.1 Sustainable Finance Disclosure Regulation
3.1.3.2 Corporate Sustainability Reporting Directive
3.1.3.3 Climate Benchmarks
3.2 The US Framework: SEC Rules and ISSB
4 Conclusion and Limitations
4.1 Data Issues
4.1.1 Low Data Quality
4.1.2 Data Reporting Challenges
4.2 Divergence in ESG Ratings
4.2.1 Sources of Divergence
4.2.2 How Does Divergence Affect the Use of ESG Ratings?
4.2.3 Divergence Is a Way to Stand Out
References
Cash Flow Valuation and ESG
1 Reminders
2 DCF and the Many Challenges Raised by ESG
2.1 The Recency of ESG
2.2 A Wide Variety of Uncertainties Associated with ESG Stakes
2.3 Impact of ESG on DCF
2.3.1 Forecasts and ESG
2.3.1.1 Revenues and Market Addressed
2.3.1.2 Revenues and Market Shares
2.3.1.3 Cost of Production
2.3.1.4 Production Tools, Investments, and Technology
2.3.1.5 Regulations and Industry
2.3.2 Long-Term Growth Rate and ESG
2.3.3 Cost of Capital and ESG
2.3.3.1 Cost of Capital Components
2.3.3.2 Practicalities in the Assessment of the Beta Factor
2.3.3.3 Beta Factor and ESG
2.3.3.4 Differentiation Between Companies from the Same Industry
3 ESG Within the Forecasts and Sensitivities on the Value
3.1 DCF Sensitivity on ESG-Adjusted Forecasts
3.1.1 Objective of the Simplified Model
3.1.2 Simplified DCF Model, Part I: A Unique Investment
3.1.3 Simplified DCF Model, Part II: Periodically Renewed Investments
3.1.4 Sensitivity of ESG-Related Parameters on DCF, Based on the Simplified Models
3.1.4.1 First Example, the Power of ESG Dynamics on DCF
3.1.4.2 Real Options
3.1.4.3 Irreversibility of the Investment
3.1.4.4 The Pressure of Stakeholders for ESG Improvements
3.1.4.5 Incentives for ESG Improvements with Stronger Dynamics than the Discount Rate
3.1.4.6 NPV Are Sensitive to ESG Projects
3.2 Terminal Value and Normative Cash Flows
3.2.1 Truncation and Extension of Cash Flows Series
3.2.2 Projects that Are Planned Beyond the Horizon of the Business Plan
3.2.3 Scenarios of Cash Flows
References
Cash Flow Valuation and ESG: Case Study
1 An Example of ESG Integration
2 Gathering Data
2.1 Internal Data
2.1.1 Quantitative Data
2.1.2 Qualitative Data
2.2 External Data
2.2.1 ESG Data Providers
2.2.2 Sell-Side Research
2.3 Company Data
2.3.1 Company Reports
2.3.2 Company Meetings
2.4 Controversies
3 Setting Out the Framework
3.1 Which Framework to Choose?
3.1.1 Absolute View
3.1.2 Relative View
3.2 ESG Momentum
4 Assessing Materiality
4.1 Is the Data Material?
4.2 Document
4.3 Other Considerations
5 Implementation in the DCF Model
5.1 What Line Should Be Amended?
5.1.1 Growth
5.1.2 Costs
5.1.3 Liabilities
5.1.4 Weighted Average Cost of Capital
5.2 Is the Impact Quantifiable?
5.2.1 Yes (a Quantifiable Impact)
5.2.2 No (an Unquantifiable Impact)
5.3 The Specific Case of WACC
5.4 Document Final Impact on Target Price
6 Practical Examples
6.1 Template
6.2 Positive Case: Renewable Utility
6.2.1 Data Gathering
6.2.2 Framework
6.2.3 Materiality
6.2.4 Valuation
6.2.5 Conclusion
6.3 Negative Case: Basic Material Company
6.3.1 Data Gathering
6.3.2 Framework
6.3.3 Materiality
6.3.4 Valuation
Reference
Multiple Valuation and ESG
1 Reasons to Adjust Valuation Multiples
2 Simple Multiple Adjustment
2.1 Conceptual Framework
2.2 Adjustment Method
2.3 Example
3 Statistics-Based Multiple Valuation Adjustment
3.1 Fundamental Value Drivers
3.2 Multiple Regression Analysis
3.3 Ordinary Least Squares Estimation
3.4 Model Assumptions Testing
3.4.1 Linearity
3.4.2 Multicollinearity
3.4.3 Homoscedasticity
3.4.4 Normality
3.5 Evaluating the Model Quality
3.5.1 T-Test and P-Value
3.5.2 F-Test
3.5.3 Goodness-of-Fit Test
3.5.3.1 R-Squared and Adjusted R-Squared
3.5.3.2 Akaike´s Information Criteria and Bayesian Information Criteria
3.5.4 Using the Model to Estimate Enterprise Value or Equity Value
3.5.5 Using the Empirical Model of Damodaran (2023)
3.5.5.1 Regression
3.5.6 Testing Linearity, Multicollinearity, Homoscedasticity, and Normality
3.5.6.1 Linearity
3.5.6.2 Multicollinearity
3.5.6.3 Homoscedasticity
3.5.6.4 Normality
3.5.6.5 Updated Model
3.5.7 Goodness-of-Fit Measures
3.5.7.1 An Example of Firm Multiple Computation
4 Conclusion
5 R Code to Perform Tasks Provided in this Chapter
5.1 Regression Analysis
5.2 Predicted Vs Residuals Plot
5.3 Pearson´s Correlation Matrix
5.4 Variance Inflation Factor
5.5 Breusch-Pagan Test
5.6 Normality: Quantile-Quantile Plot Variable Beta
5.7 Normality: Jarque-Bera Test
5.8 Akaike Information Criterion and Bayesian Information Criterion Calculation
References
Research Advances in Valuation and ESG
1 Theoretical Frameworks
1.1 Market Equilibria Between Green and Nongreen Investors (Heinkel et al., 2001)
1.1.1 Overview
1.1.2 Equilibrium Between Green and Neutral Investors and Cost of Capital
1.1.3 Equilibrium Model
1.1.4 Empirical Evidence
1.1.5 Contributions and Implications
1.2 Nonpecuniary Utility (Baker et al., 2018)
1.2.1 Overview
1.2.2 Framework to Price Green Bonds
1.2.3 Prediction to Test Pricing Patterns of Green Bonds
1.2.4 The Role of Certification
1.2.5 Framework to Examine Ownership Concentration of Green Bonds
1.2.6 Prediction to Test Ownership Concentration of Green Bonds
1.2.7 Contributions and Implications
2 Empirical Evidence of the Link Between ESG, Cost of Capital, and Firm Valuation
2.1 Environmental Profile´s Impact on the Cost of Capital (Chava, 2014)
2.1.1 Overview
2.1.2 Environmental Profile
2.1.3 Estimating Cost of Equity and Cost of Debt Capital
2.1.4 The Link Between Environmental Profile and Cost of Equity and Cost of Debt Capital
2.1.5 Contributions and Implications
2.2 Corporate Environmental Responsibility´s Impact on the Cost of Equity (El Ghoul et al., 2018)
2.2.1 Overview
2.2.2 The Link Between CER and Equity Pricing
2.2.3 Estimating Cost of Equity and Environmental Costs
2.2.4 Results
2.2.5 Contributions and Implications
3 New Asset Pricing Models
3.1 The ESG-Efficient Frontier (Pedersen et al., 2021)
3.1.1 Overview
3.1.2 Portfolio Choice Depending on ESG Appetency
3.1.3 ESG-Adjusted CAPM
3.1.4 Contributions and Implications
3.2 Sustainable Capital Asset Pricing Model (Zerbib, 2022)
3.2.1 Introduction to S-CAPM
3.2.2 S-CAPM for Investable Assets
3.2.3 S-CAPM for Excluded Assets
3.2.4 Empirical Analysis
3.2.4.1 Proxy for the Cost of Externalities
3.2.4.2 Proxy for the Proportion of Green Integrators´ Wealth
3.2.5 Empirical Results
3.3 Conclusion on New Asset Pricing Models Articles
4 Market Reactions Facing Green Sentiments
4.1 Green Sentiment in Financial Markets (Bessec & Fouquau, 2020)
4.1.1 Methodology and Environmental Media Indices
4.1.2 Fama-French Model (Fama Fama & French, 2015)
4.1.3 Empirical Analysis: Green Sentiment Matters
4.2 Green Sentiment, Stock Returns, and Corporate Behavior (Brière & Ramelli, 2021)
4.2.1 Overview
4.2.2 Green Sentiment Index: Arbitrage Activities of ETF
4.2.3 Effects of Green Sentiment on Stock Returns
4.2.4 Effects of Green Sentiment on Corporate Behavior
5 Conclusion
References

Citation preview

Sustainable Finance

Dejan Glavas   Editor

Valuation and Sustainability A Guide to Include Environmental, Social, and Governance Data in Business Valuation

Sustainable Finance

Sustainable Finance is a concise and authoritative reference series linking research and practice. It provides reliable concepts and research findings in the ever growing field of sustainable investing and finance, SDG economics and Leadership with the declared commitment to present the theories, methods, tools and investment approaches that can fulfil the United Nations Sustainable Development Goals and the Paris Agreement COP 21/22 alongside with de-risking assets and creating triple purpose solutions that ensure the parity of profit, people and planet through choice architecture passion and performance. The series addresses market failure, systemic risk and reinvents portfolio theory, portfolio engineering as well as behavioural finance, financial mediation, product innovation, shared values, community building, business strategy and innovation, exponential tech and creation of social capital. Sustainable Finance and SDG Economics series helps to understand keynotes on international guidelines, guiding accounting and accountability principles, prototyping new developments in triple bottom line investing, cost benefit analysis, integrated financial first plus impact first concepts and impact measurement. Going beyond adjacent fields (like accounting, marketing, strategy, risk management) it integrates the concept of psychology, innovation, exponential tech, choice architecture, alternative economics, blue economy shared values, professions of the future, leadership, human and community development, team culture, impact, quantitative and qualitative measurement, Harvard Negotiation, mediation and complementary currency design using exponential tech and ledger technology. Books in the series contain latest findings from research, concepts for implementation, as well as best practices and case studies for the finance industry.

Dejan Glavas Editor

Valuation and Sustainability A Guide to Include Environmental, Social, and Governance Data in Business Valuation

Editor Dejan Glavas ESSCA, School of Management Boulogne-Billancourt, France

ISSN 2522-8285 ISSN 2522-8293 (electronic) Sustainable Finance ISBN 978-3-031-30532-0 ISBN 978-3-031-30533-7 (eBook) https://doi.org/10.1007/978-3-031-30533-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book aims to provide readers with methods to value businesses using financial models that factor in environmental, social, and governance (ESG) data. While business valuation is key to scholars and practitioners in finance, there are currently no books providing applicable methods to value companies using both financial data and ESG data. Existing books discussing business valuation mainly rely on methodologies that became popular in the 1930s. While these methodologies remain robust and widely used, they were developed during a period when issues like climate change and board diversity were not central to social sciences or society. This book aims to enhance these methodologies by integrating ESG factors into valuation. Interest in sustainability has recently gained ground in the minds of social science scholars and finance professionals. As proof of this growing interest, sustainable investing has grown from $14tr in 2014 to $35tr in 2020, according to the Global Sustainable Investment Alliance. According to Google Scholar data, 96 academic articles discussing sustainable investment were published in 2000, this number increased to 4480 in 2022. This expansion in both practice of sustainable investment and research in the field calls for work to reconcile both worlds. In short, bridges need to be continuously built between sustainable investment and the latest knowledge in the field to improve practices and to keep knowledge in close contact with the latest developments in practice. Our book intends to build on this knowledge to provide a rigorous method to value firms taking the impact of ESG factors into consideration. The book primarily aims at reaching two types of audiences: practitioners in finance and students in finance (graduate and undergraduate level). Practitioners in finance will find this book interesting, as it presents academic knowledge in a format that suits their needs. Academic research has substantially advanced in business valuation and ESG. The book intends to transform this knowledge into practical and rigorous methodologies to take ESG into account when valuing a company. Graduate and undergraduate courses have recently developed in business schools, universities, and engineering schools. As an example, eight of the leading European business schools have joined forces to offer climate training for all. These courses usually directly refer to v

vi

Preface

academic articles or valuation companies’ web documentation, but not to academic books. Therefore, the book will allow students to have access to centralized and organized information on business valuation and ESG. Boulogne-Billancourt, France

Dejan Glavas

Acknowledgments

I would first like to express my deepest appreciation to the authors of this book who have given their time, efforts and were willing to share their knowledge to the wider audience. Throughout this whole journey, I was impressed by the level of commitment of each author that were willing to work and rework countless numbers of times their respective chapters. I would also like to extend my deepest gratitude to my family that allowed this work to exist. Alexa, your unwavering support and willingness to assist me through moments of uncertainty have been invaluable. Your constant presence has not only provided answers but also bestowed comfort in times of doubt.To Miona and Elio, you fill my world with immeasurable joy and contentment. Your bright spirits and remarkable demeanors continue to be a source of pride and motivation. I express my deepest gratitude to you both for being such remarkable children. The completion of this book would not have been possible without Professor Franck Bancel, who shared his insights on how such projects should be conducted. I would also sincerely like to thank Professor George Andrew Karolyi for his advice to conduct purposeful research and for having inspired countless numbers of scholars in the field. Lastly, I extend my heartfelt gratitude to Cédric Villani. Despite not being able to pen the preface for this book due to time constraints, your moral support played an invaluable role and made a significant impact. I would like to thank my business school, ESSCA School of Management for providing its faculty the opportunity to work on projects of this nature. In this context, I would specifically like to thank our President, Jean Charroin, for enabling our faculty to conduct purposeful research, Guillaume Schier our Head of Research who has always been supportive of my research, Wael Louhichi my department Head of Research who has also shown strong support for my research, and my colleagues who have always been supportive in this project (https://www.esscaknowledge.fr/institut-ai-sustainability/). I am grateful to Elena Moisei, who has provided valuable advice and insights on ways to improve this collective work. I would like to thank Youssef Belatar, whose contributions have been instrumental in shaping the valuation chapters. Ultimately, this book project catalyzed the establishment of the ‘Artificial Intelligence for Sustainability Institute,’ which will continue and expand upon the work presented in the book. vii

Contents

Value and Externalities in Economics and Finance . . . . . . . . . . . . . . . . . Dejan Glavas

1

Reminder on Common Valuation Techniques . . . . . . . . . . . . . . . . . . . . Paul Jouy

19

ESG Data and Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mathieu Joubrel and Elena Maksimovich

67

Cash Flow Valuation and ESG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laurent Inard

99

Cash Flow Valuation and ESG: Case Study . . . . . . . . . . . . . . . . . . . . . . 129 Frédéric Le Meaux Multiple Valuation and ESG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Dejan Glavas Research Advances in Valuation and ESG . . . . . . . . . . . . . . . . . . . . . . . 173 Paul Jouy and Dejan Glavas

ix

Abbreviations and Acronyms

Chapter 1 CSR DCF ESG EU EU ETS GDP MMT

Corporate Social Responsibility Discounted Cash Flow Environmental, Social, and Governance Europe European Union’s Emissions Trading System Gross Domestic Product Modern Monetary Theory

Chapter 2 capex CAPM CIT CoE COGS D&A DCF DIO DPO DSO EBIT EBITDA EBT EQ ERP ESG ETFs EV

capital Expenditures Capital Asset Pricing Model Corporate Income Tax cost of Equity cost of Goods Sold Depreciation and Amortization Discounted Cash Flow days inventory outstanding days payable outstanding days sales outstanding earnings before interest and taxes earnings before interest, taxes, depreciation, and amortization earnings before taxes equity value equity risk premium Environmental, Social, and Governance Exchange Traded Funds enterprise value

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FTE GDP IMF M&A ND PER P&L ROIC SG&A SPTR WACC

Abbreviations and Acronyms

full-time equivalent the gross domestic product International Monetary Fund mergers, acquisitions, and divestitures net debt price-earnings ratio profit and loss return on invested capital selling, general, and administrative expenses S&P 500 Total Return Index weighted average cost of capital

Chapter 3 BASF CDP CDSB COP 26 CSR CSRD CTB DLT DNSH EEOC EFRAG EFPD ESG ESMA EU FTSE GHG GRI HLEG IFRS IIRC IMP ISS ISSB KLD MSCI NFRD NGOs NOAA NLP PAB

Badische Anilin- und Soda-Fabrik Carbon Disclosure Project Climate Disclosure Standards Board 26th Conference of the Parties Corporate Social Responsibility Corporate Sustainability Reporting Directive Climate Transition Benchmarks Distributed Ledger Technology Do No Significant Harm Equal Employment Opportunity Commission European Financial Reporting Advisory Group Extra-Financial Performance Declaration Environmental, Social, and Governance European Securities and Markets Authority European Union Financial Times Stock Exchange greenhouse gas Global Reporting Initiative High-level Expert Group International Financial Reporting Standards International Integrated Reporting Council Impact Management Project Institutional Shareholder Services International Sustainability Standards Board Kinder, Lydenberg, Domini, and Co. Morgan Stanley Capital International Nonfinancial Reporting Directive Non-Governmental Organizations National Oceanic and Atmospheric Administration Natural Language Processing Paris Aligned Benchmarks

Abbreviations and Acronyms

PRI RTS SASB SDGs SEC SFDR TCFD UNCP VRF

Principles for Responsible Investment Regulatory Technical Standards Sustainability Accounting Standard Board Sustainable Development Goals Securities and Exchange Commission Sustainable Financial Disclosure Regulation Task Force on Climate-related Financial Disclosure United Nations Country Programme Value Reporting Foundation

Chapter 4 B2B CAPM DCF ESG EU ETS FCEV GDP IMF KPIs LTGR NPV opex SOTP WACC

Business to Business Capital Asset Pricing Model Discounted Cash Flow Environmental, Social and Governance European Union Emissions Trading System Fuel Cell Electric Vehicle Gross Domestic Product International Monetary Fund Key Performance Indicators Long-term Annual Growth Rate Net Present Value Operational Expenditure Sum of the Parts Weighted Average Cost of Capital

Chapter 5 CSRD DCF ESG PPAs PRI SASB

Corporate Sustainability Reporting Directive Discounted Cash Flow Environmental, Social and Governance Power Purchase Agreements Principles of Responsible Investment Sustainability Accounting Standards Board

Chapter 6 AIC BIC BP CAPM DDM EPS ESG EV/EBITDA OLS

Akaike’s Information Criteria Bayesian Information Criteria Breusch-Pagan Capital Asset Pricing Model Dividend Discount Model earnings per share Environmental, Social, and Governance enterprise-value-to-EBITDA Ordinary Least Squares

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PEGR PER PERE p-value Q-Q S&P 500 VIF WACC

Abbreviations and Acronyms

PER-to-growth ratio price-earnings ratio PER-to-ESG ratios probability value quantile-quantile Standard & Poor’s 500 variance inflation factors Weighted Average Cost of Capital

Chapter 7 AMEX AMT APs capex CAPM CBI CER CREB CSR ENVCOST EPA EPS ESG ETF FEPSt+1 GDP GHG GMM HHI I/B/E/S ICC IT IRR KFEYD KLD KPEG KTYED LIBOR NASDAQ NAV NBER NYSE NYSE Arca Pt

American Express alternative minimum tax authorized participants Capital Expenditures Capital Asset Pricing Model Climate Bond Initiative Corporate Environmental Responsibility Clean Renewable Energy Bonds corporate social responsibility environmental costs/total assets Environmental Protection Agency earnings per share environmental, social, and governance Exchange Trade Funds forward earning per share of the next period gross domestic product greenhouse gas generalized method of moments Herfindahl-Hirschman Index Institutional Brokers’ Estimate System integrated cost of capital Information Technology internal rate of return a forward earnings-to-price ratio Kinder, Lydenberg, Domini, and Co. a price-earnings-growth model trailing earnings forecasts London Interbank Offered Rate National Association of Securities Dealers Automated Quotations net asset value National Bureau of Economic Research New York Stock Exchange New York Stock Exchange Arca stock price

Abbreviations and Acronyms

Q1 Q4 QECB R&D S&P 500 SR SRI S-CAPM

first quarter fourth quarter Qualified Energy Conservation Bonds research and development Standard & Poor’s 500 Sharpe ratio socially responsible investing sustainable Capital Asset Pricing Model

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Value and Externalities in Economics and Finance Dejan Glavas

1 Value Theories in Economics 1.1

Definitions of Value in Economics

In this section, we will examine how various economic theories have attempted to understand the concept of value. The intrinsic theory posits that the value of an object originates from the object itself, specifically from its characteristics. The labor theory of value posits that labor required to produce an object determines its value. The subjective theory, on the other hand, argues that value is determined by consumer perceptions and the satisfaction that the object brings. Lastly, monetary theories propose that value is rooted in the supply and demand of money in the economy. Each of these theories has contributed to the development of current valuation tools used in finance. The intrinsic theory has led to the use of intrinsic valuation methods, the most prevalent of which is the discounted cash flow (DCF) model, which is discussed in depth in Chapter “Reminder on Common Valuation Techniques.” The labor theory has influenced the incorporation of salary expenses as important factors in modern valuation. The subjective theory provides the basis for comparison-based valuation methods in finance, which are also discussed in detail in Chapter “Reminder on Common Valuation Techniques.” Finally, monetary theories of value have led to the inclusion of factors such as inflation and interest rates as significant drivers in valuing a firm or asset.

D. Glavas (✉) ESSCA School of Management, Boulogne-Billancourt, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_1

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2

1.2

D. Glavas

Intrinsic Theory of Value

The intrinsic value of something refers to its worth “in and of itself.” The concept of intrinsic value has its origins in ancient Greek philosophy and remains a topic of discussion today (McShane, 2007). Aristotle’s work defined something as intrinsically good if it does not derive its goodness from another object (Ameriks & Clarke, 2000). This foundational definition, while debated and revised over time, has served as a foundation for understanding value in economics. The concept of intrinsic value prompts the question of what specific types of objects can possess intrinsic value. There has been a debate among scholars about whether it is the state of an object that has intrinsic value (the Moorean view) or if only concrete objects can have value (the Kantian view) (Bradley, 2006). Our book focuses on financial assets, which possess both concrete and abstract characteristics. For example, a company’s stock is a concrete object, as it is a document that demonstrates the holders’ property rights. Yet, it also carries an abstract element as it functions as a marketable financial instrument. As evaluators, we seek to assess the value of this abstract concept, not the concrete legal document. Ultimately, our understanding of intrinsic value aligns with the Moorean view, where we evaluate the state of an object, in this case, the marketable financial instrument, by calculating the value of shares. Having defined intrinsic value and explored the types of objects that can be valued, we now turn to the question of how this value can be measured according to the intrinsic theory. Scholars have long debated the feasibility of determining intrinsic value. Two main limitations arise in this endeavor: first, the value of some objects may be immeasurable, and second, it may be challenging to compare the relative values of different objects. In finance, the issue of measurability is addressed using monetary value. The monetary value of a stock, for example, can be estimated and placed on a quantitative scale. As this book will demonstrate, the financial industry has developed methods to determine the intrinsic value of an asset, which are discussed in more detail in Chapters “Reminder on Common Valuation Techniques” and “Cash Flow Valuation and ESG.” While financial assets can be traded like any other good, they cannot be valued in the same manner. Financial assets have the potential to generate monetary returns for their holder over time. Therefore, in finance, it is widely accepted that the intrinsic value of an asset is rooted in the cash flows it generates for the holder. This model has served as a useful guide for finance professionals for many years. However, in this book, we aim to adapt this model to better reflect the impact of environmental, social, and governance (ESG) factors on the intrinsic value of an asset. These factors, while not new, have recently become more widely available to the public. An important aspect of intrinsic models is their ability to incorporate all value-related information. By incorporating ESG factors into intrinsic valuation, we can enhance the predictive power of these models. As Alex Edmans’ book Grow the Pie: How Great Companies Deliver Both Purpose and Profit (Edmans, 2021)

Value and Externalities in Economics and Finance

3

illustrates, incorporating ESG not only contributes to societal well-being but also benefits business practices.

1.3

Labor Theory of Value

In the late eighteenth century, Adam Smith and David Ricardo both developed their versions of the labor theory of value, starting from the assumption of a simplified world where only commodities are exchanged (Ricardo, 1817; Smith, 1776). This was a thought experiment rather than an accurate representation of historical or factual reality. In this hypothetical scenario, only self-producers own the materials, equipment, and tools required for production. The labor theory of value asserts that the comparative worth of two products is determined by the amount of labor required to produce each one. For instance, if it takes twelve months of labor to make a cheese wheel and three months to craft a custom chair and if both items are traded evenly, workers will move from cheesemaking to chair-crafting. As a result, there will be more chairs available, causing their value to decrease, while the value of cheese wheels will increase due to their relative scarcity. Adam Smith believed that the value of a commodity is derived solely from the direct amount of labor put into producing it (e.g., the time workers dedicate to making cheese). David Ricardo proposed that the labor cost of production includes not only the direct costs but also indirect costs (e.g., wages of workers working in cheese storage facilities). Therefore, Ricardo’s theory includes both direct and indirect labor time in the calculation of the value of a commodity—this is known as vertically integrated labor time. Ricardo attempted to develop a theory of the actual or natural price of a commodity by considering all labor components in the calculation of value. Labor theories of value aim to explain broad price patterns rather than specific prices for specific items or services. They may account for discrepancies in prices that result from market transactions or price-fixing regimes. Current empirical evidence shows that the labor share (share attributed to labor out of gross domestic product [GDP]) accounted for 47% of GDP on average in 2019 in all countries on which there are available data, 57% in Europe (EU) and 58% in the USA (International Labour Organization, 2022). These data contradict Smith’s hypothesis of labor being the only component of value, but they confirm the key role of labor in generating value. As is the case for the intrinsic theory of value, the financial industry has integrated labor as a key determinant of value. When computing an intrinsic value through the discounted cash flow model (explained in detail in Chapter “Reminder on Common Valuation Techniques”), the cost of labor is integrated into the cash flow computation. The cash flow is the base of value generation under this model. When integrating ESG in valuation, we improve our assessment of value stemming from work. Indeed, the social component of ESG includes several factors (such

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as promotion or workforce health and safety) that were not specifically factored in valuation. The use of these new data will therefore improve the accuracy of DCF models.

1.4

Subjective Theory of Value

The subjective theory of value is an economic concept that posits that the value of a product or service is determined by the individual customer’s subjective assessment of its worth. This theory contrasts with the intrinsic theory of value, which holds that the value of an item or service is determined by its inherent qualities or utility in satisfying an objective need. A crucial element of the subjective theory is acknowledging that individuals have varying preferences and priorities, leading them to assign different values to things. This wide range of tastes implies that what one person finds valuable could be seen as unimportant by someone else. For instance, while one person might highly appreciate a cutting-edge smartphone, another could value a reliable washing machine more. Due to this diversity in preferences, the market value of a product or service is established by the combined demand from all market participants. The contemporary subjective theory of value was developed in the late nineteenth century by economists William Stanley Jevons, Léon Walras, and Carl Menger, interestingly, without much contact or influence from one another. William Stanley Jevons believed that value originates from utility. According to Jevons, utility refers to the satisfaction or enjoyment a person can obtain from the use of a good. Building on this concept, Jevons (Jevons, 1879) introduced the concept of marginal utility. The theory of marginal utility posits that the consumption of each additional unit of a good will decrease the utility or satisfaction obtained from that good. This theory contradicts the idea that value stems from labor, as discussed in the previous section. Carl Menger (Menger, 1936) believed that satisfaction from the use of a good diminishes over time until reaching a point of saturation. Therefore, like Jevons, Menger also proposed the idea of decreasing marginal utility, the idea that the satisfaction from consuming each additional unit of a good decreases as well. Menger used a different way to approach this question but came to the same idea as Jevons. Léon Walras (Walras, 1883) developed the idea of market equilibrium. According to Walras, the price of a good depends on the equilibrium between the offer and demand of that good. Again, this idea was a clear refutation of the intrinsic theory, as value here is determined by extrinsic market factors rather than the own characteristics of the good. It is the aggregate willingness to buy a good compared to the quantity of the good produced that will determine value. In summary, the subjective theory of value is a fundamental principle in economics that asserts that the value of a product or service is determined by the individual customer’s personal perception of its worth. This theory provides a

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valuable perspective for interpreting consumer and producer behavior and has significant implications for policy-making and economic research. We use the subjective theory of value to explain policy initiatives that aim to reduce greenhouse gas emissions (see Sect. 2). The subjective theory also has key importance in the work done in this book. As we consider that ESG factors bring new costs and opportunities that should be factored in value, we give a subjective value to such factors. This subjective view of ESG value is the result of previous years of work and research on the topic.

1.5

Monetary Theory of Value

To date, we have largely ignored the role that the money supply plays in prices. Monetary policy, carried out by governments and central banks, has the power to influence economic development by altering the amount of money and other liquid assets in circulation. The basic formula that governs monetary theory is the equation of exchange: MV=PT. In this equation, M represents the aggregate quantity of money in circulation, V represents the velocity of money, P represents the price of goods and services, and T represents the volume of transactions in the economy. This equation was first formulated by John Stuart Mill (Mill, 1884) and has since been refined and expanded upon by monetary economists. The equation proposes that an increase in M will affect P, as V is relatively stable and given a certain volume of transactions in the economy, T. Therefore, a central bank increasing the amount of money in circulation will cause a general rise in prices, known as inflation. Some economists, such as John Maynard Keynes (Moggridge & Howson, 1974), while acknowledging the equation of exchange, argue that the aggregate quantity of money is less important than the other components in the equation. Keynes believed that an increase in the aggregate amount of money was not the primary cause of inflation and that monetary expansion could be one the solutions to economic crises, such as the 1930s crisis. On the other hand, Milton Friedman (Friedman, 1982) argued that inflation is directly caused by the amount of money put into circulation by central banks. This is based on the idea that the volume of transactions in an economy is not influenced by monetary policy and that velocity stays constant in the short term, meaning that the only remaining cause of inflation is the amount of money added to the economy by the central bank. In recent times, economists have developed the modern monetary theory (MMT). This theory, which has been debated since the early twentieth century, was brought to the forefront by Stephanie Kelton in her book The Deficit Myth (Kelton, 2020). One of the key concepts of MMT suggests that governments’ constraints lie not in their budgets (government spending and/or income from taxes) but in their ability to issue debt purchased by their central bank. The central bank can always print money to purchase government debt, thus preventing the possibility of government default. Opponents of MMT argue that, while government default may not be an immediate

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constraint, inflation remains a key limitation to monetary creation. In light of current inflationary trends, central banks such as the Federal Reserve (Fed) and European Central Bank have not adopted the MMT approach, but have instead followed traditional monetary policy. As a result, considering the rising inflation, the Fed has raised interest rates from 0.25% in January 2022 to 4.00% in November 2022 and has decreased its investments in securities on the open market. The monetary theory of value has contributed to the understanding of the relationship between money, inflation, and valuation. Monetary policy affects stock prices through two channels: inflation’s impact on future cash flows and changes in interest rates. These two factors are critical in the discounted cash flow (DCF) method, which will be explored in more detail in Chapter “Reminder on Common Valuation Techniques.” Additionally, the transition from a carbon-intensive economy to a low-carbon economy has also led to what is known as “greenflation.” Specifically, greenflation refers to the potential increase in prices of products and services as a result of the cost of adopting climate change mitigation and renewable energy transition policies. These actions may include investments in renewable energy infrastructure, the implementation of carbon pricing systems, and the introduction of legislation to limit emissions from certain industries. The idea is that when these policies are put in place, the cost of producing goods and services will increase, leading to higher consumer prices. Integrating ESG factors in valuation allows us to acknowledge and sometimes limit the effects of greenflation on valuations. Indeed, when financial analysts integrate greenflation in the value of a firm, they anticipate the price of that firm more precisely as compared to an analyst that does not.

2 What Are Externalities, and Why Should We Factor in Environmental, Social, and Governance Factors in Valuation? 2.1

Market Equilibrium

In economics, markets serve as venues for buyers and sellers to exchange goods and assets. A perfect market is a specific type of market, characterized by a large number of producers (sellers) and buyers, complete information available to all market participants, and no barriers to entry or exit. Under these conditions, both producers and buyers have no market power, and are considered “price takers.” They are unable to significantly influence the market price and must accept the price, which is determined by market supply and demand. The law of demand states that consumers will buy less when prices increase. The corresponding demand curve is represented in Fig. 1 in orange. The law of supply states that producers will generate more products as the price increases. The corresponding supply curve is shown in Fig. 1 in blue. In the situation of equilibrium, supply and demand, curves intersect, allowing an equilibrium price and an

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Price (P)

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Supply

P*

Demand

Q*

Quantity (Q)

Fig. 1 Market equilibrium

equilibrium quantity to emerge. When equilibrium is reached, no factors are pushing for a price change, and consequently, quantities exchanged in the market remain constant. For example, let us imagine a perfect market including milk producers and consumers. We can express demand and supply curves with equations: The demand (D) can be expressed depending on the price (P) as: D = 150 - P Logically, we can see that as price increases, demand decreases. The supply (S) can be expressed depending on the price (P) as: S = 100 þ P On the contrary, we can see that as price increases, offer increases. The equilibrium is reached when (D) equals (S) at the equilibrium price (P*): 150 - P = 100 þ P P = 25 The equilibrium quantity (Q*) is therefore: Q = 125

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What Are Externalities?

As explained in the previous paragraph, markets, under perfect competition, will naturally reach the equilibrium price and volume produced at the point where supply and demand curves meet. Nevertheless, there are several types of market failures, such as imperfect competition or information asymmetry, that do not allow the market to reach equilibrium. Externalities are one case of market failure. Externalities occur when the activity of an economic agent leads to additional costs or benefits to other economic agents not involved in this activity. There are two types of externalities: negative and positive externalities. Negative externalities occur when one economic agent’s activity causes losses to other economic agents. Positive externalities occur when such activity causes benefits to other economic agents. When discussing environmental, social, and governance issues, negative externalities are the primary point of focus. We can find a powerful example of a negative externality in real estate. When you buy a flat for $400,000 in a coastal town with a view of the sea, the flat’s price will factor in this key characteristic. We assume that the view accounts for $50,000 of the total value of one flat. In your building, there are four other flats with a similar size and view; thus, the cumulative value of the view of all flats, including yours, amounts to $200,000 (4 x $50,000). Let us imagine a case where a project of a 10-story building between your flat and the sea is approved right after your acquisition. While the promoter of the 10-story building will now benefit from flats with the view, you and other residents of your building will lose that view. The promoter benefits from this new building without bearing the cost of the lost view from your building. We assume that the private benefit of making and selling flats from the 10-story building is $5 m. Therefore, the private benefit to the promoter will be $5 m, while the negative externalities suffered by all the residents in your building will be $200,000. Such a situation is defined as suboptimal because it does not match a market equilibrium where all costs and gains would have been efficiently priced. Suboptimal situations can lead to incorrect investment decisions from a macroeconomic perspective, even if they may benefit some economic agents on a local scale. Coase (2013) found a solution to this type of suboptimal situation. This solution supposes that property rights are attributed and that negotiation is not costly. Under these conditions, the market can resolve externalities in an economically efficient manner through bargaining between the economic agents involved. Coming back to the real estate example, we could imagine that the flat owners in your building organize and threaten to sue the promoter. The lawsuit may lead to the project being canceled, leading to a promoter’s $5 million loss plus legal costs. The second outcome is that the project may go on, leading to a loss for flat owners in your building of $200,000, plus legal costs. The most efficient outcome then becomes direct negotiation between flat owners and the promoter, where the promoter compensates flat owners for their loss of value (pays $200,000 to the residents) and still

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makes a $4.8 m profit on the new building project. This situation is viable because the property rights of the flat owners and the promoter are clearly defined.

2.3

Climate Change as an Example of Externality

We can now discuss an example involving climate change—the coal for power plants market. Coal-fired power plants, without carbon capture technology, emit between 751 g and 1095 g of carbon dioxyde equivalent per kilowatt hour of electricity produced (UNECE, 2021). This is, on average, higher than any other existing technology. Carbon emissions from coal-fired power plants lead to increased global warming and increased costs to society as a whole (Summerfield et al., 1993). Let us suppose a price of $35 per ton of coal (P1 in Fig. 2). We assume that the cost of coal to society is $70 per ton of coal (price P2 in Fig. 2). Therefore, we consider that the producers of coal do not pay the full cost of a ton of coal to society. In economics, the difference in production costs caused by creating one more unit is known as the marginal cost. The marginal private cost is the difference in costs to the producers due to the production of one additional unit. The marginal social cost is the cost to society of the production of an additional unit by the producers. In our example, the production of one additional unit of coal will increase costs to society as a whole because of climate change consequences. The marginal social benefit is the gains to society generated by consuming an extra unit of an item or service. The social benefit is equal to the private benefit plus/ Marginal Social Cost

Price

Marginal Private Cost

P2 P1

Marginal Social Benefit Q2 Fig. 2 Social and private cost of coal

Q1

Quantity

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minus the positive/negative externalities. In the case of coal, one additional unit produced will decrease the marginal social benefit. In our example, the marginal private cost is below the marginal social cost (see Fig. 2). If the price was set to optimize social welfare, it would be at P2 ($70), and the socially optimal output should be Q2, while the actual output is Q1 (Q1 > Q2). When an item or service is not produced in the socially desirable quantity, deadweight loss is used in economics as an estimate of the loss to society as a whole. The deadweight loss due to the private optimal output (Q1) being higher than the social optimal output (Q2) is represented by the blue triangle.

2.4

Solutions to the Externalities Issue

Externalities due to climate change cannot be resolved by the Coase Theorem described earlier mainly because property rights are not clearly defined. We can hardly attribute the ownership of the air to a specific economic agent. The second issue comes from the difficulty of organizing negotiations between all parties involved, as the climate effectively affects all human beings and all types of organizations. Therefore, any solution relying on bargaining between the economic agents suffering from the externalities and those generating the externality is bound to fail. However, States and public authorities can intervene to either resolve or limit externalities. We will discuss here three main mechanisms used to limit negative externalities: (1) taxes, (2) regulation, and (3) carbon markets.

2.4.1

Carbon Taxes

First, we can test the effect of taxes in our example. If we imagine that governments implement a carbon tax on coal of $20 per ton and that this tax is directly transferred to the price of coal, leading to a new price of P3 ($55 per ton of coal), which is greater than P1 ($35 per ton of coal). The private cost curve would shift up and become closer to the social cost curve, which in turn will reduce the deadweight loss (see Fig. 3’s representation of deadweight loss compared to that in Fig. 2). Two key observations here are (1) the new optimal output of coal becomes Q3, which is smaller than Q1 under the previous price P1, and more importantly, (2) the deadweight loss represented by the blue triangle in Fig. 3 has shrunk. We can also observe that determining a carbon tax level may prove to be a difficult exercise since it is difficult to precisely determine the price that cancels the deadweight loss.

2.4.2

Regulations

Second, we can discuss how regulations help reduce deadweight loss. We will focus on one type of regulation: quantity regulation. Returning to our previous example on

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Marginal Social Cost Price

Marginal Private Cost

P2 P3 P1

Marginal Social Benefit Q2 Q3 Q1

Quantity

Fig. 3 Social and private cost of coal after the implementation of a carbon tax

coal, let us imagine that regulators want to limit coal production as it leads to damaging carbon emissions. In such a case, we can imagine that coal producers can only produce up to a given quantity of coal, Q4, which is less than the initial equilibrium quantity Q1 (from Fig. 2). The lower quantity produced will have the effect of increasing the price of coal (same demand but lower supply). Therefore, the equilibrium price would increase from P1 to P4. This would shift the marginal private cost curve upward and reduce the deadweight loss compared to Fig. 3 (see Fig. 4).

2.4.3

Carbon Markets

The last way to address negative externalities due to climate change is through the use of carbon markets. It is a system that is intended to minimize pollution in our environment. The cap on greenhouse gas emissions is a fixed pollution limit, which tightens with time. Corporations can purchase and sell permits that enable them to emit just a specific quantity of pollutants, with the permit price determined by supply and demand in the market. Through this system, companies are strongly encouraged to save money by reducing emissions using the most cost-effective methods. Prominent examples of carbon markets include the European Union’s Emissions Trading System (EU ETS), the California Carbon Market, and the markets established by Canadian provinces. The EU ETS was established in 2005 as the world’s first multinational carbon market. The California Carbon Market, launched in 2013 after careful examination of the EU ETS, covers 450 entities and aimed to

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Marginal Social Cost Price

Marginal Private Cost

P2 P4 P1

Marginal Social Benefit Q2 Q4 Q1

Quantity

Fig. 4 Social and private cost of coal after implementation of a quantity regulation

reduce greenhouse gas emissions to 1990’s levels by 2020. In Canada, the Quebec Carbon Market is the most widely recognized, although other provinces have also implemented carbon markets. The Quebec market was introduced in 2013 and covers approximately 80% of the carbon emissions in the province. To better understand the theoretical functioning of carbon markets, we can use the example of two coal producers. The more (respectively, less) coal they produce, the higher (respectively, lower) their CO2 emissions. To illustrate the impact of trade on price, we use two firms, Firm A and Firm B, which have different marginal private cost curves. Let us imagine that the acceptable quantity of CO2 emissions, also called the cap, is Q’ = 80 tons for both firms (i.e., both firms can emit a total maximal quantity of 80 tons of CO2). The government first decides to assign forty permits to emit (40 tons of CO2 equivalent) to firm A and 40 permits to firm B. In Fig. 5, we illustrate the emissions of tons of CO2 (horizontal axis) and the cost required to reduce one ton of CO2 emission (vertical axis). Reducing pollution for a coal producer implies two types of costs: reducing the quantity of coal produced and/or the cost of improving the production process to be more CO2 emissions-efficient. Each company incurs different costs to reduce pollution, which may include costs of reducing production or modifying the production process. This is illustrated by the marginal abatement cost curve. Without the possibility of trading the pollution permits, Firm A would be in a situation to pay a cost of $10 (C(A) = $10) to reduce its pollution by one ton, whereas Firm B would have to pay only $2 (C(B) = $2). If only the cap had existed, this would have been the final outcome for both firms. This situation is suboptimal

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Cost of reducing pollution by one ton Marginal Abatement Cost Firm A

Marginal Abatement Cost Firm B C(A) =$10 C(B)=$2 Q(A)=Q(B)=40 tons

Emissions in tons of CO2(Q)

Fig. 5 Carbon market and marginal abatement cost—Initial situation

because firm B could easily reduce its pollution further while a small effort from firm A is extremely costly. If pollution permits are tradable, Firm B will agree to sell its permits for at least $2 or higher, while Firm A saves $10 per permit purchased. If Firm B sells the permit at $5, it will have to pay $2 to reduce its pollution by one ton (equivalent to one permit). In this case, Firm B makes a profit of $3 (cost of $2 and revenue of $5). Therefore, Firm A has an incentive to pay Firm B to acquire emissions permits. We will take a first transaction where Firm A buys five permits from Firm B. Buying new permits will allow Firm A to emit 45 tons of CO2 instead of 40, while Firm B will be allowed to emit only 35 tons of CO2 (see Fig. 6). Each time a transaction occurs, it will become more and more costly for firm B to reduce its level of pollution (because further reducing the production of coal may for instance lead to a loss in economies of scale). From Fig. 6, we observe that the cost increases for Firm B to C(B) = $4 and decreases for Firm A to C(A) = $5. Again, firm B will accept at least $4 to sell a permit, and firm A is willing to pay $5 for a new permit. Since Firms A and B have an interest in exchanging permits, transactions will continue. The point at which no firm has an incentive to exchange permits anymore is when C(A) = C(B), as illustrated in Fig. 7 below. The point at which C(A) = C(B) is the point at which no more exchanges will occur. It is also the equilibrium price of carbon emission permits. Now, what is the impact of carbon markets on deadweight loss? Thanks to the cap-and-trade system, regulators can directly set the quantity that minimizes deadweight loss. This quantity of emissions corresponds to a quantity Q’ of coal produced, as shown in Fig. 8. This means that marginal social cost and marginal private cost become equal and that deadweight loss becomes nil. Where there is a

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Cost of reducing pollution by one ton Marginal Abatement Cost Firm A Marginal Abatement Cost Firm B C(A)=$5 C(B)=$4 Q(B)=35

Q(A)=45

Emissions in tons of CO2 (Q)

Fig. 6 Carbon market and marginal abatement cost—After the first transaction

Cost of reducing pollution by one ton

Marginal Abatement Cost Firm A Marginal Abatement Cost Firm B CA(A)=C(B)=$4.5 Q(B)=34

Q(A)=46

Emissions in tons of CO2(Q)

Fig. 7 Carbon market and marginal abatement cost—Equilibrium

permit trading market, companies that are less effective in reducing carbon emissions will buy licenses from the most effective ones. The market will set the optimal price of carbon to reach the regulator’s desired quantity of carbon emitted. This increases the efficiency of the carbon reduction process (Hutchinson et al., 2017).

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Marginal Social Cost = Marginal Private Cost

Price

P=C(A)=C(B)

Q’=50 tons

Quantity

Fig. 8 Social and private cost of coal after implementation of a carbon market

3 Conclusion The current scientific consensus is that climate change and biodiversity loss will have profound economic impacts (Zhong et al., 2022). Social issues such as social unrest can also lead to strong economic value losses (Hadzi-Vaskov et al., 2021). This is also the case for governance issues that have a known link in the scientific literature with loss of value (Leal & Carvalhal-da-Silva, 2007). These externalities will lead to long-term losses that valuation models should reflect. One example is the case of physical risks arising from climate change which are clear examples of the translation of environmental externalities into value losses. Therefore, the risk of losses due to these externalities is undervalued if we do not consider ESG factors in business valuations. As discussed in the previous section, governments may intervene to limit externalities through regulation, taxes, or carbon markets. These interventions may lead to immediate value losses, instead of longer-term losses. In the case of taxes and regulations aimed at limiting climate change, this phenomenon is more commonly described as transition risk. One example is the proposed implementation of a carbon tax in Australia (Luo & Tang, 2014; Meng et al., 2013). These carbon taxes, by reducing deadweight losses and decreasing private benefits, lead to lower business valuations. Luo and Tang (2014) find that proposed carbon tax legislation leads to a loss of shareholder wealth and that this loss is even more pronounced in some specific industries such as materials, industrial, and financials. Therefore, having limited consideration for the transition risk in business valuation can lead to overvaluation of firms exposed to the transition risk. It is, again, key to consider ESG in business valuation to limit this overvaluation risk.

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ESG factors may also lead to opportunities for some industries and companies that would decide to improve their ESG performance. Friede et al. (2015) apply a meta-analysis of empirical research on the link between ESG and financial performance in a sample of approximately 2200 studies. They uncover that a large majority of studies find a positive relationship between ESG and financial performance. Therefore, the state of the knowledge on the topic seems to point toward advantages in improving ESG performance. This represents the third reason to consider ESG performance in business valuation models. Finally, aside from the intervention of taxes or regulation, stakeholders may pressure companies to factor in ESG in their decision-making processes and, therefore, in valuation. Helmig et al. (2016) find that not all stakeholders are equal when pushing firms into implementing corporate social responsibility (CSR) practices. According to the authors, employees are key to the implementation of CSR through loyalty or word-of-mouth. The financial literature widely supports the idea that investors (stockholders and bondholders) are central in the implementation of CSR within firms (Glavas & Bancel, 2022). To conclude, integrating ESG has become central to any business valuation model (Bancel et al., 2023). Aside from the social welfare necessity of integrating negative externalities, it is key to properly factor in risks and opportunities in business valuation and to respond to growing stakeholder pressure.

References Ameriks, K., & Clarke, D. M. (2000). Aristotle: Nicomachean ethics. Cambridge University Press. Bancel, F., Glavas, D., & Karolyi, G. A. (2023, February 20). Do ESG factors influence firm valuation? Evidence from the field. Available at SSRN https://ssrn.com/abstract=4365196 or https://doi.org/10.2139/ssrn.4365196 Bradley, B. (2006). Two concepts of intrinsic value. Ethical Theory and Moral Practice, 9, 111–130. https://doi.org/10.1007/s10677-006-9009-7 Coase, R. H. (2013). The problem of social cost. The Journal of Law and Economics, 56, 837–877. Edmans, A. (2021). Grow the pie: How great companies deliver both purpose and profit–updated and revised. Cambridge University Press. Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2,000 empirical studies. Journal of Sustainable Finance & Investment, 5, 210–233. https://doi.org/10.1080/20430795.2015.1118917 Friedman, M. (1982). Monetary policy: Theory and practice. Journal of Money, Credit and Banking, 14, 98–118. Glavas, D., & Bancel, F. (2022). Does state ownership impact green bond issuance? International evidence. Finance, 2022, 120. https://doi.org/10.3917/fina.pr.020 Hadzi-Vaskov, M., Pienknagura, S., & Ricci, L. A. (2021). The macroeconomic impact of social unrest. IMF Working Papers, 2021, 1. https://doi.org/10.5089/9781513582573.001 Helmig, B., Spraul, K., & Ingenhoff, D. (2016). Under positive pressure: How stakeholder pressure affects corporate social responsibility implementation. Business & Society, 55, 151–187. https:// doi.org/10.1177/0007650313477841 Hutchinson, Emma, & University of Victoria. (2017). 5.3 Directly targeting pollution. University of Victoria.

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International Labour Organization. (2022). ILO modelled estimates database. ILOSTAT. Accessed December 7, from https://ilostat.ilo.org/data/ Jevons, W. S. (1879). The theory of political economy. Macmillan and Company. Kelton, S. (2020). The deficit myth. Public Affairs. Leal, R. P. C., & Carvalhal-da-Silva, A. L. (2007). Corporate governance and value, Brazil (and in Chile). In F. Lopez-de-Silanes & C. Alberto (Eds.), Investor protection and corporate governance—Firm level evidence across Latin America (pp. 213–288). World Bank, Stanford University Press. Life Cycle Assessment of Electricity Generation Options | UNECE. (2021). Accessed March 8, 2022, from https://unece.org/sed/documents/2021/10/reports/life-cycle-assessment-electric ity-generation-options Luo, L., & Tang, Q. (2014). Carbon tax, corporate carbon profile and financial return. Pacific Accounting Review, 26, 351–373. https://doi.org/10.1108/PAR-09-2012-0046 McShane, K. (2007). Why environmental ethics Shouldn’t give up on intrinsic value. Environmental Ethics, 29, 43–61. Meng, S., Siriwardana, M., & McNeill, J. (2013). The environmental and economic impact of the carbon tax in Australia. Environmental and Resource Economics, 54, 313–332. https://doi.org/ 10.1007/s10640-012-9600-4 Menger, C. (1933–1936). The collected works of Carl Menger. London: London School of Economics and Political Science. Mill, J. S. (1884). Principles of political economy. D. Appleton. Moggridge, D. E., & Howson, S. (1974). Keynes on monetary policy, 1910-1946. Oxford Economic Papers, 26, 226–247. Ricardo, D. (1817). The theory of comparative advantage. Pierro Sraffa with Collaboration of M.H. Dobb. Cambridge University Press. Smith, A. (1776). The Wealth of Nations: An Inquiry into the Nature and Causes of the Wealth of Nations. Vol. 11937. Summerfield, I. R., Goldthorpe, S. H., & Bower, C. J. (1993). Combating global warming— Reducing CO2 emissions from coal-fired power plant. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 207, 81–88. Walras, L. (1883). Théorie Mathématique de la Richesse Sociale. Corbaz. Zhong, M., Zhao, W., & Shahab, Y. (2022). The philanthropic response of substantive and symbolic corporate social responsibility strategies to COVID-19 crisis: Evidence from China. Corporate Social Responsibility and Environmental Management, 29, 339–355. https://doi.org/ 10.1002/csr.2204

Reminder on Common Valuation Techniques Paul Jouy

1 Introduction and Purpose The purpose of this chapter is to introduce the two most widely used asset valuation methods, the discounted cash flow (DCF) approach and the valuation approach based on multiples (relative approach). The readers will be provided with the main steps of the valuation process and some common tools and techniques, enabling them to carry out their own valuations. This chapter is based on the theoretical framework usually used in finance, in which investors are only maximizing the riskreturn trade-off. How ESG considerations can impact these common valuation techniques will be addressed in the following chapters. This chapter is intended to be a practical guide and therefore, only goes into the theory to a limited extent. However, the main concepts underlying the valuation will be discussed: After a brief definition of the company’s value and its sharing among the different stakeholders, a few accounting reminders will permit an understanding of how a company generates profits and cash flows. The focus will then be placed on the cost of capital from both a theoretical and practical perspective. Finally, the last two sections cover the implementation of the DCF and relative approaches, illustrated with concrete examples.

P. Jouy (✉) Corporate Finance, BDO AG Wirtschaftsprüfungsgesellschaft, Frankfurt am Main, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_2

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2 Define the Value of a Stock or an Asset 2.1

Introduction to the Value of an Asset

In finance, the value of an asset is viewed as a sum of the future benefits that can be expected from that asset. Rational investors seeking to maximize profits will only buy an asset if the future cash flows they can obtain from this asset exceed the price they will have to pay to purchase it. If the sum of future profits is lower than the purchase price, investors have no interest in the bargain; they will lose money. The maximum price they will be willing to pay is, therefore, the value of the expected profits. The seller, on their side, will seek to sell to the buyer with the highest expectation of future profits. This definition of the value is quite simple but already reveals two major difficulties of the valuation. The first one is that the valuation depends entirely on the future, or more precisely, on the investor’s expectations of the future. The second is that the value of an asset can be quite personal and subjective, not only because everyone has their own anticipation of the future but also because not all investors will receive equal benefits from the same asset. Think of a company that has identified synergies with the enterprise it wishes to purchase, for example, a market leader considering buying a competitor in a niche market. It could leverage its own distribution network to sell the target company’s products to new customers and may thus expect more benefit from the target company than a financial investor without any synergies. Consequently, the former company will likely be ready to pay a higher price for that acquisition than the latter. An investor buying company stocks should consider not only whether the company will perform well in the future but whether the firm can outperform the expected profits that are implied by the stock price. In other words, if you buy a stock at an excessive price, you can lose money even if the company makes good profits in the future. Defining the value of an asset as the expected future profits leads to a first valuation approach known as the discounted cash flow (DCF) approach. It consists of weighing future cash flows presumably generated by the asset against the risk and the opportunity costs of the investment by applying a discount rate. Correspondingly, the value of an asset will be the sum of all discounted future cash flows that are expected between the valuation date and the end of the asset’s life. Asset value = with: Cash flow t: cash flow in year t n: year of the last cash flow d: discount rate

n t=1

Cash flow t ð d þ 1Þ t

ð1Þ

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21

This approach could be, however, quite difficult to implement in practice as it requires first estimating the future cash flows and then determining a discount rate reflecting cash flow forecast uncertainty. The second approach presented in this chapter, the relative approach, is much easier to implement because it bypasses the difficulty associated with the prospective dimension of the value. Rather than making fastidious cash flow forecasts, the relative approach consists of just looking at what other investors are willing to pay for similar assets. Due to its simplicity, this approach is the most widely used among professionals in finance. It is based on the pragmatic idea that the value of an asset is simply the price for which a buyer is willing to pay and a seller to sell. Despite its practicability, the relative approach harbors a greater risk because when there is high market volatility the market approach may yield inconsistent results. Solely looking at transactions made by others to get an idea of the value without paying attention to the fundamentals generates risks of sheep-like behavior with market movements leading to bubbles or cracks. Eventually, as both approaches have their intrinsic advantages and disadvantages, it is recommended to use them in combination when performing a valuation. The different results should be interpreted accordingly and will lead to a better understanding and assessment of the value.

2.2

Sharing Value Between the Stakeholders: Enterprise and Equity Value

When trying to estimate the value of a company, it is important to distinguish between two main concepts: enterprise value (EV) and equity value (EQ). The first is, as the name suggests, the value of the company as a whole. It is the expected value that could be created in the future by all the means of production within the company, including employees, machines, equipment, etc. However, it needs to be clarified that the value is understood here from the perspective of the capital holders (stockholders and debtholders). Consequently, it is the value generated by the production factors that goes to capital holders after having paid wages to employees, the purchase (or rental) of productive assets, and taxes to the state. In fact, if we take a step back, the value of the company is broader than its financial definition. First, the company can create noneconomic value by delivering non-trading goods and services. The company’s activity can also generate negative or positive externalities that are not captured in the company’s financials. Furthermore, the economic value created by a company corresponds to what is defined as the value added in the economy, i.e., the revenue from the outputs sold by the company (sales of goods and services) minus the cost of the inputs required for production (purchases of goods and services). It is this value added that contributes to the gross domestic product (GDP) at the macroeconomic level. Part of it is given to the state through taxes and social contributions, whereas the remains are shared

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between the employees (via wages and salaries) and the capital holders. In financial asset valuation, we focus on the share that goes to the latest. The financial interpretation of enterprise value, therefore, corresponds to the enterprise value for capital holders (shareholders and debtholders), which eventually differs from the enterprise value for the employees or the society. Capital holders can be divided into two categories: shareholders and debtholders. Debtholders are all people or entities that lend money to the company, for instance, banks that grant loans to the company. In return, debtholders also receive a share of the value added through interest payments. Shareholders, on their side, have invested their money in the company in exchange for property rights (stocks) that give them the possibility to participate in the company’s decisions and receive part of future earnings. The residual income received by shareholders is the remaining part of the value added after having paid all other stakeholders. The part of the enterprise value that belongs to shareholders (the stocks) is called equity value. In the stock price of a listed company, it is the equity value that is reflected and not the enterprise value: a listed company’s share price is equal to the equity value divided by the number of shares issued. The company creates value for society and receives monetary compensation for this (through sales). This gain is redistributed among the various stakeholders (Fig. 1): 1. Part of the created value is, intentionally or not (positive externalities), uncaptured by the stakeholders. In some cases, this uncaptured value can be negative (negative externalities), which implies a cost to society.

Fig. 1 Illustrative breakdown of the enterprise value for the society

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2. The state has invested in the country’s infrastructure and education and receives its share of the created value via taxes. 3. Employees have invested their time and work and receive a share of the created value through wages and salaries. 4. Debtholders have lent money and are rewarded by interest. 5. The rest of the created value goes to the shareholders who invested their money in the company.

2.3

Direct and Indirect Valuation Approach

When performing a valuation, whatever approach is used, it is always possible to determine either the enterprise value or the equity value. The valuation model will, of course, be different in each case. With the DCF approach, the cash flow and the discount rate to be used (formula 1) will be different whether enterprise value or equity value is pursued. The enterprise value will be obtained by discounting the cash flows returning to the capital holders, which are called cash flows to the firm (formula 2). It is the operating cash flow resulting from the activity reduced by taxes and required investments but before payments to either debt or equity holders. Accordingly, the discount rate will be a rate reflecting the weighted average risk taken by the shareholders and the debtholders together, which is called the weighted average cost of capital (WACC). Enterprise value ðEVÞ =

n t=1

CF to the firm t ðWACC þ 1Þt

ð2Þ

with: CF to the firm t: cash flow to the firm in year t n: year of the last cash flow WACC: weighted average cost of capital If the equity value is sought in the valuation model, the cash flows returning exclusively to shareholders, called cash flow to equity, must be discounted (formula 3). It is the cash flow to the firm reduced by the share accruing to the debtholders, i.e., after interest and principal payments. As the cash flow to equity is subjected to the payment of interests and the debt principal (the bankers are served before the shareholders), the risk is higher for the cash flow to equity than for the cash flow to the firm. Consequently, the rate used to discount cash flow to equity, called the cost of equity, is higher than the WACC. Equity value ðEQÞ = with:

n t=1

CF to equity t ðCoE þ 1Þt

ð3Þ

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Direct approach ¾ The

value of equity is determined directly by discounting cash flows to equity.

DCF

=

Relative valuation (multiples)

(

+ 1)

Indirect approach ¾ The enterprise value is first determined by discounting the cash flow to the firm. =

ℎ (

+ 1)

¾ The equity value is deducted from the enterprise value in a second step. EQ=EV-ND

¾ The value of equity is determined directly by applying a multiple.

¾ The enterprise value is first determined by applying a multiple.

EQ=multiple×variable

EV=multiple×variable ¾ The equity value is deducted from the enterprise value in a second step. EQ=EV-ND

Fig. 2 Four ways of determining equity value

CF to equity t: cash flow to equity in year t CoE: cost of equity Similarly, when using a relative valuation method, it is possible to determine either the enterprise value or the equity value. In the first case, enterprise values that were implicitly paid by investors during transactions on comparable assets are considered, whereas, in the second case, the disbursed equity values are surveyed. In fact, since the difference between the enterprise value and the equity value is the share of the debtholder, it is always possible to calculate the equity value from the enterprise value by subtracting the portion of the debtholder, i.e., by subtracting the net debt1 (ND) as shown in formula (4). Equity value ðEQÞ = Enterprise value ðEVÞ - Net debt ðNDÞ

ð4Þ

Hence, there are always two paths to estimate the equity value: either directly or indirectly by determining the enterprise value and subtracting the net debt. This being true for both the DCF and the relative valuation methods, four ways of determining equity value are presented in this chapter, summarized in Fig. 2: 1 In theory, the equity value is obtained by subtracting the fair value of the net debt (which may differ from its book value).

Reminder on Common Valuation Techniques

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3 Generating Value and Cash Flows 3.1

Accounting for Valuation

To introduce the valuation approaches, it is necessary to briefly discuss some major accounting concepts. Accounting is a set of rules that aims to represent the reality of the company’s financial situation. Just like real-life situations may be interpreted and described subjectively depending on the observer, the same financial situation can be presented in different ways depending on the accounting rules used. When valuing an asset, it is, therefore, essential to understand the interpretation rules that have been implemented to translate the reality of the company’s financial situation into accounting financial reports. This comprehension will allow investors to adjust the accounting figures in their valuation model if their own interpretation of reality differs from the one used in the accounting system. Fortunately, accounting rules have been established to avoid too much diversion in the range of possible interpretations for the same financial situation. However, these rules still allow for a certain degree of flexibility and can vary significantly from one country to another. In accounting, three financial documents are particularly important for the valuation: • The profit and loss (P&L) statement presenting revenues and expenses related to the goods sold for a given reporting period (see Fig. 3). • The balance sheet summarizing all the assets and liabilities owned by the company at the reporting date (see Fig. 4). • The cash flow statement, which presents the cash inflows and outflows that occurred during the reporting period (see Fig. 5). Those three statements act in concert and serve as the raw material for financial analysis and valuation.

Profit and loss statement (simplified example) (+) Sales (+) Other revenues (=) Total revenue (-) Raw materials (-) Personnel expenses (-) Other operating expenses/income (=) Earnings before interest, taxes, depreciation, and amortization (EBITDA) (-) Depreciation and amortization (=) Earnings before interest and taxes (EBIT) (-) Interest expenses (=) Earnings before taxes (EBT) (-) Income tax expenses (=) Net income

Fig. 3 Simplified income statement example

Comments:

EBITDA/EBIT reflects the operating result.

Interests are the created value returning to debtholders. Net income is the created value for shareholders.

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Balance sheet (simplified example) Assets Fixed assets Intangible assets Property, plant, and equipment Financial assets Current assets Inventories Trade receivables Other receivables Cash and cash equivalents

Liabilities Equity Issued capital Retained earnings Liabilities Long-term debts Short-term borrowings Trade payables Other liabilities

Fig. 4 Simplified balance sheet example

Cash flow statement (simplified example) (+) Cash flows from operations (+) Cash flows from investments (+) Cash flows from financing (=) Total cash flow

Comments: Cash flows from acquisition and disposal of fixed assets. Cash flows from issue and repayment of debt/equity. Change in cash.

Fig. 5 Simplified cash flow statement example

3.2

Capital Employed and Financial Resources

In the context of financial analysis and valuation, a slightly different presentation of the balance sheet is preferred in which the usage of the capital employed to run the business and its related financing resources can be clearly distinguished. For this purpose, two concepts must be introduced, the net working capital and the net debt: • The net working capital corresponds to the capital employed in day-to-day trading operations and results from the time delay between production, sales, and their conversion into cash flows. Indeed, most companies must incur expenses for their operations before being paid by their customers. For this reason, a company must allocate financing resources to cover all these expenses unfinanced by customer payments. Similarly, all the services and products received by the company from its suppliers or employees that have not yet been paid by the company are reducing the capital requirements to run the business. The net working capital is the link between the operating result from the P&L and the cash flow. Basically, the net working capital includes inventories and trade receivables from customers on the one side and is reduced by trade payables to suppliers and other current liabilities (such as liabilities to employees) on the other side. Working capital management is a critical issue for many companies. In some cases, the net working capital can be negative, meaning that the company receives cash from its customers faster than it pays its employees and suppliers. In this case, the working capital becomes a financing resource, i.e., the company can finance its own business on the shoulders of its suppliers or employees. This can happen in sectors where suppliers have little bargaining power: supermarkets have, for example, often a negative working capital.

Reminder on Common Valuation Techniques Capital Employed (simplified example) Usage Fixed assets Intangible assets Property, plant, and equipment Financial assets Net working capital (+) Inventories (+) Trade receivables (+) Other receivables (-) Trade payables (-) Other liabilities

Resources Equity Issued capital Retained earnings Net debt (+) Long-term debts (+) Short-term borrowings (-) Cash and cash equivalents

27 Comments: The book value of equity is the capital invested by shareholders, including the past profits not distributed as dividends (retained earnings), which have been reinvested in the company. The net debt is part of the capital employed, which has been financed by debtholders.

Fig. 6 Capital employed

• The net debt is the debt raised to finance the business minus the available cash and cash equivalent. It is part of the capital employed, which has been financed by debtholders. After reorganization, the balance sheet in Fig. 5 can be presented as in Fig. 6. The presentation of Fig. 6 offers a clear understanding of where the capital originates (on the right, with a distinction between shareholders and debtholders) and of the capital allocation (on the left, allocation between fixed assets and net working capital). However, this presentation remains a vision of the past because it is based on accounting. It enables a good understanding of the allocation of realized investments but does not provide the enterprise value, which is based on future profit expectations. Similarly, the amonut of equity reported in financial statements, the book value of equity, represents all past investments of the shareholders. This book value is composed of the following aspects: • The issued capital: the sum of all cash received by a company from shareholders upon the issuance of shares; and • The retained earnings: all profits (net incomes from the P&L) generated in the past years which have not been distributed as dividends and that have been reinvested in the company. As those profits belong to the shareholders, they correspond to new investments on their part. The company can use the cash from undistributed profits to acquire new capital expenditures. The current value of the equity and stocks, which is the purpose of the valuation, mostly differs from the book value of equity, and it is quite unusual for an investor to buy shares at their accounting book value.

3.3

Creating Value: Return on Investment and Return on Equity

Companies are expected to generate profits thanks to the work of employees and the capital employed. Although a company may incur losses temporarily, it is usually

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expected to generate profits in the long run. If nobody believes in the ability of the company to deliver future profits, the company will be shut down. The operating earnings generated by a company can be found in the P&L and correspond to the EBIT. The profit belonging to capital holders is obtained by removing the corporate taxes from the EBIT. As the capital employed creates this profit, the return on invested capital (ROIC) is defined by the formula (5). ROIC = =

EBIT × ð1 - CITÞ EBIT × ð1 - CITÞ = Capital employed Fixed assets þ NWC

EBIT × ð1 - CITÞ Book value of equity þ Book value of ND

ð5Þ

with: ROIC: return on invested capital CIT: corporate income tax rate NWC: net working capital ND: net debt The ROIC is a very important metric for investors. If the ROIC is higher than the cost of capital (which will be discussed in further detail in the following section), then there is value creation. If the ROIC is lower than the cost of capital, there is value destruction. All in all, it is a relatively basic but quite important rule in the investment world: capital must generate more profit than it costs. This rule can be applied to the totality of the invested capital, as is the case in formula (5), but it can also be applied to each investment of the company taken individually. The management should wonder: is the return on this specific investment or project greater than the cost of the capital? Is the return higher than the costs that the company must pay via interest and dividend payments to obtain capital to finance the project? It is, in this sense, interesting to determine the marginal return on investment, i.e., the additional profit generated by the last capital expenditure. In fact, if the marginal return on investment is lower than the cost of capital, it is not financially rational to continue investing in the company. Instead, the company should stop investing and give the profits back to the shareholders by distributing dividends. The shareholders should be able to invest the money received in more profitable companies that generate more value for them and potentially for society. It is this confrontation between return on capital and cost of capital that should allow the right allocation of available savings to the most profitable projects at the macroeconomics level. The marginal return on investment tends to decrease once a company reaches its optimal level of capacity. Imagine a company with old industrial equipment and machines that undertakes a renewal and automation of its machine park. In the beginning, the company will certainly achieve considerable productivity gains with each investment in a new machine—the marginal return on investment will be high. However, once the machine park has been completely modernized, it will be more

Reminder on Common Valuation Techniques

29

difficult to continue to improve productivity. Each increase in productivity will be more costly, and the marginal return on investment will decrease. In short, the more developed a company is, the lower the marginal return on its new investments. For this reason, mature companies tend to distribute higher dividends. So far, we have looked at the value created for capital holders as a whole. As already mentioned, capital holders are divided into two categories: debtholders and shareholders. We will focus now on the value created for shareholders only. This value consists of a company’s earnings after the payment of wages and taxes but also after interest payment to the debtholders. The gains entitled to the shareholders relate to the net income and can be found at the bottom of the P&L. As the capital invested by shareholders in the past corresponds to the book value of the equity, the return on their investments is the ratio of the net income divided by the book value of equity (see formula 62). ROE =

Net income Book value of equity

ð6Þ

Since the ROE is based on earnings after interest payments, it is affected by a company’s financing structure. ROE can be linked to the financing structure and the ROIC, as shown in formula (7). ROE = ROIC þ

Net debt × ½ROIC- i × ð1- CITÞ Book value of equity

ð7Þ

with: i: interest rate CIT: corporate income tax rate The second term on the right in formula (7) reflects the financial leverage effect. As long as the ROIC stays above the cost of debt (defined as interests minus the tax savings from interest deductibility), a firm can increase the return on equity by borrowing. However, the leverage can work in both directions. Suppose the ROIC drops under the cost of debt. In that case, the return on investment for the shareholders will be significantly lower if the company is highly leveraged (if the company has a huge amount of debt). If the operating income is sufficient to pay the interest, the leverage works well. Otherwise, shareholders must assume a deteriorated operating income and high-interest expenses. To summarize, leverage increases the risk for shareholders, and an appropriate balance of return and risk must be found.

2

Formula 6 provides the global return on equity directly invested in the company. However, an individual investor who acquired shares on the secondary market may have a different return. Formula 12 in Section 3 would be appropriate to compute the return for this individual investor.

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3.4

P. Jouy

Generating Cash Flows

The value created by the company is measured by the P&L. However, this created value is not directly converted into cash flows. There are several factors that generate a difference between the P&L and cash flow statements received by the company: • Change in net working capital: the first factor influencing cash flow is the time lag between the recognition of the accounting revenue, which is often recognized at delivery or invoicing, and the effective payment by the customers, which could arise later depending on payment terms. Similarly, on the expenses side, the same kind of time difference can be observed between the expense recognition in the P&L and the effective payments to suppliers. Furthermore, the use or construction of inventory strongly influences cash flow. • Noncash items: the P&L statement contains accounting losses and incomes that have no cash impact. The most common ones are depreciation and amortization (D&A) on fixed assets. These accounting depreciation losses are recognized in the P&L every year to reflect the continuous deterioration of the fixed assets all along their useful life. However, D&A do not translate into any real cash outflow. • Capital expenditures (capex): all investments (or disinvestments) in fixed assets executed by a company are not directly included in the P&L. These capital expenditures are, however, cash outflows. Capex are not recognized in the P&L because they are not directly attributable to the goods sold during the current period but are considered as investments in the assets required for the business. Those assets can be used over several periods, and it would therefore be misleading to allocate all capital expenditures in a single period in the P&L. • Financing cash flows: the cash flows from financing (debt repayment or issuance, dividends distribution, or equity increase) do not appear in the P&L, as financing is not part of the created value measured in the P&L. Only the interest expenses (or income) are considered in the P&L as the share of the created value which returns to the debtholders. As mentioned before, for the valuation, it is interesting to make a distinction between the cash flow that goes to the capital holders (cash flow to the firm) and the cash flow that returns to the shareholders (cash flow to equity). Cash flow to the firm, cash flow to equity, and total cash flow are summarized in Fig. 7.

4 The Cost of Capital 4.1

Understanding the Cost of Capital

In a DCF approach, the value of an asset is determined by applying a discount rate to future cash flows. After having introduced the components of cash flows, the following section will focus on the discount rate. A short introduction to the main concepts underlying this rate and basic practical calculation methodologies based on

Reminder on Common Valuation Techniques

31

Cash flow Comments: Created value for capital holders (+) EBITx(1–CIT*) (+) D&A (and other noncash items not included in NWC**) (-) Change in net working capital Investment in net working capital (-) Capital expenditures (net of fixed asset disposals) Investment in fixed assets (=) Cash flow to the firm Cash flow to capital holders (-) Interest payments/(+) Interest received (+) Tax saving on interests Interest deductibility (-) Debt repayments (+) Debt issuance (=) Cash flow to equity Cash flow to shareholders (-) Dividend payments (+) increase in equity (=) Total cash flow Change in cash *CIT: Corporate income tax rate **all noncash items (provisions) included in net working capital are already neutralized in the change in net working capital

Fig. 7 The cash flow

the capital asset pricing model (CAPM) will be provided. For simplification and better comprehensibility, we avoid going too deeply into theoretical details and make some shortcuts and approximations. The interested reader may refer to the existing literature on portfolio theory to get a better view of the problematic (Lintner, 1965; Mossin, 1966; Poncet & Portait, 2022; Sharpe, 1964; Treynor, 1961).

4.2

The Perspective of the Investor

The discount rate is the financial translation of the adage “a bird in the hand is worth two in the bush.” It is the idea that in some cases, it is better to receive €100 today than €200 in five years, while in other situations, the opposite could be true. It is this uncertainty that the investor tries to capture in the discount rate: is it better to receive €100 now or €200 in five years? To answer this question, the investor will try to estimate the present value of the promise to receive €200 in five years. If this present value is greater than €100, they will accept the promise rather than obtain the €100 right now. However, knowing the present value of the €200 in five years is not so intuitive. The problem can be apprehended in the form of an opportunity cost problematic. Let us assume that the investor has a choice between two options: Option 1: The investor can buy a ticket for a value of €100. In five years, they will have the opportunity to convert the ticket into €200 (we consider here that there is no doubt that they will receive the €200). Option 2: Instead of buying the ticket, they could alternatively put the money required for the acquisition of the ticket into a bank account bearing a 10% interest rate per year. In this case, the solution is quite simple. The investor will take option 1 without hesitation as here they will receive €200 in five years, whereas in option 2, they would only have obtained €161 (161 = 100x[1 + 10%]^5). If now the ticket price in the first option increases to €130, the investor will prefer the second option and invest the €130 bearing a 10% interest rate to obtain €209 after five years. By solving

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the equation in formula (8), it can be demonstrated that the investor will only purchase the ticket if its price is below €124. p × ð10% þ 1Þ5 ≤ 200

ð8Þ

with: p: price of the ticket In a scenario where the ticket price in option 1 is not fixed but is listed for auction between competing investors who could all benefit from the second option, the selling price of the ticket will likely be close to €124. This price limit results from the fact that investors are not willing to purchase the ticket above this threshold. In this case, the present value of €200 in five years will be €124 given a constant interest rate (see formula 9). p=

200 200 = = 124 5 ð i þ 1Þ 5 ð10% þ 1Þ

ð9Þ

with: i: interest rate Note that there is a correlation between the ticket value and the interest rate. If the interest rate of the bank account decreases to 8%, the present value of the ticket will increase to €136. Indeed, with a decreasing interest rate, the option to receive €200 in five years will become more attractive. It is also interesting to note that the value of the ticket increases over time. If the buyer sells their ticket after one year to other investors (assuming that they still benefit from option 2 with an interest rate of 10%), the present value will be €137 rather than the previously calculated €124 because the considered time frame is four instead of five years. In a sense, the discount rate represents the time value of money (see formula 10). p=

200 = 137 ð10% þ 1Þ4

ð10Þ

Now, let us assume that our investor stops gambling with tickets and returns to a more serious and valuable activity: the investment. This time, the investor no longer has the choice between options 1 and 2 but needs to select assets with diverse levels of risk. As their investment capacity is limited, they are obliged to make choices. Suppose that they have identified an investment opportunity and offers a purchase price based on a DCF valuation. It can be easily shown3 that the investor’s annual 3

This can be demonstrated by mathematical induction. The value of the investment in year n (including dividends paid) will always be equal to the value in year n - 1 plus the discount rate, provided that the cash flows are effectively realized as forecasted.

Reminder on Common Valuation Techniques

33

return will be equal to the discount rate used in the valuation, provided that the cash flows are effectively realized as forecasted. By offering a purchase price, the investor is, therefore, implicitly asking for a rate of return via the discount rate. How does an investor determine this requested return? As in the case of the ticket example, this depends on the opportunity costs and, therefore, on the returns that the investor can expect to obtain from other assets in the market. If the investor thinks that the market is full of assets with high returns, they will tend to ask for a higher level of return on investment. The other major driver is the risk. We have not introduced it until here to simplify the purpose, but risk is the basis of the investment. In the harsh reality of investment, you can never be sure that the expected cash flows will be achieved; thus, you can never be sure to receive the €200 in five years from our example. The riskier an asset is perceived to be, the higher the return requested by an investor needs to be to compensate for the risk of nonrealization of future cash flows. When investing in several risky assets, some of them will not realize the anticipated cash flows, while others will meet the expectations. The high returns generated on the assets that have realized the expected cash flows can cover the losses incurred on the other assets. Therefore, the risk-return combination, and not the return only, is considered by the investor and used to compare one asset to another. In conclusion, from an investor’s point of view, the discount rate of the DCF method is the return on investments requested by the investor to finance an asset in regard to the risk associated with its acquisition.

4.3

The Perspective of the Company

Having understood what the discount rate is for the investor, we will now look at what it represents for the company. A company tries to create value for its various stakeholders as well as to develop itself. For this purpose, the company may need capital to finance new investments and projects. To realize this, there are two options. The first is to finance investments by debt, i.e., ask banks or other financial institutions to lend cash against interest payments. The second option is to get cash from its current shareholders or from new investors in exchange for shares in the company (stocks). In the last case, the company will be financed through equity. In fact, the company can also finance itself with the profits generated by its own activities. However, as these profits belong to shareholders, reinvesting profits (i.e., not distributing past profits as dividends) is equivalent to new shareholder investments. So, in the end, there are only two options: being financed by debt or by equity. Let us start with the case of debt financing. When a business secures a line of credit, it gains access to funds, which essentially constitutes the principal amount of the loan. This loan will, however, generate future cash outflows via the payment of principal and interest. Because of the interests, the sum of the future cash outflows will exceed

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the amount of the principal received at the time of borrowing. Nevertheless, the company needs the cash now and is willing to pay this additional cost. The company assigns a value to time and to collecting cash at this specific time. From the company’s point of view, the amount of money received today should have at least the same value as the present value of future cash outflows generated by the loan. Loan value =

n t=1

CFL t ð d þ 1Þ t

ð11Þ

with: CFL t: cash flow related to the loan in year t n: year of the last cash flow d: discount rate It can be shown that the discount rate in formula (11) is equal to the interest rate. The discount rate, which enables to equalization of the value of the debt borrowed and the discounted cash flow to the debtholders, is the cost of the debt, i.e., the interest rate. In many countries, interests allow tax savings. In this case, the equation remains valid, but the tax savings reduce both the cash outflows and the cost of the debt (the discount rate). Example 1 Bullet Loan A company subscribes to a bullet loan of €100k from its bank. The loan has a maturity of five years and bears a 5% annual interest rate. Furthermore, the company whose tax rate is 30% can fiscally deduct the interest. The principal must be repaid in full after five years. The cash flows related to the loan are summarized below: Year Loan drawdown Interest payments Tax savings Principal repayment Total cash flow

t=0 100.0

100.0

t=1

t=2

t=3

t=4

-5.0 1.5

-5.0 1.5

-5.0 1.5

-5.0 1.5

-3.5

-3.5

-3.5

t=5

-5.0 1.5 -100.0 -3.5 -103.5

The discount rate which enables to match the discounted cash flow with the value of the principal can be calculated as follows: 100 =

3.5 3.5 3.5 3.5 103.5 + + + + ( + 1)1 ( + 1)2 ( + 1)3 ( + 1)4 ( + 1)5

Solution

Reminder on Common Valuation Techniques

35

The same logic can be applied to equity. A company receives cash from its shareholders by raising equity and will reward them by paying out its future profits in the form of dividends. The discounted value of future cash flows to shareholders equals the value of the equity raised today, and the discount rate is the cost of equity. Unlike cash flows to debtholders, which follow a fixed payment schedule, cash flows to shareholders are not subdued to any timetable. If the company does not make any profit, it is not committed to paying its shareholders. Cash flows to equity thus allow more flexibility but are, on the other hand, also more expensive: the cost of equity surpasses the cost of debt. To sum up, companies need money to finance their business and projects, and for them, the discount rate is the required price to obtain this capital. Companies will, of course, try to minimize this cost. If the cost of capital becomes too high, companies will probably request less financing and undertake only the most profitable projects. Indeed, as seen in Sect. 2, only the projects with an ROIC above the cost of capital are creating value.

4.4

Matching Both Perceptions

On the one hand, there are investors who compare several assets to find the best combination of risk/return, whereas, on the other hand, there are companies striving to finance their projects with the lowest cost of capital possible. Both sides are trying to pull the rate (requested rate of return or cost of capital) in opposing directions. The supply of capital and the demand for investment confront each other in the market and find an equilibrium thanks to the rate. If savings increase, more capital becomes available, which induces investors to be more competitive and accept lower returns. These lower rates will then allow less profitable projects to find financing, as projects are financed only if their expected return exceeds the return requested by the investors. The supply of capital and the demand for financing will be matched at a new level and at a new interest rate. On the contrary, less available capital (savings) on the market implies higher interest rates because companies will have difficulties in finding investors, and ultimately, fewer projects (only the more profitable ones) will receive financing. As not all assets (or projects) carry the same risk, the matching of supply and demand through the rate is based on the risk/return combination of each asset. This is true in most financial models assuming that investors are only maximizing the risk/ return combination. However, as environmental, social, and governance (ESG) criteria are a central topic in this book, it should be mentioned that if some investors do not only maximize the risk/return combination but also consider good ecological, social, and governmental behaviors in their investments, the market equilibrium and its related rate could possibly (at least locally) be modified. This may happen, for example, if some investors are able to request less return from companies trying to improve their ecological footprint. However, the valuation approaches presented in this chapter are based on the usual assumption that investors are only maximizing the risk/return combination.

36

4.5

P. Jouy

Measuring Risk and Portfolio Theory

In Sect. 2, the return-on-investment formulae (formulas 5 and 6) have been presented in the context of a P&L and cash flow analysis. These formulas are important because they allow the analysis of the return on investment generated during the full life of a project as well as an understanding of how the activity generates value. However, returns on equity investments can also be directly deduced from stock market data. Imagine an investor buys a stock and holds it for five years before selling it. In that case, they can calculate the total return of their operation based on the difference between the sell and the purchase price of their stock and the gains due to all dividends received over the five years of holding the stock. More generally, the return on stock between two periods t1 and t2 is given by formula (12). 1 þ returnt2t1 =

Stock Pricet2 þ Divt2t1 = ð1 þ r t2t1 Þt2 - t1 Stock Pricet1

ð12Þ

with: returnt2t1: total return over the period from t1 to t2 Stock Pricet2: stock price at t2 Stock Pricet1: stock price at t1 Divt2t1: dividends received between t1 and t2 rt2t1: annualized return over the period from t1 to t2 Looking at formula (12), one could imagine that dividends increase shareholder returns and that these returns are further enhanced the more dividends a company disburses. In practice, the dividend is more or less neutral4 on the return on equity. Indeed, if the dividend brings money into the shareholder’s pocket, the same amount is lost for the company and, therefore, reduces the value of the equity, which is, in the end, reflected in the stock price. In fact, as seen in the first sections, the driver for the return on equity is the generation of net income. This net income can then be returned to the shareholder in the form of dividends or be reinvested in the company. In both cases, the net income remains the shareholder’s property, and its full value is included in the return (either in the dividend or in the stock price). In standard models, investors seek not only to maximize their return but also to minimize the risk. In finance, risk is associated with uncertainty and is measured by volatility. On a set of values, the volatility (or standard deviation) is defined as the measure of the deviations of each value from the average of the whole set. The volatility of a given stock return observed on a sample of returns is given by formula (13) (Fig. 8).

4

There may be a tax impact because dividends and capital gains are not taxed at the same rate.

return

Reminder on Common Valuation Techniques

14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% -2.0% -4.0% -6.0%

37

if volatility is high, the returns will be far from average.

7 3

̅

6

10

4

1

8

9

5 2 i=1 i=2 i=3 i=4 i=5 i=6 i=7 i=8 i=9 i=10 period

Fig. 8 Volatility—illustrative example

1 N

σ=

N

ðr i - r Þ2 where r =

i=1

1 N

N

ð13Þ

ri i=1

with: σ:volatility or standard deviation N: number of returns observed ri: return observed in period i r: average return observed One way of reducing risk is to diversify investments, i.e., buying several assets with different risk profiles and not putting all eggs in one basket. This is quite intuitive: it is more prudent to invest in several risky projects, some of which will succeed and some of which will fail than to put all money on only one of them. Diversification neutralizes risk if the returns of the assets held in the investment portfolio are not dependent on each other. Of course, if the assets are highly correlated, i.e., if the return on the assets in the portfolio tends to go down or up together, then diversification is no longer an effective hedge against risk. The correlation of the two assets is given by formula (14). Corrðx, yÞ = Covðx, yÞ = σxy =

with:

1 N

N i=1

Covðx, yÞ σx σy

ðxi - xÞ × ðyi - yÞ where x =

ð14Þ 1 N

N i=1

xi and y =

1 N

N i=1

yi

38

P. Jouy

σ xy: covariance between stock x and stock y σ x: volatility of stock x σ y: volatility of stock y N: number of returns observed xi: return of stock x in period i x: average return of stock x yi: return of stock y in period i y: average return of stock y Theories were established in the 1950s, in particular by H. Markowitz (Markowitz, 1952), on how to build an optimal portfolio. It has been shown that based on the expected return and the volatility of each asset but also depending on the correlation between the different assets, the weighting of each asset in a portfolio can be adjusted to optimize the risk/return combination. Example 2 Effect of Diversification Consider three assets A, B, and C with a volatility of 30%, 45%, and 20% and a return of 12%, 18%, and 6%, respectively. Suppose that the correlation between A and B is 0.4, between A and C is 0.3, and between B and C is 0.5. • If investors build a portfolio with only asset C, they will get a 6% return with a 20% volatility. • If now they build a portfolio composed of 37% of asset A and 63% of asset C. The return on the portfolio is given by: rp = 37 % × rA +63 % × rC = 37 % × 12%+ 63 % × 6% =8.8% and the volatility of the portfolio is equal to:

• If now they decide to add asset B and weights their portfolio with 34% of asset A, 9% of asset B, and 57% of asset C, then the return and the volatility of the portfolio will be as follows:

In the end, all three portfolios bear the same risk with a volatility of 20%. The third portfolio is, however, better because it offers a return of 9.1%, which is higher than the return of the other portfolios.

Reminder on Common Valuation Techniques

39

From a given investment universe, it is possible to build an optimal portfolio that optimizes the return for a given level of risk (or volatility). Depending on their level of risk aversion, investors working in this universe are, therefore, able to build an optimal portfolio (efficient portfolio) that corresponds to the level of risk they are willing to accept (see Example 2: For a given volatility of 20% an investor can build several portfolios with different returns). However, this calculation of the efficient portfolio is relatively subjective because it depends on the return expectations on the various assets. According to their own expectations of the future performance of the assets, each investor will have a different estimation of the efficient portfolio and will, therefore, have their own weighting of assets to optimize their portfolio. When an investor constructs a portfolio, the return and the volatility of each of the assets taken separately could be expressed in relation to the portfolio (see formula 15). r a = μa þ β × ðr p - μp Þ þ Undiversifiable risk

þ

σ2ε

εa Diversifiable risk

where β =

Diversifiable risk

and σ2a = β2 σ2p

σap σ2p

ð15Þ

with: μa, μp: constants ra: asset return rp: portfolio return εa: specific asset return σ a: volatility of the asset σ p: volatility of the portfolio σ ap: covariance between the asset and the portfolio σ ε: diversifiable risk If investors optimize their portfolio, they can neutralize the diversifiable risk by playing with the weightings of the different assets. The optimal solution is obtained when the diversifiable risk of each asset has been entirely suppressed, which means for εa and σ ε to equal zero in formula (15). In this case, there is a linear affine relationship between the stock’s return and the covariance of the stock’s return with the optimal portfolio’s return (see formula 16).

with:

r a = μa þ β × ðr p - μp Þ and σ2a =

β2 σ2p

Undiversifiable risk

Undiversifiable risk

where β =

σap σ2p

ð16Þ

40

P. Jouy

μa, μp*: constants ra: asset return rp*: optimal (or efficient) portfolio return σ a: volatility of the asset σ p*: volatility of the optimal (or efficient) portfolio

4.6

Introduction to the Capital Asset Pricing Model

The CAPM was established and developed in the early 1960s (Lintner, 1965; Mossin, 1966; Sharpe, 1964; Treynor, 1961). Although it is subject to criticism because of its strong underlying assumptions, this model is the most widely used by analysts to determine the cost of capital. The strength and popularity of this model are derived from its assumption of a simple linear relationship between return and risk, which could be applied to all assets independently from each investor’s portfolio. The main assumptions underlying CAPM are the following: • Investors base their investments on the pursuit of only two criteria, minimization of volatility and maximization of expected return. • The market portfolio, composed of all assets in the market (apart from risk-free assets), is assumed to be efficient, i.e., to optimize the risk/return trade-off. This is a bold hypothesis because, as we have seen, the efficient risky portfolio should be specific to each investor since it depends on personal expectations. However, the CAPM assumes that the market as a whole is optimal and maximizes the riskreturn trade-off, even though investors may have different expectations. An investor who holds all the assets in the market would therefore maximize the risk-return trade-off. This strong assumption makes it possible to eliminate the relative dimension of each investor’s expectation. • In its standard version, the CAPM assumes the existence of a risk-free asset. • Furthermore, the CAPM is a partial equilibrium model in which investors compete for a fixed demand for capital from firms. CAPM is not a global equilibrium model considering interactions and elasticity between the supply and demand of capital. Since the market portfolio is assumed to be the optimal risky portfolio, the return on any asset can be decomposed in terms of its contribution to the risk of the market portfolio in the same way as in formula (16). This result is very powerful because it allows finding the rate of return of any asset according to its correlation with the market portfolio. The CAPM is widely used in practice to determine the cost of equity using formula (17).

Reminder on Common Valuation Techniques

CoE = r f þ β × ðr m - r f Þ = r f þ β × ERP where β =

41

σsm σ2m

ð17Þ

with: CoE: cost of equity rf: risk-free rate rm: market return ERP: equity risk premium σ sm: covariance between stock and the market σ m: volatility of the market The assumptions of the CAPM raise some major philosophical questions for investors. Indeed, accepting that the market portfolio is the optimal portfolio is a big argument in favor of a passive investment strategy based on Exchange Traded Funds (ETFs) tracking broad stock indices rather than on stock picking. ETFs will indeed be closer to the optimal portfolio than a portfolio composed of a few hand-picked stocks. This makes the role of fundamental analysis and valuation less relevant. There are, however, some counterarguments. First, it is possible that the market is not always efficient or at least locally or temporarily inefficient. Furthermore, if all investors pursue a passive investment strategy based on buying broad indices, it is likely that the market will become less efficient. Some investors will always have to continue their analytical job to keep the market efficient. In addition, there are unlisted assets and companies (private equity firms) without directly available price indications, and where an estimation of the value is, therefore, indispensable when buying such assets.

4.7 4.7.1

Computing the Cost of Capital Cost of Equity

The CAPM provides a theoretical framework to compute the cost of equity; the next section will focus on how to find proxies to determine each parameter of formula (17) in practice. One of the difficulties that cross the determination of these proxies is that all these parameters are dynamic and change over time. Indeed, the balance between the supply and demand of capital, the correlation between assets and the expected returns are not fixed in time. However, the data used to estimate these proxies are historical market data observed over a more or less long period. The shorter the observation period, the more representative the data will be of the current market situation. However, if the observation period is too short, statistical noise may disturb the data. A more extended observation period of the market data will thus allow a more substantial statistical relevance of the proxies. There is, therefore, a

42

P. Jouy

compromise to be found in the observation period; The period should be not too short to avoid statistical noise but not too long to capture the current market situation.

4.7.1.1

The Risk-Free Rate

The risk-free rate is the rate of return on the risk-free asset, which results from the global balance between savings and investment demand. In practice, government debt in developed countries is considered a risk-free asset. Governments issue bonds for different maturities, and it appears that interest rates are not the same for each maturity. This is the famous yield curve where short-term maturity bonds generally offer a lower yield than long-term maturity bonds. The existence of a differential between short- and long-term rates is not intuitive, the asset being risk-free and the rate being proportional to the risk, the rates should be the same for short- and longterm maturities (the yield curve should be flat). However, this rate differential reflects a differentiated capital supply-demand equilibrium depending on the asset’s maturity: more firms are looking for long-term financing, while investors are probably more inclined to finance the short term. This implies a higher interest rate for long-term financing. The curve is also explained by the market’s expectations on monetary policy, inflation, and economic growth, i.e., expectations of the future equilibrium of capital supply and demand. From a theoretical point of view, it would be thus appropriate to use a different discount rate for each cash flow period in the DCF approach. As a practical compromise, a single rate can be retained, corresponding to a maturity that would match the duration of the asset being valued. As the companies being valued often have an indefinite life, the durations observed are usually long. In practice, analysts often use maturities between ten and thirty years (Figs. 9 and 10).

4.7.1.2

Equity Risk Premium (ERP)

The ERP is the difference between the expected market return and the risk-free rate. It is the return premium requested by investors to cover the nondiversifiable risk of 12% 11% 10% 8%

8% 7%

9% 8%8%

8% 6%6%

5%

6% 5% 6%

6% 5%

5%

4%

3%3%

4%5% 4% 3%

3%

2.3% 3% 1.6% 2% 2%2%2%2%2% 2% 1% 1%1% 0%

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

2%

US Government Debt—ten-year maturity

Average twenty years

Fig. 9 The US government bonds: Ten-year maturity (Source: Capital IQ)

Average ten years

Reminder on Common Valuation Techniques 4.2% 4.3% 3.7%

4.0% 3.4%

43

4.4% 3.0%

3.4%

3.0% 1.9%

2.0%

1.8% 1.3%

0.6% 0.7%

0.3% 0.5% 0.3%

0.4%

-0.3% -0.4% -0.7% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 German Government Bonds—ten-year maturity

Average twenty years

Average ten years

Fig. 10 The interest rate of the German government bonds: Ten-year maturity (Source: Capital IQ)

the market. This risk premium can be a bit challenging to estimate. In practice, three approaches could be implemented. The first approach is to make a benchmark by looking at the premium used by other equity research analysts and investors. This parameter is not specific to the asset but to the market, which makes a benchmark relevant. The second solution is to use a large stock index as a proxy for the market and to look at the historical return of the selected index over a long period. For example, the S&P 500 could be used as a proxy for the US market. The total return, including price changes and dividends, must be considered for the calculation. It is required to be careful when selecting the index because some indices include the dividend yield (called total return indices, such as the German DAX), whereas others reflect only price changes (called price return indices, such as the French CAC 40). If the last ones are retained, the dividend yield should be added back. The disadvantage is that this approach is based on historical data to estimate the expected return, and of course, the past is not always a good indication of the future. Furthermore, given the volatility of stock markets, it is necessary to observe data over long periods to obtain a statistically relevant average. The graph below (Fig. 11) shows a risk premium calculation using the Euro Stoxx (EUR) (Gross Total Return) Index as a proxy for the market return in Europe and the German ten-year government bonds as a proxy for the risk-free asset. The difference between the annual return of the index and the German bond rate was calculated for each of the last twenty years. High volatility of the annual risk premium can be observed (20% volatility over twenty years). Consequently, the risk premium calculated over a ten-year average of 11.5% is significantly different from the one calculated over a twenty-year average of 5.6%. This argues for using a long observation period to avoid too much statistical noise. In fact, calculations over thirty or fifty years could be considered. The risk-free rate and the market premium form the total market expected return. Therefore, it is crucial to be consistent with the approach used for estimating these two parameters. Using a ten-year average for the risk-free rate (0.4% in Fig. 9) and a twenty-year average for the ERP (5.6% in Fig. 11) would, for example, not be appropriate.

44

P. Jouy

24% 13%

26%

19%

19%

10% 2%

27%

23% 4%

1%

10%

24% 11.5%

13% 5.6%

5%

1%

-12%

-17% -35%

-46% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Average twenty years

Annual return minus risk-free rate

Average ten years

Fig. 11 Historical equity risk premium: Europe (Source: Capital IQ)

32% 24% 24%

23% 11% 11%

11%

7%

28% 24% 15%

24%

17%

1% 4%

8.7%

23%

19% 12% 14% 12% 10% 0%

11% 7% 1% 1%

4.9% -5%

-4%

-11%

-7%

29%

7.6%

-14% -17% -26%

-1%

30% 27% 17% 8.2%

-7%

3.7%

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

-39%

Annual return minus risk-free rate

Rolling average twenty years

Fig. 12 Historical equity risk premium: the USA (Source: Capital IQ)

Figure 12 illustrates, once again, the risk premium volatility, this time observed on the US market (S&P 500 Total Return Index [SPTR]) is used as a proxy). It can be observed that the rolling average over twenty years ranges between 3.7% (in 2018) and 8.7% (in 2001), depending on the year of observation. Based on the following set of data and computation, the average equity risk premium in the USA amounts to 8.4% over forty years (from 1982 to 2021). The last approach to determine an ERP is using earnings forecasts and an index’s current price. As for a single company, the future cash flows of all the companies included in the index must, after discounting, be equal to the index’s price. As the index is a proxy for the market portfolio, the discount rate that allows the index price to be reconciled with the earnings forecasts is the implied ERP. This method has the advantage of being future-oriented but is very sensitive to forecasting assumptions, particularly the long-term growth premise.

4.7.1.3

A Brief Remark on Inflation

As can be observed, the data collected in the US and European markets are not the same. For example, the interest rates on German government bonds are significantly

Reminder on Common Valuation Techniques

45

different from those on US government bonds. This is due to the specificities of each of these economies and, notably, to inflation. State interest rates are linked to inflation and inflation forecasts. Inflation can, therefore, play an essential role in the valuation result. Attention should be paid to the consistency of the inflation assumptions used for the cash flow forecasts, on the one hand, and the rate calculation, on the other. If future cash flows are calculated based on German inflation, the discount rate should be accordingly based on German inflation and German parameters. It is also possible to do a valuation with real rates, i.e., without inflation. In this case, cash flow forecasts should also be performed without inflation.

4.7.1.4

Beta

The beta measures the correlation between a stock and the market. It shows how the stock reacts to market fluctuations. A beta higher than one means that the stock tends to overreact to market movements by amplifying them. For example, banks generally have a beta higher than one. Conversely, a stock with a beta lower than one will tend to be less affected by market movements. Since beta is always related to the fluctuation of the market, it is possible for a stock to be very volatile but to have a low beta if the stock’s movements are independent from the market. As for the ERP, a stock index can be used as a proxy for the market portfolio when computing the beta. The following formula could be applied to the stock and index returns to determine the beta of a specific stock. β=

σ index,stock σ 2index

ð18Þ

with σ index, stock: covariance between the index and the stock σ index: volatility of the index There are several ways to estimate the beta. First, stock returns can be calculated over different periods: daily, weekly, monthly, etc. The shorter the calculation period, the more likely the statistical noise will disturb the measurement. It is, therefore, preferable to use weekly or monthly returns. Then it should be defined what is the better observation period for the correlation: should we consider the correlation between the returns during the last year or the last fifteen years? As mentioned before, it is a compromise between the statistical and temporal relevance of the measure. The longer the observation period, the greater the statistical accuracy because of the larger dataset. On the other hand, as stocks and companies are dynamic objects, the correlation with the market may change over time. If the observation period is too long, these changes will not be well reflected in the beta. In practice, most analysts use either a beta based on weekly returns observed over two years or a beta based on monthly returns over five years. Graphically, the beta

46

P. Jouy

corresponds to the angle of the linear regression line between the index return and the stock return. Example 3 Beta Estimation of the Company Albioma In this example, the beta of Albioma, an independent renewable energy producer, is estimated as of 31.12.2021. The CAC AllShares index is used as a proxy for the market portfolio. The computation is based on the weekly returns of the Albioma stock and of the CAC AllShares index observed over the two years 2021 and 2020. Pricing date

Stock Price (€) 34.28 32.70 31.80 32.80 32.36

31.12.2021 24.12.2021 17.12.2021 10.12.2021 03.12.2021

…

16.39 15.19 14.25 14.00 13.59

7.93% 6.58% 1.78% 3.04% -4.47%

14,197.15 14,044.99 13,740.64 13,920.78 13,539.99

10,802.66 11,172.32 11,327.22 11,168.11 11,163.41

The beta is computed based on the observed weekly returns.

Regression between Albioma stock return and CAC AllShares index return over the two years 2021 and 2020 17.00%

Albioma return

12.00% 7.00%

= 0.79

2.00% -3.00% -8.00%

-13.00% -18.00% -18.00%

-13.00%

-8.00% -3.00% 2.00% 7.00% CAC AllShares Index return

Source: Capital IQ for the stock and index prices

12.00%

Index return 1.08% 2.22% -1.29% 2.81% -0.18%

…

…

31.01.2020 24.01.2020 17.01.2020 10.01.2020 03.01.2020

Index

…

…

Weekly returns are computed over two years

Stock return 4.83% 2.83% -3.05% 1.36% 11.03%

17.00%

-3.31% -1.37% 1.42% 0.04% 0.10%

Reminder on Common Valuation Techniques

47

Several methods can assess the statistical relevance of the beta. The simplest is to look at the coefficient of determination (R2). Other statistical tests, such as the student test (t-test), can also be used. The statistical significance is generally low which can be observed in practice by calculating a company’s beta with the same approach, e.g., weekly return over two years, but using a different day of the week as the starting point for the calculation. The beta calculated using Friday-toFriday returns may be very different from the one based on Monday-to-Monday returns. It is possible to reduce the uncertainty of the beta by using a peer group, i.e., calculate the beta for each peer and retain the average beta over the peer group as the best estimate. Indeed, the average of uncertain estimates will always be less uncertain than each estimate taken separately.5 It is the same logic as diversification. The statistical noise of each of the peers’ betas will neutralize each other. A peer group can also be useful when the company to be valued is not listed (and there is no data available for the beta calculation). More details on the constitution of a peer group are given in the section on relative valuation. The idea is to select a group of peers composed of listed companies evolving in the same sector and carrying a similar risk. One of the problems when using a peer group is that the financial structure of each peer affects the beta. Indeed, as seen in Sect. 2, the risk is higher for strongly indebted companies. This difficulty can be resolved by introducing the following formula developed by Hamada (Hamada, 1972), which enables the computation of an unlevered beta that is independent of the financial structure (unlevered beta or asset beta). The unlevered beta would be the beta that would have been observed if the company was only financed by equity. βlevered = βunlevered × ½1 þ

D × ð1CITÞ E

ð19Þ

with: E: market value of equity D: market value of debt CIT: corporate income tax rate According to this formula, the risk of a stock (levered beta or equity beta) increases linearly with the debt-to-equity ratio (gearing). It is thus possible to calculate the unlevered beta of each of the peers, deduce an average beta independent of the financial structure, and then leverage the peers’ average beta by using the company’s gearing.

5

Assuming that the estimators are unbiased.

48

P. Jouy

Example 4 Beta Estimation Based on a Peer Group In this example, we estimate a beta that could be used for the valuation of a European independent renewable energy producer. This beta calculation is based on the weekly returns of listed companies observed over a period of two years from 01/01/2020 to 31/12/2021. The different steps of the calculation are described below: 1. The first step is to identify comparable listed companies. Based on their activity, the following European independent renewable energy producers have been selected for the calculation: Albioma, Alerion Clean Power S.p. A., EDP Renováveis, S.A., Encavis AG, Neoen S.A., Scatec ASA, Solaria Energía y Medio Ambiente, S.A. and Voltalia SA. The business of the selected companies varies from one to another, some being more specialized in wind energy, others in solar or other forms of renewable energy. However, since there are few independent players, the companies were all selected despite these differences. The large energy producers owning just a part of their activity in the renewable energy segment have been excluded due to a lack of comparability with the pure players. 2. In the second step, the levered beta of each company is estimated. A specific reference index for each peer was used according to their country location. 3. According to their financial structure, the unlevered beta of each peer was then determined. e.g. for Albioma, the unlevered beta is obtained by resolving: βunlevered = 1þ89:2% 0:79 × ð1 - 26:5%Þ 4. The average beta of the peers is 0.52. Assuming that the company to be valued is paying 25.0% of corporate tax and has a debt structure composed of 40% debt and 60% equity, it is possible to calculate the leveraged beta as follows: β_levered = 0.52 × [1 + (40%)/(60%) × (1–25.0%)] = 0.78

Company name 1

Country

2

Tax rate Index name

Levered Beta

D/E(1)

Unlevered Beta

3

Albiomia

France

26.5%

CAC AllShares

0.79x

89.2%

0.47x

Alerion Clean Power S.p.A.

Italy

24.0%

FTSE Italia All-Share

0.82x

79.7%

0.51x

EDP Renováveis, S.A.

Spain

25.0%

Madrid Ibex 35

0.57x

36.0%

0.45x

Encavis AG

Germany

30.0%

CDAX (Total Return)

0.98x

67.7%

0.67x

Neoen S.A.

France

26.5%

CAC AllShares

0.44x

56.2%

0.31x

Scatec ASA

Norway

22.0%

Oslo OBX Total Return

0.84x

51.0%

0.60x

Solaria Energía y Medio Ambiente, S.A.

Spain

25.0%

Madrid Ibex 35

0.79x

21.7%

0.68x

Voltalia SA

France

26.5%

CAC AllShares

0.65x

36.8%

Average (1)

Average D/E ratio over the last 2 years

4

0.51x 0.52x

Source: Capital IQ for the stock and index prices

(continued)

Reminder on Common Valuation Techniques

49

Example 5 Cost of Equity Estimation of a European Independent Renewable Energy Producer In continuation of Example 4, the cost of equity as of 31/12/2021 of a European independent renewable energy producer with a capital structure composed of 40% of debt and 60% of equity is estimated below. The components of the cost of equity can be estimated as follows: 1. Risk-free rate: The German government bonds with ten-year maturity could be used as a proxy for the risk-free rate. Based on the average over 20 years between 2001 and 2022, as displayed in Fig. 9, the risk-free rate amounts to 1.9%. 2. Beta: The beta has been estimated at 0.78 in Example 3. 3. Equity risk premium (ERP): The market return can be estimated by using an index. The Euro Stoxx (EUR) (Gross Total Return) Index is used here as a proxy. As shown in Fig. 11, based on a twenty-year average, the ERP amounts to 5.6%. • According to the capital asset pricing model, the cost of equity could be obtained by solving the following equation: CoE = 1.9 % + 0.78 × 5.6 % = 6.3 %.

4.7.2

Cost of Debt

The other component of the cost of capital is the cost of debt which is usually divided into three components: • The risk-free rate, which has already been presented in the previous section and which corresponds to the rate of return on the risk-free asset, is often approximated by government bond rates. • The credit spread is the risk premium requested by the creditor to cover the risk of default. The risk-free rate and the spread together form the interest rate of the debt. • The tax deduction generated by the interests when fiscally deductible. Be careful that since the tax deduction is considered in the WACC, it should not be taken into account a second time in the cash flow to the firm. Cost of debt = interest rate × ð1 - CITÞ = ðriskfree rate þ credit spreadÞ × ð1 - CITÞ with CIT: corporate income tax rate

ð20Þ

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The cost of the debt can be estimated in different ways. A simple approach is to use the interest rate actually paid by the company. This rate has the advantage of having been estimated by the banks according to the risk of the company. However, this rate reflects a view of the past. The company may have changed since the debt issuance and the market conditions as well. So, if the company had to borrow money today, the interest rate would certainly be different. Otherwise, the current cost of debt can be approached by benchmarking. Some debt instruments, such as corporate bonds, are traded on the market. The interest rates paid by comparable companies in the same sector and with the same level of risk can therefore be directly observed. To ensure greater comparability with peers in terms of credit risk, ratings from specialized agencies, such as Fitch, Standard and Poor’s, and Moody’s, can be used.

4.7.3

Weighted Average Cost of Capital

The cost of capital, used to discount the cash flow to the firm, can be expressed based on the financial structure, the cost of debt, and the cost of equity. WACC = CoE ×

E D þ CoD × DþE DþE

ð21Þ

with: CoE: cost of equity E: equity’s market value CoD: cost of debt D: debt’s market value Example 6 Cost of Capital Estimation of a European Independent Renewable Energy Producer As an extension of Examples 3 and 4, an estimate of the WACC as of 31/12/ 2021 for a European independent renewable energy producer is given here. As assumed before, the company has a capital structure composed of 40% of debt and 60% of equity and is paying a corporate income tax rate of 25%. • Cost of equity (CoE): The CoE has been estimated at 6.3% according to the capital asset pricing model in Example 4. • Cost of debt: Based on the same set of peers used in previous examples, it can be observed that the Standard & Poor’s ratings of the peers range from CCC to B (except for Albioma, rated BBB), which corresponds to the investment-grade category. According to Capital IQ data, the yield of a large basket of EUR-denominated corporate bonds with a long-term (continued)

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maturity (20 years) issued by B-rated companies in the utilities sector was around 3% as of 31/12/2021. The effective interest rate paid by peers in 2021 was also about 3%. By considering a tax deductibility of interests, the cost of debt of the company could be estimated as follows: CoD = 3:00% × ð1- 25:00Þ% = 2:25% • According to formula (21), the WACC could be obtained by solving the following equation: WACC = 6.30 % × 60 % + 2.25 % × 40 % = 4.68 % .

5 DCF Valuation After having computed the cost of capital, this rate can be applied to future cash flows to determine the enterprise value. However, estimating future cash flows is not so easy. To help the reader in this exercise, the following section presents some basic ideas that could be used to model future cash flows. In a DCF approach, forecasts are usually performed in two phases: a first phase in which the cash flows are estimated for each period (usually one period equals one year). This phase generally corresponds to a period of growth for the company and ends when the firm reaches certain stability and maturity. The first phase can also end when the time horizon is too far away or uncertain about making accurate forecasts. Then begins the second phase of projections, in which the cash flows will no longer be estimated individually but will be considered stable or in constant moderate growth. In most cases, the company’s life and the second forecasting phase are assumed to be unlimited, and consequently, cash flows grow forever.

5.1

Estimating Cash Flows in the First Phase (the Business Plan)

There are many ways to build a business plan, and it is definitely not an easy exercise because it is about anticipating the future. Often the person conducting the valuation, or the investor is not necessarily a specialist in the company’s sector or business, making this task even more complicated. Extensive work of research and information analyses should therefore be undertaken. It is relatively easy to build a model disconnected from any reality on an Excel sheet. The assumptions supporting the business plan should be based on a maximum of concrete arguments and information to avoid such a situation. Several sources can be used in this context. For global economic data such as price indices, inflation, or GDP growth, institutes like the IMF or the World Bank publish data regularly, most of the time freely available. When

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looking at information on a specific market or sector, it could be interesting to access detailed studies from specialized consulting companies. For listed companies covered by analysts, benchmarks of the analysts’ forecasts can be compiled. These analysts usually have privileged access to management, enabling them to construct their projections and assess their opinions. Their investment notes could contain an extensive description of their analyses that can be valuable. There is also a lot of material simply available in the company’s or peers’ financial reports, such as in the risk and opportunity analysis section. The best is to use a maximum of different information sources and to cross them. In some cases, it is possible to have direct access to the company’s management or to obtain a business plan prepared by the management. As the management may have an optimistic bias due to its involvement (or to its financial interest in the case of mergers and acquisitions), it may be necessary to challenge the forecasts. To do this, it may be interesting to back-test previous management forecasts and compare them with the actual performance retrospectively. It is also necessary to test the relevance of the assumptions used in light of factual information, such as the order book, the latest financial data available, etc. The cash flow modeling can differ from one business to another. The modeling of a bank’s business plan will be significantly different from the one of a car parts manufacturer. However, we tried to sum up below some common ideas that could be used to forecast the main components of cash flows: • Revenue: there are many ways to model revenue. One approach is to make assumptions about future market share. Otherwise, assumptions are often based on sales growth rates. It is possible to distinguish between volume and price effects in sales development. More detailed modeling can be carried out by product, product group, or project. • Cost of goods sold (COGS): these are all costs directly related to the production of goods, typically the cost of raw materials and the labor working on the goods. These costs are related to sales, and assumptions based on a percentage of sales make sense. Here again, an approach differentiating volume and price effects can be implemented, as well as modeling by product. • Selling, general, and administrative expenses (SG&A): There are all the operating costs not directly related to the production of goods but still necessary to run the business. These costs could include information technologies, marketing, accounting, advertising, human resources, rent, or other operating expenses. Growth rate assumptions are often applied for forecasting SG&A. • Personnel costs6: assumptions on the number of employees or full-time equivalent (FTE) can be used for the modeling together with salary assumptions (such as applying a growth rate on the average wage per employee). The modeling can be done for the whole payroll or by division and department.

6

Be careful—there is an overlapping here because personnel costs are basically shared between COGS and SG&A, so it is necessary to be consistent in the modeling.

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• Net working capital: forecasting working capital as a percentage of revenues is quite efficient when the working capital is linked to the activity level. More precise modeling can also be done using days inventory outstanding (DIO), days sales outstanding (DSO), and days payable outstanding (DPO) ratios. • Capex: investments can be quite difficult to model. A distinction can be made between replacement capital expenditures and production capital expenditures, the last ones being expended to increase production capacity or to improve productivity. The amount of depreciation and amortization can be a good indicator of the required replacement capex. Otherwise, growth and inflation rate assumptions could be applied to forecasting investment. The consistency can be checked by calculating the implied return and marginal return on investment in the business plan, ensuring that it remains in line with past figures and comparable companies. In the case of a direct approach, based on a cash flow to equity model, debt repayment and issuance must be considered as well in the cash flow. The forecasting can be done based on the current debt schedule. The difficulty here is to estimate the new debt issuances. Assumptions can be made to target a specific financial structure (a targeted debt-to-equity ratio) at the end of the business plan. The average structure of peers or mature companies could be used as a reference.

5.2

Terminal Value

The second forecasting phase starts when the company has reached a certain degree of maturity or when the time horizon is too far away to make accurate forecasts. Cash flows in this phase are assumed to grow at a constant rate. As companies are often considered to have an indefinite life, cash flows are supposed to grow to infinity. This may seem aberrant, and one might think at first sight that the company’s value is infinite as a sum of increasing cash flows. However, as the cash flows are discounted, the discount rate reduces the value of the most distant cash flows. If the long-term growth rate is inferior to the discount rate, the infinite sum of discounted cash flows is mathematically convergent. This makes it possible to calculate the value for the second phase using the Gordon–Shapiro formula shown in formula (22) (Gordon & Shapiro, 1956). If ðLTG < dÞ then Terminale value = with:

CFT × ðLTG þ 1Þt CFT × ð1 þ LTGÞ = t=1 ðd - LTGÞ ð d þ 1Þ t þ1

ð22Þ

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CFT: cash flow terminal LTG: long-term growth rate d: discount rate The result of the calculation is called the terminal value and captures the value of all cash flows occurring after the business plan of the first forecasting phase. It is essential to see that the Gordon–Shapiro formula gives the total value of future cash flows from their starting point. As this starting point is after the first forecasting phase (after the business plan), the terminal value should be discounted to consider the “time value” between the valuation date and the start of the second forecasting phase. The terminal value is very sensitive to the long-term growth assumption. Variations of a few basis points in the growth rate can significantly impact the valuation. It is, therefore, necessary to carry out sensitivity analyses on this assumption. It is generally accepted that this growth rate should not be too far from the economy’s growth rate, i.e., the GDP growth forecasts. More conservatively, long-term inflation expectations can also be used as a reference. The terminal cash flow used in the Gordon–Shapiro formula is generally constructed based on the last cash flow of the business plan. This cash flow may be adjusted to take into account the normative and perpetual nature of the terminal cash flow. The exit multiple implied by the terminal value can be calculated to test the plausibility of the assumptions. For example, calculate the ratio of the terminal value divided by the EBITDA at the end of the business plan. The marginal return on investment implied by the long-term growth rate in the normative year can also be determined. It is important to ensure that these ratios (multiples and ROIC) are not too far away from the ratios usually observed in the market. If they are, the investor should be comfortable with the company’s competitive advantages, which would justify deviations from the market ratios. Example 7 DCF Valuation of a European Independent Renewable Energy Producer In this case, a DCF valuation will be implemented based on an indirect approach using discounted cash flows to the firm to determine the enterprise value. The company and the figures used are fictional. As in Sect. 3, the case of a European independent renewable energy producer is taken as an example (see Examples 5 and 6). To run its business, the company has invested in fixed assets with a total book value of €1000 m and currently has a working capital of €20 m. Furthermore, the company owns €70 m of cash and €820 m of debt. The company’s future cash flow forecasts are presented below, together with the main underlying assumptions: (continued)

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First phase based on a business plan

Cash flows €m

year 1

year 2

year 3

year 4

Second phase

Terminal year 5 value

Revenue 500 550 583 606 625 637 Revenue growth 10.0% 10.0% 6.0% 4.0% 3.0% 2.0% 5 Purchase of material and services (150) (165) (175) (182) (187) Personnel expenses (70) (76) (79) (81) (83) Other operating expenses (150) (158) (162) (167) (172) EBITDA 130 152 167 176 182 185 EBITDA margin 26.0% 27.6% 28.7% 29.1% 29.1% 29.1% 6 Depreciation & amortisation EBIT EBIT margin (-) Corporate income tax Tax rate (+) Depreciation & amortisation (-) Change in net working capital (-) Capital expenditures Cash flow to the firm

(90) (94) (97) (101) (102) (103) 40 58 70 75 79 82 8.0% 10.6% 12.0% 12.4% 12.7% 12.9% 1

(10) (15) (17) (19) (20) (21) 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 90 94 97 101 102 103 2 3 (2) (2) (1) (1) (1) (0) 4 (110) (125) (120) (115) (105) (105) 7 8 10 28 42 56 59

Invested capital €m Net working capital in % of revenue Fixed assets ROIC

3 4

year 1

year 2

year 3

year 4

Terminal year 5 value

20 4.0% 1,000 2.9%

22 4.0% 1,031 4.2%

23 4.0% 1,054 4.9%

24 4.0% 1,068 5.2%

25 4.0% 1,071 5.4%

25 4.0% 1,072 5.6%

First phase: In the first five years, the cash flows are forecasted based on several assumptions. The revenue is expected to grow strongly in the first years, the EBITDA margin to improve to 29.1% and the ROIC to reach 5.4%. 1. The company pays 25% corporate tax; the interest deductibility is not included into the cash flow. The deductibility will be accounted for in the WACC. 2. D&A from the P&L are neutralized into cash flow as noncash items. 3. Net working capital is supposed to be equal to 4% of revenue. The change in working capital has thus a limited impact on cash flow. 4. More capital expenditures are expected at the beginning of the business plan. The change in the book value of fixed assets from one year to another is computed as the capital expenditures reduced by the D&A. Fixed assets and working capital forecasts enable ROIC computation. If ROIC is above the WACC, the company is creating value for shareholders. Second phase: In the second phase, cash flows are expected to grow at a constant rate of 2.0%. Therefore, a terminal value is computed based on a normative cash flow and the following assumptions: (continued)

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5. The long-term growth rate of 2.0% has been applied to the revenue of the fifth year. 6. The EBITDA margin has been assumed to be equal to the EBITDA margin of the fifth year. 7. Capital expenditures have been assumed slightly above D&A to reflect that capex are required to maintain the business but also to maintain the growth. The enterprise value is obtained by applying a discount rate equal to the WACC to the forecasted cash flows. In Example 6, the WACC of a European independent renewable energy producer has been estimated at 4.68%. Details of the calculation of discounted cash flows and enterprise value are given in the table below: Cash flows €m

year 1

year 3

year 4

Cash flow to the firm Discount factor Discounted cash flows Sum of discounted cash flows Discounted terminal value Enterprise value (+) Cash (-) Debt Equity value

8 10 28 0.96 0.91 1 0.87 8 9 25 121 1,760 1,881 4 70 (820) 1,131 5

42 0.83 35

year 2

Terminal year 5 value 56 0.80 45

2,212 0.80 1,760

2 3

1. The discount factor is determined for each year based on a WACC of 4.68%. Discount factor of year 2 =

1 = 0:91 ð1 þ 4:68%Þ2

Discount factor of year 3 =

1 = 0:87 ð1 þ 4:68%Þ3

2. The terminal value is obtained using the Gordon–Shapiro formula. The growth rate is not reapplied in the formula on the normative cash flow, because it has already been applied once when computing the normative cash flow (see bullet 5 of the second phase above). (continued)

Reminder on Common Valuation Techniques

Terminal value =

57

59:27 = 2,212 ð4:68% - 2:00%Þ

3. As the Gordon–Shapiro formula indicates the terminal value at the end of the business plan, the terminal value must be discounted with the same discount factor as in the fifth year to obtain the present value of the terminal value. Present value of the terminal value =

2,212 = 1,760 ð1 þ 4:68%Þ5

4. The enterprise value is the sum of the discounted cash flows of the first phase and the discounted terminal value. 5. The equity value is obtained by deducting the net debt from the enterprise value. According to this DCF model, the equity value of the independent renewable energy producer is €1131 m. If the capital of this company is divided into 100,000,000 stocks, the price of one stock is thus valued at €11.3.

6 Relative Valuation (Multiples) The relative valuation approach consisting of using multiples to value an asset is the most basic and intuitive approach but also the most used in practice. Multiples are used by investment professionals as a reference and basis for negotiations in most corporate transactions. Many offers and purchase agreements refer to a given multiple to derive the purchase price. The relative approach consists of simply comparing the asset to other similar assets using ratios (multiples). It is based on the common and reassuring human reflex of looking at what others are doing (are paying) to do the same. It consists, for example, of an analysis of the ratio of euros paid per square meter to determine the price of a house in a real estate transaction. When initiating a property purchase, a prospective buyer often begins by investigating recent real estate transactions in the target area or city to ascertain the average price per square meter paid by other purchasers. This enables the buyer to form an initial offer. Subsequently, the buyer may fine-tune this initial figure based on the insights gained from property visits and due diligence. Although it's a simplified approach, utilizing a relative valuation grounded in a square meter multiple can provide a reliable preliminary estimate of the property's value, serving as a solid foundation for negotiation.

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Choosing a Relevant Variable

A multiple is calculated by dividing the asset value (enterprise or equity value) by a defined variable. Many variables can be used to construct multiples; these should be as reflective as possible of the characteristics of the asset and its underlying business: For example, a square meter multiple could be used in a real estate transaction, a multiple of users for the valuation of a company like Twitter, a multiple of revenue for an advisory company, etc. In practice, earning multiples are commonly used, with, here again, a wide variety of possibilities in the definition of earnings, such as EBITDA, EBIT, net income, etc. EBITDA multiples and net income multiples (also known as the price-earnings ratio [PER]) are the most common ones: • EBITDA because it reflects the operating result without being affected by the accounting depreciation policy and the financing structure. It is also often argued that EBITDA is closer to the operating cash flow (even if changes in working capital may imply a significant difference between EBITDA and operating cash flow in some cases). • Net income is often used as well because it is simple. Its definition is clear: it is the company’s profit, including all incomes and expenses of the P&L. So, it does not require any costs or income identification (EBITDA, in contrast, requires an allocation between financial and operating results).

6.2

Indirect and Direct Relative Valuation Approach

The relative valuation approach can be used to determine the equity value directly or indirectly. In the first case, the multiples will be calculated by dividing the equity value by the selected variable (e.g., net income). A company with an equity value of €150 m and a net income of €10 m will have a PER of 15x. In the case of an indirect approach, the enterprise value is first determined before deriving the equity value by subtracting the net debt. The multiples will then be calculated by dividing the enterprise value by the selected variable (for example, EBITDA). A company with an enterprise value of €200 m and an EBITDA of €25 m will have an EBITDA multiple of 8x. The question then becomes which approach is better to adopt, direct or indirect? There is no absolute answer, and each method has advantages and disadvantages. The direct one is easier to implement, as the equity value is derived from the observable transaction price (directly derived from the stock price in the case of a listed company). In contrast, the enterprise value used in the indirect approach has to be recalculated by adding the net debt to the equity value. That requires the availability of balance sheet data and identifying the items to be included in net debt. However, the disadvantage of the direct approach is that the multiple relies on the financing structure. According to a major theorem of corporate finance developed by

Reminder on Common Valuation Techniques

59

Relative valuation (multiples)

Calculation

Multiple=

EQ

Multiple=

Variable

EV Variable

Advantage

¾ Simplicity: Equity value (EQ) is directly derived from the transaction price (or from the stock price).

¾ The multiple does not depend on the financing structure.

Disadvantage

¾ The multiple depends on the financing structure which limit the comparability with peers.

¾ Necessity to compute the net debt (ND) to obtain the multiple : EV = EQ +ND

Examples of multiples

EQ

EQ

Net income

Equity book value

EV Revenue

EV EBITDA

Fig. 13 Relative valuation calculation

Modigliani and Miller, in the absence of taxes,7 bankruptcy costs, and agency costs and in an efficient market, the enterprise value is independent of the financing structure and thus depends only on the company’s operating performance (Modigliani & Miller, 1958; Modigliani & Miller, 1963). In contrast, equity value, which is obtained by subtracting net debt, is dependent on the financing structure. For this reason, an indirect approach is often preferred for the valuation of traditional industry companies, as it will avoid paying too much attention to the financing structure differences which could arise among the peers. The table below summarizes the differences between the two approaches (Fig. 13):

6.3

Relevant Observation Period

Once a reference variable has been chosen for the multiple, e.g., EBITDA, the question arises of the relevant observation period to be selected. Should we look at the EBITDA of the current year, which may not yet be reported at the valuation date and will therefore have to be composed of a mix between actual and forecast? Should we rather consider the previous year’s EBITDA, which is already disclosed and therefore has the advantage of being based on actual results? Or should we look at the forecasted EBITDA for the coming year, which may be less reliable but has the 7

Modigliani and Miller (1963) have demonstrated that in the case of tax deductibility of interest, the financing structure has an impact on the enterprise value. In this case, the enterprise value would be increased by the value of the discounted future cash flows resulting from the tax savings.

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advantage of being forward-looking? In fact, it will always be interesting to look at both actual and forecasted figures, as they carry different information. It is helpful to understand the evolution of the multiples according to the selected periods and to compare it with the peer multiples. For example, suppose a company has a particularly low EBITDA in the current year, but the consensus expects a comeback to a normal level in the following year. In this case, the multiple calculated based on the current year will be much higher than the one calculated based on the forecasted EBITDA of the following year. In this example, it is also likely that the difference between the company’s multiple and the peers’ multiples will be much higher with a calculation using the current EBITDA. In general, as earnings are expected to grow, the multiples based on forecasts are often lower than those found on actuals.

6.4

Peer Group

One of the main steps to implementing a relative valuation approach is identifying comparable companies or transactions for which appropriate financial data are available. For this purpose, two information sources can be considered: • Information from listed companies whose stocks are traded daily, and which are legally required to publish financial statements. Their stock prices enable the calculation of the equity value. • Information from recent merger and acquisition transactions, for which data would have been published. However, information on corporate deals could be difficult to find as the data for these transactions is often not publicly disclosed, especially for European deals. To construct a peer group, the screening of comparable companies (peers) can be performed according to different criteria. It is, of course, required to find peers who operate in the same business/sector or at least in a business that is close and has the same risk profile. Depending on the number of companies identified as comparable, the screening criteria can then be restricted, for example, by selecting only companies with similar size (enterprise value) or market positions (market leader, challenger, nicher, etc.). Attention should also be paid to the location to avoid the analysis being disturbed by the specificities of local markets, primarily regional legal and tax disparities. In the case of listed companies, it will be necessary to check the stock’s liquidity, since poorly traded companies could have a stock price that is not representative of their value. It is also useful to analyze the peers’ financial statements and remove companies that have unusually weak or good results due to one-off items. For example, a company with an EBITDA weakened by restructuring expenses (such as severance payments) will have a high multiple that will not be representative of the sector. How many peers should be included in a peer group? There is no correct answer to this question, depending on data availability. On the one hand, if more companies are selected in the peer group, the chance to obtain an average multiple that would be

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representative of the sector is better. Indeed, the effects of eventual nonrepresentative peers, whose earnings should have been disturbed by one-off items, will be mitigated by the number of peers. On the other hand, the more companies there are in the peer group, the higher the risk of having selected irrelevant peers (not very comparable in terms of business, in different locations, etc.). In practice, personal judgment is required to find a compromise between the number of peers retained and the quality/relevance of the selected peers. Although peer screening can be a tedious and time-consuming process, it is a crucial step in the valuation work as the value will rely on the quality of the screening. It is a process that requires careful consideration and personal judgment, but it is not a meaningless effort. Indeed, a thorough analysis of peers’ financial and nonfinancial data could permit the identification of current business trends and value drivers, which could also be used for the DCF valuation.

6.5

Application

Example 8 Relative Valuation of a European Independent Renewable Energy Producer In this relative valuation case, the same (fictional) company as in the DCF section is used as an example. This independent renewable energy producer is assumed to own €820 m of debt and €70 m of cash. The actual EBITDA in 2020 and 2021 is €80 m and €110 m, respectively. Furthermore, the EBITDA forecasts amount to €130 m and €152 m for the years 2022 and 2023. The valuation date is 31/12/2021. The main steps of the valuation approach are described below: 1. The same peer group of listed companies as for the WACC computation in Example 3 has been retained. 2. The market capitalization or equity value of the peers is calculated as the stock price as at 31/12/2021 multiplied by the number of outstanding shares. Company 1

Albioma Alerion Clean Power S.p.A. EDP Renováveis, S.A. Encavis AG Neoen S.A. Scatec ASA Solaria Energía y Medio Ambiente, S.A. Voltalia SA

Shares Price per outst. share m € 31.7 54.0 960.6 160.5 106.9 158.8 125.0 95.2

34.3 29.6 21.9 15.6 38.2 15.2 17.1 19.7

Market 2 cap. €m

Net debt €m

1,086 1,596 21,036 2,497 4,078 2,421 2,139 1,872

1,083 508 5,307 1,235 2,370 1,676 552 836

3

EV €m

2,169 2,104 26,343 3,732 6,448 4,097 2,691 2,708

(continued)

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3. The enterprise value has been computed as the sum of the market capitalization and the net debt. 4. The actual EBITDA of each peer for 2020 is derived from their financial statements. The forecasted EBITDA for 2021 to 2023 are based on the consensus of equity research analysts. The last quarter of 2021 was not yet published as of 31/12/2021. For this reason, the EBITDA for 2021 is based on three-quarters of actuals and one-quarter of forecasts. 4

Company

EBITDA (€m)

Dec-20

Dec-21

Dec-22

Dec-23

196 73 993 205 271 199 48 85

194 114 1,023 232 298 213 91 124

218 130 1,762 259 368 320 139 225

229 155 1,946 269 458 392 194 279

Albioma Alerion Clean Power S.p.A. EDP Renováveis, S.A. Encavis AG Neoen S.A. Scatec ASA Solaria Energía y Medio Ambiente, S.A. Voltalia SA

5. A multiple per year and peer can be calculated by dividing the enterprise value by the related EBITDA. 5

Company

Multiples (EV/EBITDA)

Dec-20

Dec-21

Dec-22

Dec-23

Albioma Alerion Clean Power S.p.A. EDP Renováveis, S.A. Encavis AG Neoen S.A. Scatec ASA Solaria Energía y Medio Ambiente, S.A. Voltalia SA

11.1x 29.0x 26.5x 18.2x 23.8x 20.6x 56.6x 32.0x

11.2x 18.5x 25.7x 16.1x 21.6x 19.2x 29.5x 21.8x

10.0x 16.2x 15.0x 14.4x 17.5x 12.8x 19.3x 12.0x

9.5x 13.5x 13.5x 13.9x 14.1x 10.4x 13.9x 9.7x

Average Median

27.2x 25.2x

20.5x 20.4x

14.7x 14.7x

12.3x 13.5x

It can be observed that the average multiple of the peer group is much higher by using 2020 EBITDA (multiple of 27.2x) than with 2023 forecasted EBITDA (multiple of 12.3x), which is due to the strong growth expectation by analysts covering the peers. Furthermore, it can be observed that Albioma was traded at a lower multiple than the other peers. However, the company was subject to a takeover bid by KKR a few months later, in June 2022 (announced 28/04/2022), based on a share price of around 15x the 2021 EBITDA. (continued)

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6. An enterprise value could be calculated by applying the peer median multiple to the EBITDA of the company to be valued for each respective year. 7. The equity value of the company can be derived from the enterprise value by deducting the net debt. Valuation Dec-20 EBITDA of the company Multiple (median of the peer group) 6 Enterprise value (€m) (+) Cash (-) Debt Equity value (€m) Average 7

80 25.2x 2,014 70 (820) 1,264

Dec-21

Dec-22

Dec-23

110 130 20.4x 14.7x 2,245 1,910 70 70 (820) (820) 1,495 1,160 1,306

152 13.5x 2,057 70 (820) 1,307

In this example, the equity value based on EBITDA multiples is ranged between €1160 m and €1495 m, with an average value of €1306 m. This value is a bit higher than the value obtained with the DCF approach (see example 7), which would imply that the forecasts of the business plan are less optimistic than the market expectations for the peers.

6.6

Going Further with Multiples

One of the weaknesses of the relative approach is that the company value, which is an anticipation of the future, is estimated based on current or past financial data. This can be ineffective if the current earnings and the balance sheet are not sufficiently representative of the future, which may occur if the company is changing its structure (e.g., through M&A activities) or if the current financial statements are disturbed by one-off events. It is possible to adjust the current and past financial data to compensate for those effects and make it more future-oriented. These kinds of adjustments are often used by professionals in M&A transactions and serve as a support for negotiations between buyers and sellers.

6.6.1

EBITDA Adjustments

Many items can be adjusted in EBITDA or any other variable selected to compute the multiple. In practice, sorting out recurring or nonrecurring items to adjust the EBITDA is quite subjective and could lead to extensive negotiations between buyers and sellers. It is not the purpose of the book to provide an exhaustive review of the items to be adjusted and analyzed in a due diligence process. Still, some examples of one-off

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items that may lead to an EBITDA adjustment are listed below to give an idea of what these adjustments could be: • A fire in a warehouse: the costs related to the damage as well as the insurance compensation received can be neutralized in the EBITDA. • A special subsidy for a specific investment: the grant could be accounted for in the P & L as other income and would increase the EBITDA. If a multiple is applied to this EBITDA without adjustment, there is a risk of overestimating the enterprise value. • An exceptional contract with unusual volumes and margin could be normalized, as well as sales resulting from an extraordinary situation. For example, the impact of COVID-19 tests temporarily sold during the pandemic by a distributor of personal protective equipment could be adjusted. • The expenses or incomes due to a legal dispute. In addition to the adjustments related to nonrecurring items, there is another adjustment category called proforma. They are applied to consider eventual changes in the company’s structure that imply that past financials will not fully represent the future. Several examples of proforma adjustments are listed below: • Acquisition of a subsidiary during the year: if a company with a reporting period from 1 January to 31 December acquires a subsidiary in June, the consolidated EBITDA for the acquisition year will only include the last six months’ earnings of the acquired company (from the acquisition date in June to the closing of the accounts in December). The subsidiary’s earnings should be annualized by adding the missing part for the period from January to June before applying a multiple. • The management significantly increased their salary two months before the transaction. The EBITDA may be adjusted to include the annualization of the salary increase and the new salary structure. • Sale and leaseback: if a company has owned an asset (e.g., real estate) and sold it in the current year before leasing it back, then the company will incur additional lease expenses in the future. These expenses can be added to the historical EBITDA when applying the multiple to take into account the new lease situation. This may occur in a transaction where the seller decides to keep the real estate on its own and to sell only the operating business.

6.6.2

Net Debt Adjustments

In the case of an indirect approach implying the determination of the enterprise value, the definition of net debt is also an essential factor. In a restrictive sense, net debt comprises cash, cash equivalents, and interest-bearing financing instruments, typically loans, bonds, and overdrafts. In a broader definition, particularly in a relative valuation approach, net debt can include all balance sheet items that are likely to generate future cash outflows and that are not directly related to the ongoing

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business. For example, provisions for pensions are commonly accepted as a debtlike item, as pension payments will lead to future cash outflow. The definition becomes more or less extensive depending on the negotiating positions. It could include many items and accruals, such as tax liabilities, lease commitments, management bonus accruals, provisions for litigation, etc. Another critical thing to have in mind is the connection between cash, which is included in the net debt, and the net working capital. The net working capital is the capital employed to run the business on a daily basis. The net working capital varies over time with fluctuations that can be more or less important depending on the industry and situation. The higher the net working capital, the lower the cash, and vice versa. The net working capital at a given date may not be representative of the net working capital during the rest of the year. Some companies may seek to maximize their cash and minimize their net working capital just before the closing of accounts or just before an acquisition to improve appearances. In practice, an annual average of the monthly net working capital is often retained as an estimate of the normative level of net working capital in corporate deals. Further analysis of the working capital items can otherwise provide a more accurate estimate of the normative level. The difference between the current and normative net working capital could be used as a cash adjustment when determining the purchase price.

7 Conclusion This chapter has summarized the main valuation techniques used to value assets based on the DCF approach and the relative approach (multiples). It can be observed that these tools and techniques are more or less sophisticated, going from the calculation of a cost of capital based on quite complex financial and mathematical theories to the use of rough ratios in the relative approach. Financial analysts are caught between the scientific rigor involved in developing and applying complex models and the pragmatism required to trade on the market or successfully complete corporate deal negotiations. This ambivalence often makes practitioners say that valuation is somewhere between science and art. The more scientific souls may be disappointed, but this state of fact does not make valuation any less attractive because it also relies on human behavior. The truth is that valuation is based on the future, investor behaviors, expectations, and many other uncertain inputs, which makes the accuracy of the output, even of the most theoretically pure and complex valuation model, never perfect. A little pragmatism will allow to keep feet on the ground and not get lost in over-complexity. However, a bit of rigor and analytical work is not meaningless either. It will never allow estimating the asset’s value with a precision of two digits. Still, it will enable a good understanding of the value drivers and bring some rationale to the negotiation. A good balance between complexity, theory, and pragmatism must therefore be found to perform a valuation. Combining different valuation approaches to assess the value better is also helpful.

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We hope this chapter will allow the reader to perform their own valuation and that it will arouse their curiosity. If it is the case, we can only recommend the reader refer to more extensive literature on this topic (Damodaran, 2012; Koller et al., 2010). The techniques discussed in this chapter take place in the classical theoretical framework of finance, in which investors seek to maximize profit and reduce risk. Therefore, the question of the impact of ESG criteria on valuation has been set aside but will be addressed in the following chapters. The effect of ESG is an exciting problem where changes in company and investor behaviors require adapting the conventional valuation tools introduced here.

References Damodaran, A. (2012). Investment valuation: Tools and techniques for determining the value of any asset. John Wiley & Sons. Gordon, M. J., & Shapiro, E. (1956). Capital equipment analysis: The required rate of profit. Management Science, 3. Informs, 102–110. Hamada, R. S. (1972). The effect of the firm’s capital structure on the systematic risk of common stocks. The Journal of Finance, 27. JSTOR, 435–452. Koller, T., Goedhart, M., & Wessels, D. (2010). Valuation: Measuring and managing the value of companies (Vol. 499). Wiley. Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20. JSTOR, 587–615. Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance., 7(1), 77–91. Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American Economic Review, 48, 261–297. Modigliani, F., & Miller, M. H. (1963). Corporate income taxes and the cost of capital: A correction. The American Economic Review, 53. JSTOR, 433–443. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the Econometric Society. JSTOR, 34(4), 768–783. Poncet, P., & Portait, R. (2022). The capital asset pricing model. In P. Poncet & R. Portait (Eds.), Capital market finance (pp. 929–962. Springer Texts in Business and Economics). Springer International Publishing. https://doi.org/10.1007/978-3-030-84600-8_22 Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19. Wiley Online Library, 425–442. Treynor, J. L. (1961, August 8). Market value, time, and risk. Time, and Risk.

ESG Data and Scores Mathieu Joubrel and Elena Maksimovich

1 Definition and Use Cases 1.1

An Aggregation of ESG Indicators

Scoring models are algorithms developed by finance professionals to aggregate raw environmental, social, and governance (ESG) data into ESG scores that can support investment decisions along with a financial analysis. One of the challenges faced by investors is to accurately measure and quantify the sustainability performances of their investments, i.e., to develop effective sustainable finance indicators. Such indicators would allow investors and companies to assess whether their investments are making progress toward a more sustainable society. They would also reduce the risk of greenwashing, i.e., unsubstantiated claims about the supposedly positive environmental impact of certain investment products. These indicators are mostly used to determine the performance of a company’s activities on ESG issues. Because the focus is on a company’s operations and not its products or services, companies whose products are inherently unsustainable, such as fossil fuel producers, can achieve high ESG scores. It is only necessary for them to implement effective internal ESG policies to compensate for the poor durability of their products. Another reason why companies that might at first glance be considered harmful from an extra-financial point of view can receive high scores is due to the concept of best-in-class. Many of the indicators currently available are assessments of a company’s performance relative to its peers; they are not absolute. Oil and gas companies are only compared to companies in their sector of activity when it M. Joubrel (✉) ValueCo, Paris, France e-mail: [email protected] E. Maksimovich Weather Trade Net, Paris, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_3

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comes to their environmental performances and only to companies from the same country for their governance performances. These best-in-class assessments can, therefore, generate very surprising sustainability indicators. More recently, a company’s sustainability has begun to be quantified by considering the impact of its products and services. This approach is more promising than the previous one focusing on the ESG policies of companies and investors. These more recent indicators assess whether and how a company’s products and services contribute to sustainable development, most often based on financial data and industry reports. For example, FTSE provides estimates of companies’ green revenues, i.e., the share of their revenues derived from products and services that provide environmental solutions. Of course, the very definition of these products and services remains a debatable issue in the absence of a global standard for what can be called a green business. While early ESG indicators were generic and aggregated into a single score, more attention has recently been paid to specific climate data. There are now metrics that estimate, for example, the greenhouse gas (GHG) emissions for which a company is responsible or whether the company has GHG reduction targets. In addition to these company-level measures, scores qualifying financial products are also becoming more common. These scores are generally based on two elements: • Information about the policies of the company offering the financial product, such as reports from these companies on the processes and strategies they use to integrate sustainability issues into their activities. • Sustainability metrics aggregated into a few scores, most of the time targeting the ESG pillars. In this spirit, calculating the average sustainability characteristics of an investment product’s holdings is somewhat like calculating the average return of an investment product (i.e., a weighted average of its components). Figure 1 illustrates how raw data is aggregated into these scores through specific metrics and indicators. From one step to the next, each component is weighted relative to the others depending on the company’s characteristics, such as their activity sector, size, or main regions of activity: There are many reasons why constructing consistent and effective indicators that are useful to support decision-making is difficult, including the following: • Sustainability is, by nature, a multidimensional concept. It encompasses a disparate set of ESG issues, such as climate change, carbon emissions, human rights, and executive compensation. These issues, particularly those related to climate change, often focus on long-term risks and opportunities. • When constructing an overall indicator of sustainable finance, the relative importance of a specific sustainability issue in some way reflects normative and subjective biases. For example, the Commonwealth Climate Law Initiative and the Climate Governance Initiative have shown in their Primer on Climate Change: Directors’ Duties and Disclosure Obligations that the country of origin of ESG data providers plays a role in determining the methodological importance of a particular ESG issue (Climate and Law Initiative, 2021). In civil law

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Fig. 1 Example of aggregation steps of an ESG scoring model

countries (such as France or Germany), data providers may place more emphasis on employee protection issues. Conversely, in common law countries (such as Canada, the USA, or the United Kingdom), these providers may focus more on issues that have a financial impact on shareholders. • In addition to the wide range of issues that are typically included in sustainable finance indicators (among others, climate change, inequality, and the issues reflected in the UN’s 17 Sustainable Development Goals [SDGs]), these indicators are intended to measure not only quantitative but also qualitative aspects.

1.2

A Wide Diversity of Rating Methodologies

The rapidly shifting scientific state of the art in sustainable finance and the relative immaturity of the ESG rating industry, coupled with the fierce competition of a few major providers, lead to frequent evolutions of these providers’ rating methodologies. Morningstar has updated their rating methodology twice since 2016 to keep up with the latest scientific and market trends. Sustainalytics shifted from a best-in-class approach to a risk-based approach in 2018. MSCI, in turn, updates their methodology annually to rebalance the weight of relevant ESG indicators for each activity sector. In the absence of relevant global standards or regulations, this update involves consultation of relevant committees along with the agency’s clients, as

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explained in their ESG rating process report (MSCI, 2023). While methodologies should be tailored to evolving market trends and expectations, it becomes more difficult with each change to perform historical analysis and track companies’ progress. The volatility of ratings resulting from methodology updates remains a challenge for investors and highlights the importance for ESG rating providers to work with their clients when such a change occurs to ensure that the new methodology is fully understood. Berg et al. (2022) showed that Refinitiv’s rewriting of ESG ratings has important, negative consequences for analyses linking ESG ratings to financial variables, such as company valuations or stock returns. Another challenge in the design of extra-financial indicators is related to the concept of materiality. Some indicators focus exclusively on issues that are financially material to the company and, therefore, relevant to capital providers such as shareholders or creditors. Other indicators are based on the concept of double materiality. They seek to consider not only the financial impact of certain ESG issues on the company but also the way in which the company itself materially affects the well-being of a wider set of stakeholders (e.g., their partners and employees), the environment, or society. The concept of double materiality thus attempts to aggregate both positive and negative externalities. Therefore, indicators based on the concept of double materiality are relevant not only for companies and the stakeholders who provide them with capital but also for a much broader audience (e.g., politicians, civil society, regulators, or NGOs). As they must take into account the interests of a wider range of stakeholders, these scores are more challenging to design. They cannot focus on a single target in the same way single materiality-based scores focus on investors.

1.2.1 1.2.1.1

Risk-Based Approaches Target and Context

Financial entities around the world agree that climate change is a source of risk to the stability of the global financial system. Unlike most other types of risk, some aspects of ESG risks arise with unique characteristics. Herring et al. (2018) state that climate-related risks tend to be poorly associated with relevant historical data, are nonlinear in nature, and mostly manifest themselves over the long term. Pricing these risks is a challenge for companies, financial institutions, and financial markets. As with other risks faced by these actors, it is, however, important to understand the nature of ESG risks, identify them, quantify them, and then manage and mitigate them. The transition to fairer and more sustainable production practices has become a priority concern for investors. They can no longer simply maximize profits but must also integrate sustainability factors by considering the ESG performances of companies. As these companies improve their practices and impact on the environment, better data plays a crucial role in understanding sustainability risks. By assessing ESG risks and making changes to mitigate them, companies can reduce costs and

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meet the rising sustainable expectations of investors and stakeholders while improving their company’s reputation as an environmental steward, thus attracting new customers and enhancing their ability to attract and retain talent. ESG factors and their disclosure were not regulated in the USA until recently. The evolving regulatory landscape has resulted in new ESG initiatives and proposals to create greater accountability and consistency between reporting frameworks and compliance requirements. Regarding European markets, regulators are more advanced and have already adopted multiple directives that are shaping the way they do business. Sustainability disclosure requirements under the Sustainable Financial Disclosure Regulation (SFDR) and the Taxonomy Regulation have introduced new structures and incentives to provide more comprehensive ESG data. Although the overall cost of adaptation and mitigation is in the trillions of dollars, it is still far less than the cost of inaction. For insurable risks, insured losses in recent years have increased significantly, which mechanically drives up insurance costs. The National Oceanic and Atmospheric Administration (NOAA) summarizes the alarming increase in climate-related events very clearly in their study “U.S. Billiondollar Weather and Climate Disasters,” covering climate-related incidents in the USA since 1980 (Smith, 2020): 2020 sets a new annual record of 22 events—shattering the previous annual record of 16 events that occurred in 2011 and 2017. The year 2020 marks the sixth consecutive year (2015–2020) in which 10 weather and climate disaster events of $1 billion or more have affected the United States. Over the past 41 years (1980–2020), years with 10 separate billion-dollar or larger catastrophic events include 1998, 2008, 2011–2013, and 2015–2020.

1.2.1.2

Interconnectivity of ESG Risks

ESG risks can have interrelated impacts, leading to not only financial distress but also social and reputation-related issues. The three components of ESG (environmental, social, and governance) are interdependent, and effective risk assessment requires considering the potential ripple effects across all areas of sustainability. Among other examples, in numerous regions of the world, biodiversity loss caused by rising ocean temperatures can impact fish stocks, which, in turn, impacts the subsistence and economic well-being of fishing communities. In late 2018, severe droughts caused water levels in the Rhine River to decline, significantly disrupting supply chains. Chemical giant BASF was forced to reduce production due to a lack of transportation capacity and subsequently reduced its annual profit forecast (Reuters, 2018). Given the crosscutting nature of ESG risks, and climate-related risks in particular, it is critical to address them within the context of the company’s business. Each company is uniquely exposed to these risks, and they cannot be treated as separate challenges or projects. The governance of these risks, their inclusion in the corporate strategy, their management, and their transparent and regular disclosure are of crucial importance.

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Assessment of ESG Risks

The purpose of an ESG risk assessment is to identify the specific measures in place and their effectiveness in addressing ESG issues that are material to a company’s business. ESG risk ratings measure the economic value of a company’s outstanding ESG risks or, in other words, the extent of a company’s unmanaged ESG risks. They may consist of unmanageable risks or manageable risks that have not been specifically addressed by the company’s leadership. ESG risk frameworks have been developed to help organizations manage ESG risks and better address regulatory and disclosure requirements. For climate-related risks, the most widely used and recognized is the Task Force on Climate-related Financial Disclosure (TCFD, 2017). The TCFD presents its framework through the four areas of governance, strategy, risk management, and metrics and objectives. ESG risk assessment methodologies should consider both internal and external risks, including the perspectives of all stakeholders, such as the company’s environment, relationships, business partners, customers, and employees, within the context of industry and company-specific expertise. It is essential to consider material ESG risks and opportunities as a crucial factor for a company’s long-term economic success in terms of strategy, business model, distribution, and supply chain. To be considered relevant, an issue must have the potential to significantly impact a company’s economic value and, subsequently, its risks and financial return profile from an investment perspective. It is also important to keep in mind that ESG risk assessments assume that the world is moving toward a more sustainable economy and that effective ESG risk management can lead to increased long-term corporate value. Assessing the materiality and priority of each ESG issue for a company involves two main dimensions: • The company’s exposure to material ESG risks or the extent to which it is exposed to these risks. This exposure can be viewed as a set of factors related to ESG issues that present potential economic risks to the firm. Another way of looking at exposure is merely analyzing a company’s sensitivity or vulnerability to ESG risks. • The way a company manages its exposure, whether it is through its internal governance, stakeholder management, or transition plans. Among the potential risks that companies may face, environmental risks are the most obvious and best studied today, especially climate-related risks. These climate risks are generally split into two categories: • Physical risks are those related to the direct, tangible impacts of events whose intensity or probability of occurrence is made higher due to climate change. They refer to the economic consequences of damage to infrastructure, supply chains, and any constructed environment. Climate change will put a strain on these systems, and it is important that we take steps to ensure their resilience. As weather disasters, rising sea levels, flooding, extreme heat, water shortages, and

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climate migration increase, businesses will not be able to carry on as usual. Assessing these risks involves modeling a series of possible scenarios to determine how these climate-related events will affect a company’s operations in the short, medium, and long term. • Transition risks are risks related to the transformation of the economy toward more sustainable and low-carbon models of production. They are related to the process of transition from fossil fuels to a low-carbon economy. For example, the potential implementation of a carbon tax, carbon disclosure requirements, or the transition to renewable energy are actions that companies need to anticipate so that they become opportunities rather than risks. All companies will be affected and need to assess their transition risks, but some industries will be hit much harder than others. For example, the fossil fuel sector is most likely to face high transition risk as companies phase down their use of fossil fuels to remain consistent with their climate goals.

1.2.1.4

Reputational Risks

Failing to consider relevant ESG factors and anticipate the risks they entail can result in the materialization of reputational risk, both for the company and for the asset managers that own it. Reputational risk refers to the likelihood of negative events as well as public opinions and perceptions negatively impacting an entity’s revenue, brand, support, and public image. The public is increasingly aware of the social responsibility of companies and is putting increasing pressure on them to demonstrate exemplary behavior. The UNCP and the University of Oxford polled 1.2 million people regarding their beliefs on the climate emergency and the most relevant policies to carry out (Flynn et al., 2021). 64% of the respondents consider climate change as a global emergency, and 50% believe investing in greener businesses should be a priority. Anticipating the potential damage that ESG litigation can cause to a company’s reputation has become a priority for many boards of directors. In the words of Warren Buffett, “it takes twenty years to build a reputation and five minutes to ruin it.” These reputational risks cover a broad range of issues that include climate change, board gender composition, workplace culture, human rights, anti-corruption efforts, and data privacy, among others. From climate change activism to diversity in leadership positions, boards and asset managers are increasingly challenged by the extent to which ESG factors are considered in their operations. Social and governance issues such as workplace culture, management behavior, and data privacy have an increasingly material impact on corporate performance and reputation. Risks to a brand’s reputation pose a danger to the overall reputation of the company that owns it. Customers and investors expect strong environmental responsibility, fair treatment of employees, and highly ethical behavior from corporate management. For instance, Aderibigbe and Fragouli (2020) report the Equal Employment Opportunity Commission (EEOC) has filed fifteen lawsuits against Walmart over the past twenty years based on discrimination complaints. Many other

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complaints, ranging from poor employment conditions to the selling of recalled products, are targeting this company. They show that failing to meet the market standards on these topics was greatly detrimental to Walmart’s reputation, even though they were the largest and most profitable retailer in the world. Conversely, applying a stakeholder-centered approach can help mitigate risks for corporates thanks to a better reputation and communication with key stakeholders. A better understanding of the financial and reputational impact of ESG issues has led stakeholders to appreciate the importance of these issues to a company’s financial performance. Research suggests that addressing ESG factors increases company value and fosters institutional ownership. Ultimately, reputational risks could threaten a company’s stability and competitiveness if it is not prevented or properly managed. Another key reason why reputation risk management is vital is that the balance between tangible and intangible assets has shifted in favor of values such as trust, reputation, and goodwill, which are not as easy to manage as equipment and personnel. As a result, a company’s value is increasingly found in its intangible assets (Edmans, 2022).

1.2.2 1.2.2.1

Impact-Based Approaches Motivations for Seeking Impact

Companies and investors can have a wide range of values and motivations and, therefore, different impact intentions. Corporate Counsel and Morrison Foerster polled senior executives from companies in various activity sectors to explore the practices, priorities, and motivations behind corporate ESG strategies. Some actors have a mission at their core and want to live up to the goals they set for themselves to have a positive influence on their stakeholders (Corporate Counsel and Morrison Foerster, 2023). Others seek to protect themselves from regulatory or reputational risks. Others again see it as a way to create business or shareholder value, for instance, by reducing costs through energy savings or increasing workforce retention or customer loyalty. Depending on their motivation, investors’ intentions thus range from general commitments, such as mitigating their risk, achieving long-term financial performance, or leaving a positive footprint in the world, to more detailed goals, such as supporting a specific group of people in a specific a place, achieving a specific outcome, or addressing a specific social or environmental challenge. Proponents of simple materiality generally argue that financial institutions have a fiduciary duty to their customers and shareholders and should only consider issues that are financially material. They claim that all stakeholder issues are financially material in the long run and, therefore, should be considered in a single materiality approach. This is correct in theory but rarely the case in practice or over the long term (Lee & Suh, 2022). Single materiality investors may miss risks that are not only important from a stakeholder’s perspective today, which is already a valid argument for taking them into account, but that will most likely become important from a financial perspective later. Supporters of double materiality assert that if we are to

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make a real systemic change in the practices that have led us to where we are today, we need to embrace a broader perspective on making an impact.

1.2.2.2

Impact Is Still Difficult to Define

An impact can be defined as a change in an outcome caused by an organization; it can be positive or negative, intended or unintended. An outcome refers to the level of well-being experienced by a group of people or the state of the environment as a result of an event or action. Impact management is the process of identifying the positive and negative impacts of an enterprise on people and the planet and then reducing the negative impacts and increasing the positive ones. Three criteria are used to assert impact: • Intentionality refers to the intentional and preexisting desire of the investor to generate a positive social or environmental impact through its activities. The investor is, therefore, looking for a double performance, both financial and nonfinancial. This intentionality must be apparent at every stage of their investment process and must be present in the analysis and selection of projects. • Additionality refers to the investor’s specific contribution that enables the company to increase its positive impact. This additionality can be financial, for example, when financing projects that are not covered by traditional actors, or extra-financial, for example, when providing the investor with sustained and active support to maximize the social and environmental impact of their investment. • Measurability refers to the assessment of the positive and negative externalities of the investment. An impact investor must measure the impact of its investments and must be able to compare them with their initial targets. This evaluation can be qualitative or quantitative and concerns the impact on the company’s various stakeholders as well as that generated by the products and services it offers. It is important to note that the purpose of this measurement is to quantify the impact of the funds entrusted to the company, not that of the company itself. Investors set targets and manage performance based on the impact they want, or do not want, the underlying companies to have on people and the planet, as well as their own contribution to that impact. They also set targets for the contribution they want to make to enable the companies in which they invest to have an impact. Investors’ preexisting intentions for the companies they invest in must match some of these specific impact goals. By being explicit about their goals, they can identify relevant metrics relating to their investments to assess whether they are achieving them.

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Positioning Impact Initiatives

Taking a double materiality approach to ESG is not enough to manage the impact. An impact analysis involves managing both the positive impacts and the negative externalities of one’s activities. The objectives pursued by impact investors are diverse, but their scope can be classified into three main categories described by the Impact Management Project (IMP) in their Guide to Classifying the Impact of an Investment: avoiding harm, benefiting stakeholders, and contributing to solutions (IMP, 2018). The ABCs of impact developed by the IMP help link these high-level intentions to more granular dimensions of the impact that are easier to measure and manage: • A—Act to avoid harm: at the very least, companies can act to avoid harm by identifying areas where they are harming the well-being of people and the environment. This allows them to improve their performance in the identified areas until they reach an acceptable threshold, defined internally or by a chosen standard. This target is chosen when an improvement in the company’s performance in a given area is expected, but without hoping to reach a high level of performance in a sustainable way during the period for which the target is set. • B—Benefit stakeholders: in addition to acting to avoid harm, companies can actively generate positive impact by improving the management of their environment and their stakeholders. This objective should be chosen when the level of performance expected at the end of the set period is consistent with that of a sustainable and responsible business in the context of the company’s activity. • C—Contribute to solutions: the principle of this objective is to go further by addressing companies with significant negative externalities to bring them to a high level of extra-financial performance. The aim is not only to avoid damage to stakeholders but also to improve their state or level of well-being. This objective is that of investors who want to help a company make a transition from a harmful to a beneficial state to society. While any investor managing their impact must act to avoid harming their stakeholders, they can use the ABC framework to show their intention to go further beyond the A goal and determine the role they want to play. For example, an investor positioning itself on the B or C goals will not only act to avoid certain harms, such as workplace accidents or greenhouse gas emissions from the companies in which it has invested but will also seek to generate positive outcomes for its stakeholders, for example by improving employee training and supporting projects that transition to a more circular economy.

1.2.2.4

Choosing the Right Impact Strategy

Once the ambition of the impact investment journey has been set, a strategy must be determined to achieve the stated goals. A strategy designed to generate positive impact must identify areas for improvement in company management and

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implement them effectively. The European Sustainable Forum, an NGO promoting sustainable investment, defines five categories of responsible investment strategies and ranks them according to their potential to generate impact: • Exclusion-focused: taking ESG into account in this type of strategy is quite simple and consists in excluding companies with certain characteristics, such as a particular sector of activity. The objective here is to align the portfolio with the values held by the fund and not to better manage the risks or opportunities identified by any ESG analysis. • Basic ESG: the primary objective of basic ESG investments is to mitigate ESG risks. This category includes most investors focused on long-term risk-adjusted returns. They have a low ambition to support the transition to a more sustainable economy. Basic ESG investors are not a priori sustainable investors. • Advanced ESG: advanced ESG investors aim to manage the risks and opportunities associated with ESG issues. They address ESG analysis in a purely material way and focus on financially material ESG issues. With a clear focus on opportunities, they go beyond the pure risk mitigation perspective pursued by mainstream ESG investors. They support a transition to a more sustainable economy to the extent that it might go hand in hand with addressing ESG risks and opportunities. Consequently, the ambition to actively support the transition to a more sustainable economy is not the primary objective of advanced ESG investors. • Impact-aligned: impact-aligned investors adopt a double materiality approach. Their goal is to align with internationally recognized goals such as the UN SDGs. They do this by focusing on companies with already healthy ESG performance to support them in their journey. This includes, for example, investments that select companies whose GHG reductions are already aligned with the Paris Agreement before the investment. As a result, impact-aligned investors are considered to have a medium level of ambition to actively support a transition to a more sustainable economy. • Impact-generating: impact investors actively contribute to finding solutions to social and environmental challenges in the real economy. They adopt a double materiality approach and have a high ambition to support the transition to a more sustainable economy. They use capital allocation as a mechanism to positively influence the performance of the companies they invest in. These investors exclude nontransformable companies and use shareholder engagement to transform those that can be transformed, with a structured approach that includes an escalation strategy and divestment as leverage. Of these five categories of strategy, only the last three are considered responsible, and only the last one is truly impactful. An impact investor manages the impact of their investments on people and the planet, whether it is generated by their products, services, distribution channels, operations, or supply chain, without assuming the relative importance of each of these elements. A major point of attention is that companies cannot offset their negative externalities with positive impacts. Developing a corporate philanthropy policy does not in any way allow one to ignore an increase in greenhouse gas

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emissions. On the contrary, it is necessary to question on a case-by-case basis whether the pursuit of a certain positive impact is worth generating a negative impact and to ensure that the conclusions and decisions taken are properly communicated. By being thoughtful in making decisions, an investor recognizes that the negative impacts generated still need to be managed by actively setting goals to reduce or mitigate them over time. Positive impacts are rarely achieved without any cost or the addition of a negative externality as compensation. Impact assessment is the process of estimating the relative value that a company creates, preserves, or erodes for its stakeholders in areas that are material to it.

1.2.2.5

Implementing Impact Strategies

Four mechanisms can be identified by which investors may contribute to the positive impact of the assets in which they invest. They represent the nonexclusive roles that an impact investor may choose to perform in the market. This choice depends on the investor’s financial and impact goals, opportunities, and constraints. Not all investors will be able to implement all these approaches, and not all investors will try to adopt one: • Promoting impact: investors employ this strategy when they communicate their impact approach to the beneficiaries of their investments and to the market. This communication about their investment strategy proactively and systematically focuses on the measurable positive and negative impacts of portfolio companies. It must be adopted only when extra-financial impact considerations have an influence on the investment decision. The objective is to drive the financial markets to consider the social and environmental effects of investments. This strategy reflects investors’ values but is unlikely to result in progress on societal issues compared to other forms of contribution. • Active engagement: beyond signaling that impact is important, investors can proactively support or advocate for assets that reduce negative impacts and increase positive impacts. Concrete ways to do this include filing a resolution at a general assembly, securing a seat on the board of directors, providing advice or mentoring, or participating in industry or regulatory efforts to promote the consideration of sustainability in the capital markets. Investors choosing active engagement often rely on a systematic strategy to identify the assets that enable them to pursue their impact goals, the levers of action to improve performance, and the strategy to make them a management priority. • Approaching new opportunities: committed investors can increase their impact by gaining exposure to new or previously overlooked opportunities. These may include assets that are less liquid or for which the perceived risk is beyond market standards. The objective is to create a change in the amount, cost, or terms of capital available to a company that allows it to achieve an impact that would not likely occur otherwise. Another possible scheme is to create a reward mechanism

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indexed to the company’s extra-financial performance that incentivizes it to improve its impact. • Risk-adjusted return flexibility: a small group of investors is willing to accept a lower financial return than they could hope for in investments with similar risk and liquidity in exchange for a bigger or higher probability of positive impact. Similarly, they can be willing to accept the same financial return but with greater risk or less liquidity. These are often investors developing or operating in new or undersupplied capital markets. For a portfolio of investments, a comprehensive impact report should include all the data necessary to understand what approach the investor is taking, from pre-investment impact goals to the strategy implemented to bring those goals to the board of invested companies. This data should include the total positive and negative impacts of these companies on society and the planet, as well as data on the specific contribution of the investor’s invested funds. For example, if the objective of a specific portfolio was to improve water resource management in a defined geographic area, the investor would highlight the C-rated impacts of the underlying companies to demonstrate how they actively contribute to solutions. In doing so, they must focus on the data related to significant changes in the pursuit of that objective that would not likely occur without their contribution, be they positive or negative. This approach communicates the full range of impacts of these enterprises, either already present or created as offsets, to justify the implementation of the projects it stands for. This information can then be shared with the investor’s various stakeholders to promote the impact-generating strategy of the portfolio.

2 The ESG Rating Landscape 2.1

Data Providers and Rating Agencies

Most of the time, ESG indicators are produced by third-party data providers, who rely on proprietary methodologies that are confidential, nonstandardized, and based on unobservable data. The ESG rating industry has been subject to a wave of buyouts and mergers in recent years, leading to the near disappearance of small, independent, and specialized companies in favor of an oligopoly of large, mostly North American, players. Notable examples include: • • • • •

MSCI’s acquisition of GMI Ratings and Carbon Delta, Moody’s acquisition of Vigeo Eiris, S&P’s acquisition of Trucost and the ESG rating activities of RobecoSAM, Morningstar’s acquisition of Sustainalytics, Deutsche Börse’s acquisition of ISS after ISS’s acquisition of Oekom Research.

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Data providers and rating agencies collect publicly available ESG data from documents published by the companies themselves (such as annual reports or mandatory extra-financial performance statements) and solicit additional information through questionnaires sent directly to the companies. This material is combined with data on company-related news and controversies from third parties, such as NGOs and the press. Together, these sources are used to generate an overall score that reflects the way each company manages ESG issues. It is important to note that the rating methodologies used by data providers are often opaque and can change over time, which makes temporal comparisons difficult.

2.2

Main Corporate Reporting Standards

As the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) reporting framework are not fully implemented and widespread among corporates, many existing reporting standards support their extra-financial reporting. Some, like the Global Reporting Initiative (GRI), the SFDR, and the Principles for Responsible Investment (PRI), are actual reporting frameworks, whereas others, like Sustainability Accounting Standard Board (SASB) and the Carbon Disclosure Project (CDP), are rather industry standards aiming at improving reported data quality (Fig. 2).

Fig. 2 ESG reporting standards and frameworks overview

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Global Reporting Initiative

With more than 10,000 reporters in over a hundred countries, the GRI is the world’s most widely used sustainability reporting standard. As the official reporting standard of the UN Global Compact, the GRI is the default reporting framework for the Compact’s more than 5800 associated companies. It requires companies to disclose material ESG topics as well as how those ESG topics were selected. The GRI states that the materiality assessment should cover topics that reflect the reporting organization’s significant economic, environmental, and social impacts on stakeholders, indicating an outward-looking approach.

2.2.2

Sustainability Accounting Standard Board

The SASB, endorsed by investors representing more than $40 tn in assets under management, has developed a materiality map describing the ESG topics that are material to more than seventy-five activity sectors. This approach is often considered less onerous than the GRI by finance professionals and is becoming increasingly popular. SASB focuses on financial materiality and aims to align organizations and investors with the financial impacts of ESG factors, thereby influencing investment or lending decisions.

2.2.3

Task Force on Climate-Related Financial Disclosure

The TCFD encourages companies to align climate-related risks disclosure with investors’ needs but does not address the companies’ impact on the environment. This framework assesses the current and potential impacts of climate-related risks and opportunities on companies’ strategies and financial results. It was created to deliver a set of consistent and comparable disclosures that companies can use to display their climate change resilience to investors and capital providers.

2.2.4

CDP

The CDP focuses on GHG emissions as well as governance actions and business strategies to mitigate climate change and deforestation and promote water security. It holds the largest repository of GHG emissions and energy use data in the world. More than 590 investors amounting to more than $110 tn in assets requested companies to disclose through CDP in 2021. CDP’s scoring standard helps respondents understand what investors expect of them. Each year, the self-disclosed information is used to generate scores to benchmark the environmental performance of the participating companies and cities.

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Fintech and Innovation

The various reporting frameworks proposed above give an idea of the complexity of the ESG data landscape at the company level. The outcomes and indicators that ensue are, unfortunately, no better than the data on which they are based. Companies may need to report under several different frameworks to accommodate global guidelines and industry standards. They rely on a combination of external providers and internal services to extract data on a variety of topics, such as their supply chain or human resources. Collecting, sorting, and leveraging this data is increasingly difficult and time consuming. Quarterly SEC filings, which used to be about 10,000 words in the 1990s, are now, on average, twice as long; annual filings have soared to about 50,000 words. The indicators computed based on this extensive data can become very complex, to the point where professional investors are no longer able to use them. For available data and its exploitation to truly improve the sustainability performances of the financial sector, the data must demonstrate high quality in several dimensions: • Accuracy: does the data correctly reflect real-world events or objects? • Completeness: is the data sufficient to allow for meaningful interpretations and decisions? • Consistency: does the information stored in one place match the data stored elsewhere? • Timeliness: to what extent does the data represent reality at a given point in time? • Validity: does the information follow a specific format or rule that can be interpreted by the user of the data? Because of the digital nature of the industry, sustainability-focused fintech have a particular ability to provide holistic and innovative approaches to improving industry practices and achieving these goals. They are building solutions in a range of areas affected by ESG issues: retail banking, lending, payments, asset management, trading, risk assessment, etc. The speed of transformation of the industry is such that traditional investors who fail to integrate the new ESG investment technologies developed by these players are often not able to properly manage the volume of new information and knowledge available to them. The consequences for their reputation and performance can quickly become significant. The main sources of innovation that have developed in recent years involve artificial intelligence, the exploitation of alternative data sources such as satellite images, and decentralized systems built on blockchain technologies.

2.3.1

Artificial Intelligence and Machine Learning

The major advantage of artificial intelligence over traditional analysis is its ability to process large volumes of data quickly. Natural Language Processing (NLP) methods

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can continuously sift through millions of articles, texts, documents, speeches, or even tweets. These features increase the ability of investors to obtain comprehensive data in real time. By using sentiment analysis algorithms, these solutions can determine a company’s ESG goals and practices from its public communications. These algorithms also have the potential to improve the efficiency of foreign language data analysis. Cross-referencing data sources in this way is a good approach to ensuring the consistency and validity of the data collected. In addition, massive data processing is an efficient method for creating consistent datasets and streamlining compliance activities. Companies get more robust, valid data and gain credibility in their audits.

2.3.2

Computer Vision and Satellite Imagery

Rating agencies and financial data providers have long sought to leverage alternative data, i.e., data other than that directly reported by companies. As the availability of satellite imagery increases, significant results can be achieved by employing image recognition algorithms to track countless ESG metrics, from potential infrastructure leakage and pollution to the conditions of farm workers and the supply chain. Their goal is to be able to objectively measure indicators with greater frequency than corporate reporting. Some companies offer services to measure emissions from factories, the affluence of certain strategic places, the level of activity of transport or production infrastructures, etc. Even the intensity of the night light provides alternative macroeconomic data for investors. In addition to activity measurements, these tools can be used to measure climate risks by tracking weather and climate events in real time, making it possible to identify and manage the risks associated with them faster.

2.3.3

Blockchain and Distributed Ledger Technology (DLT)

Blockchain technology has enormous potential to redefine supply chain management and ensure the validity of resulting data. As the blockchain is a digital ledger, it can record every transaction in a decentralized infrastructure and store the details in multiple locations simultaneously. The history of a transaction can be immutably embedded during its production in these decentralized databases. Companies can use blockchain-based reporting platforms to collect data and produce credible ESG reports. They offer transparency, traceability, and enhanced data protection to improve the reliability of their data. Blockchain can be used for data protection to prevent data corruption and protect against cyberattacks. Blockchain and DLTs are, however, not a universal solution: the regulatory framework for these technologies is still not well defined, and its future is uncertain. In particular, the fact that blockchain data cannot be modified may mean that its use does not comply with the “right to be forgotten” rule adopted by the European Union (EU) in 2014.

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3 Legal Framework 3.1 3.1.1

The EU Framework A Comprehensive Framework

Voluntary approaches to sustainability disclosure have been around for a long time but have not produced satisfying results. Consequently, sustainable finance is receiving increased attention from regulators and policymakers around the world. The EU has made sustainable finance one of its priorities and one of its pillars to achieve carbon neutrality in 2050. The European Commission has taken the most rigorous regulatory approach to sustainable finance in general and sustainable finance indicators in particular. In 2016, the EU established a High-level Expert Group (HLEG) composed of observers from European and international institutions and experts from a wide range of backgrounds and specialties, including civil society, the finance industry, and academia. The group was tasked with providing advice on how to: • Increase the flow of public and private capital into sustainable investments, • Identify measures that financial institutions and supervisory bodies should take to protect the stability of the financial system from environmental risks, • Deploy these policies at the European level. Following the publication of its Sustainable Finance Action Plan in March 2018, the European Commission has implemented several regulatory initiatives: • The Taxonomy Regulation, aimed at creating a single definition of environmentally sustainable economic activities, • The SFDR, aimed at improving ESG-related transparency toward investors; broadly inspired by French regulations (Article 173 of the Law on Energy Transition for Green Growth), • The CSRD, aimed at reinforcing the obligations of listed and large companies regarding the Extra-financial Performance Declaration (EFPD) introduced by the Nonfinancial Reporting Directive (NFRD) in 2014, • Amendments to the Benchmark Regulation aimed at improving the transparency of ESG benchmarks as well as creating benchmarks (climate transition, aligned with the objectives of the 2015 Paris Agreement), • Amendments to sectorial directives (Alternative Investment Fund Managers, Markets in Financial Instruments Directive II) to integrate sustainability preferences and risks into the organizational rules of asset management companies and the assessment of the product’s suitability to the client’s ESG preferences and product governance aspects. These various directives and regulations shall work together as a comprehensive sustainability framework supporting the collective effort towards the EU Green Deal targets (Fig. 3).

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Fig. 3 The foundations of the EU Sustainable Finance Framework. Source: Strategy for Financing the Transition to a Sustainable Economy, European Commission, 06/07/2021

3.1.2 3.1.2.1

The European Taxonomy What Is the Taxonomy?

The first purpose of the (EU) 2020/852 Regulation, or Taxonomy Regulation, is to define what counts as a “green” activity. Six major environmental objectives are defined by the Regulation, which can be summed up as follows: (1) climate change mitigation, (2) climate change adaptation, (3) water, (4) circular economy, (5) pollution, and (6) biodiversity. An economic activity or an investment can, therefore, only be considered environmentally sustainable if it contributes to at least one of these environmental objectives while not significantly harming any of the other five. This definition of sustainability set out by the Taxonomy is not intended to be limited to this regulation; it is binding on states as soon as they legislate on financial activities or products identified as environmentally sustainable. In short, the Taxonomy creates and sets a shared definition of sustainability within the EU. Its second purpose is, of course, the transparency of companies and investors regarding the sustainability of their activities. Companies that are already required to publish extra-financial information will have to extend their reporting by publishing the proportion of their turnover, capital expenditure, and operational expenditure that is environmentally sustainable. Large investors or investors who promote the sustainability of their products through SFDR will do the same reporting on their investments. The aim here is to make it possible to compare several financial activities or products based on legally established quantitative indicators and, ultimately, to promote the financing of sustainable activities.

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3.1.2.2

How Does It Work?

The Taxonomy is based on a simple principle of classifying eligible economic activities and determining specific criteria to be met to certify their sustainability. However, applying the Taxonomy requires analyzing technical criteria that are much more complex than those used in ESG analysis until now. This technical complexity is justified by the fact that the EU wants to deprive companies and investors of any subjective approach to the sustainability of their activities. The Taxonomy Regulation is complemented on the technical side by the Delegated Regulation (EU) 2021/2139. This “level 2” regulation identifies the activities eligible for the Taxonomy for the first two objectives (mitigation and adaptation). Once identified as eligible, an activity is considered aligned (i.e., effectively contributing to the ecological transition) if it substantially contributes to an environmental objective by meeting all the criteria that are specific to this activity within the Delegated Regulation.

3.1.2.3

How to Apply It?

Alignment with the Taxonomy is more demanding than eligibility. To say that an activity is eligible for the European Taxonomy only means that this activity is concerned by the Taxonomy and cited in the Delegated Regulation. An economic activity is aligned if: • • • •

It is eligible, It meets the activity-specific substantial contribution criteria, It meets the Do No Significant Harm (DNSH) criteria, It meets the minimum safeguards.

An activity must be eligible to be aligned, but an eligible activity is not always aligned. For an economic activity to be considered sustainable as defined by the Taxonomy, it must, therefore, verify both technical criteria, such as GHG emissions per unit of output but also not undermine the other five objectives of the Taxonomy and not violate international labor or human rights conventions. While there is some debate about whether to include certain activities in the Taxonomy, it is unarguably extremely demanding when it comes to achieving the criteria for alignment. Some analyses suggest that the average alignment of European companies does not exceed 5% today and could even be around 3% overall. Some activities are even assigned criteria that imply innovations that do not even exist yet, such as zero-carbon road transport.

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SFDR, CSRD, and Benchmarks Sustainable Finance Disclosure Regulation

The EU has enacted new regulations regarding investor sustainability reporting. The SFDR, which came into force in 2021 and was implemented at the end of 2022, aims to: • Ensure that investors and the public are informed, • Encourage investment professionals to clarify their level of ESG integration and assess ESG impacts on financial performance, • Establish common standards for financial products that present or claim ESG or sustainable aspects. It imposes an ESG reporting obligation on asset managers and other financial market participants. It requires them to provide standardized reporting on how ESG factors are integrated into their investment processes. This information must be provided at both the company and investment product levels: • Each of the products must be classified according to a typology that can be schematized as follows: • Products classified as “Article 6” take little or no account of ESG issues, • Products classified as “Article 8” include an obligation of means such as the deployment of an exclusion policy, the performance of ESG due diligence before investment, the measurement of the CSR impacts of the underlying assets, etc., • Products classified as “Article 9” have a formalized objective: they are, therefore, the most demanding and virtuous on ESG issues. An “Article 9” fund can thus set itself an objective of decarbonization or contribution to an SDG. • In addition to this classification, there is the obligation to produce Regulatory Technical Standards (RTS). These indicators are related to the following: • Climate and the environment, • Social issues and treatment of employees, including respect for human rights, • Anti-corruption and bribery issues. 3.1.3.2

Corporate Sustainability Reporting Directive

Corporate reporting of their sustainability impacts is a cornerstone of building sustainability indicators at the investor level. Since 2014, European law requires certain large companies to explain how they manage social and environmental issues. The NFRD has defined rules on nonfinancial disclosure and regarding diversity for large companies. These rules currently apply to large public interest companies with more than 500 employees. One of the major flaws of the NFRD was

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being widely nonprescriptive, leaving companies with significant leeway on the nature and extent of what they should report. In April 2021, the European Commission adopted a draft CSRD, which would be a revision of previous regulations. The CSRD Proposal addresses the following: • It extends the scope of reporting requirements to additional companies, bringing the number of companies eligible for this reporting from 11,000 initially to about 49,000, • It requires full assurance of sustainability information by external auditors, • It specifies in more detail the information that companies should report in line with mandatory EU sustainability reporting standards, • It requires all information to be published as part of companies’ management reports and disclosed in a digital, machine-readable format. It should eventually allow them to feed these reports into a single European data repository. The CSRD requires verification of sustainability information reported by companies and introduces more detailed reporting requirements than the NFRD. These standards are currently being drafted by a working group of the European Financial Reporting Advisory Group (EFRAG) and follow the principle of double materiality. They should address the following: • The resilience of the business model and strategy to sustainability-related factors, • Opportunities related to sustainability, • Plans to align the business model and strategy with the transition of a sustainable economy, defined as limiting the rise in global average temperature to 1.5 °C above preindustrial levels, in line with the Paris Agreement, • Stakeholder engagement practices and their implications for the business model and strategy, • Implementation of the strategy as it relates to sustainability, • Sustainability-related targets and progress achieved against them, • The role of functional areas and business units, as well as of the board, whether one-tier or two-tier, as per local practice in different Member States, regarding sustainability, • Principal actual or potential impacts related to the company’s broader value chain and any action taken and results achieved to prevent, mitigate, or remediate negative impacts, • Indicators to measure and report on the above. The CSRD is a real effort toward double materiality in its focus on linking environmental performance to social considerations. It allows the investor, in the end, to ensure that ESG performance is aligned with the scientific requirements and what is acceptable in society. This gives credibility to business plans and the expected performance figures of the assets to be financed.

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Climate Benchmarks

The Sustainable Finance Action Plan has proposed changes to the existing regulations on benchmarks. As a result of this action plan, two types of climate benchmarks have been created: Climate Transition Benchmarks (CTB) and Paris Aligned Benchmarks (PAB). The goal of the CTB is to bring benchmark portfolios on a decarbonization trajectory, and that of the PAB is to bring it in line with the Paris Climate Agreement. The amended regulations define the minimum technical requirements (e.g., in terms of carbon emission reductions and exposure to specific sectors) required to comply with each EU climate benchmark. In addition to introducing these new climate benchmarks, the regulations also define ESG reporting requirements that apply to all investment benchmarks.

3.2

The US Framework: SEC Rules and ISSB

The International Financial Reporting Standards (IFRS) created the ISSB in 2021 to establish its own ESG framework. The mission of the IFRS is to publish international financial reporting standards intended to unify the presentation of accounting data exchanged at the international level. The IFRS standards have essentially become the norm since 2005. IFRS is a private initiative based in the USA, but IFRS standards are not limited to North America. Since 2002, companies in the EU that are publicly traded have been required to present their financial statements using the IFRS standard. The IFRS thus enjoys a strong legitimacy in the accounting and financial world. Of course, the fact remains that the legitimacy and history of the IFRS’s accounting and financial standards do not give any legitimacy or expertise to grasp the reality of the issues and extra-financial standardization carried out by ESG criteria. This is where the composition of the ISSB becomes valuable. The official announcement was made at COP 26 in November 2021 that the ISSB would be organized as a committee by bringing together the joint forces of the CDSB (Climate Disclosure Standards Board) and the VRF (Value Reporting Foundation). The CDSB is itself the result of a coalition of organizations (such as CDP, SASB, etc.) aiming to standardize the integration of climate-related information in financial reporting. The VRF is the alliance formalized in June 2021 of two initiatives that have been working on the harmonization of nonfinancial information for the past decade: the IIRC (International Integrated Reporting Council) and SASB. The IIRC is exploring the appropriate ways to integrate ESG data into financial reporting. SASB is mandated to provide ESG information to financial and accounting actors in the USA by structuring tools to help prioritize the extra-financial issues to be explored by the economic sector. In the spirit of the IFRS standards, the extra-financial information produced must be “relevant, understandable, reliable and of materiality.” The philosophies behind

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ISSB and CSRD are very different: while CSRD serves society, ISSB serves investors. These two initiatives also differ in their geographical scope and their format, as the CSRD is an official EU law, whereas the ISSB standards are guidelines delivered by a private entity. Officially, the two initiatives are not in competition with each other and have committed to cooperating. In any case, the work is substantial, and any bridge between the initiatives will obviously be welcome. The ISSB states that its corporate reporting standard is designed to meet the needs of investors. It focuses primarily on the financial materiality of ESG and climate risks that could affect investors, not the impact of the company on its stakeholders. Moreover, if we look at the IFRS accounting standards, they lay down principles rather than rules, which gives companies leeway. These principles are the following: the balance sheet approach (priority of the balance sheet over the income statement), the precedence of substance over form, the principle of neutrality, the priority given to the investor’s vision, the significant room for interpretation, and the principle of prudence. Thus, ISSB remains fundamentally in line with the work of its founders over the last ten years (IFRS, CDSB, SASB, IIRC) as an approach aimed at informing investors of their extra-financial risks. These risks tend to focus primarily on governance and the environment, which itself tends to focus primarily on the climate issue. In any case, recent SEC rulings in the USA are pushing for an ESG vision that is highly focused on climate issues.

4 Conclusion and Limitations 4.1 4.1.1

Data Issues Low Data Quality

The availability and homogeneity of data (or rather its unavailability and heterogeneity) can be an obstacle to the development of sustainability scores. Despite ongoing regulatory initiatives, sustainability reporting at the company level is still largely voluntary or at least nonprescriptive. In most cases, the information provided by companies and investors remains unstructured and sometimes incomplete, which makes benchmarking and comparison between companies difficult (Berg et al., 2022). Judging the quality of sustainable finance indicators is generally tricky because of the difficulty of observing a company’s actual sustainability performance, as opposed to some financial indicators. To compute earnings per share forecasts or determine credit ratings, analysts observe company results periodically according to a well-defined and globally standardized methodology. In contrast, a company’s actual sustainability performance is perhaps most significantly expressed when it is involved in a scandal or receives negative media coverage on a sustainability issue.

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Data Reporting Challenges

Companies must monitor the evolution of investors’ and stakeholders’ expectations to adjust their priorities and revise their strategy if necessary. Extra-financial rating agencies periodically update their rating methodologies, which implies fluctuations in companies’ ratings and rankings even if there is no change in strategy on their side. The process of updating a methodology normally includes consultations with external stakeholders, including companies, and an analysis of quantitative data, which assesses the level of risk exposure caused by key ESG factors in certain sectors. Companies are increasingly pressured to disclose extra-financial information, both by their customers and stakeholders and by regulations that are gradually coming into force in various regions of the world. Annual reports, and in particular their CSR and ESG components, are becoming lengthier due to the ever-increasing recommendations proposed by standard-setting bodies. Many companies and investors are taking the lead and prefer to start disclosing the relevant ESG information requested by the different market players rather than waiting for a common regulatory framework to be established. Publicly traded companies are under intense pressure to provide information and audited data from a broad range of stakeholders to assess and monitor their ESG performance. How companies organize data collection and reporting efforts internally is, therefore, increasingly important to meet the growing demand for progressive ESG practices and associated disclosures. For instance, Schneider Electric and ABN AMRO Bank both report having more than ten employees answering requests from ESG rating providers and contributing to sustainability reporting. Both companies have integrated sustainability into their long-term corporate strategy; they, therefore, allocate considerable resources to these tasks to support their vision and deliver on their goals. However, not all companies are able to commit this level of resources to monitor ESG trends and improve their ESG reporting, putting smaller companies at a disadvantage with ESG rating providers. As a matter of fact, larger companies tend to have better ESG disclosure practices and, therefore, better ESG ratings than smaller ones. For them, concentrating on ESG issues that are relevant to the largest portion of their investor base is preferable to ensure that they get the best return on their investment of resources and time. It is critical for companies to remember that these ratings are used to make investment decisions, leaving them little choice but to improve their ESG practices and reporting. Therefore, taking the time to understand investors’ methodologies and underlying datasets is important for companies to identify the key ESG data and ratings providers used by their shareholders. This will allow them to identify where improvements are needed to achieve higher ESG ratings and how best to achieve them, i.e., either through improved practices or more comprehensive disclosures. Given the number of players in the ESG ratings and data market, engaging with these ESG ratings and data agencies should be prioritized by considering the impact of an ESG data and ratings provider on a company’s core investors.

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A major challenge for extra-financial rating agencies is to succeed in covering a large volume of companies. This objective is especially difficult to achieve with small and mid-cap companies and in emerging markets. These categories of issuers are likely to experience the most significant inequalities. Sustainalytics uses a slightly different evaluation framework for large and small companies to account for the different quality of reporting. This approach can help offset the disadvantage of smaller companies with limited resources that can be devoted to disclosure and progressive ESG practices. This approach is also used by most investors who adopt an active investment strategy. ESG rating agencies are not the only ones monitoring and reviewing companies’ ESG statements to assess their sustainability efforts and progress. Investors, proxy advisers, NGOs, and regulators also make use of this data. It is, therefore, in the interest of companies to invest resources in their sustainability performance reports to improve the quality and availability of the data. Since the analysis and update intervals of these different stakeholders tend to differ, companies can benefit from communicating their ESG performance on an ongoing basis without waiting for the annual report date. Companies that do wait may experience a six to eighteen-month lag in their ESG ratings as their reports are evaluated and integrated by third-party organizations.

4.2 4.2.1

Divergence in ESG Ratings Sources of Divergence

The way investors use these ratings and the underlying ESG data has become increasingly complex in recent years, from integrating ESG into their overall investment decision-making process to impact investing, to negative screening/ exclusion, to engagement, for instance. Two main areas have garnered attention and investor criticism of ESG rating providers: 1. Their focus on past performance and their lack of predictive value for future performance, 2. The sometimes divergent opinions of ESG rating providers for the same company. To illustrate the second point, The Wall Street Journal pointed out that in 2018, Tesla was rated well by MSCI regarding environmental issues, while FTSE came to the opposite conclusion, giving Tesla a poor score on these same issues (Mackintosh, 2018). While knowing that discrepancies between ESG ratings exist is, of course, important, understanding why they occur is even more crucial. First, it should be noted that these discrepancies are found in other financial market indicators, not just in sustainable finance ones. While credit ratings show a high degree of convergence, earnings per share forecasts, price targets, and buy/sell

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recommendations issued by financial analysts or broker reports are generally much more heterogeneous. Nevertheless, researchers have begun to find explanations for why sustainable finance indicators tend to diverge. Early research by Chatterji et al. (2016) focused on corporate social responsibility (CSR) assessments. They found that these divergences were primarily due to a lack of consensus on what the term corporate social responsibility encompassed and a lack of consensus on what indicators to use to measure CSR. This lack of a common definition is likely the result of the societal and normative aspects involved in defining CSR. More recently, Berg et al. (2022) examined the reasons for discrepancies in ESG ratings using data from six leading ESG rating providers. They identified three main sources of divergence: • Measurement: the indicators used to measure a specific attribute (accounts for 56% of the divergence), • Scope: the set of attributes used in the scoring model (accounts for 38% of the divergence), • Weights: the relative importance of the attributes used in the model (accounts for 6% of the divergence). Among these three elements, measurement and scope divergence are the most important in explaining ESG rating differences. Measurement divergence is the most critical factor and is particularly important when measuring a company’s ESG sustainability performance. Weighting discrepancy plays a lesser role, although one could argue that it could be considered a particular form of scope discrepancy. The measurement-related divergence is rather linked to the model’s underlying data. Improving the quality of raw data to build relevant indicators is the responsibility of governments and standard setters more than that of academic research. Overall, their study shows the ESG scores of six major rating agencies were only pairwise correlated at 54% on average, which is way lower than credit ratings, for instance. Figure 4 shows the pairwise correlation they observed at the ESG, E, S, and G levels. SA, SP, MO, RE, KL, and MS are short for Sustainalytics, S&P Global, Moody’s ESG, Refinitiv, KLD, and MSCI, respectively. Other studies examining the reasons for ESG score discrepancies have focused on disclosure as a source of divergence. Unlike financial reporting, which is subject to numerous mandatory and prescriptive regulations, ESG reporting is still largely voluntary and nonprescriptive in most jurisdictions. Although, as mentioned above, there is now a growing effort to harmonize and enforce ESG disclosure,

Fig. 4 Pairwise correlation between major ESG rating provider’s scores. Source: Berg et al. (2022)

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until recently, there were few prescriptive regulations on how companies should report on ESG issues. A Harvard Business School study published by Kotsantonis and Serafeim (2019) found that companies can disclose information on the same ESG topic using very different indicators. When the researchers examined how fifty randomly selected companies from the Fortune 500 list published health and safety information, they found that they used more than twenty different indicators. In addition to the divergence in data disclosure issues, the same publication cites data discrepancies, choice of peer groups in best performance calculations, data imputation, and information overload as important reasons for these divergences in ESG ratings. One might think that increasing the amount of information would reduce the discrepancies between ESG ratings. However, recent research concludes the opposite: greater data availability leads to more diverse interpretations and, thus, more divergence (Christensen et al., 2022). The researchers demonstrate empirically that these discrepancies increase with the amount of sustainability data readily available. They caution that merely requiring more information from companies may not solve the problem but, on the contrary, make the situation worse from this point of view. By making data more readily available, investors and nonfinancial rating agencies have more latitude to take ownership of companies’ ESG performance and build scores into management processes. It remains to be seen whether an increase in the quality of sustainability information will reduce the gap between ESG scores.

4.2.2

How Does Divergence Affect the Use of ESG Ratings?

The inherent complexity of finding correlations between extra-financial data and the financial performances of companies is enhanced by the low correlation between ESG scores themselves. The raw data underlying these scores also still lacks the required quality to support efficient decision-making. Classic issues for ESG scoring models’ designers include data availability, transparency, update frequency, and even the lack of a consensual proxy to measure the chosen indicator (biodiversity, happiness, etc.). Scores sold by rating agencies and data providers show large divergences and, combined with their lack of transparency, are almost impossible to compare and understand properly. Many asset managers develop internal proprietary scoring models to make up for this opacity and justify their ESG commitment. The problem with the multiplicity of scores and methodologies is that they make the ESG ratings landscape even harder to read and reflect disagreements among investors that affect asset prices (Fama & French, 2007). The wide range of scoring models entering the market is not harmful as such, as every investor may be concerned by different aspects of the ESG performances of a company and therefore attribute it a different score. The issue comes from the lack of transparency on the methodologies, biases, and interests of the raters that only use the scores internally. The consequences of this lack of consensus and collaboration affect all the stakeholders in the ecosystem:

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• Asset managers: even for those trying to hold well-performing ESG assets, the biased data and scores available on the market make it hard to identify top and bottom performers. They are confronted with contradictory views that introduce noise in their decisions and, therefore, on asset prices (Pástor et al., 2021). • Fund selectors and individual investors: there is no way to make sure who is really managing their assets responsibly or not. The increasing pressure for ESG products associated with the lack of maturity of the ESG reporting and scoring ecosystem generates greenwashing risks that are hard to monitor, even for dedicated institutions. At the European level, the European Securities and Markets Authority (ESMA) identified these risks as being a priority for their 2022–2024 roadmap. • Corporates: ESG ratings and investments by responsible investors are relevant signals to figure out the best way to improve their CSR policies. As both elements are noisy, corporates receive mixed signals that can prove counterproductive for them. Their reaction to these signals can, in turn, potentially trigger a negative reaction from stakeholders (Krüger, 2015). • Research institutions: state of the art needs to evolve if we are to step up the quality of ESG scoring. The problem is that, with divergent scores, the results of academic papers depend on the chosen scores and data providers. We need to devise a methodology for score comparisons to be able to explain and quantify this divergence in results in academic papers. A key finding of the study from Berg et al. (2022) is that the divergence is not only due to differences in opinion but also due to differences in the underlying data. Different rating agencies tend to measure similar sustainability issues using different indicators and approaches. This divergence in measurement is the main factor explaining the overall divergence in ESG ratings. While it is certainly good to have differences of opinion and not to prescribe which aspects (or scopes) of sustainability are most important, particularly because users may have different preferences for sustainability data and ratings, measurement divergence is clearly problematic. Similar categories should be measured in the same way. However, as long as there is no uniform standard for ESG data disclosure, measurement divergence is likely to remain an important factor explaining differences in ESG ratings. When selecting issues and metrics to include in their sustainability score, raters must be careful to select a set of clearly defined attributes for which verifiable, auditable, and transparent indicators exist. Ideally, scenario analyses or temperature alignment measures should also be subject to disclosure, but it is unclear whether existing methodologies allow for meaningful reporting. It may be more appropriate to take a less prescriptive approach in this regard until a common framework is found. In addition, efforts should be made to encourage harmonization and collaboration between the many existing standards (TCFD, SASB, GRI, ESRS, ISSB, etc.). Developing new standards and not building on existing frameworks risks amplifying the well-known problem of the “ESG label jungle.” For example, research shows that if ESG rating agencies used a standardized taxonomy, their methodologies and ratings would be more aligned (Berg et al., 2022).

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While differences in opinion are useful, research shows that some prescriptive guidance on the most important categories would also reduce divergence. For example, Berg et al. (2022) use the SASB taxonomy to create new ratings in which weights and scope are made more consistent across providers. This exercise leads to greater alignment between ESG ratings. Finally, greater methodological transparency from data providers on issues such as peer groups, scopes, and weights could also reduce inconsistencies.

4.2.3

Divergence Is a Way to Stand Out

Some market players are calling for a convergence of ratings to provide the market with clarity and a true measure of companies’ extra-financial performance to better manage their efforts in this area. Despite the voice given by the media to these concerns about the lack of consensus on nonfinancial ratings, not all financial professionals consider this divergence to be problematic. Rather, it reflects the added value of each ESG data and ratings provider and allows each to differentiate themselves in the market. Variations in methodologies are their value proposition and will likely remain distinctive for each one in the future. Instead, leveraging the services of different ESG rating providers seems to be a valuable approach as they all bring different perspectives. Depending on their focus and the sustainability criteria they consider, they merely address different concerns. This diversity of opinion is necessary because ESG scores are, by nature, subjective. Unlike a credit rating, ESG ratings are not “verifiable” by an event such as a company default. They reflect the convictions of the entity that expresses them according to the goals it has set for this score. The pursuits of performance, impact, or risk mitigation through ESG analysis are not likely to lead to the same result. It would make no sense to look at companies through the same lens with such dissimilar objectives. As such, understanding the data being captured, the weights and assumptions underlying each ESG rating provider’s methodologies may be of greater interest than the final score. There is no single perfect rating, and investors are seeking to develop analytical frameworks that fit their investment style, beliefs, and client base. They are increasingly engaging with shareholders to enhance individual company ratings and enable them to improve their performances. Investor demand for the granular underlying data that makes up ESG scores has increased in recent years. A growing number of investors are also developing their own ESG ratings using the underlying datasets of these ESG data and rating providers, filtering the criteria and assigning weights to ESG factors to differentiate themselves from their peers and produce more meaningful results. According to SquareWell Partners’ “The Playing Field” study, thirty of the world’s fifty largest asset managers have created their own extra-financial rating methodologies. Moreover, thirty-eight of them use at least two ESG data and rating providers to inform their responsible investment strategies. The most common ESG data providers are MSCI, Sustainalytics, ISS-ESG, and Vigeo Eiris (now Moody’s ESG). These internal ratings are used in the construction and management

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of portfolios as well as in stewardship activities. The raw data that feeds into ESG ratings often sends stronger signals about future performance than the overall ESG rating itself, which is only the starting point for analyzing a company’s ESG performance. The lack of global consensual reporting standards and agreement on what should be considered material for each sector has led ESG rating agencies to each design their own methodologies and processes, making it difficult for companies to manage their sustainability discourse and determine how best to allocate internal sustainability reporting resources. This decision is further complicated by the fact that investors increasingly tend to leverage multiple data sources to develop their own rating methodologies. The most critical issues come from the lack of transparency regarding the methodologies, biases, and assumptions behind ESG scores. Without a clear understanding of what differentiates two different approaches, comparing two issuers with scores such as 72/100 or BB provides little information. Investors need relevant context to compare companies to their peers and understand how that score was achieved. These two scores most likely do not measure the same factors and are not suitable for the same investor use. Unfortunately, whether it is the scores of rating agencies or those computed internally by investors, this background is often lacking or nonexistent. The cost of developing and maintaining these methodologies deters most score owners from disclosing them too transparently and at the individual issuer level. Asset managers provide aggregate ratings at the fund level, and rating agencies publish methodologies whose complexity makes comparison difficult. They are often unclear about the exact data points that have been estimated or reported and sometimes use complex artificial intelligence algorithms whose own mechanics are only partially explainable. All these elements hinder the comparability of ESG scores and, therefore, their integration into responsible investment processes. Communication around ESG scores is also problematic in the absence of a consensus and reference points on the market. Some initiatives seek to bring visibility to the sector by comparing the available signals to obtain an objective assessment of each company’s positioning. For instance, CSRHub seeks to bring consistency by scanning the scores of different ESG rating agencies, while Valueco directly collects scores developed internally by investors to help them position themselves against their peers. Although concerns about greenwashing and/or social washing remain, investors are increasingly aware of ESG issues and are developing tailored investment strategies to take them into account. Some investors are implementing responsible investment strategies by targeting companies with low or medium ESG ratings and interacting with them to improve their extra-financial performance or mitigate their risks. The divergence of ESG ratings is a priori a major obstacle for them, but it can become an edge for those who are able to understand the causes of this diversity of opinion and use it to promote their own approach and convictions.

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References Aderibigbe, A., & Fragouli, E. (2020). Reputation risk from a stakeholder management perspective. Risk and Financial Management, 2, 1. https://doi.org/10.30560/rfm.v2n2p1 BASF. (2018, December 7). Cuts outlook, now sees profits falling 15–20 percent in 2018. Reuters, sec. Business News. Berg, F., Kölbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26, 1315–1344. https://doi.org/10.1093/rof/rfac033 Chatterji, A. K., Durand, R., Levine, D. I., & Touboul, S. (2016). Do ratings of firms converge? Implications for managers, investors and strategy researchers: Do ratings of firms converge? Strategic Management Journal, 37, 1597–1614. https://doi.org/10.1002/smj.2407 Christensen, D. M., Serafeim, G., & Sikochi, A. (2022). Why is corporate virtue in the eye of the beholder? The case of ESG ratings. The Accounting Review, 97, 147–175. https://doi.org/10. 2308/TAR-2019-0506 Climate, Commonwealth and Law Initiative. 2021. Primer on climate change: Directors’ duties and disclosure obligations. Edmans, A. (2022). The end of ESG. SSRN Electronic Journal. https://doi.org/10.2139/ssrn. 4221990 ESG Ratings Methodology. (2023). Accessed January 12, 2023, from https://www.msci.com/esgand-climate-methodologies Fama, E. F., & French, K. R. (2007). Disagreement, tastes, and asset prices. Journal of Financial Economics, 83, 667–689. https://doi.org/10.1016/j.jfineco.2006.01.003 Flynn, C., Yamasumi, E., Fisher, S., Snow, D., Grant, Z., Kirby, M., Browning, P., Rommerskirchen, M., & Russell, I. (2021). Peoples’ Climate Vote: Results. UNDP. Herring, S. C., Christidis, N., Hoell, A., Kossin, J. P., Schreck, C. J., & Stott, P. A. (2018). Explaining extreme events of 2016 from a climate perspective. Bulletin of the American Meteorological Society, 99, S1–S157. https://doi.org/10.1175/BAMSExplainingExtremeEvents2016.1 IMP. (2018). Guide to classifying the impact of investments. Impact Management Project. Krüger, P. (2015). Corporate goodness and shareholder wealth. Journal of Financial Economics, 115, 304–329. https://doi.org/10.1016/j.jfineco.2014.09.008 Lee, M. T., & Suh, I. (2022). Understanding the effects of environment, social, and governance conduct on financial performance: Arguments for a process and integrated modelling approach. Sustainable Technology and Entrepreneurship, 1, 100004. https://doi.org/10.1016/j.stae.2022. 100004 Mackintosh, J. (2018). Is Tesla or Exxon More Sustainable? It Depends Whom You Ask. Wall Street Journal, September 17, sec. Markets. MoFo ESG Survey in Partnership with Corporate Counsel: In-House Legal Teams Overwhelmingly Lead Company ESG Strategy; DEI, Climate Change, and Board Oversight of Environmental and Sustainability Issues Emerge as Top Priorities for Legal Teams. 2022. Morrison Foerster. Accessed January 12, 2023., from https://www.mofo.com/resources/news/220519-mofo-esgsurvey-corporate-counsel Pástor, Ľ., Stambaugh, R. F., & Taylor, L. A. (2021). Sustainable investing in equilibrium. Journal of Financial Economics, 142, 550–571. https://doi.org/10.1016/j.jfineco.2020.12.011 Smith, A. B. (2020). U.S. In Billion-dollar weather and climate disasters, 1980–present (NCEI Accession 0209268). NOAA National Centers for Environmental Information. https://doi.org/ 10.25921/STKW-7W73 Task Force on Climate-Related Financial Disclosures. (2017). Recommendations of the task force on climate-related financial disclosures: Final report. Task Force on Climate-Related Financial Disclosures.

Cash Flow Valuation and ESG Laurent Inard

1 Reminders The valuation of a company or branch of activity is a vast and complex process to embrace. It is not the purpose of this chapter to deal with such an extensive topic, and it is hereafter assumed that the reader is well aware of the many issues and subtleties of valuations, beginning with the very concept of value (e.g., fair value, value-in-use, distressed value, differences between value and price, impact of acquirer’s and/or market participants’ synergies, etc.). The various techniques made available to the valuation practitioner when assessing the value of an asset, of shares, or of an enterprise mainly fall into the following families of approaches: • Market approaches: these approaches encompass both direct market approaches (market price of the traded asset, price of recent investment) and indirect market approaches (analogical approaches such as multiples based on listed companies or on recent transactions of comparable companies). These approaches are dealt with in the “Multiple valuation and ESG” chapter. • Cost approaches: these approaches are either based on the past (capitalization of incurred costs) or on the assumption of replacement/replication of assets (introducing modern-equivalent assets where the actual asset is no longer available on a first or secondhand market). • Income approaches: these approaches are based on the premise that the value of an asset relies on the profitability it will deliver in the future to its owner, so that the value depends on the expectations of further income, on the risk attached to these expectations and on the remuneration expected for taking such risk. The

L. Inard (✉) Partner Valuation & Modelling/Research & Development, Mazars, Paris La Défense, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_4

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discounted cash flows (DCF) method clearly belongs to this family of approaches. Each and every one of these approaches has pros and cons and is circumstantially more or less relevant for a specific valuation, depending on the context of the valuation and characteristics of the considered asset. Where possible, a multi-criteria approach is implemented to get a broad vision of the values derived from the approaches deemed relevant, eventually enabling one to make an informed decision on a final reasonable range of values. Again, the characteristics, underlying assumptions, and pros and cons of each of these approaches are discussed and available in many valuation books, thus will not be reminded here. However, focusing on DCF, two main features are to be highlighted: • DCF is a prospective approach: while environmental, social, and governance (ESG) concerns are very actual and relevant actions are taken every day, ESG is nonetheless fundamentally about the long term. Most of today’s ESG steps will have, in fact, an impact in the future, near or far. When trying to capture these effects for valuation purpose, DCF is found to possess interesting features, especially the use of company-specific forecasts. • DCF explicitly handles risk and remuneration: ESG actions aim to mitigate the many risks that threaten a sustainable environment and development. However, some of these risks reveal uneasiness to anticipate and grasp, in timing as in magnitude, generating uncertainty in both the choice of a solution and the timing of its implementation. DCF proves interesting in such a context, taking into account (1) company-specific decisions (such as the selection of an ESG-positive impact technology and the timing of its investment) and (2) the industry-specific risk exposure through the cost of capital, which balances risk and remuneration as measured by the market. For these reasons, the study of DCF in the context of ESG issues, objectives, and actions is of particular interest. This chapter will first explore the various challenges that the ESG context presents to DCF’s main parameters and inputs. Then, a specific focus will be placed on the forecasts, the timing of ESG actions, and scenarios of forecasts.

2 DCF and the Many Challenges Raised by ESG While ESG concerns are not new, the sense of emergency attached to the issues they address and the expansion of the people involved, i.e., basically everyone, are new. The challenges to be met are somewhat unprecedented, so the contemplated solutions, the speed of their ramping up, and their actual impact on ESG factors are not under similar control as that of mature activities. Obviously, organizations still

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evolve in a context of uncertainty when it comes to ESG data. DCF approaches and especially their parameters and inputs are, of course, impacted by such a context.

2.1

The Recency of ESG

ESG data expand the objectives of the organizations so that they achieve sustainability and create value for them as stakeholders. Expressed this way, ESG data should have been eagerly implemented by all organizations decades ago, which is obviously not the case. It appears indeed that ESG objectives are not so natural or straightforward, despite their ultimate positive effects, so ESG appears to be a somewhat recent matter. Several reasons for such a situation may be contemplated: • Great changes of certain ESG stakes over time: the evolution of human societies and cultures, the awareness of a finite world with finite resources, and capped ability to regenerate have led to dramatic increases in ESG challenges (e.g., gender equality, working conditions in the supply chain, air, soil, and sea pollution, global warming, biodiversity loss, etc.). These stakes have lately become visible, material, and global. • Unattractiveness in the short run: achieving ESG objectives is a real challenge that may be costly in the short run and may appear counterproductive in terms of spot competitivity when facing ESG-careless competitors, while returns usually require a long-term vision and may prove difficult to measure (e.g., factual vs counterfactual scenarios are not easy to process). In some cases, returns are even suspected of benefiting external parties and not significantly the initiator of the ESG action (especially the reduction of negative externalities). These obstacles have progressively been lifted with ESG incentives and with the progressive increase of ESG awareness of clients, lenders, and investors that put more pressure on ESG-careless actors or even discard them. • Lack of club effect until recently: certain ESG actions do not produce any significant impact, if any impact at all, unless many contributors undertake similar actions. For example, struggling against pollution and global warming takes massively collective and multiple actions to make a difference. Therefore, ESG has taken time to emerge as many organizations waited for the club effect to achieve critical mass. • Incentives vs penalties tactics: where the whole society and global welfare are at stake, governments often promote the required changes through incentives, if not subsidies. However such policies have a drawback: in a given specific area where no incentive has been enacted yet, operators may speculate on the probability that such measures may be decided on a short to medium run, thus encouraging waiting strategies. Therefore, although ESG shall eventually create value for the active players as for its related parties, ESG objectives are relatively recent in organizations’ strategies. This lack of past collective experience generates uncertainty per se.

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A Wide Variety of Uncertainties Associated with ESG Stakes

Beyond the lack of experience of organizations when confronted with ESG stakes, these very stakes are evolving and moving swiftly, along with mankind’s better understanding of the dynamics of certain issues (global warming, loss of biodiversity, etc.). Facing these changes, many domains of organizations are concerned with their own level of uncertainty. Among these domains, the following may be pointed out: • Employees: ESG actions may have many positive effects on employees, based on more respect, equality, a sense of purpose, well-being, etc. These may benefit organizations through (1) greater attractiveness on the recruitment market, (2) higher retention rate of talents, (3) lesser absenteeism and higher productivity, and (4) higher creativity and innovation. However, the relation between ESG actions, their nature, and magnitude, and the actual consequences for the above KPIs are not easy to grasp, let alone measure. • Sourcing: ESG strategy may lead to increasing costs through (1) the selection of suppliers under ESG criteria (narrowing down the market—due to additional selection criteria—results in a less competitive, thus more onerous market and the fact that ESG-compliant suppliers usually experience a more onerous cost of production), (2) the changes in market prices of certain raw materials due to their rarefaction and/or to the increase of their cost of production or of logistics, and (3) the increase of the cost of financing of certain infrastructure with negative ESG impact (e.g., coal power plants). Sourcing reveals an uneasy-to-understand magnitude and rhythm of the increase of such costs so that alternatives and countermeasures can be designed and implemented at optimal timing. • Regulations: depending on the industry, organizations may generate negative externalities, some of them relating to ESG stakes (e.g., pollution, greenhouse gas [GHG] emissions, thermal or noise effects, etc.). Governments progressively issue regulations that internalize the cost of externalities—to some extent—so that the economics gradually turn in favor of processes with reduced negative externalities. However, the timing and magnitude of these regulations are not easy to anticipate, and their effects over time are sometimes uncertain even for the regulator itself (for example, when rights of emissions are tradable, their price depends on the market that has its own dynamics). • Technologies: certain ESG objectives may be processed through new infrastructure or equipment that, thanks to modern technologies, are more effective than older ones in terms of ESG (e.g., reduction of unwanted outputs and waste, greater energy sobriety, predictive maintenance, etc.). However, the related technologies evolve quickly as worldwide R&D focuses more and more on ESG stakes, so it is not easy for organizations to scrap old tech assets and implement a new technology, when this one may prove obsolete only a few years after.

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• Customers: while citizens are more and more educated and sensitive to ESG issues, it is still uneasy to grasp to what extent ESG is now part of the decision process of consumers, especially where the prices differ from one product to the other. Strong ESG fundamentals grant pricing power, but there is still uncertainty in certain markets as to the actual coverage of ESG incremental costs. Furthermore, in certain markets, several niches may co-exist (e.g., organic food addresses a part of the market with higher prices): where the market can be segmented, the problem is far more complex than a single “ESG-to-price” elasticity factor and its time derivative.

2.3

Impact of ESG on DCF

There are usually four main components in a DCF approach: • Business plan, covering the “explicit horizon:” the business plan is composed of forecasts of cash flows, either inflows or outflows, and covers several years, usually from three to five, in certain sectors, ten years or even more where the industry specifics are consistent with longer visibility (e.g., industries with longterm backlog). • Terminal year normative cash flow, covering the “implicit horizon:” the value generated by a company does not stop where the business plan stops,1 as further cash flows are still expected beyond the explicit horizon. Of course, forecasts of the far future cannot be as accurate as the ones from the business plan; as such, they are modeled through an assessment of a “normative” yearly cash flow (in other words, variations around this normative flow are considered as not material and/or reasonably compensate each other; furthermore, these inaccuracies are arithmetically mitigated as far future is more heavily discounted). • Long-term annual growth rate (LTGR): this parameter applies to the normative cash flows, so that the cash flows implicitly grow at this LTGR pace. In a business targeting and hopefully achieving sustainability, perhaps the clearest manifestation of ESG impact on valuation is the assumption that the business shall run indefinitely. Of course, before ESG grew to become a significant concern, most businesses were already assumed as everlasting, so the creation of value did not appear so obvious. However, the increasing awareness of certain threats addressed by ESG makes it clear that this assumption should not be taken for granted for businesses ignoring ESG while deeply exposed. Thus, there is indeed an impact on the value when considering duration insofar as without ESG actions, the value of certain sectors would lower. • Weighted average cost of capital (WACC): this parameter reflects the riskremuneration balance for both shareholders and debt lenders (hence the 1

Except, of course, in certain circumstances where the organization runs a business with a definite life, for example, for-special-purpose organizations linked to a particular project or infrastructure.

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“weighted average”). The DCF approach consists of the calculation of the sum of all discounted cash flows. This series converges even though the implicit horizon is indefinite insofar as the WACC is higher than the LTGR (convergence of geometric series). Under a common DCF approach, these four components are arithmetically combined in order to compute the enterprise value (see Eq. 1). NPV =

N i=1

FCFi TFCF iþ ð1 þ K Þ ðK - G Þ  ð1 þ K ÞN

ð1Þ

where: FCFi: free cash flows series from the n-years business plan TFCF: terminal normative cash flow G: long-term annual growth rate K: weighted average cost of capital NPV: net present value (of the business, asset, etc.) Nota bene: under this simplified approach, each FCF is positioned at the end of its annual period. In certain contexts, the DCF shall use evolutive discount rates or shall include financial cash flows so that the DCF is adjusted into equity cash flows. In certain other contexts, the dynamics are so strong at the end of the business plan that an intermediate horizon is introduced between the explicit and the implicit horizon, and the trajectory is progressively smoothed in a more consistent way, etc. However, the above components properly reflect the standard DCF approach widely used by practitioners. ESG objectives and their associated uncertainties have a potential effect on several, if not all, of these four DCF components.

2.3.1

Forecasts and ESG

The DCF approach deals with forecasts of cash flows; ESG may have an impact and generate uncertainty on this component.

2.3.1.1

Revenues and Market Addressed

Depending on the sector, the size of the market may be altered by ESG fundamentals. For example, the size of the private car market, in the long run, might be altered with the rise of urban soft mobility. The air traffic market may also be altered due to its reputation for high GHG emissions on routes where alternatives do exist. Reversely, wind turbine farms shall experience a boost in their potential market.

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These trends are not easy to anticipate and measure and further have to be mitigated considering the bias of present, meaning that thoughts are biased by the currently existing or close-to-come technologies, though, in fact, technologies evolve quickly in the ESG domain. As such, wind farms may be outsmarted by new ways of generating sustainable power; reversely, aircraft may benefit in the future from new technology. The uncertainty here is about not only risk but also opportunity. However, reflecting a wide field of possibilities is usually not a task to be performed by the forecasts.2 In fact, DCF forecasts are to reflect expectancy in order to ensure consistency with the discount rate (WACC). For instance, a highly volatile sector in terms of possible outcomes, due to ESG or any other driver, shall relate to a high WACC.3 Therefore, the forecasts shall stick to reasonable expectations (as in the mathematical expectation of a statistic variable). In very sensitive sectors, such as the previously mentioned automotive, air, or power sectors, external data concerning the trends and target of the market may prove useful to improve the documentation of the forecasts and the LTGR and to check consistency with the organization’s own predictions.

2.3.1.2

Revenues and Market Shares

Within its own market, the organization may intend to implement a specific ESG strategy, also expecting an impact on its market shares. Although the market may indeed react positively to ESG proactive players, the global effect on market shares may have to be mitigated where an increase in unit price is also planned, depending on the elasticity to the price of the market addressed. For instance, in many sectors, clients expect that certain ESG criteria are met (especially in B2B, through purchase departments policies) without any increase in prices where the services provided are not assumed to be materially cost-sensitive to ESG. Further, ESG actions are also contemplated by the competition. As such, it is important to understand whether the organization is indeed part of the ESG leaders in its market or is rather among the followers so that the forecasted change in market share is consistent (especially when intending to address the narrow ultra-ESG part of the market).

2

Forecasts are nonetheless useful to simulate the consequences of certain events should they happen or to simulate differentiated tactics. 3 Indeed, under the portfolio theory, risk is volatility, and risk is balanced with expected remuneration through the WACC, which is thus higher where the risk (and hence the volatility) is higher.

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Cost of Production

As previously mentioned, the employees, the sourcing, and the regulations are domains that shall have an impact on the cost of production of the organization. As such, it is important to understand whether and to what extent the business plan considers these effects. These effects may be easy to assess in the present but more difficult to extend over time, as certain parameters can change significantly (for example, the cost of energy or regulation incentives and fines mechanisms that are not always firmly scheduled).

2.3.1.4

Production Tools, Investments, and Technology

Regulations and technologies are domains that shall have an impact on cash flows. One of the most critical issues for a valuation practitioner is to ensure that, when a cost is incurred within the business plan, all its further effects are indeed embedded in the forecasts or through the LTGR. The main point here is that there is a material delay between the initial investment and its return. It is, therefore, highly possible that an investment is positioned within the horizon of the business plan, but its positive effects are not: consistency demands that, in such a case, the normative cash flow component (i.e., implicit horizon) takes into account these phenomena. Where the investment generates recurring sustainable savings, the normative cash flow may easily achieve consistency, but where the effects are limited in time, an intermediary horizon may have to be considered.

2.3.1.5

Regulations and Industry

ESG regulations target more precisely certain industries than others, considering their impact on the environment, the negative externalities they emit, etc. Furthermore, depending on their typology, they have differentiated impact and generate more or less uncertainty: • Nonmonetary obligations: regulations may consist of restricted authorizations with scheduled increasing restrictions. For example, a real estate owner whose asset has a bad energy balance can be prohibited from renting; a car whose particle emissions are too high may be prohibited from selling; combustion engine vehicles may be prohibited from certain urban areas; certain pesticides may be prohibited, etc. Under such regulations, time schedules provide a certain level of visibility. Business plans have to address these regulations; the terminal normative flow also has to address prohibitions that are to happen beyond the horizon of the plan (even though it is eventually assumed that the sector shall find a way to handle these prohibitions without any significant change in normative profitability).

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107

• Additional taxes: for example, the cost of recycling certain appliances is financed upstream through an additional tax applied on the selling price. The quantum is known, may evolve if the level of tax proves miscalibrated, and the effect on the market is quickly known after the first application of the tax. • Quotas of allowances: policymakers may grant allowances (for example, CO2) and decide on their reduction over time. Then, CO2 allowances become tradable so that the prices are subject to bid and ask market balance.4 Of course, the trajectory of reduction somewhat leads to a rarefaction of allowances and, therefore, to a price uptrend. However, this trend is highly dependent on the rhythm of decarbonization of all companies as compared to the trajectory. It is also dependent on other jurisdictions’ regulations so the changes in the price of a metric ton of carbon are difficult to anticipate over time. For example, while at circa €20/ton in 2008, the EU ETS price decreased in the following years. On the contrary, from 2018 to 2022, the rise revealed drastically steep, with a price above €80/ton.

2.3.2

Long-Term Growth Rate and ESG

The long-term growth rate is a parameter that predominantly relates to exogenous factors (i.e., external to the company), such as the geographical (country, region) dynamics and the sector dynamics.5 This situation facially lowers the difficulty of assessing the specific impact of ESG on the LTGR since exogenous factors relate to consensus and benchmarks that implicitly embed any item with material impact, including ESG. As such, external data such as long-term regional inflation and GDP growth from the World Bank and the IMF, sector data from brokers, etc., are valuable material to derive an LTGR. In addition, other valuation approaches, such as multiples of listed comparable companies, may provide additional hints as to the implicit long-term dynamics considered by the market for the sector. However, there are a number of situations where the LTGR is not easy to determine. For example, many companies do not address one single market and tend to diversify, especially where they evolve in ESG-risky environments. In such situations, the LTGR is more complex to address, given that certain businesses are more recent and less mature than others. For instance, the core business of a tobacco company may eventually decline and be associated with a negative LTGR, while at the same time, the company may have addressed a strategy of transformation in 4 The EU Emissions trading system, for example, is a cap-and-trade system, a cap on greenhouse gas emissions is set, and this cap is gradually reduced over time. 5 Where the market shares of the company are modest and swiftly evolving, it may be relevant to contemplate an adjusted LTGR rather than stick to the sector’s LTGR. Of course, such an adjustment creates an inconsistency in the very far future (e.g., at one point, the revenues of the company shall exceed the revenues of its entire sector), but at this stage, the discount rate usually makes such inconsistency insignificant, thus acceptable.

108

L. Inard

which new products and new services are developed, with a strong LTGR. In such cases, the global LTGR is hybrid and complex to assess. Where possible, separate business plans shall be considered so that differentiated DCF parameters (LTGR, but also WACCs) are consistently applied; a sum of the parts (SOTP) of DCFs provides a final global value. However, sometimes the businesses are intertwined and difficult to unbundle; the understanding of each business, their strategy, and the way they are supposed to combine are then important for the determination of the global LTGR. Again, the market approaches and consensus of multiples may also provide valuable information as to the dynamics of the sector. Finally, in certain situations, the business may rely on raw materials or resources that are finite: • Long horizon: in this situation, the global duration of the business may be definite, but the horizon is deemed sufficiently far to assume that a standard LTGR approach remains reasonable and relevant. In fact, all companies fall into this category, as they all ultimately rely, one way or the other, on certain finite resources. • Extended horizon: in this situation, certain resources are finite, but their use may last more and more due to improvements in recycling. For example, aluminum can be recycled indefinitely (i.e., no deterioration in chemical and physical properties), thus enabling the extension of the horizon for businesses needing aluminum, along with the development and improvement of recycling channels. • A sense of purpose: in this situation, certain resources are finite but needed for a broader purpose than for themselves. Thus, they may be replaced even though current technologies are not able to do so (in a scalable way or at all). For example, a global power company will certainly not produce electricity in the future the same way it currently does, using the same resources, but the production of energy will still be required, and being the raison d’être of this company, it is probable that it will do its best to transform and remain a player of this market. In such a situation, the LTGR is often of lesser importance than the determination of the normative terminal flow (e.g., will the next ways of producing power generate similar cash flows as the current ones, will the next cars and/or mobility solutions generate the same level of profitability as the production of contemporary cars, etc.). • Limited horizon: in certain situations, the company is totally committed to a definite-life project and does not or cannot plan to diversify (e.g., a mine, a quarry). In other cases, the contracts or regulations set a definite date of termination of the business (e.g., wind farm, concessions, etc.), usually with dismantling and remediation obligations. In these situations, an LTGR is not needed: the DCF shall be fed with a business plan covering the entire duration. The last year shall not omit the one-off specific cash flows that relate to the dismantling, depollution, and remediation costs (unless they are already processed as an obligation included in the passage from the DCF enterprise value to the equity value).

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109

In a nutshell, a sustainable business is a business that shall last. ESG is thus of primary concern when it comes to the assessment of the duration of the business. For the moment, the finite resources in which humans evolve and the very fluctuations of certain reserves (e.g., the quick diminution of certain living resources) have an impact on the DCF on a limited number of industries and on certain companies. For most industries, a long-and-similar-to-indefinite duration is still implemented as the customers’ needs are there, and the technologies are assumed to evolve into and timely address the issues. The underlying uncertainties of these assumptions are mainly reflected in the industry cost of capital (see the next section). In the coming years, though, the ESG issues and the extension of their reach will get clearer, and so will the rhythm of new regulations, new technologies, and behaviors. No doubt that valuation practitioners will be able—and shall have—to be more and more specific as to the understanding of the possible horizon for a considered business.

2.3.3 2.3.3.1

Cost of Capital and ESG Cost of Capital Components

A lot of research has been performed on the concept of cost of capital: Ross (Ross, 1976) and the arbitrage pricing theory, Fama and French (1995) and their threefactor model, and before that, the capital asset price model from Sharpe (1964), prelude of the Capital Asset Pricing Model, known as CAPM, and the works on the portfolio theory from Markowitz (1952). The CAPM is the frequently most used approach by valuation practitioners; it is a one-factor model, introducing the unique beta factor in order to address the sector’s systematic risk as a whole; as such, it proves relatively easy to use even though it appears theoretically less accurate than other approaches. It is no coincidence that recent research trying to showcase an ESG impact on the cost of capital considers a multifactor approach, trying to separate the beta factor from another one representing the ESG factor. The CAPM cost of capital is presented as in formula 2. K e,u = r f þ βa  r m- r f

ð2Þ

where: Ke, u: cost of equity for a debt-unlevered business rf: risk-free rate rm: the market equity premium βa: systematic (nondiversifiable) risk of the asset a, a factor that can be expressed as βa = covVððrram, Þrm Þ.

110

L. Inard

Where the business is debt-levered, the beta factor has to be adjusted, which does not prove so easy because of the presence of tax shields and agency costs. The reader may refer to Myers (1974) or Miles and Ezzell (1980) to know more. The Hamada (1972) formula is widely used among valuation practitioners (see formula 3). Inard (2015) summarizes these distinctive approaches of the tax shields value. βl = βu  1 þ

D  ð1- T Þ E

ð3Þ

where: βl: levered beta βu:unlevered beta T: tax rate D: debt value E: equity value Further, the weighted cost of capital is a combination of the above equity cost of capital and the cost of debt: WACC = K e 

E D þ Kd  DþE DþE

ð4Þ

with K e = r f þ βu  1 þ

2.3.3.2

D  ð1- T Þ  r m- r f E

Practicalities in the Assessment of the Beta Factor

The beta is supposed to be a prospective factor consistent with the DCF approach, which addresses the future rather than the past. Certain practitioners use target industry betas, but these are usually not thoroughly sourced. The most common technique consists of computing a retrospective beta6 based on the history of stock prices of listed comparable companies. Of course, each of these betas is unlevered, using the Hamada formula backward in order to get an empirical set of betas deemed representative of the unlevered beta of the sector (i.e., the above βu).

6 Certain public sources directly provide calculated betas as a function of parameters (depth of historical quotes, usually from two to five years, and frequency of quotes, usually weekly or yearly). These betas also include, sometimes, adjustments that are assumed to adjust a retrospective beta into a prospective beta (e.g., Blume adjustment).

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111

It is usually observed that the betas derived from each company of the peer group generate a range of βs rather than one unique β; one of the reasons for this relates to practicalities7 and the other one may relate to the fact that the risks of each company are assessed by the market beyond the vanilla industry risk (even though these risks should remain systematic). Valuation practitioners usually discard very low R2based betas and arbitrate a reasonable beta within the range.

2.3.3.3

Beta Factor and ESG

Considering the above-mentioned empirical technique used in order to retrieve the industry beta, it appears clearly that this resulting beta directly derives from the market. As such, this parameter implicitly embeds the market global anticipations for the considered sector, thus including the ESG impacts. More precisely, the beta factor reflects systematic volatility, which is a measure of the risk. Where a β>1, then the considered sector is more volatile and thus considered riskier than the global market of all assets (i.e., all industries), and the cost of capital Ke, u results mechanically higher than rm. ESG may have two impacts on the cost of capital: rm: ESG stakes may have a global impact on the economy, with an adjustment on global expectations. Such an impact is not easily measurable; thus, its existence is not easy to prove. β: The considered sector may be more (or, on the contrary, less) sensitive to ESG issues, thus creating an increase (or decrease) of volatility versus the whole market. Again, even though the β of a given sector has evolved over time, it is uneasy to unbundle any ESG effect from all potential effects. Certain approaches, such as the ones mentioned in Chapter “Research Advances in Valuation and ESG”, aim to showcase ESG effects through adjusted CAPM methods.

2.3.3.4

Differentiation Between Companies from the Same Industry

None of the above parameters are company specific. In particular, rm is global, and β is sector specific. Yet, a company that has a particular ESG positioning within its industry or that implements a differentiated ESG strategy may experience a distinctive risk profile. For instance, a proactive ESG policy could make the company more resilient to future changes (regulatory, customer sensitivity, etc.) and, therefore, could reduce the volatility profile.

7

The COV/V computation requires a series of data that may be perturbated by many phenomena and statistical noise. Consequently, R2 is barely high, and the confidence interval is significant for each company.

112

L. Inard

Tackling such a specificity reveals difficult, but it is usually not an issue as material as the effect of ESG on the forecasts (i.e., in the expectancy), which reveals more significant and thus sufficient.8 Nonetheless, where both (1) ESG stakes are significant in the considered industry and (2) companies from this industry implement clearly distinct ESG strategies and/or boast differentiated ESG dynamics, then this issue may have to be addressed. Two following techniques may be contemplated: • Betas and ESG scores: it may reveal interesting to confront the ESG scores (from ESG rating agencies) of each company within the peer group against their calculated individual betas, in case a relation between these two can be made. However, such a relationship is unlikely to appear, mainly because betas are generally subject to high statistical noise and may be affected by topics other than ESG. • ESG scores approach: adjusted cost of capital using ESG ratings may be implemented. At the end of the day, a critical issue for a DCF is to ensure consistency between its components, especially between (1) forecasts and (2) cost of capital, in order to avoid double-count or, on the contrary, omission of certain effects.

3 ESG Within the Forecasts and Sensitivities on the Value The forecasts are obviously one of the most important inputs in a DCF. ESG clearly deals with forecasts insofar as: • The issues that ESG intends to tackle do have an impact on forecasts, • Certain ESG regulations internalize negative externalities, prohibit certain processes or products promote certain activities or behaviors, all having an impact on forecasts, • The actions triggered in response to ESG issues also have an impact, usually in both the short term and the long term. The following section aims to (1) exhibit the possible magnitude of ESG impacts on DCF and its sensitivity on the chosen timing for the ESG actions, (2) contemplate practicalities when confronted with major ESG uncertainties.

8

ESG strategies shall primarily generate material changes in the expectations (investment, costs, revenues, etc.). The impact of these strategies on the expectations’ volatility is often not material or at least of lesser magnitude than the confidence intervals on the forecasts themselves. As such, it seems more practical and informative to analyze several scenarios of forecasts with an unadjusted cost of capital rather than trying to capture within the cost of capital a change in the risk profile and apply this adjusted WACC to a unique scenario.

Cash Flow Valuation and ESG

3.1

113

DCF Sensitivity on ESG-Adjusted Forecasts

ESG-oriented actions have multiple effects, such as (1) the cost savings achieved by more frugal processes, (2) the price signal implemented in certain negative environmental externalities (e.g., cost of carbon per ton), but also (3) a growing pressure from stakeholders, and among them the customers, more and more attentive to the environmental footprint of what they consume. As such, the responses to environmental stakes are not to be considered as additional costs but as investment projects. However, these investment projects are complex since they are subject to many uncertainties: for instance, some of these are based on quickly evolving technologies and/or under very dynamic learning curves. Similarly, the sensitivity of stakeholders on environmental stakes evolves swiftly over time; so are the price signals that depend on regulations and their evolutions. Depending on the relative dynamics of each of the above parameters, the optimal timing for an ESG-oriented investment may differ greatly. In other words, the valuation of an organization is not only sensitive to the said parameters but also to the very choice of the timing of investments; all of these being captured within the forecasts in a DCF. It is worth noticing that depending on the scenarios, an unprofitable investment should not be systematically discarded as it may turn out to be profitable if it is simply postponed a little. In some other situations, it may be interesting to invest in new, more efficient equipment without waiting for the entire lifespan of the current equipment. In order to better grasp the dynamics of these parameters and their sensitivities on a valuation, a simplified DCF model is elaborated and implemented in the following section.

3.1.1

Objective of the Simplified Model

The model computes the net present value (NPV) of an investment project launched in year Y (thus possibly differed when Y is later than today’s date Y0) so that the NPV sensitivity on the choice of year Y may be analyzed. The model considers certain specific dynamics: • The cost of the investment: the expense is incurred in year Y, and its amount decreases over time, following a financial progress factor, assumed to be a constant p. A strong p would thus mean that the technologies are quickly evolving and are still supposed to evolve in the future. It also means that the invested amount is lower for higher Y. • The yearly returns on the investment are experienced after year Y. These savings, either gains achieved, or losses avoided, are expressed as (1) quantities of unwanted outputs avoided (such as tons of CO2) and (2) a revenue per unit.

114

L. Inard

– Quantities of avoided outputs: the volumes that are avoided in the very first year after the investment are assumed to increase under a q constant factor (considering operational technical progress). Then, in the following years, these volumes decrease under a yearly u factor, considering a progressive loss of performance while the asset is aging. – Revenue per unit: this parameter (1) accounts for the price of a unit of output (for example, the price of a ton of CO2) and (2) captures the marginal sales that are protected or even won through the decrease of one unit of output (due to the sensitivity of customers on avoided outputs), reflecting the incremental margins earned. The revenue per unit is assumed to adjust yearly under a global g factor. • The project has a definite (D) life: as such, it has an effect between Y and Y + D. No cost for the decommissioning of the project is simulated in this simplified model. Depending on the project, D may be very high (for example, when considering a frugal process with indefinite savings) or may consist of successive investments every D years (for example, where the underlying tangible asset is to be replaced every D years). The model takes into account these scenarios. • The computation of the NPV, through a DCF, requires a discount rate, i.e., the w parameter.

3.1.2

Simplified DCF Model, Part I: A Unique Investment

For the sake of simplicity, in the following, all annual growth rates are expressed in terms of exponential growth rather than geometric growth. However, it is easy to switch to geometric growth rates at the end of the calculations (which may prove useful since “real-life” rates are usually expressed in terms of annual geometric rates) using the formula 5. rgeo = erexp - 1

ð5Þ

r exp = ln 1 þ r geo The initial investment IY is launched in year Y, and its amount is a function of Y, considering the p financial progress factor (see formula 6) I Y = I 0  e - pY

ð6Þ

Thanks to this investment, certain volumes of unwanted outputs are avoided. These volumes depend on the chosen year Y (considering the q factor that accounts for the technical progress) and afterward decrease following the u factor (considering the progressive decrease of performance). Thus, where t ≥ T, the quantities of outputs QY, t that are avoided between t and t + 1 may be expressed as in formula 7).

Cash Flow Valuation and ESG

115 tþ1

QY,t =

t

dQY = Q0  eqY  e - uðt - Y Þ

with: dQY =

u 1 - e-u

ð7Þ

 Q0  eqY  e - uðt - Y Þ  dt

As to the revenue Pt per unit of output, it may be expressed as follows, considering the g factor as shown in formula (8). Pt = P0  egt Thus, the cash flow FY, corporate tax rate T): tþ1

F Y,t = t

e

t

on year t is expressed as follows (introducing the

ð1- T Þ  Pt  dQY = ð1- T Þ 

- uðt - Y Þ

ð8Þ

tþ1

P0  egt 

t

u  Q  eqY 1 - e-u 0

 dt

ð9Þ

F Y,t = ð1- T Þ  P0  Q0  F Y,t = ð1- T Þ  P0  Q0 

u  eðqþuÞY  1 - e-u

tþ1

eðg - uÞt dt

t

u eg - u - 1  eðqþuÞY  eðg - uÞt  u g-u 1-e

F Y,t = ð1- T Þ  Pt  QY,t 

u eg - u - 1  u 1-e g-u

As a consequence, in a DCF, the forecasts should take into account these FY, t future flows and include the initial negative cash flow in year Y, i.e., IY. Where the initial investment is, in fact, an operational expenditure (opex), the cash flow in year Y includes the negative IY, but also the tax saving T  IY. Where the initial investment is a capital expenditure (capex), then the tax savings spread over time. The simplified model assumes that the duration for the deductible depreciation equals the life of the project, i.e., D. As such, the NPV of the investment and its tax savings are: NPVopex = - I 0 :e - ðwþpÞY  ð1 - T Þ YþD I0 NPVcapex = - I 0  e - ðwþpÞY þ T   e - pY  e - wt  dt D Y NPVcapex = - I 0  e - ðwþpÞY þ T 

I 0 - pY 1 - e - wD - wY  e e D w

NPVcapex = - I 0  e - ðwþpÞY þ T  I 0  e - ðwþpÞY 

1 - e - wD wD

ð10Þ

116

L. Inard

NPVcapex = - I 0  e - ðwþpÞY  1- T 

1 - e - wD wD

Thus, merging both opex and capex situations, the equation of NPV is shown in formula 11. NPVinvestment = - I 0  e - ðwþpÞY  ð1- αT Þ

ð11Þ

- wD

e , depending on whether the investment is an OPEX or Where α = 1 or α = 1 -wD a CAPEX. Now considering all cash flows, the NPV is shown in formula 12. YþD

NPV = - I 0  e - ðwþpÞY  ð1- αT Þ þ

ð1- T Þ  Pt  dQ  e - wt

ð12Þ

Y YþD

= - I 0 :e - ðwþpÞY  ð1- αT Þ þ ð1- T Þ 

P0  Q0 

Y

u  eðqþuÞY  eðg - u - wÞt 1 - e-u

 dt = - I 0  e - ðwþpÞY  ð1- αT Þ þ P0  Q0  YþD



u  eðqþuÞY  ð1- T Þ 1 - e-u

e - ðwþu - gÞt  dt

Y

= - I 0  e - ðwþpÞY  ð1- αT Þ þ P0  Q0  

u  eðqþuÞY  ð1- T Þ 1 - e-u

1 - e - ðwþu - gÞD - ðwþu - gÞY e w þ u-g

And finally: NPV = - I 0 :e - ðwþpÞY  ð1- αT Þ þ P0  Q0 

u 1 - e - ðwþu - gÞD  1 - e-u w þ u-g

 e - ðw - q - gÞY  ð1- T Þ Obviously, the NPV depends on each one of these parameters: this typology of ESG-oriented investments has indeed an effect on the valuation performed through DCF. Moreover, the value depends on the choice of year Y of the investment. It is thus interesting to analyze whether there is an optimal date of investment. Mathematically speaking, such optimum, if it exists, satisfies a zero derivative of NPV with respect to =0 Y: ∂NPV ∂Y

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117

∂NPV u = ðw þ pÞ  I 0  e - ðwþpÞY  ð1- αT Þ - ðw- q - gÞ  P0  Q0  1 - e-u ∂Y 1 - e - ðwþu - gÞD - ðw - q - gÞY   ð1- T Þ ð13Þ e w þ u-g ∂NPV ∂Y

= 0 means:

ðw þ pÞ  I 0  ð1- αT Þ = ðw- q - gÞ  P0  Q0 

u 1 - e - ðwþu - gÞD ðpþqþgÞY  e w þ u-g 1 - e-u

 ð1- T Þ e - ðpþqþgÞY =

w - q - g P0  Q0 u 1 - e - ðwþu - gÞD 1 - T     u wþp 1-e I0 w þ u-g 1 - αT

There is indeed an optimum if: ðw- q - gÞ  ðw þ pÞ > 0 This optimum is: Y=

3.1.3

w þ u-g 1 I0 w þ p 1 - e-u 1 - αT     ln  P0 Q0 w - q - g u pþqþg 1 - e - ðwþu - gÞD 1 - T

Simplified DCF Model, Part II: Periodically Renewed Investments

In such a situation, the investment is to be renewed every D years; this is, for example, the case where the investment is a capex based on an asset with a D lifetime duration. Investments are then to occur every Y + k  D with k from zero to 1. I YþkD = I 0  e - pðYþkDÞ 8t ≥ Y, the number of the current investment is k t = int expressed as follows: QY,t =

tþ1 t

ð14Þ t-Y D

, and QY, t can be

dQY = Q0  eqðYþkt DÞ  e - uðt - Y - kt DÞ

Meaning that: dQY = 1 -ue - u  Q0  eqðYþkt DÞ  e - uðt - Y - kt DÞ  dt. As to the investment cash flows, their NPV can be expressed as:

ð15Þ

118

L. Inard 1

NPVinvestment =

- I 0  e - ðwþpÞðYþkDÞ  ð1- αT Þ

ð16Þ

k=0

NPVinvestment = - I 0 

e - ðwþpÞY  ð1- αT Þ 1 - e - ðwþpÞD

As to the positive cash flows: 1

NPVCF > 0 =

Yþð1þk ÞD YþkD

k=0 Yþð1þk ÞD

k=0

=

ð1- T Þ  P0  egt 

YþkD

= ð1- T Þ  P0  Q0 

1

 eðqþuÞDk  = ð1- T Þ  P0  Q0  1

u  Q  eqðYþkDÞ  e - uðt - Y - kDÞ  dt  e - wt 1 - e-u 0

u  eðqþuÞY  eðqþuÞDk 1 - e-u k=0

= ð1- T Þ  P0  Q0 



ð1- T Þ  Pt  dQY  e - wt

Yþð1þk ÞD

e - ðwþu - gÞt  dt

YþkD 1

u  eðqþuÞY  1 - e-u k=0

1 - e - ðwþu - gÞD - ðwþu - gÞðYþkDÞ e w þ u-g

u 1 - e - ðwþu - gÞD - ðwþu - gÞY  eðqþuÞY  e u w þ u-g 1-e

e - ðw - q - gÞDk

k=0

= ð1- T Þ  P0  Q0 

u 1 - e - ðwþu - gÞD - ðw - q - gÞY 1   e u 1-e w þ u-g 1 - e - ðw - q - gÞ D

NPVCF > 0 = P0  Q0 

e - ðw - q - gÞY u 1 - e - ðwþu - gÞD   ð1- T Þ  u w þ u-g 1-e 1 - e - ðw - q - gÞD

And finally, the NPV of the whole project is: NPV = NPVinvestment þ NPVCF > 0 NPV = - I 0  

- ðwþpÞY

e u 1 - e - ðwþu - gÞD  ð 1αT Þ þ P  Q   0 0 1 - e-u w þ u-g 1 - e - ðwþpÞD

e - ðw - q - gÞY  ð1- T Þ 1 - e - ðw - q - gÞD

Similarly, to the one-time investment model, it is interesting to look for Y where = 0.

∂NPV ∂Y

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119

e - ðwþpÞY u ∂NPV  ð1- αT Þ - ðw- q - gÞ  P0  Q0  = 0 = ð w þ pÞ  I 0  ð wþp ÞD 1 e-u ∂Y 1-e 

e - ðw - q - gÞY 1 - e - ðwþu - gÞD  ð1- T Þ  w þ u-g 1 - e - ðw - q - gÞD e - ðwþpÞY u  ð1- αT Þ = ðw- q - gÞ  P0  Q0  1 - e-u 1 - e - ðwþpÞD 1 - e - ðwþu - gÞD e - ðw - q - gÞY   ð1- T Þ  w þ u-g 1 - e - ðw - q - gÞD

ð w þ pÞ  I 0 

e - ðpþqþgÞY = 

w - q - g 1 - e - ðwþpÞD P0  Q0 u 1 - e - ðwþu - gÞD     u ð w q g ÞD I0 w þ u-g w þ p 1-e 1-e

1-T 1 - αT

There is indeed an optimum, which is: Y=

1 pþqþg

 ln

3.1.4

w þ u-g w þ p 1 - e - ðw - q - gÞD 1 - e - u 1 - αT I0      P0 Q0 w - q - g 1 - e - ðwþpÞD u 1 - e - ðwþu - gÞD 1 - T

Sensitivity of ESG-Related Parameters on DCF, Based on the Simplified Models

The effect of ESG-related projects on valuation may be first approached through an example in which the parameters to date do not appear interesting, namely a spot yield of 5% for a duration of the investment of fifteen years. Indeed, considering such characteristics, the project does not seem to have any payback since 5% times fifteen equals 75%, before any discount. However, ESG projects are quickly evolving; investments that can now be contemplated were significantly onerous a few years ago. Indeed, the dynamics are strong and add together: • Technologies: they are more and more efficient and lower the breakeven point, • Sales: ESG is more and more required by customers. ESG-active organizations at least protect their business, at best, expand their market shares as ESG becomes a competitive advantage, • Negative externalities are reinternalized, often through regulations, and are more and more onerous so that ESG data indeed create value by avoiding externalities. Thus, a project that is facially not interesting may nonetheless reveal interesting in the near future.

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Technical Progress Financial progress (decrease of investment cost) p* Operational progress (increase in avoided unwanted outputs) q* ESG signal Change in revenue per unit of avoided outputs g* Performance of the investment Obsolescence (decrease in volumes of avoided outputs) u* Duration D General parameters Discount rate w* Tax rate T Investment specifics Nature of investment Initial Yield P0.Q0 / I0

10.0% 1.0% 7.0% 0.0% 15.0 15.0% 25.0% Capex 5.0%

* Expressed as geometric rates

Fig. 1 First set of parameters implemented in the simplified DCF model

Fig. 2 NPV against year Y of investment

3.1.4.1

First Example, the Power of ESG Dynamics on DCF

In the first example, the yield and duration parameters are supplemented by other parameters, as shown in Fig. 1. From these parameters, both models from the previous section can be implemented (i.e., part I: one-off investment and part II: renewals), as shown in Fig. 2.

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Not surprisingly, the NPV is negative in year zero; however, it is interesting to highlight that a perpetual project (i.e., with renewals of investments) finds its breakeven as soon as in year 3. However, investing in year three might be bold, as the one-off investment is still not profitable at such a date: any change in strategy dictated by external factors would leave the company with a loss. Thus, it may prove wiser to wait a little longer, for example, year five or six, where NPV is positive on both one-off and perpetual projects. Of course, theoretically speaking, the best year to launch the investments is eleven, and year thirteen for one-off project, but such a calculation assumes that the parameters have been predicted with a certain level of confidence and that all parameters are indeed geometric. Of course, this is not the case: for example, the prices for negative externalities may evolve drastically in short periods of time (e.g., see Eq. 1). As a consequence, it may not be wise to wait for year eleven, especially when the NPV in year six is not so different from the optimum. Finally, certain regulations enact prohibitions or decide on tombstones to comply with that can make such a wait irrelevant.

3.1.4.2

Real Options

Even though the company intends to launch the project in year six (for example), it does not mean that the organization is committed to doing so: when actually, in year six, the state of the art of technologies, the ESG-price signal, etc. may differ from today’s and another delay may prove smart. Reversely, it may be smart to anticipate the investment, for example, if regulations are reinforced and programmed to be enacted before year six. In fact, while the project is delayed, the organization benefits from a real option: the option to delay or even abandon the project if the parameters turn wrong. As such, the value of the company should include the value of such a real option. Of course, when the project is launched, there is no turning back for the first investment; theoretically, such a decision would mean that the NPV of the project has exceeded the value of the real option. Assessing the value of a real option is a complicated task, as it basically requires the gathering of volatilities of each parameter: where volatilities cannot be assessed with sufficient accuracy, the real-option approach does not provide any valuable additional material for strategic decisions and for valuation. Furthermore, it has to be noticed that all projects, ESG-related or not, are made of uncertainty, and many real options can be identified when addressing each one of them: valuation practitioners usually handle these through the industry discount rate. Real options shall only be contemplated for very significant projects that have specificities that are not accounted for in an industry beta.

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Irreversibility of the Investment

When the investment is launched, it is expected to last for its whole “normative” duration, i.e., D years. However, after such an investment, if the technological progress suddenly accelerates, for example, due to a disruption, it turns out frustrating for an organization to witness competitors that were less ESG-oriented invest in new technology with higher returns in both financial and environmental terms. For example, companies that had invested in solar panels years ago do not benefit now from the same efficiency as modern ones. In certain situations, it reveals better to invest in new equipment without waiting; indeed, in certain circumstances, the loss can be compensated by future savings. The simplified models are not able to properly simulate this, however, they can help demonstrate that such strategies may indeed prove interesting. For example, let’s consider a company that has invested in year six in a project (using the first example parameters). In year eleven, let us assume that the technologies have tremendously changed for the best (meaning that the original p and q parameters were wrong); the invested assets still have ten years left. Using the model and adjusting the duration to five, the NPV on year six is -10: benefiting from the cash flows only for five years would obviously turn out to be a loss of value. However, the new project, which would be an investment in year eleven in new assets, may compensate for this loss. In Fig. 3, the parameters have been modified so that the yield is better: in year eleven, the NPV is +13. As previously mentioned, the simplified models do not simulate this situation properly; for example, they do not account for switching costs and reversely do not implement the fact that keeping the original assets may lead to a significant competitive disadvantage now that new technologies are at hand for competitors. Making real informed decision requires more thorough simulations, but this example shows that such decisions have consequences on valuations, reflected in a DCF approach.

Fig. 3 Using the simplified model in order to illustrate the ability to anticipate certain renewals

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The Pressure of Stakeholders for ESG Improvements

In this example, there is no technical progress, so this factor does not create opportunity in the future which is not already available now. However, the price signal g is subject to strong dynamics, meaning that either the regulations, the customers, or any other stakeholders are more and more sensitive to ESG and put pressure on the business (see Fig. 4). Of course, the project is not to be launched immediately due to unfavorable year zero characteristics (i.e., a 5% yield combined with a duration of fifteen years). But the pressure of stakeholders makes it more and more onerous to keep on emitting unwanted outputs so that the project becomes viable as soon as in year six. Where the project is of indefinite duration, which occurs, for example, when it targets processes and makes them more frugal, it is interesting to notice that the NPV is positive and significant at year zero (see Fig. 5) This situation may appear theoretical since it is not expected that strong dynamics such as a 12% for g should last forever, and indeed, in many activities, such dynamics should not be applied on a long-term horizon (implementing a DCF requires specific attention on the implicit horizon, its normative flow and longterm growth rate, which often differs from the annual growth rate of the business plan). However, there are situations in which a non-ESG behavior is not an option Parameters Technical Progress Financial progress (decrease of investment cost) Operational progress (increase in avoided unwanted outputs) ESG signal Change in revenue per unit of avoided outputs Performance of the investment Obsolescence (decrease in volumes of avoided outputs) Duration General parameters Discount rate Tax rate Investment specifics Nature of investment Initial Yield

p* q*

0.0% 0.0%

g*

12.0%

u* D

0.0% 15.0

w* T

15.0% 25.0%

P0.Q0 / I0

Capex 5.0%

* Expressed as geometric rates

Fig. 4 NPV of an ESG project in a context of strong pressure from stakeholders

Parameters Technical Progress Financial progress (decrease of investment cost) Operational progress (increase in avoided unwanted outputs) ESG signal Change in revenue per unit of avoided outputs Performance of the investment Obsolescence (decrease in volumes of avoided outputs) Duration General parameters Discount rate Tax rate Investment specifics Nature of investment Initial Yield

p* q*

0.0% 0.0%

g*

12.0%

u* D

0.0% ꝏ

w* T

15.0% 25.0%

P0.Q0 / I0

Opex 5.0%

* Expressed as geometric rates

Fig. 5 NPV of an ESG project transforming processes

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since regulations have planned the prohibition of certain compounds (e.g., chemicals) or activities: in such situations, the g is even infinite at one point, though it is not at the beginning: g evolves over time, which the simplified model cannot simulate. A more thorough simulation would enable one to assess the optimal year to launch the project before such a project becomes mandatory by law.

3.1.4.5

Incentives for ESG Improvements with Stronger Dynamics than the Discount Rate

In another situation, the combination of technical progress and price signal results in a higher discount rate. This is not a theoretical situation when considering, for example, the dynamics of the price per ton of CO2, let alone the evolution of the behaviors of stakeholders toward ESG (see Fig. 6). In such a situation, viability is achieved very soon (year three). Unsurprisingly, there is no optimal year for the investment, but, of course, it would be unwise to differ it indefinitely; obviously, such strong dynamics shall not last forever, so the model is flawed in late years. It is thus interesting to launch such a project in the short run; should the technology evolve a lot afterward, then a switch to modern technology could still be contemplated.

3.1.4.6

NPV Are Sensitive to ESG Projects

Through the many examples above, it is shown that even a nonviable project can become profitable within a few years, considering the strong dynamics at work around ESG. Of course, there also exist many already viable projects that shall be contemplated; for these, the main concern is not about viability but about the risk of premature obsolescence of the project once launched and, thus, about the ability to switch to a more modern solution when available. Whatever the situation, the forecasts and, consequently, the valuation through DCF are sensitive to the timing of ESG investments that directly adjust the forecasts

Parameters Technical Progress Financial progress (decrease of investment cost) Operational progress (increase in avoided unwanted outputs) ESG signal Change in revenue per unit of avoided outputs Performance of the investment Obsolescence (decrease in volumes of avoided outputs) Duration General parameters Discount rate Tax rate Investment specifics Nature of investment Initial Yield

p* q*

0.0% 4.0%

g*

14.0%

u* D

0.0% 15.0

w* T

15.0% 25.0%

P0.Q0 / I0

Opex 5.0%

* Expressed as geometric rates

Fig. 6 NPV of an ESG project with strong incentives

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and also to the dynamics at work, i.e., technology improvements, regulations enacted, and customers’ behaviors. Companies need to handle their environmental footprint and trajectory over time for several key matters (carbon and, more globally, temperature and water, etc.) with respect to regulatory tombstones,9 while managing concomitantly the financial impacts of their portfolio of ESG projects. Modeling financial and, in parallel, nonfinancial forecasts is a powerful tool for companies deciding on their strategy and ESG path, and this tool closely relates to DCF and valuation of the business. The simplified model that has been introduced in this section does not take into account many effects, such as regulatory tombstones or the change of dynamics over time: it is not fit for strategic decision or for valuation but demonstrates the sensitivity of DCF on ESG-related parameters.

3.2

Terminal Value and Normative Cash Flows

ESG projects are like any other investment project—they generate cash flows from which can be derived an NPV. These forecasts of cash flows, either investment or returns, are implicitly embedded within the main forecasts of the company. The said returns are usually not fully visible when analyzing the whole business plan of the company. Indeed, these cash flows are representative of the discrepancy between a company implementing the project and a company not implementing it, and in the second situation, it cannot be assumed that the company would remain stable, with the same profitability as actuals. The environment of the company quickly evolves with respect to ESG, so its inaction may result in a decrease, if not a collapse, of its business plan and, ultimately, its value. For this reason, the business plans of companies that plan to implement viable ESG projects do not systematically increase. When performing a DCF, it is of utmost importance that ESG projects are not taken into account in an incomplete, thus, improper way, and this requires specific attention as their effects are usually not clearly visible on the global business plan.

3.2.1

Truncation and Extension of Cash Flows Series

The horizon of ESG projects is usually medium to long and often longer than the horizon of the business plan of the company (three to five years for many companies, longer for infrastructure, asset, or long-term contracts-based activities).

9

In Europe, carbon neutrality is targeted in 2050, as set out in the framework of the Green Deal issued in 2019. In addition, the climate package published in July 2021 and referred to as Fit for 55 aims for a reduction of greenhouse gases by 55% between 1990 and 2030.

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When ESG projects are planned to take a big step in the near future, many investments are likely to be included in the business plan, while the related returns are partially taken into account. The previously described simplified model does not have to handle this issue as its equations spread over time until the end of the project and its effects, but in a DCF, the explicit horizon and the implicit horizon work differently and have to remain consistent. Hence, for significant projects that lead to nonnormative investment expenses, the further returns may become truncated where they are not taken into account within the normative terminal flow. On the contrary, for one-off projects with definite duration, the terminal flow should not include returns that are supposed to stop at some point: an intermediate horizon may then be built to take into account these specifics. Again, such a process is rare as it is only worth implementing for industries or situations where ESG has a major impact. At last, for projects with definite duration but that are supposed to be renewed regularly (i.e., this is the case for example of tangible assets that are to be reinvested at the end of their lifetime), the returns may be implemented within the normative cash flow, but the normative capital expenditures also have to take into account the related investments that will have to get renewed regularly.

3.2.2

Projects that Are Planned Beyond the Horizon of the Business Plan

Certain projects may be planned in consistency with the agenda of regulations. For example, when an asset complies with current regulations but not with year nine regulations, its replacement may be planned in year seven or eight, beyond the business plan. (Of course, it also may be planned before, but there may be numerous constraints that prevent from doing so, for example, the compatibility of this asset with another one that is owned by a third party, such as a customer or a logistician, or the availability of the modern asset, or the fact that the current asset is still recent, etc.). Where the project does not generate significant changes, it can be considered as a daily business and already included within the normative cash flow, thus ignored. Where it makes a change, it may come from: (1) the series of investments and returns that are not as profitable as before and are supposed to be perpetual due to new regulations, thus requiring an adjustment directly in the normative flow, (2) the regulatory constraint that has led to an anticipation of the whole series (for example, the assets’ replacement that shall occur in year seven or eight are to replace assets whose remaining lifetime was still fifteen years), thus generating a loss of value to address through an intermediate horizon, where significant (probably a rare situation).

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Scenarios of Cash Flows

Due to materiality, valuation practitioners do not systematically address the ESG topic in valuations. Many activities within the tertiary sector are not significantly exposed to ESG issues in their business model, though, of course, they are expected by stakeholders to contribute to the ESG global effort at their level. Furthermore, certain businesses do not expect the various ESG challenges to have a significant impact on their model, at least because their competitors are facing the same challenges, and should these challenges reveal costly, the whole sector is believed to have pricing power. Financial analyst reviews and market analysis may indeed corroborate such assumptions. Also interesting, certain strategies tend to diversify the business, for example adding services to goods, so that incidentally, the global ESG exposure of the model decreases (to be more specific, the model is still exposed to ESG where the said services entirely rely on the sales of goods, but the ESG investments are relatively less significant). Of course, for highly exposed sectors, such as power production or powerintensive activities, or such as activities issuing significant negative externalities (e.g., transportation and logistics, mining, tobacco, etc.), specific attention is to be paid, especially with respect to the regulatory tombstones and more generally considering the various issues discussed in the previous sections. For certain sectors, the field of possibilities turns out to be vast, and it is not easy to predict, for example, (1) the ultimate winning technology (e.g., electric batteries, hydrogen, FCEV) or (2) the speed or acceleration of certain parameters, such as the price of negative externalities, the customers’ sensitivity to each ESG pillar. In these contexts, it may be considered that these uncertainties relate to the whole sector and, as such, should be reflected through the WACC and its beta, with a sensitivity analysis of the DCF valuation on the WACC. But in certain situations, industry beta is not enough (1) because beta assumes the volatility as continuous, where certain real situations are not, and (2) because peer companies from a given industry may not face ESG from the same starting point (for example, a powerintensive plant may own the hydroelectric dam next to it, while another one is supplied by a coal power plant but is better located in terms of logistics, being in a harbor zone). In these situations, besides WACC sensitivities, an analysis of several scenarios of a business plan usually proves a good practical way to assess sensitivity.

References Fama, E. F., & French, K. R. (1995). Size and book-to-market factors earnings and returns. Emerging Markets Quarterly. Hamada, R. S. (1972). The effect of the Firm’s capital structure on the systematic risk of common stocks. The Journal of Finance, 27, 435–452. JSTOR. Inard, L. (2015). L’Evaluation de l’Entreprise – Méthode DCF. Economica.

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Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. Miles, J., & Ezzell, J. (1980). The weighted average cost of capital, perfect capital markets, and project life: A clarification. The Journal of Financial and Quantitative Analysis, 15, 719. Myers, S. C. (1974). Interactions of corporate financing and investment decisions – Implications for capital budgeting. Journal of Finance., 29, 1. Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341–360. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19, 425–442. Wiley Online Library.

Cash Flow Valuation and ESG: Case Study Frédéric Le Meaux

1 An Example of ESG Integration How do we concretely assess the impact of ESG issues on valuation and, in particular, Discounted Cash Flow (DCF)? In this section, we are going to construct a framework that will help to relate all material ESG issues specific to a company with their financial impact and, therefore, their effect on valuation. This approach has been developed starting from real-life bottom-up examples. It will help the reader to work with a pragmatic and thorough analytical framework. There are various types of ESG investment strategies utilized in the financial industry. None of these strategies is inherently better than the other, as the choice of strategy largely depends on the investment manager’s ESG philosophy. Five primary ESG strategies are introduced below: • Exclusion is a strategy where specific companies or sectors are avoided to mitigate reputational or operational risk. • Best-in-class (positive screening) strategy selects companies with the highest ESG ratings or scores within a specific universe or segment of this universe. • Norms-based screening selects companies based on their compliance with international standards and rules. • Thematic investing is an approach that focuses on long-term trends in areas that have positive impacts on the environment and society. • ESG integration couples the best ESG practices with traditional financial analysis. The Principles of Responsible Investment (PRI) defines ESG integration as “the systematic and explicit inclusion of material ESG factors into investment analysis and investment decisions” (PRI, 2016).

F. Le Meaux (✉) Amundi, Paris, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_5

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ESG Analysis

Integrated Analysis

Financial Analysis

Fig. 1 Integrated analysis

Bonus/Malus • Reward good companies • Penalize bad companies

Materiality • Identify • Justify

Flexibility • Analyst's own judgment

Valuation • Growth • Costs: opex, capex • Liabilities • Discount rate

Fig. 2 The ESG integration framework

Note that some frameworks try to formalize ESG integration within the context of the DCF model by assigning a financial impact to each material ESG issue (see Fig. 1). Overall, the choice of ESG investment strategy will vary depending on the investment manager’s ESG philosophy and their approach to risk management. We must consider sustainability in the context of the double materiality perspective. The first dimension of materiality deals with how ESG issues affect the company. The second materiality deals with how ESG issues have an impact on society and the global economy. Both dimensions could have an impact on the value of an asset. The first dimension through financial impact on the P&L, cash flows, and balance sheet; the second dimension through controversies and more regulation that could result in fines, liabilities, or a higher risk profile. The ESG integration framework shown in Fig. 2 has several underlying principles: (a) Bonus/malus: The first principle is that ESG can be seen as either a positive (bonus) or negative (malus) factor in investment decisions. Companies that perform well in terms of ESG can be rewarded, while those that perform poorly can be penalized. Historically, ESG was about identifying risks that generate higher costs (opex or capex, liabilities). However, ESG can create a competitive advantage, too. For example, a more sustainable product (healthier food) encouraged by regulation may translate into increased revenues over the medium to long term.

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ESG Data

ESG Framework

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ESG Materiality

ESG Modeling

Fig. 3 Preliminary analysis

(b) Materiality: the second principle, is at the core of this framework. The aim is to focus only on ESG issues relevant to the specific company or sector and have a meaningful financial impact. (c) Flexibility: the third principle, should allow for flexibility and reflect the analyst’s (the person adjusting the DCF) judgment. This is particularly important when considering materiality and the valuation items that will be impacted. (d) Valuation: the framework should only make limited adjustments to the DCF for an accurate valuation. This step will ensure that the focus remains on the most material ESG factors, and the analyst can make informed decisions based on the adjusted DCF model. Regarding the valuation, we recommend adjusting only a limited number of items within the DCF. Before adjusting the DCF, the analyst must conduct a preliminary analysis (see Fig. 3). To do so, the work will be split into four main phases: (i) ESG Data: the objective is to ensure that the analyst has identified all ESG issues. (ii) ESG Framework: once the analyst has collected all ESG data, they need to organize and assess them to determine their respective impact on the DCF, i.e., their financial impact. (iii) ESG Materiality: among the selected ESG issues, some are more relevant than others for a given company or sector or have a meaningful financial impact. (iv) ESG Modeling: as shown in the previous part, DCF is a complicated model with many different inputs. Therefore, the analyst must choose how the material ESG data affects the DCF.

2 Gathering Data The first step in this process is to ensure that all positive and negative ESG issues are identified. If they have a material financial impact, these positive and negative ESG issues should eventually translate into a bonus or a malus on the valuation. To ensure that ESG data collection is thorough, the analyst has to inspect all data sources related to ESG topics (Fig. 4).

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Internal Quantitative Data Internal Qualitative Data

Controversies

ESG ISSUES

External Data from Specialized ESG Providers

Corporate Meetings

Corporate Data

External Data from Sell-side Research

Fig. 4 ESG data sources

2.1

Internal Data

Internal data is a critical component of developing an ESG framework. Many financial institutions have established their own specific ESG framework, reflecting their unique ESG philosophy. This diversity in approach highlights the importance of considering internal data as the starting point for any ESG analysis, as the DCF adjustment should reflect your company’s ESG philosophy.

2.1.1

Quantitative Data

Quantitative data is often obtained by aggregating external ESG data selected according to their relevance for each specific ESG issue and your company’s ESG philosophy.

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Qualitative Data

Qualitative data, provided by internal ESG analysts, offers a qualitative assessment of ESG issues for each company or industry. The internal ESG experts identify all ESG issues for a particular company or sector. Additionally, they have the expertise to consider different ESG themes, such as circular economy, biodiversity, and supply chain, and determine which companies are most affected by these themes.

2.2

External Data

External data plays a crucial role in solving issues identified with internal data. The objective of incorporating external data is to address any gaps in internal information and enhance the overall understanding of the ESG issues relevant to the company. One should remember that DCF adjustment reflects the company’s ESG philosophy.

2.2.1

ESG Data Providers

An increasing number of ESG data providers are offering a wide range of information for specific companies (cf. Chapter “ESG Data and Scores”). However, it is essential to be careful when using ESG data from different providers because it may not correspond to your view on the particular ESG issue. ESG data providers could add a qualitative comment to the quantitative assessment to help you understand the score or rating associated with the considered ESG issue.

2.2.2

Sell-Side Research

Sell-side research providers are increasingly becoming valuable sources of ESG information as they have set up their own ESG framework to match the integration framework put in place by the buy-side institutions. Even though they are late to invest in ESG research and integration, the effort has gained momentum in recent years, specifically accelerated over the last two years, and it is becoming a complementary source of information. Sell-side research process often starts with a quantitative analysis to identify the relevant ESG issues for a company or a sector. However, as these sell-side financial analysts become more involved in the process, the analysis becomes more and more qualitative. Indeed, sell-side analysts have covered the companies they look at for many years and thus have a thorough knowledge of their strengths and weaknesses. Although not identified as such, they are aware of the relevant sustainability issues and associated views.

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Company Data

The third source of ESG data comes from the company you are analyzing. Since regulation has played an important role in pushing corporates to provide reliable ESG information (e.g., EU taxonomy), the company is best positioned to provide them with this information. However, there is still a risk of greenwashing, in which the company presents itself as more sustainable than it actually is. Therefore, the analyst needs to make a judgment call to determine whether the ESG issue is fairly presented, especially if it is not supplied with internal and external data. Concerning this, two methods can provide a wealth of information for scrutiny.

2.3.1

Company Reports

Company reports provide plenty of information, including integrated reports that communicate the company’s strategy, governance, performance, and prospects and describe the sustainability practices in detail. More and more companies are publishing their integrated reports to communicate how these parameters lead to the creation of value in the short, medium, and long term. Sustainability topics relevant to the corporate sector are described with a lot of information and details. The Corporate Sustainability Reporting Directive (CSRD) is a new EU legislation that requires all large companies to publish regular reports on their environmental and social impact activities, which helps investors, consumers, policymakers, and other stakeholders evaluate the nonfinancial performance of large companies.

2.3.2

Company Meetings

Analysts have the opportunity to meet regularly with the company, whether during one-to-one meetings or conferences. This direct contact allows them to discuss and identify sustainability topics relevant to the company’s management. There are two types of company meetings. (i) Regular company meetings cover a wide range of topics, including investment case validation, forecasts, and risks. (ii) Engagement meetings focus specifically on an ESG issue to provide a better understanding of the company’s approach to it.

2.4

Controversies

Past controversies are an important source of information for ESG analysis. In other words, they provide a glimpse into the company’s approach to ESG matters.

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Corporate scandals such as bribery, corruption, workplace discrimination, and environmental incidents can have significant financial repercussions. These incidents can result in legal penalties and consumer boycotts, as well as damage the reputation of the companies and their shareholders.

3 Setting Out the Framework The second step is to select all relevant data gathered in the previous one to start assessing materiality and financial impacts. Note that it is essential to distinguish between relevant and material issues. The issues and data selected should be of significant importance for the companies and/or set them apart from their peers. However, some may have only a minimal impact and no financial consequences. Moreover, we have found that looking at momentum adds value to this analysis. While selecting the relevant ESG issues, both absolute and relative views should be considered. The following section briefly discusses which to choose.

3.1 3.1.1

Which Framework to Choose? Absolute View

An ESG issue could be relevant if it is an essential topic for its industry (see Fig. 5). For example, “green car” is a principal concern for the automotive sector, while data protection and privacy are crucial for the technology industry. Additionally, an issue can be relevant even if it differs from industry topics but is important for the specific company. For example, “green car” is an important issue

Topic where the company differs from its peers

Important topic for the industry

RELEVANT ESG ISSUES

Fig. 5 Which framework to choose?

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for a semiconductor company that supplies the automotive industry but not for its competitor, which produces chips for smartphones.

3.1.2

Relative View

Looking for criteria different from comparable companies allows identifying issues that emphasize how this company’s ESG policy is positioned vis-à-vis its peers. This examination could highlight the company’s strengths and areas where it is ahead of the competition, resulting in a positive development. For example, in the case of companies that produce electricity, the renewable strategy could differ, one being more advanced than the others. However, it could also reveal risks where the company lags behind its peers, such as frequent safety issues at production sites compared to peers.

3.2

ESG Momentum

The assessment of ESG momentum provides a dynamic view of a company’s performance on specific ESG criteria and supplements the static analysis (see Fig. 6). If the analyst has a specific view, they can add valuable information. In particular, it entails looking at how a specific ESG issue has developed over the past twelve to twenty-four months. Has it improved or gotten worse? In comparison, short-term momentum is more difficult to analyze. This is because ESG scores are based on information updated at least once every quarter or annually. Furthermore, short-term movements in ESG data may result from factors other than the improvement or deterioration of the issue under consideration. We recommend assessing ESG momentum for each ESG relevant criteria selected; it will help you to assess the magnitude of the adjustment. An issue with a positive momentum would mean that the company has made efforts to improve on a particular topic that might often result in a more meaningful change in the associated DCF item.

Static view

Fig. 6 ESG momentum

Dynamic view

MORE RELEVANT ESG ISSUES

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4 Assessing Materiality The third step in the ESG analysis framework is to assess the materiality of the relevant ESG issues selected (see Fig. 7). If and where a material issue is different from relevant issues, it calls for the analyst’s judgment. Documenting and reasoning are especially important in this assessment as they will support further discussion with other analysts or users of your analysis. Proper documentation and reasoning will enhance the validity and robustness of the analysis.

4.1

Is the Data Material?

Utilize the relevant data gathered and combine them with the analyst’s judgment to decide the materiality of items identified: is it truly material to the company and the investment case? In fact, it goes one step further; the objective is not to identify all issues relevant or different from peers (see Section “Gathering data”) but to focus on the ones that will impact the company’s business model. Typically, there are between three to five material ESG issues for each company capable of impacting its business model. SASB’s Materiality Map could be a useful source of information to get an indication of what is material for a specific industry (SASB’s Materiality Map identifies likely material sustainability issues on an industry-by-industry basis). EU taxonomy could help you identify what the sustainable activities of the company are and, therefore, what the material opportunities are. However, we recommend relying on the analyst’s judgment that can leverage deep knowledge of the company’s sustainability issues. Moreover, certain issues are company-specific and may not be addressed by a general model. For example, waste management can be a material issue for a construction company as it means more costs if the policy in place is not in line with the best standards or practices. This might result in more materials used and potential fines. On the other hand, a food company that has developed a healthier product version sold at a higher price could positively impact its revenue if it becomes significant or meaningful.

Relevant ESG issue

Fig. 7 Is it material?

Analyst's judgment

MATERIAL ESG ISSUES

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Document

The next step is documenting the reasoning for materiality. This step will help you decide what financial item it will influence and in the assessment of the magnitude of this impact. Therefore, it is recommended to also include quantitative data in addition to qualitative, which will eventually help to quantify the overall impact of the valuation.

4.3

Other Considerations

In addition to assessing each ESG issue individually, it is vital to consider the combined effect of nonmaterial issues. Companies can have many nonmaterial issues, which, when added together, represent a material risk. For example, a company may have repeated behavioral practices for which the objective is to make clients dependent on its products without having the possibility to go for competitors’. Hence, it is important to take a comprehensive approach when assessing the materiality of ESG issues.

5 Implementation in the DCF Model It is crucial to properly implement the material ESG issues identified into the DCF analysis. In doing so, you will have to justify adjustments to all financial items. Furthermore, the justification for these adjustments should be clearly documented and explained to support any discussions or questions from stakeholders using the analysis. Thus, justification is essential as it allows further discussion with people that will use your work.

5.1

What Line Should Be Amended?

Each ESG issue could be financially linked with one or many items of the DCF. In order to avoid double counting, we recommend identifying the specific line item in the DCF that will be most impacted by the ESG issue. In addition, ESG data have short- and long-term impacts. Generally, short-term impacts are negative as they represent a cost or a risk, while long-term impacts are more positive as they could mean a business opportunity or less cost and risk. Given the mechanics of the DCF, which applies the discount rate to shorter-term cash flows at a lower rate, short-term adjustments will have a greater impact.

Cash Flow Valuation and ESG: Case Study

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With a deep knowledge of the company, the analyst is best placed to decide on the proposed adjustment line. We recommend focusing on a limited number of key items, specifically four main financial impacts that can be adjusted to avoid making the adjustment too complicated. These items are growth, costs (opex or capex), liabilities, and WACC.

5.1.1

Growth

ESG factors have become increasingly important in recent years as consumers, investors, and other stakeholders place a higher value on sustainability and corporate responsibility. These ESG issues can have a major influence on a company’s financial performance, and the effects can be either positive or negative. Positive impacts could come from increased brand reputation, consumer loyalty, and improved relationships with stakeholders, leading to increased revenue growth. On the other hand, negative impacts could result from ESG controversies, declining consumer trust, and regulatory penalties, resulting in decreased revenue growth. Companies that prioritize and effectively manage ESG issues are more likely to realize positive outcomes and secure a sustainable future for their business. The growth prospects can be positively influenced by its commitment to sustainability, as consumers are increasingly willing to pay more for sustainable products. For example, younger customers tend to favor more sustainable, eco-friendly products, such as organic and healthy food. As these generations make up a larger part of your clientele, being able to propose such products will drive your growth and your market share. In that case, the analyst should compute the expected increase in sales volume of the sustainable product and its impact on overall revenue growth, taking into account any potential cannibalization of existing, less desirable products. On the other hand, ESG issues can also have a negative effect on growth prospects. Some products will be less relevant in a more sustainable world, e.g., oil. In transport, alternatives to the use of oil already exist, such as electric cars and sustainable aviation fuel, among others. In this case, oil companies will generate less revenue from oil over the long term. Hence, the analyst should assess the potential decline in sales volume and revenue and consider the company’s strategies to offset this loss, such as the development of renewable energy products.

5.1.2

Costs

ESG considerations can affect a company’s costs in both the short and long term. In the short term, ESG issues can require higher costs, considering that the company has to invest in new technology to achieve a more sustainable way of doing business. However, over the long term, it often means lower costs as the technology becomes more efficient, spreading the cost throughout the organization (operating leverage). An example of this can be seen in many industrial companies that need to transition from fossil-fuel-based production processes to more sustainable methods.

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Initially, it might mean investing in R&D to develop or utilize new technologies, then investing in capex to implement them in production. However, these investments will eventually make them more efficient with fewer costs.

5.1.3

Liabilities

An ESG-related liability can result from controversies and regulatory violations within the company. In the USA, there are collective actions that could result and have culminated in hefty fines. For instance, some chemical companies faced huge fines for producing harmful products; a few financial companies—for noncompliance with regulations and aerospace and defense companies—for involvement in bribery in strategic countries. Quantifying the potential liabilities from ESG issues is a strenuous exercise. However, it can be estimated by looking at past cases that offer a reference point to make this assessment.

5.1.4

Weighted Average Cost of Capital

WACC is a critical component in the DCF valuation model. It is often used when the impact is not quantifiable and should, therefore, be used carefully, keeping in mind that WACC could have a meaningful impact on the DCF.

5.2

Is the Impact Quantifiable?

The next question that the analyst should ask is whether the impact is quantifiable. It is important to prioritize a quantifiable adjustment as it allows for a more precise assessment of the financial impact on the company’s valuation.

5.2.1

Yes (a Quantifiable Impact)

If the impact is quantifiable, an analyst should document the proposed adjustment, its reasoning, and the quantity used as a reference. They need to also explain the calculation of the impact, the data used, the assumptions made, and the outcome. It will facilitate further discussions and help them update this adjustment once new data are available.

Cash Flow Valuation and ESG: Case Study

5.2.2

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No (an Unquantifiable Impact)

If the impact is unquantifiable, the analyst should still document the reasoning behind the proposed adjustment and any steps taken to estimate its impact. Finally, they should clarify if the issue is already accounted for in the company’s financial statements or forecasts to avoid double counting. In fact, many ESG issues are already considered in financial statements or financial forecasts. For example, the analyst may have already taken into account the revenues generated by the more sustainable product. Nevertheless, having identified it as an ESG opportunity, the analyst might better understand the financial impact and have a more precise assessment.

5.3

The Specific Case of WACC

When adjusting the WACC, proceed with caution and ensure that any changes made are reasonable owing to the fact that the impact is unquantifiable. This caution is particularly important because changing the WACC can significantly impact the overall valuation, especially for high-growth companies with a large portion of their worth in the terminal value. If possible, relate this adjustment to a past or peer-comparable issue that was quantified. We propose that the total WACC adjustment should not be adjusted beyond ±100 basis points (bp). Furthermore, the magnitude of the adjustment should be based on the importance of the ESG issue being considered. For example, a meaningful issue might warrant an adjustment of ±50 bp but a minor or distant one, only ±10 bp.

5.4

Document Final Impact on Target Price

The proposed adjustment must be related to the impact on the stock price by dividing the overall impact by the number of shares. It will help you assess whether your assessment is reasonable as a percentage of the share price calculated based on financial forecasts.

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ESG Criteria

ESG ESG Material? Rating Momentum (Yes / No)

Reasonin Proposed Proposed g Adjustment Item Adjustment

Environmental

Impact on Target Price (TP)

Target Price Impact Summary

Financial Model TP

0

Adjustment 1 +/-0 Adjustment 2 +/-0 Integrated TP 0 Social

Governance

Other Material Controversies Positive ESG Implications

Fig. 8 DCF adjustment template

6 Practical Examples 6.1

Template

We recommend using Fig. 8 as a template that summarizes all the steps discussed in this chapter. However, this is just an example. It can be adapted to one’s criteria or a new one can be created. It will help in following the process discussed so far.

6.2

Positive Case: Renewable Utility

In this first example, we will analyze the case of a renewable utility company specializing in offshore wind projects. The company’s focus on sustainability creates revenue opportunities with more offshore wind projects that produce greener electricity and is a positive factor in the ESG evaluation. See our template from Fig. 8 completed in Fig. 9.

6.2.1

Data Gathering

Our analysis involved a comprehensive evaluation of both internal and external data sources to determine the company’s ESG performance. The results of the analysis

Cash Flow Valuation and ESG: Case Study

ESG Criteria

ESG ESG Material? Reasoning Rating Momentum (Yes / No)

143 Proposed Proposed Adjustment Adjustment Item

Impact on Target Price (TP)

Financial 700 Model TP

Environmental

Carbon Emissions

Target Price Impact Summary

Good

+

Yes

The company is a global leader in offshore wind: carbon intensity 70% lower than the industry average. Revenues The company plans to be almost carbon-neutral by the mid-2020s.

The total value of €50bn, worth 120 Allocate value €/share. Discount for offshore 40% (as not wind capacity guaranteed) for 72 growth €/share impact on TP.

Carbon +72 emissions

Integrated 772 TP Social Health & Safety

Very Good

=

Yes

Supply Chain

Very Good

=

Yes

A low accident rate is a positive but is already priced None into the returns delivered on projects. Opportunity in the future to sell directly to customers instead of into the None pool/subsidies; these corporate PPAs support future return assumptions.

None

None

Governance Remuneration Good

No

Shareholder Rights

Good

No

Tax Practices

Good

No

Audit & Control

Average

No

Strong corporate governance vs global peers. Governance practices are generally well-aligned with shareholder interests. Effective tax rate disclosed by the company and included in our forecasts already. Significant issues not noted in our research with ESG analysts and other disclosure.

None None

None

None

Other Rare Earth Metals Used in Assets Positive ESG Implications

No

Currently not a supply chain issue.

Fig. 9 DCF adjustment template: positive case

showed that the company has a strong ESG rating, surpassing the industry average on several key ESG metrics.

6.2.2

Framework

The main environmental issue for the company is carbon emissions, for which the rating is good. Concerning social issues, the company rates excellent in health and safety and supply chain management. In terms of governance, there are several ESG issues relevant for this company (remuneration, shareholders’ rights, tax practices, audit and control) on which the rating is average to good. We have identified another ESG issue related to the use of rare earth metals in assets.

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Materiality

We consider the carbon emissions issue to be material to the company’s valuation as it is a leader in offshore wind electricity generation, resulting in a much lower carbon intensity than other utility companies. This will result in lower costs and more revenue streams. Alongside this, the two social ESG issues are also considered material for the valuation of this company as it directly influences the project returns. However, we do not consider the identified governance issues to be material to the investment case. Likewise, the rare earth metals issue is not material yet, as there are no supply chain concerns.

6.2.4

Valuation

Concerning the carbon emissions issue, the company’s leadership in the offshore wind projects presents a revenue opportunity that will drive its growth over the medium to long term. To quantify this impact after the explicit period in the DCF, we value the opportunity at €50bn with a probability of 60%, representing €72 per share. The two material social issues have already been considered in project returns, and therefore there is no need to adjust the valuation to avoid double counting.

6.2.5

Conclusion

The only adjustment made to the valuation is related to carbon emissions, which increases the target price by more than 10%. This adjustment is significant and reasonable, considering that offshore wind project development is key to the company’s business model (Fig. 9).

6.3

Negative Case: Basic Material Company

In the second example, we are examining a basic material extraction company that has significant impacts on the environment and communities in which it operates. In this case, sustainability presents a risk, thus having a negative effect on its valuation. See our template from Fig. 8 completed in Fig. 10.

Cash Flow Valuation and ESG: Case Study

ESG Criteria

ESG Rating

ESG Material? Reasoning Momentum (Yes/No)

145 Proposed Adjustment Proposed Adjustment Item

Impact on Target Price Target Impact Price (TP) Summary Financial Model TP

Environmental

Water

Yes

95% of operations located in waterstressed regions.

Yes

Severe flooding and widespread Cost of environmental Capital damage.

Good

No

Carbon intensity 30% lower than the None industry average.

Average

Yes

The business subject to worker strikes and criticism over None employment contracts.

This is a price of business and is already reflected in margins, so no further adjustment is proposed.

H&A safety impact Cost of from accidents. Capital

Liability for known issues already recognized in financial statements (X% of equity value); we have proposed a See above WACC uplift within environmental issues, which encapsulates this risk.

Average

Biodiversity & Very Pollution bad

Carbon Emissions

This is a price of business and is already reflected in the company's capex spend: the company spent $2bn to build a desalination plant. Liability for known issues already recognized in financial statements but propose a 50bps $1/share cost of capital uplift for future unknowns.

-

None

20

Adjustment -1 1

Integrated TP

19

Social Labor Relations

Health & Safety

Average +

Yes

Governance

Other Material Controversies Positive ESG Implications

Fig. 10 DCF adjustment template: negative case

6.3.1

Data Gathering

The internal and external data reveal that the ESG rating of the company is average, in line with industry practices. This average ESG rating suggests that the company is taking steps to address ESG concerns, but there is room for improvement compared to industry leaders in this area.

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Framework

The main issues for this company are environmental—water, biodiversity, and pollution—and carbon emissions. Regarding social issues, the company rates average for labor relations and health and safety. There are no relevant social or other issues identified.

6.3.3

Materiality

Out of these ESG issues, we consider biodiversity, pollution, and water to be material for the company. Biodiversity is included because a recent dam collapse at one of its mining sites has resulted in severe flooding and widespread environmental damage. Water is included because 95% of operations are located in waterstressed areas. Although relevant, we do not consider carbon emissions to be material for the investment case of the company, especially relative to the two other environmental issues. Labor relations and health and safety are material issues for the company as they could result in business disruption and/or liabilities.

6.3.4

Valuation

Regarding biodiversity and pollution, the company has to pay a substantial liability related to the flood provoked by the dam collapse. Therefore, it is already accounted for in the financial statements. Nevertheless, we consider it to still be a risk for the company and apply a 0.5% penalty to the discount rate representing $1 per share. Water, although a material risk, is accounted for in the capex budget for a desalination plant. Concerning labor relations, the financial analyst has taken it into account in the margin forecasts. Health and safety issues are assessed in the form of liability in the balance sheet. The target price is therefore adjusted only for the biodiversity and pollution risk. The adjustment is reasonable as it represents less than 10% of the initial target price (Fig. 10).

Reference Principles for Responsible Investment (PRI). (2016). A practical guide to ESG integration for equity investing. Accessed February 21, 2023, from https://www.unpri.org/listed-equity/apractical-guide-to-esg-integration-for-equity-investing.

Multiple Valuation and ESG Dejan Glavas

1 Reasons to Adjust Valuation Multiples The multiples valuation method has been extensively described in Chapter “Reminder on Common Valuation Techniques”; this chapter will, therefore, focus on describing how environmental, social, and governance (ESG) data can be integrated into multiples valuation. As outlined in Chapter “Reminder on Common Valuation Techniques”, this valuation technique relies on the use of an average (or median) valuation multiple (price-to-earnings, price-to-book, enterprise-valueto-EBITDA, for instance) of a group of firms similar to the one we are valuing. One key assumption of the multiples approach is that the group of firms has similar characteristics to the firm we are valuing. However, this is hardly the case in practice, where we can observe differences in risk, liquidity, sources of revenue, size, etc., between peer firms and the firm we are valuing. To account for these differences, valuation professionals often apply adjustments to the target company’s enterprise value or market value of equity resulting from the valuation. We will focus on adjustments to valuation multiples to account for different firm ESG performances between the peer group and the target firm (the company we want to value). We see three key reasons to integrate ESG in multiples valuation. First, academic literature consistently finds that ESG performance usually impacts financial performance. Friede et al. (2015), using a sample of 2200 academic studies, find a consistent relationship between ESG data and corporate financial performance. We further explain these links between ESG data, cost of capital, and valuation in Chapter “Research Advances in Valuation and ESG” of this book. Second, empirical evidence already seems to suggest an impact on valuation multiples. A study by Deloitte finds that a ten-point higher ESG score leads to an D. Glavas (✉) ESSCA School of Management, Boulogne-Billancourt, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Glavas (ed.), Valuation and Sustainability, Sustainable Finance, https://doi.org/10.1007/978-3-031-30533-7_6

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approximately 1.2× higher enterprise-value-to-EBITDA (EV/EBITDA) multiple (Deloitte, 2023). While this study recognizes an effect, it is still key to mention that it is based on a simple regression analysis and that several tests that we will see in this chapter have to be performed to ensure that the link between ESG data and multiples work. If this evidence is to be confirmed, not integrating ESG factors in multiples valuation would lead to mispricing. Lastly, there is growing evidence that finance professionals have started to incorporate ESG data into multiples valuation (Bancel et al., 2023). This research finds that it is a growing trend among finance professionals which should lead to increased pressure on firm prices in financial markets and private equity markets. While this evidence has become well-known among finance professionals, the practice of valuation multiples adjustment to account for ESG is still in its early stages. A commonly cited limitation in integrating ESG data into multiples valuations is the lack of consensus regarding which techniques should be used as well as a lack of easy-to-implement techniques (Bancel et al., 2023). To address this issue, we offer to explain in detail two methods to factor in ESG in business valuation. The first method is a simple price-earnings ratio (PER) adjustment method, which is easy to implement but has strong technical limitations. The second method is a statistical one that will yield more accurate valuation multiples but requires a deeper level of analysis.

2 Simple Multiple Adjustment 2.1

Conceptual Framework

For the first method, we build the adjustments based on existing techniques used for valuation multiples adjustment to growth. We take the case where we use the PER to value a target firm. The peer group and the target firm may differ on a key value factor which is expected growth (earnings per share expected growth, to be more precise). The PER is strongly dependent on growth and would therefore yield high price-to-earnings ratios when expected growth is high. The PER-to-growth ratio (PEGR) divides the PER by the growth rate of earnings per share, as exposed in formula 1. PEGR =

PER g × 100

ð1Þ

By computing the PEGR, analysts can compare firms more easily, even if their growth rates differ substantially, as demonstrated in Fig. 1. The main idea is that a lower PEGR indicates that a firm is undervalued with respect to its growth rate. A rule of thumb is to consider a PEGR of one as a fair trade-off between growth and value, a PEGR below one as an indicator of undervaluation, and a PEGR above one as an indicator of overvaluation.

Multiple Valuation and ESG

Company Firm A Firm B Firm C

Price 60 149 45

Earnings per share 2022 3.2 11.3 9.3

149 Earnings per share 2023e 4.3 14.3 9.9

Price-to-earnings ratio 2022 Growth 18.8 34.4% 13.2 26.5% 4.8 6.5%

Price-to-earnings to growth 0.55 0.50 0.75

Fig. 1 Price-earnings-to-growth of a group of firms

Following the same logic, we consider, based on previously cited evidence, that the ESG performance of firms has a key impact on valuation. Based on this, we built a PER-to-ESG ratio (PERE) to compare firms with different ESG performances. We use ESG scores as the best proxy for ESG performance until better metrics become common (see Chapter “ESG Data and Scores” for more details on this topic). Our approach relies on the availability of ESG data for the firm being valued. This is a strong assumption, provided the general lack of data available for private or small firms. Still, with ESG rating firms’ rapid development and the progressive implementation of regulations affecting smaller firms, we expect this coverage to increase over time (see Chapter “ESG Data and Scores”).

2.2

Adjustment Method

Inspired by the PEGR, we may compute a multiple-to-ESG ratio (see formula 2). A lower ratio would indicate undervaluation with respect to the ESG score, and a higher ratio would indicate overvaluation with respect to the ESG score. Multiple to ESG Ratio =

Valuation MultipleðtargetÞ ESGðtargetÞ

ð2Þ

where: Multiple(target): median multiple of the firm we intend to value. ESG(target): firm’s ESG score. In the case of the PER, we would have obtained the formula 3. PERE =

2.3

PERðtargetÞ ESGðtargetÞ

ð3Þ

Example

We will first extract PER and ESG data from one of the providers cited in Chapter “ESG Data and Scores”. We then divide the PER by the ESG score taking

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Multiples (PER)

ESG Score

Company

Dec-21

Dec-21

PER-to-ESG (PERE) Dec-21

Target Company 1 Company 2 Company 3 Company 4 Company 5

27.0 45.1 29.8 33.0 10.3 83.2

56.8 75.5 45.6 72.4 76.6 59.3

0.48 0.60 0.65 0.46 0.13 1.40

Average Median

40.3x 33.0x

65.9 72.4

0.65 0.60

Fig. 2 PER-to-ESG of a target firm and its peer group

Index S&P 500© Stoxx 600© CSI 300© NIFTY 500©

Geography US Europe China India

Multiples (PER)

ESG Score

Dec-21 29.0 22.9 41.0 30.8

Dec-21 70.5 74.9 45.0 51.1

Dec-22 25.7 21.2 27.1 31.1

Dec-22 71.1 70.3 NA 42.5

PER-to-ESG (PERE) Dec-21 Dec-22 0.41 0.36 0.31 0.30 0.91 0.60 0.73

Fig. 3 Indicative median PERE of key indices

into account the publication date of the PER and ESG score to keep time consistency. Looking at the PER in Fig. 2, we may deduce that our target firm is cheaper than its peer companies 1, 2, 3, and 5. After computing the PERE, we observe that it is more undervalued with respect to its ESG score than companies 1, 2, and 5 but not company 3. Therefore, an investor not interested in integrating ESG may consider the target firm as the cheapest while an investor integrating ESG may consider company 3 as the cheapest option. For comparison, the author has computed the median PERE of a set of world indices as of January 2023 in Fig. 3 based on Refinitiv (©) ESG scores.

3 Statistics-Based Multiple Valuation Adjustment 3.1

Fundamental Value Drivers

The peer group we use to compute the median valuation multiple can be similar to the firm we value on various aspects such as sector, geography, cash flow volatility, or earnings growth. In practice, it may prove to be very difficult to have a set of peer firms that are identical in all aspects to the firm we value. Firms may differ on their

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151

fundamental value drivers. Therefore it is key to understand what these value drivers are. We can take the example of the EV/EBITDA multiple and split it into fundamental components following previous literature (Damodaran, 2009; Gupta, 2018). We start with the definition of EV as shown in formula 4. EV =

Free Cash Flow ðWACC - gÞ

ð4Þ

where: WACC: weighted average cost of capital. g: long-term growth rate. When we replace free cash flow with its standard definition in finance in formula 4, we obtain formula 5. EV = EBITDA × ð1 - Tax rateÞ þ Depreciation - Capital Expenditure - Change in working capital ðWACC - gÞ ð5Þ

To obtain the EV/EBITDA, we divide Eq. 5 by EBITDA left and right, as shown in formula 6. EV EBITDA =

EBITDA × ð1 - Tax rateÞþDepreciation - Capital Expenditure - Change in working capital ðWACC - gÞ

EBITDA

ð6Þ

Here, we observe here that the EV/EBITDA depends on (1) growth, (2) weighted average cost of capital (WACC), (3) the reinvestment rate, and (4) the tax rate. We can also derive fundamental drivers of the PE ratio starting from the dividend discount model (DDM), as shown in formula 7.1 Equity value =

Dividend per share Expected return - g

ð7Þ

where the expected return is derived from the capital asset pricing model (CAPM) and is equivalent to the cost of equity. We then divide the equation above by

1

If the model is not computed in terms of expected earnings in the next period, then the formula per share ð1þgÞ should be: Equity value = Dividend Expected return - g .

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earnings per share left and right. We obtain the result shown in formula 8, knowing that the dividend per share divided by earnings per share is the dividend payout ratio. PER =

Payout Ratio Expected return - g

ð8Þ

Here (1) growth, (2) cost of equity, and (3) payout ratio are the fundamental drivers of the PER. All commonly used valuation drivers are summarized by Gupta (2018) in “3.4. Deriving the Relationship between Multiples for Valuations and Value Drivers” if the reader wants to use other valuation multiples.

3.2

Multiple Regression Analysis

A statistical technique commonly used to account for differences between firms in the peer group and the target firm is the multiple regression analysis. The multiple regression analysis allows us to test how fundamental value drivers (growth, risk, reinvestment rate, tax rate, depreciation, profitability) individually or pooled together, affect valuation multiples. In a standard multiple valuation procedure, the analyst first runs the regression analysis on a large dataset of firms in the sector of the target firm to estimate the average impact of a set of fundamental value drivers on the valuation multiple. In this step, the valuation multiple is the dependent variable of the regression, and fundamental value drivers are the independent variables. Next, the analyst multiplies the coefficients associated with each factor by the target firm’s corresponding fundamental factor value to obtain an estimated multiple. The estimated multiple is then used the same way as in the standard multiple valuation presented in Chapter “Reminder on Common Valuation Techniques”. For EV/EBITDA, we have shown that fundamental value drivers are growth, WACC, reinvestment rate, and tax rate. For PER, we have seen that growth, cost of equity, and the payout ratio are the fundamental value drivers. The question remains as to know which proxies of these fundamental value drivers to use in practice. One answer to this question is the use of real-life data for earnings growth, WACC, reinvestment rate, and tax rate, which create the regression described by Eq. 9. EV = β1 Growth þ β2 WACC þ β3 Reinvestment rate þ β4 Tax rate þ ε ð9Þ EBITDA Similarly, for the PER using its fundamental value drivers (see formula 8).

Multiple Valuation and ESG

PER = β1 Growth þ β2 Payout þ β3 Cost of Equity þ ε

153

ð10Þ

The second approach would be to rely on previous and current empirical works to build these models. We can, for instance, take regressions updated on an annual basis by Damodaran (2023). For instance, we can use the regression results for EV/EBITDA from Europe in January 2022 (see formula 11). EV = 25:86 þ 34:3 × Growth - 16:9 EBITDA Total Debt × - 27:3 × Tax rate ðTotal Debt þ Market value of EquityÞ

ð11Þ

For PER, it is shown in formula 12. PER = 30:23 - 9:06 × Beta þ 27:4 × EPS Growth þ 12:6 × Payout þ ε

ð12Þ

As evidenced by previous empirical work and in this book (see Chapters “Value and Externalities in Economics and Finance”, “ESG Data and Scores”, and “Research Advances in Valuation and ESG”), ESG factors affect value and should therefore improve multiple regressions’ quality. We, therefore, recommend that the reader tests how the aggregate ESG score impacts the regression model quality. Readers can reproduce the same method to test how each pillar—E, S, or G—or more specific ESG data (CO2 emissions per sales, ESG controversies, etc.) affect value. A typical regression analysis based on Damodaran’s (2023) empirical value drivers for EV/EBITDA or PER enhanced with ESG data would, therefore, be as in formulae 13 or 14 for European firms. EV = α þ β1 × Growth þ β2 EBITDA Total Debt þ β3 × Tax rate þ β4 × ðTotal Debt þ Market value of EquityÞ × ESG Score PE = α þ β1 × Beta þ β2 × EPS Growth þ β3 × Payout þ β4 × ESG Score

3.3

ð13Þ ð14Þ

Ordinary Least Squares Estimation

The multiple linear regression model is often used by statisticians to examine associations between numerous possible predictors and a single outcome. It is a

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forecasting method in which several elements (the predictors) are attempted to be linked to a single result (the dependent variable or the response variable). Parameters of a multivariate linear regression model may be estimated using the Ordinary Least Squares (OLS) method. Differences between actual and predicted values of a dependent variable are known as residuals. An important aspect of OLS is determining which regression line (or best-fit line) has the smallest sum of squared residuals. The OLS method may then be used to make predictions about the dependent variable, given the values of the independent variables, after the coefficients have been calculated.

3.4

Model Assumptions Testing

In order to ensure that our regression coefficients are valid and that we can use the regression results to compute an estimated valuation multiple, we need to test the key OLS model assumptions (Stewart, 2016). We kept in the list below the model assumptions that we would like to test more specifically: – Linearity—a dependent variable has a linear relationship with the independent variables, – No multicollinearity—there is no high-level correlation between independent variables, – Homoscedasticity—the variance of the errors is constant and finite, – Normality—that the errors are normally distributed. We will now see how we can test each of these assumptions.

3.4.1

Linearity

We will first test the linearity assumption. One way to do so graphically is to show a graph of residuals and predicted values and input a horizontal line at y = 0. Residuals are the difference between the estimated value of the dependent variable and the observed value of the dependent variable.2 If points are not symmetrically distributed around the horizontal line, the linearity assumption is very likely violated. In Fig. 4, we show two scatter plots of residuals on the vertical axis and predicted values on the horizontal axis. The plot on the left shows a case of violated linearity assumption with a U-shaped curve and on the right graph we show a case with no linearity assumption violation. This test is not perfect but gives a first idea of

2

For example, if we do a regression of the payout ratio on PER. We can use the estimated model to compute the PER according to the model and then compare it with the real value of the PER. The difference between the estimated and real value of the PER is the residual. Explained very simply, it is the mistake our model made.

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linearity assumption violation. The readers may also use partial regression plots to test linearity. If we detect that the linearity assumption is violated, it is possible to apply log transformations to the dependent, independent, or both types of variables. Inverse or square root transformations may also be appropriate. Please note that log transformations are not suited for variables that take zero or negative values. There are recommendations in the form of log transformations to apply in the literature (Benoit, 2011).

3.4.2

Multicollinearity

We can now test the no multicollinearity assumption. This assumption is important because when we regress independent variables on a dependent variable, we consider that the change in one independent variable (payout ratio, for instance) affects, on average, the dependent variable (PER, for instance), holding other independent variables constant. Multicollinearity occurs when independent variables are highly correlated with each other, making it difficult to disentangle the individual effects of each variable. When independent variables are highly correlated, this assumption does not hold, and we will have difficulties interpreting regression results. There are two main methods to detect multicollinearity. The first method is to compute a Pearson correlation matrix between independent variables. As a rule of thumb, if the Pearson correlation coefficient between two independent variables is close to or above 0.8, we may expect collinearity (Pearson, 1900; Kim, 2019; Shrestha, 2020). In Fig. 5, we show a correlation matrix where we see high levels of correlation between variables 1, 3, and 4. In this case, we will avoid adding these variables in the same time as the independent variables in the same model due to the risk of collinearity. We can also use a more precise measure: the variance inflation factors (VIF). Statistics software packages usually compute the VIF for each independent variable; the higher the VIF, the higher the correlation between this independent variable with others. Again, a rule of thumb says that VIF values above four need to be

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Variable 1 Variable 2 Variable 3 Variable 4

Variable 1 1.0000 0.1471 0.9956 0.9930

Variable 2 0.1471 1.0000 0.1400 0.1679

Variable 3 0.9956 0.1400 1.0000 0.9955

Variable 4 0.9930 0.1679 0.9955 1.0000

Fig. 5 Pearson’s correlation matrix

investigated, and VIF values above ten indicate multicollinearity (Micheal & Abiodun, 2014). The usual solution to multicollinearity is to remove variables that cause it, for instance variables with a VIF above four.

3.4.3

Homoscedasticity

Homoscedasticity occurs where the variance of the error term of the regression is constant. On the contrary, a dataset that is not homoscedastic is said to be heteroscedastic. If homoscedasticity is not met, our regression may lead to biased standard errors. These biased standard errors will lead readers to a wrong interpretation of regression results (in terms of coefficients’ statistical significance, for instance). Fortunately, there are tests to measure the level of data homoscedasticity. We show the homoscedastic (left) and heteroscedastic (right) sets of data in Fig. 6. In Fig. 6, we observe that in the left graph (homoscedastic data) the data points seem to have a constant variance over time while in the right graph we observe that variance is not constant over time. There are two key test statistics that may help assess homoscedasticity. The first test for homoscedasticity is the Breusch-Pagan (BP) test (Breusch & Pagan, 1979), and the second is White’s test (White, 1980). The key difference is that White’s test is better at detecting nonlinear forms of heteroscedasticity. Usually, software packages that perform the BP and White’s tests provide a test statistic and a probability

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value ( p-value). Commonly, in these packages, a p-value higher than 0.053 (not a significant test) indicates that there should be no issues with heteroscedasticity (but it is worth always checking the interpretation for each software package). When we face heteroscedasticity, we may still perform the regression analysis but using standard errors less sensitive to heteroscedasticity called robust standard errors (also called “heteroscedasticity-robust standard errors”). When we apply robust standard errors, OLS estimators will remain unbiased, consistent, and asymptotically normal even in the presence of heteroscedasticity.

3.4.4

Normality

A normal distribution of errors is important to make inferences from the regression model we have built. This assumption can be tested using a graphical analysis or a statistical test. Quantile-quantile (Q-Q) plots are useful for visually comparing the quantiles of the sample residuals from a linear regression to the theoretical quantiles of the underlying normal distribution. Model residuals are deemed to be non-normally distributed if the data points significantly deviate from the theoretical line in the Q-Q plot. We show in Fig. 7 one Q-Q plot example with non-normally distributed data (left) and one with normally distributed data (right). In Fig. 7, in the left graph we see that observations do not follow the diagonal line indicating departure from normality. On the contrary in the right graph of Fig. 7, the observations stay close to the diagonal line indicating normally distributed data. The second way to test normality is the Jarque–Bera test (Jarque & Bera, 1987). This test computes data skewness and kurtosis and compares these metrics to a normal distribution. Usually, statistics software packages will provide the Jarque– Bera test statistic and a p-value. When that p-value is above 0.05 (not a significant

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test), we can consider that the data is normally distributed (always check the interpretation of these tests in your software package).

3.5

Evaluating the Model Quality

Checking that our model follows key OLS assumptions does not mean that the regression model we have built has statistical significance or that it is better at explaining valuation multiples compared to an alternative model. We can assess this by testing whether the independent variables we include in our model are statistically significant (different from zero) using the t-test and p-value (explained in subsection “T-test and p-value”). We then verify whether our model coefficients are jointly significant using the F-test. We then test our model’s goodness-of-fit using the R-squared measure. Finally, we test whether our model performs better than the model without ESG data using the adjusted R-squared or other advanced methods.

3.5.1

T-Test and P-Value

The goal of regression is to explain a dependent variable (here, a valuation multiple) using one or several independent variables (here, fundamental value drivers and ESG data). For the independent variable to impact the dependent variable, we need to verify whether the coefficient for each independent variable is different from zero. Let us consider the following model: PER = α þ β1 × Beta þ β2 × EPS Growth þ β3 × Payout þ β4 × ESG Score ð15Þ We will use the t-test to know whether β4 is different from zero. The t-test has two main hypotheses: Hypothesis 1: β4 = 0. Hypothesis 2: β4 ≠ 0 Our statistical package then computes the following t-statistic: t=

β4 SE

ð16Þ

where: SE: standard error β4: estimated coefficient The statistical package will then compute a p-value. Hypothesis 1 (or null hypothesis) for the coefficient being zero can be rejected if the p-value is below a predefined

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level (usually 0.05). This analysis is conducted to determine whether the coefficient is statistically significant ( p-value below 0.05). In our example, if β4 is statistically significant, we can start considering that the ESG score has an effect on PER; still other tests would be necessary to claim causality, as explained in detail by Roberts and Whited (2013).

3.5.2

F-Test

We will now look at the model as a whole rather than looking at each individual independent variable; the goal here is to make sure that the model has a minimum level of quality. In our previous subsection, we tested whether one of the coefficients of the model was nil. We will now discuss the F-test that allows us to test all coefficients of the regression simultaneously. In this case, the F-test will compare two models: the unrestricted model we estimated (which includes all independent variables) and a restricted model including only the constant term. The statistical software will compute the F-test to test two competing hypotheses: – Hypothesis 1: the restricted model (including only the constant term) fits the data as well as the unrestricted model, – Hypothesis 2: the unrestricted model (with all independent variables) fits the data better than the restricted model (with only the constant term). The statistical package will provide the p-value. Usually, when the p-value is significant ( p-value below 0.05), this means that hypothesis 1 given above can be rejected (again check how to interpret results of your specific statistical package). This test is obviously key to start testing the quality of the model used as it gives a first view of whether the model has basic statistical significance.

3.5.3 3.5.3.1

Goodness-of-Fit Test R-Squared and Adjusted R-Squared

We will now further test the quality of our model by checking whether it is good at fitting the data. One way to do so is the R-squared and adjusted R-squared measures (see Eq. 17). R2 = 1 -

Sum of squared residuals Total sum of squares

ð17Þ

First, the R-squared measure is a statistical tool that allows measuring to what extent the variance of the independent variable explains the variance of the dependent variable. A model with a 100% R-squared perfectly explains the variance in the dependent variable, while one with a 0% R-squared does not explain any variation of

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the dependent variable. A higher R-squared leads to a model fitting the data better. Therefore, when choosing between several models we will aim at the one with the highest R-squared. In Fig. 8, in the left graph, we show a model with a high R-squared at about 99%, where the data points are very close to the regression line. Still in Fig. 8, we see in the right graph a model with a low R-squared of about 2%, where the observations seem randomly spread around the regression line. The R-squared is specifically important for predictions, which is our case here. If the goal is simply to infer how an independent variable impacts a dependent variable, it may be less important to have a high R-squared value. One flaw of the simple R-squared measure is that adding an independent variable will lead to an increase in the R-squared value. A model with more independent variables would, therefore, generally have a higher R-squared as compared to a model with fewer variables, even though every independent variable added to the model might not really improve the model’s goodness-of-fit. A measure, the adjusted R-squared, limits this flaw by taking into account the number of independent variables in the model. Therefore, an increase in predictors does not systematically lead to an increase in adjusted R-squared. We present the adjusted R-squared formula in Eq. 18. Adjusted R2 = 1 -

1 - R 2 × ½ n - 1 ½ n - k - 1

ð18Þ

where: n: number of observations in the dataset k: number of independent variables in the model The interpretation of the adjusted R-squared is similar to the one of the R-squared. A higher adjusted R-squared leads to a model fitting the data better. The advantage of the adjusted R-squared is that it also allows us to compare a model including more independent variables with a model including fewer independent variables.

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Akaike’s Information Criteria and Bayesian Information Criteria

After assessing F-test, R-squared, and adjusted R-squared models, two other measures may be used to compare and then select the best model—the Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) (Akaike, 1974; Schwarz, 1978). AIC and BIC allow comparing models on the basis of simplicity and fitness. When comparing models, a lower AIC and a lower BIC indicate a better model (best simplicity vs. fitness trade-off). We provide the formulae of AIC and BIC in formulae 19 and 20, respectively. AIC = 2k - 2 lnðLÞ

ð19Þ

where: k: number of parameters in the regression L: maximum likelihood function value ln(L): natural logarithm of L BIC = k lnðnÞ - 2 lnðLÞ

ð20Þ

where: k: number of parameters in the regression L: maximum likelihood function value ln(n): natural logarithm of n ln(L): natural logarithm of L It is important to understand here that contrary to the adjusted R-squared, the AIC and BIC are not absolute measures of model quality but relative measures. It means that if all models tested perform poorly, AIC and BIC will not indicate it. They will only help to choose the best model in a given list.

3.5.4

Using the Model to Estimate Enterprise Value or Equity Value

To summarize, we have (1) chosen a multiple, (2) chosen a sample of companies on which we want to train the model, (3) chosen the relevant set of fundamental value drivers using fundamental equations or empirical work from Damodaran (2023) and relevant ESG data, (4) estimated a model using fundamental value drivers and ESG data, (5) tested OLS assumptions, (6) tested the significance of all coefficients in the model as well as individual coefficients, and (7) chosen the most appropriate model based on significance and goodness-of-fit measures. Let us suppose we obtain a final model as in formula 21.

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PER = α þ β1 × Beta þ β2 × EPS Growth þ β3 × Payout þ β4 × ESG Score ð21Þ The next step to estimate the PER using the model of the target firm is to replace Beta, EPS Growth, Payout, and ESG Score with the actual values of the target firm. We can then multiply this PER by the firm’s earnings per share to estimate its market capitalization.

3.5.5

Using the Empirical Model of Damodaran (2023)

3.5.5.1

Regression

In this example, we will test a regression analysis on firms that are part of the S&P 500 index. We will use Damodaran’s (2023) empirical model as of January 2022 on this dataset (see formula 22). It is necessary to frequently check whether this empirical model has been updated before using it. We will then add the size computed as the natural logarithm of total assets, the aggregate score, and the score for each ESG pillar (see formula 23). We add a size independent variable in the regression models to factor in ESG performance independent of size. Please note that all R commands that allow us to perform the analyses used here are summarized in Section “R Code to Perform Tasks Provided in this Chapter”. PER = 33:33 - 7:11 × Beta þ 49:40 × EPS Growth þ 7:5 × Payout

ð22Þ

When we add the score for each ESG pillar, we estimate the model represented by formula 23. PER = β1 × Beta þ β2 × EPS Growth þ β3 × Payout þ β4 × Size þ β5 × E Score þ β6 × S Score þ β7 × G Score

ð23Þ

After extracting this data from our financial and ESG data provider, we then clean the dataset for outliers by winsorizing the data at the 1% and 99% levels.4 We then regress the 2021 year-end beta, EPS growth, payout ratio, size, ESG score, E score, S score, and G score on PER using the Stata© statistical package. A quick look at the model shows that the EPS growth long-term forecast is positive and significant at the 1% level in all models. The ESG score is not significant here, but the G score (governance score) is significant and negative at the 5% level. Coefficients are not jointly nil, as the significant F-test shows in each model. The model with the highest adjusted R-squared is model (4).

4

The reader can choose a different data cleaning procedure, but it is always highly recommended to clean data for outliers to limit the effect of extreme variables on the model results. Literature may, in certain cases, provide guidance on the best method to account for outliers (Ghosh & Vogt, 2012).

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3.5.6.1

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Testing Linearity, Multicollinearity, Homoscedasticity, and Normality Linearity

From now on, we will continue with model (4) of Fig. 9 as it has the highest adjusted R-squared. First, we would like to test the linearity assumption on model (4) using a residuals-to-predicted values plot. In Fig. 10, we do not see a U-shaped curve, nor do we observe perfect symmetry of residuals around the zero line. We may investigate further the linearity issue and use partial residual plots on each variable. We may then apply natural logarithm transformations to the variables we have identified as non-linear, based on partial residual plot (making sure that we deal with positive and non-zero variables).

3.5.6.2

Multicollinearity

As suggested previously, one way to assess the risk of multicollinearity is to compute a Pearson’s correlation matrix, including all independent variables. We do not observe any pairwise correlation exceeding the 0.8 threshold in Fig. 11. The environmental pillar score (E score) and social pillar score (S score) have the highest correlation (0.56). We can check for further risks of multicollinearity using the VIF test, paying special attention to the E and S scores. We apply the VIF test to our set of variables from model (4) and report the results in Fig. 12. We observe in Fig. 12, VIFs that we have computed are all below 2; based on our rule of thumb, we should be concerned about VIF values above 4. No value meets this threshold here, so we do not expect multicollinearity to be a major issue.

3.5.6.3

Homoscedasticity

We now apply the BP test for heteroscedasticity. When computing a BP test, we obtain a Chi-squared test of 4.33 and a p-value of 0.0374. The null hypothesis of the test is that we have homoscedasticity; the p-value being below the 0.05 significance threshold, we reject the null hypothesis of homoscedasticity and conclude that we have heteroscedasticity. Therefore, we should use robust standard errors to account for this issue.

3.5.6.4

Normality

First, we compute a Q-Q plot of residuals (see Fig. 13). We then use the Jarque–Bera test to check whether we have normality issues in our data. The test gives us a

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We regress the price-to-earnings ratio (PER) against the beta, the earnings per share (EPS) growth rate long-term forecast, the dividend payout ratio, the size (ln(Total Assets)), the aggregate ESG score, and the E, S, and G scores separately. All data are as of the end of 2021 and winsorized at the 1% and 99% levels. We estimate four models: model (1) with variables from the Damodaran (2023) empirical model; model (2) with the addition of size; model (3) with the addition of the aggregate ESG score; and model (4) where we remove the aggregate score and replace it by the addition of each ESG pillar separately. Model (1) (2) (3) (4) Independent Dependent Dependent Dependent Dependent variables variable: PER variable: PER variable: PER variable: PER Beta EPS_Growth Payout

7.11 (5.38) 1.62*** (0.25) 0.10* (0.06)

Size

8.02 (5.36) 1.52*** (0.25) 0.12** (0.06) -4.20** (1.79)

ESG score

8.87 (6.21) 1.63*** (0.29) 0.14** (0.07) -3.59* (2.14) -0.27 (0.23)

E score

9.50 (6.22) 1.61*** (0.29) 0.13* (0.07) -3.80* (2.15)

Constant

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88.49*** (31.52)

95.61*** (36.11)

-0.05 (0.16) 0.14 (0.21) -0.41** (0.17) 101.31*** (37.62)

Observations F-test P-value F-test R-squared Adjusted R-squared Standard errors in parentheses ***p