Financial Modeling For Equity Research: A Step-by-Step Guide to Earnings Modeling 1549832867, 9781549832864

Citation preview

FINANCIAL MODELING FOR EQUITY RESEARCH A Step-by-Step Guide to Earnings Modeling Second Edition

JOHN MOSCHELLA, CFA, CPA Copyright © 2017 Gutenberg Research LLC All rights reserved.

2

About the Author John has more than a decade of experience analyzing companies in various capacities. After earning a BSBA in Finance, MS in Accounting and MBA from Northeastern University, he began his professional career at PricewaterhouseCoopers (PwC) in New York as an Assurance Associate in the Financial Services practice. He also participated in a rotational assignment in the Financial Service Research Institute at PwC where he studied bank mergers during the financial crisis. After PwC, John spent five years at UBS Investment Bank where he worked first as a Capital Specialist, and then as an Equity Research Associate. In his research role, he maintained earnings models for companies in the Semiconductor and Semiconductor Equipment Industries, contributed to research reports, and participated in investor conferences. John moved to General Electric Capital Corp in early 2014 as a Lead Risk Analyst where he built regression models to predict asset losses based on various macroeconomic scenarios. After the sale of the majority of GE Capital’s assets, John started a consulting firm which provides capital planning support to investment banks, in addition to running Gutenberg Research.

About Gutenberg Research Gutenberg Research is a web-based, interactive earnings modeling community, which provides professional analysis based on modern portfolio theory and fundamental valuation techniques, with the mission of making earnings models available to all investors. The Gutenberg name and philosophy are inspired by the fifteenth century visionary and inventor of the printing press, Johannes Gutenberg. Gutenberg's press forever altered the state of communication and flow of information through the mass production of books, changing literacy from a luxury of an elite few, to a right of the masses. Now, more than 500 years later there are still many limitations on information. For example, investment banks restrict the distribution of their earnings models to top paying clients. The Gutenberg Research community is building an inventory of models which all investors can access for free.

Model Templates 3

Throughout this book images of models are used to demonstrate the topics covered. These Microsoft Excel-based files are available for download when you register your book at www.GutenbergResearch.com/book. The cell references in the text correspond to the cells within the spreadsheets. This will enable you to drill into the cells and trace through the equations, or alter the files to suit your modeling needs. File 1—Blank Model Template: Use this template to create your own earnings model. None of the cells are locked so you can change the accounts and equations to fit any company you choose. File 2—Apple Inc Back of the Envelope Model: The majority of the examples covered in this book are based on Apple Inc. This beginner model is perfect for those who have not had prior modeling experience. This model is covered in Chapter 2. File 3—Apple Inc Tier 2 Earnings Model: This version of the model is more sophisticated and includes a breakdown of the company’s products. The Tier 2 model is covered in Chapter 3. File 4—Apple Inc Tier 1 Earnings Model: The Tier 1 model is geared toward advanced analysts and includes financial statement integration, as well as a discounted cash flow valuation. This model is covered in Chapter 4. File 5—Equity Risk Premium (ERP) Model: Using this simple model you can quickly estimate the market ERP based on volatility, changes in interest rates, and market return expectations. You can then derive a discount rate using your ERP estimate, and the Capital Asset Pricing Model (CAPM). This model is covered in Chapter 6. File 6—Apple Inc Beta Calculation: This file demonstrates the calculation of beta, using an Excel-based regression, which is covered in Chapter 6. File 7—Boeing Co Regression Model: This file calculates a simple linear regression and related statistical tests using Boeing’s revenue and the U.S. Census Bureau’s Industrial Production report. The Boeing regression model is covered in the Appendix. File 8—Starbucks Inc Regression Model: This file demonstrates a multiple regression using Starbucks’ revenue and the U.S. Census Bureau’s Advanced Retail & Food Sales Report, Food Services & Drinking Places sales data. The Starbucks regression model is covered in the Appendix.

4

Terms of Use This book, and all associated models, files, and content published in text or on GutenbergResearch.com is for demonstration purposes only, and is presented “as is”. Neither Gutenberg Research, the author, nor any Gutenberg Research agents or associates are liable for any errors, delays, incompleteness of data presented, or for actions taken based on reliance on any information contained in this book, associated files and templates, or information presented on GutenbergResearch.com. The information presented does not represent investment advice. Investors should consult a professional investment adviser prior to making investment decisions.

5

Preface—Your Financial Modeling Toolbox Equity research is a difficult field, with a steep learning curve for new associates. The hours are long, clients are demanding, and modeling errors are unacceptable. When I first joined the semiconductor research team at a New York-based investment bank I struggled with the workload. Between the 100+ hour workweeks during earnings season, getting up-to-speed with the fundamentals of a highly technical industry, and studying for the FINRA exams, there was no additional time to learn the basics of the dayto-day tasks, most importantly how to model a company’s earnings. Most senior research analysts have little time for training, so new associates tend to teach themselves, as I did, by reverse-engineering their team’s models. I wrote this book and designed the Excel templates as resources for new associates to assist in the financial modeling learning process. This book is based on my experience as a Sell-Side Research Associate, with input from the quantitative methods I used as a Risk Analyst, and a unique consideration of financial reporting from my time as a Public Accountant. The methods covered are primarily geared toward sell-side earnings modeling, however, many of the topics are also applicable to careers in investment banking, asset management, or any other field which requires knowledge of forecasting or valuation. Remember modeling is part science and part art. The chapters to follow describe the modeling approach I use in practice. There are many different methods, variations, and techniques you can use to forecast earnings. If you have prior modeling experience, feel free to incorporate your own spin on the steps as you work through each section. If you are new to modeling, you may find some of the concepts difficult at first, but if you follow each step you will be able to build an earnings model for nearly any company.

6

Table of Contents Chapter 1: Introduction to Financial Modeling Section 1: What is an Earnings Model Section 2: The Fundamental Principles of Modeling Section 3: How to Use This Book Section 4: The Earnings Cycle Section 5: Timing of Publication Section 6: Basic Excel Functionality Section 7: Types of Models Section 8: Anatomy of a Model Chapter 2: Building a Basic Financial Statement Model Step 1: Getting Started Step 2: How to Setup Your Model Step 3: Filling in the Historic Data Step 4: Assumptions & Forecast Equations Chapter 3: Converting to a Ti e r 2 E a r n i n g s M o d e l Step 5: Build the Earnings Engine Step 6: Link Product or Segment Details Step 7: Complete the Income Statement Step 8: Adjust for Non-GAAP Items Chapter 4: Converting to a Ti e r 1 E a r n i n g s M o d e l Step 9: Complete the Historic Financials Step 10: Balance Sheet Modeling—Assets 7

Step 11: Balance Sheet Modeling—Liabilities Step 12: Balance Sheet Modeling—Equity Step 13: Cash Flow Statement Modeling Step 14: Primary Financial Statement Links Chapter 5: Model Calibration & Forecasting Step 15: Incorporate Historic Trends Step 16: Adjust for Seasonality Step 17: Adjust for New Product Launches Step 18: Guidance and the Consensus Step 19: Incorporate Your Opinions Step 20: Consider Leading Indicators Chapter 6: The DCF Inputs ( B e t a , E R P, C A P M , & WA C C ) Step 21: Calculate the Equity Risk Premium Step 22: Derive Beta Using the Regression Function in Excel Step 23: Calculate the Required Return on Equity using Beta and the CAPM Step 24: Calculate the WACC Chapter 7: Discounted C a s h F l o w Va l u a t i o n Step 25: Calculate the Stage-One DCF Step 26: Calculate the Stage-Two DCF Step 27: Calculate the DCF Valuation Step 28: Understanding the DCF Valuation Chapter 8: Market Multiple B a s e d Va l u a t i o n Step 29: Choose a Multiple Step 30: Separate Net Cash Step 31: Calculate the Historic Multiple Step 32: Create a Price Band Chapter 9: How to Use 8

Yo u r E a r n i n g s M o d e l Step 33: Run Scenario Analysis Step 34: Perform Sensitivity Analysis Step 35: Analyze Guidance & Consensus Step 36: Prepare for the Release Step 37: Updating The Model Step 38: Regular Model Maintenance Appendix 1: Using Regression Analysis to Predict Earnings Step R1: Select Your Data Set Step R2: Run the Regression Step R3: Test the Model Step R4: Analyze the Output Step R5: Perform Back Testing Step R6: Model Limitations

9

CHAPTER 1: INTRODUCTION TO FINANCIAL MODELING Section 1: What is an Earnings Model Section 2: Fundamental Principles Section 3: How to Use This Book Section 4: The Earnings Cycle Section 5: Timing of Publication Section 6: Basic Excel Functionality Section 7: Types of Models Section 8: Anatomy of a Model Chapter 1 Overview: An earnings model is a representation of a company’s financial position, disaggregated to a level which can be analyzed based on historic results and future expectations. The level of disaggregation depends on the subject company. For example, if someone asks you whether or not Apple Inc will beat the consensus Earnings Per Share (EPS) estimate next quarter, it would be very difficult to answer without thinking about how many iPhones, Macs, and iPads the company will sell, the average price for each, and the profit margin. An earnings model shows all of these components and how they work together to get to a final EPS estimate.

Section 1: What is an Earnings Model The term model is broadly used to explain many different types of financial representations, which can vary depending on the type of task the model is performing. For example, an economist may use a regression model to predict the likelihood of an economic downturn based on several macro-economic inputs. On the other hand, an option trader’s model may use various inputs to price a particular option contract. When we use the term model, or earnings model, in the context of equity research, we are 10

simply referring to a tool which uses a set of assumptions to project the financial statements (or sometimes just future earnings) for a particular company. The primary objective of creating a model is to have a dynamic tool which can be easily adjusted to see the outcome of a change in assumptions. It is critical to understand this concept before you get started. To demonstrate, take a look at the excerpt of an Apple Inc earnings model in Exhibit 1. For now, just focus on cell AO11 which represents the total revenue forecast for the next quarter. This cell is not a hard-coded number, but an equation which equals the sum of the individual product sales in rows 41 through 106. Each product’s sales estimate is calculated based on the number of units sold times the Average Selling Price (ASP) per unit. Now let’s say you are trying to determine what Apple’s revenue will be next quarter, and you believe that recent developments in iPhone technology will lead to a higher ASP. You decide to increase the ASP in cell AO45 from $650 per phone to $675. After you make the change to the ASP, you can go back to the total revenue in cell AO11 and see that it changed from $44.9B to $45.9B. The rest of the Income Statement accounts changed as well, since the cells are linked throughout the model’s equations. This example summarizes the primary purpose of earnings modeling. The goal is to break a company’s complex operations into smaller parts, and use the model as a tool to project future results.

Exhibit 1—Apple Revenue Sensitivity to iPhone ASP

My Experience: At investment banks, equity sales representatives sell the research of equity research analysts to institutional clients (usually package with other equity service offerings). This is where the phrase 11

“sell-side” research comes from since investment banks sell their research to “buy-side” clients. The bank’s clients typically have different tiers of service based on the research fees they pay. The highest-tier clients typically receive the analyst’s “working models”, which means the equations remain in the Excel file, so the client has the ability to change the assumptions and see the impact on earnings. Lower tier clients, such as in-house wealth management advisors who generate very little revenue for the research department, receive models which show the “values only” view without the embedded equations. Hard-coded models do not allow for dynamic analysis. In this book, I will be demonstrating how to create working models. FAQ 1—What is the difference between the modeling approach in this book and models built by sell-side analysts? The main difference is the level of granularity. Wall Street analysts tend to disaggregate earnings to a level which cannot be validated by company reports alone. This concept will be discussed in Step 5a, Chapter 5. In addition, analysts’ models generally forecast all three financial statements with interlinking equations. I will demonstrate how to forecast all three financial statements (Tier 1 model approach), after covering how to build a basic earnings model.

Section 2: The Fundamental Principles of Modeling There are an endless number of inputs and assumptions which could be used to build an earnings model. Two guiding principles should always be considered when assessing which metrics to use: the Principle of Balance and the Principle of Drive. The Principle of Balance: This concept relates to the level of granularity you choose to incorporate in your model. The degree of effort required to create and maintain the model, should be balanced against the resulting analytical benefit from adding each additional layer of complexity. To demonstrate this principle, consider how you might project future revenue for a company which sells widgets. You may decide to take a simplistic approach by applying a growth rate to the last reported revenue, or you may add an additional layer of complexity by disaggregating revenue by the number of widgets sold and the average price of each widget. If the analytical benefit you derive from the additional widget unit detail in the second approach outweighs the additional modeling effort, then you 12

should choose the second method. If not, you should keep the model simple by choosing the revenue growth rate method. Maintaining the balance between effort and analytical value will help you to create a model which best fits your needs. The correct balance is in the eye of the beholder. We all have different reasons for creating models. You may be building a model as a hobby because you are interested in following a particular company. In which case, your balance point would likely fall on the lower complexity and effort side of the spectrum. On the other hand, if you are a sell-side analyst, your model would be more complex to meet the needs of your clients. I will be demonstrating three different types of models in this book, beginning in the next section, each with a different level of complexity. As you read through this book consider where your balance point lies, and choose a model type that best suits your needs. The Principle of Drive: For each financial statement account within our models we must make decisions on what metrics we will use to drive our future period projections. These metrics can be internal to a particular company, external forces, or a combination of the two. The key concept is that we choose metrics which drive the future results. If we go back to our widget company example, it is safe to say that the number of widgets sold will drive future revenue. This would be an example of a metric which is internal to the company. If widgets are a cyclical product, then you could also make an argument that changes in Gross Domestic Product (GDP) could also drive future sales. This is an example of an external metric which could be incorporated into the model (likely an input to determining the number of units, which would then translate into a revenue estimate). You would not, however, use average rainfall to project the widget company’s revenue since rain does not drive the company’s results. It can be difficult to determine which metrics to use when you are creating a model from scratch. As you research your company you should continually be asking what is driving the overall results of the company, and what is driving the individual accounts. Listen for clues from management as they discuss the company on earnings calls and investor conferences. Draw conclusions from the details provided in their financial statements, or other sources of data. The Apple example I will be using for the majority of this book is just that, an example. The specific model drivers for Apple may not be directly relevant for the company you are trying to model, but the methodology is the same. If you can effectively identify the appropriate drivers you can 13

build a model for any company.

Section 3: How to Use This Book This book is designed as a step-by-step demonstration of how to create an earnings model, which you can use as a guide to build a model for a company of your choice. Throughout the book, I have included my personal experiences as well as modeling pitfalls to watch out for, and frequently asked questions I have received from a team of contributors participating in the Gutenberg Research Financial Modeling Intern Program. This demonstration will primarily use an Apple Inc earnings model as an example. You can download the Excel files shown in the next few chapters when you register at www.GutenbergResearch.com/book. I chose Apple Inc for this book because the company’s financial reporting is relatively straightforward. The concepts covered are generally applicable to most companies. In cases where an Apple example would not suffice, I have brought in examples from other companies. Using this realworld case study approach rather than a textbook-style example demonstrates that equity research and earnings modeling can be difficult, and requires a significant number of assumptions. Despite this fact, modeling is a key part of the process of developing a reasonable basis to support an investment thesis.

Section 4: The Earnings Cycle Earnings models require constant updates for new information as it is released. The most common model updates are quarterly earnings releases. Most analysts update their models multiple times each quarter. The image below is a demonstration of the fiscal second quarter 2017 earnings cycle for Apple Inc. Since Apple follows a fiscal reporting schedule, the quarterly results do not align with calendar quarters. The “consolidation period” is the period after the reporting date when the company is busy preparing the financial statements. Apple issued an earnings press release and held an earnings conference call on May 2, 2017. After that the cycle begins again.

Exhibit 2—Apple’s Second Quarter Earnings Cycle

14

Research Analysts at investment banks typically prepare earnings preview research notes and update their models about a week before the press release date. When the press release is posted to the company’s investor relations page, the Research Analysts will update their models to include the actual results for that quarter, and change their future period forecasts based on comments made by management in the press release or on the earnings call. After their model is updated they will publish it with a research note to their clients later that night. My Experience: I like to update my models as soon as the press release hits the wires so I can focus on the earnings call when it starts. Some companies start their earnings call by reading what was already written in the press release as part of the prepared remarks. Try not to zone out during this section of the call, and be sure to stay focused for the important parts, like the Q&A with the analysts. Some companies will read a quick summary on the call and then skip right to the Q&A session (Netflix and Tesla are good examples of this approach). Be sure to listen to a few earnings calls for the company you are covering to gain an understanding of how their calls are conducted.

15

Section 5: Timing of Publication This book uses a real-world, case study example with actual results disclosed in Apple’s SEC filings. As Apple releases new quarterly financial results, the tables and text will be updated and new editions of this book will be published. The publishing process takes a significant amount of time. As a result, this edition may not reflect the most up-todate information reported by Apple. This version is the second edition which was published after Apple’s fiscal second quarter 2017 results were released. Despite the constant maintenance required to sustain an up-todate example, the methodologies demonstrated in the book should remain relatively constant over time; Therefore, this book will continue to be a useful demonstration of the earnings modeling process even after Apple reports their third fiscal quarter 2017 results and beyond.

Section 6: Basic Excel Functionality As you read through this book you will notice that none of the advanced Excel features (macros, links to external spreadsheets or data sources, etc.) are used in my modeling approach. I have kept the Excel functionality relatively simple for three reasons: 1) the goal for this book is to teach about financial modeling, not Excel shortcuts, 2) if equations are run in VBA code behind the scenes, the user cannot trace the calculations through the spreadsheet, and 3) this book is primarily geared toward those looking to become equity research associates, and sell-side research firms do not typically rely on macros and links because they share their models with clients who tend to have problems with such features, and in some cases will not accept files which include macros. If you are new to Excel, there are some basic features which you should get familiar with before you begin to build your first earnings model. Freeze Panes: I like to keep the target share price in the upper left-hand corner of my models so I can see the impact assumption changes have on valuation. To do this select the cell where you want the panes to freeze. Select “View” then “Freeze Panes”. This will let you scroll left or right, up or down, and keep the header with each column’s date, and the account for each row, in place. Grouping Rows and Columns: As you add variables to your model, the row count will increase, which can get distracting. You can group sets of rows or columns to make your model more user-friendly. I tend to group 16

columns by year, and rows by product or segment. I also group the Balance Sheet and Cash Flow Statement so I can easily collapse these sections when I am focusing on earnings. To create a group, highlight the rows you would like to include. Select “Data” and “Group”. Notice that a group symbol has appeared in the left-hand column of Excel. Now you can easily collapse the group by clicking the “-” symbol, and then re-expand the group by clicking the “+” symbol. Show Equations: There is a simple and useful trick in Excel which instantly converts all of the cells in the worksheet to show the underlying equations. Hold down control and select the “~” key. To change the view back to normal, hold control and select “~” a second time. Trace Equations: Since you will be using templates with equations that I have created, you may want to trace which cells are used in certain financial statement line items. To determine which cells are used in a calculation select “Formulas” and “Trace Precedents”. Arrows will appear which will clearly show the flow of values through the equations. If you would like to see if a cell impacts other cells click “Trace Dependents”. When you are finished with your review click “Remove Arrows”. Dragging Equations and Fixed References: Most analysts setup their models to have four quarter columns followed by one column which represents the full-year. This is the method we will use in this book. Keep in mind that with this style of modeling, you cannot drag columns with quarterly calculations across the spreadsheet as the cell references will be incorrect after you pass through the full-year column. Instead of dragging, consider copying and pasting the equations which will maintain the correct cell references. In some cases you may want to keep a fixed reference on a row, column or both. To do this you can use “$” in your cell references within the equations, by using the F4 key. Within the equation point to the cell for which you are looking to fix the reference (for example cell =A1). Press F4 once to lock both the row and column (=$A$1). Press F4 again to lock the row only (=A$1). Press F4 a third time to lock the column only (=$A1).

17

Other Excel Functions: Throughout the book I will introduce other important Excel functions in the sections for which they are relevant. These functions include: 1) Using data tables for sensitivity analysis (Step 34, Ch. 9) 2) Using the goal seek function (Step 18c, Ch. 5) 3) Running regression analysis in Excel (Appendix)

Section 7: Types of Models I use different types of models for different purposes. While models can have an unlimited number of styles and variations, my models generally follow one of three different formats which I refer to as: 1) Back of The Envelope (BOTE) models, 2) Tier 2 models, and 3) Tier 1 models. These model styles vary based on the type of analysis I am performing. For example, sometimes I want to run a quick sensitivity review on a company’s earnings before an earnings release. For this type of analysis it does not make sense to populate years of historic data and all three of the financial statements. Instead I create a basic Income Statement with a few assumptions to drive the earnings forecast. This is my Back of The Envelope (BOTE) model format. In other cases, I may want to add a bit more detail to the model to allow some flexibility in the forecast. For example, if I were modeling earnings for Apple Inc, I may want to include an estimate of the number of iPhones sold in the model. For this I would have to add a breakdown of the products to the Excel file. If I include the Income Statement and the product or segment details, I consider this a Tier 2 earnings model. If I want to use a discounted cash flow valuation, then I would have to project the Cash Flow Statement and Balance Sheet. For this type of analysis I use a more advanced Tier 1 model, which links the financial statements together based on my earnings projections and other assumptions. There are many other forms that earnings models can take. I use these three basic styles to frame my analysis based on what I am trying to accomplish. For demonstration purposes, I have created each of these three distinct model types for Apple Inc, which you can download when you register this book at www.GutenbergResearch.com/book. The remainder of this chapter will go through the structure of each type of model. As you read through this section and decide which type of model you would like to create, consider your proficiency with the concepts covered. If you do 18

not have prior modeling experience, start by creating a BOTE model. As you read through the book, you may want to convert your BOTE to a Tier 2 model. After you have had a chance to get comfortable with the advanced material, you can add the Balance Sheet and Cash Flow Statement and transform your Tier 2 model to a Tier 1 with full integration of the financial statements.

19

Table 1—Types of Earnings Model Description Level of difficulty Financial Statements

BOTE Model Beginner

Tier 2 Model Intermediate

Tier 1 Model Advanced

Summarized version of the Income Statement

Income Statement Segment Details

Valuation Methodology

Market Multiple

Market Multiple

Forecast Horizon

End of current End of current fiscal year plus fiscal year plus four full quarters. four full quarters.

Income Statement Segment Details Balance Sheet Cash Flow Statement Market Multiple Discounted Cash Flow Approximately five years

Section 8: Anatomy of a Model In this section, I will explain the basic components that I include in each type of model. You will notice a natural progression of complexity as we move through each of the three types. At this point in the process, you may not fully understand the components within the models, but all of the concepts will be explained later in the book. For now, you should simply focus on recognizing the similarities and differences between each type of model. Chapter 2 will start the “step-by-step” process of creating each type of model beginning with the easiest BOTE version, and moving to a full Tier 1 model. The cell references in this section are reflected in the model print screens in Exhibits 3, 5, and 6 which show each of the three models. The blue arrows in these exhibits are used to emphasize the main differences from one model to the next, while the green arrows point out the similarities. In some cases, the rows or columns have been hidden to keep the comparisons relatively simple; however, the cell references in the exhibits are accurate and correspond to the actual rows in the Excel templates which you can explore after you have downloaded the files. Section 8a—Anatomy of a BOTE Model: BOTE models should be kept very straight forward, with at least one full year of historic data, and four 20

quarters of projections. This basic form of an earnings model is useful in comparing management’s guidance to the consensus earnings estimate, and for calculating the values implied by management when guidance is not provided for specific metrics. For example, Apple does not explicitly give guidance for Earnings Per Share (EPS), however, with the details they do provide, and a reasonable estimate for share count, we can calculate the EPS implied by their guidance, and compare it to the consensus estimate. This concept will be covered in Step 18, Chapter 5. In addition to the Income Statement and assumptions sections, I usually include sensitivity analysis which shows the estimated impact on EPS and share valuation if the actual reported results are different from my estimates. Sensitivity Analysis will be explained in Step 34, Chapter 9. There are four primary sections to a BOTE model (refer to Exhibit 3 below) including: 1) The color legend and valuation summary (cells B3 to C7), 2) Summarized version of the Income Statement (rows 9-21), 3) The primary model assumption section (rows 23-35), and 4) A summary of the inputs to the share price valuation (cells B37 to C54).

21

Exhibit 3—Apple Inc BOTE Earnings Model

Section 8a.1—Color Legend: Keeping track of all the assumptions in an Excel file can be difficult. I use a basic color coding process to help sort the different types of cells in the file. Blue cells represent my key assumptions, or inputs into the model. These are the cells you would want to focus on if you were adjusting one of my models to incorporate your own assumptions. For example, if you want to change the assumptions in my Tier 2 model for iPhone sales next quarter, you could directly input the number of iPhones you expect Apple will sell, as shown in cell AP42 in Exhibit 4 below. Notice that this cell is not shaded blue, therefore, it is not an input cell. The number of iPhone units sold is actually dependent on the year-over-year iPhone unit sales growth rate, which is the blue input in cell AP44. If you manually typed in the number over the equation in AP42, you would break the link between the growth rate and units. By shading the input cells blue, you can quickly identify which cells should be changed and which should be left as is. 22

Exhibit 4—iPhone Model Input Example

I also use colors to indicate how a certain metric has been calibrated. For example, orange cells represent financial statement line items which have been calibrated to meet consensus estimates (refer to Step 18c, Chapter 5 for details), while purple cells represent guidance from company management (refer to Step 18b, Chapter 5). Notice that the column headers are also color coded. This is to allow the user to quickly see which columns represent historic results (dark gray columns), and which represent future period forecasts (light gray). The cells within the historic columns generally do not have any color coding applied. This is because historic cells represent actual values which can be found in the company’s past financial statement filings, rather than estimates. In some cases historic results may include blue cells, which indicates that a particular metric was not disclosed by the company, hence the value represents an estimate (refer to Step 5a, Chapter 3 for details). If a cell within a future period column is not color coded, than the cell represents an equation derived from other inputs. In general, it is best to leave these cells unchanged to maintain the integrity of the embedded equations. Section 8a.2—Summarized Income Statement: If you compare the Income Statement reported by Apple to the summarized version presented in the BOTE model, you will notice that the BOTE combines the operating expenses to one line, and only shows the diluted share count and EPS. This is done to keep the format as simple as possible. Keep in mind that the point of the BOTE is to facilitate quick, high-level analysis. The details presented in the Income Statement will be expanded further in the Tier 2 and Tier 1 models. Section 8a.3—Model Assumptions Section: The next section houses the majority of the inputs which feed the Income Statement (refer to Exhibit 3 rows 25 through 35). For example, the gross margin estimate entered in 23

cell P26 of Exhibit 3 links to the cost of goods sold estimate in cell P12.The other assumptions within this section work the same way. Section 8a.4—Valuation Inputs: The valuation summary section shows the inputs used in the share price valuation and target price band (refer to Exhibit 3 rows 38 through 54). Since the BOTE model contains a limited amount of data, the share valuation methodology is also limited. As a result, the BOTE model employs a simple Price-to-Earnings (PE) market multiple-based valuation approach, which will be discussed in Chapter 8. Section 8b—Anatomy of a Tier 2 Model: There are two primary differences between the structure of the Tier 2 model (refer to Exhibit 5 below) and the BOTE model. First, the Tier 2 model includes the full Income Statement as reported by the company with the disaggregation of operating expenses. Second, and more notably, the full segment and product details are included in the assumptions section of the Tier 2 model. The Price Earnings (P/E) multiple valuation and risk estimation section in the Tier 2 model is identical to the BOTE model. The Tier 2 modeling approach is covered in Chapter 3.

Exhibit 5—Apple Inc Tier 2 Earnings Model

24

Section 8c—Anatomy of a Tier 1 Model: The Tier 1 model is the most complex and comprehensive model covered in this book. This is clear to see by simply reviewing the total row count of the Tier 1 model (312 rows, refer to Exhibit 6 below) which is much greater than the 150 rows in the Tier 2, and 54 in the BOTE model. Since all three of the Financial 25

Statements are forecasted using the Tier 1 methodology there is additional information available for more complex analysis compared to the capabilities of the other two models. There are three primary differences between the Tier 1 and Tier 2 models: 1) Level of Granularity: Notice that there are three additional rows in the Tier 1 Income Statement: Earnings Before Interest Tax Depreciation and Amortization (EBITDA), interest and dividend income, and interest expense. All three of these rows can be predicted in a Tier 1 model since the forecast for the Balance Sheet and Cash Flow Statement is available. Take interest expense for example, which can be disaggregated from other income and expense in the historic results using the details in the financial statement footnotes. Since the future debt balance is projected in the Tier 1 version of the model, you can calculate the future interest expense as a percentage of the average future debt balance. This would not be possible in a Tier 2 or BOTE model. 2) Balance Sheet and Cash Flow Statement Analysis: Having all three financial statements available in the forecast allows for much greater detail in future period analysis including cash conversion cycle component reviews, operating efficiency ratio analysis, and cash shortfall investigations. 3) Valuation Flexibility: One of the first things you may notice in the Apple Tier 1 model is the addition of the Discounted Cash Flow (DCF) metric in the valuation summary section. Without cash flow data, the Tier 2 model cannot perform this valuation calculation. There are other valuation methodologies which could also be implemented in a Tier 1 model, such as an Enterprise Value-to-EBITDA, or any other method which incorporates components from multiple financial statements. The Tier 1 modeling approach is covered in Chapter 4, and the valuation inputs are included in Chapter 6.

26

Exhibit 6—Apple Inc Tier 1 Earnings Model

27

28

Exhibit 6 (continued)—Apple Inc Tier 2 Earnings Model

29

30

CHAPTER 2: BUILDING A BASIC FINANCIAL STATEMENT MODEL Step 1: Getting Started Step 2: How to Setup Your Model Step 3: Filling in the Historic Data Step 4: Assumptions & Forecast Equations Chapter 2 Overview: In this chapter, we start our step-bystep guide to building a model. If you are new to modeling be sure to move slowly through each step to make sure you fully understand the procedures before moving on. This will ensure you do not get overwhelmed later in the process as you move to the more complex areas of modeling.

Step 1: Getting Started Step 1a—Choose a Company: To start, think of a company you want to cover. If this is your first time building a model, there are a few things you should consider when making your company choice. First, consider choosing a domestic company as foreign based companies listed on domestic exchanges will have an extra currency translation step, which can be a bit difficult for beginners. Next, make sure your company is not in the process of being acquired, as this adds another layer of complexity. Now consider the industry. Some industries are inherently difficult to cover such as biotech or energy. Finally, consider your level of comfort with the financial modeling process. The easiest companies to cover tend to be large, well establish organizations with stable earnings, and straight-forward guidance from management. The most difficult tend to be new companies with volatile earnings that do not provide earnings guidance. Step 1b—Get to Know Your Company: Before you can begin modeling earnings, you should develop an understanding of the company’s business 31

model. The fastest way to do this is by reading the “Business” section in the 10-K or 10-Q filings from the Securities and Exchange Commission (SEC) website. This section summarizes much of the general information you need to know about the company. Next read the latest earnings press release, which is available on the company’s investor relations page, or from the SEC 8-K filings. This will give you an indication of some of the important items to watch for next quarter, and usually includes the guidance management gave for the current quarter. After you read the press release, listen to the last few earnings conference calls. This will help you get acquainted with how the company conducts the calls, and what the analysts are watching for in the company’s results. Step 1c—Keep Track of Your Company: Visit the company’s investor relations page and sign-up for alerts. Some companies use Twitter or other social media outlets to publish updates, so consider following the company to stay up-to-date on developments. In addition, you can sign-up for free alert services which will notify you of important company specific news. Keep in mind that these services will send you updates for every piece of content as it is made available, so you will need to get used to sorting out what the important developments are from the clutter. The easiest way to do this is to look at the stock price when the news comes out. If it is not moving much, then the article is probably not meaningful. Some companies issue monthly updates. For example, many retailers publish monthly same store sales statistics. Check your company’s press releases to see if they publish similar reports. If so read the updates when they come out, and consider including the details in your model.

Step 2: How to Setup Your Model Step 2a—Start with an Existing Template: This book comes with a blank model template which you can download at www.GutenbergResearch.com/book. The template is a helpful starting point, but keep in mind that different companies will have vastly different financial reporting, so you will need to make significant changes to the template for the company you are working on. Open the “Blank Model Template” file and follow along as I transform the template into an earnings model for Apple Inc. Step 2b—Align the Reporting Schedule in the Header: Not every company’s fiscal year coincides with the calendar year. Take a look at the company’s last press release. Does the report date on the financials match the calendar quarter? If so there is no change required. If not, update the 32

heading sections in the model to coincide with the company’s fiscal quarters. For example, Apple Inc does not follow a calendar year. The company’s year-end date is September 30th, not December 31st, so we need to adjust the headings for Apple (refer to Exhibit 7 below). Notice I have added an “F” in front of the quarter “F1Q17” this indicates that the column represents the company’s “fiscal” quarter.

Exhibit 7—Aligning the Header with the Fiscal Reporting Calendar

Step 2c—Format the Financials: Make sure the financial statements in the model are consistent with the presentation in the company’s press release and SEC filings. For the BOTE model, we will only be creating a summarized version of the Income Statement. Rename each of the accounts based on the presentation in the SEC filing. The Income Statement for most companies is somewhat similar, although you may need to insert or delete rows from the template to fit your company’s format.

Step 3: Filling in the Historic Data Next, enter the financial values from the SEC page. Be sure to work your way backward in time because the company may have made corrections after a previous release. Keep in mind that after each set of four quarters there is a column which represents the full-year. Do not hardcode the values in these columns, instead insert equations which represent the sum of the individual quarters for that year. Input the values for the Income Statement line items in your BOTE model. Companies report on a comparable basis, meaning they show year-overyear data in their financials, so you should only need to look at four filings to get data for eight quarters (not eight filings).

Exhibit 8—Formatting the Financials and Filling in the Historic Data 33

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017, retrieved June 30, 2017.

My Experience: There have been many advancements in financial data automation over the last few years. The Financial Accounting Standards Board (FASB) publishes a taxonomy which consists of eXtensible Business Reporting Language (XBRL) tags for financial statement disclosures. These tags are used by third party data aggregators to make inputting financial data into an Excel file as easy as refreshing the worksheet. If you have access to a data aggregator it may save you some input time. I usually copy/paste the data from the SEC webpage. Some companies provide historic financial statements in Excel on their investor relation page, which can make this step much easier.

Step 4: Assumptions & Forecast Equations Step 4a—Decide on the Assumptions for Your Forecast: As discussed in Chapter 1, the primary reason for creating a dynamic model is to be able to change the assumptions for future quarters and see the impact on earnings. To do this, you must first decide on the factors that will drive your model. These factors should be based on the metrics available in the historic results. Review the line items in Exhibit 8 and think of the ratios you could calculate based on these items. For revenue, you could calculate a year-over-year growth rate. Cost of sales could be derived based on the gross profit margin percentage. Operating expenses could be stated in a ratio of operating expenses to total sales. An effective tax rate could be derived by dividing the total income tax expense by taxable income. The other income and expense line does 34

not make up a significant portion of the total earnings and can be difficult to predict, so you may decide to use an average of historic results in your future estimate. Using these ideas you can add the historic ratios to your model. Before moving on, take note that the sales line in the BOTE model is a hard coded value which is used to calculate the year-over-year growth rate in the assumptions section. This is a bit counter-intuitive, since the other accounts are driven by estimates within the assumptions section. I do it this way to make running the sensitivity analysis (discussed in Step 34, Chapter 9) easier. Feel free to reverse the equation to be driven by the growth rate if you prefer.

35

Exhibit 9—Calculate the Historic Ratios Based on the Income Statement Values

The last assumption needed to calculate EPS, is the share count. The number of shares outstanding is impacted by share repurchases, equity awards, and the dilutive impact of convertible debt. Share repurchases tend to be the largest driver of changes in shares. For this reason, I breakout repurchases separately from other share count changes and use estimates of repurchase amounts and average share price to calculate share count reductions. The remaining share changes are difficult to project so I apply a basic average growth rate to project future estimates. Having the ability to change future repurchase assumptions is important. If a company announces a significant change to its repurchase plan, the results can be incorporated into the future estimates very easily if the equations are already setup. This is where the Tier 1 model proves helpful as it automatically take the cash needed for share repurchases from the Balance Sheet through the Cash Flow Statement. For the BOTE model share count estimate, start by filling in the past share repurchase data, including the dollar amount of the repurchases, weighted 36

average share price, and number of shares repurchased from the latest SEC filings. Next, calculate the remaining quarter-over-quarter change in share count excluding the impact of repurchases. Step 4b—Set the Forecast Equations in the Income Statement: Now that we have our assumption metrics selected, we can input the future period equations into the Income Statement. Table 2 below will help clarify the inputs.

37

Table 2—Summary of BOTE Assumptions and EPS Calculation Line Item

Driver

Future Period Equation

Net sales

Direct input

N/A

Cost of sales

Gross margin percentage

Sales × (1 - gross margin)

Gross profit

N/A (subtotal equation)

Net sales – cost of sales

Operating expenses

Operating expense to sales ratio

Sales × Operating expense to sales ratio

Operating income

N/A (subtotal equation)

Gross profit – operating expenses

Other income/(expense)

Direct input

N/A

Income before taxes

N/A (subtotal equation)

Operating income – other income/(expense)

Provisions for income tax

Effective tax rate ratio

Income before tax × effective tax rate

Net income

N/A (subtotal equation)

Income before tax - provisions for income tax

Diluted shares outstanding

Repurchase assumptions & rate

Refer to template for details

Diluted EPS

N/A (subtotal equation)

Net income ÷ shares outstanding

The “drivers” in Table 2 should be shaded blue in the model to indicate that the cells are primary model inputs. For the time being, set these cells equal to the last reported value in your version of the Excel file. We will discuss how to select the values for the blue cells in the calibration and forecasting chapter. Exhibit 10 below shows what your model should look like at this point. Notice that in the previous image (Exhibit 9) the assumption section was 38

calculated based on the Income Statement results. The flow of information in the historic columns moves downward. In the forecast columns (Exhibit 10) the flow shifts, as the estimates are entered in the assumptions section and feed upward into the forecast section of the Income Statement.

39

Exhibit 10—BOTE Model Pre-Calibration

Step 4c—Decision Time: At this point you should decide if you are going to be converting your model to a Tier 2 or Tier 1 version. If you are going to stick to the basic BOTE model, then move on to Chapter 5 to cover calibration, and then proceed to Chapter 8 to add a market multiple valuation metric to your model. If you would like to convert your model then move on to Chapter 3 to learn how to expand the assumption section to include a product breakdown.

40

CHAPTER 3: CONVERTING TO A TIER 2 EARNINGS MODEL Step 5: Build the Earnings Engine Step 6: Link Product or Segment Details Step 7: Complete the Income Statement Step 8: Adjust for Non-GAAP Items Chapter 3 Overview: Chapter 2 focused on the primary assumptions of a simplistic model. In this chapter, we will begin to disaggregate earnings to a lower level of granularity, and link our future earnings estimates to our defined inputs. As you read through the modeling approach for each account, remember that the basic idea is to identify a driver of the value, and use it to project a forecast. Apple’s Income Statement accounts may differ from the company you ultimately create a model for; however, if you keep this fundamental concept in mind you should be able to develop a model for any company.

Step 5: Build the Earnings Engine Step 5a—Decide What Will Drive Your Earnings Forecast: In this section we will expand the modeling concepts we started in Step 4, Chapter 2. This part takes some judgment, and is probably the hardest part of the modeling process. You will need to decide what metrics you are going to use to build the future earnings forecast. For most companies, this will be based on the segment details provided in the press release. For others, you may need to run some calculations to develop the key drivers of your model. The most important thing is to incorporate any metric management includes in their guidance, since management’s focus indicates that the metric is a key driver of results. In addition, by including these metrics, you can calibrate the model to meet the guidance, or at least identify if your model estimates vary from management’s expectations. 41

The Apple earnings engine within our example model is relatively straight forward. Earnings are separated by product: 1) iPhones, 2) iPads, 3) Macintosh computers, 4) iWatch, 5) other products, and 6) service revenue. Each product’s revenue is disaggregated by units and Average Selling Price (ASP) per unit. This approach means that all we need to construct a future forecast for revenue are the unit sales, which is driven by year-over-year growth rates, and ASP for each product. Exhibit 11 below shows the iPad section of the earnings engine. Cells AP58 and AP59 are the input cells which are multiplied to arrive at total iPad revenue in cell AP55. The iPad revenue is then added to the other product revenue estimates to arrive at total net sales which flows up to the net sales line in cell AP11 at the top of the Income Statement.

42

Exhibit 11—iPad Section of Apple Earnings Engine

A sell-side analyst’s model would probably have greater detail in the estimates. They would likely break down each product within the subcategories above. For example, the iPad section would likely have subsections for the iPad Mini, iPad Pro, and iPad Air. They may even disaggregate each type of iPad by storage capacity, and provide estimates for gross margin of each category. This information can never be verified because Apple does not disaggregate their data at this granular level. For this reason, I usually do not disaggregate beyond the level of verification (or the ability to reconcile with past results). However, I will make an exception with the iWatch to demonstrate this point. Apple does not disclose unit and revenue results for the iWatch in their press release. Instead, iWatch sales are included in the “Other Products” category. In Exhibit 12 below, I have made assumptions for iWatch unit sales and ASP each quarter. I also make assumptions about the “Other Products” revenue growth rate (excluding my iWatch estimates) and make sure the sum of the two estimates equals the total reported “Other Products” revenue. To highlight the fact that the past estimates for the iWatch represent estimates, I keep these cells shaded in blue in the historic columns.

Exhibit 12—iWatch Unverifiable Model Estimate

43

1) Includes deferrals and amortization of related software upgrade rights and nonsoftware services. 2) Includes revenue from Digital Content and Services, AppleCare, Apple Pay, licensing and other services. 3) Includes sales of Apple TV, Apple Watch, Beats products, iPod and Applebranded and third-party accessories. Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

FAQ 2—Why should we bother disaggregating the results at all? You could apply a simple growth rate to top-line sales and not disaggregate results by segment, but remember, the point of modeling is to break the results down into smaller pieces to help us understand what the potential future outcomes could be. If you are analyzing Apple prior to a new iPhone release, wouldn’t you want the ability to run some sensitivity analysis on the number of iPhones sold and ASP ahead of the quarterly results? If your model does not break results down by product, you will not be able to do this.

44

Step 5b—Input Equations for Future Period Forecasts: Let’s revisit our estimates for iPhones, and focus on the equations used for the future forecast. Once you understand these equations it is easy to setup forecast columns for as many quarters as you would like. Remember we are using year-over-year growth rates in unit sales and average selling price per unit to project sales.

Table 3—Summary of Future iPhone Assumption Equations* Line Item

Driver (Assumptions)

Future Period (F3Q17E)

Equation

iPhone Units (cell AP42)

Growth rate (YoY)

F3Q16 units × (1+ growth rate)

iPhone unit growth rate (cell AP44)

Estimate

N/A – Direct Estimate Input

iPhone ASP (cell AP45)

Estimate

N/A – Direct Estimate Input

iPhone Revenue Forecast Units and ASP iPhones units × ASP (cell AP41) *Note that the cell references in Table 3 correspond to the Excel file in Exhibit 13 below. The growth rate used in the equations above represent the Year-over-Year (YoY) rate of change.

45

Exhibit 13—iPhone Future Period Forecast Equations

Notice that in Exhibit 13 I have entered equations in cells AP41 and AP42, which are driven by the hard-coded estimates in the blue cells AP44 and AP45 for unit growth rate and ASP. You may be wondering why we use these equations to back into the revenue estimate instead of just typing our revenue estimate in directly. The reason is that we are creating a “Working Model” (as discussed in Chapter 1). Using the equation based method will allow you to type in different estimates for the unit growth rate and selling price to instantly see the impact on earnings after the model is complete. The equation for units sold is: 1 + the year-over-year growth rate × the number of units from the comparable quarter of the previous fiscal year Once we have derived the total units for the quarter we can multiply the units by the ASP to arrive at total iPhone revenue (we divide by 1,000 since units are presented in thousands not millions). The equations for all the future periods are identical. Keep in mind the comparable quarter reference will change with each future quarter in the forecast, so ensure you are referencing the correct quarter to apply the growth rate. The equations are also identical for each product, so after you setup the iPhone forecast, you can copy and paste the rows for the other products. For this step in the process set the future forecast blue cell inputs equal to the last reported value. We will discuss how to choose the estimates for these cells in Chapter 5. Step 5c—Earning Engine Block: If you need help deciding how to breakout your company’s earnings, read the “Business” and “Management Discussion & Analysis” (MD&A) sections, and the Segment footnote (assuming the company operates in more than one segment), from the latest 10-K or 10-Q filing. In addition, check the press release and the company’s investor relations page for any quarterly metrics given by management to help you understand the best way to disaggregate earnings.

46

Step 6: Link Product or Segment Details Now that we have our sales estimates for each individual product from Step 5, we can link the total sales to the top line of the Income Statement. You can do this by writing an equation which references the sales of each product directly in Income Statement, or creating a subtotal in the section as I have done (shown in Exhibit 14 below). Be sure to add the total sales equations for each of the forecast quarters you are modeling, as we will be using the total revenue across the entire forecast horizon in Step 7.

47

Exhibit 14—Linking Product Details to the Income Statement

Step 7: Complete the Income Statement For some companies, you may decide to model other components of the Income Statement in the segment section. For example, if Apple disclosed gross profit for each product, then I would build that metric into the product details section of the model, and link the gross profit in the Income Statement to the sum of the gross profit in the product data section. Since Apple does not disclose any additional product details, the majority of the other Income Statement line item forecasts will be based on corporate average amounts, primarily driven by total revenue. In this step, I will walk through each line of Apple’s Income Statement and demonstrate how to write equations which will be used to forecast future results. Keep in mind, we are simply setting up the future period equations in this chapter, and will not be entering forecast estimates for the input cells until Chapter 5. For now, set the future forecast blue cell inputs equal to the last reported value as a placeholder until we cover calibration and forecasting in Chapter 5. Step 7a—Input Equations for Future Cost of Goods Sold Estimates: The next two lines on the Income Statement below sales, are cost of goods sold and gross profit. The Tier 2 approach for modeling these line items is consistent with the BOTE approach. First create a row in the ratio section for a gross margin assumption (refer to cell AP119 in Exhibit 15 below). Next, insert an equation in cell AP12 which uses the assumption for gross margin and the total sales estimate to derive cost of goods sold. This equation is net sales × (1 – gross margin percentage), or AP11 × (1AP119). After the equation for cost of goods sold is setup, we can calculate gross profit as revenue – cost of goods sold in cell AP13. Note that in Exhibit 15, I have calculated the historic results for the gross 48

margin in cell AO119 as gross profit divided by net sales. This historic detail will be critical for the forecasting chapter. Notice that the methodology of the historic and future period calculations are consistent; however, the flow of the equations is reversed. In the historic columns, the equations flow downward to calculate the ratios, while in the future columns, the equations flow upward based on the ratio assumption inputs. This is the same concept introduced in Step 4b, Chapter 2. It is a fundamental concept which you should get comfortable with prior to moving on to Step 37, Chapter 9 where we will be discussing how to update a model after a new quarterly earnings report is released, at which point you will need to shift the data flow.

49

Exhibit 15—Forecast Equation for Cost of Goods Sold

Pitfall: When modeling the Income Statement many new Associates will set assumptions for cost of goods sold as a percentage of total revenue since that is what we do for the other Income Statement line items. It may seem a bit counter-intuitive at first, but investors do not think in terms of cost of goods sold; instead, they focus on gross margin. To reflect this, we create an assumption for gross margin, and use an equation to back into the cost of goods sold on the Income Statement. The equation is Revenue x (1 – gross margin percentage). Step 7b—Input Equations for Future Operating Expense Items: Apple reports two operating expense line items: Research and Development (R&D), and Selling General & Administrative (SG&A) expenses. I use a slightly different approach to model these two lines compared to the cost of goods sold approach covered in Step 7a. Rather then modeling based on an operating margin percentage estimate, I model each independently using a percentage of sales ratio. This will give us some flexibility during the forecast step as R&D and SG&A have separate seasonality traits. SG&A tends to be more aligned with traditional calendar seasonality, and R&D tends to move with product release dates. Start by inserting two rows in the ratio analysis section, one for SG&A as a percentage of sales (refer to Exhibit 16 below, row 123) and one for R&D as a percentage of sales (row 124). Remember we are only entering placeholder estimates in the blue cells at this point (cells AP123 and AP124). Now insert the forecast equations in the Income Statement section. For example, the R&D forecast in cell AP14 equals net sales times the SG&A to sales ratio (AP14=AP11*AP123).

Exhibit 16—Forecast Equations for Operating Expense 50

Lines

Step 7c—Input Equations for Other Income: Companies usually have a few remaining accounts at the bottom of the Income Statement. In general, these items are not significant drivers of overall earnings; nevertheless, they must be incorporated into your model. For Apple, this account is “other income/(expenses)” which includes the interest and dividends Apple earns on investments, interest expense paid on debt, and any other items which do not fall within the other Income Statement line item classifications. With the Tier 2 modeling approach, we do not have forecasted balances for investment securities or debt, to use as a basis for estimating investment income or interest expense. Given this limitation, and the fact the line is not a driver of overall earnings, the best approach is to set the other income line equal to the average of the last few quarters, or the value guided by management. Step 7d—Input Equations for Tax Expense: Calculate an effective tax rate using historic tax expense, divided by the income before income taxes line on the Income Statement (refer to cell AO122 in Exhibit 17 below). In the future period columns, apply a similar effective tax rate to the forecasted pre-tax income, which is based on the other assumptions you have input into your Income Statement model.

Exhibit 17—Forecast Equation for Tax Expense

51

Step 7e—Input Equations for the Future Share Count Estimate: The share count forecast equations for the Tier 2 model are identical to the BOTE model. Refer back to Chapter 2, Step 4 for the details. My Experience: If you end up working in research at an investment bank you will most likely be required to submit your models into an automated system which checks for errors based on a series of calculations and comparisons. This can cause many validation problems, one of which is checking the sum of the four quarters of EPS against the annual total. If you use a simple average share count for the year-end column, you will most likely end up with a validation error. To avoid the issue, you can set the equation for the full-year share count to be a weighted average based on net income rather than a simple average. Step 7f—Calculate EPS: At this point you have all the inputs necessary to calculate Earnings Per Share (EPS), which is simply net income divided by shares outstanding. Table 4 summarizes the Tier 2 assumptions and calculation from net sales to EPS.

52

Table 4—Summary of Tier 2 Assumptions and EPS Calculation Line Item

Driver (Assumptions) Future Period Equation

Net sales

Product unit sales and ASP

Sum of individual product sales estimates

Cost of sales

Gross margin percentage

Sales × (1 - gross margin)

Gross profit

N/A (subtotal equation)

Net sales – cost of sales

R&D

R&D to sales ratio

Sales × R&D to sales ratio

SG&A

SG&A to sales ratio

Sales × SG&A to sales ratio

Total operating expenses

N/A (subtotal equation)

R&D + SG&A

Operating income

N/A (subtotal equation)

Gross profit – total operating expenses

Other income/(expense)

Direct input (historic average)

N/A

Income before taxes

N/A (subtotal equation)

Operating income – other income/(expense)

Provisions for income tax

Effective tax rate ratio

Income before tax × effective tax rate

Net income

N/A (subtotal equation)

Income before tax - provisions for income tax

Diluted shares outstanding

Repurchase assumptions & rate

Refer to template for details

Diluted EPS

N/A (subtotal equation)

Net income ÷ shares outstanding

Step 8: Adjust for Non-GAAP Items Step 8a—Incorporate Non-GAAP Adjustments into the Model: Many companies use non-GAAP adjustments to show what management 53

believes to be a more accurate representation of the company’s true earning capacity. Most non-GAAP adjustments reduce expenses for noncash items and nonrecurring gains or losses. Non-GAAP adjustments are relatively consistent between companies and tend to include adjustments for stock-based compensation, amortization of acquired intangibles, and the tax impact of these items. The adjustments are typically divided between reductions in costs of goods sold, reductions of operating expense, and adjustments to income tax expense, or directly to net income. Recently, there has been a shift in focus away from non-GAAP measures. For example, Facebook Inc, like many other technology companies, has historically focused guidance on non-GAAP measures. On the first quarter 2016 earnings conference call that approach changed: Before diving into our quarterly financial results I wanted to highlight that beginning this quarter, I will focus my prepared remarks on our GAAP results…The primary difference between our GAAP and non-GAAP metrics is stock-based compensation. Stock-based compensation plays an important role in how we compensate our employees, and therefore we view it as a real expense to the business. -David Wehner, Facebook Inc CFO, first quarter 2016 earnings conference call, April 27, 2016. Soon after Facebook’s announcement, Google parent Alphabet Inc took the GAAP movement a step further by removing the non-GAAP disclosures altogether: ...we are making changes to our non-GAAP reporting. SBC (Stock-Based Compensation) has always been an important part of how we reward our employees in a way that aligns their interests with those of all shareholders. Although it is not a cash expense, we consider it to be a real cost of running our business because SBC is critical to our ability to attract and retain the best talent in the world. Starting with our first quarter for 2017, we will no longer regularly exclude stock-based compensation expense from non-GAAP results. Non-cash stock-based compensation will continue to be reported on our cash flow statement, but we will no longer be providing a reconciliation from GAAP to non-GAAP measures that reflects SBC and related tax benefits. -Ruth Porat, Alphabet Inc CFO, Fourth quarter 2016 earnings conference call, January 26, 2017. Despite the recent shift in focus from some notable companies, non-GAAP reporting remains a common practice among the majority of U.S. public 54

companies. Therefore, it is important for new research associates to learn how to incorporate non-GAAP adjustments into their models. The key to successful non-GAAP modeling is to maintain the integrity of the GAAP financial statements, without having to create a separate nonGAAP Income Statement. To do this, add a row after each GAAP metric which the company posts an adjustment to, and run a set of equations down the Income Statement which incorporates all the non-GAAP items. Although it may appear that the GAAP and non-GAAP Income Statements are mixed together, the calculations are actually separate. This allows us to have two distinct bottom-line EPS estimates side-by-side, one on a GAAP basis the other non-GAAP. Apple discloses the allocation of stock-based compensation to various financial statement line items; however, the company does not officially publish non-GAAP earnings. Instead, I will use Adobe Systems Inc for this demonstration. Step 8b—Review the Company’s Non-GAAP Disclosure: Adobe’s nonGAAP items are relatively straight forward. In Adobe’s fiscal first quarter (period ending March 3, 2017), the company disclosed non-GAAP adjustments for stock-based compensation, amortization of intangibles, restructuring charges, investment gains and losses, and the tax impact of these items (refer to Exhibit 18 below). The company removes the impact of investment gains and losses since they are not part of the primary business. The impact of restructuring charges are also removed as they represent non-recurring items (there were no restructuring costs in the March-2017 quarter). Note that a non-GAAP gross margin is not included in Adobe’s disclosure. If it were, some of the operating expense adjustments would have to be allocated to cost of goods sold.

Exhibit 18—Adobe Systems Inc Non-GAAP Disclosure Example

55

Source: SEC.gov, Adobe Systems Inc 8-K, filing date March 16, 2017.

Step 8c—Reconcile the Company’s Non-GAAP Disclosure: At this point in the process you have completed the GAAP-based Income Statement in Step 7. The GAAP results will be used to reconcile the nonGAAP adjustments. Start the reconciliation process by adding a nonGAAP section below the segment details in your model, and list out each of the non-GAAP adjustments the company makes as I have done in rows 81 through 91 in Exhibit 19 below. Notice that I put the financial statement line impacted on each row for reference. Next, enter the amounts for the last quarter reported from the press release (refer to cells S82 through 56

S90). Now that you have all the non-GAAP items identified, you will need to link them to the Income Statement. Two Income Statement lines are impacted by adjustments in the Adobe example, operating income and net income. Insert rows after each line. Next, insert an equation to pick up the historic non-GAAP items. For example, cell S29 equals the non-GAAP adjustments for operating expenses (=S82+S86). The final step is to create an equation for non-GAAP net income (=S28+S31+S32-S35-S37) and non-GAAP diluted EPS (=S38/S40). Compare the resulting non-GAAP net income of $472M and EPS of $0.94 from your model calculation in Exhibit 19, to the results reported by the company in Exhibit 18. If they do not reconcile then you have made a mistake, and need to revisit your calculation. If the values from your model match the non-GAAP disclosure then you can move on to the next step.

57

Exhibit 19—Modeling Systems Inc Example

Non-GAAP

Items:

Adobe

Step 8d—Input Equations to Forecast Future Non-GAAP Adjustments: To predict the future period non-GAAP adjustment for stock-based compensation, I usually apply a quarterly average of the total stock-based compensation as a percentage of revenue (refer to cell T83 in Exhibit 19 above), or if the company gives specific guidance on the expectation for the next quarter I use the guidance. If the company allocates stock-based compensation to multiple line items, then use the historic allocation percentages in the future periods. If the company includes amortization of intangibles in their non-GAAP adjustments, the value tends to stay relatively consistent each quarter, so I usually set the future periods equal to the latest reported value. Other items typically are non-recurring in nature and generally difficult, if not impossible to predict.

58

CHAPTER 4: CONVERTING TO A TIER 1 EARNINGS MODEL Step 9: Complete the Historic Financials Step 10: Balance Sheet Modeling-Assets Step 11: Balance Sheet Modeling-Liabilities Step 12: Balance Sheet Modeling-Equity Step 13: Cash Flow Statement Modeling Step 14: Financial Statement Links Chapter 4 Overview: Understanding the relationship between the three financial statements is critically important for research associates. In this chapter, I will demonstrate how to model the Balance Sheet and Cash Flow Statement, and highlight the key links between the statements.

Step 9: Complete the Historic Financials In this step, you will need to bring the historic Balance Sheet and Cash Flow Statements from the SEC filings into your model. This is the same procedure performed for the Income Statement in Step 3 of Chapter 2. Remember to work your way backward in time, starting with the most recent filing, since the company may have made corrections after a previous release. Keep in mind that after each set of four quarters there is a column which represents the full-year. Do not hardcode the values in these columns. Instead, insert equations which represent the fiscal fourth quarter balance for the Balance Sheet, and the sum of the individual quarters for the Cash Flow Statement. Pitfall: First time research associates tend to have trouble with the Cash Flow Statement because most companies present it year-to-date, not quarter-to-date. As a result, to get the quarter cash flow for each line you will need to enter an equation to separate the cash flows for the quarter from the rest of the year, which is the year-to-date number minus the 59

previous quarters of the year. For example, to get the accounts receivable line for Apple’s fourth fiscal quarter ending September 2016 enter a positive inflow of $1.095B (which represents the full year cash flow) minus each cell for the first, second, and third quarters, which equals an outflow of $4.040B for the fourth quarter. Pay careful attention to the line descriptions, as they can move up or down the Cash Flow Statement from one quarter to the next.

Step 10: Balance Sheet Modeling—Assets As you read through this section, consider the fundamental concepts demonstrated in this step. The company you are building a model for may not have some of the same accounts as Apple. For example, if you are building a model for a bank or a social media internet company, you would not forecast inventory. Despite the difference in accounts from one company to the next, the basic modeling concept remains the same. For each account consider what the driver of the balance is, create a ratio using that driver, and forecast estimates using historic results as a guide, with adjustments for your future expectations. Step 10a—Cash & Marketable Securities: The first thing you should notice in the forecast section of the Balance Sheet is that the cash balance is an equation which links to the Cash Flow Statement. This is because all of the Balance Sheet changes in the model must be paid for with cash, or financed with debt or equity, and the Cash Flow Statement is where these decisions are reflected. If we did not link the Balance Sheet to the Cash Flow Statement, then our financial statements would be out of sync, and our Balance Sheet would not balance (remember assets must always equal liabilities plus equity). With this understanding of how the cash balance is determined through the Cash Flow Statement, we should also consider the balance of long-term and short-term marketable securities. In general, we are indifferent from a valuation standpoint to the balance being classified as cash and cash equivalents (i.e. money market securities), short-term investments, or longterm investments since our net cash per share calculation is added back to our final target share price in our multiple-based valuation (this will be discussed later in Chapter 8). The equation is: (Cash and equivalents + short-term marketable securities ​adjustments for deferred tax liabilities + long-term marketable securities - current portion of long-term debt - long-term debt) ÷ by diluted shares outstanding = net cash per share. 60

It is difficult to predict from one quarter to the next what the ratio of shortterm investments-to-total cash and investments will be, since the balance depends on management’s assessment of the short-term liquidity requirements, as well as the maturity profile of the existing investment portfolio. The total balance of cash and investments is the critical component in the equation, and this is determined through the full Balance Sheet assumptions which are incorporated into the Cash Flow Statement. Despite the fact that cash and investments are treated the same way from a valuation standpoint, we still must form an opinion of the breakout between the investment categories in the future to calculate the interest income on the Income Statement. To do this, I apply a quarter-over-quarter growth rate on total investments (refer to cell AP180 in Exhibit 20 below), and a ratio of short-term marketable securities to total investments (cell AP181) to get an approximate allocation between the current and noncurrent balances. If you look back through history, you will see that in general, the investment balance increases in the first and second fiscal quarters, and decreases in the third and fourth quarters. This trend is due to the fact that Apple’s sales generally increase in the first fiscal quarter (December quarter). This results in an inflow of cash, which management can invest. Some of the investments then mature in the third and fourth quarters when Apple is ramping its production and inventory ahead of the next holiday shopping season. I incorporate this seasonality into my future investment balance expectations by keeping similar quarter-over-quarter growth rates relative to each of the last four quarters.

61

Exhibit 20—Investment Balance Modeling

Step 10b—Accounts Receivable: Accounts Receivable (A/R) is the first of the working capital accounts which needs to be forecasted. Step one is to calculate the A/R turnover ratio for the historic periods as sales divided by average A/R. This calculation is shown in Exhibit 21 below in cell AO186, which equals AO13 divided by the average of AN146 and AO146. The A/R turnover ratio measures how many times the company collects on a receivable balance in each period. Next, divide the turnover ratio by the number of days in the quarter (calculated in cell AO187). The number of days represents how long it takes the company to collect its receivables. Notice that I use the average accounts receivable balance in the A/R turnover calculation. This is because the numerator, net sales, is an Income Statement account, whereas the denominator is a Balance Sheet account. Anytime a ratio mixes accounts from these two financial statements, the average of the Balance Sheet accounts should be used to reflect the fact that the Income Statement captures values over a period of time, whereas the Balance Sheet represents a distinct position at the end of the period.

62

Exhibit 21—The Historic Turnover Calculation

Accounts

Receivable

The next step is to use the historic A/R turnover ratio to predict the future A/R balance. The only cell that needs to be populated for this forecast is the A/R turnover. The equation in the accounts receivable line of the Balance Sheet will incorporate the turnover ratio and the sales estimate, which is a function of the assumptions entered in the Income Statement model from Chapter 3. Remember that we calculated the A/R turnover as the two-quarter average receivable. To reverse engineer the equation used for the forecast period, start with the historic equation and solve for the accounts receivable balance for the current quarter: A/R Equation—Step 1: A/R turnover = salesQ ÷ [(A/RQ + A/RQ1)/2]. A/R Equation—Step 2: Multiply both sides of the equation by [(A/RQ + A/RQ-1)/2]. A/R Equation—Step 3: Multiply both sides of the equation by 2. A/R Equation—Step 4: Divide both sides by the A/R turnover ratio. A/R Equation—Step 5: Subtract the A/R Q-1 from both sides of the equation. A/R Equation—Step 6: We are left with: A/RQ = (2 × salesQ ÷ A/R turnover) - A/R Q-1 63

This equation is entered in cell AP146 in Exhibit 22 below. Note that Q = the current Quarter, and Q-1 = the previous quarter.

Exhibit 22—Modeling Future Accounts Receivable

Pitfall: Seasonality is critical when determining future period A/R turnover assumptions. For example, Apple’s A/R turnover tends to increase in the December quarter when sales peak, and then decreases for the remainder of the year. Consider what would happen if you kept the A/R turnover ratio constant from the December peak throughout the year. The accounts receivable balance forecast for the next year would decrease significantly and the net cash per share would increase, causing an overstatement in share valuation. Given the potential impact of such assumptions, it is very important to consider the estimates made for the future working capital ratios. Step 10c—Inventory: Inventory is forecasted similar to A/R using the inventory turnover ratio, which is the number of times inventory is sold during the period. The first step in the forecast is to calculate the historic inventory turnover ratio as cost of goods sold divided by average inventory balance (refer to cell AO184 in Exhibit 23 below). If you plan on analyzing the cash conversion cycle, divide the turnover ratio by the number of days in the quarter. The number of days represents how long it takes the company to sell inventory. The next step is to use the historic inventory turnover ratio to predict the future inventory balance. The only cell which needs to be populated for this forecast is the inventory turnover (cell AP184). The equation on the 64

inventory line of the Balance Sheet (cell AP147) will incorporate the turnover ratio and the cost of goods sold estimate, which is based on the sales forecast and estimated gross margin from Chapter 3. Besides incorporating historic seasonality into the model, you can also consider adjustments for one-off events. For example, Apple typically gives details about channel inventory levels for each product. Inventory can be impacted by temporary supply and demand imbalances, and can fluctuate from one quarter to the next. If there is a significant increase or reduction in inventory one quarter, you may want to tweak your turnover ratio for the next quarter to get back to a normal inventory level.

65

Exhibit 23—Modeling Future Inventory

Step 10d—Vendor Non-Trade Receivable: This account includes inventory of product components which are purchased by Apple’s manufacturers. Vendor receivables tend to behave similar to the inventory balance with an increase in the September quarter, in preparation for the inventory build ahead of the holiday season. The balance typically remains high through December, then decreases in the March quarter. Similar to the forecast of inventory, a vendor receivable turnover ratio is calculated as cost of goods sold divided by average vendor non-trade receivable balance. The ratio is used with historic seasonality to project the future value of the balance. Step 10e—Property Plant and Equipment: Changes in the Property, Plant, and Equipment (PP&E) are driven by new purchases of capital assets and depreciation of existing assets. New PP&E purchases show up on the cash flow statement as “payments for acquisition of PPE” which is based on the capital expenditure (“capex”) estimates for future periods. Most companies give guidance for capex expectations for the next year, which you can incorporate into your model by adjusting the capex growth rate in the ratio section below the Cash Flow Statement.

Exhibit 24—Payments Equipment

for

66

Property,

Plant,

and

Depreciation estimates can be difficult to quantify since depreciation of PP&E and amortization of intangible assets are typically reported on the same line on the Cash Flow Statement. The account is usually disaggregated within the footnotes. For example, the details of accumulated depreciation for PP&E and amortization of acquired intangibles is included in Footnote 3: “Condensed Consolidated Financial Statement Details” and Footnote 4: “Acquired Intangible Assets” in Apple’s fiscal second quarter 2017 10-Q filing (refer to Exhibits 25 and 26 below).

Exhibit 25—Apple Inc 10-Q Footnote 3

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

Exhibit 26—Apple Inc 10-Q Footnote 4

67

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

There are two problems with the quarterly footnotes. First, compare them to the annual footnote for acquired intangibles below in Exhibit 27. Notice that in the annual filing the estimated amortization of intangibles is given, but this detail is not included in the quarterly filing. The second issue is that the carrying value changes over time through additional acquisitions, sales, and asset impairments. These events will change the accumulated depreciation or amortization, making it difficult to derive the related expense for an individual quarter.

Exhibit 27—Apple Inc 10-K Footnote 4

Source: SEC.gov, Apple Inc 10-K, filing date October 26, 2016.

To overcome these limitations in the quarterly filings, I use the following steps to estimate future amortization and allocate it between PP&E and acquired intangibles: Depreciation & Amortization—Step 1: Calculate the historic rate of depreciation and amortization expense (reported on the Cash Flow Statement) as a percentage of the total PP&E and acquired intangible assets reported on the Balance Sheet. Refer to Exhibit 28 below, cell AO193. Depreciation & Amortization—Step 2: Set the future rate of depreciation and amortization expense-to-average PP&E and acquired intangibles equal to the historic average of the last four 68

quarters. Refer to Exhibit 28, cells AP193 and AQ193. Depreciation & Amortization—Step 3: Set the total depreciation and amortization expense equal to the rate calculated in Step 2, times the prior period balance of PP&E and acquired intangibles. Refer to Exhibit 28 below, cell AP204. Depreciation & Amortization—Step 4: Input the future expected amortization of acquired intangibles based on the last 10-K filing. The estimated amount for fiscal 2017 is $1.197B, which comes from Footnote 4 (Exhibit 27). This amount is entered in cell AR202 in Exhibit 28. Note that the estimates given in the 10-K represent full year amortization. To get an approximate for each quarter, divide by four. Refer to Exhibit 28 cells AN202 through AQ202. Depreciation & Amortization—Step 5: Take the difference between the total depreciation and amortization expense (Exhibit 28 cell AP204) and the expected amortization (Exhibit 28 cell AP202). This difference will be applied to PP&E. Note that I shade the amortization of acquired intangibles blue even in the historic columns. I do this to remind myself that it represents an estimate and the company has not disclosed the figure. The only value that is known is total depreciation and amortization.

Exhibit 28—Modeling Depreciation and Amortization

Note that this process is an approximation of depreciation and 69

amortization. There are some shortcomings of the approach since it does not reflect new assets acquired with different useful lives, or impairment changes due to the disposal of specific assets. In addition, as the forecast moves further away from the last reported 10-K, the projection may drift further from the reality of the amortization estimate. Despite these limitations, the calculation should provide a reasonable estimate, and when a new 10-K is released, the estimate will be recalibrated to management’s latest forecast. Now that you have a firm understanding of how depreciation is allocated to PP&E, we can return to our estimate of the future PP&E balance. The equation is relatively straight forward. First, take the previous reported balance of PP&E (Exhibit 28, cell AO153), add to it the expected cash outflows for new PP&E acquisitions from the Cash Flow Statement (Exhibit 24, cell AP224). Then, subtract the estimate of depreciation calculated above (Exhibit 28, cell AP203). Step 10f—Goodwill: In general, goodwill is not amortized over time. Instead, it is tested periodically for impairment. Given the difficulty in estimating future impairments, I set the balance equal to the prior period balance plus the average quarter-over-quarter growth from the previous year. By applying this scaling factor, the balance will grow with the general growth of the Balance Sheet. Step 10g—Acquired Intangibles: Companies are permitted to amortize acquired intangibles with definite useful lives. The majority of Apple’s acquired intangibles (made up primarily of patents and licenses) fall into this category, and are amortized on a straight-line basis over periods of three to seven years. The current amortization expense schedule is included in Footnote 4: “Acquired Intangible Assets” as shown in the PP&E section above. After figuring out the quarterly amortization expense for intangibles, we also need to make an assumption for new acquisitions of patents and licenses over time. To do this, I take the average payments made for acquisitions of intangibles over the last four quarters from the Cash Flow Statement and apply the same average payment in the future (refer to Exhibit 29 cell AP225). After the cash flows are set, the Balance Sheet equation is simple: Take the prior period balance of intangibles (Exhibit 29, cell AO155), subtract amortization (Exhibit 29, cell AP202), and add new acquisitions (Exhibit 29, cell AP225). Note that since the acquisitions are coming from the Cash Flow Statement, we are actually subtracting a negative number because it 70

reflects the cash outflow.

Exhibit 29—Modeling Acquired Intangibles

Step 10h—Other Current and Noncurrent Assets: The other asset account is inherently difficult to predict because it is made up of different types of assets which cannot be isolated. For example, the fair value of derivative assets are recorded in other current assets, and derivative liabilities are recorded in accrued expenses. These derivative instruments are used to hedge changes in interest rates and currencies. Since the specific details of the underlying contracts are not disclosed, it is nearly impossible to predict the future balance. Rather then spend additional effort attempting to forecast the changes, I set the balance equal to the prior period balance plus the average quarter-over-quarter growth from the previous year. This is consistent with the approach used for goodwill. Remember that applying this scaling factor approach will allow the balance to move inline with the general growth of the Balance Sheet over time. Step 10i—Asset Modeling Overview: Before moving to the liability side of the Balance Sheet, here is a quick recap of the approach used for each asset account:

Table 5—Summary of Asset Modeling Assumptions Line Item Cash and

Driver (Assumptions) N/A 71

Future Period Equation Derived in the Cash Flow

Equivalents Short-term securities Growth rate and short-term to total investments ratio Accounts receivable Net sales and receivables turnover ratio Inventory Net sales, gross margin, and inventory turnover ratio Vendor non-trade Net sales and nonreceivables trade receivables turnover ratio Long-term securities Growth rate and STto-total investments ratio Property, Plant, & Capex growth rate, Equipment and depreciation estimate Goodwill Growth scaling factor Acquired Intangibles Average cash outflow for intangibles, and amortization Other Assets Growth scaling factor

Statement Prior quarter total investments × (1 + growth rate) × short-term to total investments ratio (Net sales ÷ receivables turnover ratio × 2) – prior quarter accounts receivable balance (Cost of goods sold ÷ inventory turnover ratio × 2) – prior quarter inventory balance (Net sales ÷ non-trade receivables turnover ratio × 2) – prior quarter accounts receivable balance Prior quarter total investments × (1 + growth rate) × (1 – short-term to total investments ratio) Previous quarter PP&E × (1 + capex growth rate) – depreciation estimate Previous quarter goodwill balance × (1 + comparable quarter growth rate) Previous quarter intangible balance + average cash outflow for intangibles – amortization Previous quarter other asset balance × (1 + comparable quarter growth rate)

Step 11: Balance Sheet Modeling—Liabilities Step 11a—Accounts Payable: This account is forecasted similar to accounts receivable, using the accounts payable turnover ratio, which is the number of times payables are collected during the period. The first step in the forecast is to calculate the historic payables turnover ratio as cost of goods sold divided by average payable balance (refer to Exhibit 30, cell AO188). Next, divide the turnover ratio by the number of days in the 72

quarter. The resulting number of days represents how long the company typically takes to pay back its vendors. Use the payables turnover ratio from the historic columns to predict the future accounts payable balance. The only cell which needs to be populated for this forecast is the payables turnover (Exhibit 30, cell AP188). The equation in the Balance Sheet (Exhibit 30, cell AP160) will incorporate the turnover ratio and the cost of goods sold estimate (which is based on the sales forecast and estimated gross margin), and will populate the future accounts payable balance.

73

Exhibit 30—Modeling Accounts Payable

Step 11b—Deferred Revenue: Revenue recognition is among the most important concepts for financial modeling since revenue is the basis for many of our future estimates. Most companies provide extensive details of their revenue recognition policies in the “Summary of Significant Accounting Policies” footnote in their SEC filings. Apple’s revenue recognition policy is fairly straightforward, as the majority of the company’s revenue comes from product sales, and is recognized at the time the product is shipped. There are some exceptions when payments are received before the criteria for revenue recognition has been met, which results in a deferred revenue liability on the Balance Sheet. The primary drivers of deferred revenue for Apple are: 1) Software Updates: A portion of product sales are allocated to future software updates. Revenue is recognized over the life of the product as the software updates are released. 2) AppleCare Services: Apple’s support service revenue is recognized over the life of the contract. 3) Gift cards: Payments are received for gift cards at the point of sale. Gift card revenue is not recognized until the recipient redeems the card for goods or services. Modeling Apple’s future deferred revenue balance may seem simple at first since the first two of the three drivers above are recognized over set time periods. However, consider what happens as the deferred revenue balance decreases in terms of accounting journal entries: The liability is 74

decreased with a debit entry, and the offsetting credit is booked to revenue. Unfortunately, our revenue model is driven by just two basic metrics, ASP and number of units, based on trends in historic results. If we decide to change the deferred revenue forecast in the future, we would have to increase revenue, which would cause a dislocation between our ASP × units revenue modeling technique, and deferred revenue on the Balance Sheet. Before jumping to any conclusions about the efficacy of this modeling approach, consider the fact that historic ASP results already include the recognition of prior period deferred revenue. Therefore, we can apply a ratio of average deferred revenue to total sales (I use the sales of the past four quarters), based on historic results to our future sales estimate. Refer to Exhibit 31 below for a demonstration of this calculation. The assumption is that the difference in the liability is already included in the future unit ASP estimate. The total deferred revenue then has to be allocated between the current portion, which is expected to be realized within one year, and the noncurrent portion. I allocate between current and non-current based on the historic allocation (Exhibit 31, cells AP191 and AP192).

Exhibit 31—Modeling Deferred Revenue

The balance of deferred revenue should be monitored over time to recognize any changes in the structure of the business (i.e. increases or decreases in service revenue or gift card sales) which could have an impact on future ASP estimates. Management also has some discretion over the allocation of sales between the product sold and future software updates, 75

which could give rise to earnings manipulation. As a result, changes in deferred revenue should be analyzed each quarter. Revenue recognition policies can be much more complex for different industries. For example, industrial equipment companies may sell very few pieces of equipment during a quarter. In such a scenario revenue recognition can be delayed and vary significantly from one quarter to the next, which can cause significant problems in forecasting results over time. In addition, customer testing may be required prior to acceptance of an order. If you are modeling earnings for a company in such an industry, you may choose to focus on orders or shipments instead of recognized revenue. Step 11c—Deferred Tax Liability Adjustments: Corporate Deferred Tax Liabilities (DTL) have been getting significant press coverage recently as tax reform efforts are currently underway in the United States which could have significant consequences on company valuations. This is an important topic for Apple, as well as any other company which has a large portion of cash held in foreign subsidiaries. International corporate tax rules are complex, but the concept of repatriation is relatively simple: In general, the U.S. corporate tax rate is 35%, which is higher than the rate in most other countries. When a U.S. company sells its products to foreign customers it must pay the income tax rate in that country. Then, if the company returns the earnings back to the parent in the U.S. it will receive a credit for the taxes paid in the foreign country, and will effectively pay the difference between the U.S. tax rate and the income tax rate of the country where the income was earned. To avoid the additional U.S. taxes (typically referred to as the repatriation tax) companies tend to leave the cash in the countries where it was earned. Apple estimates its Deferred Tax Liability (DTL) and discloses the amount in Footnote 5: “Income Taxes” within the 10-K annual filing in an account called “Deferred tax liabilities –Unremitted earnings of foreign subsidiaries” (refer to Exhibit 32 below). When valuing Apple shares, it is important to incorporate the repatriation tax impact to recognize the fact that the tax liability would have to be realized before the earnings could be distributed to shareholders as dividends, or otherwise reinvested by the parent company. Most analysts adjust the net cash per share to reflect this deferred liability. There is an extra layer of complexity with DTL reporting for Apple, since the company only reports the full breakdown of the gross DTL in annual filings (refer to Exhibit 32 below). In the quarterly SEC filings, the deferred tax footnote is reported net of Deferred Tax Assets (DTAs), 76

which makes it difficult to strip out the portion related to unremitted earnings of foreign subsidiaries (refer to Exhibit 33 below).

Exhibit 32—Apple DTA & DTL 10-K Footnote

Source: SEC.gov, Apple Inc 10-K, filing date October 26, 2016.

Exhibit 33—Apple DTA & DTL 10-Q Footnote

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

One way to overcome the limitation of the details provided in the quarterly filings, is to calculate the DTL as a percentage of the total cash held in foreign subsidiaries (Exhibit 34, cell AP249), and apply the same percentage for the next quarter. Once a year you can true-up the percentage when the 10-K is released, by updating the new reported results for the fiscal year. You can also use the same method to model the DTL for future periods. First, estimate the percentage of cash which you expect to be generated by foreign subsidiaries (Exhibit 34, cell AP247) and enter the percentage forecast of DTL to foreign cash and investments (Exhibit 34, cell AP249). You can use the historic trend to predict the future percentages. Setting up this section in your model will allow you to run sensitivity analysis to see what the impact would be if a favorable 77

repatriation tax reform decision is passed by Congress. For example, to see the impact of a complete repeal of the repatriation tax, set the DTL to foreign cash percentage equal to 0%. You can see that the estimated tax liability would change to $0, which would add approximately $6.83 to the share valuation.

Exhibit 34—Modeling Deferred Tax Liabilities

Keep in mind, this approach represents an approximation of the liability because corporations are not taxed on cash, rather on the foreign earnings which generated the cash. The taxable event is the remittance of the earnings (cash or investments) back to the parent. Also, the repatriation tax is effectively the difference between the U.S. income tax rate and that of the foreign tax authority multiplied by the foreign earnings, not the cash balance as the calculation above implies. Despite this fact, the ratio gives a rough approximation of the tax liability and is easy to apply. Feel free to incorporate any other DTAs or DTLs you think should be represented separately in the valuation of shares. Step 11d—Debt: Modeling a company’s future debt balance is difficult because changes in debt are driven by management’s decisions, which can be unpredictable. In addition, most companies have both fixed and floating rate debt, with varying maturities, denominated in multiple currencies, and hedges to protect against currency and interest rate fluctuations. In some cases, it may make more sense to spend the additional time and effort to model debt. For example, it would be more important to accurately model future debt balances for companies in the development stage, since cash burn would be a greater concern, compared to a well-capitalized, developed company. Similarly, if a company’s business is in decline, monitoring the debt balance would be extremely important as creditors could force the company into bankruptcy. Modeling Apple’s debt is less important since the company is in a 78

favorable financial position and has substantial cash and multiple sources of liquidity. Apple’s Balance Sheet is financed with commercial paper (short-term notes which mature within 90 days) and longer-term notes with maturities ranging from six months to 30 years. Apple’s debt is a mix of fixed and floating rate notes denominated in multiple currencies. The company discloses the details of debt balances in the financial statement footnotes, so if you choose to, you can model each debt category based on interest rate and maturity. I take a simplistic approach to modeling debt. First, I make the assumption that commercial paper will roll continuously in the future, and that the ratio of commercial paper-to-total debt will equal the historic average (refer to Exhibit 35, cell AP196). Next, I make the assumption that the company’s principal pay downs will be replaced with new debt over time, but that the company’s debt-to-equity ratio will stay consistent with the historic average (Exhibit 35, cell AP194). This assumption is important because it forces the debt balance to grow with the Balance Sheet over time. To understand this concept, consider what would happen to Apple’s future Balance Sheet if the debt balance was held constant. Equity would continue to grow through retained earnings, offset to some extent by share repurchases and dividend distributions. If debt was held constant, total leverage and interest expense would decline. More importantly the weighted average cost of capital would increase since debt would make up a smaller percentage of total capital. The required return on equity is much higher compared to the after-tax cost of debt. This would have a negative impact on share valuation. Keeping the debt-to-equity ratio constant solves this problem by allowing debt to grow with the Balance Sheet. Once the debt-to-equity and commercial paper-to-total debt assumptions are input into the model you can link the debt and commercial paper lines to the ratios to project the future balances (Exhibit 35, cells AP163 and AP166).

79

Exhibit 35—Modeling Debt

Step 11f—Remaining Liability Accounts: The remaining liability accounts including accrued expenses and other liabilities are difficult to forecast. I use the simplistic scaling factor approach discussed in Step 10h for these items.

Table 6—Summary of Liability Modeling Assumptions Line Item Accounts payable

Driver (Assumptions) Future Period Equation Cost of goods sold and (Cost of goods sold ÷ payables payables turnover ratio turnover ratio × 2) – prior quarter accounts payable balance Deferred revenue Net sales, deferred Net sales × deferred revenue to revenue to net sales net sales ratio ratio Deferred tax liability DTL to cash and Foreign cash balance × DTL to (for impact on cash investments ratio cash and investments ratio per share only) Debt Debt to equity ratio, Equity × debt to equity short-term debt to total (allocated to short-term and debt ratio long-term debt) Accrued expense and Growth scaling factor Previous quarter other asset other liabilities balance × (1 + comparable quarter growth rate) 80

Step 11g—Revisit the Income Statement: Now that we have established estimates for investments and debt, we can go back to the other income/(expenses) account from Chapter 3, Step 7c to disaggregate interest and dividend income from interest expense, and all other income and expenses. Keep in mind Apple’s Income Statement only reports one net line, however, the details are disaggregated in the company’s Footnote 3: “Condensed Consolidated Financial Statement Details” (refer to Exhibit 36 below). Using the footnote, we can disaggregate investment income from interest expense to provide a better forecast of future results, particularly, if we want to be able to run different interest rate scenarios in our future earnings analysis.

81

Exhibit 36—Apple Inc 10-Q Footnote 3

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

Since we have modeled the Income Statement and the Balance Sheet, we can use the future balances of investments to model interest and dividend income, and the balance of debt to model interest expense. Setting up the future period equations for these rows can be difficult. Take care not to reference the average asset balance, which can cause circular references in Excel. Start by calculating the interest and dividend income earned on average short-term and long-term investments for the past quarters (refer to Exhibit 37, cell AP131).

Exhibit 37—Modeling Investment Income

Your instinct is probably telling you that the next step is to set the future period equation equal to the interest and dividend income as a percentage of investments estimate in cell AQ131, times the average short and long82

term marketable securities balance in cells AP145, AP152, AQ145 and AQ152. If you set the equation up this way, it will cause a circular reference due to the links among the three financial statements. Instead, we can replicate the current quarter’s investment balances by applying the growth rate from the Balance Sheet ratio section (Exhibit 37, cell AP180) to the sum of the short-term and long-term marketable securities from the prior period. An alternative approach would be to ignore the current period investment balance and simply set the equation equal to the ratio times the previous period balance, without taking an average of the balance for the two quarters. In the Tier 1 model, I have structured the interest expense calculation this way to demonstrate. The equation is much more straight forward, but you may have to increase the future period assumptions slightly to offset the impact of not having the average of both quarters in the denominator, if there is a significant fluctuation in the investment balance from one quarter to the next. Interest expense is a bit easier to model. First, calculate the historic interest expense to average debt ratio (refer to Exhibit 38, cell AO132). Then, apply the interest expense to average debt ratio (Exhibit 38, cell AP132) to the forecasted debt balance (Exhibit 38, cells AP163 and AP166).

83

Exhibit 38—Modeling Interest Expense

For the remaining portion of the other income/(expenses) account I use a simple average applied to historic results. Give that there is no additional assumption driving this estimate, I enter the amount directly in the Income Statement section.

Step 12: Balance Sheet Modeling—Equity Step 12a—Common Stock: Common stock is impacted by Share-Based Compensation (SBC). In general, companies tend to withhold shares to cover employee taxes, and recognize a tax benefit when the shares vest. This will change with the adoption of Accounting Standard Update 201609, refer to Step 19f, Chapter 5 for details. For mature companies, the historic ratio of SBC to total sales should be a relatively good predictor of future SBC (after adjusting for any quarterly seasonality). In the case of Apple Inc, SBC is disclosed as it is awarded, but vests over a two to threeyear time frame. This reduces the variability in future projections. As a result, a simplistic ratio based on recent quarterly sales should provide a reasonable basis for future SBC (refer to Exhibit 40, cell AP256). One thing to remember when projecting this account, is that the SBC expense is already include in the Income Statement with allocations to cost of sales, research & development, and selling general & administrative expenses. Most companies disclose non-GAAP results and strip out SBC expense from the Income Statement. Although Apple does not officially report non-GAAP results, the company discloses the allocation in Footnote 9: “Benefit Plans” (Exhibit 39 below). Refer to Chapter 3, Step 8 for details on how to add non-GAAP adjustments to your earnings model. 84

Exhibit 39—Apple Inc Footnote 9

Source: SEC.gov, Apple Inc 10-Q, filing date May 3, 2017.

Since we are not stripping out SBC from the Income Statement, the forecast of SBC is used to predict the Balance Sheet and Cash Flow Statement impact only. The Cash Flow Statement is the only place SBC is presented, so I show the ratio used in the forecast under the cash flow ratios and assumptions section. Keep in mind that similar to payroll payments made to employees, equity awards have tax consequences. Apple receives a tax benefit for equity awards and records cash outflows for the related taxes paid. The impact of taxes varies from quarter-to-quarter. As a rough approximation, I model the tax benefit and taxes paid as a percentage of the total SBC expense (refer to Exhibit 40, cells AP257 and AP258). The final common stock balance is equal to the previous quarter’s balance plus SBC, including the related taxes (Exhibit 40, cell AP172).

Exhibit 40—Modeling SBC and Common Stock

85

Step 12b—Retained Earnings: This account represents the net accumulated earnings over time which have not been distributed to shareholders. The future period calculation of retained earnings is the beginning balance (meaning end of the last reported period’s balance) plus net income for the current period, minus dividends paid, minus current period stock repurchases, equals ending retained earnings (refer to Exhibit 41, cell AP173). Notice that in the equation the cell references appear to be adding back dividends and repurchases. This is due to the fact that the referenced cells are within the Cash Flow Statement where dividends and repurchases are reported as negative values to reflect the fact that the amounts represent cash outflows, so the equation is “adding” negative values, and therefore reducing retained earnings.

86

Exhibit 41—Modeling Retained Earnings

Step 12c—Dividends: Over the last two years, Apple’s dividend growth rate has average approximately 10% year-over-year. For future dividend modeling, I apply the same 10% growth for the next five years. Given Apple’s cash balance, there is no reason to believe the company’s dividend will be cut; however, the future dividend policy may be something to consider if you are modeling a company in a different financial position. The dividend per share is shown at the bottom of the Income Statement in the Tier 1 and Tier 2 models, which is consistent with the presentation in the company’s SEC filings. Future period dividend payments are reflected in the Cash Flow Statement, and reduce retained earnings on the Balance Sheet as described above. Step 12d—Accumulated Other Comprehensive Income (AOCI): Changes in AOCI are typically driven by currency translation, changes in fair value of derivative contracts classified as cash flow hedges, and unrealized gains and losses on available-for-sale securities. All three of these items are extremely difficult to predict. Given this fact and the immateriality of the AOCI balance relative to the rest of shareholders equity, I set the future period AOCI equal to the last reported period. Step 12e—Off-Balance Sheet Commitments: Some companies have substantial obligations reported off-Balance Sheet, which you may choose to bring onto the Balance Sheet in your analysis. Apple’s off-Balance Sheet exposure is primarily related to manufacturing purchase obligations, operating leases, and equipment and other asset purchase obligations. One of the fundamental assumptions we make in our attempt to model Apple’s future financial statements is the “going concern” notion, which 87

means we expect Apple to continue to operate as an entity for the foreseeable future. Given this concept, we can assume that Apple will continue to manufacture products using the equipment and raw materials for which it has made purchase commitments. In addition, we expect Apple to continue to lease retail space and other facilities as required. As these contracts materialize in the future, the financial statements will reflect the associated costs and liabilities of the transactions. Therefore, there is no need to adjust the financial statements for these off-Balance Sheet exposures at this time.

88

Table 7—Summary of Equity Modeling Assumptions Line Item Common Stock Retained Earnings

AOCI

Driver (Assumptions) Stock-Based Compensation (SBC) Dividends and share repurchases N/A

Future Period Equation Prior quarter balance + SBC Prior quarter balance + net incomes – dividend distributions – share repurchases Prior quarter balance

Step 13: Cash Flow Statement Modeling Cash Flow Statement modeling is more important for some companies and less important for others. Apple has plenty of cash, so a cash shortfall is less likely than for a developing company. For example, Tesla Motors is ramping new production lines, and burning through substantial cash each quarter. Using a full Tesla financial statement model, we can change the assumptions for earnings, operating ratios, and capital expenditures, to determine if the company will require additional debt or equity to fund its growth. In the Apple model, the main reason for forecasting the Cash Flow Statement is to arrive at a free cash flow estimate to use in a Discounted Cash Flow (DCF) valuation calculation. The Cash Flow Statement is relatively easy to model, since it essentially represents estimates already included in the Income Statement and Balance Sheet. The following steps show the references where each of the Cash Flow Statement lines are calculated. Step 13a—Net Income: The Cash Flow Statement begins with the net income from the Income Statement. For the future period estimates set the cells in row 201 equal to row 27.

Exhibit 42—Net Income in the Cash Flow Statement

89

Step 13b—Depreciation and Amortization: The calculation for depreciation and amortization is covered in Step 10e as part of the calculation of PP&E and acquired intangibles. Step 13c—Share-Based Compensation: This calculation is covered in Step 12a with the equity section of the Balance Sheet. Step 13d—Changes in Operating Assets and Liabilities: This section calculates the changes from one quarter to the next, for all the company’s assets and liabilities, other than those classified as investing or financing activities. For example, the difference between the fiscal third quarter accounts receivable balance (Exhibit 43, cell AP146), and the fiscal second quarter (Exhibit 43, cell AO146), equals the accounts receivable cash flow in the operating activities section of the Cash Flow Statement (Exhibit 43, cell AP210). Notice that since the third fiscal quarter is greater than the second quarter, the cash flow is negative, which indicates that the increase in the asset represents a cash outflow. The majority of the lines on the Balance Sheet have a corresponding line on the Cash Flow Statement. If there is not a specific line on the Cash Flow Statement, then add the amount to one of the “other” categories. Step 13e—Deferred Income Taxes: In Chapter 4, Step 11c, we discussed deferred tax assets and liabilities, which include operating loss and tax credit carryforwards, as well as temporary differences between financial reporting and tax reporting. These tax assets and liabilities are reported net in the “other asset” line on the Balance Sheet. Since we already take the change in the other assets and liabilities on a net liability basis on row 216 (refer to Exhibit 43), we do not have to breakout deferred income taxes separately, which is why this row is not projected in the future columns within the Cash Flow Statement.

Exhibit 43—Operating Assets and Liabilities in the Cash Flow Statement 90

Step 13f—Changes in Investing Activities: This section of the Cash Flow Statement shows the changes in the balance of marketable securities (refer to Step 10a), PP&E (refer to Step 10e), and intangibles (refer to 10g, calculation equals the average cash outflows from the past four quarters). Notice that I put the full change in marketable securities within the “purchases of marketable securities” row, rather than breaking out the changes among purchases, maturities, and sales. These classifications are almost impossible to forecast, and they have no impact on the results of the model, so there is no need to disaggregate the changes.

Exhibit 44—Investing Activities Section of the Cash Flow Statement

91

Step 13g—Changes in Financing Activities: The financing section of the Cash Flow Statement shows inflows and outflows of cash to fund changes in the Balance Sheet, primarily related to debt and equity issuance. For Apple, this section is made up of the following items: 1) Tax Implications of Stock-Based Compensation (SBC) Awards: Refer to Step 8d, Chapter 3 for the calculation of SBC expense. To derive the excess tax benefit and taxes paid on SBC, I apply a ratio of these two tax implications as a percentage of total SBC. This will change with the adoption of Accounting Standard Update 2016-09, discussed in Step 19f for details. 2) Dividend Payments: Dividends are represented on the Balance Sheet through retained earnings (refer to Step 12c). The calculation for dividend payments in the Cash Flow Statement is shares outstanding times dividend amount per share. 3) Share Repurchases: Repurchases were calculated in Step 4a, Chapter 2. The total dollar amount of repurchases are represented in the financing section of the Cash Flow Statement. 4) Debt Issuance: The balances of debt and commercial paper were calculated in Step 11d. The cash inflow or outflow related to these accounts is calculated as the difference from one quarter to the next. For example, the proceeds from issuance of debt in Exhibit 45, cell AP235 is calculated as the fiscal third quarter 92

projected debt balance ($15.425B, cell AP163), minus the balance from the previous quarter ($13.991B, cell AO163), which equals $1.793B. 5) Proceeds from Issuance of Common Stock: In my model, I have not included estimates for future equity issuance since I believe it is unlikely that Apple will need to raise funds through common stock. If you are modeling a company which will need to raise funds through an offering of equity, you should include the expected proceeds on the Cash Flow Statement, adjust equity on the Balance Sheet, and increase the number of shares used in the calculation of EPS on the Income Statement. The remaining “other” category represents changes in deferred revenue, which was calculated in Step 11b. Note that had we projected a common stock issuance, the funds raised by the offering would also be reported in the financing section.

Exhibit 45—Financing Activities Section of the Cash Flow Statement

93

Step 14: Primary Financial Statement Links After the Income Statement, Balance Sheet, and Cash Flow Statement are complete, take a minute to review how the three statements are interrelated. The following is a recap of the primary links. The cell references correspond to the Tier 1 Excel model template included with this book (visit www.GutenbergResearch.com/book to download) or refer to the Tier 1 model in Exhibit 6 from Chapter 1. Retained Earnings: GAAP net income (Income Statement, cell AP27 from Exhibit 6 in Chapter 1) flows through retained earnings (Balance Sheet, cell AP173, Exhibit 6). Dividends (Cash Flow Statement, cell AP231, Exhibit 6) are also paid out of retained earnings (Balance Sheet, cell AP173, Exhibit 6), and if the company does not disclose treasury 94

shares separately in the equity section, we subtract share repurchases (Cash Flow Statement, AP232, Exhibit 6) from this account. The calculation starts with the beginning retained earnings balance of $100.925B (cell AO173, Exhibit 6), plus net income of $8.142B (cell AP27, Exhibit 6), minus dividends of $3.208B (cell AP231, Exhibit 6), minus share repurchases of $5.000B (cell AP232, Exhibit 6), equals the ending retained earnings balance of $100.859B (cell AP173, Exhibit 6). Income Statement Links to the Cash Flow Statement: GAAP net income (Income Statement, cell AP27, Exhibit 6) is the starting point for the Cash Flow Statement (cell AP201, Exhibit 6). Since the starting point for the Cash Flow Statement is the ending point of the Income Statement, all of the assumptions made to the future earnings forecast will impact the projected cash flows. Balance Sheet Links to the Cash Flow Statement: All of the changes in the Balance Sheet accounts are reflected throughout the Cash Flow Statement. For example, in the projected fiscal third quarter, inventory (Balance Sheet, cell AP147, Exhibit 6) decreased from $2.910B to $1.996B. The decrease in the asset balance represents a cash inflow of $0.914B, and is reflected in the Cash Flow Statement (cell AP211, Exhibit 6). Cash Flow Links to the Balance Sheet: The Cash Flow Statement will add all the inflows and outflows of cash (cell AP237, Exhibit 6) to the beginning cash balance (cell AO239, Exhibit 6), to arrive at the ending cash balance (cell AP239, Exhibit 6) which flows to the Balance Sheet (cell AP144, Exhibit 6). This is the final link which ensures that the assets reported on the Balance Sheet equal the liabilities plus equity.

95

CHAPTER 5: MODEL CALIBRATION & FORECASTING Step 15: Incorporate Historic Trends Step 16: Adjust for Seasonality Step 17: Adjust for New Product Launches Step 18: Guidance and the Consensus Step 19: Incorporate Your Opinions Step 20: Consider Leading Indicators Chapter 5 Overview: This chapter discusses how to incorporate future estimates for the assumption cells, which are shaded blue. I typically keep two versions of models for companies I cover. One version which is calibrated to meet management’s guidance and consensus estimates (baseline-type of scenario). The second version incorporates my own opinions about the company. This allows me to easily compare my opinion to the consensus. In this chapter, I will demonstrate how to calibrate your model, and develop your own forecast.

Step 15: Incorporate Historic Trends This step should be performed first, prior to considering management’s guidance, or any other factors, to prevent yourself from developing a bias in your analysis. By extending past trends into the future, you will force yourself to question deviations from recent history. You can skip this step and jump to developing your own forecast, but if you do, you may have a hard time recognizing changes from historic trends in your results. As you perform this step, remember that history does not always repeat itself and there could be very good reasons for why future results differ from historic trends. If this is the case, you should be prepared to articulate the reasons. Starting with a trend analysis will help you accomplish this. 96

In this section, I will demonstrate how to incorporate historic results into the model using iPhone unit sales and Average Selling Price (ASP) trends as an example. As discussed in Step 5b, Chapter 3, we use forecasts of year-over-year unit sales growth rates and ASP to estimate future iPhone revenue. Without applying any judgement, we can enter an equation which will incorporate the same movement experienced in the comparable fiscal period applied to the last reported results. For example, if we want to forecast the iPhone unit growth rate for the third fiscal quarter of 2017 (Exhibit 46, cell AP50), we could set the equation equal to the growth rate in the second quarter (cell AO50), plus the change in growth rate between the second and third fiscal 2016 quarters (cell AK50 minus cell AJ50). After you have this equation set, you can copy and paste the formula into all of the future quarter cells. As you do this, take care not to reference the fiscal year-end columns in your future forecast cells. After you complete this step for iPhones, repeat the same process for the remaining products, and the other Income Statement line items which require assumptions (i.e. operating expenses, other income, and tax rate).

Exhibit 46—Incorporating Forecast

Seasonality

into

the

Step 16: Adjust for Seasonality The forecasting approach from Step 15 will automatically include the effect of seasonality, assuming the previous year did not have any nonrecurring items included in the reported results. You should review the general trend of the projections, and compare it to the past few years to see that the seasonal trend is reasonable. For example, Apple iPhone unit sales tend to spike during the holiday shopping season in the first fiscal quarter (December quarter). After you have completed Step 15, check to make sure this trend is reflected in your assumptions. If not, it could be an 97

indication of an error in the forecast, or evidence that the previous year included a one-off event which may require an adjustment to the future period assumptions.

98

In Exhibit 47 below, you can see that the results in our forecast are inline with the historic seasonality for iPhone unit sales as we see a peak in the December quarter, followed by a decline in the March quarter.

Exhibit 47—Reviewing the Effects of Seasonality

Step 17: Adjust for New Product Launches New products can disrupt the usual historic seasonality, so it is important to incorporate adjustments to reflect these events. In the case of Apple, product launches typically occur in predictable intervals. For example, new iPhones are usually released every two years, with a refresh one year after the initial launch. Sometimes it is helpful to include a product heat map within an earnings model to compare sales trends to product release dates. The following section compares the year-over-year iPhone unit growth rates to recent iPhone launch dates: iPhone 6: The iPhone 6 was released in September of 2014. The first full quarter of sales after the release was December 2014. During the December 2014 quarter, unit sales increased 46% year-over-year (Exhibit 48, cell AD50). In the following year, unit sales faced a difficult comparison. The year-over-year growth rate in December 2015, dropped to below 1% (Exhibit 48, cell AI50)). This demonstrates the importance of incorporating estimated product release dates into your future period forecast.

Exhibit 48—Incorporating New Product Launch Dates (iPhone 6)

99

iPhone 7: Of course, there are exceptions to every forecast. The iPhone 7 was released in September of 2016. The first full quarter of sales after the release was in the December 2016 quarter, which saw a year-over-year growth rate of just 5%. This was a better rate compared to that of December 2015 at less than 1%; however, it was far from the rate achieved with the iPhone 6 release. There are a number of factors which may have contributed to the iPhone 7’s results. The iPhone SE launch in March of 2016 could have pulled some sales forward. Some customers may have seen the iPhone 7’s features as marginal relative to the iPhone 6, and may be waiting for the iPhone 8 before upgrading their devices. Or, there could have been a number of macroeconomic factors involved. Despite the variability in results from one product release to the next, it is critically important to consider these events in your future earnings expectations.

Step 18: Guidance and the Consensus Most companies offer earnings guidance for the next fiscal period in their quarterly earnings press release, or on their quarterly earnings conference calls. Apple gives guidance for revenue, gross margin, operating expenses, other income, and tax rate. Refer to the bottom section of Apple’s fiscal second quarter press release in Exhibit 49 below, for details. Apple only provides guidance for five metrics; however by incorporating a few basic assumptions, you can calculate the full Income Statement including and EPS estimate, implied by management’s guidance. For example, the midpoint of management’s revenue guidance is $44.5B. The midpoint of management’s gross margin guidance is 38%. This implies that cost of goods sold will be $27.6B, which is (1 – 38%) × $44.5B. If the company you are covering is valued using a Price-Earnings ratio, then the implied EPS guidance is likely the most important metric within the press release. To derive it, you will need to make an assumption for the 100

next period’s share count, which in the case of Apple, management does not provide guidance for. Refer to Step 4, Chapter 2, for a description of how to do this. After you have your share count estimate, use the following steps to calculate the EPS implied by management’s guidance: 1) Start the calculation with the midpoint of management’s revenue guidance ($44.5B) 2) Multiply by the midpoint of management’s gross margin estimate (38%) 3) Subtract the midpoint of management’s operating expense guidance ($6.65B) 4) Add management’s other income guidance ($0.45B) 5) The result represents the pre-tax income. Multiply by (1 – management’s guided tax rate of 25.5%) 6) Divide by your share count estimate (5.153B) 7) EPS implied by management’s guidance = $1.55 per share

Exhibit 49—Apple Fiscal Second Quarter Press Release

Source: SEC.gov, Apple Inc 8-K, filing date May 2, 2017.

101

Remember that guidance can come in the direct form, such as in the press release for Apple, or in an indirect form, such as in a discussion at an investor conference. Indirect guidance tends to be focused on long-term strategic direction rather than short-term financial ranges. For example, on Apple’s fiscal first quarter conference call, management mentioned that they expect other service revenue to double in four years. If you only read the press release and did not listen to the conference call you may have missed this important point: Our services offerings are now driving over 150 million paid customer subscriptions. This includes our own services and third-party content that we offer on our stores. We feel great about this momentum, and our goal is to double the size of our Services business in the next four years. -Tim Cook, Apple Inc CEO, Apple first quarter 2017 earnings call, January 31, 2017. Most analysts place a high reliance on guidance since management has access to non-public information about the company. In addition, it usually takes about a full month for a company’s financial reporting team to consolidate the financial statements before releasing the details to the public. This means that one-third of the next reporting period has already passed by the time they release their guidance, which gives management relatively good visibility into how the next quarter is developing. Step 18a—Can We Trust Management’s Guidance: If management gives misleading guidance, they will have to answer to analysts and investors at the end of the quarter. Of course, there could be short-term events, such as a product release or debt/equity offerings, which may give management reason to be more optimistic in their guidance. In general, we should expect management to act in good faith. However, this does not mean we should accept their guidance blindly. Before I rely on management’s guidance, I review their track record. Table 8 below shows a breakdown of Apple’s guidance for revenue, gross margin, operating expenses, other income, and tax rate over the last five years. Table 9 shows Apple’s actual reported results for the same periods. In general, the reported results have been reasonably close to the guidance provided by management in the prior quarter’s press release. Revenue results have beaten the guided range 9 out of the last 16 quarters, came in at the high-end of the range five times, and the low-end just two times. This may imply that management tends to be a bit conservative in their guidance estimates. Gross margin guidance follows a similar trend. 102

Operating margin guidance tends to have a tighter range with lower variability in dollar terms. Management gives a specific dollar amount for other income, rather than a range. Given the lower materiality of this line, it is less important compared to the other metrics. Tax rates are difficult to predict. In general, Apple’s tax forecast has been relatively accurate over time.

103

Table 8—Apple’s Historic Guidance Fiscal Quarter

Date Published

Revenue Range

$41B to $43B $33.5B to F3Q2013 4/23/2013 $35.5B $34B to F4Q2013 7/23/2013 $37B $55B to F1Q2014 10/28/2013 $58B $42B to F2Q2014 1/27/2014 $48B $36B to F3Q2014 4/23/2014 $38B $37B to F4Q2014 7/22/2014 $40B $63.5B to F1Q2015 10/20/2014 $66.5B $52B to F2Q2015 1/27/2015 $55B $46B to F3Q2015 4/27/2015 $48B $49B to F4Q2015 7/21/2015 $51B $75.5B to F1Q2016 10/27/2015 $77.5B $50B to F2Q2016 1/26/2016 $53B $41B to F3Q2016 4/26/2016 $43B $45.5B to F4Q2016 7/26/2016 $47.5B $76B to F2Q2013 1/23/2013

Gross Margin Range 37.5% to 38.5% 36% to 37% 36% to 37% 36.5% to 37.5% 37% to 38% 37% to 38% 37% to 38% 37.5% to 38.5% 38.5% to 39.5% 38.5% to 39.5% 38.5% to 39.5% 39% to 40% 39% to 39.5% 37.5% to 38% 37.5% to 38% 38% to

104

Operating Expense Range $3.8B to $3.9B $3.85B to $3.95B $3.9B to $3.95B $4.4B to $4.5B $4.3B to $4.4B $4.4B to $4.5B $4.75B to $4.85B $5.4B to $5.5B $5.4B to $5.5B $5.65B to $5.75B $5.85B to $5.95B $6.3B to $6.4B $6B to $6.1B $6B to $6.1B $6.05B to $6.15B $6.9B to

Other Income $350M $300M $200M $200M $200M $200M $250M $325M $350M $350M $400M $400M $325M $300M $350M

F1Q2017 10/25/2016 F2Q2017 1/31/2017 F3Q2017 5/2/2017

$78B

38.5%

$7.0B

$51.5B to $53.5B $43.5B to $45.5B

38% to 39% 37.5% to 38.5%

$6.5B to $6.6B $6.6B to $6.7B

105

$400M $400M $450M

Table 9—Apple’s Results Relative to Guidance Fiscal Quarter

Date Published

Reported Revenue

37.5% (Low-end) 36.9% F3Q2013 7/23/2013 (Beat) 37.0% F4Q2013 10/28/2013 (High-end) 37.9% F1Q2014 1/27/2014 (Beat) 39.3% F2Q2014 4/23/2014 (Beat) 39.4% F3Q2014 7/22/2014 (Beat) 38.0% F4Q2014 10/20/2014 (High-end) 39.9% F1Q2015 1/27/2015 (Beat) 40.8% F2Q2015 4/27/2015 (Beat) 39.7% F3Q2015 7/21/2015 (Beat) 39.9% F4Q2015 10/27/2015 $51.5B(Beat) (Beat) $75.9B 40.1% F1Q2016 1/26/2016 (Low-end) (Beat) $50.6B 39.4% F2Q2016 4/26/2016 (Low-end) (High-end) $42.4B 38.0% F3Q2016 7/26/2016 (High-end) (High-end) $46.9B 38.0% F4Q2016 10/25/2016 (High-end) (Low-end) 38.5% F1Q2017 1/31/2017 $78.4B(Beat) (High-end) F2Q2013

4/23/2013

$43.6B (Beat) $35.3B (High-end) $37.5B (Beat) $57.6B (High-end) $45.6B (Beat) $37.4B (High-end) $42.1B (Beat) $74.6B (Beat) $58.0B (Beat) $49.6B (Beat)

Reported Gross Margin

106

Operating Other Expense Income $3.8B (Beat) $3.8B (Beat) $3.8B (Beat) $4.4B (Beat) $4.4B (High-end) $4.5B (High-end) $4.8B (High-end) $5.5B (High-end) $5.4B (Beat) $5.6B (Beat) $5.9B (High-end) $6.3B (Beat) $5.9B (Beat) $6.0B (Low-end) $6.1B (Low-end) $6.8B (Beat)

$0.3B (Miss) $0.2B (Miss) $0.1B (Miss) $0.2B (Beat) $0.2B (Beat) $0.2B (Beat) $0.3B (Beat) $0.1B (Miss) $0.3B (Miss) $0.4B (Beat) $0.4B (Beat) $0.4B (Beat) $0.2B (Miss) $0.4B (Beat) $0.4B (Beat) $0.8B (Beat)

$52.9B (High-end)

38.9% (Beat)

$6.494 (Beat)

$0.6B (Beat)

F2Q2017

5/2/2017

F3Q2017

Fiscal third quarter results have not been released prior to the publishing date of this edition.

107

Step 18b—Calibrate Your Model to Include Management’s Guidance: Now that we have determined we can trust the guidance Apple publishes, our next step is to set the upcoming fiscal quarter estimates to meet this guidance. Typically I set my results equal to the midpoint of management’s guidance. If you believe management tends to be conservative or aggressive with their guidance, then you may choose to set your results at the high-end or low-end of the range. Notice that Apple does not give specific guidance for each product, so in order to get back to management’s total revenue estimate you will need to make a judgement call as to which product you will adjust, and which metric (ASP or unit growth). I tend to select the product which contributes the most to total revenue, which would be the iPhone in Apple’s case. Also, I usually use the unit growth rate instead of ASP since there tends to be greater variability in unit sales. Now all you need to do is change the growth rate until your revenue estimate falls within management’s guided range. To keep track of which metrics I have calibrated to meet management’s guidance, I shade the cells purple, and insert a comment with the full details of the range given by management.

108

Exhibit 50—Calibrate Management’s Guidance

the

Model

to

Meet

Step 18c—Calibrate the Model to Meet Consensus Estimates: The consensus or average estimates among analysts for revenue and EPS are published for free from many different sources (i.e. Nasdaq, Google Finance). Additional details, such as gross margin, operating margin, and EBIDTA estimates are available with premium subscriptions (i.e. Bloomberg, FactSet, Reuters). It may seem counterintuitive to calibrate your model to meet the consensus estimates since the point of having your own model is to make your own assumptions, but it is helpful to see where your estimates deviate from the consensus. For this reason, you should perform this calibration step prior to adding your own assumptions. It is also useful to have a consensus-based model as a baseline view, which can be utilized to perform sensitivity analysis ahead of an earnings release. When considering using consensus estimates in an earnings model it is important to understand the inherent limitations of the data. For example, not all analysts enter estimates for each published metric. As a result, some individual financial statement line items may look distorted relative to the consensus top-line revenue, or bottom-line Earnings Per Share (EPS) estimates. To demonstrate this concept consider Table 10 below which shows the Apple consensus estimates for various Income Statement line items retrieved from multiple sources. The table shows Apple’s reported results for the first two fiscal quarters of 2017, the consensus estimates for the fiscal third and fourth quarters, and the next full fiscal year 2017 estimates. Table 11 shows the number of analysts who have submitted 109

estimates to be included in the consensus.

110

Table 10—Consensus Estimates for Apple Inc Account Net sales ($M) Gross margin (%) Operating profit ($M) Pre-tax profit ($M) Net income ($M) Earnings Per Share (EPS, $)

F3Q17 F4Q17 FY2017 Consensus Consensus Consensus Estimate Estimate Estimate $44,972 $50,747 $227,526 38.2% 38.3% 38.6%

F1Q17 Actual $78,351 39.5%

F2Q17 Actual $52,896 38.9%

$23,359

$14,097

$10,584

$12,557

$60,692

$24,180

$14,684

$10,987

$13,022

$63,185

$17,891 $3.36

$11,029 $2.10

$8,241 $1.58

$9,759 $1.90

$47,096 $8.95

Table 11—Number of Analysts Who Submitted Estimates Account Net sales ($M) Gross margin (%) Operating profit ($M) Pre-tax profit ($M) Net income ($M) Earnings Per Share (EPS, $)

F3Q17 # F4Q17 # FY2017 of of # of Analysts Analysts Analysts 37 37 44 28 28 33 29 29 35 31 32 37

31 32 37

40 41 43

Notice that only 28 analysts have enter fiscal third quarter estimates for gross margin and 29 entered estimates for operating profit, which is far fewer than the 37 analysts who have entered estimates for revenue and EPS. Consider what would happen if you attempt to Model Apple’s fiscal third quarter based on the consensus revenue estimates, then use the consensus operating margin estimate and reasonable assumptions for other income and taxes, you may not get back to the consensus EPS estimate. 111

This is due to the fact that the consensus estimates do not reflect the full earnings model of each analyst who participated in the earnings survey. To demonstrate this point, let’s walk through the third fiscal quarter estimates, and compare the consensus-based model to management’s guidance. The details are shown in Table 12, which uses the consensus estimates from Table 10 to derive the missing Income Statement line items. The consensus Income Statement forecast starts off inline with management’s guidance, although at the higher-end of the range, with sales of $45B. The implied cost of goods sold is based on the consensus gross margin estimate of 38.2%, which is also inline with management’s guidance, and at the higher-end of the range. At this point, the model starts to breakdown. The consensus implied operating expenses fall below management’s guided range, which is counterintuitive since the revenue and gross margin were at the high-end of the range. This is probably due to the fact that only 29 analysts submitted operating margin estimates, and the full earnings models for these analysts were probably more optimistic on average than the full population of 37 analysts. Pre-tax income estimates were submitted by 31 analysts (not necessarily the same 29 analysts who submitted operating margin estimates, plus two). The implied other income estimate based on the pre-tax income consensus estimate is just over $400M which is $47M short of management’s guidance. This is likely due to the analysts’ relatively high operating margin estimate. The implied tax expense based on the pre-tax and net income consensus estimates is also 50 basis points below management’s guidance. It is perfectly reasonable to assume that the market expectations could deviate from managements guidance; however, the up-and-down nature of the estimates as you move through the Income Statement, reinforce the need to consider the number of analysts and potential varying level of optimism, for each metric.

112

Table 12—Consensus-Based Apple Model ($ in millions, except EPS) Account Net sales Implied cost of goods sold Gross profit Implied operating expenses Operating income Implied other income/(expense) Pre-tax income Implied tax expense Net income Implied share count Earnings Per Share (EPS, $)

F3Q17 Consensus $44,972 $27,793

Management’s Guidance $43,500 to $45,500

$17,179 $6,595

37.5% to 38.5% $6,600 to $6,700

$10,584 $403

$450

$10,987 $2,746 $8,241 5,216 $1.58

25.5%

The consensus limitation is also apparent in the differing number of analysts who enter both quarterly and annual estimates. As you can see in the Table 11, only 37 analysts have submitted net sales estimates for the fiscal fourth quarter, while 44 have entered estimates for the full 2017 fiscal year. If you sum the net sales estimates for the two reported quarters and the two forecasted quarters, the result does not equal the consensus estimate for the full year ($227.0B vs $227.5B). The difference of $0.5B is due to the fact that not all analysts submit quarterly estimates. Even fewer enter estimates over a five year horizon, which makes the future consensus estimates less reliable. Given the variability in the number of analyst estimates included in different consensus metrics, it is important to consider how many analysts have entered estimates before deciding whether or not to incorporate the consensus projections into your analysis. At this point, you should understand that the calibration step in this section 113

is part art and part science. To demonstrate, let’s look again at the revenue guidance provided by management for the fourth fiscal quarter, which is expected to be between $45.5B and $47.5B. If you take a look at the consensus estimate ahead of the fourth quarter release it is $46.8B, just higher than the midpoint of management’s guidance. You may be thinking the estimates are cut and dry and easy to input into the model right? Wrong. There are several products that go into the revenue number (iPhone, iPad, Mac, etc.) each with unit and price estimates. If you review the models from several different sell-side analysts, each have very different estimates of the unit sales and average selling price of each of the products. Despite the difference in product estimates, like magic, they all get back to a point within management’s guided range. This is no coincidence. Analysts know that management has access to the best information about their companies. Unless the company has a trend of always beating or missing guidance, analysts will typically set their estimates within management’s range. They will also consider their recommendation in their estimates, meaning if they have a “buy” rating they will make sure they are at the top-end of management’s range, and vice versa for a “sell” recommendation. Similar to our calibration of guidance discussed in the previous section, we must choose one metric to back into the consensus estimate. I usually choose the estimate which is the most significant driver of earnings. In the Apple model, the most significant driver is the iPhone unit sales growth estimate. In Exhibit 50, I used an iPhone unit sales growth estimate of -0.3% to get to the midpoint of management’s sales guidance of $44.5B. We can use the Goal Seek function in Excel to reach the exact consensus estimate of $44.972B. This function is used to essentially back into a number, based on the equations setup in an Excel file. To run this analysis, click on “Data”, “What-If Analysis”, and “Goal Seek.” Once the Goal Seek window pops-up you can enter the cell you want to change in the “set cell” section (net sales in cell AP13 within Exhibit 5 in Chapter 1), set the value you want to reach (the consensus estimate of $44.972B), by changing the iPhone growth rate (in cell AP44, Exhibit 5). Excel will automatically change the growth rate to what is need to meet the consensus estimate. I selected to change the iPhone estimate because it is the largest contributor to total revenue. If you want to find the cells I used to reach the consensus estimate in my model, look for the value carried out to several 114

decimal places. In this case, the growth rate in the model is 1.48766058565808%. By looking at the number of decimal places, you can tell that it is the result of a goal seek function. Keep this in mind if you are reviewing the model of a sell side analyst and want to find out if they backed into a number using a goal seek function. If they have, the decimal place will likely be carried out several places. Pitfall: At this point in the calibration process, you should be recognizing the flow of assumptions in the forecast columns. New research associates tend to waste hours of time by not following this simple financial modeling rule: When entering future forecasts always move from left-to-right and top-to-down! This means always start by forecasting revenue across the top of the Income Statement. We do this because many of the ratios we use are based on a percentage of revenue. For example, SG&A is usually projected using a ratio of SG&A to revenue. If you are trying to reach a certain target SG&A for next quarter and you start by calibrating the SG&A line, and then the revenue line, your SG&A expense will change after your revenue estimate changes and you will end up going in circles. The same logic applies with moving left to right. Start with the earliest forecast periods and move across to the later periods in order. This is important because many of the future periods are based on growth rates applied to the earlier periods. If you reach your targets for the end of the year (a few quarters from now), then move right to left to adjust your first forecast period, you will need to re-adjust your full year data again.

Step 19: Incorporate Your Opinions As you form your views on the company’s future prospects, consider what the consensus may be missing, and how the competitive landscape has changed relative to the past few years. These factors may require adjustments to the historic trend-based model assumptions. Remember, modeling is like writing a story with numbers, and some analysts are better story tellers than others. This is the point in the modeling process for you to write your own story about the direction you think the company is headed using your own assumptions. Pitfall: Many new research associates feel uncomfortable entering forecasts into a model which deviate from the consensus. Try to remember that no one really knows for sure what the future holds and the consensus is often incorrect. The best we can do is use the available information to make an informed guess at what the future results will be. 115

Also, keep in mind that the consensus estimate represents an average of a broad range of possible outcomes, so even if your estimates look a bit high or low relative to the average consensus, it still may be within the range. The remainder of Step 19 will highlight the questions to consider in your forecast. As you go through the each item, think about what lines will be impacted in your model: revenue, gross margin, operating expenses, etc. Then, change your future period assumptions based on your analysis. Step 19a—Has the Macroeconomic Environment Changed? If the company you are modeling is in a cyclical industry, consider how current economic conditions, and your expectations for future macroeconomic developments, may impact financial results. For example, if an economic downturn occurs, then consumers may be less likely to purchase Apple’s products. In addition, foreign currency exchange rate fluctuations will make Apple’s products more or less affordable in different global markets which could impact unit sales, and change the value of profit in U.S. dollar terms. Macro impact can be very difficult to quantify, particularly the effects of changes in foreign exchange rates, since the company’s hedging strategies change over time. At the very least you should consider the financial statement lines which would be impacted, and adjust the historic ratios up or down to reflect a reasonable estimate. Management often gives insight into such effects during the quarterly earnings conference calls. Refer to FAQ 3 in Chapter 5 for an example from Apple’s fiscal fourth quarter 2016 earnings call for an example of such comments. Step 19b—Have Input Prices Changed? Input prices will have a direct impact on profitability. Any expected future changes should be incorporated into your model through changes in the gross margin assumptions. If a company you are modeling uses commodities traded on an exchange as inputs, it may be easier to incorporate future raw material prices into your forecast. Unfortunately, Apple’s material prices are not openly published. Many information technology research firms publish cost estimates for Apple’s products in reports called “product teardowns”. In a teardown report, the company will physically pull apart the product, identify each input, and estimate its cost. Teardown reports for newly released products are typically expensive. As the product ages the report prices fall, and are eventually available for free. To see an example of such a report, type “iPhone 7 teardown” into a Google search. You can also watch teardown videos on YouTube. 116

Remember to go to multiple sources to validate the information. Some reports come with pricing estimates of each part. Only insiders know exactly how much Apple pays for each component used in Apple products. To minimize the risk of over-reliance on a poor estimate, consider using multiple teardown reports and averaging the cost of each input. Using product teardowns, you can begin to form an opinion about how the iPhone’s gross margin changes from one product generation to the next, as new components are added. Notice that in my Apple model, I do not attempt to calculate the gross margin for each product, nor do I list the product subtypes (i.e. iPhone 7, 32GB, 128GB, iPhone 7+). I like to keep my earnings models somewhat simple, and take the change in profitability for each product into consideration as I form my opinion of what the corporate average gross margin should be. Feel free to add an additional layer of disaggregation to your model by showing the different product models, and estimating the gross margin of each. Besides changes in cost structure from one product generation to the next, there are also general fluctuations in input costs over time. For example, memory costs have been increasing lately, which has been a headwind for Apple. It is important to watch the market for inputs closely, and use any data available to corroborate the story management gives during investor meetings. After you have collected the evidence, you can make a judgement call by adjusting your model assumptions to incorporate any changes necessary. Consider the following conversation from Apple’s fiscal second quarter conference call: Analyst Question: How were you able to expand gross margins sequentially and guide rather seasonally for the June quarter in light of what's going on in the memory market…and also [are] your contracts around commodity prices likely to hit gross margins by more in the back half of this calendar year? Kathryn Lynn Huberty, Morgan Stanley LLC Analyst, Apple second quarter 2017 earnings call, May 2, 2017. Apple’s Response: Let me start with our performance for the March quarter, which we were very happy with. As you said, we were up 40 basis points sequentially. And this is in spite of the fact, as you know, that we lose leverage as we go from the December quarter to the March quarter. The foreign exchange headwind on a sequential basis was 100 basis points. Obviously, that was also a negative. And as you said, we started to experience some level of cost pressure on the memory side, 117

particularly on NAND and DRAM. To offset that, and actually do better than that, we had very good cost performance on other commodities, and the fact that our Services mix increases as we go through the year, that is of course also helping, given the profile of our gross margin for Services. So that answers the question around the second quarter. As we move into the June quarter, as you know, we tend to have some level of gross margin compression as we go from the March quarter to the June quarter. Again, the majority of that comes from the sequential loss of leverage. We also have a different mix of products as we move into the June quarter, and the cost pressures on memory will remain. We expect to offset partially these impacts with other cost efficiencies, and again, with a mix shift towards Services. The impact on NAND and DRAM will continue to be there, and we expect it to be there. You know we do not guide past the June quarter, but we expect it to be there for the time being. -Luca Maestri, Apple Inc CFO, Apple second quarter 2017 earnings call, May 2, 2017. There are three main points to take from the comments above: 1) Apple is currently facing unfavorable memory costs which is expected to continue for the foreseeable future, 2) other commodity input prices have offset some of the additional memory costs, 3) a recent shift towards services have had a positive effect on gross margin. Investors will interpret these effects differently. For example, if you are pessimistic about the company’s ability to continue to expand the services business, then you may decide to reduce your future period gross margin assumptions. On the other hand, you may believe that memory costs will decline in the future, and the services business will expand, in which case you would increase your future period gross margin assumptions. Step 19c—Have Customer Preferences Changed? In the previous step (step 19b), we discussed the impact business mix (products versus services) has on gross margin. Notice management also referred to “product mix” in the comments on the earnings call. In this context, product mix is referring to the specific products that customers have purchased during the quarter. If the mix is favorable, then more customers are purchasing the higher cost versions of Apple products, such as the larger storage capacity iPhone Plus, compared to lower cost iPhone SEs. If you believe that customers will shift to lower cost versions of the Apple 118

products in the future, then you should decrease your ASP and profit margin assumptions in your model. If you believe customers will trade up to the higher cost product models during the next product refresh cycle, then you should increase your ASP and profit margin assumptions. Step 19d—Has the Competitive Landscape Changed? The next step is to consider how the company’s competitors will impact future sales. Competition in the personal computing and smartphone market is intense, and new products will keep challenging Apple in the future. While all industries have differing degrees of competitive pressure, this is an important consideration in the modeling process. You should read trade reports, and watch for new patent filings to help shape your view on the company’s future prospects. If you believe a competitor’s product will have a detrimental impact on your company’s market share, then you should reduce your unit growth rate assumptions. On the other hand, if you believe your company will gain market share, you should increase your unit growth rate assumptions. Step 19e—Supply Chain Analysis: Suppliers can offer substantial insight into a company’s performance, particularly if the supply reports earnings results ahead of the company you are covering. Teardown reports are useful tools to identify the specific suppliers for a company’s inputs. You can also review the 10-K filings of companies within the industry you are covering, as any significant customers must be disclosed to investors. Step 19f—Has the Financial Reporting Changed: If the company has adopted a new accounting policy, or changed an accounting method or assumptions, then historical trends may not hold for effected accounts. To find out if the company you are covering has changed, or expects to change their financial reporting, read the “Recent Accounting Pronouncements” footnote in their 10-K SEC filing. The example below in Exhibit 51, explains that Apple intends to adopt the Financial Accounting Standards Board’s (FASB) Accounting Standard Update (ASU) number 2016-09, beginning in the first fiscal quarter of 2018. It is important to consider the implication of a change such as this, and update your model to reflect the new financial presentation requirements.

Exhibit 51—Apple’s Pronouncements Example

Recent

119

Accounting

Source: SEC.gov, Apple Inc 10-K, filing date October 26, 2016.

The first step is to develop an understanding of the accounting changes. FASB’s ASUs are available to the public free of charge at FASB.org, and include clear descriptions of the impact of new standards. In addition, public accounting firms (Deloitte, PricewaterhouseCoopers, KPMG, and Ernst & Young) typically write summary reports on new standards. If you are still unclear of how the new standard will impact the company you are modeling, consider reading one of these summary reports. You can also listen to the last few earnings conference calls to see if management has made any comments about the anticipated effects of the new standard. The final step is to look for early ASU adopters. Adobe Systems Inc (the same company used for our non-GAAP adjustment example in Step 8, Chapter 3) adopted ASU 2016-09 in their first fiscal quarter 2017 results. The impact on Adobe’s results was a reduction in the effective tax rate since the excess tax benefit on SBC is now included in the provisions for income tax line on the Income Statement instead of additional paid-in capital in the equity section of the Balance Sheet. Adobe’s CFO explained the impact on the first quarter conference call: In Q1 Adobe’s effective tax rate was 13.5% on a GAAP-basis, and 21% on a non-GAAP basis. The Q1 GAAP rate was slightly lower than targeted due to Adobe’s adoption of the new accounting standard affecting taxes related to equity-based costs. -Mark Garrett, Adobe Systems Inc CFO, Adobe fiscal first quarter 2017 earnings call, March 16, 2017. The impact on Apple’s results will likely be similar to that of Adobe. You may choose to adjust your model ahead of the company’s adoption, or you may prefer to wait for adoption. If you decide to make the adjustment, reduce the historic effective tax rate by moving the excess tax benefit on SBC to the income tax line. In addition, move the excess tax benefit on SBC from the Cash Flows From Financing Activities section of the Cash Flow Statement, to the Cash Flows From Operating Activities section. Remember that this standard will likely be adopted on a prospective basis, meaning historic results will not be adjusted. However, we recalculate the historic tax rate including the change, to develop an expectation of the future effective tax rate for our fiscal 2018 projections. Step 19g—Channel Checks: You may have access to the distribution 120

channel of the company you are covering. For example, if you are building a model for Nike Inc, then consider visiting a sample of shoe retailers and check to see how the latest Nike shoe is doing. It may be out of stock at every store you visit. On the other hand, if it is not well received by consumers, you may find it available everywhere at deep discounts. If you have the resources, you may be able to sample availability and prices of the product across the country, or better yet, globally. This data could prove extremely useful in predicting the next quarter’s results. Step 19h—Have there Been Any Other Changes or Events Which Could Impact Comparability? There are many other factors which could cause problems in using historic trends to project future results. These events typically fall into one of three categories: 1) “Big bang” events that instantly change the company’s earnings profile indefinitely, such as the announcement of a divestiture or acquisition. 2) Gradual structural changes which continue for long periods of time, such as a shift in sales from one region to another, or a change in the type of business booked over time (i.e. shift from service-based to product-based). 3) Non-recurring items such as legal settlements or large onetime orders, which could impact year-over-year comparisons for the period in which they are booked. If the company you are modeling has a similar event in the past, or if you expect such an event in the future, be sure to consider the ramifications in your future projections.

121

FAQ 3—How could I possibly predict earnings better then wall street analysts? The street is not perfect. In fact the consensus earnings estimate is wrong quite frequently. Apple’s fiscal fourth quarter 2016 (quarter ending September 30, 2016) highlights this point well. Ahead of the earnings call the consensus estimate for Apple’s gross margin for the fiscal first quarter 2017 (quarter ending December 31, 2016) was approximately 39%, which was generally in line with historic seasonality. But there were a few important factors the street underestimated, which Apple’s CFO explained during the question and answer portion of the earnings call: Analyst Question: Luca, I wanted to ask about the gross margin guidance. I think that the street [and we were] expecting something a little bit higher, [the reported margin was] about 50 basis points lower and the street it was I think 70 basis points lower, and I am just curious, do you think that people are mis-modeling that or is there something going on with pricing or mix there that you could provide us more color on? -Rod Hall, JPMorgan Securities LLC Analyst, Apple fourth quarter 2016 earnings call, October 25, 2016. Apple’s Response: Rod, let me give you some detail both on a sequential basis and I'll give you also something on a year-over-year basis because maybe that's where the disconnect comes from, looking at the last year's gross margins in the December quarter. On a sequential basis we are essentially guiding to some improvement in gross margins. We had 38% both in the June quarter and in the September quarter. We are guiding slightly higher for the December quarter because on the positive, we are going to have of course better leverage, and the mix in the December quarter tends to be better [Step 16: Adjust for Seasonality]. But we need to keep into account the fact that these positives are going to be partially offset by the cost structures of the new products [Step 17: Adjust for New Product Launches] that we are launching now and we have launched already a few during the September quarter and that will have an impact on our December quarter results. On a year-over-year basis, keep in mind that last year we did, in Q1, we did 40, around 40%, 40%, 41%, but there is a couple of things that I think need to be considered before doing a year-over-year compare. And there is the fact that last year we had this award for a patent infringement [Step: 19e Events Which Impact Comparability] of $548 million. That is, at the gross margin level, is 40 bps, and then we have 122

got the foreign exchange situation [Step 19a Macroeconomic Changes], which I mentioned before, which is worth another 60 bps, 70 bps, and so you are left with less than 100 basis points deterioration on a year-overyear basis where again, we have the reality of new cost structures into our products. It is very, very important I think for investors to understand that what has happened during the last two years. During the last two years, the U.S. dollar has appreciated by 15% over the basket of currencies where we do business, and we're a company that generates two thirds of our revenues outside the United States, 15% appreciation of the U.S. dollar. So on a year-over-year basis, just 2016 over 2015 was 340 bps impact from foreign exchange. This is something that we have offset almost entirely through a number of initiatives going from pricing actions to cost initiatives to our hedging program, but at some point, the strong dollar becomes the new normal and we need to work with that, and I think over the years, we made very good trade-offs and our gross margins have been quite stable over time. -Luca Maestri, Apple Inc CFO, Apple fourth quarter 2016 earnings call, October 25, 2016. Takeaways from this conversation: So could you have beaten the street’s gross margin forecast for the first quarter 2017 guidance given on October 25, 2016? Hindsight is 20/20, but it would have been as simple as following these steps: Step 1) Start with the previous year’s fiscal first quarter gross margin of 40.1% Step 2) Subtract the 300bps disclosed by Luca Maestri on the prior earnings conference call (fiscal second quarter 2016) as the foreign exchange year-over-year impact [40.10% minus 3.00% = 37.10%] Step 3) Derive the impact related to all other factors besides foreign exchange from the previous quarter by taking the difference between the fiscal second quarters in 2016 and 2015, and subtract the 300bps from Step 2 [38.02% minus 39.68% minus 3.00% = 1.35%] Add the result to the total from Step 2 [37.10% + 1.35% = 38.45%]. Step 4) Deduct the patent infringement benefit received from the previous year as this would not be a recurring it [38.45% minus 0.40% = 38.05%] This approach would have resulted in a gross margin estimate of 38.05% which would have been inline with the guidance management gave on October 25, 2016 of gross margin between 38% and 38.5%, and would have beaten the consensus estimate of approximately 39%. 123

124

Step 20: Consider Leading Indicators Most Industries have some form of trade publication which track important metrics for the companies within that industry. Sometimes the data is published by a government organization. For example, the Federal Reserve publishes industrial production and capacity utilization data for many different industries. This data could provide insight about the company you are covering and in some cases may even help you forecast earnings. In the case of Apple Inc consumer electronic device data is tracked by many different research companies. Two well known research firms, Gartner and IDC, track market share and shipments of Personal Computers (PCs), tablets and smartphones, and publish estimates of future shipments. Most trade journal data, including that of Gartner and IDC, is proprietary in nature, and expensive to access. Luckily, Gartner and IDC publish the highlights of their shipment estimates for free in quarterly press releases available on their websites. You may want to use trade data in your model to help inform your decision of future input estimates. If you are considering incorporating trade data assumptions, there are a few limitations and other things you should consider: Step 20a—Data Revisions: There is always a margin of error involved with forecasting. As a result trade data estimates are almost always revised after the date of the original published report. To demonstrate this point, consider Gartner’s estimate of Apple’s global PC shipments. If you download one of Gartner’s press releases you will notice that they always provide a comparison to the previous year’s quarter as a frame of reference for year-over-year shipment growth. The shipment estimate for the first calendar quarter of 2016 was first published on April 11, 2016 at 4.6M units. One year later, on April 11, 2017, Gartner published a new report with their estimate of shipments for the first quarter of 2017. In this later report, the previous year’s shipment estimate is disclosed at 4.0M (revised down from the original published estimate of 4.6M). The change was made to reflect the new information available after Apple released their March quarter (Apple’s fiscal second quarter) results, which showed that the company had sold 4.0M Mac computers. Step 20b—Reliability of the Data: As with all of the estimates you use in your earnings model, you should take steps to gain some comfort in the reliability of the data. One way to do this, is to review the historic estimates relative to actual results. In this review, be sure to make the comparisons against the pre-revised version of the estimates to capture the 125

true value in the estimate. In other words, comparing a revised estimate to actual results does not provide much value. Step 20c—Changes in Data Methodology: It is important to consider how the trade data has changed over time. For example, if a research firm had originally included tablets in their PC shipment estimates, and then later changed the policy at some point to exclude tablets, this would make it difficult to draw conclusions when comparing the estimates to Apple’s shipments over time. Sometimes the methodology used to collect trade data changes over time. Usually significant methodology changes are disclosed, but you may need to do some additional research if you find that the correlation of the data and the model input has started to decouple without explanation. Step 20d—Timing of Data Release: If the data is typically released after the company you are covering reports quarterly earnings results, then it may not provide much insight for forecasting. IDC and Gartner typically release PC shipment estimates before Apple reports earnings. If IDC and Gartner released their estimates after the Apple earnings release, then the data would be more accurate, but less useful. Step 20e—Number of Observations: It can be difficult to recognize a trend or establish an opinion about the accuracy of the data if the dataset is limited to a few observations. In Gartner’s estimate of Apple’s worldwide PC shipments, the company began disaggregating Apple’s worldwide PC shipments from the “all other” category in the third calendar quarter of 2015. This means there are only seven observations available to analyze. Given the limited number of data points it would be better to use Gartner’s total market global PC shipments, U.S. shipments, or IDC’s global Apple estimates as alternative. Step 20f—Regression Analysis: If you would like to determine the correlation between the trade data and an attribute within your model, and use the data point to project an estimate for your future forecast, then you may want to consider regression analysis. This will also give you an idea of how accurate the forecast has been over time (the higher the correlation, the more accurate the forecast). For example, if you want to determine the correlation between IDC’s projection of Apple’s Mac shipments and Apple’s actual shipments, you could regress the two variables. Part of the output from the regression would be an equation to predict Apple’s earnings release. The regression model would also give you a standard error based on historic results. Refer to the Appendix for examples of how to build a regression equation to 126

project attributes to include in your earnings model. Regression analysis may be useful if a specific metric for your company is not available. For example, if IDC only forecasted total global PC shipments, but not Apple’s specific shipments, we may use the total market shipments as a proxy for the general outlook on the PC market and regress the estimate with Apple’s historic results to see if there is a correlation. There are many limitations associated with this type of analysis (perhaps most importantly is the fact that this approach would ignore changes in market share). Be sure to refer to the limitations described in Step R6 of the Appendix. FAQ 4—How do I know if trade data exists for my company? Trade data is not ground breaking information, and does not represent the missing link required to unlock the secret of a company’s future earnings. It is usually reviewed by all analysts covering a company, so it should be relatively easy to find with a simple Google search on your company, or the industry in general.

127

CHAPTER 6: THE DCF INPUTS (BETA, ERP, CAPM, & WACC) Step 21: Calculate the Equity Risk Premium Step 22: Derive Beta Using the Regression Function Step 23: Calculate the Required Return on Equity using Beta and the CAPM Step 24: Calculate the WACC Chapter 6 Overview: Many investors struggle with the concept of linking risk metrics, particularly volatility, to stock valuation. This is due in part to the complexity of valuation techniques which incorporate volatility, relative to a simplistic market multiple approach. Adopting a Discounted Cash Flow (DCF) valuation technique is an effective way to link risk to equity valuation. In this chapter I will discuss the approach I use in a DCF model which links the change in volatility and interest rates to share value, through the Equity Risk Premium (ERP).

128

Step 21: Calculate the Equity Risk Premium The market Equity Risk Premium (ERP) is a measure of the total return an investor requires in excess of the risk-free rate (typically a U.S. Treasury security), to compensate for the additional risk of the investment. There is a direct relationship between the ERP and required return, which means that as an investment’s risk increases, investors will expect a higher return, and conversely, as risk decreases the required return on equity will also decrease. There are many different approaches to measure the market’s ERP. Each method has different assumptions, pros, cons, and results. I use the Constant Sharpe approach. The remainder of this step will demonstrate this approach. If you would like to follow along, the ERP calculation spreadsheet can be downloaded when you register your book at www.GutenbergResearch.com/book. Step 21a—Calculate the Historic Sharpe Ratio: There are two primary steps to calculate the Constant Sharpe ERP. First is a calculation of the market’s historic average Sharpe ratio, which is the average S&P500 total return, less the risk-free rate (I use the 10-year U.S. Treasury rate), divided by the standard deviation of S&P500 returns. In Exhibit 52 below, you can see I used data through 1963 to calculate the excess return over the riskfree rate each year, then took the average and divided it by the standard deviation in returns to arrive at the Constant Sharpe ratio of 0.308 for the index.

129

Exhibit 52—Constant Sharpe Ratio Calculation

Step 21b—Calculate the Current ERP: The second step in the Constant Sharpe-based ERP calculation, is the application of volatility to the Constant Sharpe. The volatility measure for the S&P500 is the Chicago Board Options Exchange (CBOE) volatility index known as the VIX. The VIX is a measure of implied short-term volatility of S&P500 options, quoted in percentage terms. As market uncertainty increases, the VIX increases, and equity valuations fall. When market uncertainty decreases, the VIX decreases, and equity valuations rise. To link the ERP to market volatility, multiply the Constant Sharpe by the VIX. Using the Constant Sharpe ratio of 0.308 calculated above and the current average VIX index value of 13.7% (12-month trailing average as of the first quarter of 2017). The resulting estimated ERP is 4.3%. Step 21c—Review of the Historic Constant Sharpe ERP: The chart below plots the Constant Sharpe-based ERP estimate through history. Two periods stand out when the ERP increased well above the long-term average for a sustained period of time. Both were driven by events which triggered periods of greater financial uncertainty. The first spike occurred in 2001 after the September 11th terrorist attacks. The second occurred at the end of 2008 as a result of the U.S. recession and credit crisis. The higher discount rates during these periods resulted in lower net present 130

values of cash flows (since the discount rate is in the denominator of the discounting equation), and lower stock valuations to reflect the heightened level of risk.

Exhibit 53—Historic Constant Sharpe-Based ERP Estimate

Step 21d—Forecasting the ERP: Since share valuation is a forward looking metric, you may want to forecast the future ERP based on your expectations. To do this, you will need to develop forecasts for the following inputs: 1) the federal funds rate, which is the rate for deposits at the federal reserve, lent overnight from one bank to another, 2) the spread between the fed funds rate and the risk-free rate, 3) volatility, and 4) equity market returns. The remainder of Step 21 will demonstrate how to input these assumptions into the ERP model, and calculate a future ERP estimate. In the Excel file, you will notice that the calculation is identical to the historic ERP calculation, except it is based on forecasted future estimates entered into the blue cells (refer to Exhibit 54 below).

Exhibit 54—Inputs to the Future ERP Estimate

131

Step 21d.i—Forecasting the ERP (The Federal Funds Rate): To develop an estimate for the federal funds rate start by reviewing the expectation of the Federal Reserve’s Federal Open Market Committee (FOMC) members. The members are the individuals who set the rate, so their forecasts are critical to your estimate. The Monetary Policy section of the Federal Reserve’s website (www.federalreserve.gov/monetarypolicy) offers all the data you need for analysis. There are three key items to download: 1) the FOMC statement, 2) the FOMC meeting minutes, and 3) the FOMC members’ median fed funds estimate for the year. Start with the FOMC Statement. Analysts tend to dissect each word in the document, looking for any change from one meeting to the next, which could indicate that the Committee is changing its view on rates. The Federal Reserve’s dual mandate is to maintain stable prices by controlling inflation, and to maximize employment. The first two paragraphs of the statement typically address these objectives. The following paragraph is where the interest rate decision is disclosed. In the example below from the May 3, 2017 meeting, the Committee decided to keep the federal funds target rate constant at ¾ to 1 percent (refer to Exhibit 55).

Exhibit 55—Example FOMC Statement

132

Source: FederalReserve.gov, FOMC Statement, May 3, 2017.

Next, read through FOMC meeting minutes. This is a much longer document, typically about 20 pages, which describes all of the economic metrics discussed during the meeting, how they have changed, how the committee members interpret the results, and whether or not the members are in agreement about FOMC’s course of action. 133

You can access the Projection Materials within the minutes, or from the FOMC Statement, which is released before the meeting minutes are available. This is where I derive my estimate for the federal fund rate. I typically do not deviate from the median committee members’ projections, although, you may decide to if you have an opposing view. Besides the federal funds rate, there are four key macroeconomic variable projections published: 1) GDP, 2) unemployment, 3) Personal Consumptions Expenditure (PCE) inflation, and 4) core inflation (excluding food and energy). If you watch these four variables over time, you can develop an expectation for the FOMC’s actions. For example, if you notice that GDP has increased above the FOMC’s median forecast for 2017 of 2.1% (refer to May Projection Material example below), the unemployment rate has decreased below the median projection of 4.5%, and inflation/core inflation have increased above the 1.9% forecast, then you may expect the FOMC to increase interest rates at the next Committee meeting.

134

Exhibit 56—FOMC Projection Materials Example

Source: FederalReserve.gov, FOMC Projections Materials, May 3, 2017.

Now, to derive the expected increase in the federal funds rate from the Projection Materials, simply take the Median federal funds projection and subtract it from the current midpoint of the federal funds target rate: 1.4% minus 0.875% = 0.525%. I usually divide the increases evenly for the remaining quarters of the year. Since the FOMC just met in May, I will enter the increases in the third and fourth quarter of my model (refer to Exhibit 54, cells W11 and W12 have increased by 0.25%). Step 21d.ii—Forecasting the ERP (Spread): When discussing spreads in this section, it is important to recognize that we are not referring to credit spreads, which reflect compensation for taking credit risk in excess of Treasury securities. We are referring instead, to the spread between the short-end of the yield curve and the intermediate/long-term portion of the 135

curve. If you plot each maturity point for the treasury rate and include the federal funds rate (overnight, 3-month, 6-month, 1-year, 2-year, 3-year, 5year, 7-year, 10-year, 20-year, and 30-year bonds), the result will be a curve, which is typically referred to as the term structure of interest rates. The chart below in Exhibit 57 shows the term structure as of May 29, 2017. Notice that the curve is positive sloping. This is generally an indication that the market expects future interest rates to increase, which reflects an expectation of economic prosperity. Now take a look at the historic yield curve from February 2007 (prior to the start of the 2008 recession). Notice that the curve was negative sloping, typically referred to as an inverted yield curve. This occurs because investors expect future interest rates to decline. In response, they are willing to pay more for long-term maturities, which drives the longer-term bond prices up, and yields down (yields and prices are inversely related).

Exhibit 57—Changes in Term Structure

Source: FederalReserve.gov.

Since we are using the 10-year treasury note as our estimate of the riskfree rate, and the federal funds rate to project future interest rates, we need to add a spread to recognize the difference in points along the yield curve. Keep in mind that the historic results (in cells W14 through W17 in Exhibit 54) already include this spread; however, we have not incorporated any additional changes in our future rate expectations for spread expansion or contraction. The chart below in Exhibit 58, shows the historic spread between the federal funds rate and 10-year note. The spread has averaged about 1.5% over the last 10+ years, and has averaged about 1.65% in 2017. If the 136

market expects an economic recession and future interest rates to fall, the spread will tighten or turn negative (inverted yield curve). If the market expects an economic expansion and future interest rates to increase, the spread will widen. If you expect economic strength in the next few quarters, you may want to add a few basis points to the risk-free rate estimates in cells W11 through W13 to reflect a widening of the federal funds to 10-year spread. Conversely, if you expect economic weakness you may want to subtract a few basis points from the estimates.

137

Exhibit 58—Historic Spread

Source: FederalReserve.gov.

Step 21d.iii—Forecasting the ERP (Volatility): Volatility is very difficult to predict. From 1995 through May of 2017 the VIX has averaged just over 20%. Through the first half of 2017, volatility has remained well below the historic average at about 12%. Volatility tends to increase in times of economic uncertainty and decrease with economic stability.If you expect an economic downturn, then you would want to increase your forecast of volatility in cells R11 through R13 in the ERP model (refer to Exhibit 54). If you have a more positive view of the market, you would want to keep volatility stable, or decrease the future estimate.

138

Exhibit 59—Historic Volatility

Source: FederalReserve.gov.

Step 21d.iv—Forecasting the ERP (Market Returns): The last step in the process, is to enter an assumption for the total market return (the S&P500 index is used as the market proxy). This estimate goes into cell C30 within the ERP model (refer to the ERP Model Excel template). You can use the historic S&P500 returns and your opinion of the future economic/market conditions to decide an appropriate future market return. After you have made inputs for the federal funds rate and spread in cells W11 through W13, volatility in cells R11 through R13, and market returns in cell C30, the new Sharpe ratio will calculate in cell J30, and the quarterly ERP estimate will recalculate in cells T11 through T13. Save your future ERP estimate, as you will need it for the calculation of the required return on equity in Step 23.

139

Step 22: Derive Beta Using the Regression Function in Excel The ERP from Step 21, represents the risk premium for the entire market. A risk premium for a specific company is required for the valuation of an individual stock. A stock’s beta can be used to link the broader market return to that of an individual company. Beta is a measure of correlation between the change in value of an equity security, and the change in value for an index. Many financial data providers publish beta estimates for stocks. If you do not have access to these sources, or if you would like to have control over the inputs, you can derive beta by regressing the stock’s returns against the S&P500 (the resulting regression beta coefficient is equal to the stock’s beta). This section demonstrates how the calculation works using Excel and Apple Inc as an example. The Excel template used for the Apple beta calculation is available for download when you register your book at www.GutenbergResearch.com/book. Step 22a—Obtain Daily Price Data: Download the daily price of Apple shares and the S&P500 Index value. The index will represent the proxy for the overall market. Calculate the percentage change for each observation. Step 22b—Install Data Analysis Add-In: You will need the Data Analysis Tool Pack in Excel. It is available for free with Excel, but must be downloaded. If you have not installed the Tool Pack, open an Excel file, select go to “File”, “Options”, “Add-ins”, “Analysis Tool Pack”, “Go” to install. Step 22c—Run the Regression: In Excel, select “Data Analysis” on the “Data” tab, then select “Regression”. Enter the percentage change in Apple shares column for the first variable, and the percentage change in the index column for the second variable. Select an output range and click “OK” to run.

Exhibit 60—Deriving Beta Using Excel

140

Exhibit 60 shows the regression output. The beta is the slope coefficient in the regression equation, which is 1.12 (cell H17). The interpretation of the result is relatively straight forward: the slope or beta represents the expected percentage change in the value of Apple shares based on the percentage change in the S&P500 index. For example, if the S&P500 increased by 10%, we would expect Apple shares to increase by 11.2%, if we isolate our analysis to include only the market return and beta. Notice that the t-Stat is relatively high (cell J17) and the P-value is nearly zero (cell K17). These metrics indicate that the correlation between the two variables is likely statistically significant; therefore, the S&P500 index is likely an appropriate market proxy to measure Apple against. For additional details on the t-stat, P-value and regression analysis in general, refer to the regression section in the Appendix. There is one more important item to point out in the beta regression. Notice that the R Square (cell H5) is relatively low, at just 25%. The interpretation of the R Square is that approximately 25% of the variation in Apple shares is explained by changes in the S&P500 index. The fact that this correlation is low makes sense, since there are many other factors which cause Apple shares to increase or decrease in value, other than how the general market performs.

Step 23: Calculate the Required Return on Equity 141

using Beta and the CAPM Beta and the ERP are not direct inputs into the Weighted Average Cost of Capital (WACC) calculation, which is the discount rate used to value shares. Instead, these metrics are used to derive the required Return on Equity (ROE). To calculate the ROE, use the Capital Asset Pricing Model (CAPM) which states that the required return on equity for a stock is equal to the risk-free rate, plus beta times the ERP. After inputting the beta, ERP, and risk-free rate into the CAPM equation, we can solve for the required return. I calculate the required return on equity in the DCF section of my Tier 1 models. In the Apple example, the required return on equity is 6.48% calculated as the risk-free rate of 2.45%, plus a beta of 1.12 times the market risk premium of 3.6%. The calculation is shown in Exhibit 61 below. Notice that 6.48% is a very low required return on equity relative to historic averages, which reflects the historic low volatility and interest rates. I use this rate only for the stage-one portion of the valuation, and use a higher long-term average required return for the stage-two valuation (refer to Chapter 7 Step 25 for details).

Exhibit 61—The Required Return on Equity

Step 24: Calculate the WACC The required return on equity is used to calculate the Weighted Average Cost of Capital (WACC). This calculation is also done in the DCF section of a Tier 1 model. First, apply the weight of equity in the company’s capital structure to the cost of equity, then add the result to the weight of debt times the after-tax cost of debt. The final WACC will be used as the discount rate in our DCF valuation. The equation is: WACC = (% of Debt × After Tax Cost of Debt) + (% of Equity × Required Return on Equity). This demonstration represents one of many approaches used to derive a discount rate. Rather than calculating the WACC, some analysts use a 142

competitor’s WACC, a competitor’s beta in the calculation, or use a sector average discount rate. You can change any of the inputs in the model to suit your needs. If you choose one of these options, keep in mind that you may need to make an adjustment to account for the differences in capital structure between the companies included in your derivation of a discount rate.

143

CHAPTER 7: DISCOUNTED CASH FLOW VALUATION Step 25: Calculate the Stage-One DCF Step 26: Calculate the Stage-Two DCF Step 27: Calculate the DCF Valuation Step 28: Understanding the DCF Valuation Chapter 7 Overview: In this chapter, I will discuss how to use the inputs we calculated in Chapter 6, to derive a DCF-based share valuation. Keep in mind that the DCF is much more complicated than a market multiple-based valuation, with many more required inputs and assumptions. DCF analysis is useful for demonstrating the fundamental concept that any asset’s fair value is equal to the present value of the future cash flows it produces. The DCF has three basic parts: 1) Projected cash flows, 2) a future growth rate, and 3) a discount rate. This chapter will cover all three, and demonstrate the full DCF calculation.

Step 25: Calculate the Stage-One DCF There are many different approaches to calculating a DCF-based valuation. I use a two-stage approach, with five years of cash flow projections in the first stage, which represents the high-growth stage. Then, I calculate a terminal value in stage-two, with the assumption that the company reaches a constant growth stage in the sixth year. This is a rather large assumption for most companies since they will likely go through many business cycles throughout their existence, rather than reaching a flat growth rate held in perpetuity. However, once the model is established and calibrated with this assumption, it gives a comparable valuation over time. With each additional year added to the forecast as time progresses, the constant growth stage will be continually pushed out if management is able to keep 144

growth on track. In the Tier 1 model, I calculate the free cash flow at the bottom of the Cash Flow Statement as the cash from operations, less capital expenditures, plus after-tax interest expense. I then discount the free cash flow using the WACC calculated in Step 24. This calculation is performed for each of the five, stage-one years. The sum of the five discounted cash flows is saved in the DCF section of the model.

145

Exhibit 62—Stage-One DCF Calculation

Step 26: Calculate the Stage-Two DCF The stage-two calculation estimates the terminal value of the company holding growth constant. The calculation may look daunting at first (in cell C297 of Exhibit 62): =((((BK217*(1+C293))-(C294*BK13*(1+C292))+ (C289*BK166)))/(C295-C292))/(1+$C$295)^5 It is actually a simple perpetuity equation. Breaking the equation down helps to show that it represents the cash flow for the first year of the constant growth stage, divided by long-term WACC, minus the constant growth rate. Since this represents the terminal value at the end of the fifth year, we discount the result back five years to present value terms. The numerator, which represents the cash flow of year six, is the complex part with the following three components: Part 1: Cash Flow From Operations (CFO) = CFO of year 5 x (1 + constant CFO growth rate) Part 2: Minus: capex = Average capex to sales ratio x [sales x (1 + constant sales growth rate)] 146

Part 3: Plus: the after-tax cost of debt = After tax cost of debt x long-term debt Notice that I calculate two discount rates, one for stage-one, and another for stage-two. This approach differs from that of other analysts who tend to use a basic market-based WACC which does not change much over time. I do this because I want to maintain the link between changes in the macroeconomic environment (i.e. changes in interest rates, volatility, etc.) and share valuation. At the same time, it is important to recognize that over the long-term, business cycles will come and go, but the key inputs in the valuation will have long-term averages similar to historic results. Since the majority of a DCF-based valuation comes from the terminal value, adjusting the WACC used for the second stage cash flows to reflect shortterm changes in interest rates, volatility, or the correlation of shares to the market, would have an unjustified impact on the price target. Maintaining two distinct discount rates for the short-term and long-term portions of the valuation, allow the model user to maintain the macro link, without over or under estimating the impact on target share price.

Step 27: Calculate the DCF Valuation Now that we have the stage-one and stage-two cash flows in present value terms, all we have to do is add them together with the adjusted net cash per share (the adjustment to cash per share is discussed in Steps 10a and 11c, in Chapter 4). The result is the final DCF-based share valuation (refer to cell C300 in Exhibit 62).

Step 28: Understanding the DCF Valuation At this point, you should be able to recognize how the different inputs impact valuation under different scenarios. As you think about the impact, keep these basic equations in mind: Market ERP = Required Return on Equity – Risk-Free Rate, or Constant Sharpe × Volatility CAPM = Company Specific Required Return Equity = Risk-Free Rate + Beta × Market Risk Premium WACC = Weight of Debt × After-Tax Cost of Debt + Weight of Equity × Required Return on Equity Using these equations, we can draw the following conclusions about the DCF inputs: Volatility: The higher the VIX, the greater the cost of equity. Increases in volatility will have a greater impact on companies with a greater 147

proportion of their capital structure coming from equity rather than debt. This scenario can tilt if a large debt balance results in a high probability of default, in which case, the company’s credit spread would widen and the cost of debt and equity would spike. Interest Rates: An increase in market interest rates will increase the required return on equity through the CAPM, and result in lower asset values. If a company has fixed rate liabilities, higher rates will not impact the cost of debt in the WACC when rates change. If the company’s debt has adjustable features, then changes in interest rates can have an impact on the cost of debt. Beta: A high beta coefficient will result in greater variability in the cost of equity. Final Note on DCF Valuation: After reading this chapter you may be thinking “I have been using market multiples and missing the risk in my valuation all this time?!” That is not necessarily the case. Market multiples are very useful tools, arguably more useful than discounted cash flow analysis which requires many more assumptions. Just remember that if you are going to use a market multiple approach, you must adjust the multiple during times of increased risk to reflect the greater required return on equity. Regardless of the valuation approach you choose, keeping the fundamental risk and return theory in mind will ultimately lead to better investment decisions.

148

CHAPTER 8: MARKET MULTIPLE BASED VALUATION Step 29: Choose a Multiple Step 30: Separate Net Cash Step 31: Calculate the Historic Multiple Step 32: Create a Price Band Chapter 8 Overview: Most companies can be valued by applying a market multiple to forecasted earnings. In this chapter, I will discuss some of the key factors to consider when choosing a multiple, and demonstrate how to use the earnings forecasted in Chapters 2 through 5 to derive a market multiple-based valuation.

Step 29: Choose a Multiple When choosing an appropriate multiple to use for valuation, the following factors should be considered: Industry/Sector: Each sector has a set of metrics which are widely accepted by analysts and investors, including which multiples to use for valuation. For banks, it is the Price to Book Value multiple, for energy companies, it is the Enterprise Value (EV) to EBITDA multiple, and for developed companies with relatively stable earnings, it is the Price to Earnings (PE) multiple. Cyclical vs Non-Cyclical: If the company’s industry is currently in decline, then the company could be operating at a loss. In this case, a PE multiple cannot be used for valuation. As an alternative, it may be appropriate to use a Price to EBITDA ratio or Price to Sales ratio. Another alternative would be to forecast earnings on a normalized basis, assuming a return to average market conditions at some point in the future, and apply a PE ratio to this later stage in the business cycle. Lifecycle Stage: Companies in the development stage have different earnings profiles compared to those which have reached maturity. 149

Developing companies may not be profitable or may have volatile earnings streams with high growth rates, in which case it may make more sense to apply a multiple to a later stage of earnings, rather than using a Next Twelve Month (NTM) PE. For example, Netflix is currently trading at a PE multiple of about 600x the NTM consensus EPS estimate, while Apple trades at about 10x. The reason that investors are willing to pay more for Netflix’ earnings, is that the company is currently in a hyper-growth stage. As a result, investors are valuing shares based on the ultimate long-term earning capacity which will not be reached for many years. Therefore, the NTM earnings are less relevant for valuation purposes. For companies in a hyper-growth stage, a growth adjustment could be used make the PE ratio more meaningful. Other than using a historic average multiple you may want to consider using the multiple of a competitor, a sector average, or a cyclical peak/trough multiple. The multiple is easy to change in the model. Feel free to try different multiple choices to see what the impact is on valuation.

Step 30: Separate Net Cash Next, review the company’s Balance Sheet and decide if any of the company’s assets should be removed and valued separately. A good example of assets that should be valued separately, is an investment in shares of a separate company. Another important item you may want to value separately, is cash and investments. I typically separate net cash and investments from the equity valuation in my models after deducting debt, because I do not want to give a valuation benefit to companies that rack-up large debt balances and let the cash sit on the Balance Sheet. Some companies, including Apple, generate substantial cash balances in other countries. If this foreign domiciled cash was brought back to the U.S., the company would be subject to a repatriation tax. To account for this, I apply an adjustment to net cash (refer to Step 11c in Chapter 4 for details).

Step 31: Calculate the Historic Multiple Now that we have the inputs, the calculation is straight forward: the PE multiple times the sum of the next four quarters of diluted EPS, plus adjusted net cash per share = the implied share valuation. For the Tier 1 model, I apply a 50% weight to the DCF-based valuation and a 50% weight to the market multiple valuation to arrive at the final implied 12150

month target share price.

Step 32: Create a Price Band It is important to recognize that we have made a significant number of assumption in the model. For this reason, having one specific target price may misrepresent the level of certainty in the share valuation. To reflect this fact, I like to create a price band around the implied share target, based on the historic changes in share price over the last 12 months. To do this, first calculate the mean monthly return for shares. Next, calculate the standard deviation in monthly returns. The standard deviation is a measure of dispersion around the mean monthly return. The larger the historic standard deviation, the greater the volatility in prices. Using a normal distribution, approximately 95% of observations fall within two standard deviations of the mean. Therefore, at the 95% confidence interval, the lower bound of the price band is equal to the target price × (1 – (2 × the standard deviation × mean monthly return)). The upper bound is equal to the target price × (1 + (2 × the standard deviation × mean monthly return)). This approach has multiple limitations, including the fact that is assumes historic results can predict future return characteristics, and it assumes the stock's returns are normally distributed. Despite these limitations, it offers some insight into how wide the level of certainty is around the valuation expectation.

151

CHAPTER 9: HOW TO USE YOUR EARNINGS MODEL Step 33: Run Scenario Analysis Step 34: Perform Sensitivity Analysis Step 35: Analyze Guidance & Consensus Step 36: Prepare for the Release Step 37: Update the Model Step 38: Regular Model Maintenance Chapter 9 Overview: Now that you have completed your earnings model, you can use it to analyze the company’s future prospects. This is the point where all the hard work put into creating a dynamic equation-based forecast pays off. In this chapter, I will explain some of the tasks you can perform with your model, such as running scenario and sensitivity analysis, analyzing management’s guidance, and the consensus estimates. This chapter will also describe how to update and maintain your model over time.

Step 33: Run Scenario Analysis If you look at your finished model, you can easily identify all of the inputs, as they have been shaded blue. You can change any of these inputs and instantly see how the change would impact earnings. Using the input cells, you can create multiple upside and downside scenarios. For example, you may want to see what the impact on earnings would be if the iPhone fell out of favor with consumers and year-over-year sales dropped dramatically for a sustained period of time. For this downside scenario, try adjusting the iPhone unit sales estimates downward each quarter for the next few quarters. You could also decrease the iPhone Average Selling Price (ASP). Next, consider the impact this would have on profitability. With a lower 152

ASP, the average gross margin would probably decline as the company would lose some of the benefit of scale. In addition, the lower overall revenue base would likely increase the operating expense ratios. To reflect this, you may want to increase the SG&A to revenue and R&D to revenue ratios. After the adjustments to the model, the Next Twelve Month (NTM) EPS estimate decreases and as a result, the target share price also declines from your original assumptions. You could also create an upside scenario. Perhaps you believe that the iPhone will continue to gain market share, and that the next iPhone refresh cycle will be particularly strong. You could reflect this by increasing the unit growth rates and ASP. With the higher ASP comes a higher gross margin as the company captures the benefit of increased scale. You could also adjust the operating expense ratios downward to reflect the leverage on the larger revenue base. After the adjustments for this upside scenario, you can see the NTM EPS and target share estimates increase. Besides running company specific scenarios, you could also analyze the impact of macroeconomic changes. For example, if you want to see the impact tax reform could have on earnings and valuation, try adjusting the effective tax rate or the tax assumptions on the repatriation of foreign earnings remittance. You could also input higher interest rates, changes in correlation with the market (beta), increases in volatility, or change the market multiple.

Step 34: Perform Sensitivity Analysis Sensitivity analysis attempts to capture and quantify the impact of changes in a sample of variables, when there is uncertainty about their future value. The primary limitation of sensitivity analysis is that the calculation only works for one or two variables at a time, whereas scenario analysis allows you to change all variables at the same time. Despite the variable limitation, sensitivity analysis is a useful exercise to perform prior to an earnings release to establish a quick view on the potential outcomes of the earnings release. Excel’s data table function can be used to create quick sensitivity charts for two variables. For example, let’s assume that Apple will release fiscal third quarter 2017 results tonight after the close. The consensus estimate for revenue and gross margin are $44.97B and 38.2%. You can run a sensitivity table to show what the impact on EPS would be if the revenue and gross margin come in at different points above or below the consensus estimate. To do this, start by setting up a chart with one variable across the 153

top (revenue) and the other running along the left side (gross margin). Keep in mind that the table will not calculate if the variables are not hardcoded values. For example, revenue is not hardcoded in the Tier 1 version of the Apple model, it is an equation based on ASP and units for each product. In order to run a sensitivity on revenue, you will need to copy and paste revenue as a value. After you run the data table you can reset the cell back to an equation. For simplicity, I will use the Apple BOTE model for this demonstration (refer to Exhibit 63 below for details). Next, input the values you want to see for each variable. I like to have my base case fall in the middle of the chart. To do this, set the value for each variable in the middle cells equal to the value in the model. For revenue, this would be $44.97B and for gross margin this would be 38.2%. Now set the remaining points for the chart with equal increments above and below the midpoint.

Exhibit 63—Apple’s EPS Sensitivity to Revenue and Gross Margin

After the variables are setup, you can link the table to the model. To do this, set an equation which points to the result in the model you are looking to solve for, in this case EPS. Place this equation in the upper left-hand corner of the chart where the variables meet (cell Z11 in Exhibit 63). This is a reference used by the data table. If you want to hide the result as I have, change the color of the text to be the same as the shade of the cell it is in (which will be white text if you are not using color in the chart header). Next, highlight the entire chart (cells Z11 through AE16). From the “Data” tab select “What-If Analysis”, “Data Table” to open the data table input selection window. In the “Row input cell” range point to the gross margin cell (refer to Chapter 1, Exhibit 3, cell P13) within the model. In the “Column input cell” range point to the revenue cell within the model (refer to Chapter 1, Exhibit 3, cell P11). Click “OK” to run the table. For each 154

point in the data table, Excel will take the revenue and gross margin assumptions, plug them into the earnings model, recalculate the EPS, and populate the result in the data table. Notice that in this analysis, all other variables are held constant to the assumptions contained in the model (i.e. operating expenses, tax rate, etc.). Now that you have the estimates of the impact on EPS for different revenue and margin possibilities, you could apply the Next-Twelve Month (NTM) Price-Earnings (PE) multiple that shares are trading at ahead of the press release, plus the next three quarters of consensus EPS estimates, to calculate the approximate impact these outcomes could have on share price. Keep in mind, this holds all other factors constant, and if there is a significant beat or miss of the revenue or margin estimates, then the consensus for the next few quarters would likely need to be revised. Irrespective of the ever-changing estimates of future earnings, a data table can be a very useful tool.

Step 35: Analyze Guidance & Consensus In Step 18a, we analyzed management’s guidance relative to historic results. Before an earnings release, you should compare management’s guidance to the consensus estimate, and your final model assumptions. This will help you understand where the market estimates stand relative to the company’s internal forecast.

Step 36: Prepare for the Release Whether your company’s earnings press release comes in the morning or evening after the market closes, updating the model quickly can give you an edge as the market absorbs the new data. There are some simple steps which can assist in updating the model as quickly and efficiently as possible. You should perform these steps prior to the release: Step 36a—Consider What You Are Going to Update: You will be updating the actual results from the company’s press release in the cells which were previously a forecast. In addition, you will be recalibrating your forecast to reflect any new guidance provided by management. Step 36b—Prepare the Headers: In your model, there should be some indication in the headers distinguishing the forecast columns from the actuals. For example, in my models, I shade the historic columns dark gray, and the forecast columns light gray with an “E” in the date to indicate that the column represents estimates. Before the earnings release, I remove the “E” for the column which will be reported, and change the 155

color to dark gray. I also highlight the column above the header and freeze the panes so I do not lose track of which column I am updating when the release comes out. Step 36c—Change Forecast Equations to Values: In Chapter 1, Section 1, I discussed the concept of creating a dynamic model driven by equations which are fed by input assumptions. Remember that the equations generally move downward in historic columns, and upward in the forecast columns. After the quarterly results are reported, the equations change to calculate the “inputs”, which are used to forecast the next quarter’s results. To demonstrate this point, look at one quarter of historic results and one quarter projections for Selling, General & Administrative (SG&A). Notice that in the historic column SG&A is a hard coded value (since it came from the company’s actual filed results), and the ratio of SG&A to revenue is an equation. Now look at the forecast column. The forecast SG&A line on the Income Statement is an equation, which points to the hard coded estimate of the ratio of SG&A to revenue. In the moments leading up to the earnings press release, it is no longer important to maintain the equations for the next quarter. At this point, the primary objective is to prepare the model to consume the new results. To do this, go through the equations which point to an input assumption. Copy and paste these inputs as values. Start at the top of the Income Statement and work your way down to the end of the model. Be sure to maintain the equations for metrics which should not be hard coded. For example, the gross profit line on the Income Statement should remain the equation of sales minus cost of goods sold. If you are having trouble determining which metrics should be values and which should be equations, just look to the last historic quarter and mimic the structure. After you complete the hard coding, you will need to copy over the equations for the input fields. Again, look to the last quarter if you are having trouble with the equations. There are two benefits of following this process ahead of the release: 1) You will be able to update the model much quicker since all the equations will already be setup, and 2) as you fill in the actual values you will instantly see how close your estimates were for each line item. If you keep the equations, rather than saving the values, you would have lost the visibility on your estimates as soon as you input the first value for revenue. This is the result of the ratio based equations which are driven by revenue. By saving the inputs as values, you prevent the loss of visibility as you update the model.

156

Step 37: Updating The Model 37a—Update the Actuals: Manually typing in the values from the press release may seem like a primitive approach to updating the model. There are many software companies trying to automate the process, however they rely on technologies which have not been fully developed. For example, as discussed in Step 3, there is an XBRL taxonomy process managed by the Financial Accounting Standards Board (FASB), which is used by third party data service providers to populate financial data automatically. One major limitation to this process is that companies are not yet required to tag the details in their 8-K SEC filings (quarterly earnings press releases are published as 8-K filings). This means there is no standard setting body overseeing the automation of the quarterly press release data. When you consider this point along with the fact that all parties receive the press release at the exact same moment, there is currently no faster, more accurate, and reliable way of accessing the data and getting it into your model then the old fashion way of taking it from the press release yourself. There are many groups working on improving this process, so this may not be the case in the not so distant future. For the time being the practice for most sell-side research teams is to manually type in the results. My Experience: Learning to update a model quickly and accurately after an earnings release takes practice. When I was a new associate in research I found this to be among the most stressful tasks, given the tight time-line for completion. Typically the lead analyst cannot discuss a release with clients until a report is published, and you cannot publish a report until you update the model. Research teams have different structures, but typically the associate updates the model while the analyst writes the report. The most important thing you can do as a new associate is to get familiar with the model. You may want to practice updating the model using the previous quarter’s press release. This will also help you get familiar with where the information is presented (some metrics are buried in text, while others are easy to find in tables). Preparation is the key to success when it comes to updating an earnings model. 37b—Update the Future Quarter Forecast: Now that you have input the latest quarterly results, the values in your forecast quarters have probably changed quite a bit. This is driven by the fact that the future growth rates are based in part on the historic results, which have changed after the latest release. For example, if you projected iPhone unit sales for the third fiscal 157

quarter of 2017 to be 41M units and the actual units sales end up being 50M units, then your third quarter 2018 unit sales estimate will be much higher then it was prior to the release since it is based on a year-over-year growth rate. As a result, you may find it necessary to adjust your future forecast to align it with your expectations. You should also consider incorporating the new guidance provided by management into your forecast. In addition, you should evaluate how accurate management’s guidance was for the last quarter to determine where on the new range you would like your future estimates to be. The final item you should consider is the accuracy of your own estimates in the latest reported quarter. There are behavioral finance theories which say we tend to focus on areas where we are correct in our predictions, and tend to forget the areas where we are wrong. Keep this idea in mind as you update your model, and force yourself to seek out the metrics where you missed the mark. Accept that you were wrong, and correct the issue going forward. For example, let’s assume you were projecting that gross margin for Apple would decline by 100 basis points in the latest quarter due to an increase in raw material costs and foreign exchange headwinds. Now assume that the company reports results and gross margin actually increased by 50 basis points. Management explains that due to recurring cost efficiencies and long-term foreign exchange hedging contracts, they expect future gross margin fluctuations to remain relatively stable. You should recognize that your prior forecast was inaccurate, and adjust the future quarter gross margin estimates accordingly. Do not refuse to believe management and leave your future gross margin overstated to justify your past mistake (unless of course there is sufficient evidence which contradicts management’s assessment).

Step 38: Regular Model Maintenance Everyday, new information is released which may impact your model. Economic data may be published which could impact the Equity Risk Premium (ERP) used in the Discounted Cash Flow (DCF) valuation section of a Tier 1 model. Company specific news may also be released, such as a new product or the issuance of debt or equity, which would need to be incorporated into the model. You should use judgment when deciding how often to update your model. If you have a long-term horizon and you are modeling a company as a passive hobby, you may not feel the need to update your model for company specific news or macroeconomic changes throughout the quarter. 158

On the other hand, if you are a sell-side analyst and your clients are relying on your model, you should keep it as up-to-date as possible.

159

APPENDIX 1: USING REGRESSION ANALYSIS TO PREDICT EARNINGS Step R1: Select Your Data Set Step R2: Run the Regression Step R3: Test the Model Step R4: Analyze the Output Step R5: Perform Back Testing Step R6: Model Limitations Appendix 1 Overview: Regression analysis is a mathematical process which estimates the relationship between two or more variables. As investors, we must forecast our expectations for future periods. Regression models can be helpful tools when reliable data is available ahead of a company’s press release. In this appendix, I will demonstrate how to use regression models for earnings projections using a simple linear regression example for Boeing Co and a multiple regression for Starbucks Corp. I will also explain the limitations associated with using regression analysis to predict results. Both regression example templates are available for download when you register your book at www.GutenbergResearch.com/book. Keep in mind that the regression models have not been updated to include the latest reported results. Notice in this section the step count has started over at “R1” (Regression-1). This is because the financial model has been completed in the previous chapter. The steps in this appendix are only relevant for those who wish to use regression analysis to predict inputs into their earnings 160

model.

Step R1: Select Your Data Set The first step is selecting the appropriate variables to use in the model. It can be difficult to find variables with strong explanatory power. Typically, we try to find data which is effective at predicting top-line revenue, since anything below the revenue line can be distorted over time due to fluctuations in profit margins, and other factors. Ideally, we should find variables with limited impact from outside forces. Given the nature of financial data, this is much easier said then done. For example, you will see in the Boeing regression I have used revenue as the dependent variable, but revenue is subject to changes over time due to fluctuations in inflation, foreign exchange rates, and revenue recognition principals. A better variable would be inputs into revenue, such as the number of units shipped which is not impacted by these factors. Unfortunately, it can be very difficult to find a statistically significant relationship among variables, so sometimes we must make do with what we can find. Start by generating a list of potential leading indicators for a company’s revenue. Then, test each variable using the steps in this chapter to find the best predictors. For Boeing and Starbucks, I found the U.S. Census Bureau’s reports to be helpful in providing leading indicators. For Boeing, I selected the Aircraft & Parts Shipments section, including defense and non-defense shipments, in the Industrial Production report, which is published by the U.S. Census Bureau on a monthly basis. The final monthly report for each calendar quarter is typically released about a week before Boeing reports quarterly results which makes it a good data point for prediction. The two variables (Boeing’s revenue and the shipment data) appear to have a strong correlation based on the time series plot below. I will test this correlation in Step R2. In this example, Boeing’s revenue is what we would like to predict, so it is the dependent variable, also know as the “Y” variable, or the variable which “depends” on the results of another variable. Aircraft & Parts Shipments from the Industrial Production report is the independent variable, also known as the “X” variable.

161

Exhibit 64—Boeing Revenue vs Aircraft & Parts Shipments

Source: Company reports, U.S. Census Bureau Manufacturers’ Shipments, defense & non-defense.

For Starbucks, I chose the Food Services & Drinking Places sales data, contained in the Advanced Retail & Food Sales report published by the U.S. Census Bureau. Similar to the Boeing example, this report is also available prior to the company’s quarterly earnings release and appears to show a strong correlation to Starbucks’ revenue. In the chart, below there is a clear de-coupling between the two variables in the fourth calendar quarter each year. This is because Starbucks’ fourth calendar quarter sales typically outperform the Food Service & Drinking Places total results. If an adjustment to account for this seasonality is not included, then the model will likely have a larger standard error, which is the standard deviation of the residuals (residuals are the differences between each observation and the model predicted value for that observation). The smaller the standard error, the better the model will be at predicting results. I will be using a second independent variable to account for the seasonality, which is a “dummy” variable to represent the fourth calendar quarter. A dummy variable has a value of 0 or 1. In the Starbucks example, a value of 1 represents the fourth calendar quarter.

Exhibit 65—Starbucks Revenue vs U.S. Food Services 162

Retail Sales

Source: Company reports, U.S. Census Bureau Advanced Retail and Food Sales.

Step R2: Run the Regression The next step is to run the regression models for Boeing and Starbucks. I will be using Excel for this analysis but feel free to use your favorite statistical software. If you have not installed the Excel Data Analysis addin, refer to the installation instructions in Step 22b in Chapter 6. Starting with the Boeing model, follow these steps: Step R2a: In Excel, on the “Data” tab, click on “Data Analysis”, then “Regression”. Select the quarterly percentage change in Boeing’s revenue column for the Y input range (dependent variable), and the percentage change in shipments column for the X input range (independent variable). Step R2b: Check the “Labels” box, which indicates that the Y and X ranges include the data headings. Step R2c: For this regression, we want to set the constant in the regression equation equal to zero, so check the “Constant is Zero” box. You can try running the regression with this box unchecked to see which result is better. The constant is the term before the beta coefficient in the regression equation, which will be explained in greater detail in Step R4. Step R2d: Select the “Output Range” where you would like the regression results to be populated. Step R2e: Click the residual details you would like to analyze. We will 163

discuss how to analyze residuals in Step R3. Step R2f: Click “OK” to run the regression. We follow a similar procedure to run the Starbucks multiple regression, except the X range includes two columns of data, since it is a multiple regression, and we do not check the “Constant is Zero” box.

Step R3: Test the Model Before we can draw any conclusions about the correlation between the variables, we must test the validity of the models. To do this, we perform a series of statistical reviews, including the following (starting with the Boeing Model): Step R3a—P-Value and t-Stat Review: These two statistics are used to test whether or not the relationship between the variables in the regression is different from zero. If the t-stat is greater than +2.79 or less than -2.79, then a statistically significant relationship exists at the 99% confidence interval (also known as the 1% level of significance). The 2.79 figure comes from the Critical t-value of a distribution chart representing 0.005 probability in each tail with 25 degrees of freedom. In this case, the model’s t-stat of 13.71 (refer to cell S19 in Exhibit 66) is well above the critical t-value of 2.79; therefore, a statistically significant relationship exists between the variables. It is important to note that pure statisticians would not say that a relationship between the variables exists, but that we can reject the notion that no relationship exists, or we reject the null hypothesis. The P-value represents the lowest level of significance the model can reach while still showing a statistically significant relationship. In this case, the P-value is close to zero (cell T19 in Exhibit 66), which means we would reach the same conclusion even at a confidence interval greater than 99%.

164

Exhibit 66—Boeing Regression Model Output: P-Value & t-Stat

Step R3b—Breusche-Pagan Test: To make sure the independent variables do not explain variation in the residuals (a situation known as heteroskedasticty), run the Breusche-Pagan test by regressing the independent variable against the squared residuals. The resulting R-square is relatively low with a high p-value, and a t-stat between the critical-t points (refer to cells AE19 and AF19 in Exhibit 67 below); therefore, heteroskedasticity is not an issue with this model. To put it another way, the correlation between shipments and the squared residuals is not statistically different from zero.

165

Exhibit 67—Boeing Model Breusche-Pagan Results

In addition to the Breusche-Pagan test, we can also plot the residual with a linear trend line showing the R Square to determine if the independent variables explain variation in the residuals (this was the box we checked in Step R2e). In the Boeing example, the trend line and R Square are close to zero so the residuals and independent variable are not correlated.

Exhibit 68—Boeing Model Residual Plot

166

Step R3c—Serial Correlation Test: Serial correlation is the relationship of a variable with itself over time. If serial correlation is present, past observations will influence future results. To ensure the model does not exhibit signs of serial correlation, run the Durbin-Watson (DW) test, by calculating the DW statistic. To calculate the Durbin-Watson statistic, first square the residuals from the regression output. Then, sum the difference of the actual residuals and the residuals on a one-period lag squared. Next, divide the result by the squared residuals. Durbin-Watson statistics range in value between 0 and 4. A value near 0 indicates positive serial correlation, a value near 4 indicates negative serial correlation, and a value near 2 indicates no significant serial correlation. Note that these ranges represent approximations. For exact measures refer to a DW Significance Table. The Boeing model DW statistic is near 2, which indicates that serial correlation is not likely an issue for this model. Step R3d—The Normal Distribution Assumption: To test that the data does not violate the assumption of a normal distribution, calculate the skew and kurtosis. A distribution is considered to be skewed if more observations are found on one side of the mean. A perfect normal distribution would have a skew of 0. A skew of +/-1 can be considered approximately normally distributed. Kurtosis is the measure of the distribution’s peak. A kurtosis of less than 3 can be considered approximately normally distributed. Excel has equations for these formulas: =SKEW(residuals) and =KURT(residuals). If you feel you need additional evidence, you can also run a Chi-sq test, and plot the residuals in a histogram to see if they appear to be normally distributed. In the Boeing model the skew is 0.02 (which is below +/-1), and the kurtosis is -0.53 (which is below 3). These statistics show that the sample is approximately normally distributed. Step R3e—Model Stability: To test the stability of the model, remove two random observations from the sample and re-run the regression. The new regression should pass all the prior tests with a similar standard error compared to the first model. This will prove that the regression model is fairly stable. Step R3f—Other Considerations: Textbook examples for regression analysis tend to be relatively straightforward. In the real world, data and external conditions will change over time which makes a regression model less powerful, and in most cases ineffective. Even if your model passes all the statistical tests, it is important to think qualitatively about whether or 167

not there is a true economic reason for the variables’ correlation and if the direction of the coefficients makes sense. Using the Boeing example, it stands to reason that U.S. Aircraft & Parts Shipments could have predictive power for Boeing’s revenue, and the positive regression coefficient makes sense as it implies a direct relationship (when shipments increase Boeing’s revenue increases). In addition, it is important to assess whether or not the population has changed over time. If there have been significant changes, for example, if Boeing began selling medical devices or hamburgers, then the model could not be used (violates the homogenous population condition). Another potential issue is that the Boeing regression model uses U.S. Shipment data as a proxy for global revenue. Using a proxy to represent a larger population of data has problems (refer to limitations in Step R6). In addition, if Boeing’s global share of revenue shifts significantly, foreign exchange rates fluctuate, or revenue recognition principals change, this model may fail to provide a reliable estimate. The first five tests are relevant for both of the Boeing and Starbucks regression models. Since the Starbucks example is a multiple regression, we have a few additional tests to run, including the following: Step R3g—F-Stat Review: This statistic is similar to the t-stat, except it is used for multiple regressions. If the F-stat is greater than or less than the critical F-values, then a statistically significant relationship exists. Critical F-values are published by degrees of freedom on a critical F chart which is available in most statistical textbooks. In the Starbucks model, the F-stat of 29.88 (refer to cell U17 in Exhibit 69 below) is well above the critical Fvalue of 2.86; therefore, a statistically significant relationship exists between the variables (or we reject the null hypothesis that no relationship exists).

168

Exhibit 69—Starbucks Regression Model Output

Step R3h—Significance F: The Significance F for a multiple regression has the same interpretation as the P-value for a simple linear regression: It represents the lowest level of significance the model can reach while still showing a statistically significant relationship. In this case, the Significance F value is close to zero (refer to cell V17 in Exhibit 69 above) which means we would reach the same conclusion even at a confidence interval greater than 99%. Step R3i—Multicollinearity: To test for multicollinearity, regress the two independent variables against each other to ensure they are not correlated. In the Starbucks regression model, the resulting regression between the calendar fourth quarter dummy variable and the percentage change in U.S. Food Service and Drinking Places Sales is not statistically significant at the 95% confidence interval; therefore, multicollinearity is not a significant issue.

Step R4: Analyze the Output Now that we have determined that our regression models are valid, we can analyze the results and use them to predict future period revenue, which can be included in the earnings model built in the previous chapters. The R Square, also known as the coefficient of determination, represents the 169

amount of variation in the dependent variable, explained by the independent variable. In the Boeing example, we can conclude that 84% of the variation in the percentage change in Boeing’s revenue is explained by the percentage change in U.S. Defense and Non-Defense Aircraft & Parts Shipments, based on the R Square in the regression output (refer to Exhibit 66 cell Q8). Since we ran the Boeing regression without a constant intercept, the regression equation is: Percentage change in Boeing Revenue = 0.88 × the percentage change in U.S. Aircraft & Parts Shipments The 0.88 in the equation represents the coefficient in the regression output (refer to Exhibit 66 cell Q19). We can now use this equation to predict Boeing’s future quarterly revenue by inputting the quarterly percentage change in Aircraft & Parts Shipments, which is published before Boeing releases quarterly results. The R Square in our Starbucks Model is 0.76, which means that 76% of the variation in Starbucks revenue is explained by changes in U.S. Food Services & Drinking Places Sales (refer to Exhibit 69 cell R11). The Standard error gives the predicted range of +/- 4% (Exhibit 69 cell R12). The regression equation is: Percentage change in Starbucks’ revenue = -0.02 + 0.89 × percentage change in Food Services Sales + 0.15 × 4Q Dummy Variable The coefficients from the equation come from cells R22, R23, and R24. Plugging in the new U.S. Census data and the dummy variable for each quarter will result in next quarter’s predicted revenue value, which we could incorporate into our earnings model ahead of the company’s release.

Step R5: Perform Back Testing Using the regression equation for Boeing, we can back test what the model would have predicted based on the shipment data for the last three quarters. The data in Table 13 below shows that the model predicted revenue outperformed the consensus revenue estimate in two of the last three quarters used in the regression model.

Table 13—Boeing Regression Model Back Testing Model Predicted

Consensus 170

Actual Reported

Quarter March 2015 June 2015 September 2015

Revenue ($M) $22,471

Estimate ($M) $22,488

Revenue ($M) $22,149

$24,600

$24,216

$24,543

$24,725

$24,738

$25,849

Analysis More accurate then consensus More accurate then consensus Less accurate then consensus

The results for the Starbucks model, shown in Table 14 below, were not as good as the Boeing model. The regression beat the consensus estimate in only one of the last four quarters used in the model. This is due in part to the fact that consensus estimates for Starbucks tend to match management's guidance, and since guidance is generally reliable for Starbucks, earnings surprise is usually relatively low. The regression model would be most useful when sales change sharply after management has already issued guidance.

Table 14—Starbucks Regression Model Back Testing

Quarter December 2014 March 2015 June 2015 September 2015

Model Predicted Revenue ($M)

Consensus Estimate ($M)

Actual Reported Revenue ($M)

$4,801

$4,797

$4,803

$4,700

$4,530

$4,564

$4,821

$4,863

$4,881

$4,722

$4,897

$4,915

Analysis More accurate then consensus Less accurate then consensus Less accurate then consensus Less accurate then consensus

Step R6: Model Limitations As with all regression analysis, there are limitations to our models which should be considered when deciding whether or not to use regression analysis. Some examples of limitations include the following: Sample: Regression analysis draws conclusions based on a limited sample of observations, typically at least 30 observations if possible. As a result, there is always a degree of error expected. Ahead of an earnings release, 171

we quantify the expected error with a range of values, but actual results can exceed the model’s standard error, particularly if the assumptions in the model are not held constant in the actual results. Historic Trends: Regression analysis assumes that historic relationships will hold constant in the future and does not consider changes such as market share or currency rate fluctuations. The residuals from the predicted values show instances in the past where the model predicted revenue differs significantly from the actual result. Similar instances will likely occur in the future. Revisions: Economic data is often revised after it is released, which can result in impaired predictability. Company Policies: Revenue recognition policies can change over time and may not align with shipment data (Boeing example). Proxies: The Boeing model uses U.S. Aircraft & Parts Shipments as a proxy for global sales of products and services. The percentage of Boeing’s revenue generated outside of the U.S. fluctuates over time, as does the ratio of service-to-product revenue. Both of which will impact the model’s ability to accurately predict revenue. The Starbucks model uses Food Service & Drinking Places Sales as a proxy for global sales. The percentage of Starbucks’ revenue generated outside of the U.S. fluctuates over time, which will impact the model’s ability to accurately predict revenue. There are a few ways we could improve the predictive power of these models. For example, we could segment the revenue between geographic regions, and products versus services, and re-run the regression. To accomplish this, we would need to find similar reliable economic statistics for each region to arrive at the total revenue number. We could also include additional observations, although the further back you go the more likely it is that your population attributes have changed. Due to the inherent limitations, most equity research analysts do not rely on regression analysis in their forecasts. Despite the limitations, regression models can provide important insight to help forecast results, and if nothing else provide a few extra data points to consider in our earnings sensitivity analysis ahead of quarterly releases.

172

Table of Contents Chapter 1: Introduction to Financial Modeling

10 10

Section 1: What is an Earnings Model Section 2: The Fundamental Principles of Modeling Section 3: How to Use This Book Section 4: The Earnings Cycle Section 5: Timing of Publication Section 6: Basic Excel Functionality Section 7: Types of Models Section 8: Anatomy of a Model

Chapter 2: Building a Basic Financial Statement Model

10 12 14 14 16 16 18 20

31 31

Step 1: Getting Started Step 2: How to Setup Your Model Step 3: Filling in the Historic Data Step 4: Assumptions & Forecast Equations

Chapter 3: Converting to a Tier 2 Earnings Model

31 32 33 34

41 41

Step 5: Build the Earnings Engine Step 6: Link Product or Segment Details Step 7: Complete the Income Statement Step 8: Adjust for Non-GAAP Items

Chapter 4: Converting to a Tier 1 Earnings Model

41 47 48 53

59 59

Step 9: Complete the Historic Financials Step 10: Balance Sheet Modeling—Assets Step 11: Balance Sheet Modeling—Liabilities Step 12: Balance Sheet Modeling—Equity Step 13: Cash Flow Statement Modeling Step 14: Primary Financial Statement Links 173

59 60 72 84 89 94

Chapter 5: Model Calibration & Forecasting

96 96

Step 15: Incorporate Historic Trends Step 16: Adjust for Seasonality Step 17: Adjust for New Product Launches Step 18: Guidance and the Consensus Step 19: Incorporate Your Opinions Step 20: Consider Leading Indicators

Chapter 6: The DCF Inputs (Beta, ERP, CAPM, & WACC) Step 21: Calculate the Equity Risk Premium Step 22: Derive Beta Using the Regression Function in Excel Step 23: Calculate the Required Return on Equity using Beta and the CAPM Step 24: Calculate the WACC

Chapter 7: Discounted Cash Flow Valuation

96 97 99 100 115 125

128 128 129 140 141 142

144 144

Step 25: Calculate the Stage-One DCF Step 26: Calculate the Stage-Two DCF Step 27: Calculate the DCF Valuation Step 28: Understanding the DCF Valuation

Chapter 8: Market Multiple Based Valuation

144 146 147 147

149 149

Step 29: Choose a Multiple Step 30: Separate Net Cash Step 31: Calculate the Historic Multiple Step 32: Create a Price Band

Chapter 9: How to Use Your Earnings Model

149 150 150 151

152 152

Step 33: Run Scenario Analysis Step 34: Perform Sensitivity Analysis Step 35: Analyze Guidance & Consensus 174

152 153 155

Step 36: Prepare for the Release Step 37: Updating The Model Step 38: Regular Model Maintenance

155 157 158

Appendix 1: Using Regression Analysis to Predict Earnings

160 160

Step R1: Select Your Data Set Step R2: Run the Regression Step R3: Test the Model Step R4: Analyze the Output Step R5: Perform Back Testing Step R6: Model Limitations

161 163 164 169 170 171

175