Preface

For more than 20 years, since the emergence of PCs, Lotus 1-2-3, and Microsoft Excel in the 1980’s, spreadsheet models have been the dominant vehicles for finance professionals in the business world to implement their financial knowledge. Yet even today, most Corporate Finance textbooks rely on calculators as the primary tool and have little coverage of how to build and estimate Excel models. This book fills that gap. It teaches students how to build and estimate financial models in Excel. It provides step-by-step instructions so that students can build and estimate models themselves (active learning), rather than being handed already completed spreadsheets (passive learning). It progresses from simple examples to practical, real-world applications. It spans nearly all quantitative models in the corporate finance.

My goal is simply to change finance education from being calculator based to being Excel based. This change will better prepare students for the 21^st century business world. This change will increase student evaluations of teacher performance by enabling more practical, real-world content and by allowing a more hands-on, active learning pedagogy.

New to this edition, the biggest innovation is Ready-To-Build Spreadsheets on the CD. The CD provides ready-to-build spreadsheets for every chapter with:

· The new Ready-To-Build spreadsheets on the CD are very popular with students. They can open a spreadsheet that is set up and ready to be constructed. Then they can follow the on-spreadsheet instructions to complete the Excel model and don’t have to refer back to the book for each step. Once they are done, they can double-check their work against the completed spreadsheet shown in the book. This approach concentrates student time on implementing financial formulas and estimation.

o Estimating firm valuation or project valuation in a two-stage framework using five alternative techniques and demonstrating their equivalence:

o Estimating the cost of capital using the Static CAPM based on the Fama-MacBeth method,

o Estimating the cost of capital using the APT or Intertemporal CAPM based on the Fama-MacBeth method, including the Fama-French three factor model, and

Text Box: Excel 2003 Equivalent
To call up a Data Table in
Excel 2003, click on Data | Table

The Ready-To-Build spreadsheets on CD and the explanations in the book are based on Excel 2007 by default. However, the CD also contains a folder with Ready-To-Build spreadsheets based on Excel 97-2003 format. Also, the book contains “Excel 2003 Equivalent” boxes that explain how to do the equivalent step in Excel 2003 and earlier versions.

· The instruction boxes on the Ready-To-Build spreadsheets are bitmapped images so that the formulas cannot just be copied to the spreadsheet. Both the instruction boxes and arrows are objects, so that all of them can be deleted in one step when the spreadsheet is complete and everything else will be left untouched. Click on Home | Editing | Find & Select down-arrow | Select Objects, then select all of the instruction boxes and arrows, and press the delete key. Furthermore, any blank rows can be deleted, leaving a clean spreadsheet for future use.

· Plain Vanilla Excel. Other books on the market emphasize teaching students programming using Visual Basic for Applications (VBA) or using macros. By contrast, this book does nearly everything in plain vanilla Excel. Although programming is liked by a minority of students, it is seriously disliked by the majority. Plain vanilla Excel has the advantage of being a very intuitive, user-friendly environment that is accessible to all. It is fully capable of handling a wide range of applications, including quite sophisticated ones. Further, your students already know the basics of Excel and nothing more is assumed. Students are assumed to be able to enter formulas in a cell and to copy formulas from one cell to another. All other features of Excel (such as built-in functions, Data Tables, Solver, etc.) are explained as they are used.

· Build From Simple Examples To Practical, Real-World Applications. The general approach is to start with a simple example and build up to a practical, real-world application. In many chapters, the previous Excel model is carried forward to the next more complex model. For example, the chapter on binomial option pricing carries forward Excel models as follows: (a.) single-period model with replicating portfolio, (b.) eight-period model with replicating portfolio, (c.) eight-period model with risk-neutral probabilities, (d.) eight-period model with risk-neutral probabilities for American or European options with discrete dividends, (e.) full-scale, fifty-period model with risk-neutral probabilities for American or European options with discrete dividends using continuous or discrete annualization convention. Whenever possible, this book builds up to full-scale, practical applications using real data. Students are excited to learn practical applications that they can actually use in their future jobs. Employers are excited to hire students with Excel modeling and estimation skills, who can be more productive faster.

· Supplement For All Popular Corporate Finance Textbooks. This book is a supplement to be combined with a primary textbook. This means that you can keep using whatever textbook you like best. You don’t have to switch. It also means that you can take an incremental approach to incorporating Excel modeling and estimation. You can start modestly and build up from there.

· A Change In Content Too. Excel modeling and estimation is not merely a new medium, but an opportunity to cover some unique content items which require computer support to be feasible. For example, the full-scale estimation Excel model in Corporate Financial Planning uses three years of historical 10K data on Nike, Inc. (including every line of their income statement, balance sheet, and cash flow statement), constructs a complete financial system (including linked financial ratios), and projects these financial statements three years into the future. Using 10 years of monthly returns for individual stocks, U.S. portfolios, and country portfolios to estimate the cost of capital using the Static CAPM based on the Fama-MacBeth method and to estimate the cost of capital using the APT or Intertemporal CAPM based on the Fama-MacBeth method. The Excel model to estimate firm valuation or project valuation demonstrates the equivalence of the Free Cash Flow To Equity, Free Cash Flow to the Firm, Residual Income, Dividend Discount Model, and Adjusted Present Value technique, not just in the perpetuity case covered by some textbooks, but for a fully general two-stage project with an arbitrary set of cash flows over an explicit forecast horizon, followed by a infinite horizon growing perpetuity. As a practical matter, all of these sophisticated applications require Excel.

· Time Goes Across The Columns And Variables Go Down The Rows. When something happens over time, I let each column represent a period of time. For example in life-cycle financial planning, date 0 is in column B, date 1 is in column C, date 2 is in column D, etc. Each row represents a different variable, which is usually a labeled in column A. This manner of organizing Excel models is so common because it is how financial statements are organized.

· Color Coding. A standard color scheme is used to clarify the structure of the Excel models. The Ready-To-Build spreadsheets on CD uses: (1) yellow shading for input values, (2) no shading (i.e. white) for throughput formulas, and (3) green shading for final results ("the bottom line"). A few Excel models include choice variables. Choice variables use blue shading. The Constrained Portfolio Optimization spreadsheet includes constraints. Constaints use pink-purple shading.

· The Time Line Technique. The most natural technique for discounting cash flows in an Excel model is the time line technique, where each column corresponds to a period of time. As an example, see the section labeled Calculate Bond Price using a Timeline in the figure below.

· Using As Many Different Techniques As Possible. In the figure above, the bond price is calculated using as many different techniques as possible. Specifically, it is calculated three ways: (1) discounting each cash flow on a time line, (2) using the closed-form formula, and (3) using Excel’s PV function. This approach makes the point that all three techniques are equivalent. This approach also develops skill at double-checking these calculations, which is a very important method for avoiding errors in practice.

· Symbolic Notation is Self-Contained. Every spreadsheet that contains symbolic notation in the instruction boxes is self-contained (i.e., all symbolic notation is defined on the spreadsheet). Further, I have stopped using symbolic notation for named ranges that was used in prior editions. Therefore, there is no need for alternative notation versions that were provided on the CD in the prior edition and they have been eliminated.

I challenge the readers of this book to dramatically improve your finance education by personally constructing all of the Excel models in this book. This will take you about 7 – 15 hours hours depending on your current Excel modeling skills. Let me assure you that it will be an excellent investment. You will:

· gain a practical understanding of the core concepts of the Corporate Finance,

When you complete this challenge, I invite you to send an e-mail to me at cholden@indiana.edu to share the good news. Please tell me your name, school, (prospective) graduation year, and which Excel modeling book you completed. I will add you to a web-based honor roll at:

This book is part of a series on Excel Modeling and Estimation by Craig W. Holden, published by Pearson / Prentice Hall. The series includes:

Each book teaches value-added skills in constructing financial models in Excel. Complete information about the Excel Modeling and Estimation series is available at my web site:

All of the Excel Modeling and Estimation books can be purchased any time at:

If you have any suggestions or corrections, please e-mail them to me at cholden@indiana.edu. I will consider your suggestions and will implement any corrections in the next edition.

This book provides educational examples of how to estimate financial models from real data. In doing so, this book uses a tiny amount of data that is copyrighted by others. I rely upon the fair use provision of law (Section 107 of the Copyright Act of 1976) as the legal and legitimate basis for doing so.[1]

There is no single best way to use Excel Modeling and Estimation in Corporate Finance. There are as many different techniques as there are different styles and philosophies of teaching. You need to discover what works best for you. Let me highlight several possibilities:

1. Out-of-class individual projects with help. This is a technique that I have used and it works well. I require completion of several short Excel modeling projects of every individual student in the class. To provide help, I schedule special “help lab” sessions in a computer lab during which time myself and my graduate assistant are available to answer questions while students do each assignment in about an hour. Typically about half the questions are Excel questions and half are finance questions. I have always graded such projects, but an alternative approach would be to treat them as ungraded homework.

2. Out-of-class individual projects without help. Another technique is to assign Excel modeling projects for individual students to do on their own out of class. One instructor assigns seven Excel modeling projects at the beginning of the semester and has individual students turn in all seven completed Excel models for grading at the end of the semester. At the end of each chapter are problems that can be assigned with or without help. Faculty members can download the completed Excel models at http://www.prenhall.com/holden. See your local Pearson / Prentice Hall (or Pearson Education) representative to gain access.

3. Out-of-class group projects. A technique that I have used for the last fifteen years is to require students to do big Excel modeling projects in groups. I have students write a report to a hypothetical boss, which intuitively explains their method of analysis, key assumptions, and key results.

4. In-class reinforcement of key concepts. The class session is scheduled in a computer lab or equivalently students are required to bring their (required) laptop computers to a technology classroom, which has a data jack and a power outlet at every student station. I explain a key concept in words and equations. Then I turn to a 10-15 minute segment in which students open a Ready-To-Build spreadsheet and build the Excel model in real-time in the class. This provides real-time, hands-on reinforcement of a key concept. This technique can be done often throughout the semester.

5. In-class demonstration of Excel modeling. The instructor can perform an in-class demonstration of how to build Excel models. Typically, only a small portion of the total Excel model would be demonstrated.

6. In-class demonstration of key relationships using Spin Buttons, Option Buttons, and Charts. The instructor can dynamically illustrate comparative statics or dynamic properties over time using visual, interactive elements. For example, one spreadsheet provides a “movie” of 37 years of U.S. term structure dynamics. Another spreadsheet provides an interactive graph of the sensitivity of bond prices to changes in the coupon rate, yield-to-maturity, number of payments / year, and face value.

I’m sure I haven’t exhausted the list of potential teaching techniques. Feel free to send an e-mail to cholden@indiana.edu to let me know novel ways in which you use this book.

I thank Mark Pfaltzgraff, David Alexander, Jackie Aaron, P.J. Boardman, Mickey Cox, Maureen Riopelle, and Paul Donnelly of Pearson / Prentice Hall for their vision, innovativeness, and encouragement of Excel Modeling and Estimation in Corporate Finance. I thank Susan Abraham, Kate Murray, Lori Braumberger, Holly Brown, Debbie Clare, Cheryl Clayton, Kevin Hancock, Josh McClary, Bill Minic, Melanie Olsen, Beth Ann Romph, Erika Rusnak, Gladys Soto, and Lauren Tarino of Pearson / Prentice Hall for many useful contributions. I thank Professors Alan Bailey (University of Texas at San Antonio), Zvi Bodie (Boston University), Jack Francis (Baruch College), David Griswold (Boston University), Carl Hudson (Auburn University), Robert Kleiman (Oakland University), Mindy Nitkin (Simmons College), Steve Rich (Baylor University), Tim Smaby (Penn State University), Charles Trzcinka (Indiana University), Sorin Tuluca (Fairleigh Dickinson University), Marilyn Wiley (Florida Atlantic University), and Chad Zutter (University of Pittsburgh) for many thoughtful comments. I thank my graduate students Scott Marolf, Heath Eckert, Ryan Brewer, Ruslan Goyenko, Wendy Liu, and Wannie Park for careful error-checking. I thank Jim Finnegan and many other students for providing helpful comments. I thank my family, Kathryn, Diana, and Jimmy, for their love and support.

CRAIG W. HOLDEN

Craig Holden is the Max Barney Faculty Fellow and Associate Professor of Finance at the Kelley School of Business at Indiana University. His M.B.A. and Ph.D. are from the Anderson School at UCLA. He is the winner of many teaching and research awards. His research on security trading and market making (“market microstructure”) has been published in leading academic journals. He has written four books on Excel Modeling and Estimation in finance, which are published by Pearson / Prentice Hall and Chinese editions are published by China Renmin University Press. He has chaired sixteen dissertations, been a member or chair of 46 dissertations, served on the program committee of the Western Finance Association for nine years, and served as an associate editor of the Journal of Financial Markets for eleven years. He chaired the department undergraduate committee for eleven years, chaired three different schoolwide committees over six years, and is currently chairing the department doctoral committee. He has lead several major curriculum innovations in the finance department. More information is available at Craig’s home page: www.kelley.iu.edu/cholden.

                                               [1] Consistent with the fair use statute, I make transformative use of the data for teaching
                                                            purposes, the nature of the data is factual data that is important to the educational
                                                            purpose, the amount of data used is a tiny, and its use has no significant impact on the
                                                            potential market for the data.