Financial Modeling, 5th edition
- Length: 1048 pages
- Edition: 5
- Language: English
- Publisher: The MIT Press
- Publication Date: 2022-02-01
- ISBN-10: 0262046423
- ISBN-13: 9780262046428
- Sales Rank: #79909 (See Top 100 Books)
A substantially updated new edition of the essential text on financial modeling, with revised material, new data, and implementations shown in Excel, R, and Python.
Financial Modeling has become the gold-standard text in its field, an essential guide for students, researchers, and practitioners that provides the computational tools needed for modeling finance fundamentals. This fifth edition has been substantially updated but maintains the straightforward, hands-on approach, with an optimal mix of explanation and implementation, that made the previous editions so popular. Using detailed Excel spreadsheets, it explains basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds. This new edition offers revised material on valuation, second-order and third-order Greeks for options, value at risk (VaR), Monte Carlo methods, and implementation in R. The examples and implementation use up-to-date and relevant data.
Parts I to V cover corporate finance topics, bond and yield curve models, portfolio theory, options and derivatives, and Monte Carlo methods and their implementation in finance. Parts VI and VII treat technical topics, with part VI covering Excel and R issues and part VII (now on the book’s auxiliary website) covering Excel’s programming language, Visual Basic for Applications (VBA), and Python implementations. Knowledge of technical chapters on VBA and R is not necessary for understanding the material in the first five parts. The book is suitable for use in advanced finance classes that emphasize the need to combine modeling skills with a deeper knowledge of the underlying financial models.
Cover Title Page Copyright Dedication Table of Contents Preface and Acknowledgments Before All Else 0.1. Data Tables 0.2. What Is Getformula? 0.3. How to Put Getformula into Your Excel Notebook 0.4. Saving the Excel Workbook: Windows 0.5. Saving the Excel Workbook: Mac 0.6. Do You Have to Put Getformula into Each Excel Workbook? 0.7. Using Formulatext() Instead of Getformula 0.8. A Shortcut to Use Getformula and Formulatext 0.9. Recording Getformula: The Windows Case 0.10. Recording Getformula: The Mac Case 0.11. Using R I. Corporate Finance 1. Basic Financial Analysis 1.1. Overview 1.2. Present Value and Net Present Value 1.3. The Internal Rate of Return (IRR) and Loan Tables 1.4. Multiple Internal Rates of Return 1.5. Flat Payment Schedules 1.6. Future Values and Applications 1.7. A Pension Problem—Complicating the Future Value Problem 1.8. Continuous Compounding 1.9. Discounting Using Dated Cash Flows Exercises 2. Corporate Valuation Overview 2.1. Overview 2.2. Three Methods to Compute Enterprise Value (EV) 2.3. Using Accounting Book Values to Value a Company: The Firm’s Accounting Enterprise Value 2.4. The Efficient Markets Approach to Corporate Valuation 2.5. Enterprise Value as the Present Value of the Free Cash Flows: DCF “Top Down” Valuation 2.6. Free Cash Flows Based on Consolidated Statement of Cash Flows 2.7. Free Cash Flows Based on Pro Forma Financial Statements 2.8. Summary Exercises 3. Calculating the Weighted Average Cost of Capital (WACC) 3.1. Overview 3.2. Computing the Value of the Firm’s Equity, E 3.3. Computing the Value of the Firm’s Debt, D 3.4. Computing the Firm’s Tax Rate, TC 3.5. Computing the Firm’s Cost of Debt, rD 3.6. Two Approaches to Computing the Firm’s Cost of Equity, rE 3.7. Three Approaches to Computing the Expected Return on the Market, E(rM) 3.8. What’s the Risk-Free Rate rf in the CAPM? 3.9. Computing the WACC 3.10. When Don’t the Models Work? 3.11. Summary Exercises 4. Pro Forma Analysis and Valuation Based on the Discounted Cash Flow Approach 4.1. Overview 4.2. Setting the Stage—Discounting the Free Cash Flow (FCF) 4.3. Simplified Approach Based on Consolidated Statement of Cash Flows 4.4. Pro Forma Financial Statement Modeling 4.5. Using the FCF to Value the Firm and Its Equity 4.6. Setting the Debt to Be the Absorbing Item and Incorporating Target Debt/Equity Ratio into the Pro Forma 4.7. Calculating the Return on Invested Capital 4.8. Project Finance: Debt Repayment Schedules 4.9. Calculating the Return on Equity 4.10. Tax Loss Carryforwards 4.11. Conclusion Exercises 5. Building a Pro Forma Model: The Case of Merck 5.1. Overview 5.2. Merck’s Financial Statements, 2015–2018 5.3. Analyzing the Financial Statements 5.4. A Model for Merck 5.5. Using the Model to Value Merck 5.6. Valuation Model for Merck Using Multiples 5.7. Summary 6. Financial Analysis of Leasing 6.1. Overview 6.2. A Simple but Misleading Example 6.3. Leasing and Firm Financing—the Equivalent-Loan Method 6.4. The Lessor’s Problem: Calculating the Highest Acceptable Lease Rental 6.5. Asset Residual Value and Other Considerations 6.6. Mini-Case: When Is Leasing Profitable for Both the Lessor and the Lessee? 6.7. Leveraged Leasing 6.8. A Leveraged Lease Example 6.9. Summary Exercises II. Bonds 7. Bond’s Duration 7.1. Overview 7.2. Two Examples 7.3. What Does Duration Mean? 7.4. Duration Patterns 7.5. The Duration of a Bond with Uneven Payments 7.6. Convexity of a Bond 7.7. Immunization Strategies 7.8. Summary Exercises 8. Modeling the Term Structure 8.1. Overview 8.2. The Term Structure of Interest Rates 8.3. Bond Pricing Using the Equivalent Single Bond Approach 8.4. Pricing with Several Bonds at the Same Maturity 8.5. The Nelson-Siegel Approach of Fitting a Functional Form to the Term Structure 8.6. The Properties of the Nelson-Siegel Term Structure 8.7. Term Structure for Treasury Notes 8.8. Summary Appendix: VBA Functions Used in This Chapter 9. Calculating Default-Adjusted Expected Bond Returns 9.1. Overview 9.2. Calculating the Expected Return in a One-Period Framework 9.3. Calculating the Bond Expected Return in a Multi-period Framework 9.4. A Numerical Example 9.5. Experimenting with the Example 9.6. Computing the Bond Expected Return for an Actual Bond 9.7. Semiannual Transition Matrices 9.8. Computing Bond Beta 9.9. Summary Exercises III. Portfolio Theory 10. Portfolio Models—Introduction 10.1. Overview 10.2. Computing Descriptive Statistics for Stocks 10.3. Calculating Portfolio Means and Variances 10.4. Portfolio Mean and Variance—Case of N Assets 10.5. Envelope Portfolios 10.6. Summary Exercises Appendix 10.1: Continuously Compounded versus Geometric Returns Appendix 10.2: Adjusting for Dividends 11. Efficient Portfolios and the Efficient Frontier 11.1. Overview 11.2. Some Preliminary Definitions and Notation 11.3. Five Propositions on Efficient Portfolios and the CAPM 11.4. Calculating the Efficient Frontier: An Example 11.5. Three Notes on the Optimization Procedure 11.6. Finding the Market Portfolio: The Capital Market Line (CML) 11.7. Computing the Global Minimum Variance Portfolio (GMVP) 11.8. Testing the SML—Implementing Propositions 3–5 11.9. Efficient Portfolios without Short Sales 11.10. Summary Exercises Mathematical Appendix 12. Calculating the Variance-Covariance Matrix 12.1. Overview 12.2. Computing the Sample Variance-Covariance Matrix 12.3. The Correlation Matrix 12.4. Four Alternatives to the Sample Variance-Covariance Matrix 12.5. Alternatives to the Sample Variance-Covariance: The Single-Index Model 12.6. Alternatives to the Sample Variance-Covariance: Constant Correlation 12.7. Alternatives to the Sample Variance-Covariance: Shrinkage Methods 12.8. Using Option Information to Compute the Variance Matrix 12.9. Which Method to Compute the Variance-Covariance Matrix? 12.10. Summing Up Exercises 13. Estimating Betas and the Security Market Line 13.1. Overview 13.2. Testing the SML 13.3. Did We Learn Something? 13.4. The Non-efficiency of the “Market Portfolio” 13.5. So What’s the Real Market Portfolio? How Can We Test the CAPM? 13.6. Conclusion: Does the CAPM Have Any Uses? Exercises 14. Event Studies 14.1. Overview 14.2. Outline of an Event Study 14.3. An Initial Event Study: Procter & Gamble Buys Gillette 14.4. A Fuller Event Study: Impact of Earnings Announcements on Stock Prices 14.5. Using a Two-Factor Model of Returns for an Event Study 14.6. Using Excel’s Offset Function to Locate a Regression in a Data Set 14.7. Conclusion 15. The Black-Litterman Approach to Portfolio Optimization 15.1. Overview 15.2. A Naive Problem 15.3. Black and Litterman’s Solution to the Optimization Problem 15.4. BL Step 1: What Does the Market Think? 15.5. BL Step 2: Introducing Opinions—What Does Joanna Think? 15.6. Using BL for International Asset Allocation 15.7. Summary Exercises IV. Options 16. Introduction to Options 16.1. Overview 16.2. Basic Option Definitions and Terminology 16.3. Some Examples 16.4. Option Payoff and Profit Patterns 16.5. Option Strategies: Payoffs from Portfolios of Options and Stocks 16.6. Option Arbitrage Propositions 16.7. Summary Exercises 17. The Binomial Option Pricing Model 17.1. Overview 17.2. Two-Date Binomial Pricing 17.3. The State Prices 17.4. The Multi-period Binomial Model 17.5. Pricing American Options Using the Binomial Pricing Model 17.6. Programming the Binomial Option Pricing Model 17.7. Convergence of Binomial Pricing to the Black-Scholes Price 17.8. Using the Binomial Model to Price Employee Stock Options 17.9. Using the Binomial Model to Price Nonstandard Options: An Example 17.10. Summary Exercises 18. The Black-Scholes Model 18.1. Overview 18.2. The Black-Scholes Model 18.3. Programming the Black-Scholes Option Pricing Model 18.4. Calculating the Volatility 18.5. Programming a Function to Find the Implied Volatility 18.6. Dividend Adjustments to the Black-Scholes 18.7. “Bang for the Buck” with Options 18.8. The Black Model for Bond Option Valuation 18.9. Using the Black-Scholes Model to Price Risky Debt 18.10. Using the Black-Scholes Formula to Price Structured Securities 18.11. Summary Exercises 19. Option Greeks 19.1. Overview 19.2. Defining and Computing the Greeks 19.3. Delta Hedging a Call 19.4. The Greeks of a Portfolio 19.5. Greek-Neutral Portfolio 19.6. The Relationship between Delta, Theta, and Gamma 19.7. Summary Exercises Appendix 19.1: VBA for Greeks Appendix 19.2: R Code for Greeks 20. Real Options 20.1. Overview 20.2. A Simple Example of the Option to Expand 20.3. The Abandonment Option 20.4. Valuing the Abandonment Option as a Series of Puts 20.5. Valuing a Biotechnology Project 20.6. Summary Exercises V. Monte Carlo Methods 21. Generating and Using Random Numbers 21.1. Overview 21.2. Rand() and Rnd: The Excel and VBA Random-Number Generators 21.3. Scaling Uniformly Distributed Numbers 21.4. Generating Normally Distributed Random Numbers 21.5. Norm.Inv: Another Way to Generate Normal Deviates 21.6. Scaling Normally Distributed Numbers 21.7. Generating Correlated Random Numbers 21.8. What’s Our Interest in Correlation? A Small Case 21.9. Multiple Random Variables with Correlation: The Cholesky Decomposition 21.10. Multivariate Uniform Simulations 21.11. Summary Exercises 22. An Introduction to Monte Carlo Methods 22.1. Overview 22.2. Computing π Using Monte Carlo 22.3. Programming the Monte Carlo Approach to Estimate π 22.4. Another Monte Carlo Problem: Investment and Retirement 22.5. A Monte Carlo Simulation of the Investment Problem 22.6. Summary Exercises Appendix: Some Comments on the Value of π 23. Simulating Stock Prices 23.1. Overview 23.2. What Do Stock Prices Look Like? 23.3. Lognormal Price Distributions and Geometric Diffusions 23.4. What Does the Lognormal Distribution Look Like? 23.5. Simulating Lognormal Price Paths 23.6. Technical Analysis 23.7. Calculating the Parameters of the Lognormal Distribution from Stock Prices 23.8. Summary Exercises Appendix: The Itô’s Lemma 24. Monte Carlo Simulations for Investments 24.1. Overview 24.2. Simulating Price and Returns for a Single Stock 24.3. Portfolio of Two Stocks 24.4. Adding a Risk-Free Asset 24.5. Multiple Stock Portfolios 24.6. Simulating Savings for Pensions 24.7. Beta and Return 24.8. Summary Exercises 25. Value at Risk (VaR) 25.1. Overview 25.2. The Three Types of VaR Models 25.3. VaR of an N-Asset Portfolio 25.4. Backtesting 26. Replicating Options and Option Strategies 26.1. Overview 26.2. Imperfect but Cashless Replication of a Call Option 26.3. Simulating Portfolio Insurance 26.4. Some Properties of Portfolio Insurance 26.5. Digression: Insuring Total Portfolio Returns 26.6. Simulating a Butterfly 26.7. Summary Exercises 27. Using Monte Carlo Methods for Option Pricing 27.1. Overview 27.2. Pricing Plain-Vanilla Options Using Monte Carlo Methods 27.3. State Prices, Probabilities, and Risk-Neutrality 27.4. Pricing Plain-Vanilla Options—Monte Carlo Binomial Model Approach 27.5. Pricing Asian Options 27.6. Barrier Options 27.7. Basket Options 27.8. Rainbow Options 27.9. Binary Options 27.10. Chooser Options 27.11. Lookback Options 27.12. Summary Exercises VI. Technical 28. Data Tables 28.1. Overview 28.2. An Example 28.3. Creating a One-Dimensional Data Table 28.4. Creating a Two-Dimensional Data Table 28.5. An Aesthetic Note: Hiding the Formula Cells 28.6. Excel Data Tables Are Arrays 28.7. Data Tables on Blank Cells (Advanced) 28.8. Data Tales Can Stop Your Computer Exercises 29. Matrices 29.1. Overview 29.2. Matrix Operations 29.3. Matrix Inverses 29.4. Solving Systems of Simultaneous Linear Equations Exercises 30. Excel Functions 30.1. Overview 30.2. Financial Functions 30.3. Dates and Date Functions 30.4. Statistical Functions 30.5. Doing Regressions with Excel 30.6. Conditional Functions 30.7. Reference Functions 30.8. Large, Rank, Percentile, and Percentrank 30.9. Count, CountA, Countif, Countifs, Averageif, Averageifs 31. Array Functions 31.1. Overview 31.2. Some Built-In Excel Array Functions 31.3. Homemade Array Functions 31.4. Array Formulas with Matrices Exercises 32. Some Excel Hints 32.1. Overview 32.2. Fast Copy: Filling in Data Next to Filled-In Column 32.3. Filling Cells with a Series 32.4. Multi-line Cells 32.5. Multi-line Cells with Text Formulas 32.6. Writing on Multiple Spreadsheets 32.7. Moving Multiple Sheets of an Excel Notebook 32.8. Text Functions in Excel 32.9. Chart Titles That Update 32.10. Putting Greek Symbols in Cells 32.11. Superscripts and Subscripts 32.12. Named Cells 32.13. Hiding Cells (in Data Tables and Other Places) 32.14. Formula Auditing 32.15. Formatting Millions as Thousands 32.16. Excel’s Personal Notebook: Automating Frequent Procedures 32.17. Quick Number Formatting 33. Essentials of R Programming 33.1. Rule #1: Use the Provided Help for R Functions 33.2. Installing a Package 33.3. Setting a Default Folder (Working Directory) 33.4. Understanding Data Types in R 33.5. How to Read a Table from a CSV File 33.6. How to Directly Import Stock Price Data to R 33.7. Defining a Function 33.8. Plotting Data in R 33.9. The apply Function 33.10. The lapply and sapply Functions Selected References Index
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