Financial modeling /

This book is the standard text for explaining the implementation of financial models in Excel. As in previous editions, this fourth edition maintains the "cookbook" features and Excel dependence; it explains basic and advanced models in the areas of corporate finance, portfolio management,...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Benninga, Simon (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Massachusetts : The MIT Press, [2014]
Edición:Fourth edition.
Temas:
Microsoft Visual Basic for applications.
Microsoft Visual Basic pour applications.
Microsoft Visual Basic for applications
Finance > Mathematical models.
Finances > Modèles mathématiques.
BUSINESS & ECONOMICS > Finance.
Finance > Mathematical models
EXCEL
Finanzierung
Mathematisches Modell
VisualBASIC für Applikationen
Wiskundige modellen.
Bedrijfsfinanciering.
Leermiddelen (vorm)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.A Shortcut to Use Getformula
  • 0.8. Recording Getformula: The Windows Case
  • 0.9. Recording Getformula: The Mac Case
  • 1. Basic Financial Calculations
  • 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. Four Methods to Compute Enterprise Value (EV).
  • Note continued: 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 (EV) 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 (CSCF)
  • 2.7. ABC Corp., Consolidated Statement of Cash Flows (CSCF)
  • 2.8. Free Cash Flows Based on Pro Forma Financial Statements
  • 2.9. 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. Implementing the Gordon Model for rE
  • 3.8. The CAPM: Computing the Beta.
  • Note continued: 3.9. Using the Security Market Line (SML) to Calculate Merck's Cost of Equity, rE
  • 3.10. Three Approaches to Computing the Expected Return on the Market, E(rM)
  • 3.11. What's the Risk-Free Rate rf in the CAPM?
  • 3.12.Computing the WACC, Three Cases
  • 3.13.Computing the WACC for Merck (MRK)
  • 3.14.Computing the WACC for Whole Foods (WFM)
  • 3.15.Computing the WACC for Caterpillar (CAT)
  • 3.16. When Don't the Models Work?
  • 3.17. Summary
  • Exercises
  • 4. Valuation Based on the Consolidated Statement of Cash Flows
  • 4.1. Overview
  • 4.2. Free Cash Flow (FCF): Measuring the Cash Produced by the Business
  • 4.3.A Simple Example
  • 4.4. Merck: Reverse Engineering the Market Value
  • 4.5. Summary
  • Exercise
  • 5. Pro Forma Financial Statement Modeling
  • 5.1. Overview
  • 5.2. How Financial Models Work: Theory and an Initial Example
  • 5.3. Free Cash Flow (FCF): Measuring the Cash Produced by the Business.
  • Note continued: 5.4. Using the Free Cash Flow (FCF) to Value the Firm and Its Equity
  • 5.5. Some Notes on the Valuation Procedure
  • 5.6. Alternative Modeling of Fixed Assets
  • 5.7. Sensitivity Analysis
  • 5.8. Debt as a Plug
  • 5.9. Incorporating a Target Debt/Equity Ratio into a Pro Forma
  • 5.10. Project Finance: Debt Repayment Schedules
  • 5.11. Calculating the Return on Equity
  • 5.12. Tax Loss Carryforwards
  • 5.13. Summary
  • Exercises
  • 6. Building a Pro Forma Model: The Case of Caterpillar
  • 6.1. Overview
  • 6.2. Caterpillar's Financial Statements, 2007-2011
  • 6.3. Analyzing the Financial Statements
  • 6.4.A Model for Caterpillar
  • 6.5. Using the Model to Value Caterpillar
  • 6.6. Summary
  • 7. Financial Analysis of Leasing
  • 7.1. Overview
  • 7.2.A Simple but Misleading Example
  • 7.3. Leasing and Firm Financing-The Equivalent-Loan Method
  • 7.4. The Lessor's Problem: Calculating the Highest Acceptable Lease Rental
  • 7.5. Asset Residual Value and Other Considerations.
  • Note continued: 7.6. Leveraged Leasing
  • 7.7.A Leveraged Lease Example
  • 7.8. Summary
  • Exercises
  • 8. Portfolio Models-Introduction
  • 8.1. Overview
  • 8.2.Computing Returns for Apple (AAPL) and Google (GOOG)
  • 8.3. Calculating Portfolio Means and Variances
  • 8.4. Portfolio Mean and Variance-Case of N Assets
  • 8.5. Envelope Portfolios
  • 8.6. Summary
  • Exercises
  • Appendix 8.1: Adjusting for Dividends
  • Appendix 8.2: Continuously Compounded Versus Geometric Returns
  • 9. Calculating Efficient Portfolios
  • 9.1. Overview
  • 9.2. Some Preliminary Definitions and Notation
  • 9.3. Five Propositions on Efficient Portfolios and the CAPM
  • 9.4. Calculating the Efficient Frontier: An Example
  • 9.5. Finding Efficient Portfolios in One Step
  • 9.6. Three Notes on the Optimization Procedure
  • 9.7. Finding the Market Portfolio: The Capital Market Line (CML)
  • 9.8. Testing the SML-Implementing Propositions 3-5
  • 9.9. Summary
  • Exercises
  • Mathematical Appendix.
  • Note continued: 10. Calculating the Variance-Covariance Matrix
  • 10.1. Overview
  • 10.2.Computing the Sample Variance-Covariance Matrix
  • 10.3. The Correlation Matrix
  • 10.4.Computing the Global Minimum Variance Portfolio (GMVP)
  • 10.5. Four Alternatives to the Sample Variance-Covariance Matrix
  • 10.6. Alternatives to the Sample Variance-Covariance: The Single-Index Model (SIM)
  • 10.7. Alternatives to the Sample Variance-Covariance: Constant Correlation
  • 10.8. Alternatives to the Sample Variance-Covariance: Shrinkage Methods
  • 10.9. Using Option Information to Compute the Variance Matrix
  • 10.10. Which Method to Compute the Variance-Covariance Matrix?
  • 10.11. Summary
  • Exercises
  • 11. Estimating Betas and the Security Market Line
  • 11.1. Overview
  • 11.2. Testing the SML
  • 11.3. Did We Learn Something?
  • 11.4. The Non-Efficiency of the "Market Portfolio"
  • 11.5. So What's the Real Market Portfolio? How Can We Test the CAPM?
  • 11.6. Using Excess Returns.
  • Note continued: 11.7. Summary: Does the CAPM Have Any Uses?
  • Exercises
  • 12. Efficient Portfolios Without Short Sales
  • 12.1. Overview
  • 12.2.A Numerical Example
  • 12.3. The Efficient Frontier with Short-Sale Restrictions
  • 12.4.A VBA Program for the Efficient Frontier Without Short Sales
  • 12.5. Other Position Restrictions
  • 12.6. Summary
  • Exercise
  • 13. The Black-Litterman Approach to Portfolio Optimization
  • 13.1. Overview
  • 13.2.A Naive Problem
  • 13.3. Black and Litterman's Solution to the Optimization Problem
  • 13.4. BL Step 1: What Does the Market Think?
  • 13.5. BL Step 2: Introducing Opinions-What Does Joanna Think?
  • 13.6. Using Black-Litterman for International Asset Allocation
  • 13.7. Summary
  • 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.
  • Note continued: 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. Summary
  • 15. Introduction to Options
  • 15.1. Overview
  • 15.2. Basic Option Definitions and Terminology
  • 15.3. Some Examples
  • 15.4. Option Payoff and Profit Patterns
  • 15.5. Option Strategies: Payoffs from Portfolios of Options and Stocks
  • 15.6. Option Arbitrage Propositions
  • 15.7. Summary
  • Exercises
  • 16. The Binomial Option Pricing Model
  • 16.1. Overview
  • 16.2. Two-Date Binomial Pricing
  • 16.3. State Prices
  • 16.4. The Multi-Period Binomial Model
  • 16.5. Pricing American Options Using the Binomial Pricing Model
  • 16.6. Programming the Binomial Option Pricing Model in VBA
  • 16.7. Convergence of Binomial Pricing to the Black-Scholes Price
  • 16.8. Using the Binomial Model to Price Employee Stock Options
  • 16.9. Using the Binomial Model to Price Non-Standard Options: An Example
  • 16.10. Summary
  • Exercises.
  • Note continued: 17. The Black-Scholes Model
  • 17.1. Overview
  • 17.2. The Black-Scholes Model
  • 17.3. Using VBA to Define a Black-Scholes Pricing Function
  • 17.4. Calculating the Volatility
  • 17.5.A VBA Function to Find the Implied Volatility
  • 17.6. Dividend Adjustments to the Black-Scholes
  • 17.7. Using the Black-Scholes Formula to Price Structured Securities
  • 17.8. Bang for the Buck with Options
  • 17.9. The Black (1976) Model for Bond Option Valuation
  • 17.10. Summary
  • Exercises
  • 18. Option Greeks
  • 18.1. Overview
  • 18.2. Defining and Computing the Greeks
  • 18.3. Delta Hedging a Call
  • 18.4. Hedging a Collar
  • 18.5. Summary
  • Exercises
  • Appendix: VBA for Greeks
  • 19. Real Options
  • 19.1. Overview
  • 19.2.A Simple Example of the Option to Expand
  • 19.3. The Abandonment Option
  • 19.4. Valuing the Abandonment Option as a Series of Puth
  • 19.5. Valuing a Biotechnology Project
  • 19.6. Summary
  • Exercises
  • 20. Duration
  • 20.1. Overview
  • 20.2. Two Examples.
  • Note continued: 20.3. What Does Duration Mean?
  • 20.4. Duration Patterns
  • 20.5. The Duration of a Bond with Uneven Payments
  • 20.6. Non-Flat Term Structures and Duration
  • 20.7. Summary
  • Exercises
  • 21. Immunization Strategies
  • 21.1. Overview
  • 21.2.A Basic Simple Model of Immunization
  • 21.3.A Numerical Example
  • 21.4. Convexity: A Continuation of Our Immunization Experiment
  • 21.5. Building a Better Mousetrap
  • 21.6. Summary
  • Exercises
  • 22. Modeling the Term Structure
  • 22.1. Overview
  • 22.2. Basic Example
  • 22.3. Several Bonds with the Same Maturity
  • 22.4. Fitting a Functional Form to the Term Structure
  • 22.5. The Properties of the Nelson-Siegel Term Structure
  • 22.6. Term Structure for Treasury Notes
  • 22.7. An Additional Computational Improvement
  • 22.8. Nelson-Siegel-Svensson Model
  • 22.9. Summary
  • Appendix: VBA Functions Used in This Chapter
  • 23. Calculating Default-Adjusted Expected Bond Returns
  • 23.1. Overview.
  • Note continued: 23.2. Calculating the Expected Return in a One-Period Framework
  • 23.3. Calculating the Bond Expected Return in a Multi-Period Framework
  • 23.4.A Numerical Example
  • 23.5. Experimenting with the Example
  • 23.6.Computing the Bond Expected Return for an Actual Bond
  • 23.7. Semiannual Transition Matrices
  • 23.8.Computing Bond Beta
  • 23.9. Summary
  • Exercises
  • 24. Generating and Using Random Numbers
  • 24.1. Overview
  • 24.2. Rand() and Rnd: The Excel and VBA Random-Number Generators
  • 24.3. Testing Random-Number Generators
  • 24.4. Generating Normally Distributed Random Numbers
  • 24.5. Norm. Inv: Another Way to Generate Normal Deviates
  • 24.6. Generating Correlated Random Numbers
  • 24.7. What's Our Interest in Correlation? A Small Case
  • 24.8. Multiple Random Variables with Correlation: The Cholesky Decomposition
  • 24.9. Multivariate Normal with Non-Zero Means
  • 24.10. Multivariate Uniform Simulations
  • 24.11. Summary
  • Exercises.
  • Note continued: 25. An Introduction to Monte Carlo Methods
  • 25.1. Overview
  • 25.2.Computing IT Using Monte Carlo
  • 25.3. Writing a VBA Program
  • 25.4. Another Monte Carlo Problem: Investment and Retirement
  • 25.5.A Monte Carlo Simulation of the Investment Problem
  • 25.6. Summary
  • Exercises
  • 26. Simulating Stock Prices
  • 26.1. Overview
  • 26.2. What Do Stock Prices Look Like?
  • 26.3. Lognormal Price Distributions and Geometric Diffusions
  • 26.4. What Does the Lognormal Distribution Look Like?
  • 26.5. Simulating Lognormal Price Paths
  • 26.6. Technical Analysis
  • 26.7. Calculating the Parameters of the Lognormal Distribution from Stock Prices
  • 26.8. Summary
  • Exercises
  • 27. Monte Carlo Simulations for Investments
  • 27.1. Overview
  • 27.2. Simulating Price and Returns for a Single Stock
  • 27.3. Portfolio of Two Stocks
  • 27.4. Adding a Risk-Free Asset
  • 27.5. Multiple Stock Portfolios
  • 27.6. Simulating Savings for Pensions
  • 27.7. Beta and Return
  • 27.8. Summary.
  • Note continued: Exercises
  • 28. Value at Risk (VaR)
  • 28.1. Overview
  • 28.2.A Really Simple Example
  • 28.3. Defining Quantiles in Excel
  • 28.4.A Three-Asset Problem: The Importance of the Variance-Covariance Matrix
  • 28.5. Simulating Data: Bootstrapping
  • Appendix: How to Bootstrap: Making a Bingo Card in Excel
  • 29. Simulating Options and Option Strategies
  • 29.1. Overview
  • 29.2. Imperfect but Cashless Replication of a Call Option
  • 29.3. Simulating Portfolio Insurance
  • 29.4. Some Properties of Portfolio Insurance
  • 29.5. Digression: Insuring Total Portfolio Returns
  • 29.6. Simulating a Butterfly
  • 29.7. Summary
  • Exercises
  • 30. Using Monte Carlo Methods for Option Pricing
  • 30.1. Overview
  • 30.2. Pricing a Plain-Vanilla Call Using Monte Carlo Methods
  • 30.3. State Prices, Probabilities, and Risk Neutrality
  • 30.4. Pricing a Call Using the Binomial Monte Carlo Model
  • 30.5. Monte Carlo Plain-Vanilla Call Pricing Converges to Black-Scholes.
  • Note continued: 30.6. Pricing Asian Options
  • 30.7. Pricing Asian Options with a VBA Program
  • 30.8. Pricing Barrier Options with Monte Carlo
  • 30.9. Using VBA and Monte Carlo to Price a Barrier Option
  • 30.10. Summary
  • Exercises
  • 31. Data Tables
  • 31.1. Overview
  • 31.2. An Example
  • 31.3. Setting Up a One-Dimensional Data Table
  • 31.4. Building a Two-Dimensional Data Table
  • 31.5. An Aesthetic Note: Hiding the Formula Cells
  • 31.6. Excel Data Tables Are Arrays
  • 31.7. Data Tables on Blank Cells (Advanced)
  • 31.8. Data Tables Can Stop Your Computer
  • Exercises
  • 32. Matrices
  • 32.1. Overview
  • 32.2. Matrix Operations
  • 32.3. Matrix Inverses
  • 32.4. Solving Systems of Simultaneous Linear Equations
  • 32.5. Some Homemade Matrix Functions
  • Exercises
  • 33. Excel Functions
  • 33.1. Overview
  • 33.2. Financial Functions
  • 33.3. Dates and Date Functions
  • 33.4. The Functions XIRR, XNPV
  • 33.5. Statistical Functions
  • 33.6. Regressions with Excel.
  • Note continued: 33.7. Conditional Functions
  • 33.8. Large and Rank, Percentile, and PercentRank
  • 33.9. Count, CountA, CountIf, CountIfs, AverageIf, AverageIfs
  • 33.10. Boolean Functions
  • 33.11. Offset
  • 34. Array Functions
  • 34.1. Overview
  • 34.2. Some Built-In Excel Array Functions
  • 34.3. Homemade Array Functions
  • 34.4. Array Formulas with Matrices
  • Exercises
  • 35. Some Excel Hints
  • 35.1. Overview
  • 35.2. Fast Copy: Filling in Data Next to Filled-In Column
  • 35.3. Filling Cells with a Series
  • 35.4. Multi-Line Cells
  • 35.5. Multi-Line Cells with Text Formulas
  • 35.6. Writing on Multiple Spreadsheets
  • 35.7. Moving Multiple Sheets of an Excel Notebook
  • 35.8. Text Functions in Excel
  • 35.9. Chart Titles That Update
  • 35.10. Putting Greek Symbols in Cells
  • 35.11. Superscripts and Subscripts
  • 35.12. Named Cells
  • 35.13. Hiding Cells (in Data Tables and Other Places)
  • 35.14. Formula Auditing
  • 35.15. Formatting Millions as Thousands.
  • Note continued: 35.16. Excel's Personal Notebook: Automating Frequent Procedures
  • 36. User-Defined Functions with VBA
  • 36.1. Overview
  • 36.2. Using the VBA Editor to Build a User-Defined Function
  • 36.3. Providing Help for User-Defined Functions in the Function Wizard
  • 36.4. Saving Excel Workbook with VBA Content
  • 36.5. Fixing Mistakes in VBA
  • 36.6. Conditional Execution: Using If Statements in VBA Functions
  • 36.7. The Boolean and Comparison Operators
  • 36.8. Loops
  • 36.9. Using Excel Functions in VBA
  • 36.10. Using User-Defined Functions in User-Defined Functions
  • Exercises
  • Appendix: Cell Errors in Excel and VBA
  • 37. Variables and Arrays
  • 37.1. Overview
  • 37.2. Defining Function Variables
  • 37.3. Arrays and Excel Ranges
  • 37.4. Simple VBA Arrays
  • 37.5. Multidimensional Arrays
  • 37.6. Dynamic Arrays and the ReDim Statement
  • 37.7. Array Assignment
  • 37.8. Variants Containing an Array
  • 37.9. Arrays as Parameters to Functions
  • 37.10. Using Types.
  • Note continued: 37.11. Summary
  • Exercises
  • 38. Subroutines and User Interaction
  • 38.1. Overview
  • 38.2. Subroutines
  • 38.3. User Interaction
  • 38.4. Using Subroutines to Change the Excel Workbook
  • 38.5. Modules
  • 38.6. Summary
  • Exercises
  • 39. Objects and Add-Ins
  • 39.1. Overview
  • 39.2. Introduction to Worksheet Objects
  • 39.3. The Range Object
  • 39.4. The With Statement
  • 39.5. Collections
  • 39.6. Names
  • 39.7. Add-Ins and Integration
  • 39.8. Summary
  • Exercises.

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