Financial Modeling with Crystal Ball and Excel

• Record Nr.: UNINA9910816079703321
• Autore: Charnes John
• Pubbl/distr/stampa: New York, : Wiley, 2012
• ISBN: 1-119-20321-X; 1-280-59278-8; 9786613622617; 1-118-22705-0
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (336 p.)
• Collana: Wiley Finance
• Classificazione: BUS036000
• Disciplina: 332.0113; 332.0285/554; 332.0285554
• Soggetti: BUSINESS & ECONOMICS / Investments & Securities; Finance -- Mathematical models; Microsoft Excel (Computer file); Finance - Mathematical models; Investments - Mathematical models
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Include bibliographical references and index.
• Nota di contenuto: Financial Modeling with Crystal Ball and Excel; Contents; Preface; Acknowledgments; About the Author; CHAPTER 1 Introduction; 1.1 FINANCIAL MODELING; 1.2 RISK ANALYSIS; 1.3 MONTE CARLO SIMULATION; 1.4 RISK MANAGEMENT; 1.5 BENEFITS AND LIMITATIONS OF USING CRYSTAL BALL; 1.5.1 Benefits; 1.5.2 Limitations; CHAPTER 2 Analyzing Crystal Ball Forecasts; 2.1 SIMULATING A 50-50 PORTFOLIO; 2.1.1 Accumulate.xls; 2.1.2 Frequency Chart; 2.1.3 Cumulative Frequency Chart; 2.1.4 Statistics View; 2.1.5 Forecast Window Percentiles View; 2.2 VARYING THE ALLOCATIONS; 2.2.1 Decision Table Tool; 2.2.2 Trend Chart; 2.2.3 Overlay Chart 2.3 PRESENTING THE RESULTS; CHAPTER 3 Building A Crystal Ball Model; 3.1 SIMULATION MODELING PROCESS; 3.1.1 Example: AKGolf.xls; 3.2 DEFINING CRYSTAL BALL ASSUMPTIONS AND FORECASTS; 3.2.1 Defining Assumptions; 3.2.2 Defining Profit as a ; Forecast Cell; 3.3 RUNNING CRYSTAL BALL; 3.4 SOURCES OF ERROR; 3.5 CONTROLLING MODEL ERROR; CHAPTER 4 Selecting Crystal Ball Assumptions; 4.1 CRYSTAL BALL'S BASIC DISTRIBUTIONS; 4.1.1 Yes-No; 4.1.2 Binomial; 4.1.3 Discrete Uniform; 4.1.4 Uniform; 4.1.5 Triangular; 4.1.6 Normal; 4.1.7 Lognormal; 4.2 USING HISTORICAL DATA TO CHOOSE DISTRIBUTIONS 4.2.1 Direct Sampling; 4.2.2 Sampling from a Fitted Distribution; 4.2.3 Fitting Distributions to Data; 4.2.4 Goodness-of-Fit Testing; 4.2.5 Eyeball Test; 4.2.6 Caveats; 4.2.7 What If No Historical Data Are Available?; 4.3 SPECIFYING CORRELATIONS; 4.3.1 Pearson Correlation Statistic; 4.3.2 Spearman (Rank) Correlation Statistic; 4.3.3 Using Crystal Ball to Calculate Correlations Between Two Assumptions; 4.3.4 Batch Fit; 4.3.5 Correlation Tool; CHAPTER 5 Using Decision Variables; 5.1 DEFINING DECISION VARIABLES; 5.2 DECISION TABLE WITH ONE DECISION VARIABLE 5.2.1 Trend Chart; 5.2.2 Overlay Chart; 5.3 DECISION TABLE WITH TWO DECISION VARIABLES; 5.3.1 Model; 5.3.2 Threshold Values; 5.3.3 Two-Way Decision Table; 5.3.4 Interpreting the Results; 5.4 USING OPTQUEST; 5.4.1 Terminology; 5.4.2 Example; CHAPTER 6 Selecting Run Preferences; 6.1 TRIALS; 6.1.1 Number of Trials to Run; 6.1.2 Stop on Calculation Errors; 6.1.3 Stop When Precision Control Limits Are Reached; 6.2 SAMPLING; 6.2.1 Random Number Generation; 6.2.2 Sampling Method; 6.3 SPEED; 6.3.1 Run Mode; 6.3.2 Chart Windows; 6.4 OPTIONS; 6.5 STATISTICS; CHAPTER 7 Net Present Value and Internal Rate of Return 7.1 DETERMINISTIC NPV AND IRR; 7.2 SIMULATING NPV AND IRR; 7.3 CAPITAL BUDGETING; 7.3.1 Tornado Chart Tool; 7.3.2 Risk Analysis; 7.3.3 Caveats; 7.4 CUSTOMER NET PRESENT VALUE; 7.4.1 Results; CHAPTER 8 Modeling Financial Statements; 8.1 DETERMINISTIC MODEL; 8.2 TORNADO CHART AND SENSITIVITY ANALYSIS; 8.3 CRYSTAL BALL SENSITIVITY CHART; 8.4 CONCLUSION; CHAPTER 9 Portfolio Models; 9.1 SINGLE-PERIOD CRYSTAL BALL MODEL; 9.2 SINGLE-PERIOD ANALYTICAL SOLUTION; 9.3 MULTI-PERIOD CRYSTAL BALL MODEL; CHAPTER 10 Value at Risk; 10.1 VAR; 10.2 SHORTCOMINGS OF VAR
• Sommario/riassunto: Updated look at financial modeling and Monte Carlo simulation with software by Oracle Crystal Ball This revised and updated edition of the bestselling book on financial modeling provides the tools and techniques needed to perform spreadsheet simulation. It answers the essential question of why risk analysis is vital to the decision-making process, for any problem posed in finance and investment. This reliable resource reviews the basics and covers how to define and refine probability distributions in financial modeling, and explores the concepts driving the simulation modeling process