Financial Modeling with Crystal Ball and Excel
Financial Modeling with Crystal Ball(r) and Excel(r)
"Professor Charnes's book drives clarity into applied Monte Carlo analysis using examples and tools relevant to real-world finance. The book will prove useful for analysts of all levels and as a supplement to academic courses in multiple disciplines."
-Mark Odermann, Senior Financial Analyst, Microsoft
"Think you really know financial modeling? This is a must-have for power Excel users. Professor Charnes shows how to make more realistic models that result in fewer surprises. Every analyst needs this credibility booster."
-James Franklin, CEO, Decisioneering, Inc.
"This book packs a first-year MBA's worth of financial and business modeling education into a few dozen easy-to-understand examples. Crystal Ball software does the housekeeping, so readers can concentrate on the business decision. A careful reader who works the examples on a computer will master the best general-purpose technology available for working with uncertainty."
-Aaron Brown, Executive Director, Morgan Stanley, author of The Poker Face of Wall Street
"Using Crystal Ball and Excel, John Charnes takes you step by step, demonstrating a conceptual framework that turns static Excel data and financial models into true risk models. I am astonished by the clarity of the text and the hands-on, step-by-step examples using Crystal Ball and Excel; Professor Charnes is a masterful teacher, and this is an absolute gem of a book for the new generation of analyst."
-Brian Watt, Chief Operating Officer, GECC, Inc.
"Financial Modeling with Crystal Ball and Excel is a comprehensive, well-written guide to one of the most useful analysis tools available to professional risk managers and quantitative analysts. This is a must-have book for anyone using Crystal Ball, and anyone wanting an overview of basic risk management concepts."
-Paul Dietz, Manager, Quantitative Analysis, Westar Energy
"John Charnes presents an insightful exploration of techniques for analysis and understanding of risk and uncertainty in business cases. By application of real options theory and Monte Carlo simulation to planning, doors are opened to analysis of what used to be impossible, such as modeling the value today of future project choices."
-Bruce Wallace, Nortel
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Building a Crystal Ball Model
Selecting Crystal Ball Assumptions
Using Decision Variables
Selecting Run Preferences
Net Present Value and Internal Rate
Modeling Financial Statements
Value at Risk
Simulating Financial Time Series
Other editions - View all
30 Wealth allocation Asian option assumption cell autocorrelation Ball’s Batch Fit Bernoulli trials Black-Scholes calculated call option cash flows Chapter correlation coefficient Crystal Ball assumptions Crystal Ball forecast Crystal Ball model CVaR Decision Table Decision Table tool decision variables deterministic dialog window distribution with mean estimate example Excel function financial modeling forecast cell forecast chart forecast window formula inputs integer investment kurtosis Latin Hypercube sampling lognormal distribution maximum method Monte Carlo simulation normal distribution number of trials optimal output Overlay Chart parameters percent percentile portfolio probability distribution Project.xls model random numbers random variable random walk rate of return real options risk analysis ROV tool Run Preferences sampling sensitivity analysis shown in Figure shows simulation model specified spreadsheet spreadsheet model Spreadsheet segment standard deviation statistics Step stochastic assumptions stock price strike price variance reduction variates white noise process worksheet