Optimal Portfolio Modeling, CD-ROM includes Models Using Excel and R Models to Maximize Returns and Control Risk in Excel and R

by McDonnell, Philip

• ISBN13: 9780470117668
• ISBN10: 0470117664
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-02-08
• Publisher: Wiley

Summary

Optimal Portfolio Modeling is an easily accessible introduction to portfolio modeling for those who prefer an intuitive approach to this discipline. While early chapters provide engaging insights on the statistical properties of markets, this book quickly moves on to illustrate invaluable trading and risk control models based on popular programs such as Excel and the statistical modeling language R. This reliable resource presents modeling formulas that will allow you to effectively maximize the performance, minimize the drawdown, and manage the risk of your portfolio.

Author Biography

Philip J. Mcdonnell is a trader and software/trading methodologies developer who has created proprietary data collection and analysis tools for real-time analysis of market direction and stock selection, with an emphasis on options analysis. He has handled network operations for a venture capital incubator, The Inception Group, and developed and sold options analysis software packages. McDonnell served as a research assistant at the University of California, Berkeley, School of Business, under Victor Niederhoffer. He holds degrees in mathematics and computer science from the University of California, Berkeley.

Table of Contents

Preface	p. xi
Acknowledgments	p. xiii
Modeling Market Microstructure-Randomness in Markets	p. 1
The Random Walk Model	p. 3
What You Cannot Predict Is Random to You	p. 5
Market Microstructure	p. 7
Efficient Market Hypothesis	p. 9
Arbitrage Pricing Theory	p. 10
Distribution of Price Changes	p. 13
The Normal Distribution	p. 13
Reflection Principle	p. 17
Approximation of the Normal Distribution by Rational Polynomial	p. 18
Lognormal Distribution	p. 19
Symmetry of the Normal and Lognormal	p. 22
Why Pick a Distribution at All?	p. 23
The Empirical Distribution	p. 24
The Lognormal as an Approximation	p. 26
Investment Objectives	p. 29
Statistician's Fair Game	p. 29
A Fair Game Is a Loser!	p. 30
Criteria for a Favorable Game	p. 30
Gambler's Ruin	p. 31
Optimal Return Models	p. 32
Markets Are Rational, Psychologists Are Not	p. 34
The St. Petersburg Paradox	p. 36
Compounded Return Is the Real Objective	p. 37
Defining Risk	p. 38
Minimum Risk Models	p. 41
Correlation of Assets	p. 41
Summary of Correlation Relationships	p. 42
Beta and Alpha	p. 43
The Efficient Frontier and the Market Portfolio	p. 46
The Sharpe Ratio	p. 47
Limitations of Modern Portfolio Theory	p. 48
Modeling Risk Management and Stop-loss Myths	p. 51
Stop-loss Orders	p. 52
Stops: Effect on the Mean Return	p. 53
Stops: Effect on the Probability of Gain	p. 56
Stops: Probability of Being Stopped Out	p. 56
Stops: Effect on Variance and Standard Deviation	p. 58
Effect on Skew	p. 59
Effect on the Kurtosis	p. 60
Stop-loss: Summary	p. 61
Modeling Stops	p. 61
Identifying When to Use Stops and When Not To	p. 62
Stop-Profits	p. 64
Puts and Calls	p. 65
Maximal Compounded Return Model	p. 67
Optimal Compound Return Models	p. 68
Relative Returns	p. 68
Average Stock Returns, but Compound Portfolio Returns	p. 70
Logarithms and the Optimal Exponential Growth Model	p. 71
Position Sizing as the Only Guaranteed Risk Control	p. 71
Controlling Risk through Optimal Position Sizing	p. 72
Maximize Compounded Portfolio Return	p. 72
Maximal Compounded Return Models	p. 73
What the Model Is and Is Not	p. 74
Modeling the Empirical Distribution	p. 75
Correlations	p. 76
The Enhanced Maximum Investment Formulas	p. 77
Expected Drawdowns May Be Large	p. 78
Utility Models-Preferences Toward Risk and Return	p. 79
Basis for a Utility Model	p. 80
History of Logarithms	p. 81
Optimal Compounded Utility Model	p. 84
The Sharpe Ratio	p. 85
Optimal Model for the Sharpe Ratio	p. 85
Optimization with Excel Solver	p. 88
Money Management Formulas Using the Joint Multiasset Distribution	p. 93
The Continuous Theoretical Distributions	p. 94
Maximal Log Log Model in the Presence of Correlation	p. 94
Optimal Sharpe Model with Correlation	p. 95
The Empirical Distribution	p. 96
Maximal Log Log Model in the Presence of Correlation	p. 97
Maximizing the Sharpe Ratio in the Presence of Correlation	p. 97
Proper Backtesting for Portfolio Models	p. 101
Assuring Good Data	p. 102
Synchronize Data	p. 102
Use Net Changes Not Levels	p. 103
Only Use Information from the Past	p. 104
Predictive Studies versus Nonpredictive Studies	p. 106
Use Intraday Highs and Lows for Model Accuracy	p. 107
Adjusted Data May Be Erroneous	p. 108
Adjusting Your Own Data	p. 109
Miscellaneous Data Pitfalls	p. 109
Tabulate and Save the Detailed Results with Dates	p. 110
Overlapping Dates Are Important for Correlations	p. 110
Calculate Mean, Standard Deviation, Variance, and Probability of Win	p. 111
Robust Methods to Find Statistics	p. 111
Confidence Limits for Robust Statistics	p. 112
The Combined Optimal Portfolio Model	p. 113
Choosing the Theoretical Distribution	p. 114
The Empirical Distribution	p. 115
Selecting Sharpe versus a Log Log Objective Function	p. 116
Model Simulation	p. 117
Professional Money Manager versus Private Investor	p. 119
About the CD-Rom	p. 121
Introduction	p. 121
System Requirements	p. 121
What's on the CD	p. 122
Updates to the CD-ROM	p. 124
Customer Care	p. 124
Table of Values of the Normal Distribution	p. 125
Installing R	p. 129
Introduction to R	p. 131
R Language Definition	p. 233
Index	p. 295
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