Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation

by Richard O. Michaud, Robert O. Michaud

Overview

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz's solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud's new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A simple global asset allocation problem illustrates portfolio optimization techniques. A final chapter includes practical advice for avoiding simple portfolio design errors. With its important implications for investment practice, Efficient Asset Management 's highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.

Product Details

ISBN-13:	9780199887194
Publisher:	Oxford University Press
Publication date:	03/03/2008
Series:	Financial Management Association Survey and Synthesis
Sold by:	Barnes & Noble
Format:	eBook
File size:	4 MB

About the Author

Richard O. Michaud is President and Chief Investment Officer at New Frontier Advisors. His research and consulting has focused on asset allocation, investment strategies, global investment management, optimization, stock valuation, portfolio analysis, and trading costs. He is co-inventor and patentee of Resampled Efficiency optimization. He earned a Ph.D. in Mathematics from Boston University and taught investment management at Columbia University. Robert O. Michaud, the co-inventor of the patented portfolio optimization processes, is the Managing Director of Research and Development at New Frontier Advisors. Mr. Michaud holds a Masters in Mathematics from Boston University and pursued a Ph.D. in finance from the Anderson School of Management at the University of California at Los Angeles before joining NFA. His research interests include risk models, empirical asset pricing, and international finance.

Table of Contents

Introduction     3
Markowitz Efficiency     3
An Asset Management Tool     4
Traditional Objections     5
The Most Important Limitations     5
Resolving the Limitations of Mean-Variance Optimization     6
Illustrating the Techniques     6
Classic Mean-Variance Optimization     7
Portfolio Risk and Return     7
Defining Markowitz Efficiency     9
Optimization Constraints     9
The Residual Risk-Return Efficient Frontier     10
Computer Algorithms     10
Asset Allocation Versus Equity Portfolio Optimization     11
A Global Asset Allocation Example     13
Reference Portfolios and Portfolio Analysis     14
Return Premium Efficient Frontiers     16
Appendix: Mathematical Formulation of MV Efficiency     17
Traditional Criticisms and Alternatives     20
Alternative Measures of Risk     20
Utility Function Optimization     22
Multiperiod Investment Horizons     23
Asset-Liability Financial Planning Studies     25
Linear Programming Optimization     27
Unbounded MV Portfolio Efficiency     29
Unbounded MVOptimization     30
The Fundamental Limitations of Unbounded MV Efficiency     31
Repeating Jobson and Korkie     32
Implications of Jobson and Korkie Analysis     33
Statistical MV Efficiency and Implications     34
Linear Constrained MV Efficiency     35
Linear Constraints     35
Efficient Frontier Variance     37
Rank-Associated Efficient Portfolios     39
How Practical an Investment Tool?     40
The Resampled Efficient Frontier     42
Efficient Frontier Statistical Analysis     42
Properties of Resampled Efficient Frontier Portfolios     45
True and Estimated Optimization Inputs     47
Simulation Proofs of Resampled Efficiency Optimization     48
Why Does It Work     51
Certainty Level and RE Optimality     51
FC Level Applications     52
The REF Maximum Return Point (MRP)     53
Implications for Asset Management     55
Conclusion     55
Rank- Versus [lambda]-Associated RE Portfolios     56
Robert's Hedgehog     57
Portfolio Rebalancing, Analysis, and Monitoring     60
Resampled Efficiency and Distance Functions      61
Portfolio Need-to-Trade Probability     62
Meta-Resampling Portfolio Rebalancing     63
Portfolio Monitoring and Analysis     64
Conclusion     66
Appendix: Confidence Region for the Sample Mean Vector     66
Input Estimation and Stein Estimators     68
Admissible Estimators     69
Bayesian Procedures and Priors     69
Four Stein Estimators     70
James-Stein Estimator     70
James-Stein MV Efficiency     71
Out-of-Sample James-Stein Estimation     72
Frost-Savarino Estimator     73
Covariance Estimation     74
Stein Covariance Estimation     76
Utility Functions and Input Estimation     77
Ad Hoc Estimators     77
Stein Estimation Caveats     78
Conclusions     78
Appendix: Ledoit Covariance Estimation     78
Benchmark Mean-Variance Optimization     80
Benchmark-Relative Optimization Characteristics     80
Tracking Error Optimization and Constraints     81
Constraint Alternatives     83
Roll's Analysis     85
Index Efficiency     85
A Simple Benchmark-Relative Framework     86
Long-Short Investing     86
Conclusion     88
Investment Policy and Economic Liabilities     89
Misusing Optimization     90
Economic Liability Models     90
Endowment Fund Investment Policy     91
Pension Liabilities and Benchmark Optimization     92
Limitations of Actuarial Liability Estimation     92
Current Pension Liabilities     93
Total and Variable Pension Liabilities     93
Economic Significance of Variable Liabilities     94
Economic Characteristics of VBO Liabilities     95
An Example: Economic Liability Pension Investment Policy     96
Past and Future of Defined Benefit Pension Plans     98
Conclusion     99
Bayes and Active Return Estimation     101
Current Practices     102
Bayes Principles     102
The Bayes Return Formula     102
A Bayes Panel Illustration     103
Bayesian Mixed Estimation Issues     104
Enhanced Inputs or Enhanced Optimizer     106
Bayesian Caveats     107
Avoiding Optimization Errors     109
Scaling Inputs     109
Financial Reality     111
Liquidity Factors     111
Practical Constraint Issues     112
Biased Portfolio Characteristics     112
Index Funds and Optimizers     113
Optimization from Cash     114
Forecast Return Limitations     115
Conclusion     116
Epilogue     117
Bibliography     119
Index     125

Editorial Reviews

Arguing that most optimization plans fail to live up to their potential because of the wide belief that they can be treated as a computational technique, the author claims that a better way would be to approach optimization as a statistical estimation of Markowitz mean-variance efficiency. He explains a number of techniques within this approach, including improved input estimation, the definition and use of portfolio priors, the integration of forecasts with historical data, and tests for portfolio revisions. Annotation c. by Book News, Inc., Portland, Or.

Booknews

"Michaud focuses on the fact that sampling errors in input estimates can have extreme effects on the portfolios produced by a mean-variance analysis. While some may debate his conclusions, everyone should understand the problem as Michaud so ably presents it and his proposed solution to it. This is a must-read, must-think-about book."—Harry M. Markowitz, Nobel Laureate, Economics, 1990

"In his long-standing tradition, Michaud once again pushes the envelope of mean-variance optimization. Quantitative analysts of all stripes will enjoy his work. Resampling is bound to be of growing interest."—Kenneth L. Fisher, Chairman & CEO, Fisher Investments, Inc.;Forbes Portfolio Strategy Columnist; and MarketPlace Commentator, Public Radio International

"Efficient Asset Management is essential reading for all investment managers. Michaud persuasively and eloquently shows that portfolio management is an inherently statistical task and is therefore subject to significant sampling error. More important, he provides a new tool, the resampling efficient frontier, for mitigating the problem. Michaud's tour de force is a rare example of a product with genuine merit for all investors."—Richard Roll, Allstate Chair in Finance and Insurance, John E. Anderson Graduate School of Management, UCLA

"This book is a delightfully sophisticated yet practical introduction to portfolio management that will appeal to industry professionals as well as finance students."—Andrew W. Lo, Harris & Harris Group Professor and Director of the Laboratory for Financial Engineering, MIT

"Efficient Asset Management offers an exciting and innovative approach to asset construction that builds on the established literature. It provides the practitioner with a new dimension for incorporating value-added investment judgments in the portfolio-building process. Readable and useful, this book makes a significant contribution to better portfolio management."—Gary P. Brinson, President, Brinson Partners, Inc.

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