Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation / Edition 2

Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation / Edition 2

by Richard O. Michaud, Robert O. Michaud
     
 

ISBN-10: 0195331915

ISBN-13: 9780195331912

Pub. Date: 12/28/2007

Publisher: Oxford University Press, USA

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

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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.

This edition includes a CD that contains a demo of the patented, internet-based optimization software created by the authors at their consulting firm, New Frontier Advisors, which has been chosen to cosponsor the new Harry M. Markowitz Award.

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Product Details

ISBN-13:
9780195331912
Publisher:
Oxford University Press, USA
Publication date:
12/28/2007
Series:
Financial Management Association Survey and Synthesis Series
Pages:
144
Sales rank:
902,276
Product dimensions:
9.30(w) x 6.20(h) x 0.80(d)

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

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