We propose a new adjustment in mean-variance portfolio weights to incorporate uncertainty caused by the fact that, in general, we have to use estimated expected returns when determining optimal portfolios. The adjustment amounts to using a higher pseudo risk-aversion rather than the actual risk-aversion and has a straightforward interpretation. The difference between the actual and the pseudo risk-aversion depends on the sample size, the number of assets in the portfolio, and the curvature of the mean-variance frontier. We show how short sales constraints and time-varying expected returns are incorporated in our framework. Applying the adjustment to international portfolios, we show that the adjustments are nontrivial for G5 country portfolios and that they are even more important when emerging markets are included. The exclusion of short sales is found to have a further important impact on the adjusted portfolio weights. In case expected country returns are time- varying, our adjustment induces a significantly smaller variability in portfolio weights that is commonly found.

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 volume invites young scientists and doctoral students in the fields of capital market theory, informational economics, and mana gement science to visualize the many different ways to arrive at a thorough understanding of risk and capital. Rather than focusing on one subject only, the sample of papers collected may be viewed as a representative choice of various aspects. Some contributions have more the character of surveys on the state of the art while others stress original research. We fou~d it proper to group the papers under two main themes. Part I covers information, risk aversion, and capital market theory. Part II is devoted to management, policy, and empirical evidence. Two contributions, we think, deserved to break this allocation and to be placed in a prologue. The ideas expressed by Jost B. Walther, although meant as opening address, draw interesting parallels for risk and capital in genetics and evolution. An old, fundamental pro blem was asked and solved by Martin J. Beckmann: how does risk affect saving? The wise answer (Martin's 60th birthday is in July 1984) is both smart and simple, although the proof requires sophisticated dynamic programming. As always, such a work must be the result of a special occasion.