Three Studies On Portfolio Optimization And Performance Appraisal

This thesis studies three important issues in portfolio management: the impact of estimation risk on portfolio optimization, the role of fundamental analysis in portfolio selection and the power of the bootstrap approach for separating skill from luck across a sample of portfolio managers. The first study examines the practical value of the mean-variance portfolio optimization. This issue arises from the concern that the performance of the meanvariance portfolio suffers seriously from estimation errors in input parameters. Based on simulated asset returns, we compare the performance of selected popular portfolios against the naïve equally weighted portfolio (1/N) in terms of the Sharpe Ratio. We conclude that given relatively small and persistent anomalies, some sophisticated portfolio rules can outperform the naïve one at estimation windows of reasonable lengths. We find that (1) an estimation window of 120 months is needed for the optimization-based portfolio rules to outperform the 1/N rule when annual abnormal returns lie between a certain range; (2) given the same abnormal returns, even longer estimation windows are needed when asset returns exhibit fat tails; (3) our preferred portfolio rule, which combines optimally the sample tangency portfolio with MacKinlay and Pástor's (2000) portfolio, performs well relative to other rules. Our second study examines the role of fundamental analysis in portfolio selection. Fundamental analysis assumes implicitly that asset prices mean-revert to their fundamental values. We solve the instantaneous mean-variance portfolio choice problem when asset prices mean-revert to their fundamentals and analyze how this meanreversion feature affects the performance of the optimal portfolio. Our analytical results show that the expected appraisal ratio of the optimal portfolio is increasing in the meanreversion speed for a given stationary distribution of the mispricing and it is increasing in the standard deviation of the stationary distribution for a given level of the meanreversion speed. The contribution from dividends is positive, increasing in the dividend yield and is tantamount to increasing the mean-reversion speed. Our numerical examples indicate that fundamental analysis can be more helpful than practitioners' performance shows. One implication of this is that it must be very challenging to obtain reasonable forecasts of the mispricing. Our third study provides a simulation analysis of the power of the bootstrap approach for identifying skill among a large population of mutual funds. Unlike the standard t-test, this approach does not require ex ante parametric assumption on fund alphas and allows us to infer on the existence of genuine skill across a large sample of fund managers. Its recent applications in mutual fund performance analysis have produced strikingly different findings from those documented in the classical literature. However, as far as we know, its power has not been subject to any rigorous statistical analysis. We provide a Monte Carlo simulation analysis of the validity and power of this method by applying it to evaluating the performance of hypothetical funds under varieties of parameter assumptions. We find that this method can be misleading, which is true regardless of using alpha estimates or their t-statistics. This makes the recent findings dubious. The major problem with this method lies in the inappropriate use or misinterpretation of what Fama and French (2010) call "likelihoods" in testing for difference between realized and bootstrapped alphas at selected percentiles. We also show that the variance decomposition and the Kolmogrov-Smirnov test can lead to correct inferences on fund managers' skill when likelihoods fail to do so.

In answer to the intense development of new financial products and the increasing complexity of portfolio management theory, Portfolio Optimization and Performance Analysis offers a solid grounding in modern portfolio theory. The book presents both standard and novel results on the axiomatics of the individual choice in an uncertain framework, cont

This paper provides a review of the methods for measuring portfolio performance and the evidence on the performance of professionally managed investment portfolios. Traditional performance measures, strongly influenced by the Capital Asset Pricing Model of Sharpe (1964), were developed prior to 1990. We discuss some of the properties and important problems associated with these measures. We then review the more recent Conditional Performance Evaluation techniques, designed to allow for expected returns and risks that may vary over time, and thus addressing one major shortcoming of the traditional measures. We also discuss weight-based performance measures and the stochastic discount factor approach. We review the evidence that these newer measures have produced on selectivity and market timing ability for professional managed investment funds. The evidence includes equity style mutual funds, pension funds, asset allocation style funds, fixed income funds and hedge funds.

Based on an exclusive business-to-business database comprising nearly 1,000 customers, the applicability of portfolio analysis is documented, and it is examined how such an optimization analysis can be used to explore the growth potential of a company. As opposed to any previous analyses, optimal customer portfolios are determined, and it is shown how marketing decision-makers can use this information in their marketing strategies to optimize the revenue growth of the company. Finally, our analysis is the first analysis which applies portfolio based methods to measure customer performance, and it is shown how these performance measures complement the optimization analysis.

Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University

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.

For many years asset management was considered to be a marginal activity, but today, it is central to the development of financial industry throughout the world. Asset management's transition from an "art and craft" to an industry has inevitably called integrated business models into question, favouring specialisation strategies based on cost optimisation and learning curve objectives. This book connects each of these major categories of techniques and practices to the unifying and seminal conceptual developments of modern portfolio theory. In these bear market times, performance evaluation of portfolio managers is of central focus. This book will be one of very few on the market and is by a respected member of the profession. Allows the professionals, whether managers or investors, to take a step back and clearly separate true innovations from mere improvements to well-known, existing techniques Puts into context the importance of innovations with regard to the fundamental portfolio management questions, which are the evolution of the investment management process, risk analysis and performance measurement Takes the explicit or implicit assumptions contained in the promoted tools into account and, by so doing, evaluate the inherent interpretative or practical limits

Here is a chapter from Portfolio Performance Measurement and Benchmarking, which will help you create a system you can use to accurately measure your performance. The authors highlight common mechanical problems involved in building benchmarks and clearly illustrate the resulting fallouts. The failure to choose the right investing performance benchmarks often leads to bad decisions or inaction and, inevitably, lost profits. In this book you will discover a foundation for benchmark construction and discuss methods for all different asset classes and investment styles.

Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.