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

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

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.

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.

This collection of articles in investment and portfolio management spans the thirty-five-year collaborative effort of two key figures in finance. Each of the nine sections begins with an overview that introduces the main contributions of the pieces and traces the development of the field. Each volume contains a foreword by Nobel laureate Harry Markowitz. Volume I presents the authors' groundbreaking work on estimating the inputs to portfolio optimization, including the analysis of alternative structures such as single and multi-index models in forecasting correlations; portfolio maximization under alternative specifications for return structures; the impact of CAPM and APT in the investment process; and taxes and portfolio composition. Volume II covers the authors' work on analysts' expectations; performance evaluation of managed portfolios, including commodity, stock, and bond portfolios; survivorship bias and performance persistence; debt markets; and immunization and efficiency.

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.

Optimization of a portfolio involves the efficient allocation of assets given a specific goal and its application to a portfolio will improve performance to some degree. Combining alpha calculation and analysis with optimization may enhance this improved portfolio performance. Thus, it can be assumed that portfolio rebalancing is worth the cost over time. This relationship may be explained by the significance of a security's alpha and the goals of an optimization scenario.