Portfolio Performance Measures Research Articles

On the Economic Evaluation of Volatility Forecasts

We analyze the applicability of economic criteria for volatility forecast evaluation based on unconditional measures of portfolio performance. The main theoretical finding is that such unconditional measures generally fail to rank conditional forecasts correctly due to the presence of a bias term driven by the variability of the conditional mean and portfolio weights. Simulations and a small empirical study suggest that the bias can be empirically substantial and lead to distortions in forecast evaluation. An important implication is that forecasting superiority of models using high frequency data is likely to be understated if unconditional criteria are used.

A Generalisation of the Mean‐Variance Analysis

Abstract In this paper we consider a decision maker whose utility function has a kink at the reference point with different functions below and above this reference point. We also suppose that the decision maker generally distorts the objective probabilities. First we show that the expected utility function of this decision maker can be approximated by a function of mean and partial moments of distribution. This ‘mean‐partial moments’ utility generalises not only mean‐variance utility of Tobin and Markowitz, but also mean‐semivariance utility of Markowitz. Then, in the spirit of Arrow and Pratt, we derive an expression for a risk premium when risk is small. Our analysis shows that a decision maker in this framework exhibits three types of aversions: aversion to loss, aversion to uncertainty in gains, and aversion to uncertainty in losses. Finally we present a solution to the optimal capital allocation problem and derive an expression for a portfolio performance measure which generalises the Sharpe and Sortino ratios. We demonstrate that in this framework the decision maker's skewness preferences have first‐order impact on risk measurement even when the risk is small.

Measuring the Effects of Foresight and Commitment on Portfolio Performance

A central question in finance is how to measure portfolio performance. We assert that foresight (i.e., the ability of managers to forecast relative returns) and commitment (i.e., the aggressiveness with which managers act on their foresight) are necessary ingredients to generate portfolio performance not attributable to chance. We create two portfolio performance measures free from the need to pre-specify a benchmark portfolio. Our measures allow for many investment styles because they use benchmarks created from the style implied in the portfolio being tested. To calibrate these new measures, we use a weight-generating process that simulates various combinations of foresight and commitment. We decompose our measures to show how foresight and commitment act jointly to generate statistically measurable excess performance. An outcome of our measures is a scoring mechanism that groups portfolios by commitment, and then ranks portfolio performance by foresight. We apply our measures to a series of actual asset allocation recommendations made by a panel of Investment Houses. Largely, we find little foresight, but we document considerable aggression. The Investment Houses made allocation recommendations to equity, bonds, and cash. However, our measures easily generalize to a wide variety of potentially hard to measure investment styles, like those of hedge funds and other alternative investment managers.

The Risk Study and Control in Investment Decision

We give the descriptions of the concepts of investment and risk related to investment first. Then, we consider how to measure the rates of return and the risk for alternative investments, and what factors determine an investor’s required rate of return on an investment in the second section. In order to control the risk systematically, the portfolio control process is proposed in section 3. Section 4 describes in detail the three major composite equity portfolio performance measures that combine risk and return performance into a single value. We describe each measure and its intent, and then demonstrate how to compute it and interpret the results. We also compare two of the measures and discuss how they differ and why they rank portfolios differently. The real application on the risk study and control in investment is discussed at the end.

An output approach to property portfolio performance measurement

PurposeThe purpose of this paper is to demonstrate the analysis of portfolios of office properties using measures of business outputs, namely occupation efficiency and staff satisfaction.Design/methodology/approachSatisfaction is measured using a proprietary online survey instrument that has proved highly reliable and repeatable in three separate trials. The data on 192 buildings are analysed using data envelopment analysis.FindingsInstant and significant differences are revealed between clusters of buildings and individual properties. The approach reveals inefficiencies that are concealed by more conventional cost‐based metrics.Practical implicationsThe study has proven to be of use in gaining organisational commitment to strategic property improvements.Originality/valueThe authors are not aware of this approach having been applied elsewhere in either research or application.

Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance

This paper presents a theoretically sound portfolio performance measure that takes into account higher moments of distribution. This measure is motivated by a study of the investor’s preferences to higher moments of distribution within Expected Utility Theory and an approximation analysis of the optimal capital allocation problem. We show that this performance measure justifies the notion of the Generalized Sharpe Ratio (GSR) introduced by Hodges (1998). We present two methods of practical estimation of the GSR: nonparametric and parametric. For the implementation of the parametric method we derive a closed-form solution for the GSR where the higher moments are calibrated to the normal inverse Gaussian distribution. We illustrate how the GSR can mitigate the shortcomings of the Sharpe ratio in resolution of Sharpe ratio paradoxes and reveal the real performance of portfolios with manipulated Sharpe ratios. We also demonstrate the use of this measure in the performance evaluation of hedge funds.

The 101 Ways to Measure Portfolio Performance

This paper performs a census of the 101 performance measures for portfolios that have been proposed so far in the scientific literature. We discuss their main strengths and weaknesses and provide a classification based on their objectives, properties and degree of generalization. The measures are categorized based on the general way they are computed: asset selection vs. market timing, standardized vs. individualized, absolute vs. relative and excess return vs. gain measure. We show that several categories have been exhausted while some others feature very heterogeneous ways to assess performance within the same sets of objectives.Note. The definitive version of this working paper was published by the of Performance Measurement in two parts:- “The (more than) 100 Ways to Measure Portfolio Performance - Part 1: Standardized Risk-Adjusted Measures”, Journal of Performance Measurement, Vol. 13, N° 4, Summer 2009, pp. 56-71.- “The (more than) 100 Ways to Measure Portfolio Performance - Part 2: Special Measures and Comparison”, Journal of Performance Measurement, Vol. 14, N° 1, Fall 2009, pp. 56-69.

Does a Bayesian Approach Generate Robust Forecasts? Evidence from Applications in Portfolio Investment Decisions

We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio performance) to compare the forecasting accuracy of three model selection approaches: Bayesian information criterion (BIC), model averaging, and model mixing. While the more recent approaches of model averaging and model mixing surpass the Bayesian information criterion in their out-of-sample hit rates, the predicted portfolios from these new approaches do not significantly outperform the portfolio obtained via the BIC subset selection method.

After-Tax Performance Measurement

Measuring portfolio performance on an after-tax basis is a challenging matter. Whether one uses simple or complex models, one implicitly or explicitly makes certain assumptions about a taxable investor9s time horizon and capital gains recognition behavior. This article integrates the after-tax performance measurement literature with recent advances in after-tax portfolio valuation. It implements a variation of Stein9s [1998] full cost equivalent model using after-tax valuation techniques developed by Horan [2007a, b]. The approach has several advantages. It can be applied relatively easily without sacrificing precision; it accounts for the impact of taxes on portfolio risk; and it can be used to develop a customized after-tax benchmark. <b>TOPICS:</b>Portfolio construction, legal/regulatory/public policy, risk management, performance measurement

Portfolio Performance Evaluation with Generalized Sharpe Ratios: Beyond the Mean and Variance

The main purpose of this paper is to present a theoretically sound portfolio performance measure that takes into account higher moments of the distribution of returns. First, we perform a study of the investor's preferences to higher moments of distribution within expected utility theory and discuss the performance measurement. To illustrate the investor's preferences to higher moments and the computation of a performance measure, we provide an approximation analysis of the optimal capital allocation problem and derive a formula for the Sharpe ratio adjusted for skewness of distribution. This performance measure justifies the notion of the Generalized Sharpe Ratio (GSR) introduced by Hodges (1998) which presumably accounts for all moments of distribution. We present two methods of practical estimation of the GSR: nonparametric and parametric. For the implementation of the parametric method we derive a closed-form solution for the GSR where the higher moments are calibrated to the normal inverse Gaussian distribution. We illustrate how the GSR can mitigate the shortcomings of the Sharpe ratio in resolution of Sharpe ratio paradoxes and reveal the real performance of portfolios with manipulated Sharpe ratios. We also demonstrate the use of this measure in the performance evaluation of hedge funds.

A Generalization of the Mean-Variance Analysis

In this paper we consider a decision maker whose utility function has a kink at the reference point with different functions below and above this reference point. We also suppose that the decision maker generally distorts the objective probabilities. First we show that the expected utility function of this decision maker can be approximated by a function of mean and partial of distribution. This mean-partial moments utility generalizes not only the mean-variance utility of Tobin and Markowitz, but also the mean-semivariance utility of Markowitz. Then, in the spirit of Arrow and Pratt, we derive an expression for a risk premium when risk is small. Our analysis shows that a decision maker in this framework exhibits three types of aversions: aversion to loss, aversion to uncertainty in gains, and aversion to uncertainty in losses. Finally we present the solution to the optimal capital allocation problem and derive an expression for a portfolio performance measure which generalizes the Sharpe and Sortino ratios. We demonstrate that in this framework the decision maker's skewness preferences have first-order impact on risk measurement even when the risk is small.

Portfolio performance: factors or benchmarks?

The suitability of using factors or benchmarks to measure portfolio performance is analysed. Fama and French factors are constructed from Russell US stock indexes and then directly utilized as benchmarks. The interpretation of factors as zero-investment benchmarks makes it difficult to explain performance measurement as the comparison of active versus passive management, given the short selling restrictions often applied to mutual funds. Empirical results reveal similar biases in extended Jensen's alphas in models with both factors and with benchmarks and with convexity and nonnegativity restrictions. Selection of the benchmarks has a more important effect than the model type chosen.

Predicting stock returns and assessing prediction performance

We use regression methods to predict the expected monthly return on stocks and the covariance matrix of returns, the predictor variables being a company's ‘fundamentals’, such as dividend yield and the history of previous returns. Predictions are evaluated out of sample for shares traded on the London Stock Exchange from 1976 to 2005. We explore and evaluate many modelling and inferential approaches, including the use of weighted regression, discounted regression, shrinkage of regression coefficients and the transformation to normality of predictor variables. We also investigate alternative covariance matrix models, such as a two-index model and a shrinkage model. Using suitable statistics to enable the out-of-sample performance of competing methodologies to be compared is crucial, and we develop some new statistics and a graphical aid for this purpose. What is original in this paper is an evaluation of many modelling and inferential procedures for which conflicting claims have been made in the literature and the development of new measures of portfolio performance.

Effective IT governance in healthcare organisations: a tale of two organisations

Extracting value from investments in IT has been a perennial problem for healthcare organisations. IT governance provides a systematic framework to establish governing principles and decision rights over IT investments, IT architecture, IT infrastructure and applications. Set against the IT Governance Institute&#39;s five domains of IT governance, this case study provides insight into two distinctly different healthcare organisations and their incredibly similar efforts at building and sustaining effective IT governance. Both organisations have hierarchical IT governance committee structures with broad representation from across the organisation. Additionally both have chosen to implement an IT program/;portfolio management office &#40;PMO&#41; capability to deliberately and explicitly build IT governance supporting processes to assess strategic alignment, risk management, resource utilisation, IT portfolio performance measurement and IT value delivery. Using case study data, evidence is presented to suggest that the 5 domains of IT governance &#40;strategic alignment, risk management, resource management, performance measurement and value delivery&#41; are antecedents of IT governance effectiveness.

Portfolio performance evaluation in a mean–variance–skewness framework

Astronomical amounts of money are being invested in financial markets. Consequently the evaluation of portfolio performance has created a great deal of interest among practitioners as well as academic researchers. The literature suggests that portfolio efficiency based on mean–variance–skewness is more desirable than the one based on mean–variance. However, there are no well-established procedures to measure efficiency in this framework, mainly due to the computational difficulties. The aim of this paper is to develop a portfolio performance measure based on mean–variance–skewness framework by utilizing a non-parametric efficiency analysis tool, namely ‘Data Envelopment Analysis’.

On the Market Discipline of Informationally-Opaque Firms: Evidence from Bank Borrowers in the Federal Funds Market

Using plausibly exogenous variation in demand for federal funds created by daily shocks to reserve balances, we identify the supply curve facing a bank borrower in the inter-bank market, and study how access to overnight credit is affected by changes in public and private measures of borrower creditworthiness. While there is evidence that lenders respond to adverse changes in public information about credit quality by restricting access to the market in a fashion consistent with market discipline, there is also evidence that borrowers are able to respond to adverse changes in private information about credit quality by increasing leverage as if to offset the future impact on earnings. While the responsiveness of investors to public information is comforting, we document evidence which suggests that banks are able to manage the real information content of these disclosures. In particular, public measures of loan portfolio performance have information about future loan chargeoffs, but only in quarters when the bank is examined by supervisors. However, the loan supply curve is not any more sensitive to public disclosures about nonperforming loans in an exam quarter, suggesting that investors are unaware of this information management.

Selecting Project Portfolios by Optimizing Simulations

New advances in the area of simulation optimization allow managers to go beyond traditional ranking rules, CAPM, and real options analysis in order to select optimal sets of projects to fund. Furthermore, these advances make use of portfolio performance measures and goals that can be defined to directly relate to corporate strategy and are easy to communicate and understand. We present a real-world example to illustrate this methodology.

Portfolio performance sensitivity for various asset-pricing kernels

This paper examines the sensitivity of various measures of portfolio performance to the choice of the benchmark model using the asset-pricing kernel methodology. It derives the appropriate estimation frameworks that are suitable to perform evaluations of fixed-weight and dynamic portfolio strategies. Various asset-pricing kernel-based benchmark models are tested using a comprehensive sample of Canadian equity mutual funds over the period, 1989–1999. The performance statistics and inferences are sensitive to the choice of the kernel-based benchmark model and to the choice of liquidity as an alternative sorting variable for forming the passive benchmark portfolios. However, they are robust to the removal of ex post index mimickers and somewhat less robust to the presence of nonlinearities in the structure of the pricing kernel. Since conditioning has a more pronounced impact on absolute than on relative performance inferences, this supports the common industry practice of comparing the performance of funds against each other.

Portfolio Performance: Factors or Benchmarks?

The suitability of using factors or benchmarks to measure portfolio performance is analyzed. Fama and French factors are constructed from Russell US stock indexes and then directly utilized as benchmarks. The interpretation of factors as zero-investment benchmarks makes it difficult to explain performance measurement as the comparison of active versus passive management, given the short selling restrictions often applied to mutual funds. Empirical results reveal similar biases in extended Jensen's alphas in models with both factors and with benchmarks, and with convexity and non-negativity restrictions. Selection of the replicate benchmark has a more important effect than the model type chosen.

Performance Measurement Manipulation

Single scalar measures of portfolio performance are inevitably susceptible to manipulation. The Morningstar stars awarded to mutual funds on the basis of huge flows into or out of funds are an example. The system was updated in 2002 and corrects a number of problems with the older system, but—like any single scalar measure—it can be gamed.