Portfolio Excess Returns Research Articles

A note on the returns from minimum variance investing

Abstract Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular concepts. I show in this paper that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk adjusted outperformance. This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition. The paper also shows that a direct combination of market weighted benchmark portfolio and risk based characteristic portfolios will provide a statistically significant improvement over the indirect pickup via the minimum variance portfolio.

Out-of-Sample Performance of Asset Allocation Strategies

Using data for the S&P 500 Sector portfolios between 1989 and 2007, I find that sample-based mean-variance portfolios are very unstable and perform poorly out of sample in terms of Sharpe ratios, certainty equivalent returns and turnover. Minimum-variance and Bayes-Stein portfolios, which are supposed to be less susceptible to the estimation error plaguing mean-variance, also fall significantly short of a naive equally-weighted policy. Imposing shortsale or turnover constraints limits the fluctuation of portfolio weights and improves performance considerably. When the normality assumption about excess portfolio returns is relaxed, there is no evidence of the constrained portfolios performing worse than the exulted equally-weighted portfolio. I propose a sophisticated turnover constraint rule which recognizes the path dependency of the optimal portfolio policy and enhances the out-of-sample monthly Sharpe ratio by 13% relative to the best of the other strategies considered in this paper.

A New Look at Minimum Variance Investing

Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular rule driven, i.e. new passive concepts. I show in this paper theoretically and empirically that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk-adjusted outperformance (alpha). This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition.

Fundamental Analysis and the Modelling of Normal Returns in the UK

Fundamental analysis involves the use of accounting data to predict future stock returns, future stock prices, or both. One aspect of such research is whether accounting information can be used to generate profitable portfolio strategies. Central to this style of fundamental analysis, is the measurement of the benchmark of ‘normal’ returns against which ‘abnormal’ returns can be identified. One approach is to regress a time series of portfolio excess returns on variables thought to capture risk. The Fama-French three factor model has been used to capture risk in both the UK and the US and we evaluate its effectiveness for this role using UK data. Numerous versions of the model are found in the UK literature and we test nine such versions. None of them can be awarded a clean bill of health in terms of standard asset pricing tests. Therefore, we conclude that fundamental analysis studies using UK data must recognise that, when using the three factor model, different ways of applying the model can result in quite different estimates of abnormal returns. Investigation of the robustness of results to different applications of the model then seems desirable. By implication, it is also desirable that fundamental analysis researchers should investigate the robustness of their results to other available methods of estimating normal returns.

The Limits of Standard Risk and Macroeconomic Factors in Explaining the Return Premia: Evidence from the Tokyo Stock Exchange

This paper examines whether various factors suggested in standard finance can explain the excess returns of portfolios formed on size and book-equity-to-market equity (BE/ME) in Japan. First, we find that, unlike for the US, five risk factors, comprising the three Fama?French factors and the momentum and reversal factors of Chan et al. (1998, 2001), cannot adequately explain the return premia in Japan. Namely, statistically significant positive alphas were observed for the excess returns of all 25 Fama?French (1996) type portfolios, formed on the basis of size and BE/ME ratios. Second, the representative macroeconomic factors of Chen et al. (1986) cannot explain the positive alphas left unexplained by the five risk factors. Third, not only is there little contemporaneous relationship between the alphas left unexplained by the five risk factors and the representative macroeconomic factors of Chen et al. (1986) but also there is little evidence of any causal relationship between these macroeconomic factors and the alphas. Thus, for Japan, unlike for the US, well-known risk factors and macroeconomic factors cannot adequately explain the return premia.

Drivers of expected returns in Istanbul stock exchange: Fama–French factors and coskewness

We investigate the impact of coskewness on the variation of portfolio excess returns in Istanbul Stock Exchange (ISE) over the period July 1999 to December 2005. We form portfolios according to size, industry, size and book-to-market ratio, momentum and coskewness and compare alternative asset pricing models. The traditional capital asset pricing model (CAPM) and the three-factor model of Fama and French are tested in the multivariate testing procedure of Gibbons–Ross–Shanken (1989). Coskewness is introduced as a fourth factor and its incremental effect over CAPM and Fama–French factors is examined both in multivariate tests and in cross-sectional regressions. The findings reveal that coskewness is able to explain the size premium in ISE. Hence, the basic two-moment CAPM without the coskewness factor would underestimate the expected return of size portfolios. Multivariate test results indicate that coskewness reduces the pricing bias, albeit insignificantly. Cross-sectional analysis uncovers that coskewness has a significant additional explanatory power over CAPM, especially for size and industry portfolios. However, coskewness does not have a significant incremental explanatory power over Fama–French factors in ISE.

The security market plane

The relation between market risk and asset returns can be modelled with the Security Market Line (SML), a positive linear relation between expected excess asset returns and the asset's β. Pettengill et al. (1995) make the case that tests of β must be conditioned upon excess market returns to obtain meaningful results. This study proceeds from and extends the work of Pettengill et al. (1995), and in the process introduces the notion of the Security Market Plane (SMP). The SMP is a conditional relation between expected excess asset returns, β and realized excess market returns and is derived directly from the market model. Econometric testing on equities traded at the Australian Securities Exchange (ASX) based on a model motivated by the SMP offers strong evidence of the relevance of β to asset returns. The analysis does not reject the hypothesis that factors other than the market portfolio may be relevant to excess portfolio returns.

The Security Market Plane

The relation between market risk and asset returns can be modeled with the Security Market Line (SML), a positive linear relation between expected excess asset returns and the asset’s beta. Pettengill et al (1995) make the case that tests of beta must be conditioned upon excess market returns to obtain meaningful results. This study proceeds from and extends the work of Pettengill et al (1995) and in the process introduces the notion of the Security Market Plane (SMP). The SMP is a conditional relation between expected excess asset returns, beta and realized excess market returns and is derived directly from the market model. Econometric testing on equities traded at the Australian Securities Exchange (ASX) based on a model motivated by the SMP offers strong evidence of the relevance of beta to asset returns. The analysis does not reject the hypothesis that factors other than the market portfolio may be relevant to excess portfolio returns.

Fund of Funds, Portable Alpha, and Portfolio Optimization

The authors examine the portfolio optimization problem that arises when a manager9s mandate is a fund of funds with an asset allocation benchmark. The portfolio manager9s objective is to maximize the portfolio excess return over the benchmark subject to given tracking errors. The authors decompose total tracking error into two components—the deviation from the benchmark and the additional risk factors associated with fund alpha. By quantifying the contribution of each component of portfolio excess return, the authors show that the performance of the style-constrained portfolio is determined by the active alpha–seeking skill of the portfolio manager and that fund alphas can be separated from their style loadings and are portable. These findings will not only help investors determine optimal tracking error constraints, but will also provide a framework for portfolio managers to identify funds with certain characteristics in order to achieve an optimal portfolio return. <b>TOPICS:</b>Manager selection, statistical methods, VAR and use of alternative risk measures of trading risk

Investment and Stock Returns: Role of Financial Constraints

Existing studies have identified two opposite investment-returns relations: a negative relation between capital investment and expected stock returns, but a positive relation between R&D investment and expected stock returns. However, explanations of these patterns usually are distinct from each other. This paper provides a new unified explanation of both relations through the channel of financial constraints. I find that both R&D-returns and capital investment-returns relations mainly exist in financially constrained segment and are insignificant in unconstrained firms. The strength of these relations increases with financial constraints status. In the constrained segment, the excess returns of the investment spread portfolios cannot be explained by either characteristic-based benchmark portfolios or existing risk factor models. Furthermore, high capital investment portfolio is less constrained, while high R&D portfolio is more constrained. These results suggest that financial constraints can help reconcile these two opposite investment-returns relations.

An examination of alternative CAPM-based models in UK stock returns

We evaluate the performance of unconditional and conditional versions of seven stochastic discount factor models in UK stock returns between January 1975 and December 2001. We find that the conditional four-moment capital asset pricing model (CAPM) has the best performance among the models we consider in terms of the lowest [Hansen, L.P., Jagannathan, R., 1997. Assessing specification errors in stochastic discount factor models. Journal of Finance 52, 591–607] distance measure and explaining the time-series predictability of industry portfolio excess returns. Conditional models also do a better job than unconditional models. However we find that the superior performance of the conditional four-moment CAPM, and conditional models in general, arises in part due to overfitting the data.

Corporate Investment and Asset Price Dynamics: Implications for the Cross‐section of Returns

ABSTRACTWe show that corporate investment decisions can explain the conditional dynamics in expected asset returns. Our approach is similar in spirit to Berk, Green, and Naik (1999), but we introduce to the investment problem operating leverage, reversible real options, fixed adjustment costs, and finite growth opportunities. Asset betas vary over time with historical investment decisions and the current product market demand. Book‐to‐market effects emerge and relate to operating leverage, while size captures the residual importance of growth options relative to assets in place. We estimate and test the model using simulation methods and reproduce portfolio excess returns comparable to the data.

Interpreting the Sharpe ratio when excess returns are negative

Extracted from text ... Can the small investor beat the market by 'Dow investing'? 15 Interpreting the Sharpe ratio when excess returns are negative 1. INTRODUCTION* Fund performance evaluation has evolved from the simple comparison of portfolio returns, to the comparison of excess portfolio returns (returns relative to a risk-free rate), to the highly acclaimed Sharpe Ratio in which excess portfolio returns per unit of risk taken to achieve those returns are compared. Of these developments, the Sharpe Ratio enjoys arguably the greatest success and most widespread implementation. It has undergone several refinements and augmentations in its 37-year life, but the basic ..

Fund Managers May Cause Their Benchmarks to Be Priced 'Risks'

The presence of a positive intercept (alpha) in a regression of an investment fund's excess returns on a broad market portfolio's excess return (as in the CAPM) and other factor portfolios' excess returns (e.g. the Fama and French factors) is frequently interpreted as evidence of superior fund performance. This paper theoretically and empirically supports the notion that the additional factors may proxy for benchmark portfolios that fund managers try to beat, rather than proxying for state variables of future risks that investors (in conventional theory) are supposed to care about.

Analysis of Linear Factor Models with Multivariate Stochastic Volatility for Stock and Bond Returns

We explore high-dimensional linear factor models in which the covariance matrix of excess asset returns follows a multivariate stochastic volatility process. We test crosssectional restrictions suggested by the arbitrage pricing theory, compare competing stochastic volatility specifications for the covariance matrix, test for the number of factors, and analyze possible sources of model misspecification. Estimation and testing of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that five latent factors with multivariate stochastic volatility best explain excess returns for a sample of seventeen stock and bond portfolios. Analysis of cumulative latent factor shocks suggests that APT pricing restrictions, coupled with constant factor risk premia, do not adequately explain cross-sectional variation in average portfolio excess returns.

The pricing of systematic liquidity risk: Empirical evidence from the US stock market

In this study, we examine whether aggregate market liquidity risk is priced in the US stock market. We define a bivariate Garch (1,1)-in-mean specification for the market portfolio excess returns and the changes in the standardized number of shares in the S&P 500 Index, the aggregate market liquidity proxy. The findings, based on monthly data, suggest that systematic liquidity risk is priced in the US over the period January 1973–December 1997. The liquidity premium represents a non-negligible, negative and time-varying component of the total market risk premium whose magnitude is not influenced by the October’87 Crash.

Optimal Contracts in a Continuous-Time Delegated Portfolio Management Problem

This article studies the contracting problem between an individual investor and a professional portfolio manager in a continuous-time principal-agent framework. Optimal contracts are obtained in closed form. These contracts are of a symmetric form and suggest that a portfolio manager should receive a fixed fee, a fraction of the total assets under management, plus a bonus or a penalty depending upon the portfolio's excess return relative to a benchmark portfolio. The appropriate benchmark portfolio is an active index that contains risky assets where the number of shares invested in each asset can vary over time, rather than a passive index in which the number of shares invested in each asset remains constant over time.

Do Beta Pricing Models Explain January Mean Reversion in Stock Returns?

AbstractJegadeesh (1991) finds evidence of January mean reversion in stock returns. In this paper we attempt to distinguish between two competing economic explanations of January mean reversion in returns: (1) mispricing in irrational markets versus (2) predictable time variation in security risk premia. Excess portfolio returns are decomposed into “explained” and “unexplained” components using the Fama‐French (1993) pricing model. The explained excess returns exhibit January mean reversion. The unexplained excess returns are not mean reverting. Mean reversion is therefore consistent with rational pricing in the framework of the Fama‐French model. Mean reversion can be attributed to the component of return related to a relative distress factor (SMB). A comparison with the Chen, Roll, and Ross (1986) macroeconomic factors reveals that mean reversion is due to the components related to SMB and bond default premium.

Interpreting mean reversion in stock returns

This research attempts to distinguish between two competing economic explanations of mean reversion in stock returns: (1) mispricing in irrational markets versus and (2) predictable time variation in security risk premia. Excess portfolio returns are decomposed into “explained” and “unexplained” components using the CAPM. if one restricts the market risk premium to be constant over time, then mean reversion could be interpreted as a manifestation of mispricing in irrational markets. But changing the assumption about the time-series behavior of the market risk premium dramatically alters the test results. If one permits the conditional market risk premium to vary and to differ from the unconditional market risk premium, then mean reversion in stock returns is consistent with rational pricing in the framework of the CAPM. This suggests one need not abandon models based on rationality to explain this puzzling return behavior.

The Wealth Effect of International Joint Ventures: The Case of U.S. Investment in China

This study provides direct evidence of the wealth effect of international joint venture announcements on the value of U.S. firms investing in China from 1979 to 1990. Using a sample of 88 announcements, we find statistically significant positive portfolio excess returns at the joint venture announcements. The results lend strong support to the positive-multinational-network hypothesis, which predicts that a firm's value increases when it expands into global markets. Further, shareholders of U.S. firms investing a relatively small amount in the joint ventures gain positive excess returns, while shareholders of those investing a relatively large amount earn insignificant excess returns. The implication is that the wealth gain arises primarily from the value of a collection of real options acquired by the U.S. firms through their investments in China.