The use of predetermined variables to represent public information and time-variation has produced new insights about asset pricing models but the literature on mutual fund performance has not exploited these insights. This paper advocates conditional performance evaluation in which the relevant expectations are conditioned on public information variables. The authors modify several classical performance measures to this end and find that the predetermined variables are both statistically and economically significant. Conditioning on public information controls for biases in traditional market timing models and makes the average performance of the mutual funds in the authors' sample look better.

Most of the evidence on consumption-based asset pricing is based on seasonally adjusted consumption data. The consumption-based models have not worked well for explaining asset returns, but with seasonally adjusted data there are reasons to expect spurious rejections of the models. This paper examines asset pricing models using not seasonally adjusted aggregate consumption data. The authors find evidence against models with time-separable preferences, even when the models incorporate seasonality and allow seasonal heteroskedasticity. A model that uses not seasonally adjusted consumption data and nonseparable preferences with seasonal effects works better according to several criteria. The parameter estimates imply a form of seasonal habit persistence in aggregate consumption expenditures.

The performance of portfolio managers depends on market timing, volatility timing, and security selection. We develop holdings-based performance measures that adjust for risk using stochastic discount factors, display all three components in a consistent framework, and avoid strong assumptions about managers’ behavior. Previous models leave out some of the components of performance, and correcting for this we deliver better measures of selectivity. Sorting stocks held by funds on selectivity produces a quintile spread in four-factor alphas greater than 2.5% per year before costs and more than 1.7% greater than found using the Daniel, Grinblatt, Titman, and Wermers (1997) measure.

This paper combines the use of portfolio holdings data and conditioning information to create a new performance measure. Our conditional weight-based measure has several advantages. Using conditioning information avoids biases in weight-based measures as discussed by Grinblatt and Titman (J. Business 60 (1993)). When conditioning information is used, returns-based measures face a bias if managers can trade between observation dates. The new measures avoid this interim trading bias. We use the new measures to provide fresh insights about performance in a sample of U.S. equity pension fund managers.

This article shows how to evaluate the performance of managed portfolios using stochastic discount factors (SDFs) from continuous-time term structure models. These models imply empirical factors that include time averages of the underlying state variables. The approach addresses a performance measurement bias, described by Goetzmann, Ingersoll, and Ivkovic (2000) and Ferson and Khang (2002), arising because fund managers may trade within the return measurement interval or hold positions in replicable options. The empirical factors contribute explanatory power in factor model regressions and reduce model pricing errors. We illustrate the approach on US government bond funds during 1986-2000.

Portfolio performance measures using holdings data are panel regressions. The returns of a fund’s stocks are regressed on its lagged portfolio weights. Stock fixed effects isolate average performance from time-series predictive ability. Control variables condition for fund performance on the characteristics of the stocks held. The long-term performance of average holdings drives some of the classical measures, while predictive ability drives others. A “buy-and-hold drift,” where portfolio weights increase over time in the higher alpha stocks, affects performance measures. Investor flows respond to average performance net of the buy-and-hold drift. (JEL G11, G14, G23, G29).

ABSTRACT Multibeta asset pricing models are examined using proxies for economic state variables in a framework which exploits time‐varying expected returns to estimate conditional betas. Examples include multiple consumption‐beta models and models where asset returns proxy for the state variables. When the state variables are not specified, the tests indicate two or three time‐varying expected risk premiums in the sample of quarterly asset returns. Conditional betas relative to consumption generate less striking evidence against the model than betas relative to asset returns, but both the consumption and the market variables fail to proxy for the state variables.

ABSTRACT Regressions of security returns on treasury bill rates provide insight about the behavior of risk in rational asset pricing models. The information in one‐month bill rates implies time variation in the conditional covariances of portfolios of stocks and fixed‐income securities with benchmark pricing variables, over extended samples and within five‐year subperiods. There is evidence of changes in conditional “betas” associated with interest rates. Consumption and stock market data are examined as proxies for marginal utility, in a general framework for asset pricing with time‐varying conditional covariances.

Regressions of security returns on treasury bill rates provide insight about the behavior of risk in rational asset pricing models. The information in one-month bill rates implies time variation in the conditional covariances of portfolios of stocks and fixed-income securities with benchmark pricing variables, over extended samples and within five-year subperiods. There is evidence of changes in conditional “betas” associated with interest rates. Consumption and stock market data are examined as proxies for marginal utility, in a general framework for asset pricing with time-varying conditional covariances.