Asset Pricing Specification Errors and Performance Evaluation

Many evaluation techniques typically measure performance as deviations of average returns on actively managed funds from those predicted by some asset pricing model. Empirical evidence, however, has so far suggested that all asset pricing models lack empirical support, implying that the models contain mis-specification errors to various degrees. Evaluating mutual fund performance relative to any of these models thus becomes problematic. In this paper, we propose an approach to performance measurement that emphasizes minimizing explicitly the pricing error associated with an asset pricing function which is employed to compute performance measures. This approach is henceforth called the minimum specification-error (MSE) method. We also discuss the statistical properties for implementing MSE performance measures. To demonstrate the significance of the pricing error confounded in evaluation measurement, we contrast our methodology with the Grinblatt and Titman (1989) period weighting approach and with the empirical implementation of Chen and Knez (1996). We find that the greater the pricing error of passive assets, the larger the performance measures. Given the average pricing error generated from a collection of 163 diverse passive portfolios used in this analysis, the performance values assigned to a large number of the funds become statistically and economically insignificant.