We show how to use asset market data to restrict the admissible region for the first-order autocorrelation of the stochastic discount factor (SDF). We interpret this statistic as a measure of a model’s economic time variation across two periods. Estimating bounds for nominal and real SDFs at monthly and quarterly frequencies, we find that the admissible autocorrelations are significantly negative, but greater than −0.02, implying that the bounds impose a strong restriction on candidate SDFs. We illustrate the relevancy of these findings by showing that some widely used consumption-based models are misspecified with respect to the autocorrelation bound. Finally, we examine the implications of our results for the admissibility of linear factor models and the appropriateness of empirical pricing factors.

This paper investigates investor disagreement and clientele effects in performance evaluation by developing a measure that considers the best potential clienteles of mutual funds. In an incomplete market under law-of-one-price and no-good-deal conditions, we obtain an upper bound on admissible performance measures that identifies the most favorable alpha. Empirically, we find that a reasonable investor disagreement leads to generally positive performance for the best clienteles. Performance disagreement by investors can be significant enough to change the average evaluation of mutual funds from negative to positive, depending on the clienteles.

Using simulations controlling for the ability to time the equity, bond, and money markets, we compare daily and monthly performance measures. Our main results highlight the joint importance of the fictitious timer’s trading frequency and the data sampling frequency for estimation. Specifically, daily timing measures are superior to those estimated monthly for daily timers, but inferior for occasional or monthly timers. Global measures show more robustness to differences in trading and data sampling frequencies. Finally, conditional measures do not improve upon unconditional ones, and results are similar for performance detection versus ranking.