A Bayesian approach is used to investigate a sample's information about a portfolio's degree of inefficiency. With standard diffuse priors, posterior distributions for measures of portfolio inefficiency can concentrate well away from values consistent with efficiency, even when the portfolio is...

This article presents a mean-variance framework for likelihood- ratio tests of asset pricing models. A pricing model is tested by examining the position of one or more reference portfolios in sample mean-standard-deviation space. Included are tests of both single-beta and multiple-beta...

The authors find that conditional means and variances of consumption growth vary through time, and this variation appears to be associated with the business cycle. A pricing model with fluctuating means and variances of consumption growth provides implications about conditional moments of...

The authors characterize the sets of mimicking positions whose returns can serve in place of factors in an exact K-factor arbitrage pricing relation for a set of N assets. All of the sets are K-dimensional nonsingular linear transformations of each other. The authors interpret three examples of...

Tests of asset-pricing models are developed that allow expected risk premiums and market betas to vary over time. These tests exploit the relation between expected excess returns and current market values. Using weekly data for 1963-82 on ten common stock portfolios formed according to equity...

Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk-averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock...

The capital asset pricing model implies that the market portfolio is efficient and expected returns are linearly related to betas. Many do not view these implications as separate, since either implies the other, but the authors demonstrate that either can hold nearly perfectly while the other...