Informative Starting Points and Asset-Return Puzzles

Abstract

Decision-makers typically rely on informative starting points that are somewhat incorrect and then attempt to make appropriate adjustments. Such reliance on informative starting points may be an optimal response of a Bayesian decision-maker who faces finite computational resources (Lieder et al 2013). Professional equity-analysts take the same approach when they co-categorize closely related firms in peer-groups and routinely extrapolate the analysis pertaining to a prominent firm to other firms in the same peer-group. We show that if the representative agent behaves like a professional equity analyst, then a unified explanation for 5 asset-market phenomena emerges. The phenomena explained include high and counter-cyclical equity premium, size effect, value premium, and media-coverage effect. A novel prediction of the model is that stocks with less volatile payoffs outperform stocks with more volatile payoffs. Empirical evidence strongly supports this prediction.