Upper Bounds on Return Predictability

Abstract

Can the degree of predictability found in the data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R-squares of predictive regressions. Using data on the market and component portfolios, we find that the empirical R-squares are significantly greater than the theoretical upper bounds. Our results suggest that the most promising direction for future research should aim to identify new state variables that are highly correlated with stock returns, instead of seeking more elaborate stochastic discount factors.

Keywords: Return predictability, asset pricing, stochastic discount factor, habit formation, long-run risks, rare disaster

JEL Classification: C22, C53, C58, G10, G12, G14, G17