Can the degree of predictability found in data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R 2 of predictive regressions. Using data on the market portfolio and component portfolios, we find that the empirical R2 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.
Return predictability, asset pricing, stochastic discount factor, habit formation, long-run risks, rare disaster
Business | Finance and Financial Management
Journal of Financial and Quantitative Analysis
Cambridge University Press
HUANG, Dashan and ZHOU, Guofu.
Upper Bounds on Return Predictability. (2017). Journal of Financial and Quantitative Analysis. 52, (2), 401-425. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/4569
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