Publication Type
Journal Article
Version
submittedVersion
Publication Date
4-2017
Abstract
Can the degree of predictability found in data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R2 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.
Keywords
Return predictability, asset pricing, stochastic discount factor, habit formation, long-run risks, rare disaster
Discipline
Business | Finance and Financial Management
Research Areas
Finance
Publication
Journal of Financial and Quantitative Analysis
Volume
52
Issue
2
First Page
401
Last Page
425
ISSN
0022-1090
Identifier
10.1017/S0022109017000096
Publisher
Cambridge University Press
Citation
HUANG, Dashan and ZHOU, Guofu.
Upper bounds on return predictability. (2017). Journal of Financial and Quantitative Analysis. 52, (2), 401-425.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4569
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S0022109017000096