Upper bounds on return predictability

Upper bounds on return predictability

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 the...

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Bibliographic Details
Main Authors: HUANG, Dashan, ZHOU, Guofu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Return predictability
asset pricing
stochastic discount factor
habit formation
long-run risks
rare disaster
Business
Finance and Financial Management
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/4569
https://ink.library.smu.edu.sg/context/lkcsb_research/article/5568/viewcontent/UpperBounds.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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