Persistent Predictors and the Cross-Section of Stock Returns

61 Pages Posted: 5 Sep 2019 Last revised: 19 Sep 2019

See all articles by Devraj Basu

Devraj Basu

SKEMA Business School - Lille Campus

Marta Szymanowska

Erasmus University Rotterdam (EUR) - Department of Finance; Erasmus Research Institute of Management (ERIM)

Date Written: August 28, 2019

Abstract

We show that when returns are predictable, persistent predictors, known to bias time-series predictive regressions, also bias the estimation of the cross-sectional moments of asset return distribution, especially the variance-covariance matrix of returns. Hence, they also bias the estimation of the various stochastic discount factor bounds, Shanken's (1985) cross-sectional regression test statistic, and Hansen and Jagannathan's (1997) distance measure, except when conditioning information is used efficiently. However, the empirical distributions of these statistics are biased, even with efficient use of conditioning information. We define a test statistic that is immune to this bias and shows consistent results across the persistence levels in our simulations.

Keywords: asset pricing, persistent predictors, stochastic discount factor bounds, conditioning information

JEL Classification: G11, G12

Suggested Citation

Basu, Devraj and Szymanowska, Marta, Persistent Predictors and the Cross-Section of Stock Returns (August 28, 2019). Available at SSRN: https://ssrn.com/abstract=3444841 or http://dx.doi.org/10.2139/ssrn.3444841

Devraj Basu

SKEMA Business School - Lille Campus ( email )

Avenue Willy Brandt, Euralille
Lille, 59777
France

Marta Szymanowska (Contact Author)

Erasmus University Rotterdam (EUR) - Department of Finance ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands
+31104089607 (Phone)

HOME PAGE: http://www.rsm.nl/mszymanowska

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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