Sparse Signals in the Cross-Section of Returns

50 Pages Posted: 16 May 2015 Last revised: 17 Feb 2017

Alexander Chinco

University of Illinois at Urbana-Champaign - College of Business

Adam D. Clark-Joseph

University of Illinois at Urbana-Champaign

Mao Ye

University of Illinois at Urbana-Champaign; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: February 16, 2017

Abstract

How do arbitrageurs find variables that predict returns? If a predictor lasts 30 days or more, then a clever arbitrageur can use his intuition to get the job done. But, what’s an arbitrageur supposed to do if a predictor lasts 30 minutes or less? An arbitrageur’s intuition is useless if the predictor decays before he can finish his morning coffee. Motivated by this observation, we show how arbitrageurs can find these sorts of rare, short-lived, “sparse” predictors by replacing intuition with a statistical procedure known as the LASSO. Using the LASSO boosts out-of-sample predictability in 1-minute returns by 23% relative to standard OLS-regression models. This out-of-sample predictive power comes from quickly identifying the right predictors at the right time, not from better estimating the effects of some new factor. What’s more, the predictors chosen by the LASSO correspond to real-world events: the lagged returns of stocks with announcements are 18.3% more likely to be used by the LASSO as predictors.

Keywords: Return Predictability, Out-of-Sample Fit, Sparsity, The LASSO

JEL Classification: C55, C58, G12, G14

Suggested Citation

Chinco, Alexander and Clark-Joseph, Adam D. and Ye, Mao, Sparse Signals in the Cross-Section of Returns (February 16, 2017). Available at SSRN: https://ssrn.com/abstract=2606396 or http://dx.doi.org/10.2139/ssrn.2606396

Alexander Chinco (Contact Author)

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

Adam D. Clark-Joseph

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

Mao Ye

University of Illinois at Urbana-Champaign ( email )

406 Wohlers
1206 South 6th Street
Champaign, IL 61820
United States
2172440474 (Phone)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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