Sparse Signals in the Cross-Section of Returns

55 Pages Posted: 16 Oct 2017

See all articles by Alexander Chinco

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

Multiple version iconThere are 2 versions of this paper

Date Written: October 2017

Abstract

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling 1-minute-ahead return forecasts using the entire cross section of lagged returns as candidate predictors. The LASSO increases both out-of-sample fit and forecast-implied Sharpe ratios. And, this out-of-sample success comes from identifying predictors that are unexpected, short-lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.

Suggested Citation

Chinco, Alexander and Clark-Joseph, Adam D. and Ye, Mao, Sparse Signals in the Cross-Section of Returns (October 2017). NBER Working Paper No. w23933. Available at SSRN: https://ssrn.com/abstract=3053723

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 )

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