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Journal of Finance Vol. 74 No. 1 2019

Sparse Signals in the Cross‐Section of Returns

Alex Chinco; Adam D. Clark-Joseph; Mao Ye1,2,3,4,5,6

1 University of Illinois Urbana-Champaign · 2 Hungarian National Bank · 3 Heatherwood Hospital · 4 TiGenix (Spain) · 5 Victor (Japan) · 6 Kelly Services (United States)

Abstract

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐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. 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.

DOI
10.1111/jofi.12733
Volume
74
Issue
1
Pages
449-492
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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