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Sparse Signals in the Cross-Section of Returns

Alexander M. Chinco, Adam D. Clark-Joseph, Mao Ye

NBER Working Paper No. 23933
Issued in October 2017
NBER Program(s):The Asset Pricing Program, The Corporate Finance Program

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.

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Document Object Identifier (DOI): 10.3386/w23933

Published: ALEX CHINCO & ADAM D. CLARK-JOSEPH & MAO YE, 2019. "Sparse Signals in the Cross-Section of Returns," The Journal of Finance, vol 74(1), pages 449-492.

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