University of Illinois at Urbana-Champaign
Institutional Affiliation: University of Illinois at Urbana-Champaign
NBER Working Papers and Publications
|October 2017||Sparse Signals in the Cross-Section of Returns|
with Alexander M. Chinco, Mao Ye: w23933
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.
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.