Sparse Signals in the Cross-Section of Returns

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.