Sparse Signals in the Cross-Section of Returns

• 成果类型: Article
• 署名作者: Chinco, Alex; Clark-Joseph, Adam D.; Ye, Mao
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; National Bureau of Economic Research
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12733
• 发表日期: 2019
• 页码: 449-492
• 关键词: market; selection; sample; MODEL; regularization; frequency; profits; Lasso; LINKS

摘要

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.