Sparse Signals in the Cross‐Section of Returns
用LASSO方法对股票收益做滚动一分钟预测，发现该方法能识别出意外、短暂且稀疏的预测因子，这些因子与基本面新闻相关，从而提升样本外预测效果和夏普比率。
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.