Sparse Signals in the Cross-Section of Returns

Alex Chinco, Adam D. Clark-Joseph, Mao Ye

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)449-492
Number of pages44
JournalJournal of Finance
Issue number1
StatePublished - Feb 1 2019

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics


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