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Sparse Signals in the Cross-Section of Returns / Alexander M. Chinco, Adam D. Clark-Joseph, Mao Ye.
- Format:
- Book
- Author/Creator:
- Chinco, Alexander M.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w23933.
- NBER working paper series no. w23933
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2017.
- Summary:
- 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.
- Notes:
- Print version record
- October 2017.
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