University of Illinois, Urbana-Champaign
343K Wohlers Hall
1206 South Sixth Street
Champaign, IL 61820
NBER Program Affiliations:
NBER Affiliation: Research Associate
Institutional Affiliation: University of Illinois at Urbana-Champaign
NBER Working Papers and Publications
|June 2019||Who Provides Liquidity, and When?|
with , : w25972
We model competition for liquidity provision between high-frequency traders (HFTs) and slower execution algorithms designed to minimize transaction costs for buy-side institutions (B-Algos). Under continuous pricing, B-Algos dominate liquidity provision by using aggressive limit orders to stimulate HFTs’ market orders. Under discrete pricing, HFTs dominate liquidity provision if the bid–ask spread is binding at one tick. If the tick size is not binding, B-Algos choose between stimulating HFTs and providing liquidity to other non-HFTs. Flash crashes arise under certain parameter values. Transaction costs can be negatively correlated with the bid–ask spread when all traders can provide liquidity.
|October 2017||Sparse Signals in the Cross-Section of Returns|
with , : w23933
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.
Published: ALEX CHINCO & ADAM D. CLARK-JOSEPH & MAO YE, 2019. "Sparse Signals in the Cross-Section of Returns," The Journal of Finance, vol 74(1), pages 449-492.
|August 2017||Investment-Horizon Spillovers|
with : w23650
This paper uses wavelets to decompose each stock’s trading-volume variance into frequency-specific components. We find that stocks dominated by short-run fluctuations in trading volume have abnormal returns that are 1% per month higher than otherwise similar stocks where short-run fluctuations in volume are less important—i.e., stocks with less of a short-run tilt. And, we document that a stock’s short-run tilt can change rapidly from month to month, suggesting that these abnormal returns are not due to some persistent firm characteristic that’s simultaneously adding both short-run fluctuations and long-term risk.