Alexander M. Chinco
University of Illinois at Urbana-Champaign
1206 S 6th St, Rm 343J
Champaign, IL 61820
Institutional Affiliation: University of Illinois at Urbana-Champaign
NBER Working Papers and Publications
|November 2019||Estimating The Anomaly Base Rate|
with Andreas Neuhierl, Michael Weber: w26493
The academic literature literally contains hundreds of variables that seem to predict the cross-section of expected returns. This so-called "anomaly zoo" has caused many to question whether researchers are using the right tests of statistical significance. But, here's the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors---i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly.
So, what are the right priors? What is the correct anomaly base rate?
We develop a first way to estimate the anomaly base rate by combining two key insights: 1) Empirical-Bayes methods capture the implicit process by which researchers form priors b...
|October 2017||Sparse Signals in the Cross-Section of Returns|
with Adam D. Clark-Joseph, Mao Ye: 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 Mao Ye: 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.
|January 2014||Misinformed Speculators and Mispricing in the Housing Market|
with Christopher Mayer: w19817
This paper uses transactions-level deeds records to examine how out-of-town second house buyers contributed to mispricing in the housing market. We document that out-of-town second house buyers behaved like misinformed speculators and drove up both house price and implied-to-actual rent ratio (IAR) appreciation rates in cities like Phoenix, Las Vegas, and Miami in the mid 2000s. Our analysis has 3 parts. First, we give evidence that out-of-town second house buyers behaved like misinformed speculators. Compared to local second house buyers, out- of-town second house buyers had worse exit timing (i.e., were likely misinformed) and were also less able to consume the dividend from their purchase (i.e., were likely speculators). Second, we show that increases in out-of-town second house buyer d...
Published: Alex Chinco & Christopher Mayer, 2016. "Misinformed Speculators and Mispricing in the Housing Market," Review of Financial Studies, vol 29(2), pages 486-522. citation courtesy of