Risk-Neutral Skewness, Informed Trading, and the Cross Section of Stock Returns

AbstractIn this article, we use volatility surface data from options contracts to document a strong, robust, and positive cross-sectional relation between risk-neutral skewness (RNS) and subsequent stock returns. The differential return between high- and low-RNS stocks amounts to 0.17% per week. Preannouncement RNS is positively related to earnings announcement returns, and the positive RNS&ndash;return relation is more pronounced for other nonscheduled news releases. This suggests that it is informed trading that drives the positive relation between RNS and subsequent stock returns. We also find that RNS contains incremental information beyond trading signals captured by option-implied volatility and volume.</jats:p

Order flow volatility and equity costs of capital

Ministry of Education, Singapore under its Academic Research Funding Tier 1; Sim Kee Boon Institute for Financial Economics at Singapore Management Universit

Trading on Algos

Abstract This paper studies the impact of algorithmic trading (AT) on asset prices. We find that the heterogeneity of algorithmic traders across stocks generates predictable patterns in stock returns. A trading strategy that exploits the AT return predictability generates a monthly risk-adjusted performance between 50-130 basis points for the period 1999 to 2012. We find that stocks with lower AT have higher returns, after controlling for standard market-, size-, book-to-market-, momentum, and liquidity risk factors. This effect survives the inclusion of many cross-sectional return predictors and is statistically and economically significant. Return predictability is stronger among stocks with higher impediments to trade and higher predatory/opportunistic algorithmic traders. Our paper is the first to study and establish a strong link between algorithmic trading and asset prices