We find that among stocks dominated by retail investors, the lottery anomaly is amplified by high investor attention (proxied by high analyst coverage, salient earnings surprises, or recency of extreme positive returns) and intense social interactions (proxied by Facebook social connectedness or...

We propose a comprehensive measure of systematic risk for corporate bonds as a nonlinear function of robust risk factors and find a significantly positive link between systematic risk and the time-series and cross-section of future bond returns. We also find a positive but insignificant relation...

Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level...

Recent theoretical models based on dynamic human capital formation, or social influence, suggest an inverse relationship between criminal activity and economic opportunity and between criminal activity and deterrence, but predict an asymmetric response of crime. In this paper we use three...

Stocks with large increases in call implied volatilities over the previous month tend to have high future returns while stocks with large increases in put implied volatilities over the previous month tend to have low future returns. Sorting stocks ranked into decile portfolios by past call...

We introduce a new, hybrid measure of stock return tail covariance risk, motivated by the under-diversified portfolio holdings of individual investors, and investigate its cross-sectional predictive power. Our key innovation is that this covariance is measured across the left tail states of the...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...