Bing Han and Gang Li ♦ We show theoretically and empirically that the cross-section of stock return idiosyncratic volatilities contains useful information about the ICAPM. We construct a proxy cross-sectional bivariate idiosyncratic volatility (CBIV) for the covariance risk between the…
Tao Li, Donghwa Shin, and Baolian Wang ♦ We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature significant increases in prices, volume, and volatility, followed by quick reversals. The evidence we…
Ryan G. Chacon, Rachel E. Gordon, and Adam S. Yore ♦ We investigate whether compensation consultants recommend excessive pay to earn repeat business by studying consultant changes. Our results show consultants’ interests are aligned with shareholders to appropriately pay the…
Xiang Zheng ♦ This study utilizes U.S. Patent Office data to explore potential improvements in the patent examination process through machine learning. It shows that integrating machine learning with human expertise can increase patent citations by up to 26%. Using…
Raymond Kan and Nathan Lassance ♦ Existing portfolio combination rules that optimize the out-of-sample performance under parameter uncertainty assume multivariate normally distributed returns. However, we show that this assumption is not innocuous because fat tails in returns lead to poorer…
Chukwuma Dim ♦ This paper documents that 56% of nonprofessional social media investment analysts (SMAs) are skilled and declare beliefs that generate positive abnormal returns, while 44% produce negative abnormal returns. Some 13% high-type SMAs produce a one-week three-factor alpha of 61…
