While it is established that idiosyncratic volatility has a negative impact on the cross-section of future stock returns, the relationship between idiosyncratic volatility and future hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility...

This paper investigates how the stock market reacts to firm level liquidity shocks. We find that negative and persistent liquidity shocks not only lead to lower contemporaneous returns, but also predict negative returns for up to six months in the future. Long-short portfolios sorted on past...

We find that the stock market underreacts to stock level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate...

We find that the stock market underreacts to stock level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate...

We find that the stock market underreacts to stock level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate...

We introduce a measure of regret for stock market investors and investigate its cross-sectional asset pricing implications. We propose a theoretical framework in which investors experience regret due to not achieving the highest possible return in the same industry with their stock investment,...

While it is established that idiosyncratic volatility has a negative impact on the cross-section of future stock returns, the relationship between idiosyncratic volatility and future hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility...

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...

We propose a belief-generating model from which we build a statistical measure of investor disagreement. We simulate differences in beliefs across investors by endowing them with different machine learning models for forecasting returns from the same set of inputs. We measure disagreement as the...

Hedge funds' extensive use of derivatives, short-selling, and leverage and their dynamic trading strategies create significant non-normalities in their return distributions. Hence, the traditional performance measures fail to provide an accurate characterization of the relative strength of hedge...