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...

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...

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...

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...

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,...

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative...

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...

We investigate the relation between downside beta and stock returns in a global context using more than 170 million daily return observations. Contrary to the findings in the U.S. equity market, we show that downside beta does not explain the cross-sectional differences in future and...

This paper investigates the relationship between upside potential and future hedge fund returns. We measure upside potential based on the maximum monthly returns of hedge funds (MAX) over a fixed time interval, and show that MAX successfully predicts cross-sectional differences in future fund...