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 investigate the cross-sectional return predictability of delta-hedged equity options using machine learning and big data. Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample...

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...

This paper provides an explanation of investing in stock market anomalies in an expected utility paradigm. Classical selection rules fail to provide a preference for high expected return portfolios. The paper utilizes the almost dominance rules to examine the practice of investing in size,...

This paper provides an explanation of investing in stock market anomalies in an expected utility paradigm. Classical selection rules fail to provide a preference for high expected return portfolios. The paper utilizes the almost dominance rules to examine the practice of investing in size,...

This paper investigates whether realized and implied volatilities of individual stocks can predict the cross-sectional variation in expected returns. Although the levels of volatilities from the physical and risk-neutral distributions cannot predict future returns, there is a significant...

This paper provides an analysis of the predictability of stock returns using market, industry, and firm-level earnings. Contrary to Lamont (1998), we find that neither dividend payout ratio nor the level of aggregate earnings can forecast the excess market return. We show that these variables do...

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...

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and...

A conditional asset pricing model with risk and uncertainty implies that the time-varying exposures of equity portfolios to the market and uncertainty factors carry positive risk premiums. The empirical results from the size, book-to-market, and industry portfolios as well as individual stocks...