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...

This paper proposes alternative specifications of the conditional CAPM with dynamic conditional beta and tests the models' performance in explaining the value premium for the period 1963-2011. The conditional alphas on the value-minus-growth portfolio are estimated to be economically and...

We develop an ex-ante measure of expected stock returns based on analyst price targets. We then show that ex-ante measures of volatility, skewness, and kurtosis implied from stock option prices are positively related to the cross section of ex-ante expected stock returns. While expected returns...

We propose the first factor model that explains cross-sectional variation in optionable stock returns. Our model includes new factors based on option-implied volatility minus realized volatility, the call minus put implied volatility spread, and the difference between changes in call and put...

This paper examines an issue overlooked in the finance and economics literature: time variation in announcement volatility or event risk. We combine long spans of high-frequency data with a flexible parametric model of returns, which al- lows to identify announcement returns, capture intraday...

This is the first study on the risk-neutral distribution of option returns. We derive solutions for the risk-neutral variance, skewness, and kurtosis of call and put option returns and document several properties of these ex-ante moments. We find that the volatility, skewness, and kurtosis of...