Florian Weigert

Drawing upon more than 12 million observations over the period from 1996 to 2020, we  nd that allowing for nonlinearities signi cantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizeable pro ts in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures o er substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH, and we empirically confirm that stocks with high MCRASH earn significantly higher future returns than stocks with low MCRASH. The premium is not explained by linear factor exposures, alternative downside risk measures, or stock characteristics. Extending market-based definitions of crash risk to other well-established factors helps to determine the cross-section of expected stock returns without further expanding the factor zoo.

This paper examines whether investors receive compensation for holding crash-sensitive stocks. We capture the crash sensitivity of stocks by their lower tail dependence (LTD) with the market based on copulas. We find that stocks with weak LTD serve as a hedge during crises, but, overall, stocks with strong LTD have higher average future returns. This effect cannot be explained by traditional risk factors and is different from the impact of beta, downside beta, coskewness, and cokurtosis. Our findings are consistent with results from the empirical option pricing literature and support the notion that investors are crash-averse.

We develop a new tail risk measure for hedge funds to examine the impact of tail risk on fund performance and to identify the sources of tail risk. We find that tail risk affects the cross-sectional variation in fund returns, and investments in both, tail-sensitive stocks as well as options, drive tail risk. Moreover, managerial incentives and discretion as well as exposure to funding liquidity shocks are important determinants of tail risk. We find evidence that is consistent with funds being able to time tail risk exposure prior to the recent financial crisis.

We investigate whether value-relevant foreign information only gradually dilutes into stock prices of multinational firms worldwide. Using an international sample of firms from 22 developed countries, we find that a portfolio strategy based on firms' foreign sales information yields future returns of more than 10% p.a. globally. The return spread due to foreign information is substantial across different geographical regions and cannot be explained by traditional risk factors, firm characteristics, and industry momentum. Our results are in line with limited attention of investors being the main driver of this effect worldwide.

This paper examines whether investors receive compensation for holding stocks with a strong sensitivity to extreme market downturns in a sample covering forty countries. Worldwide, stocks with strong crash sensitivity deliver average returns of more than 7% p.a. higher than stocks with weak crash sensitivity. The effect is robust across geographical subsamples and is not explained by systematic risk factors and alternative firm characteristics. I show that the risk premium is particularly pronounced in countries that display negative market skewness, high income per capita, and rank high on Hofstede's individualism index.