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. Received July 2, 2015; accepted November 20, 2015 by Editor Raman Uppal.

We investigate whether value-relevant foreign information only gradually dilutes into stock prices of multinational firms worldwide. Using an international sample of firms from twenty-two 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 to foreign information being the main driver of this effect worldwide.

While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds’ idiosyncratic volatility is a measure of managerial skill.

This paper investigates how investors’ abnormal attention affects the cross-section of cryptocurrency returns in the period from 2018 to 2022. We capture abnormal attention using the (log) number of Twitter posts on individual cryptocurrencies on the current day minus a 30-day average. Our results reveal that abnormal attention is positively associated with contemporaneous and one-day ahead crypto performance. Among the different Twitter tweets, return predictability arises due to Ticker-tweets from investors, but not due to tweets from the cryptocurrency channel. These Official-tweets, however, are able to forecast technological innovations on the blockchain.

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 performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits 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 offer 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We utilize Bayesian model averaging to estimate a stochastic discount factor (SDF) for single-stock options. A Bayesian model averaging SDF outperforms reduced-form benchmark models in-sample and out-of-sample in pricing option return anomalies and portfolios. We document that the SDF is dense in characteristics with the implied-realized volatility spread, option return momentum, and jump risk emerging as the most likely included factors. The option SDF exhibits a distinct business cycle pattern and aligns more closely with its counterpart in the stock market than in the bond market.

We develop a new systematic tail risk measure for equity-oriented 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 that investments in both tail-sensitive stocks and options drive tail risk. Moreover, leverage and exposure to funding liquidity shocks are important determinants of tail risk. We find evidence of some funds being able to time tail risk exposure prior to the 2008–2009 financial crisis.

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.

We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside liquidity (EDL) risks. The cross-section of stock returns reflects a premium if a stock’s return (liquidity) is lowest at the same time when the market liquidity (return) is lowest. This effect is not driven by linear or downside liquidity risk or extreme downside return risk and is mainly driven by more recent years. There is no premium for stocks whose liquidity is lowest when market liquidity is lowest.

This article 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 strong LTD have higher average future returns than stocks with weak LTD. This effect cannot be explained by traditional risk factors and is different from the impact of beta, downside beta, coskewness, cokurtosis, and Kelly and Jiang’s (2014) tail risk beta. Hence, our findings are consistent with the notion that investors are crash-averse.