Investors tend to be crash averse and are thus willing to pay higher prices for stocks with low crash sensitivity. Stocks characterized by strong lower-tail dependence (LTD), a measure of crash sensitivity, earn significantly higher average future returns than those with weak LTD. This effect cannot be explained by traditional risk factors.
How Is This Research Useful to Practitioners?
The authors examine the potential effect of crash aversion on the pricing of the cross section of individual stocks. Stock market crashes result in significant wealth destruction. As a result, stocks that tend to perform poorly during crashes also tend to become unattractive assets for crash-averse investors. Crash-sensitive stocks should thus bear a return premium.
The authors calculate copula-based lower-tail dependence (LTD) coefficients for US stocks over the period 1963–2012. They find that stocks with previously weak LTD have significantly higher returns than strong LTD stocks during extreme market downturns. This result suggests that weak LTD stocks can provide some protection against market crashes. A value-weighted portfolio consisting of stocks with the highest LTD is actually seen to generate a higher average future return of 0.36% per month (annualized spread of 4.32%) when compared with a portfolio composed of the weakest LTD stocks. There is a higher risk premium for LTD following large stock market declines. The effect of LTD, however, must be distinguished from the effects of traditional risk factors, downside beta, coskewness, cokurtosis, and tail risk beta.
Portfolio managers and investors in general will find the conclusions of this research useful. Investors who are more risk averse may opt for stocks with weak LTD and eventually accept lower returns for such stocks. Expected stock returns reflect a premium for crash sensitivity, as measured by a stock’s LTD with the market return. It would seem that investors can receive compensation for holding stocks with a strong sensitivity to extreme market downturns. Some portfolio managers may use crash sensitivity as a strategy to potentially generate superior returns following a market crisis.
How Did the Authors Conduct This Research?
The authors estimate measures of tail dependence based on copulas. They use a sample consisting of all common stocks—CRSP share codes 10 and 11 from CRSP trading on the NYSE, AMEX, and NASDAQ between 1 January 1963 and 31 December 2012. The authors also exclude return data from firms in the bottom 1% of market capitalization of all stocks in the previous year so that their results are not influenced by very small stocks. Furthermore, they require at least 100 valid daily return observations per year.
Overall, there are 2,613,440 firm-month observations after application of the filters. The number of firms in each month over the sample period ranges from 1,904 to 6,778. Stocks with high LTD are seen to earn a higher future return of 0.36% per month (4.32% per year), which is statistically significant at the 1% level. LTD is highest on average for firms in manufacturing, financials, and consumer durables. Industries with strong LTD also earn significantly higher average future returns than industries with weak LTD. The authors find that the impact of LTD on future returns and alphas is always positive and statistically significant for two-month-ahead and three-month-ahead horizons.
Based on bivariate portfolio sorts, they provide evidence that the risk associated with LTD is related to but different from risks associated with regular market beta, downside beta, coskewness, cokurtosis, and tail risk. The authors’ findings indicate that the effect of LTD is stronger in years after a market crash.
Stock market crashes can significantly affect both market sentiment and willingness to take risk. Stocks that are characterized by strong LTD can nonetheless benefit from potentially higher average future returns than stocks with weak LTD. The most risk-averse investors may opt to invest in stocks with weak LTD given that such securities are expected to offer protection against extreme negative portfolio returns.
The research is interesting and is a further addition to the relatively few articles that cover the effects of crash sensitivity. I have concerns that the authors evaluate only four financial crises (Black Monday, the Asian financial crisis, the dot-com bubble, and Lehman Brothers’ collapse), which each arose because of very different factors. We cannot always expect trends that prevailed in a few past crises to necessarily apply in future.
In addition, the authors look only at US data. They could also have covered a few more developed markets. The findings, however, remain interesting. The cross section of expected stock returns seems to reflect a premium for a stock’s crash sensitivity measured by its LTD with the market. Investors choosing to invest in crash-sensitive stocks could thus potentially expect higher average returns in the future when compared with crash-insensitive stocks. This return premium might be of interest to certain investors.