Extreme Abhängigkeiten und das "Idiosyncratic Volatility Puzzle"
Zusammenfassung der Projektergebnisse
In this project, we provide answers to two main research questions. First, can alternative ways of computing the idiosyncratic volatility of a stock based on non-linear models for the dependence between individual and market returns contribute to solving the idiosyncratic volatility puzzle? Second, do ranking-induced attention effects lead to an overpricing and subsequent return reversal of ranked stocks that can be used to construct a profitable trading strategy and possibly provide an explanation for the idiosyncratic volatility puzzle. In short, the answer to the first question is ‘no’ and the answer to the second question is ‘yes’. Regarding the first question, we mainly used a newly developed copula-based methodology to compute idiosyncratic volatility. However – and against our expectations - it turned out that taking into account non-linear dependencies by using copulas did not materially alter the resulting numbers for the idiosyncratic volatility of most stocks. The correlation between our newly introduce measure and standard measures of idiosyncratic volatility based on a simple linear onefactor model was close to one. Thus, it comes as no surprise, that this measure led to very similar conclusions regarding the idiosyncratic volatility puzzle as the existing literature and thus does not contribute so solving the puzzle. However, one takeaway for future research is that researchers can safely neglect the more complicated computation of idiosyncratic volatility and results can be expected to be stable using simpler measures at least in the context of the common stocks that we analyzed. Regarding the second question, we found that it has important implications for the future returns of stocks if they are ranked as daily winners or losers in the previous month. A trading strategy going long in stocks that were never ranked in the previous month and shorting all stocks that were both, at least once ranked as daily winner and at least once as daily loser, delivered a very strong positive abnormal return. This ‘neve-minus-both’ (NMB) strategy delivered more than 10% p.a. abnormal returns after controlling for the exposure to systematic risk factors and is at most partially explained by limits to arbitrage. Furthermore, our findings provide a new, attention-based unifying solution to the idiosyncratic volatility puzzle of Ang et al. (2006) and related anomalies. We demonstrate that the significantly negative return premium of high volatility stocks only exists among stocks that were daily winners or losers in the previous month, and not for the clear majority of all other stocks (that make up 93% of total market capitalization). We show that the daily winner and loser effect is by far the best available candidate explanation for the idiosyncratic volatility puzzle. The price patterns we document might give rise to incentives for firm executives to opportunistically time SEOs or insider sales after periods in which the firm regularly appeared in the daily rankings. Executives might even try to manipulate their daily returns to make it more likely for their firm to appear in rankings prior to such events in order to artificially inflate shortterm stock prices. For example, a firm could try to spread a (positive or negative) rumor and later officially deny the rumor, which might lead to appearances in both, a daily winner and a daily loser ranking in a short period of time and eventually to temporarily inflated prices. However, whether firms really engage in such activities is pure speculation at this point and was beyond the scope of the current project.
Projektbezogene Publikationen (Auswahl)
- 2016. Crash Aversion and the Cross-Section of Expected Stock Returns Worldwide, in: Review of Asset Pricing Studies 6, 135-178
Weigert, F.
(Siehe online unter https://doi.org/10.1093/rapstu/rav019) - 2018. Extreme Dependence and the Cross-Section of Expected Stock Returns, in: Journal of Financial and Quantitative Analysis 53, 1059-1100
Chabi-Yo, F., Ruenzi, S., Weigert, F.
(Siehe online unter https://doi.org/10.1017/S0022109018000121) - 2018. Tail risk in hedge funds: A unique view from portfolio holdings, in: Journal of Financial Economics 125, 610-636
Agarwal, V., Ruenzi, S., Weigert, F.
(Siehe online unter https://doi.org/10.1016/j.jfineco.2017.06.006) - 2018: Momentum and Crash Sensitivity, in: Economics Letters 165, 77-81
Momentum and Crash Sensitivity, in: Economics Letters 165,
(Siehe online unter https://doi.org/10.1016/j.econlet.2018.01.031) - 2020. Daily Winners and Losers
Kumar, A., Ruenzi, S., Ungeheuer, M.
(Siehe online unter https://doi.org/10.2139/ssrn.2931545) - 2020. Extreme Downside Liquidity Risk, in: Journal of Banking and Finance 15, Article 105809
Ruenzi, S., Ungeheuer, M., Weigert, F.
(Siehe online unter https://doi.org/10.1016/j.jbankfin.2020.105809)
