Idiosyncratic volatility and the cross-section of anomaly returns: is risk your Ally?

Adam Zaremba , Alina Maydybura


Due to arbitrage risk asymmetries, the relationship between idiosyncratic risk and expected returns is positive (negative) among overpriced (underpriced) stocks. We offer a new active anomaly-selection strategy that capitalizes on this effect. To this end, we consider 11 equity anomalies in the U.S. market for years 1963–2016. Buying (selling) long (short) legs of the anomaly portfolios with the highest idiosyncratic volatility produces monthly abnormal returns ranging from 0.97% to 1.14% per month, outperforming a naive benchmark that equally weights all the anomalies by 45–70%. The effect cannot be subsumed by any other established anomaly-return predictor, such as momentum or seasonality. The results are robust to many considerations, including different numbers of anomalies in the portfolios, subperiod analysis, as well as estimation of idiosyncratic risk from the alternative models and throughout different periods.
Author Adam Zaremba (WZ / KIiRK)
Adam Zaremba,,
- Department of Investment and Capital Markets
, Alina Maydybura - University of Dubai, UAE
Alina Maydybura,,
Journal seriesApplied Economics, ISSN 0003-6846, e-ISSN 1466-4283, (N/A 40 pkt)
Issue year2019
Publication size in sheets0.5
Keywords in Polishanomalie rynku kapitałowego, ryzyko specyficzne, zmienność specyficzna, wycena aktywów, przekrój stóp zwrotu, prognozowanie stóp zwrotu
Keywords in EnglishEquity anomalies, idiosyncratic risk, idiosyncratic volatility, asset allocation, asset pricing, return predictability, the cross-section of stock returns
ASJC Classification2002 Economics and Econometrics
Languageen angielski
Score (nominal)40
Score sourcejournalList
ScoreMinisterial score = 40.0, 03-04-2020, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.781; WoS Impact Factor: 2017 = 0.75 (2) - 2017=0.906 (5)
Citation count*1 (2020-09-10)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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