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Interacting Anomalies

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  • UserKarsten Müller (Princeton University) World_link
  • ClockThursday 26 November 2020, 13:00-14:00
  • HouseOnline.

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An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy based on double-sorted portfolios performs on par with state-of-the-art machine learning strategies, suggesting that simple combinations of characteristics can capture a similar amount of variation in expected returns.

This talk is part of the Cambridge Finance Workshop Series series.

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