Does It Pay to Follow Anomalies Research? Machine Learning Approach with International Evidence
51 Pages Posted: 8 Mar 2018 Last revised: 24 Sep 2018
Date Written: May 1, 2018
We study out-of-sample returns on 153 anomalies in equities documented in academic literature. We show that machine learning techniques that aggregates all the anomalies into one mispricing signal are 4 times more profitable than a strategy based on individual anomalies and survive on a liquid universe of stocks. The machine learning also leads to 2 times larger Sharpe ratios with respect to the corresponding standard finance methods. We next study value of international evidence for selection of quantitative strategies that outperform out-of-sample. Past performance of quantitative strategies in the regions other than the US does not help to pick out-of-sample winning strategies in the US. Past evidence from the US, however, captures most of the predictability within the other regions. The value of international evidence in empirical asset pricing is thus very limited.
Keywords: Anomalies, International Finance, Machine Learning, Neural Network, Random Forest.
JEL Classification: G11, G12, G15
Suggested Citation: Suggested Citation