EconPapers    
Economics at your fingertips  
 

Does it pay to follow anomalies research? Machine learning approach with international evidence

Ondrej Tobek and Martin Hronec

Journal of Financial Markets, 2021, vol. 56, issue C

Abstract: We study out-of-sample returns on 153 anomalies in equities documented in the academic literature. We show that machine learning techniques that aggregate all the anomalies into one mispricing signal are profitable around the globe and survive on a liquid universe of stocks. We investigate the value of international evidence for selection of quantitative strategies that outperform out-of-sample. Past performance of quantitative strategies in regions other than the United States does not help to pick out-of-sample winning strategies in the U.S. Past evidence from the U.S., however, captures most of the return predictability outside the U.S.

Keywords: Anomalies; Machine learning; International finance (search for similar items in EconPapers)
JEL-codes: G11 G12 G15 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1386418120300574
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:56:y:2021:i:c:s1386418120300574

DOI: 10.1016/j.finmar.2020.100588

Access Statistics for this article

Journal of Financial Markets is currently edited by B. Lehmann, D. Seppi and A. Subrahmanyam

More articles in Journal of Financial Markets from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2022-01-15
Handle: RePEc:eee:finmar:v:56:y:2021:i:c:s1386418120300574