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

0533567 - ÚTIA 2022 RIV NL eng J - Článek v odborném periodiku
Hronec, Martin - Tobek, O.
Does it pay to follow anomalies research? Machine learning approach with international evidence.
Journal of Financial Markets. Roč. 56, č. 1 (2021), č. článku 100588. ISSN 1386-4181. E-ISSN 1878-576X
Grant CEP: GA ČR(CZ) GX19-28231X
Institucionální podpora: RVO:67985556
Klíčová slova: Anomalies * Machine Learning * International Finance
Obor OECD: Finance
Impakt faktor: 3.095, rok: 2021
Způsob publikování: Omezený přístup
http://library.utia.cas.cz/separaty/2020/E/hronec-0533567.pdf https://www.sciencedirect.com/science/article/pii/S1386418120300574

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.
Trvalý link: http://hdl.handle.net/11104/0311938