0533567 20250313101721.420201026235959.9 85089487417 000808118800002 10.1016/j.finmar.2020.100588 73 s. P cav_un_epca*02585061386-418156Elsevier Anomalies Machine Learning International Finance cav_un_auth*0398132 Hronec Martin UTIA-B Ekonometrie Department of Econometrics E E SK Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0398133 Tobek O. GB http://library.utia.cas.cz/separaty/2020/E/hronec-0533567.pdf https://www.sciencedirect.com/science/article/pii/S1386418120300574 GX19-28231X GA ČR CZ cav_un_auth*0385135 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. SCOPUS AH 50000 50200 50206 2022 2 2 4 R hod 4 4rh 4 20250310151316.1 20250310153313.5 RVO:67985556 http://hdl.handle.net/11104/0311938 1 cav_un_auth*0300540 University of Cambridge GB S 100588 C BUSINESS.FINANCE 3.825 0.795 12.2 0.00219 1.502 2519 74 1.661 51.97 2.56 272 Q1 3.079 39 Q2 59.9 Q2 Q1 0.99 Q2 59.91 2021 Soubory v repozitáři: hronec-533567.pdf 85089487417 SCOPUS PUBMED 000808118800002 WOS cav_un_epca*0258506 Journal of Financial Markets Roč. 56 č. 1 2021 1386-4181 1878-576X Elsevier