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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00533567" target="_blank" >RIV/67985556:_____/21:00533567 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11230/21:10413843
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1386418120300574" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1386418120300574</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.finmar.2020.100588" target="_blank" >10.1016/j.finmar.2020.100588</a>

Result language
angličtina
Original language name
Does it pay to follow anomalies research? Machine learning approach with international evidence
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
50206 - Finance

Project
<a href="/en/project/GX19-28231X" target="_blank" >GX19-28231X: DyMoDiF - Dynamic Models for the Digital Finance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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