| bibtype |
J -
Journal Article
|
| ARLID |
0533567 |
| utime |
20250313101721.4 |
| mtime |
20201026235959.9 |
| SCOPUS |
85089487417 |
| WOS |
000808118800002 |
| DOI |
10.1016/j.finmar.2020.100588 |
| title
(primary) (eng) |
Does it pay to follow anomalies research? Machine learning approach with international evidence |
| specification |
| page_count |
73 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0258506 |
| ISSN |
1386-4181 |
| title
|
Journal of Financial Markets |
| volume_id |
56 |
| publisher |
|
|
| keyword |
Anomalies |
| keyword |
Machine Learning |
| keyword |
International Finance |
| author
(primary) |
| ARLID |
cav_un_auth*0398132 |
| name1 |
Hronec |
| name2 |
Martin |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| country |
SK |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0398133 |
| name1 |
Tobek |
| name2 |
O. |
| country |
GB |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GX19-28231X |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0385135 |
|
| abstract
(eng) |
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. |
| result_subspec |
SCOPUS |
| RIV |
AH |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50206 |
| reportyear |
2022 |
| num_of_auth |
2 |
| mrcbC52 |
2 4 R hod 4 4rh 4 20250310151316.1 20250310153313.5 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0311938 |
| mrcbC61 |
1 |
| cooperation |
| ARLID |
cav_un_auth*0300540 |
| name |
University of Cambridge |
| country |
GB |
|
| confidential |
S |
| article_num |
100588 |
| mrcbC91 |
C |
| mrcbT16-e |
BUSINESSFINANCE |
| mrcbT16-j |
1.502 |
| mrcbT16-s |
1.661 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2021 |
| mrcbTft |
\nSoubory v repozitáři: hronec-533567.pdf |
| mrcbU14 |
85089487417 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000808118800002 WOS |
| mrcbU63 |
cav_un_epca*0258506 Journal of Financial Markets Roč. 56 č. 1 2021 1386-4181 1878-576X Elsevier |
|