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Enhancing cross-sectional currency strategies by context-aware learning to rank with self-attention

Abstract:

The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. Although this ranking step is traditionally performed using heuristics or by sorting the outputs produced by pointwise regression or classification techniques, strategies using learning-to-rank algorithms have recently pre-sented themselves as competitive and viable alternatives. Although the rankers at the core of these strategies are learned globally an...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3905/jfds.2022.1.099

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9305-9268
Publisher:
Portfolio Management Research Publisher's website
Journal:
The Journal of Financial Data Science Journal website
Volume:
4
Issue:
3
Pages:
89-107
Publication date:
2022-07-01
Acceptance date:
2022-01-01
DOI:
EISSN:
2640-3951
ISSN:
2640-3943
Language:
English
Keywords:
Pubs id:
1275597
Local pid:
pubs:1275597
Deposit date:
2023-01-20

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