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Building cross-sectional systematic strategies by learning to rank

Abstract:

The success of a cross-sectional systematic strategy depends critically on accurately ranking assets before portfolio construction. Contemporary techniques perform this ranking step either with simple heuristics or by sorting outputs from standard regression or classification models, which have been demonstrated to be suboptimal for ranking in other domains (e.g., information retrieval). To address this deficiency, the authors propose a framework to enhance cross-sectional portfolios by incor...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3905/jfds.2021.1.060

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:
Journal of Financial Data Science Journal website
Volume:
3
Issue:
2
Pages:
70-86
Publication date:
2021-03-26
DOI:
EISSN:
2640-3951
ISSN:
2640-3943
Language:
English
Keywords:
Pubs id:
1251190
Local pid:
pubs:1251190
Deposit date:
2023-01-20

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