Skip to main content

Main menu

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JFDS
    • Editorial Board
    • Published Ahead of Print (PAP)
  • IPR logos x
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

User menu

  • Sample our Content
  • Request a Demo
  • Log in

Search

  • ADVANCED SEARCH: Discover more content by journal, author or time frame
The Journal of Financial Data Science
  • IPR logos x
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Request a Demo
  • Log in
The Journal of Financial Data Science

The Journal of Financial Data Science

ADVANCED SEARCH: Discover more content by journal, author or time frame

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JFDS
    • Editorial Board
    • Published Ahead of Print (PAP)
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

Building Cross-Sectional Systematic Strategies by Learning to Rank

Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts
The Journal of Financial Data Science Spring 2021, 3 (2) 70-86; DOI: https://doi.org/10.3905/jfds.2021.1.060
Daniel Poh
is a DPhil student with the Machine Learning Research Group and the Oxford-Man Institute of Quantitative Finance at the University of Oxford in Oxford, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bryan Lim
is an associate member with the Machine Learning Research Group and the Oxford-Man Institute of Quantitative Finance at the University of Oxford in Oxford, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Zohren
is an associate professor (research) with the Machine Learning Research Group and the Oxford-Man Institute of Quantitative Finance at the University of Oxford in Oxford, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stephen Roberts
is the RAEng/Man Professor of Machine Learning at the University of Oxford and the director of the Oxford-Man Institute of Quantitative Finance at the University of Oxford in Oxford, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Click to login and read the full article.

Don’t have access? Click here to request a demo 
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
UK: 0207 139 1600

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 incorporating learning-to-rank algorithms, which lead to improvements in ranking accuracy by learning pairwise and listwise structures across instruments. Using cross-sectional momentum as a demonstrative case study, the authors show that the use of modern machine learning ranking algorithms can substantially improve the trading performance of cross-sectional strategies—providing approximately threefold boosting of Sharpe ratios compared with traditional approaches.

TOPICS: Big data/machine learning, portfolio construction, performance measurement

Key Findings

  • ▪ Contemporary approaches (e.g., simple heuristics) used to score and rank assets in portfolio construction are sub optimal as they do not learn the broader pairwise and listwise relationships across instruments.

  • ▪ Learning to rank algorithms can be used to address this shortcoming, learning the broader links across assets, which consequently allow them to be ranked more accurately.

  • ▪ Using Cross-sectional Momentum as a demonstrative use-case, we show that more precise rankings produce long/short portfolios that significantly outperform traditional approaches across various financial and ranking-based measures.

  • © 2021 Pageant Media Ltd
View Full Text

Don’t have access? Click here to request a demo

Alternatively, Call a member of the team to discuss membership options

US and Overseas: +1 646-931-9045

UK: 0207 139 1600

Log in using your username and password

Forgot your user name or password?
PreviousNext
Back to top

Explore our content to discover more relevant research

  • By topic
  • Across journals
  • From the experts
  • Monthly highlights
  • Special collections

In this issue

The Journal of Financial Data Science: 3 (2)
The Journal of Financial Data Science
Vol. 3, Issue 2
Spring 2021
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on The Journal of Financial Data Science.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Building Cross-Sectional Systematic Strategies by Learning to Rank
(Your Name) has sent you a message from The Journal of Financial Data Science
(Your Name) thought you would like to see the The Journal of Financial Data Science web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Building Cross-Sectional Systematic Strategies by Learning to Rank
Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The Journal of Financial Data Science Apr 2021, 3 (2) 70-86; DOI: 10.3905/jfds.2021.1.060

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Save To My Folders
Share
Building Cross-Sectional Systematic Strategies by Learning to Rank
Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The Journal of Financial Data Science Apr 2021, 3 (2) 70-86; DOI: 10.3905/jfds.2021.1.060
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
    • Abstract
    • RELATED WORKS
    • PROBLEM DEFINITION
    • SCORE CALCULATION METHODOLOGIES
    • PERFORMANCE EVALUATION
    • CONCLUSIONS
    • APPENDIX
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar
LONDON
One London Wall, London, EC2Y 5EA
0207 139 1600
 
NEW YORK
41 Madison Avenue, 20th Floor, New York, NY 10010
646 931 9045
pm-research@pageantmedia.com

Stay Connected

  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

MORE FROM PMR

  • Home
  • Awards
  • Investment Guides
  • Videos
  • About PMR

INFORMATION FOR

  • Academics
  • Agents
  • Authors
  • Content Usage Terms

GET INVOLVED

  • Advertise
  • Publish
  • Article Licensing
  • Contact Us
  • Subscribe Now
  • Sign In
  • Update your profile
  • Give us your feedback

© 2022 Pageant Media Ltd | All Rights Reserved | ISSN: 2640-3943 | E-ISSN: 2640-3951

  • Site Map
  • Terms & Conditions
  • Privacy Policy
  • Cookies