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
  • Subscribe Now
  • 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
  • Subscribe Now
  • 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

Machine Trading: Theory, Advances, and Applications

Dilip B. Madan and Yazid M. Sharaiha
The Journal of Financial Data Science Summer 2020, 2 (3) 8-24; DOI: https://doi.org/10.3905/jfds.2020.1.039
Dilip B. Madan
is professor emeritus at the Robert H. Smith School of Business in College Park, MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yazid M. Sharaiha
is global head of Systematic Strategies at Norges Bank Investment Management in London, 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? Sign up today to begin your trial to the PMR platform 

Abstract

Dynamic contributions to trading are evaluated using covariations between position and price changes over a horizon. Other performance measures such as Sharpe ratios, gain–loss ratios, acceptability indexes, and drawdowns are also employed. Machine learning strategies based on Gaussian process regression (GPR) are compared with least squares (LSQ). Furthermore, both are generalized by invoking conservative valuation schemes that lead to the study of conservative conditional expectations modeled by distorted expectations. The latter lead to the development of distorted least squares (DLSQ) and distorted Gaussian process regression (DGPR) as the associated estimation or prediction schemes. Trading strategies are executed for nine sectors of the US economy using 14 different predictive factor sets. Results indicate improvements are made by GPR, DGPR over LSQ, and DLSQ, with the distorted versions also favorably affecting the drawdowns.

TOPICS: Statistical methods, simulations, big data/machine learning

Key Findings

  • • The authors demonstrate how finance theory reduced the potential contributions of data science by assumption.

  • • The concept of conservative conditional expectations is defined, developed, and implemented to enhance predictive technologies such as least squares and Gaussian process regression.

  • • Using factor attributes as input, the trading of stock return predictions classically and conservatively illustrates the practical benefits of the advocated theoretical advances.

  • © 2020 Pageant Media Ltd
View Full Text

Don’t have access? Register today to begin unrestricted access to our database of research.

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: 2 (3)
The Journal of Financial Data Science
Vol. 2, Issue 3
Summer 2020
  • 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.
Machine Trading: Theory, Advances, and Applications
(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
Machine Trading: Theory, Advances, and Applications
Dilip B. Madan, Yazid M. Sharaiha
The Journal of Financial Data Science Jul 2020, 2 (3) 8-24; DOI: 10.3905/jfds.2020.1.039

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
Machine Trading: Theory, Advances, and Applications
Dilip B. Madan, Yazid M. Sharaiha
The Journal of Financial Data Science Jul 2020, 2 (3) 8-24; DOI: 10.3905/jfds.2020.1.039
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
    • Abstract
    • PROFITABILITY AND POSITIONING
    • PREDICTION TECHNOLOGIES
    • DESIGN OF TRADING STRATEGIES
    • RESULTS FOR THE FOUR TRAINING CRITERIA
    • CONCLUSION
    • ADDITIONAL READING
    • Disclaimer
    • 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

© 2021 Pageant Media Ltd | All Rights Reserved | ISSN: 2640-3943 | E-ISSN: 2640-3951

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