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

Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases

Adriano Koshiyama and Nick Firoozye
The Journal of Financial Data Science Fall 2019, 1 (4) 63-83; DOI: https://doi.org/10.3905/jfds.2019.1.013
Adriano Koshiyama
is a PhD student in the department of computer science at the University College London in London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nick Firoozye
is an honorary senior research fellow in the department of computer science at the University College London 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? 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

Systematic trading strategies are rule-based procedures that choose portfolios and allocate assets. To attain certain desired return profiles, quantitative strategists must determine a large array of trading parameters. Backtesting, the attempt to identify the appropriate parameters using historical data available, has been highly criticized because of its abundance of misleading results. Hence, there is increasing interest in devising procedures for the assessment and comparison of strategies (i.e., devising schemes for preventing what is known as backtesting overfitting). So far, many financial researchers have proposed different ways to tackle this problem that can be broadly categorized into three types: data snooping, overestimated performance, and cross-validation evaluation. In this article, the authors propose a new approach to dealing with financial overfitting, a covariance-penalty correction, in which a risk metric is lowered given the number of parameters and amount of data used to underpin a trading strategy. They outline the foundation and main results behind the covariance-penalty correction for trading strategies. After that, the authors pursue an empirical investigation and compare its performance with some other approaches in the realm of covariance-penalties across more than 1,300 assets, using ordinary and total least squares. Their results suggest that covariance-penalties are a suitable procedure to avoid backtesting overfitting, and total least squares provides superior performance when compared to ordinary least squares.

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

Key Findings

  • • A literature review of backtest overfitting, putting in perspective the different approaches available.

  • • A new Covariance-Penalty formula to correct Sharpe ratios based on the number of parameters in a model.

  • • An empirical investigation across 1300 assets, using ordinary and total least squares, comparing our technique with others from the Covariance-Penalty literature.

  • © 2019 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: 1 (4)
The Journal of Financial Data Science
Vol. 1, Issue 4
Fall 2019
  • 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.
Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases
(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
Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases
Adriano Koshiyama, Nick Firoozye
The Journal of Financial Data Science Oct 2019, 1 (4) 63-83; DOI: 10.3905/jfds.2019.1.013

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
Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases
Adriano Koshiyama, Nick Firoozye
The Journal of Financial Data Science Oct 2019, 1 (4) 63-83; DOI: 10.3905/jfds.2019.1.013
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
    • LITERATURE REVIEW
    • COVARIANCE-PENALTY FOR TRADING STRATEGIES
    • EMPIRICAL ASSESSMENT
    • RESULTS AND DISCUSSION
    • CONCLUSIONS
    • ACKNOWLEDGMENT
    • ADDITIONAL READING
    • 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

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

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