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

Style Rotation Revisited

John Galakis, Ioannis Vrontos and Spyridon Vrontos
The Journal of Financial Data Science Spring 2021, jfds.2021.1.059; DOI: https://doi.org/10.3905/jfds.2021.1.059
John Galakis
is a partner with Iniohos Advisory Services in Geneva, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ioannis Vrontos
is an associate professor in the Department of Statistics at the Athens University of Economics and Business in Athens, Greece
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Spyridon Vrontos
is a senior lecturer in the Department of Mathematical Sciences at the University of Essex in Colchester, United Kingdom
  • 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

Style rotation strategies have enjoyed growing interest in the academic and practitioner communities over the last decades. This study investigates the ability of innovative modeling approaches to effectively forecast equity style performance. Single–multifactor logit models and several machine learning techniques are employed to generate directional style spread forecasts. Their efficacy is assessed in both a statistical and economic evaluation context. The analysis reveals that certain univariate logit models and machine learning techniques, such as naïve Bayes, bagging, Bayes generalized linear models, discriminant analysis models, and k-nearest neighbor, enhance the accuracy of the generated forecasts and lead to profitable investment strategies.

TOPICS: Style investing, security analysis and valuation, big data/machine learning, performance measurement

Key Findings

  • ▪ In this study, the authors employ logit and supervised machine learning models to generate directional forecasts of equity style performance, employing a plethora of well-known predictors.

  • ▪ The generated forecasts are evaluated in both a statistical and economic evaluation setting.

  • ▪ Apart from the widely known and tracked value and size spreads, the study also investigates the predictability of the betting-against-beta spread.

  • ▪ The analysis reveals that certain univariate logit and machine learning techniques enhance the accuracy of the generated forecasts and lead to profitable investment strategies.

  • © 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 (1)
The Journal of Financial Data Science
Vol. 3, Issue 1
Winter 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.
Style Rotation Revisited
(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
Style Rotation Revisited
John Galakis, Ioannis Vrontos, Spyridon Vrontos
The Journal of Financial Data Science Mar 2021, jfds.2021.1.059; DOI: 10.3905/jfds.2021.1.059

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
Style Rotation Revisited
John Galakis, Ioannis Vrontos, Spyridon Vrontos
The Journal of Financial Data Science Mar 2021, jfds.2021.1.059; DOI: 10.3905/jfds.2021.1.059
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
    • THE DATA
    • EMPIRICAL DESIGN AND ANALYSIS
    • CONCLUSION
    • APPENDIX
    • 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