Skip to main content

Main menu

  • Home
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About the JOT
    • Editorial Board
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow PMR on LinkedIn
  • Follow PMR 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 Trading
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Subscribe Now
  • Log in
The Journal of Trading

The Journal of Trading

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

  • Home
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About the JOT
    • Editorial Board
  • Follow PMR on LinkedIn
  • Follow PMR on Twitter
Article

Dark Pool DNA: Improving Dark Pool Assessment

Ben Polidore
The Journal of Trading Spring 2012, 7 (2) 69-74; DOI: https://doi.org/10.3905/jot.2012.7.2.069
Ben Polidore
is the director of algorithmic trading at ITG in New York, NY.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ben.polidore@itg.com
  • 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

This empirical study based on 517,000 observations from 17 dark pools used by ITG trading systems over a one-month period shows the value in predicting execution quality by fill type rather than simply by dark pool. Using the granular approach in this study, one can better identify low-quality trading situations to avoid, without the opportunity cost of cutting out an entire market center. A simulated trading strategy using the conclusions in this research is compared with the typical approach for improving market quality. The simulated strategy has better access to liquidity with lower adverse selection and a better mix of likely contraparties. Given the availability of complex order types in modern dark pools, it is possible to constrain orders ahead of the trade such that they may fill only in a certain way. This means that traders and algorithm designers can make practical use of this research to improve market quality for institutions.

Footnotes

  • I am grateful to Jeff Bacidore, Kathryn Berkow, Phil Pearson, and Jamie Selway for their support, comments and suggestions.

  • Disclaimer These materials are for informational purposes only, and are not intended to be used for trading or investment purposes or as an offer to sell or the solicitation of an offer to buy any security or financial product. The information contained herein has been taken from trade and statistical services and other sources we deem reliable but we do not represent that such information is accurate or complete and it should not be relied upon as such. No guarantee or warranty is made as to the reasonableness of the assumptions or the accuracy of the models or market data used by ITG. These materials do not provide any form of advice (investment, tax or legal). The positions taken in this document reflect the judgment of the individual author(s) and are not necessarily those of ITG.

  • © 2012 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 Trading: 7 (2)
The Journal of Trading
Vol. 7, Issue 2
Spring 2012
  • Table of Contents
  • Index by author
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 Trading.

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.
Dark Pool DNA: Improving Dark Pool Assessment
(Your Name) has sent you a message from The Journal of Trading
(Your Name) thought you would like to see the The Journal of Trading web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Dark Pool DNA: Improving Dark Pool Assessment
Ben Polidore
The Journal of Trading Mar 2012, 7 (2) 69-74; DOI: 10.3905/jot.2012.7.2.069

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
Dark Pool DNA: Improving Dark Pool Assessment
Ben Polidore
The Journal of Trading Mar 2012, 7 (2) 69-74; DOI: 10.3905/jot.2012.7.2.069
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
    • ADVERSE SELECTION—ISOLATING DARK POOL PERFORMANCE
    • THE RELATIONSHIP BETWEEN ADVERSE SELECTION AND FILL TYPES
    • APPLICATION—MORE EFFICIENT ROUTING DECISIONS
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • Dancing in the Dark: Optimal Liquidity Search under Portfolio Constraints
  • Ranking the Performance of U.S. Dark Venues
  • Google Scholar

More in this TOC Section

  • COMMENTARY: Space Unicorns and the Intermarket Trading System: Revisiting Myths
  • COMMENTARY: Commentary on “If Best Execution Is a Process, What Does That Process Look Like?”1
  • COMMENTARY: Beyond the Black Box Revisited: Algorithmic Trading and TCA Analysis Using Excel
Show more Article
LONDON
One London Wall, London, EC2Y 5EA
United Kingdom
+44 207 139 1600
 
NEW YORK
41 Madison Avenue, New York, NY 10010
USA
+1 646 931 9045
pm-research@pageantmedia.com
 

Stay Connected

  • Follow PMR on LinkedIn
  • Follow PMR 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
  • Log In
  • Update your profile
  • Give us your feedback

© 2021 Pageant Media Ltd | All Rights Reserved | ISSN: 1559-3967 | E-ISSN: 2168-8427

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