A Study of Dark Pool Trading Using an Agent-Based Model

Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2013

8 Pages Posted: 27 Nov 2012 Last revised: 23 Nov 2013

See all articles by Sheung Yin Mo

Sheung Yin Mo

Stevens Institute of Technology

Mark E. Paddrik

Government of the United States of America - Office of Financial Research

Steve Y. Yang

Stevens Institute of Technology

Date Written: November 27, 2012

Abstract

A dark pool is a securities trading venue with no published market depth feed. Such markets have traditionally been utilized by large institutions as an alternative to public exchanges to execute large block orders which might otherwise impact settlement price. It is estimated that the trading volume of dark pool markets was 9% to 12% of the total U.S. equity market share volume in 2010. This phenomenon raises questions regarding the fundamental value of securities traded through dark pool markets and their impact on the price discovery process in traditional “visible” markets. In this paper, we establish a modeling framework for dark pool markets through agent-based modeling. It presents and validates the costs and benefits of trading small orders in dark pool markets. Simulated trading of 78 selected stocks demonstrates that dark pool market traders can obtain better execution rate when the dark pool market has more uninformed traders relative to informed traders. In addition, trading stocks with larger market capitalization yields better price improvement in dark pool markets.

Keywords: dark pool, agent-based model, informed vs. uninformed trader, algorithmic trading

JEL Classification: G1, C15

Suggested Citation

Mo, Sheung Yin and Paddrik, Mark Endel and Yang, Steve Y., A Study of Dark Pool Trading Using an Agent-Based Model (November 27, 2012). Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2013, Available at SSRN: https://ssrn.com/abstract=2181209 or http://dx.doi.org/10.2139/ssrn.2181209

Sheung Yin Mo (Contact Author)

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

Mark Endel Paddrik

Government of the United States of America - Office of Financial Research ( email )

717 14th Street, NW
Washington DC, DC 20005
United States

Steve Y. Yang

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

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