TY - GEN
T1 - A study of dark pool trading using an agent-based model
AU - Mo, Sheung Yin Kevin
AU - Paddrik, Mark
AU - Yang, Steve Y.
PY - 2013
Y1 - 2013
N2 - 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 [1]. 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.
AB - 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 [1]. 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.
KW - Dark pool
KW - agent-based model
KW - algorithmic trading
KW - informed vs. uninformed trader
UR - http://www.scopus.com/inward/record.url?scp=84886057590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886057590&partnerID=8YFLogxK
U2 - 10.1109/CIFEr.2013.6611692
DO - 10.1109/CIFEr.2013.6611692
M3 - Conference contribution
AN - SCOPUS:84886057590
SN - 9781467359214
T3 - Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 19
EP - 26
BT - Proceedings of the 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
T2 - 2013 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -