DocumentCode :
120790
Title :
Do dark pools stabilize markets and reduce market impacts? Investigations using multi-agent simulations
Author :
Mizuta, T. ; Matsumoto, Wataru ; Kosugi, Shintaro ; Izumi, Kiyotaka ; Kusumoto, Takuya ; Yoshimura, Satoru
Author_Institution :
Sch. of Eng., Univ. of Tokyo, Tokyo, Japan
fYear :
2014
fDate :
27-28 March 2014
Firstpage :
71
Lastpage :
76
Abstract :
In financial stock markets, dark pools, which never provide any order books and quotes, are becoming widely used. It is said that dark pools may lead to stabilization of markets. However, an increased use of dark pools does raise regulatory concerns as it may ultimately affect the quality of the price discovery mechanism. In this study, we built an artificial market model, multi-agent simulation, including one lit market, which provides all order books to investors, and one dark pool to investigate whether dark pools stabilize markets or not. We found that as the dark pool is increasingly used, markets become more stable. We also found that using the dark pool more reduces the market impacts. However, if other investors´ usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When tick size of a lit market is large, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggested the threshold might be much lager than the usage rate in present real financial markets.
Keywords :
digital simulation; multi-agent systems; pricing; stock markets; artificial market model; dark pools; financial stock markets; lit market; market impact reduction; market stabilization; multiagent simulation; price discovery mechanism; usage rate; Educational institutions; Electronic mail; Gaussian distribution; Random variables; Security; Stability analysis; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on
Conference_Location :
London
Type :
conf
DOI :
10.1109/CIFEr.2014.6924056
Filename :
6924056
Link To Document :
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