Evolutionary and Institutional Economics Review

, Volume 12, Issue 2, pp 375–394 | Cite as

Effects of dark pools on financial markets’ efficiency and price discovery function: an investigation by multi-agent simulations

  • Takanobu Mizuta
  • Shintaro Kosugi
  • Takuya Kusumoto
  • Wataru Matsumoto
  • Kiyoshi Izumi


In financial stock markets, dark pools, in which order books or quotes are not provided, are becoming widely used. However, increasing the use of dark pools would raise regulatory concerns as it may ultimately affect the quality of the price discovery function in the lit markets, which are normal markets in which all order books are provided to investors. This may destabilize a market and heighten financial systemic risk. In this study, we investigated effects of a dark pool on financial markets’ efficiency and the price discovery function by using an artificial market model. We found that the markets are made more efficient by raising the share of the trading value amount of the dark pool by a certain level. However, raising the share above the level makes the market significantly inefficient. This indicates that the dark pool has an optimal usage rate for market efficiency . The smart order routing (SOR) is transmitting market orders to the dark pool, and this leads the depth of limit orders to become thicker. The thicker limit orders absorb market orders, and thus a market price is still stable near a fundamental price. On the other hand, when too many waiting orders are stored in the dark pool, the orders absorb market orders in the lit market by SOR and prevent the market price converging to the fundamental price. This causes the market price to stay very different from the fundamental price and makes the lit market inefficient. We also discuss mechanisms by which a dark pool makes a market efficient or inefficient by using a simple equation model. The equations suggest that if the trading value amount is higher in dark pools than in lit markets, markets become inefficient. This suggests that when the usage rate of dark pools is low, dark pools rarely destroy the price discovery function even though a large buy–sell imbalance occurs. On the other hand, when the usage rate of dark pools is very high, dark pools very easily destroy the price discovery function by a very slight buy–sell imbalance. We also compared results of the equations with those of simulations and found similar tendencies .


Dark pool Market efficiency Price discovery function Artificial market model Multi-agent simulation 

JEL Classification

G14 G17 G18 G19 



The authors are grateful to Execution Service Department, Nomura Securities Co., Ltd., for their valuable comments from a practical financial perspective on this paper. This research was partially supported by CREST, Japan Science and Technology Agency, and Japan Society for the Promotion of Science, KAKENHI Grant Number 15H02745.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


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Copyright information

© Japan Association for Evolutionary Economics 2015

Authors and Affiliations

  • Takanobu Mizuta
    • 1
  • Shintaro Kosugi
    • 5
  • Takuya Kusumoto
    • 2
  • Wataru Matsumoto
    • 2
  • Kiyoshi Izumi
    • 3
    • 4
  1. 1.SPARX Asset Management Co., Ltd.TokyoJapan
  2. 2.Nomura Securities Co., Ltd.TokyoJapan
  3. 3.School of EngineeringThe University of TokyoTokyoJapan
  4. 4.CREST, Japan Science and Technology AgencyTokyoJapan
  5. 5.TokyoJapan

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