Evolutionary and Institutional Economics Review

, Volume 12, Issue 2, pp 375–394

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
Article

Abstract

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 .

Keywords

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

JEL Classification

G14 G17 G18 G19 

References

  1. Bowley A (2014) Agreement of mifid ii reforms, instinet incorporated. http://instinet.com/docs/msr/2014/Agreement_of_MiFID_II_Reforms-Quick_Analysis
  2. Chen SH, Chang CL, Du YR (2012) Agent-based economic models and econometrics. Knowl Eng Rev 27(2):187–219CrossRefGoogle Scholar
  3. Chiarella C, Iori G (2002) A simulation analysis of the microstructure of double auction markets. Quant Finance 2(5):346–353CrossRefGoogle Scholar
  4. Commodity Futures Trading Commission and Securities Exchange Commission (2010) Concept release on equity market structure. Fed Regist 75(13):3594–3614Google Scholar
  5. Cristelli M (2014) Complexity in financial markets, modeling psychological behavior in agent-based models and order book models. Springer, BerlinGoogle Scholar
  6. European Commission (2010) Public consultation review of the markets in financial instruments directive (mifid). Consultation Report 8Google Scholar
  7. Friedman D (1993) The double auction market institution: a survey. The double auction market: institutions, theories, and evidence, pp 3–25Google Scholar
  8. Johnson B (2010) Algorithmic trading and DMA: an introduction to direct access trading strategies. 4Myeloma PressGoogle Scholar
  9. Kobayashi S, Hashimoto T (2011) Benefits and limits of circuit breaker: institutional design using artificial futures market. Evol Inst Econ Rev 7(2):355–372CrossRefGoogle Scholar
  10. LeBaron B (2006) Agent-based computational finance. Handb Comput Econ 2:1187–1233CrossRefGoogle Scholar
  11. Mizuta T, Hayakawa S, Izumi K, Yoshimura S (2013) Investigation of relationship between tick size and trading volume of markets using artificial market simulations. In: JPX working paper, Japan Exchange Group, 2, http://www.jpx.co.jp/english/corporate/research-study/working-paper/
  12. Mizuta T, Izumi K, Yagi I, Yoshimura S (2015a) Investigation of price variation limits, short selling regulation, and uptick rules and their optimal design by artificial market simulations. Electron Commun Jpn 98(7):13–21. doi:10.1002/ecj.11684 CrossRefGoogle Scholar
  13. Mizuta T, Kosugi S, Kusumoto T, Matsumoto W, Izumi K, Yagi I, Yoshimura S (2015b) Effects of price regulations and dark pools on financial market stability: an investigation by multiagent simulations. Intell Syst Account Finance Manag. doi:10.1002/isaf.1374
  14. Mizuta T, Noritake Y, Hayakawa S, Izumi K (2015c) Impacts of speedup of market system on price formations using artificial market simulations. In: JPX working paper, Japan Exchange Group, 9, http://www.jpx.co.jp/english/corporate/research-study/working-paper/
  15. Mo SYK, Yang MPSY (2013) A study of dark pool trading using an agent-based model. In: Computational intelligence for financial engineering economics (CIFEr), 2013 IEEE symposium series on computational intelligence on, pp 19–26Google Scholar
  16. Sewell M (2006) Characterization of financial time series. http://finance.martinsewell.com/stylized-facts/
  17. Stöckl T, Huber J, Kirchler M (2010) Bubble measures in experimental asset markets. Exp Econ 13(3):284–298CrossRefGoogle Scholar
  18. Thurner S, Farmer J, Geanakoplos J (2012) Leverage causes fat tails and clustered volatility. Quant Finance 12(5):695–707CrossRefGoogle Scholar
  19. Verheyden T, De Moor L, Van den Bossche F (2013) A tale of market efficiency. Rev Bus Econ Lit 58(2):140–158Google Scholar
  20. Westerhoff F (2008) The use of agent-based financial market models to test the effectiveness of regulatory policies. Jahrbucher Fur Nationalokonomie Und Statistik 228(2):195Google Scholar
  21. Yagi I, Mizuta T, Izumi K (2010) A study on the effectiveness of short-selling regulation using artificial markets. Evol Inst Econ Rev 7(1):113–132CrossRefGoogle Scholar
  22. Ye M (2012) Price manipulation, price discovery and transaction costs in the crossing network. In: Price discovery and transaction costs in the crossing networkGoogle Scholar
  23. Yeh C, Yang C (2010) Examining the effectiveness of price limits in an artificial stock market. J Econ Dyn Control 34(10):2089–2108CrossRefGoogle Scholar

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

Personalised recommendations