Abstract
Recently, investors have become more interested in market liquidity, which is regarded as a measure of a booming financial market. When market liquidity is high, market participants are able to smoothly buy and sell their intended amount at a price close to the market mid-price. When discussing market liquidity in empirical studies, researchers have defined liquidity indicators that are consistent with their research objectives. However, it has not been clarified which market factors affect these indicators. In the present paper, we investigated which market factors affect major liquidity indicators, including Volume, Tightness, Resiliency, and Depth, using an artificial market, which is a type of agent-based simulation system. As a result, market liquidity based on Volume is completely opposite to market liquidity based on Tightness, Resiliency, or Depth. Moreover, we confirmed the price decline rate from the fundamental price and the price convergence periods to the fundamental price as a measure of the convergence speed, which is the original meaning of Resiliency, from the price level, which has been brought about by random price changes. Therefore, the trades of fundamentalists have the effect of shortening the convergence period, i.e., causing market liquidity to increase.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
For example, if the difference between the highest and lowest prices of the trading day is smaller, then the P/T ratio tends to become lower (Kurosaki et al. 2015).
- 2.
For example, the P/T ratio may be small, although the price does not return from the price level that has been brought about by random price changes if the price fluctuates randomly far from the original price level.
References
Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 31–56
Arthur W, Holland J, Lebaron B, Palmer R, Tayler P (1997) Asset pricing under endogenous expectations in an artificial stock market. The economy as an evolving complex system II. Addison-Wesley, pp 15–44
Chen SH, Chang CL, Du YR (2012) Agent-based economic models and econometrics. Knowl Eng Review 27(2):187–219
Chiarella C, Iori G, Perellò J (2009) The impact of heterogeneous trading rules on the limit order book and order flows. J Econ Dyn Control 33(3):525–537
Chung KH, Kim KA, Kitsabunnarat P (2005) Liquidity and quote clustering in a market with multiple tick sizes. J Financ Res 28(2):177–195. https://doi.org/10.1111/j.1475-6803.2005.00120.x, https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-6803.2005.00120.x
Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quant Financ 1(2):223–236
Kurosaki T, Kumano Y, Okabe K, Nagano T (2015) Liquidity in JGB markets: an evaluation from transaction data. Bank of Japan Working Paper Series 15-E-2, Bank of Japan. https://ideas.repec.org/p/boj/bojwps/wp15e02.html
Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53:1315–1336
Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multiagent model of a financial market. Nature 397:498–500
Mizuta T, Izumi K, Yagi I, Yoshimura S (2014) Regulations’ effectiveness for market turbulence by large erroneous orders using multi agent simulation. In: 2014 IEEE conference on computational intelligence on for financial engineering and economics (CIFEr), pp 138–143
Mizuta T, Izumi K, Yagi I, Yoshimura S (2014) 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
Muranaga J (1999) Dynamics of market liquidity of Japanese stocks: an analysis of tick-by-tick data of the Tokyo Stock Exchange. In: Bank for International Settlements (ed) Market liquidity: research findings and selected policy implications, vol 11. Bank for International Settlements, pp 1–25. https://EconPapers.repec.org/RePEc:bis:biscgc:11-13
Nakada T, Takadama K (2013) Analysis on the number of XCS agents in agent-based computational finance. In: IEEE conference on computational intelligence on for financial engineering and economics (CIFEr), pp 8–13
Nishizaki K, Tsuchikawa A, Yagi T (2013) Indicators related to liquidity in JGB markets. Bank of Japan Review Series 13-E-3, Bank of Japan. https://EconPapers.repec.org/RePEc:boj:bojrev:13-e-3
Bank for International Settlements (1999) Recommendations for the design of liquid markets. Bank for International Settlements. https://www.bis.org/publ/cgfs13.htm
Sewell M (2006) Characterization of financial time series. http://finance.martinsewell.com/stylized-facts/
Yagi I, Nozaki A, Mizuta T (2017) Investigation of the rule for investment diversification at the time of a market crash using an artificial market simulation. Evol Inst Econ Rev 14(2):451–565. https://link.springer.com/article/10.1007%2Fs40844-017-0070-9
Yamamoto R, Hirata H (2013) Strategy switching in the Japanese stock market. J Econ Dyn Control 37(19):2010–2022. http://EconPapers.repec.org/RePEc:eee:dyncon:v:37:y:2013:i:10:p:2010-2022
Disclaimer
Note that the opinions expressed herein are solely those of the authors and do not necessarily reflect those of SPARX Asset Management Co., Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Yagi, I., Masuda, Y., Mizuta, T. (2019). Detection of Factors Influencing Market Liquidity Using an Agent-Based Simulation. In: Chakrabarti, A., Pichl, L., Kaizoji, T. (eds) Network Theory and Agent-Based Modeling in Economics and Finance. Springer, Singapore. https://doi.org/10.1007/978-981-13-8319-9_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-8319-9_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8318-2
Online ISBN: 978-981-13-8319-9
eBook Packages: Economics and FinanceEconomics and Finance (R0)