Journal of Evolutionary Economics

, Volume 25, Issue 5, pp 901–924 | Cite as

A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach

  • Haijun YangEmail author
  • Harry Jiannan Wang
  • Gui Ping Sun
  • Li Wang
Regular Article


The market microstructure literatures study how the traders work in the financial market. In this paper, we propose a novel heterogeneous agent-based multi-asset artificial stock market based on Santa Fe Artificial Stock Market (SFI-ASM) to compare the financial market microstructure between U.S. and China. We first develop a set of new parameters for the single stock market simulation to improve the way that agents monitor the market and choose different strategies, which make our model closer to the real financial market. Secondly, we construct a multiple assets financial market by incorporating two new types of agents, namely, zero-intelligence agents and less-intelligence agents, and conduct simulations for different evolution speeds, strategies, and intelligence levels to achieve the optimal models of Chinese and U.S. financial markets before and after the financial crisis. Based on the simulation results, we present a comprehensive analysis of the market microstructure for the two financial markets.


Heterogeneous agent Agent-based model Multi-asset artificial stock market Microstructure 

JEL Classification

C6 D8  G1 



We would like to thank those anonymous reviewers who gave us much valuable advice to revise this manuscript. This work was supported in part by the National Natural Science Foundation of China under grants 71171010.


  1. Alfarano S, Lux T, Wagner F (2008) Time variation of higher moments in a financial market with heterogeneous agents: an analytical approach. J Econ Dyn Control 32(1):101–136MathSciNetCrossRefGoogle Scholar
  2. Bray M (1982) Learning, estimation and the stability of rational expectations. J Econ Theory 26(2):318–339MathSciNetCrossRefGoogle Scholar
  3. Brock WA, Hommes CH (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22:1235–1274MathSciNetCrossRefGoogle Scholar
  4. Chen CH (2003) Social learning mechanism in java-swarm artificial stock market. Master’s Thesis, National Central University, TaiwanGoogle Scholar
  5. Chen SH, Yeh CH (2001) Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market. J Econ Dyn Control 25:363–393CrossRefGoogle Scholar
  6. Cincotti S, Ponta L, Raberto M (2011) Information-based multi-assets artificial stock market with heterogeneous agents. Nonlinear Anal Real World Appl 12(2):1235–1242MathSciNetCrossRefGoogle Scholar
  7. Ehrentreich N (2005) The Santa Fe artificial stock market re-examined-suggested corrections. J Econ Dyn Control 56(1):24–31Google Scholar
  8. Grossman S, Stiglitz J (1980) On the impossibility of informationally efficient markets. Am Econ Rev 70(3):393–408Google Scholar
  9. Hauser F, Kaempff B (2013) Evolution of trading strategies in a market with heterogeneously informed agents. J Evol Econ 23:575–607CrossRefGoogle Scholar
  10. Hoffmann A, Post T, Pennings J (2013) Individual investor perceptions and behavior during the financial crisis. J Bank Financ 37:60–74CrossRefGoogle Scholar
  11. Hommes CH (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, vol 2. North-Holland, Amsterdam, pp 1109–1186Google Scholar
  12. Joshi S, Parker J, Bedau MA (2000) Computational finance 99. MIT Press, Cambridge, pp 465–479Google Scholar
  13. LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market model. J Econ Dyn Control 23:1487–1516CrossRefGoogle Scholar
  14. Lux T (1995) Herd behavior, bubbles and crashes. Econ J 105:881–896CrossRefGoogle Scholar
  15. Lux T, Marchesi M (2000) Volatility clustering in financial markets, a microsimulation of interacting agents. Int J Theoretical Appl Finance 3:675–702MathSciNetCrossRefGoogle Scholar
  16. Madhavan A (2000) Market microstructure: a survey. J Financ Mark 3:205–258CrossRefGoogle Scholar
  17. Pascual J, Pajares J, López-Paredes A (2006) Explaining the statistical features of the Spanish Stock Market from the bottom-up. In Lecture Notes in Economics and Mathematical Systems. Adv Artif Econ 584:283–294CrossRefGoogle Scholar
  18. Tay N, Linn SC (2001) Fuzzy inductive reasoning, expectation formation and the behavior of security prices. J Econ Dyn Control 25:124–141CrossRefGoogle Scholar
  19. Waltman L, Van Eck NJP, Dekker R, Kaymak U (2011) Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies. J Evol Econ 21:737–756CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Haijun Yang
    • 1
    Email author
  • Harry Jiannan Wang
    • 2
  • Gui Ping Sun
    • 1
  • Li Wang
    • 1
  1. 1.School of Economics and ManagementBeihang UniversityBeijingChina
  2. 2.Alfred Lerner College of Business and EconomicsUniversity of DelawareNewarkUSA

Personalised recommendations