Journal of Economic Interaction and Coordination

, Volume 13, Issue 3, pp 511–535 | Cite as

Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models

  • Hazem Krichene
  • Mhamed-Ali El-Aroui
Regular Article


This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.


Agent-based model Multi-assets trading Immature stock markets Information asymmetry Herd behavior Assortativity 

JEL Classification

C58 C63 G12 G14 G15 G17 O16 


  1. Alagidede P (2011) Return behaviour in Africa’s emerging equity markets. Q Rev Econ Financ 51:133–140CrossRefGoogle Scholar
  2. Alvarez-Ramirez J, Rodriguez E, Alvarez J (2012) A multiscale entropy approach for market efficiency. Int Rev Financ Anal 21:64–69CrossRefGoogle Scholar
  3. Amaral LAN, Scala A, Barthélémy M, Stanley HE (2000) Classes of small-world networks. Proc Natl Acad Sci USA 97:11149–11152CrossRefGoogle Scholar
  4. Baker M, Wurgler J (2007) Investor sentiment in the stock market. J Econ Perspect 21(2):129–151CrossRefGoogle Scholar
  5. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:506–512Google Scholar
  6. Barber B, Odean T, Zhu N (2009) Systematic noise. J Financ Mark 12(4):547–569CrossRefGoogle Scholar
  7. Bekaert G, Harvey C (2003) Emerging markets finance. J Empir Financ 10:3–55CrossRefGoogle Scholar
  8. Bikhchandani S, Sharma S (2001) Herd behavior in financial markets. IMF Staff Pap 47(3):279–310Google Scholar
  9. Bollobás B, Borgs C, Chayes T, Riordan O (2003) Directed scale-free graphs. In: Extreme value theory, Proceedings of the 14th ACM-SIAM symposium on discrete algorithms, pp 132–139Google Scholar
  10. Brock W, Hommes C (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22:1235–1274CrossRefGoogle Scholar
  11. Brown S, Hillegeist A (2007) How disclosure quality affects the level of information asymmetry. Rev Account Stud 12:443–477CrossRefGoogle Scholar
  12. Bushman RM, Smith AJ (2003) Transparency, financial accounting information, and corporate governance. Econ Policy Rev 9(1):65–87Google Scholar
  13. Chiarella C, Iori G, Perello J (2009) The impact of heterogeneous trading rules on the limit order book and order flows. J Econ Dyn Control 33:525–537CrossRefGoogle Scholar
  14. Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quant Financ 1:223–236CrossRefGoogle Scholar
  15. De Santis G, Imrohoroglu S (1997) Stock returns and volatility in emerging financial markets. J Int Money Financ 16:561–579CrossRefGoogle Scholar
  16. Foster G, Foster D, Grassberger P, Paczuski M (2010) Edge direction and the structure of networks. In: Proceedings of the national academy of sciences. Proceedings of the 14th ACM-SIAM symposium on discrete algorithms, pp 1–6Google Scholar
  17. Guo Q, Zhou T, Liu J, Bain W, Wang B, Zhao M (2006) Growing scale-free small-world networks with tunable assortative coefficient. Phys A 371:814–822CrossRefGoogle Scholar
  18. Ionescu C (2012) The herd behavior and the financial instability. Ann Univ Petroşani Econ 12(1):129–140Google Scholar
  19. Jurkatis S, Kremer S, Nautz D (2012) Correlated trades and herd behavior in the stock market. SFB 649 economic risk Berlin discussion paper 2012-035Google Scholar
  20. Kim W, Wei S (2002) Foreign portfolio investors before and during a crisis. J Int Econ 56:77–96CrossRefGoogle Scholar
  21. Krichene H, El-Aroui M (2016) Agent-based simulation and microstructure modeling of immature stock markets: case of a single risky asset. Comput Econ. doi: 10.1007/s10614-016-9615-y Google Scholar
  22. Laloux L, Cizeau P, Bouchaud JP, Potters M (1999) Noise dressing of financial correlation matrices. Phys Rev Lett 83(7):1467–1470CrossRefGoogle Scholar
  23. LeBaron B, Yamamoto R (2008) The impact of imitation on long memory in an order-driven market. East Econ J 34:504–517CrossRefGoogle Scholar
  24. Maffett M (2011) Who benefits from corporate opacity? International evidence from informed trading by institutional investors. University of North Carolina (unpublished paper)Google Scholar
  25. Mantegna R, Stanley H (1994) Stochastic process with ultraslow convergence to a gaussian: the truncated lévy flight. Phys Rev Lett 22(73):2946–2949CrossRefGoogle Scholar
  26. Mantegna R, Stanley H (1995) Scaling behaviour in the dynamics of an economic index. Nature 376:46–49CrossRefGoogle Scholar
  27. Newman M (2002) Assortative mixing in networks. Phys Rev Lett 89:208701CrossRefGoogle Scholar
  28. Pan R, Sinha S (2007) Collective behavior of stock price movements in an emerging market. Phys Rev 76(046):116Google Scholar
  29. Pincus S (1995) Approximate entropy (apen) as a complexity measure. Chaos 5(1):17CrossRefGoogle Scholar
  30. Ponta L, Pastore S, Cincotti S (2011) Information-based multi-assets artificial stock market with heterogeneous agents. Nonlinear Anal Real World Appl 12:1235–1242CrossRefGoogle Scholar
  31. Shen J, Zheng B (2009) Cross-correlation in financial dynamics. Europhys Lett 86(48):005Google Scholar
  32. Tedeshi G, Iori G, Gallegati M (2012) Herding effects in order driven markets: the rise and fall of gurus. J Econ Behav Organ 81:82–96CrossRefGoogle Scholar
  33. Wang J (1993) A model of intertemporal asset prices under asymmetric information. Rev Econ Stud 60:249–282CrossRefGoogle Scholar
  34. Weron R (2002) Estimating long range dependence: finite sample properties and confidence intervals. Phys A 312:286–299CrossRefGoogle Scholar
  35. Yartey C (2008) The determinants of stock market development in emerging economies: is South Africa different? Working paper 08/32, International Monetary Fund, WashingtonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.ISG de Tunis, LARODECUniversité de TunisTunisTunisia
  2. 2.Graduate School of Simulation StudiesUniversity of HyogoKobeJapan
  3. 3.FSEG Nabeul and LARODECUniversité de CarthageTunisTunisia

Personalised recommendations