A Behavioral and Rational Investor Modeling to Explain Subprime Crisis: Multi Agent Systems Simulation in Artificial Financial Markets

  • Yosra Ben SaidEmail author
  • Dalel Kanzari
  • Marwa Bezzine
Part of the Multiple Criteria Decision Making book series (MCDM)


The aim of this paper is to explain the financial crisis via the investors’ psychological behavior and rational reasoning. We specifically focus on three main biases: overconfidence, loss aversion and mimetic behavior.

We propose a new conceptual model of financial decision-making representing the stock market dynamics during the crisis period. We construct an artificial financial market that has two types of investors: institutional and individual. The latter are classified into two groups: the noise traders and the mimetic investors.

A simple experimentation of our model is elaborated to simulate the behavior of the investors during the different phases of a crisis: the formation and the break-up of the speculative bubble. We conclude that the interaction between rational and irrational behavior and the investor’s psychology must be considered in the explanation of financial crises, overconfidence and loss aversion are two behavioral biases very relevant to explain the formation and bursting of bubbles. Finally, mimetic behavior amplifies disturbances in the financial market and limits arbitrage.


Financial crisis Behavioral finance Rational behavior Investor’s psychology Overconfidence Loss aversion Mimetic behavior Multi-agents simulation 


  1. Abbes, M. B. (2012). Does overconfidence bias explain volatility during the global financial crisis? Transition Studies Review, 19(3), 291–312.CrossRefGoogle Scholar
  2. Alfarano, S. Lux, T. & Wagner, F. (2010). Excess volatility and herding in an artificial financial market: Analytical approach and estimation. MPRA_paper_24719.pdf.Google Scholar
  3. Barberis, N., Huang, M., & Santos, T. (2001). Prospect theory and asset prices. Quarterly Journal of Economics, 116, 1–53.CrossRefGoogle Scholar
  4. Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343.CrossRefGoogle Scholar
  5. Broihanne, M. H., Merli, M., & Roger, P. (2014). Overconfidence, risk perception and the risk-taking behavior of finance professionals. Finance Research Letters, 11(2), 64–73.CrossRefGoogle Scholar
  6. Chuang, W., & Lee, B. (2006). An empirical evaluation of then overconfidence hypothesis. Journal of Banking and Finance, 30, 2489–2515.CrossRefGoogle Scholar
  7. Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and overreactions. Journal of Finance, 53(6), 1839–1885.CrossRefGoogle Scholar
  8. De Bondt, W., & Thaler, R. (1985). Does the stock market overreact. Journal of Finance, 40(3), 793–805.CrossRefGoogle Scholar
  9. Demyanyk, Y., & Hemert, O. V. (2011). Understanding the subprime mortgage crisis. Review of Financial Studies, 24(6), 1848–1880.CrossRefGoogle Scholar
  10. Easterwood, J. C., Stacey, R., & Nutt, S. R. (1999). Inefficiency in analysts’ earnings forecasts: Systematic misreaction or systematic optimism? Journal of Finance, 54(5), 1777–1797.CrossRefGoogle Scholar
  11. Graham, J. R. (1999). Herding among investment newsletters: Theory and evidence. Journal of Finance, 54(1), 237–268.Google Scholar
  12. Hirshleifer, D., Subrahmanyam, A., & Titman, S. (1994). Security analysis and trading patterns when some investors receive information before others. Journal of Finance, 49(5), 1665–1698.CrossRefGoogle Scholar
  13. Hoffmann, A., Post, T., & Pennings, J. (2013). Individual investor perceptions and behavior during the financial crisis. Journal of Banking and Finance, 37(1), 60–74.CrossRefGoogle Scholar
  14. Hoffmann, A. O. I., WJager, W., & von Eije, J. H. (2007). Social simulation of stock markets: Taking it to the next level. Journal of Artificial Societies and Social Simulation, 10(2), 7.Google Scholar
  15. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk by. Econometrica, 47(2), 263–291.CrossRefGoogle Scholar
  16. Kouwenberg, R., & Zwinkels, R. C. J. (2015). Endogenous price bubbles in a multi-agent system of the housing market. PLoS One, 10(6), e0129070.CrossRefGoogle Scholar
  17. Levitin, A., & Wachter, S. (2012). Explaining the housing bubble. Georgetown Law Journal, 100, 1177–1258.Google Scholar
  18. Mah-Hui, M. L. (2008). Old wine in new bottles: Subprime mortgage crisis - causes and consequences. Journal of Applied Research in Accounting and Finance, 3(1), 3–13.Google Scholar
  19. Manzan, S., & Westerhoff, F. (2005). Representativeness of news and exchange rate dynamics. Journal of Economic Dynamics and Control, 29(4), 677–689.CrossRefGoogle Scholar
  20. Odean, T. (1998). Are investors reluctant to realize their losses? Journal of Finance, 53(5), 1775–1798.CrossRefGoogle Scholar
  21. Odean, T., & Gervais, S. (2001). Learning to be overconfident. Review of Financial Studies, 30, 1–27.Google Scholar
  22. Rekik, Y., Hachicha, W., & Boujelbene, Y. (2014). Agent-based modeling and investors’ behavior explanation of asset. Procedia Economics and Finance, 13, 30–46.CrossRefGoogle Scholar
  23. Scharfstein, D., & Stein, J. (1990). Herd behavior and investment. American Economic Review, 80(3), 465–479.Google Scholar
  24. Shefrin, H. (2002). Beyond greed and fear: Understanding behavioral finance and the psychology of investing. New York: Oxford University Press.CrossRefGoogle Scholar
  25. Shefrin, A., & Statman, M. (2011). Behavioral finance in the financial crisis: Market efficiency, Minsky and Keynes. Working paper, Santa Clara University.Google Scholar
  26. Shleifer, A., Summer, L., & Waldmann, R. (1990). Noise trader risk in financial markets. Journal of Political Economics, 98, 703–738.CrossRefGoogle Scholar
  27. Shleifer, A., & Vishny, R. W. (1997). The limits of arbitrage. Journal of Finance, 52(1), 35–55.CrossRefGoogle Scholar
  28. Statman, M. (2011). Efficient Markets in Crisis. Journal of Investment Management, 9(2).Google Scholar
  29. Tisue, S., & Wilensky, U. (2004, May 16–21). NetLogo: A simple environment for modeling complexity. The International Conference on Complex Systems, Boston.Google Scholar
  30. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.CrossRefGoogle Scholar
  31. Zwiebel, J. (1995). Corporate conservatism and relative compensation. Journal of Political Economy, 103(1), 1–25.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yosra Ben Said
    • 1
    • 2
    • 3
    Email author
  • Dalel Kanzari
    • 4
    • 5
  • Marwa Bezzine
    • 6
  1. 1.Financial and Accounting Methods DepartmentFSEGMahdiaTunisia
  2. 2.Monastir UniversityMonastirTunisia
  3. 3.E.A.S Research UnitMahdiaTunisia
  4. 4.Computer Science Department, ISSATSoSousse-UniversitySousseTunisia
  5. 5.COSMOS-ENSI LaboratoryTunisTunisia
  6. 6.ISG-SousseSousseTunisia

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