Skip to main content
SpringerLink
Log in

An Agent-Based Model to Study Informational Cascades in Financial Markets

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

The purpose of this paper is to build an agent-based model which is adapted to study the information cascades in financial markets. Thus, we design and implement a model with asynchronous continuous-time management, populated by heterogeneous traders, connected by an interaction network with directed and weighted edges, which allows them to observe and learn about the actions of their peers. The proposed model relaxes unrealistic assumptions of previous analytical models, particularly, homogeneity of traders and access to all previous decisions (full network). Therefore, the proposed model allows to study the impact of different factors in the emergence and magnitude of information cascades: mainly, the impact of the heterogeneity of traders behaviours and characteristics of the interaction network. Moreover, unlike analytical models which focus only on how information cascades occur, the proposed model allows studying the impact of information cascades on the price dynamics. Then, we use the model to perform a series of experiments to investigate the impact of various factors in the formation and magnitude of information cascades. The obtained results show in particular that informational cascades only occur when the market is dominated by an overwhelming majority of traders who have significant uncertainty in their own signals. This shows the benefit of designing policies to reduce uncertainty among investors to avoid informational cascades, whether by financial regulatory authorities or by companies with high degrees of information uncertainty.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bikhchandani, S., Sharma, S.: Herd behavior in financial markets. IMF Staff Pap. 47(3), 279–310 (2000)

    Google Scholar 

  2. Botsvadze, I.: Herd behavior in equity markets-the international evidence. J. Bus. 2(2), 41–46 (2013)

    Google Scholar 

  3. Banerjee, A.V.: A simple model of herd behavior. Q. J. Econ. 107(3), 797–817 (1992)

    Article  Google Scholar 

  4. David, S., Jeremy, C.S.: Herd behavior and investment. Am. Econ. Rev. 1, 465–479 (1990)

    Google Scholar 

  5. Oksana, D.: Informational cascades in financial markets: review and synthesis. Rev. Behav. Financ. (2018). https://doi.org/10.1108/RBF-05-2016-0030

    Article  Google Scholar 

  6. Devenow, A., Welch, I.: Rational herding in financial economics. Eur. Econ. Rev. 40(3–5), 603–615 (1996)

    Article  Google Scholar 

  7. Nihan, D., Cumhur, E., Oğuz, E.: Daily and intraday herding within different types of investors in borsa istanbul. Emerg. Mark. Financ. Trade 57(6), 1793–1810 (2019)

    Google Scholar 

  8. Shyu, J., Sun, H.-M.: Do institutional investors herd in emerging markets? evidence from the Taiwan stock market. Asian J. Financ. Acc. 2(2), 1 (2010)

    Google Scholar 

  9. Christopher, A., Peter, Z.: Multidimensional uncertainty and herd behavior in financial markets. Am. Econ. Rev. 1, 724–748 (1998)

    Google Scholar 

  10. Orléan, A.: Comprendre les foules spéculatives: mimétismes informationnel, autoréférentiel et normatif. In: Gravereau, J., Trauman, J. (eds.) Crises Financières, Economica, Paris (2001)

  11. Viktor, M., Robert, H.: Herd behaviour experimental testing in laboratory artificial stock market settings behavioural foundations of stylised facts of financial returns. Phys. A Stat. Mech. Appl. 392(19), 4351–4372 (2013)

    Article  Google Scholar 

  12. Hong, H.: Information cascade and share market volatility: a Chinese perspective. J. Asian Financ. Econ. Bus. 3(4), 17–24 (2016)

    Article  Google Scholar 

  13. Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100(5), 992–1026 (1992)

    Article  Google Scholar 

  14. Levy, H., Levy, M., Solomon, S.: Microscopic simulation of financial markets: from investor behavior to market phenomena. Elsevier, Amsterdam (2000)

    Google Scholar 

  15. Iryna, V.: A Reexamination of Modern Finance Issues Using Artificial Market Frameworks. Université Panthéon-Sorbonne-Paris, Paris (2012)

    Google Scholar 

  16. Matthieu, C., Luciano, P., Andrea. Z.: Critical overview of agent-based models for economics. Complex. Financ. Mark (2014)

  17. Martinez-Jaramillo, S., Tsang, E.P.K.: An heterogeneous, endogenous and coevolutionary gp-based financial market. IEEE Trans. Evolut. Comput. 13(1), 33–55 (2009)

    Article  Google Scholar 

  18. Benhammada, S., Amblard, F., Chikhi, S.: An asynchronous double auction market to study the formation of financial bubbles and crashes. New Gener. Comput. 35(2), 129–156 (2017)

    Article  Google Scholar 

  19. Mark, P.B.: Social learning, herd behaviour and information cascades: a review of the recent developments in relation to their criticisms. J. Econ. Res. (JER) 18(3), 205–236 (2013)

    Article  Google Scholar 

  20. Joohyun, K., Ohsung, K., Duk, H.L.: Observing cascade behavior depending on the network topology and transaction costs. Comput. Econ. 53(1), 207–225 (2019)

    Article  Google Scholar 

  21. Richard, G.P., Arthur, W.B., John, H.H., Blake, L.B., Paul, T.: Artificial economic life: a simple model of a stockmarket. Phys. D Nonlinear Phenom. 75(1–3), 264–274 (1994)

    MATH  Google Scholar 

  22. LeBaron, B.: Agent-based computational finance. Handbook Comput. Econ. 2, 1187–1233 (2006)

    Article  Google Scholar 

  23. Chen, S.-H., Chang, C.-L., Ye-Rong, D.: Agent-based economic models and econometrics. Knowl. Eng. Rev. 27(2), 187–219 (2012)

    Article  Google Scholar 

  24. Takanobu, M.: An agent-based model for designing a financial market that works well. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 400–406. IEEE (2020)

  25. Jondeau, E.: Le comportement mimétique sur les marchés de capitaux. Bull. Banq. Fr. 95, 85–95 (2001)

    Google Scholar 

  26. Welch, I.: Sequential sales, learning, and cascades. J. Financ. 47(2), 695–732 (1992)

    Article  Google Scholar 

  27. Easley, D., Kleinberg, J., et al.: Networks, crowds, and markets: reasoning about a highly connected world. Significance 9(1), 43–44 (2012)

    MATH  Google Scholar 

  28. Çelen, B., Kariv, S.: Observational learning under imperfect information. Games Econ. Behav. 47(1), 72–86 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  29. Daron, A., Munther, A.D., Ilan, L., Asuman, O.: Bayesian learning in social networks. Rev. Econ. Stud. 78(4), 1201–1236 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  30. Lisa, R.A., Charles, A.H.: Information cascades in the laboratory. Am. Econ. Rev. 1, 847–862 (1997)

    Google Scholar 

  31. Çelen, B., Kariv, S.: An experimental test of observational learning under imperfect information. Econ. Theory 26(3), 677–699 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  32. Frank, A.H.: Unobservability, tractability and the battle of assumptions. J. Econ. Methodol. 12(3), 383–406 (2005)

    Article  Google Scholar 

  33. Boer, K., Kaymak, U., Spiering, J.: From discrete-time models to continuous-time, asynchronous modeling of financial markets. Comput. Intell. 23(2), 142–161 (2007)

    Article  MathSciNet  Google Scholar 

  34. Bargigli, L., Tedeschi, G.: Interaction in agent-based economics: a survey on the network approach. Phys. A Stat. Mech. Appl. 399, 1–15 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  35. Ponta, L., Pastore, S., Cincotti, S.: Information-based multi-assets artificial stock market with heterogeneous agents. Nonlinear Anal. Real World Appl. 12(2), 1235–1242 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  36. Valentyn, P., Sergiy, G., Oleg, V.P.: Asset price dynamics with heterogeneous beliefs and local network interactions. J. Econ. Dyn. Control 37(12), 2623–2642 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  37. Kodia, Z., Said, L.B., Ghedira, K.: A study of stock market trading behavior and social interactions through a multi agent based simulation. In: KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, pp. 302–311. Springer, Berlin (2010)

  38. Sadek, B., Frédéric, A., Salim, C.: An artificial stock market with interaction network and mimetic agents. In:  9th International Conference on Agents and Artificial Intelligence (ICAART 2017), pp. 390–397 (2017)

  39. Cont, R., Tanimura, E.: Small-world graphs: characterization and alternative constructions. Adv. Appl. Probab. 40(4), 939–965 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  40. Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    Article  MATH  Google Scholar 

  41. De Finetti, B.: Theory of probability: a critical introductory treatment, vol. 6. Wiley, Hoboken (2017)

    Book  MATH  Google Scholar 

  42. Cipriani, M., Guarino, A.: Transaction costs and informational cascades in financial markets. J. Econ. Behav. Organ. 68(3–4), 581–592 (2008)

    Article  Google Scholar 

  43. Stöckl, T., Huber, J., Kirchler, M.: Bubble measures in experimental asset markets. Exp. Econ. 13(3), 284–298 (2010)

    Article  MATH  Google Scholar 

  44. Mizuta, T., Kosugi, S., Kusumoto, T., Matsumoto, W., Izumi, K., Yagi, I., Yoshimura, S.: Effects of price regulations and dark pools on financial market stability: an investigation by multiagent simulations. Intell. Syst. Acc. Financ. Manag. 23(1–2), 97–120 (2016)

    Article  Google Scholar 

  45. del María, M.M., Catalina García, G., José, G.: Treatment of kurtosis in financial markets. Phys. A Stat. Mech. Appl. 391(5), 2032–2045 (2012)

    Article  Google Scholar 

  46. Lux, T., Marchesi, M.: Volatility clustering in financial markets: a microsimulation of interacting agents. Internet J. Theor. Appl. Financ. 3(04), 675–702 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  47. Frédéric, A., Denis, P., et al.: Modélisation et simulation multi-agents. Hermes Lavoisier, Paris (2006)

  48. Christian, L., Peter, D.W.: Economic consequences of financial reporting and disclosure regulation: a review and suggestions for future research. Working Paper, University of Chicago (2008)

  49. Willenborg, M.: Empirical analysis of the economic demand for auditing in the initial public offerings market. J. Acc. Res. 37(1), 225–238 (1999)

    Article  Google Scholar 

  50. Chien-Heng, J.C., Ling-Tai, L.C.: Auditor choice under client information uncertainty. Rev. Integr. Bus. Econ. Res. 5(4), 329–370 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MISC Laboratory, Department of Computer Science and its Applications, University Constantine 2 Abdelhamid Mehri, El Khroub, Algeria

    Sadek Benhammada & Salim Chikhi

  2. UMR 5505 CNRS-IRIT, Université Toulouse 1 Capitole, Toulouse, France

    Frédéric Amblard

Authors
  1. Sadek Benhammada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frédéric Amblard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salim Chikhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadek Benhammada.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benhammada, S., Amblard, F. & Chikhi, S. An Agent-Based Model to Study Informational Cascades in Financial Markets. New Gener. Comput. 39, 409–436 (2021). https://doi.org/10.1007/s00354-021-00133-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-021-00133-3

Keywords

Search

Navigation