Asymmetric Information and Learning by Imitation in Agent-Based Financial Markets

  • Luca Gerotto
  • Paolo PellizzariEmail author
  • Marco Tolotti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1047)


We describe an agent-based model of a market where traders exchange a risky asset whose returns can be partly predicted purchasing a costly signal. The decision to be informed (at a cost) or uninformed is taken by means of a simple learning by imitation mechanism that periodically occurs.

The equilibrium is characterized describing the stationary distribution of the price and the fraction of the informed traders. We find that the number of agents who acquire the signal decreases with its cost and with agents’ risk aversion and, conversely, it increases with the signal-to-noise ratio and when learning is slow, as opposed to frequent. Moreover, price volatility appears to directly depend on the fraction of informed traders and, hence, some heteroskedasticity is observed when this fraction fluctuates.


Agent-based modeling Bounded rationality Information in financial markets 


  1. 1.
    Schredelseker, K.: Is the usefulness approach useful? Some reflections on the utility of public information. In: McLeay, S., Riccaboni, A. (eds.) Contemporary Issues in Accounting Regulation, pp. 135–153. Springer, Boston (2001). Scholar
  2. 2.
    Kurlat, P., Veldkamp, L.: Should we regulate financial information? J. Econ. Theory 158, 697–720 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Veldkamp, L.L.: Media frenzies in markets for financial information. Am. Econ. Rev. 96(3), 577–601 (2006)CrossRefGoogle Scholar
  4. 4.
    Grossman, S.J., Stiglitz, J.E.: On the impossibility of informationally efficient markets. Am. Econ. Rev. 70(3), 393–408 (1980)Google Scholar
  5. 5.
    Routledge, B.R.: Adaptive learning in financial markets. Rev. Financ. Stud. 12(5), 1165–1202 (1999)CrossRefGoogle Scholar
  6. 6.
    Routledge, B.R.: Genetic algorithm learning to choose and use information. Macroecon. Dyn. 5(02), 303–325 (2001)CrossRefGoogle Scholar
  7. 7.
    Chen, T.T., Zheng, B., Li, Y., Jiang, X.F.: Information driving force and its application in agent-based modeling. Phys. A: Stat. Mech. Its Appl. 496, 593–601 (2018)CrossRefGoogle Scholar
  8. 8.
    Billett, M.T., Garfinkel, J.A., Yu, M.: The effect of asymmetric information on product market outcomes. J. Financ. Econ. 123(2), 357–376 (2017)CrossRefGoogle Scholar
  9. 9.
    Krichene, H., El-Aroui, M.A.: Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models. J. Econ. Interact. Coord. 13(3), 511–535 (2018)CrossRefGoogle Scholar
  10. 10.
    Cont, R., Bouchaud, J.P.: Herd behavior and aggregate fluctuations in financial markets. Macroecon. Dyn. 4(2), 170–196 (2000)CrossRefGoogle Scholar
  11. 11.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999).
  12. 12.
    Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., Tayler, P.: Asset pricing under endogenous expectations in an artificial stock market. In: Arthur, W., Lane, D., Durlauf, S. (eds.) The Economy as an Evolving, Complex System II, pp. 15–44. Addison Wesley, Redwood City (1997)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of EconomicsCa’ Foscari UniversityVeniceItaly
  2. 2.Department of ManagementCa’ Foscari UniversityVeniceItaly

Personalised recommendations