Information dissemination in an experimentally based agent-based stock market

Regular Article
  • 335 Downloads

Abstract

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.

Keywords

Agent-based modeling Experiments Stock market   Asymmetric information Learning 

References

  1. Anufriev M, Hommes C (2012) Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. Am Econ J Microecon 4(4):35–64CrossRefGoogle Scholar
  2. Anufriev M, Arifovic J, Ledyard J, Panchenko V (2011) Efficiency of continuous double auctions under individual evolutionary learning with full or limited information. J Evol Econ 1–35. doi:10.1007/s00191-011-0230-8
  3. Axtell R (2000) Why agents? On the varied motivations for agent computing in the social sciences. Center on Social and Economic Dynamics Working Paper (17)Google Scholar
  4. Barner M, Feri F, Plott CR (2005) On the microstructure of price determination and information aggregation with sequential and asymmetric information arrival in an experimental asset market. Ann Financ 1:73–107CrossRefGoogle Scholar
  5. Boero R, Bravo G, Castellani M, Squazzoni F (2010a) Why bother with what others tell you? An experimental data-driven agent-based model. J Artif Soc Soc Simul 13(3). http://jasss.soc.surrey.ac.uk/13/3/6.html
  6. Bravo G, Boero R, Squazzoni F (2012) Trust and partner selection in social networks: an experimentally grounded model. Soc Netw 34(4):481–492CrossRefGoogle Scholar
  7. Camerer C, Weigelt K (1991) Information mirages in experimental asset markets. J Bus 64(4):463–493CrossRefGoogle Scholar
  8. Chan NT, LeBaron B, Lo AW, Poggio T (2001) Agent-based models of financial markets: a comparison with experimental markets. MIT Sloan Working Paper (4195–01)Google Scholar
  9. Cliff D, Bruten J (1997) Minimal-intelligence agents for bargaining behaviors in market based environments. HP Laboratories Bristol (HPL-97-91)Google Scholar
  10. Contini B, Leombruni R, Richiardi M (2007) Exploring a new expace the complementarities between experimental economics and agent-based computational economics. J Soc Complex 3(1):13–22Google Scholar
  11. Copeland TE, Friedman D (1986) The effects of sequential information arrival on asset prices: an experimental study. J Financ 42(3):763–797CrossRefGoogle Scholar
  12. Copeland TE, Friedman D (1991) Partial revelation of information in experimental asset markets. J Financ 46(1):265–295CrossRefGoogle Scholar
  13. Duffy J (2006) Agent-based models and human subject experiments. In: Handbook of computational economics, vol 2. Elsevier, Amsterdam, pp 949–1011Google Scholar
  14. Duffy J, Ünver MU (2006) Asset price bubbles and crashes with near-zero-intelligence traders. Econ Theory 27:537–563CrossRefGoogle Scholar
  15. Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. The MIT Press, CambridgeGoogle Scholar
  16. Fano S, LiCalzi M, Pellizzari P (2011) Convergence of outcomes and evolution of strategic behavior in double auctions. J Evol Econ. doi:10.1007/s00191-011-0226-4
  17. Forsythe R, Lundholm R (1990) Information aggregation in an experimental market. Econometrica 58(2):309–347CrossRefGoogle Scholar
  18. Gjerstad S, Dickhaut J (1998) Price formation in double auctions. Games Econ Behav 22(1):1–29CrossRefGoogle Scholar
  19. Gjerstad S, Sachat JM (2007) Individual rationality and market efficiency. Institute for Research in the Behavioral, Economic and, Management Science (1204)Google Scholar
  20. Gneezy U (1996) Probability judgments in multi-stage problems: experimental evidence of systematic biases. Acta Psychol 93:59–68CrossRefGoogle Scholar
  21. Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101(1):119–137CrossRefGoogle Scholar
  22. Grazzini J (2012) Analysis of the emergent properties: stationarity and ergodicity. J Artif Soc Soc Simul 15(2)Google Scholar
  23. Holden CW, Subrahmanyam A (1992) Long-lived private information and imperfect competition. J Financ 47(1):247–270CrossRefGoogle Scholar
  24. Hommes C (2011) The heterogeneous expectations hypothesis: some evidence from the lab. J Econ Dyn Control 35:1–24CrossRefGoogle Scholar
  25. Hommes C, Sonnemans J, Tunistra J, van de Velde H (2005) Coordination of expectations in asset pricing experiments. Rev Financ Stud 18(3):955–980CrossRefGoogle Scholar
  26. King RR, Smith VL, Williams A, van Boening M (2001) The robustness of bubbles and crashes in experimental stock markets. Econometrica 69(4):831–859CrossRefGoogle Scholar
  27. Kyle AS (1985) Continuous auction and insider trading. Econometrica 53(6):1315–1335CrossRefGoogle Scholar
  28. Lei V, Noussair CN, Plott CR (2001) Nonspeculative bubbles in experimental asset markets: lack of common knowledge of rationality vs actual irrationality. Econometrica 69(4):831–859CrossRefGoogle Scholar
  29. Mitchell TM (1997) Machine learning. McGraw-Hill, New YorkGoogle Scholar
  30. Noussair C, Robin S, Ruffleux B (2001) Price bubbles in laboratory asset markets with constant fundamental values. Exp Econ 4:87–105Google Scholar
  31. Plott CR (2000) Market as information gathering tools. South Econ J 67(1):1–15CrossRefGoogle Scholar
  32. Plott CR, Sunder S (1982) Efficiency of experimental security markets with insider information: an application of rational-expectations models. J Polit Econ 90(4):663–698CrossRefGoogle Scholar
  33. Plott CR, Sunder S (1988) Rational expectations and the aggregation of diverse information in laboratory security markets. Econometrica 56(5):1085–1118CrossRefGoogle Scholar
  34. Smith VL (1962) An experimental study of competitive market behavior. J Polit Econ 70(2):111–137CrossRefGoogle Scholar
  35. Smith VL (1982) Microeconomic systems as an experimental science. Am Econ Rev 72:923–955Google Scholar
  36. Smith VL, Suchanek GL, Williams AW (1988) Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica 56(5):1119–1151CrossRefGoogle Scholar
  37. Smith VL, van Boening M, Wellford CP (2000) Dividend timing and behavior in laboratory asset markets. Econ Theory 16(3):567–583Google Scholar
  38. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157): 1124–1131Google Scholar
  39. Zhang T, Brorsen BW (2011) Oligopoly firms with quantity-price strategic decisions. J Econ Inter Coord 6:157–170CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Economic Theory and Quantitative Methods Catholic University of MilanMilanoItaly

Personalised recommendations