Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-18T15:28:18.446Z Has data issue: false hasContentIssue false
  • English
  • Français

Evolution of market heuristics

Published online by Cambridge University Press:  26 April 2012

Mikhail Anufriev  and
Cars Hommes
Mikhail Anufriev*
Affiliation:
Economics Discipline Group, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia; e-mail: Mikhail.Anufriev@uts.edu.au
Cars Hommes*
Affiliation:
CeNDEF, Department of Economics, University of Amsterdam, Roetersstraat 11, NL-1018 WB Amsterdam, The Netherlands; e-mail: C.H.Hommes@uva.nl
Get access Rights & Permissions [Opens in a new window]

Abstract

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neoclassical economic theory assumes that individuals form expectations rationally, thus forcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that individuals do not behave fully rationally but instead follow simple heuristics. In laboratory markets, prices may show persistent deviations from fundamentals similar to the large swings observed in real stock prices.

Here we show that evolutionary selection among simple forecasting heuristics can explain coordination of individual behavior, leading to three different aggregate outcomes observed in recent laboratory market-forecasting experiments: slow monotonic price convergence, oscillatory dampened price fluctuations, and persistent price oscillations. In our model, forecasting strategies are selected every period from a small population of plausible heuristics, such as adaptive expectations and trend-following rules. Individuals adapt their strategies over time, based on the relative forecasting performance of the heuristics. As a result, the evolutionary switching mechanism exhibits path dependence and matches individual forecasting behavior as well as aggregate market outcomes in the experiments. Our results are in line with recent work on agent-based models of interaction and contribute to a behavioral explanation of universal features of financial markets.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 255 - 271
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anufriev, M., Hommes, C. 2012. Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments. American Economic Journal: Microeconomics, forthcoming.CrossRefGoogle Scholar
Arthur, W. 1991. Designing economic agents that act like human agents: a behavioral approach to bounded rationality. American Economic Review 81, 353–359.Google Scholar
Brock, W. A., Hommes, C. H. 1997. A rational route to randomness. Econometrica 65(5), 1059–1095.CrossRefGoogle Scholar
Brock, W. A., Hommes, C. H. 1998. Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 1235–1274.CrossRefGoogle Scholar
Conlisk, J. 1996. Why bounded rationality. Journal of Economic Literature 34(2), 669–700.Google Scholar
Diks, C., Weide, R. V. D. 2005. Herding, a-synchronous updating and heterogeneity in memory in a CBS. Journal of Economic Dynamics and Control 29, 741–763.CrossRefGoogle Scholar
Erev, I., Roth, A. E. 1998. Prediction how people play games: reinforcement learning in games with unique strategy equilibrium. American Economic Review 88, 848–881.Google Scholar
Evans, G. W., Honkapohja, S. 2001. Learning and Expectations in Macroeconomics. Princeton University Press.CrossRefGoogle Scholar
Farmer, J., Lo, A. 1999. Frontiers of finance: evolution and efficient markets. Proceedings of the National Academy of Science 96, 9991–9992.CrossRefGoogle ScholarPubMed
Hommes, C. 2006. Heterogeneous agent models in economics and finance. In Handbook of Computational Economics, 2: Agent-Based Computational Economics (Handbooks in Economics Series), Judd, K. & Tesfatsion, L. (eds). Elsevier/North-Holland.Google Scholar
Hommes, C., Huang, H., Wang, D. 2005. A robust rational route to randomness in a simple asset pricing model. Journal of Economic Dynamics and Control 29, 1043–1072.CrossRefGoogle Scholar
Hommes, C., Sonnemans, J., Tuinstra, J., Velden, H. V. D. 2005. Coordination of expectations in asset pricing experiments. Review of Financial Studies 18(3), 955–980.CrossRefGoogle Scholar
Kahneman, D. 2003. Maps of bounded rationality: psychology for behavioral economics. American Economic Review 93, 1449–1475.CrossRefGoogle Scholar
LeBaron, B. 2006. Agent-based computational finance. In Handbook of Computational Economics, 2: Agent-Based Computational Economics (Handbooks in Economics Series), Judd, K. & Tesfatsion, L. (eds). Elsevier/North-Holland.Google Scholar
Lux, T., Marchesi, M. 1999. Scaling and criticality in a stochastic multi-agent model of financial market. Nature 397, 498–500.CrossRefGoogle Scholar
Mantegna, R. N., Stanley, H. E. 1995. Scaling behaviour in the dynamics of an economic index. Nature 376, 46–49.CrossRefGoogle Scholar
Muth, J. F. 1961. Rational expectations and the theory of price movements. Econometrica 29(3), 315–335.CrossRefGoogle Scholar
Sargent, T. J. 1993. Bounded Rationality in Macroeconomics. Oxford University Press.CrossRefGoogle Scholar
Simon, H. A. 1957. Models of Man: Social and Rational. John Wiley.Google Scholar
Tversky, A., Kahneman, D. 1974. Judgement under uncertainty: heuristics and biases. Science 185, 1124–1130.CrossRefGoogle ScholarPubMed