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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: [email protected]
Cars Hommes*
Affiliation:
CeNDEF, Department of Economics, University of Amsterdam, Roetersstraat 11, NL-1018 WB Amsterdam, The Netherlands; e-mail: [email protected]
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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

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