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2012 Volume 27
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RESEARCH ARTICLE   Open Access    

Evolution of market heuristics

  • 1.

    Economics Discipline Group

  • 2.

    CeNDEF

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  • Corresponding authors: Mikhail Anufriev ;  Cars Hommes
  • 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.
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  • Cite this article

    Mikhail Anufriev, Cars Hommes. 2012. Evolution of market heuristics. The Knowledge Engineering Review 27(2)255−271, doi: 10.1017/S0269888912000161
    Mikhail Anufriev, Cars Hommes. 2012. Evolution of market heuristics. The Knowledge Engineering Review 27(2)255−271, doi: 10.1017/S0269888912000161
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RESEARCH ARTICLE   Open Access    

Evolution of market heuristics

  • 1.

    Economics Discipline Group

  • 2.

    CeNDEF

  • Corresponding authors: Mikhail Anufriev ;  Cars Hommes
  • Published online: 26 April 2012
The Knowledge Engineering Review  27,  2012, 27(2): 255−271  |  Cite this article

Abstract: 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.

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    Acknowledgments

    • We would like to thank the participants of the ‘Computation in Economics and Finance’ conference, Montreal, June 2007, the ‘Complexity in Economics and Finance’ workshop, Leiden, October 2007, and seminar participants at the University of Amsterdam, Sant'Anna School of Advanced Studies (Pisa) for stimulating discussions. We are especially grateful to an anonymous referee and the editor, Robert Marks, for their helpful comments, which lead to significant improvement of the paper. This work was supported by the ComplexMarkets E.U. STREP project 516446 under FP6-2003-NEST-PATH-1, and by the EU FP7 POLHIA project, grant no. 225408.

    • Price dynamics in group 3 (not shown) was more difficult to classify, due to a possible typing error of one of the participants.

    • However, we will use the AA heuristic (8) in the stability analysis in Section 5.

    • Notice that we do not use the actual experimental data during simulations: the simulations should thus be viewed as 50-period-ahead forecasts of the patterns of aggregate price behavior and underlying individual forecasting behavior. In fact, the model performs much better (in particular, it does not go out-of-phase in the oscillating groups) if the actual price realizations from the experiment are used at each step. See Anufriev and Hommes (2012) for the model performance in one- or two-periods-ahead forecasting.

    • The four heuristics of Section 3 are obtained with w = 0.65 in the first rule, and with α2 = 0, β2,1 = 1.4, β2,2 = −0.4 for h = 2; α3 = 0, β3,1 = 2.3, β3,2 = −1.3 for h = 3; and α4 = 30, β4,1 = 1.5, β4,2 = −1 for h = 4.

    • Simulations of the model show that the generated frequencies are more affected by the choice of heuristics than by the learning parameters. With other extrapolative coefficients in the heuristics or with additional heuristics, the quantitative fit of the model can be improved. Recall, however, that our choice of heuristics was driven by the estimation of the experimental data and simplicity of the model.

    • The simulation program for the model described in this paper together with brief documentation and configuration settings used for the reported simulations is freely available at http://www.cafed.eu/evexex.

    • We find the best parameters in Anufriev and Hommes (2012) through the grid search for a model with the four heuristics reported here.

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    Cite this article
    Mikhail Anufriev, Cars Hommes. 2012. Evolution of market heuristics. The Knowledge Engineering Review 27(2)255−271, doi: 10.1017/S0269888912000161
    Mikhail Anufriev, Cars Hommes. 2012. Evolution of market heuristics. The Knowledge Engineering Review 27(2)255−271, doi: 10.1017/S0269888912000161
    shu

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