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Reverse Engineering Financial Markets with Majority and Minority Games using Genetic Algorithms

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  • J. Wiesinger
  • D. Sornette
  • J. Satinover

Abstract

Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index.

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  • J. Wiesinger & D. Sornette & J. Satinover, 2010. "Reverse Engineering Financial Markets with Majority and Minority Games using Genetic Algorithms," Papers 1002.2171, arXiv.org.
    • Handle: RePEc:arx:papers:1002.2171
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    Cited by:

    1. Yang, G. & Chen, Y. & Huang, J.P., 2016. "The highly intelligent virtual agents for modeling financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 98-108.
    2. Krause, Sebastian M. & Bornholdt, Stefan, 2013. "Spin models as microfoundation of macroscopic market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4048-4054.

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