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Machine learning in sentiment reconstruction of the simulated stock market

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  • Mikhail Goykhman
  • Ali Teimouri

Abstract

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior.

Suggested Citation

  • Mikhail Goykhman & Ali Teimouri, 2017. "Machine learning in sentiment reconstruction of the simulated stock market," Papers 1708.01897, arXiv.org.
    • Handle: RePEc:arx:papers:1708.01897
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    References listed on IDEAS

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    1. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    2. Mikhail Goykhman, 2017. "Wealth dynamics in a sentiment-driven market," Papers 1705.07092, arXiv.org.
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    7. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
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