Machine learning in sentiment reconstruction of the simulated stock market

Mikhail Goykhman and Ali Teimouri

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 1729-1740

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. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Keywords: Market microstructure; Artificial stock market; Agent-based market model; Hidden Markov model; Recurrent Neural Network (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:1729-1740

DOI: 10.1016/j.physa.2017.11.093

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