Machine learning in sentiment reconstruction of the simulated stock market

Goykhman, Mikhail and Teimouri, Ilia (2018) Machine learning in sentiment reconstruction of the simulated stock market. Physica A: Statistical Mechanics and its Applications, 492. pp. 1729-1740. ISSN 0378-4371

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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.

Item Type:
Journal Article
Journal or Publication Title:
Physica A: Statistical Mechanics and its Applications
Additional Information:
This is the author’s version of a work that was accepted for publication in Physica A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physica A, 492, 2018 DOI: 10.1016/j.physa.2017.11.093
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3104
Subjects:
ID Code:
88879
Deposited By:
Deposited On:
23 Nov 2017 00:29
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2020 05:15