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    Rights statement: 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

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Machine learning in sentiment reconstruction of the simulated stock market

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>15/02/2018
<mark>Journal</mark>Physica A: Statistical Mechanics and its Applications
Volume492
Number of pages12
Pages (from-to)1729-1740
Publication statusPublished
Early online date20/11/17
Original languageEnglish

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.

Bibliographic note

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