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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

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Machine learning in sentiment reconstruction of the simulated stock market. / Goykhman, Mikhail; Teimouri, Ilia.

In: Physica A: Statistical Mechanics and its Applications, Vol. 492, 15.02.2018, p. 1729-1740.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Goykhman, M & Teimouri, I 2018, 'Machine learning in sentiment reconstruction of the simulated stock market', Physica A: Statistical Mechanics and its Applications, vol. 492, pp. 1729-1740. https://doi.org/10.1016/j.physa.2017.11.093

APA

Goykhman, M., & Teimouri, I. (2018). Machine learning in sentiment reconstruction of the simulated stock market. Physica A: Statistical Mechanics and its Applications, 492, 1729-1740. https://doi.org/10.1016/j.physa.2017.11.093

Vancouver

Goykhman M, Teimouri I. Machine learning in sentiment reconstruction of the simulated stock market. Physica A: Statistical Mechanics and its Applications. 2018 Feb 15;492:1729-1740. https://doi.org/10.1016/j.physa.2017.11.093

Author

Goykhman, Mikhail ; Teimouri, Ilia. / Machine learning in sentiment reconstruction of the simulated stock market. In: Physica A: Statistical Mechanics and its Applications. 2018 ; Vol. 492. pp. 1729-1740.

Bibtex

@article{8bc448e28aaf4fbdb1eb8b79d89596cb,
title = "Machine learning in sentiment reconstruction of the simulated stock market",
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",
author = "Mikhail Goykhman and Ilia Teimouri",
note = "This is the author{\textquoteright}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",
year = "2018",
month = feb,
day = "15",
doi = "10.1016/j.physa.2017.11.093",
language = "English",
volume = "492",
pages = "1729--1740",
journal = "Physica A: Statistical Mechanics and its Applications",
issn = "0378-4371",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Machine learning in sentiment reconstruction of the simulated stock market

AU - Goykhman, Mikhail

AU - Teimouri, Ilia

N1 - 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

PY - 2018/2/15

Y1 - 2018/2/15

N2 - 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.

AB - 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.

KW - Market microstructure

KW - Artificial stock market

KW - Agent-based market model

KW - Hidden Markov model

KW - Recurrent neural network

U2 - 10.1016/j.physa.2017.11.093

DO - 10.1016/j.physa.2017.11.093

M3 - Journal article

VL - 492

SP - 1729

EP - 1740

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

ER -