Machine learning in sentiment reconstruction of the simulated stock market

TL;DR

This paper explores using Hidden Markov Models and Recurrent Neural Networks to reconstruct sentiment states and transition probabilities in a simulated stock market driven by Markov chain sentiment processes.

Contribution

It introduces a novel application of machine learning techniques to recover underlying sentiment states from simulated stock market data.

Findings

Successful reconstruction of sentiment states from stock prices

Effective estimation of transition probabilities in sentiment processes

Demonstration of machine learning methods in market sentiment analysis

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.