Machine learning in sentiment reconstruction of the simulated stock market
This work addresses the challenge of sentiment analysis in financial markets for researchers and practitioners, but it appears incremental as it builds on existing simulated frameworks and methods.
The authors tackled the problem of reconstructing hidden sentiment states and their transition probabilities from observed stock price data in a simulated market driven by Markov chain sentiment processes, using Hidden Markov Models and Recurrent Neural Networks to achieve this reconstruction.
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.