  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.
