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In this paper we continue the study of the simulated stock market frameworkdefined by the driving sentiment processes. We focus on the market environmentdriven by the buy/sell trading sentiment process of the Markov chain type. Weapply the methodology of the Hidden Markov Models and the Recurrent NeuralNetworks to reconstruct the transition probabilities matrix of the Markovsentiment process and recover the underlying sentiment states from the observedstock price behavior.

Machine learning in sentiment reconstruction of the simulated stock market