Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory

This paper intends to apply the Hidden Markov Model into stock market and and make predictions. Moreover, four different methods of improvement, which are GMM-HMM, XGB-HMM, GMM-HMM+LSTM and XGB-HMM+LSTM, will be discussed later with the results of experiment respectively. After that we will analyze the pros and cons of different models. And finally, one of the best will be used into stock market for timing strategy.

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References (4)

03Estimate probability P { S t = i } , i = { 1 , 2 , .., N } , t = { 0 , 1 , .., T − 1 } by using Viterbi algorithm in gmm-hmm
04Estimate model parameters λ = ( A, B, π ) by using Baum-Welh algorithm at a given observation sequence ORecord model parameters

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