Application Of Hidden Markov Model In Stock Market

The Hidden Markov Model(HMM)is a statistical model used to simulate a Markov process with invisible state sequences.Hidden Markov models have been used in more and more fields,especially in the fields of NPL,fault diagnosis and biological information.In emerging fields such as artificial intelligence,hidden Markov models also play a role.An important role.Hidden Markov models need to solve three related problems of recognition,learning and decoding.This article will introduce the principles of the hidden Markov chain and the general algorithm.The hidden Markov model applies to a system that changes its intrinsic state over time and affects external performance.Therefore,the use of hidden Markov chains in financial markets is also very valuable.Based on Hidden Markov Theory,this paper combines EM algorithm and Wirtbit algorithm to construct hidden Markov model,which improves the accuracy of Hidden Markov Model for the identification of hidden states and studies the trend of stock market trend..This paper will start from the stock market forecast,use hidden Markov model,select different data features and latitude,adopt different classification methods,classify and classify stocks according to actual data.Based on the established model,a simple trading strategy is used to evaluate the model using stock market historical data.