Prediction of Stock Prices Based on Markov Chain

Markov Chain is a random process with Markov property in probability theory and mathematical statistics, which exists in discrete exponential set and state space. The essence of the Markov chain prediction model is “no after effect”. No after effect generally refers to the state of things in the future is only related to the state of this stage and has nothing to do with the state in any previous stage [3]. The Markov chain suitable for continuous exponential set is called Markov process. But it is sometimes regarded as a subset of Markov chain, namely Continuous-Time MC, CTMC, and Discrete-Time MC, DTMC correspondingly. So, Markov chain is a relatively broad concept. Based on the Markov chain, this paper makes a prediction on the closing price of Shanghai Stock Exchange Index. The stock market is risky. There are many ways to predict the stock market, which can be summarized into two categories: stock price fluctuation prediction models based on statistical theory [1] and artificial intelligence prediction models [2]. The paper introduces a brief introduction of Markov chain and uses the case of the closing price of the above stock index to measure the accuracy of Markov chain price prediction.