Predicting Markov Chain Behavior Using Markov Chain Models

Abstract

In the stock market, the stock price is a random variable that changes over time, and its fluctuation exhibits the characteristics of a random walk. This research focuses on the A-share "Shanghai Kweichow Moutai Co., Ltd." listed on the Shanghai Stock Exchange, utilizing data from 100 trading days. The study begins with confirming the presence of Markov properties within the stock’s price movements, followed by the construction of a Markov model to analyze and forecast its price trends. The analysis reveals that the Markov model provides reasonably accurate results, offering valuable insights into the cyclical patterns of stock prices. This approach aids in understanding the underlying mechanisms that drive price fluctuations and enhances the ability to predict future price movements, which can be beneficial for investors and financial analysts in decision-making processes. Additionally, the model demonstrates the potential of applying Markov processes to financial markets, particularly in predicting trends and making more informed predictions based on historical data.