The Principle of the Markov Chain Prediction Model and Its Application in Stock Price Forecasting

Abstract

Markov chains, due to their "memoryless" property, align well with the characteristics of many phenomena in life, making Markov prediction models an important tool for predicting random events in daily life. This paper aims to summarize the rich findings obtained by researchers on Markov prediction models, and to specifically demonstrate the powerful capabilities of Markov prediction models in forecasting stock prices, allowing readers to intuitively experience the differences in prediction accuracy brought about by continuously improved models. Specifically, the paper first introduces the origin and development history of Markov chains. It then briefly covers the relevant knowledge of Markov chains and the basic steps for using them in predictions. Following this, it presents the effects of three progressively advanced Markov chain prediction methods in stock price forecasting. The study finds that when more careful consideration is given to the relationships between variables and more complex and precise algorithms are integrated, the prediction results are more accurate, and the predictability extends over longer periods. Overall, although there are difficulties in data collection and state classification, its advantages in handling stochastic processes make it one of the important choices for practical applications in various fields. With the advancement of technology, these models will become more efficient and accurate, allowing them to be applied in a wider range of fields. Moreover, attempts to combine various algorithms with Markov chain models will be a direction for improving prediction results.