Stock Market Analysis Based On The Theory Of Gray Markov Chains

To accurately predict stock price, in combination with gray system theory and the theory of Markov chains, the Gray Markov Prediction Model is established. And the idea of weighted is blended in among them, to further improve the Gray Markov Prediction Model, and made an empirical analysis. The empirical analysis results show that the improved weighted Grey Markov Prediction Model has a better forecast effect and forecast performance. Stock price prediction by using the model can provide reference to part with the investment of investors.This paper first introduced the grey system in detail and the knowledge of the basic theory of Markov chain, analyzed the Grey Prediction Model and Markov Prediction Model of characteristic and scope of application:Grey Prediction Model is built using gray system theory. Gray Forecast Model required less information, ease of calculation, high prediction accuracy. But mainly for small data modeling has better accuracy. And Gray Forecast Model for predicting short time, less data, with a clear upward trend in the data series forecasting effect, when the random fluctuations in the data sequence are relatively large, the prediction accuracy will be significantly reduced. The study of Markov chain theory is a random dynamic system. More suitable for those who predict a relatively large stochastic fluctuations in stochastic processes, and Markov prediction model is more suitable for a large sample of data to predict. But the drawback is that the model required not only an object of Markov prediction, but also asked to predict the data sequence to be subject to certain typical distribution. However. For systems with little information, it is difficult to determine what data to predict the sequence is subject to distribution. Therefore, in this paper, the combination of the Grey System Prediction and Markov Prediction is reasonable, and the advantages of both can complement each other. The Gray Prediction Model predicted the general trend of the data sequence, and on the basis of data on trends in processing, the paper applied to the Markov Prediction Model, then the Gray Markov Prediction Model was formed.Then, the article improved Grey Markov Prediction Model, the Weighted Grey Markov Prediction Model is established. This article puts forward the Weighted Gray Markov Prediction Model of the weighted method is different, this paper proposes a new weighted method based on the Genetic Algorithm. The last part is the empirical analysis part, the author of using the improved Grey Markov Prediction Model to predict the stock price, and using the improved Weighted Grey Markov Prediction Model to predict the stock price, the two kinds of model prediction results and the prediction of the performance, it is concluded that the improved Weighted Grey Markov Prediction Model has better forecast effect and forecast performance. Stock price prediction by using the model can provide reference to part with the investment of investors.