股票市场预测的组合方法研究-控制工程专业论文.docxVIP

华 中科 技大学 华 中 科 技 大 学 硕 士 学 位 论 文 II II ABSTRACT Stocks has become an indispensable part of financial markets， the ups and downs of stock prices, and the changes of stock market, is not only the impact by the internal law, but also by the external environment such as political, economic and other factors, but investors still want to be able to analyze and predict stock prices accurately in order to obtain lucrative income. So how to build a more ideal forecasting model has been the research content by many scholars. select two combined model is selected to analysis, forecasting and compare the Shanghai Composite Index in this article，which is Grey - Markov model and genetic algorithm optimization of BP Neural Network model. First, create a gray system model GM (1,1), which describes a gray variables, and is more suitable for system objects with little data， short-term, small fluctuations, but is not ideal for relatively large fluctuations in the data series, the fitting is relatively poor, the prediction accuracy. So Markov process is introduced because of this- disadvantage against gray model, then use the combination method Grey - Markov model to predict the Shanghai Composite Index; Secondly, through the modeling and analysis of BP Neural Network, found there are some problems in a local minimum point and on the convergence rate，and introduce the analysis and research of genetic algorithm, establish a second combined model based on genetic algorithm BP Neural Network, and made a single comparison with BP Neural Network, then get the conclusion that the combined model prediction is better; Finally, by comparing two combinations methods to analyze their respective characteristics of the data presented, in the end of the article ，predict the data of the Shanghai index relatively large fluctuations. The experimental results showed that: O n the whole， GM (1, N)-Markov is prediction accuracy higher than the GA-BP model ，in the case of stable data and the III III data