中文摘要
随着中国经济的迅速发展，中国的股票市场不断完善，人们对于股市的参与越来越多，渴望对股票价格的预测和好的预测精度。这是目前全世界都在关注的一个股票问题。这个问题吸引着无数的学者进行研究。
本文针对这一问题，系统地介绍了股票价格的预测理论的发展进程，以及各种常用的时间序列预测方法，包括ARIMA，神经网络，灰色理论，以及最新发展起来的支持向量机(Support Vector Machine,SVM)理论，并重点对支持向量机(Support Vector Machine,SVM)理论作了详细介绍。分别从理论背景，基本含义，基本原理，常用做法，基本步骤，及其改进和发展等几个方面作了全面介绍，并对他们的优缺点作了简单比较和分析。
另外，本文还运用支持向量机(Support Vector Machine,SVM)和ARIMA分别对招商银行2006年下半年的股票价格做实证分析。通过做回归分析和预测，找出其中的最优参数，并对最后12月份的价格作出了预测，证明了支持向量机对于短期的时间序列预测具有非常高的预测精度，是一种非常好的数据分析和预测的方法，具有很大的使用价值。
关键词：股票价格预测支持向量机(SVM)ARIMA神经网络灰色理论
ABSTRACT
With the development of Chinese economy,the stock market has been gradually improved.And more and more people put their momey into the stock market and long to know the stock price in advance at a high degree of accuracy as possible.This issue has been focused ever since the stock market comes into being and many scholors
have tried to work out it.
The theory and methords on the stock price forecast are presented comprehensively in this thesis in terms of the fundamentals,the basic concepts,brief theory,and the simple usage.Methods include ARIMA,neural networks,gray theory and Support Vector Mechine(SVM),placing emphasis on the last one.For better understanding of these methods,a detailed analysis and comparison of advantages and disadvantages of the methods are made.
In addition,an empirical analysis of stock prices based on the Support Vector Mechine theory and ARIMA is carried out through regression models and,thus, prediction of stock price in the last month of 2006 is made after optimal parameters of SVM-based model are found.The result from the two methods shows that SVM-based method is better than ARIMA in forecast of the stocks price.
KEYWORDS：Stock Price Prediction;Support Vector Machine;ARIMA;Neural Networks;Gray System Theory
目录
中文摘要...........................................................I
ABSTRACT..........................................................II
目录（图）......................................................V
目录（表）.....................................................VI
第一章绪论........................................................1
1.1研究背景和意义......................................................................................1
1.2本文主要研究工作及内容......................................................................5
第二章股票价格预测理论与发展......................................7
2.1股票价格预测理论的发展史..................................................................7
2.2股票价格预测方法的最新发展............................................................10
2.3本章小结................................................................................................11
第三章股票价格预测方法的比较.....................................12
3.1ARIMA模型...........................................................................................12
3.2神经网络................................................................................................17
3.3灰色理论................................................................................................23
3.4本章小结...............................................................................................26
第四章支持向量机的基本理论.......................................27
4.1支持向量机的背景................................................................................27
4.2支持向量机的基本理论........................................................................28
4.3支持向量机的回归................................................................................33
4.4支持向量机的常用训练算法................................................................36
4.5支持向量机与其他方法的优缺点比较................................................39
4.6本章小结..............................................................................................42
第五章实证分析...................................................43
5.1 SVM建模步骤........................................................................................43
5.2参数确定分析........................................................................................43
5.3 SVM方法的实证分析.........................................................................45
5.4 ARIMA模型的分析............................................................................46
5.5不同模型预测结果比较......................................................................49
5.6本章小结...............................................................................................51
结束语............................................................52
本文总结........................................................................................................52
展望................................................................................................................52
参考文献..........................................................53
