论文编号:XXLW101 论文字数:13668,页数:34

摘  要
 股票市场是一个风险和利益共存的市场,股票市场的建模和预测研究对我国的经济发展和金融建设具有重要意义。掌握好股票预测能力，就可以更好地选择买卖时机，获得更大的利益。
 人工神经网络具有广泛的适应能力, 学习能力和映射能力,在多变量非线性系统的建模和控制方面取得了惊人的成就。针对股票市场的不确定性，神经网络具有比其他算法更有优势，预测的结果更加精确，更加有效。
 SAS Enterprise Miner简称EM，是一个集成的数据挖掘系统,它的运行方式是通过在一个工作空间(workspace)中按照一定的顺序添加各种可以实现不同功能的节点，然后对不同节点进行相应的设置，最后运行整个工作流程(workflow)，便可以得到相应的结果。
    模型建立中，本文通过1990年12月19日到2009年12月31日上证指数日线数据中的开盘价、最高价、最低价、收盘价、成交量以及成交金额延伸出一些专用指标来预测短期股票的涨跌，得出其中的规律，判断股票买卖时机，从而应用于股票预测。
关键字：股价预测  神经网络  SAS EM

Abstract
    Stock market is a market in which risks and benefits of co-existence. It is very important to stock market modeling and prediction on China's economic and financial. Mastering the predictive power of stock, you can choose better trading opportunities to gain more benefits.
    There is a wide range of adaptability, learning ability and mapping capabilities in artificial neural network which has made remarkable achievements in multivariable modeling and control of nonlinear systems. Because of the uncertainty of the stock market, neural network is superior to other algorithms and the predictions are more efficient and accurate.
    SAS Enterprise Miner referred to as EM, is an integrated data mining system. It runs through a workspace (workspace). In the workspace, a variety of nodes, which can achieve different functions, can be added in accordance with a certain order. By setting different node, than running the entire workflow (workflow), we can obtain the corresponding results.
 In this model, the opening price, highest price, lowest price, closing price, trading volume and transaction value of Shanghai Stock Index Date Line Data from December 19, 1990 to December 31, 2009 are as the input. Some extension of the input can predict the stocks, either ups or downs. We can get the regular pattern, determine stock trading opportunity, than use it in stocks prediction.
Keywords: Stock price prediction; Neural Networks; SAS EM

目  录
摘要 i
Abstract ii
目  录 iii
第一章 绪论 1
1.1研究背景 1
1.2研究目的 1
1.3论文框架 2
第二章 神经网络 3
2.1神经网络简介 3
2.2 神经网络的基本原理 3
第三章 SAS Enterprise Miner 5
 3.1 SAS EM 简介 5
 3.2 EM 基本原理 5
第四章 建立模型 7
4.1数据准备 7
4.2数据分割 7
4.3数据指标延伸 7
 4.3.1 MACD指标 7
 4.3.2 KDJ指标 8
 4.3.3威廉指标—W%R 9
 4.3.4 RSI指标 10
 4.3.5 CR指标 10
 4.3.6动量指标—MTM 11
 4.3.7 MIKE指标 12
 4.3.8 DMA指标 13
 4.3.9 AR和BR指标 13
4.4模型的建立 15
4.4.1技术流程 15
4.4.2测试 18
4.5模型的应用 21
第五章 总结 22
致  谢 23
参考文献 24
附  录 25

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