信息計算科學論文編號:XXLW101 論文字數(shù):13668,頁數(shù):34

目  錄
摘要 i
Abstract ii
目  錄 iii
第一章 緒論 1
1.1研究背景 1
1.2研究目的 1
1.3論文框架 2
第二章 神經(jīng)網(wǎng)絡(luò) 3
2.1神經(jīng)網(wǎng)絡(luò)簡介 3
2.2 神經(jīng)網(wǎng)絡(luò)的基本原理 3
第三章 SAS Enterprise Miner 5
 3.1 SAS EM 簡介 5
 3.2 EM 基本原理 5
第四章 建立模型 7
4.1數(shù)據(jù)準備 7
4.2數(shù)據(jù)分割 7
4.3數(shù)據(jù)指標延伸 7
 4.3.1 MACD指標 7
 4.3.2 KDJ指標 8
 4.3.3威廉指標—W%R 9
 4.3.4 RSI指標 10
 4.3.5 CR指標 10
 4.3.6動量指標—MTM 11
 4.3.7 MIKE指標 12
 4.3.8 DMA指標 13
 4.3.9 AR和BR指標 13
4.4模型的建立 15
4.4.1技術(shù)流程 15
4.4.2測試 18
4.5模型的應(yīng)用 21
第五章 總結(jié) 22
致  謝 23
參考文獻 24
附  錄 25

摘  要
 股票市場是一個風險和利益共存的市場,股票市場的建模和預(yù)測研究對我國的經(jīng)濟發(fā)展和金融建設(shè)具有重要意義。掌握好股票預(yù)測能力，就可以更好地選擇買賣時機，獲得更大的利益。
 人工神經(jīng)網(wǎng)絡(luò)具有廣泛的適應(yīng)能力, 學習能力和映射能力,在多變量非線性系統(tǒng)的建模和控制方面取得了驚人的成就。針對股票市場的不確定性，神經(jīng)網(wǎng)絡(luò)具有比其他算法更有優(yōu)勢，預(yù)測的結(jié)果更加精確，更加有效。
 SAS Enterprise Miner簡稱EM，是一個集成的數(shù)據(jù)挖掘系統(tǒng),它的運行方式是通過在一個工作空間(workspace)中按照一定的順序添加各種可以實現(xiàn)不同功能的節(jié)點，然后對不同節(jié)點進行相應(yīng)的設(shè)置，最后運行整個工作流程(workflow)，便可以得到相應(yīng)的結(jié)果。
    模型建立中，本文通過1990年12月19日到2009年12月31日上證指數(shù)日線數(shù)據(jù)中的開盤價、最高價、最低價、收盤價、成交量以及成交金額延伸出一些專用指標來預(yù)測短期股票的漲跌，得出其中的規(guī)律，判斷股票買賣時機，從而應(yīng)用于股票預(yù)測。
關(guān)鍵字：股價預(yù)測  神經(jīng)網(wǎng)絡(luò)  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