Using Rough Set ,Support Vector Machines, and Optimization Algorithm for Financial System
吳忠原、陳郁文 ; 白炳豐
E-mail: [email protected]
ABSTRACT
The problem studied here was about the stock price prediction for use of investors.Technical analysis is mainly concerned with market indicators.These technical indicators look at the trend of price indices and individual securities.In order to solve because difficulty of analysis that technical indicator causes and categorised accuracy，Application of rough set theory(RST) and Support Vector Machines(SVM) to set up decision system.In order to deal with uncertain problem of Stock price , set up dependence of materials in order to as decision maker(DM).Application of Self-organizing map(SOM) to discretize the continuous attributes in reconstructed decision table for the succeeding rough sets processing. In our experiments,utilize SVM to choose the best parameter association to adjust decision rule,enable improving its decision rule and predicting ability.Utilize RST to combine the occupation mode of the technical indicator,let investors know the range of ups and downs of the stock price clearly .
Keywords : Rough Set Theory ; Technical Indicator ; Self-organizing map ; Support Vector Machines Table of Contents
授權書 iii 中文摘要 iv ABSTRACT v 誌謝 vi 目錄 vii 圖目錄 ix 表目錄 x 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 研 究流程 2 第二章 文獻探討 4 2.1 技術指標分析 4 2.2 粗略集合理論 9 2.3自組織映射圖網路 13 2.4支援向量機支援向量 機(Support Vector Machions; SVMs) 14 第三章 研究方法 20 3.1 研究架構 20 3.2組織映射圖網路結構（Self-Organizing Map
，SOM） 21 3.3略集合理論模型(Rough-Sets Theory,RST) 22 3.4支援向量機模型(Support Vector Machions; SVMs) 29 第四章 預測股價實例 31 4.1預測公司股價實例一(電子面板公司奇美電子) 31 4.2預測公司股價實例二(中國鋼鐵公司) 37 4.3預測公司 股價實例三(半導體台積電公司) 43 4.4預測公司股價實例四(統一食品公司) 46 4.5預測公司股價實例五(台塑化學公司) 48 4.6 結果分析與討論 51 第五章 結論及末來研究方向 53 5.1 結論 53 5.2 末來研究方向 54 參考文獻 55
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