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研究生： 蘇瑞魁
Su, Jui-kuei
論文名稱： 台灣加權股價指數極短線之預測
Extreme Short-Term of Forecasting in Taiwan weighting stock price index
指導教授： 利德江
Li, Der-Chiang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2009
畢業學年度： 97
語文別： 中文
論文頁數： 36
中文關鍵詞： 趨勢力道函數 、星期五行為 、類神經網路 、支撐向量迴歸 、預測 、小樣本 、統計方法
外文關鍵詞： small data, forecasting, Friday behavior, neural network, regression, support vector regression
相關次數： 點閱：73  下載：6 
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    • 針對股市價預測而言，利用過去大量的歷史資料去做分析並找出一個趨勢可能失去部份意義，故本研究不同於過去之研究，主要是針對每週的星期五去做行為模式的預測。根據實驗結果發現，星期五效應在過去三年間並不顯著，且若加上跨週之資訊，將會使得星期五之行為更難以捉摸。最後本研究測試了多種預測模式:傳統的線性回歸、計量模型中的ARIMMA、類神經網路的倒傳遞類神經(BPNN)、輻狀基底類神經(RBF)、支撐向量回歸(SVR)以及趨勢力道函數簡單型(CLTM-S)、複雜型(CLTM)，在各種方法之測試下，不同於傳統之結果，針對星期五行為預測以類神經網路的倒傳遞類神經(BPNN)結果最佳，而在三種群組下，大盤預測績效為1.22%、電子類股為1.11%以及金融保險類股為1.1%皆優於其它模式。

    This paper is to study using large data to analysis and find a tendency maybe lose some meanings in forecasting tendency of stock market price index. For the reason, different with traditional method, using small data to find the observations for forecasting Friday by Trend and potency function(CLTM-S) .
    Experimental result reveal in that the best observations is four days in the same week in forecasting the stock price of Friday behavior. And using these observations to build establish prediction model with statistics method (REGRESSION)、ARIMA、Neural network(BPNN) 、radial base function(RBF)、 Support vector regression(SVR) and CLTM-S CLTM. The result reveal BPNN is better than other tradition method and the mean absolute percentages error are 1.22%、1.11% and 1.10% in Taiwan weighting stock price index Electric index Finance index.

    摘要.................................................I Abstract ............................................II 誌謝.................................................III 目錄.................................................IV 表目錄...............................................VI 圖目錄...............................................VII 第一章 緒論..........................................1 1.1 研究動機與背景..............................1 1.2 研究目的....................................2 1.3 研究流程....................................3 第二章 文獻探討....................................4 2.1 價格預測.........................................4 2.1.1 預測模型.......................................4 2.1.2 混合式預測模型.................................5 2.2 理論基礎.........................................6 2.2.1 股票價格形成理論...............................6 2.2.2 分析股市價格變動之方法.........................7 2.3 小樣本相關概念 ...................................10 2.3.1 小樣本的相關問題................................10 2.3.2 小樣本資料學習..................................10 2.4 支撐向量機........................................11 2.4.1 最大邊際分類器..................................12 2.4.2 線性不可分割問題................................16 2.4.3 支撐向量迴歸 ....................................17 2.4.4 核心函數........................................19 第三章 研究方法.......................................21 3.1 研究方法架構......................................21 3.2 研究方法描述......................................22 3.2.1 資料收集與最適觀察數............................22 3.2.2 研究資料部分....................................24 3.2.3 星期五行為預測..................................24 第四章 實證分析.............................. ........25 4.1 資料分析..........................................25 4.2 模型評估及比較....................................29 第五章 結論與建議...................................31 5.1 研究結論..........................................31 5.2 後續研究建議......................................33 參考文獻 .............................................34

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