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研究生:黃瀞萱
論文名稱:支援向量迴歸結合真實波動分類預測股票指數
論文名稱(外文):Forecasting Stock Market Indices Using RVC-SVR
指導教授:洪瑞鍾
指導教授(外文):Jui-Chung Hung
學位類別:碩士
校院名稱:臺北市立大學
系所名稱:資訊科學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:102
語文別:中文
論文頁數:46
中文關鍵詞:支援向量迴歸股票預測基因演算法股票真實波動
外文關鍵詞:Support vector regressionForecasting index of stock marketGenetic algorithmReal volatility clustering
相關次數:
  • 被引用被引用:9
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  • 下載下載:83
  • 收藏至我的研究室書目清單書目收藏:1
股價的變化複雜且不規則,並且尚未有通用模型可以參考,因此股票指數預測通常是相當困難的。我們在此篇文章提出利用股票指數的真實波動特性-大波動伴隨大波動小波動伴隨小波動,將股價依波動大小程度分類,分別以支援向量迴歸(Support Vector Regression, SVR)建立預測股價行為的混合式模型(Real Volatility Clustering of Support Vector Regression, RVC-SVR),然而要選擇RVC-SVR模型中的適合參數是非常複雜的問題,因此本論文使用基因演算法(Genetic Algorithm, GA)來選擇適當的RVC-SVR參數。本實驗取臺灣股市加權指數、香港恒生指數、美國納斯達克綜合指數,計算估測值與實際股票收盤價的誤差,其結果顯示本實驗提出的方法可得到好的預測結果。
This paper addresses stock market forecasting indices. Generally, the stock market index exhibits clustering properties and irregular fluctuation. This paper presents the results of using real volatility clustering (RVC) to analyze the clustering in support vector regression (SVR), called “real volatility clustering of support vector regression” (RVC-SVR). Combining RVC and SVR causes the parameters of estimation to become more difficult to solve, thus constituting a highly nonlinear optimization problem accompanied by many local optima. Thus, the genetic algorithm (GA) is used to estimate parameters.
Data from the Taiwan stock weighted index (Taiwan), Hang Seng index (Hong Kong), and NASDAQ (USA) were used as the simulation presented in this paper. Based on the simulation results, the stock indices forecasting error is significantly improved when the SVR model considers the RVC.
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 3
第三節 論文架構 4
第二章 文獻探討 5
第一節 股票波動 5
第二節 支援向量迴歸與財務金融研究 6
第三節 基因演算法參數估測應用 9
第三章 研究方法 10
第一節 股票報酬率與真實波動 12
第二節 支援向量迴歸 13
第三節 RVC-SVR 16
第四節 基因演算法 18
第四章 實驗結果與分析 23
第一節 資料分析及處理 23
第二節 實驗結果 29
第五章 結論與建議 32
第一節 結論 32
第二節 建議 32
參考文獻 33
網頁參考 33
中文參考 33
英文參考 34
附錄 38
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