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研究生:游緯鴻
研究生(外文):Richard Yu
論文名稱:基於決策樹之股價趨勢預測研究─以臺股為例
論文名稱(外文):A Study of Stock Trends Forecasting Based on Decision Tree – The Case Study of Taiwan Stock Market
指導教授:左杰官左杰官引用關係
指導教授(外文):Brandt Tso
學位類別:碩士
校院名稱:國防大學管理學院
系所名稱:資源管理及決策研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:124
中文關鍵詞:資料探勘決策樹技術分析股價趨勢
外文關鍵詞:Data MiningDecision TreeTechnical AnalysisStock Trends
相關次數:
  • 被引用被引用:4
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理想的股票投資方式應是掌握股價變動趨勢,低點時買進,高點時賣出,本研究即在探討利用資料探勘技術中的決策樹找出個股技術指標與股價走勢間的關聯性以預測股價趨勢。本研究以七檔臺灣上市公司股票為研究對象,研究期間為2007年1月至2009年12月。首先將各股日線技術指標資料設計成九種組合,接著分別利用CART、C4.5、CHAID及QUEST演算法決策樹針對各股進行分析並比較各演算法的預測成效,最後試算利用本研究預測結果進行投資所能獲得之投資報酬率。研究結果顯示,預測準確率最佳可達81%以上,在投資報酬率方面,最佳甚可獲得50%以上的投資報酬率。此外,從設計的九種資料組合分析結果中可發現,如果能同時考慮數天(三天、五天)的技術指標將可提升預測的準確率。在決策樹的預測成效方面,平均而言,CART決策樹略優於其它三種演算法決策樹。
This study attempts to predict stock trends based on technical indicators by using CART, C4.5, CHAID and QUEST decision trees. The prediction results of these decision trees are analyzed. The study objects are chozen from the stocks listed in Taiwan Stock Exchange (TWSE), and the relating technical indicators being extracted is during the period from January 2007 to December 2009. The empirical results of this study show that the prediction accuracy can be as high as 85%, and the investment return can be also as high as 50%. The study proves the usefulness of the methods and the results also reveal that, CART decision tree perform slightly better than other decision trees.
表目錄.......................................iii
圖目錄........................................iv
第一章 緒論.....................................1
1.1 研究背景與動機..............................1
1.2 研究目的...................................2
1.3 論文架構...................................3
第二章 文獻探討.................................5
2.1 資料探勘(Data Mining) .....................5
2.1.1 資料探勘定義.............................5
2.1.2 資料探勘模式.............................6
2.1.3 資料探勘流程.............................7
2.2 決策樹....................................8
2.2.1 C4.5 決策樹.............................10
2.2.2 CART決策樹..............................12
2.2.3 CHAID決策樹.............................14
2.2.4 QUEST決策樹.............................14
2.3 技術分析..................................16
2.3.1 技術分析定義.............................17
2.3.2 技術指標介紹.............................17
2.4 資料探勘運用於股市預測之文獻.................21
第三章 研究方法.................................27
3.1 研究流程...................................27
第四章 實證結果與分析............................37
4.1 國泰金.....................................37
4.2 宏達電.....................................42
4.3 統一.......................................46
4.4 臺塑.......................................50
4.5 台積電.....................................55
4.6 友達.......................................59
4.7 聯發科.....................................64
第五章 結論與建議...............................68
5.1 結論.......................................68
5.2 未來研究建議................................69
參考文獻........................................70
中文部分........................................70
英文部份........................................71
附錄一..........................................74
中文部分
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2.杜金龍(2008)。最新技術指標在台灣股市應用的訣竅增定三版。台北市:財訊出版社。
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6.張政一(2001)。類神經網路於有價證券預測股價及漲跌之研究。中國文化大學國際企業研究所碩士論文,未出版,台北市。
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8.廖述賢、溫志皓(2009)。資料採礦與商業智慧。台北市:雙葉書廊。
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11.鄭淵仲(2002)。模糊決策樹於資料探勘的應用─以台股為例。國立中山大學資訊管理研究所碩士論文,未出版,高雄市。
12.鄭忠樑(2002)。運用分類樹於股價報酬率預測之研究。元智大學資訊管理學系碩士論文,未出版,桃園縣。
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英文部份
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