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研究生:鍾念庭
研究生(外文):Chung, Nien-Ting
論文名稱:應用資料探勘技術於股票投資 -以元大台灣50 ETF為例
論文名稱(外文):Applications of Data Mining Techniques for Stock Investment-A case study of Yuanta Taiwan Top 50 ETF
指導教授:巫木誠巫木誠引用關係洪暉智
指導教授(外文):Wu, Muh-CherngHung, Hui-Chih
口試委員:陳文智劉建良巫木誠洪暉智
口試委員(外文):Chen, Wen-ChihLiu, Chien-LiangWu, Muh-CherngHung, Hui-Chih
口試日期:2019-05-27
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:57
中文關鍵詞:資料探勘機器學習投資決策投資報酬率
外文關鍵詞:Data MiningInvestment DecisionReturn on Investment
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最近Wu, Chung, and Hung (2019)提出一個應用資料探勘於股票投資的研究架構,本論文是此架構的應用案例研究,研究的股票標的是元大台灣50 ETF,此ETF涵蓋台灣市值排名前50名的上市公司。本研究使用資料探勘技術來建構兩個二元分類器,一種是買點信號分類器;另一種是賣點信號分類器。當兩個分類器達成共識時才做出交易決策。本研究使用特徵篩選方法為12種機器學習演算法選擇重要的特徵子集。股票交易數據集涵蓋11年(從2008/1/2到2018/12 / 28),其中6年用於訓練分類器,2年用於選擇最佳分類器,3年用於測試最佳分類器及交易方法,其中考慮五種常見的技術指標和買入持有(B&H)策略作為比較基準。實驗結果顯示,在季度審核中,本研究提出的交易方法的投資報酬率(ROI)在72項比較基準中可勝過61項比較基準;並且在3年內的投資報酬率達到62.72%,優於所有比較基準(Benchmark)。
This thesis is an application of a research framework of data mining on stock investment proposed by a recent study (Wu, Chung, and Hung 2019). We apply this research framework to the case of Taiwan Top 50 ETF. This ETF involves the top 50 listed companies in terms of market capitalization. On a trading day, the ETF investment decisions can be of three types: buy, sell, and hold (no action). We use a data mining technique to develop two binary classifiers. One is a buy-signal classifier (buy or hold); the other is a sell-signal classifier (sell or hold). We make trading decisions only when the two classifiers reach consensus. Namely, we buy stock only when the buy-signal classifier appears “buy” and the sell-signal classifier appears “hold”; likewise we sell stock only when “hold” and “sell” both appear. We also used feature selection methods to select a subset of important features for twelve data mining techniques. The stock trading dataset covers 11 years (from 2008/1/2 to 2018/12/28), of which 6 years are used in training classifiers, 2 years in selecting the best classifier, and 3 years in testing performance. Experiments reveal that the return on investment (ROI) of the proposed method in quarterly review can outperform 61 benchmarks out of 72 benchmarks; herein, five common technical indicators and the Buy and Hold (B&H) strategy are considered, and with 62.72% in 3 years and is also capable of outperforming all the benchmarks.
中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 ix
一、 緒論 1
1.1研究背景 1
1.2研究動機 1
1.3研究方法與目的 2
1.4章節安排 2
二、 文獻回顧 3
2.1 股市領域的預測 3
2.1.1預測問題—迴歸 3
2.1.2預測問題—分類 7
2.2 本論文的特色 14
三、 研究架構 15
3.1 買點與賣點信號定義 16
3.1.1買點信號 16
3.1.2賣點信號 17
3.2 特徵子集與模型建立階段 17
3.3 變數組合與模型選擇階段 18
3.3.1選擇最佳的變數與模型 18
3.3.2共識預測模型 19
3.4 模型測試階段 19
3.4.1交易的假設與限制 20
3.4.2交易額外成本與股利政策 20
3.4.3績效衡量 22
3.4.4比較基準 24
四、 輸入變數 27
4.1 總經指標 28
4.2 ETF相關指標 30
4.3 市場交易 31
4.4 技術分析指標 33
五、 研究成果 38
5.1元大台灣50資料 38
5.2 資料前處理 40
5.2.1資料時間單位不同 40
5.2.2缺失值處理 41
5.2.3資料正規化 41
5.3 變數與預測模型的篩選 42
5.3.1變數篩選 42
5.3.2變數與預測模型的篩選結果 44
5.4 預測結果分析 47
六、 結論 51
6.1 研究貢獻 51
6.2 研究結論 52
6.3未來研究方向 52
參考文獻 54
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