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論文中文名稱:利用關聯法則分析個股報酬率的關係 [以論文名稱查詢館藏系統]
論文英文名稱:Using Association Rule Mining to Study the Return Relationships Among Stocks [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與財金管理系碩士班
畢業學年度:104
畢業學期:第二學期
中文姓名:吳重漢
英文姓名:CHUNG HAN WU
研究生學號:103AB8016
學位類別:碩士
語文別:中文
口試日期:2016/06/08
指導教授中文名:吳建文
口試委員中文名:陳育威;李炯三
中文關鍵詞:關聯法則Apriori規則有趣性
英文關鍵詞:Association RulesAprioriRule Interestingness
論文中文摘要:  長久以來人們使用各種工具與方法預測股票價格的變化,選擇合適的分析方法,就是投資能夠獲利的重要關鍵。如何從證券市場中龐大的股票數據資訊找出有用的訊息提供投資者做投資參考,已是近年來重要的研究方向。

  然而股票市場的預測方向非常多樣,主要為股價漲跌趨勢的研究。本研究使用R軟體透過關聯規則挖掘個股間彼此的關聯性,並導入規則有趣性判斷找出的關聯規則是否有價值,挖掘出股票報酬率之間的關聯關係,同時對照實際股票數據,驗證找出的規則,提供投資者更具體有用的資訊。本研究的Source1共有118天交易日,產生223條規則;在Source2共有991天交易日,產生12條規則。研究結果顯示不同資料區間能挖掘出的規則多寡有很大得差異,公司間的報酬率有相當程度的關聯性。
論文英文摘要:A variety of tools and methods were used to predict the changes in stock prices. Choosing an appropriate method of analysis is the key to getting profit. In recent years, finding the correlation of stock markets to provide significant rules for the financial market is an important direction of research.

However, there are many ways of predicting stock market. Most of them were studying the price relationships among stocks. The study is using association rule mining to study the return relationships among stocks by using R software. And the study add Rule Interestingness to find valuable rules. To provide more specific information, the rules compared with the actual stock data and be verified at the same time. The Source1 in this study included 118 trading days and generated 223 rules; the Source2 in this study included 991 trading days and generated only 12 rules. The results show that the rules in different data sources diverge greatly. The returns between companies have relationships to some extent.
論文目次:摘要 i
英文摘要 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 1
1.2 研究目的 2
1.3 研究架構 2

第二章 文獻探討 3
2.1 資料探勘 3
2.1.1 資料探勘的定義 3
2.1.2 資料探勘的模式 4
2.1.3 資料探勘的方法 5
2.2 關聯法則 6
2.2.1 關聯法則定義 6
2.2.2 Apriori演算法 9
2.2.3 資料前序處理 13
2.2.4 規則有趣性 13

第三章 研究方法 19
3.1 研究架構 19
3.1.1 研究方法與流程 20
3.2 股票報酬率 21
3.3 資料處理 22
3.3.1 資料庫建置 22
3.3.2 資料的選擇 23
3.3.3 資料的分割 23

第四章 實驗結果與分析 25
4.1 交易區間的探勘結果與分析 25
4.1.1 實驗一 S1探勘結果 25
4.1.2 實驗二 S2探勘結果 29
4.1.3 實驗三 S3探勘結果 33
4.2關聯規則探勘總結分析 38

第五章 結論 39

參考文獻 41
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