本研究從股價漲跌記錄與財經新聞以關聯分析去找出關聯規則。結果顯示，關聯規則存在於這些資料之中，並且這些規則可以用來繪製產業關聯圖。此外，從漲跌記錄產生的關聯規則可預測股價漲跌。當漲、跌被定義為1%的價格變化時，統計結果顯示，這些規則可以預測下跌趨勢。也就是說，當這些記錄產生關聯規則：「股票A下跌股票B下跌」，此規則顯示當股票A股價下跌時，股票B有很高的機率在當日下跌。若股票B當天股價持平時，股票B隔日有很高的機率會下跌。

This research used association mining to find out the association rules from the stock market rise and drop records and financial and economic news. The results showed that association rules exist in these data, and these rules can be used to draw the industry relationship graph. Moreover, the association rules produced from the rise and drop records can forecast the rise and drop of stock price. When rise and drop are defined as 1% change of price, the statistical results showed that these rules can forecast drop trend. That is, when these records produced association rule: “Stock A drop  Stock B drop”, the rule shows that when stock A drops, stock B drops with high probability on the same day. If stock B doesn’t drop or rise on the same day, it will drop with high probability on the next day.

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 導論 1
1-1 研究背景 1
1-2 研究目的 2
1-3 研究方法 2
1-4 研究內容 4
第二章 文獻回顧 6
2-1 股市的投資分析 6
2-1-1 技術面分析 6
2-1-2 基本面分析 7
2-1-3 消息面分析 7
2-2 關聯分析 8
2-3 關聯分析與股市的投資分析 13
第三章 關聯分析在台灣股市消息面分析之應用 15
3-1 導論 15
3-2 方法 15
3-3 結果 16
3-3-1建立新聞資料 16
3-3-2建立關聯規則 18
3-3-3分析關聯規則 19
3-4 關聯規則與產業結構之關係—關聯圖之應用 24
3-5 結論 31
第四章 關聯分析在台灣股市技術面分析之應用 32
4-1 導論 32
4-2 方法 32
4-3 結果 32
4-3-1建立漲跌資料 32
4-3-2建立關聯規則 34
4-3-3分析關聯規則 36
4-4 關聯規則與產業結構之關係－關聯圖之應用 41
4-5 以關聯規則預測股價漲跌之可行性分析 48
4-6 結論 62
第五章 結論與建議 63
5-1 結論 63
5-2 建議 65
參考文獻 67
附錄A：財經新聞關聯規則 70
附錄B：股市漲跌關聯規則 76

施惠萍，1999，“結構性變化的偵測與其在技術分析中的應用”，台灣大學經濟學研究所碩士論文。
洪志豪，1988，“技術指標KD、MACD、RSI 與WMS%R 之操作績效實證”，台灣大學國際企業學研究所碩士論文。
陳建全，1988，“台灣股市技術分析之實證研究”，台灣大學商學研究所碩士論文。
葉怡成，2006，“資料探勘–方法應用與實作”，未出版。
蔡斌仕，1995，“台灣股市技術分析之實證研究”，台灣大學財務金融學研究所碩士論文。
蕭正南，1988，“資料探勘應用於股市股價趨勢之研究”，輔仁大學資訊管理學研究所論文。
賴宏祺，1987，“技術分析有效性之研究”，中興大學企業管理研究所碩士論文。
鍾任明，2004，“運用文字探勘於日內股價漲跌趨勢預測之研究”，中原大學資訊管理學研究所論文。
Anthony, T., Lu, H., Han, J., and Feng, L., 1999, “Breaking the Barrier of Transaction: Mining Inter-transaction Association Rules,” Proceedings of 1999 ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp. 297–301.
Brock, W., Josef L., and Blake L. B., 1992, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance (47), pp. 1731-1764.
Corrado, C, J., and S. H. Lee, 1992, “Filter Rule Tests of the Economic Significance of Serial Dependencies in Daily Stock Returns,” Journal of Financial Research (15), pp. 369-387.
Fama, E. F., and M. E. Blume, 1966, “Filter Rules and Stock-Market Reading,” Journal of Business (39), pp. 226-241.
Han, J., Cai, Y. D., Cercone, N., 1992, “Knowledge Discovery in Database An Attribute-Oriented Approach,” Proc. of 1992 Int''l Conf. on Very Large Data Bases (VLDB''92), Vancouver, Canada, August 1992, pp. 547-559.
James, F. E., 1968, “Monthly Moving Averages-An Effective Investment Toll,” Journal of Financial and Quantitative Analysis, September 1968, pp. 315-326.
Jones, C.P., 1988, “Investment : analysis and management,” Journal of Financial and Quantitative Analysis(23), pp. 315-326.
Levy, R. A., 1967, “Relative Strength as a Criterion for Investment Selection,” Journal of Finance (22), pp. 595-610.
Lu, H., Han, J., and Feng, L., 1998, “Stock Movement Prediction and N-dimensional Inter-transaction Association Rules,” In Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 12:1-12:7.
Miller, R. J., Yang, Y., 1997, “Association rules over interval data,” ACM SIGMOD Record, v.26 n.2, pp. 452-461.
Nottola, C., Condamin, L., and Naim, P., 1991, "On the Use of Hard Neural Networks for Symbolic Learning Application to Company Evaluation," in Proceedings of the International Joint Conference on Neural Networks IJCNN-91, pp. 509-522.
Ou, J. and Penman H., 1989, “Financial Statement Analysis and the Prediction of Stock Returns,” Journal of Accounting and Economics (11), pp. 295-329.
Saito, K., Ueda, N., Katagiri, S., Fukai, Y., Fujimaru, H., and Fujinawa, M., 2000, “Law Discovery from Financial Data Using Neural Network,” Proceedings of the IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering, pp. 209-212.
Sullivan, Ryan, A. Timmermann, and H. White, 1999, “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap,” Journal of Finance (54), pp. 1647-1691.
Sweeney, Richard, 1986, “Beating the Foreign Exchange Market,” Journal of Finance (41), pp. 163-182.
Van Horne, Jame C., George G. C. Parker, 1967, “The Random Walk Theory: An Empirical Test,” Financial Analyst’s Journal (23), pp. 87-92.
Van Horne, Jame C., George G. C. Parker, 1968, “Technical Trading Rules: A Comment,” Financial Analyst’s Journal (24), pp. 128-132.
Wong, P.C., Whitney, P., and Thomas, J., 1999, “Visualizing Association Rules for Text Mining,” Proc. of the IEEE Symposium on Information Visualization InfoVis'99, 1999, pp. 120-123.
Wuthrich, B., Permunetilleke, D., Leung, S., Cho, V., Zhang, J., Lam, W., 1998, “Daily Prediction of Major Stock Indices from Textual WWW Data,” Proceedings of 1998 ACM International Conference on Knowledge Discovery and Data Mining, pp. 364-368.