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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/32689
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32689


    Title: 利用股價連動關係發展股票推薦系統
    Developing a stock recommendation system by stock prices correlation
    Authors: 簡志偉
    Contributors: 陳良弼
    簡志偉
    Keywords: 股票
    資料探勘
    Date: 2006
    Issue Date: 2009-09-17 14:03:54 (UTC+8)
    Abstract: 累積財富的方法隨著時代背景的不同而改變,在二十一世紀的今天,投資就是一個可以快速累積財富的方法。近年來國民所得與理財知識的提升,使得今日的台灣證劵市場交易活絡,根據台灣證劵交易所與財政部證劵暨期貨管理委員會統計資料顯示,股票市場已成為國內投資者重要的理財管道。

      而試圖在股市或是衍生性商品中投資獲利者,不可不重視股票價格的變化。然而影響股票價格的因素極為廣泛,對於如此大量且複雜的資訊,實非一般投資人可以輕易掌握的。

    近年來,藉資訊科技的快速發展,資料探勘應用於股市金融領域變的可行,優點是可以在大量的資訊裡找出有用的資訊。本研究目的在利用資料探勘的技術來尋找股票市場之買進標的與切入時機。

    本研究探討單一個股的價格走向,是否會跟群體股票有所關連。利用歷史交易資料找出股票之間的股價漲跌關連度與技術指標關連度,進而發展出條件機率法則與投票法則來求出每檔股票的買進與賣出推薦值,最後再依推薦值的變化來判斷買進與賣出的標的股票。

      本研究以2004年3月到2006年3月為訓練期間;2006年3月到2007年3月為預測時間。研究結果經由報酬率分佈分析、交易次數分析、正報酬比例分析、總獲利分析與「買進後持有」策略比較分析,顯示本研究所提出的四種預測模式中,以技術指標關連度搭配投票法則的方法最能夠有效的打敗「買進後持有」的策略。
    The method to accumulate wealth changes during different times. Making the investment is a method that can accumulate the wealth quickly in 21st century. The improvement of the national income makes today`s stock market of Taiwan activate recently. From the statistical data of Taiwan Stock Exchange Corporation (TSEC), the stock market has already become important financing channel of investor.

    People attempting to earn profits in stock market must pay attention to the change of the stock price. However, many factors widely influence the price of the stock and make the investors hard to predict.

    In recent years, the fast development of computer science makes the technology of data mining applied to the stock market. The advantage of data mining is that we can find out useful information in a large amount of information. The purpose of this research is to use the technology of data mining to look for buying time and selling time of the stocks.

    This research investigates the correlation between a single stock and other stocks. By using the historical data of the stocks to find out the correlation between stocks, and developing the rules to calculate a prediction value, the recommendation of the buying or selling time of a stock can be done.

    The training analysis in this paper is collected from March, 2004 to March, 2006 and the prediction time is from March, 2006 to March, 2007. The empirical result shows that: from the distribution analysis of the profit rate, the trade number of the times analysis, the buy-and-hold policy comparative analysis, and the positive profit rate analysis: in the four models discussed the index with the rule of vote performs better than the buy-and-hold policy.
    Reference: [1] 朱富春,「股價分析」,聯經出版有限公司,民國66年。
    [2] 林宗永,「證券投資技術分析指標獲利性之實證研究」,碩士論文,政治大學企業管理研究所,民國78年。
    [3] 林良炤,「KD技術指標應用在台灣股市之實證研究」,碩士論文,國立台灣大學商學研究所,民國86年。
    [4] 洪志豪,「技術指標KD、MACD、RSI與WMS%R之操作績效實證」,碩士
    論文,國立台灣大學國際企業學研究所,民國88年。
    [5] 黃怡芳,「道氏理論、濾嘴法則與買入持有策略在台灣股市投資績效之比較」,碩士論文,國立成功大學企業管理研究所,民國90年。
    [6] 董茲莉,「由技術分析效果驗證我國股市效率性」,碩士論文,國立中山大學企業管理研究所,民國84年。
    [7] 蔡宜龍,「台灣股票市場技術分析有效性之衡量」,碩士論文,國立成功大學工業管理學系研究所,民國79年。
    [8] 蔡金豐,「類神經網路於台灣股市預測之應用」,碩士論文,國立高雄第一科技大學電腦與通訊工程研究所,民國90年。
    [9] 廖廣毅,「以類神經網路預測股價指數漲跌」,碩士論文,私立元智大學工業工程研究所, 民國88年。
    [10] Brock, W., J. Lakonishok and B. Lebaron, Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, Journal of Finance, Vol. 47, No. 5, pp.1731–
    1764, Dec. 1992.
    [11] Gerald, A. and H. Fredick , Stock Market Trading Systems: A Guide to Investment Strategy, Irwin Professional Pub, 1979.
    [12] Jovina, R. and J. Akhtar, Backpropagation and Recurrent Neural Networks in Financial Analysis of Multiple Stock Market Returns, Proceedings of the 29th Annual Hawaii
    International Conference on System Sciences, 1996.
    [13] Mizuno, H., M. Kosaka, H. Yajima and N. Komoda, Application Of Neural Network To Technical Analysis of Stock Market Prediction, Studies in Informatics and Control, 7(3),111-120, 1998.
    [14] Mark, O. and O. Olatoyosi, Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM), Proceedings of the Fortieth Annual Hawaii International
    Conference on System Sciences, 2007.
    [15] Norio, B., I. Naoyuki and A. Hiroyuki, Utilization of Neural Networks & GAs for Constructing Reliable Decision Support Systems to Deal Stocks, Proceedings of the IEEE International Joint Conference on Neural Networks, 2000.
    [16] Pruitt, S. W. and R. E. White, The CRISMA Trading System: Who Says Technical Analysis Can’t Beat the Market?, Journal of Portfolio Management, pp. 55-58, Sep.
    1988.
    [17] Qiong, L., L. Xin, R. Fuji and K. Shingo, Automatic Estimation of Stock Market Forecasting and Generating the Corresponding Natural Language Expression,Proceedings of the International Conference on Information Technology: Coding and Computing, 2004.
    Description: 碩士
    國立政治大學
    資訊科學學系
    94753038
    95
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094753038
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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