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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/60223
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/60223


    Title: 整合文件探勘與類神經網路預測模型之研究 -以財經事件線索預測台灣股市為例
    Authors: 歐智民
    Contributors: 楊建民
    歐智民
    Keywords: 事件偵測與追蹤
    kNN分群
    倒傳遞類神經網路預測模型
    Date: 2010
    Issue Date: 2013-09-04 17:00:34 (UTC+8)
    Abstract:   隨著全球化與資訊科技之進步,大幅加快媒體傳播訊息之速度,使得與股票市場相關之新聞事件,無論在產量、產出頻率上,都較以往增加,進而對股票市場造成影響。現今投資者多已具備傳統的投資概念、觀察總體經濟之趨勢與指標、分析漲跌之圖表用以預測股票收盤價;除此之外,從大量新聞資料中,找出關鍵輔助投資之新聞事件更是需要培養的能力,而此正是投資者較為不熟悉的部分,故希望透過本文加以探討之。
      本研究使用2009年自由時報電子報之財經新聞(共5767篇)為資料來源,以文件距離為基礎之kNN技術分群,並採用時間區間之概念,用以增進分群之時效性;而分群之結果,再透過類別詞庫分類為正向、持平及負向新聞事件,與股票市場之量化資料,包括成交量、收盤價及3日收盤價,一併輸入於倒傳遞類神經網路之預測模型。自台灣經濟新報中取得半導體類股之交易資訊,將其分成訓練及測試資料,各包含168個及83個交易日,經由網路之迭代學習過程建立預測模型,並與原預測模型進行比較。
      由研究結果中,首先,類別詞庫可透過股票收盤價報酬率及篩選字詞出現頻率的方式建立,使投資者能透藉由分群與分類降低新聞文件的資訊量;其次,於倒傳遞類神經網路預測模型中加入分類後的新聞事件,依統計顯著性檢定,在顯著水準為95%及99%下,皆顯著改善隔日股票收盤價之預測方向正確性與準確率,換言之,於預測模型中加入新聞事件,有助於預測隔日收盤價。最後,本研究並指出一些未來研究方向。
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    網站資料
    [1]A Tutorial on Clustering Algorithms (2011), 2011年2月3日取自 http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
    [2]自由時報電子報。2011年2月1日取自http://www.libertytimes.com.tw/index.htm
    [3]中研院CKIP。2011年1月17日取自 http://ckipsvr.iis.sinica.edu.tw
    [4]Yahoo API (2011)。2011年1月22日取自http://tw.developer.yahoo.com/cas
    Description: 碩士
    國立政治大學
    資訊管理研究所
    98356033
    99
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098356033
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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