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論文中文名稱:以空間性及時間性獨立成份分析與分類迴歸樹為基礎的投資決策模式 [以論文名稱查詢館藏系統]
論文英文名稱:Investment Decision Making Model Based on Spatiotemporal Independent Component Analysis and Classification and Regression Tree [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:商業自動化與管理研究所
畢業學年度:99
出版年度:100
中文姓名:李泓緯
英文姓名:Hung-Wei Li
研究生學號:98488049
學位類別:碩士
語文別:中文
口試日期:2011-06-21
論文頁數:40
指導教授中文名:邱志洲
口試委員中文名:蔡榮發;呂奇傑
中文關鍵詞:財務時間序列預測空間性及時間性獨立成份分析分類迴歸樹
英文關鍵詞:Financial Time Series ForecastingSpatiotemporal Independent Component AnalysisClassification and Regression Tree
論文中文摘要:財務時間序列的未來走勢一直是投資人所關心的議題,因此,投資人常以過去的財務時間序列資料為對象進行分析,以建立具有良好預測能力的模式,然而,財務時間序列資料中的雜訊常會干擾模式的預測能力。因此,本研究以空間性及時間性獨立成份分析方法處理雜訊問題,再以分類迴歸樹建立投資決策模式,提供投資人客觀的投資決策。為了驗證本模式的穩健性及有效性,本研究以美國道瓊工業指數、香港恆生指數以及臺灣加權指數為對象進行實證分析,研究結果發現,本研究所提模式除了具有良好的預測能力外,績效亦優於傳統方法。
論文英文摘要:Financial time series forecasting is an important issue for the investors. However, the accuracy of the financial time series forecasting model is always affected by the noise. In order to properly handle this problem, spatiotemporal independent component analysis is adopted in this research. Besides, classification and regression tree is also adopted to build a financial decision making model to provide the investors with objective investment suggestions. Finally, Dow Jones Industrial Average Index, Hang Seng Index, and Taiwan Stock Exchange Capitalization Weighted Stock Index are used to verify the efficiency and robustness of our proposed model. The results show that our proposed model outperforms other models.
論文目次:摘 要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究架構 2
第二章 文獻探討 4
2.1 投資決策分析 4
2.2 獨立成份分析 5
2.3 空間性及時間性獨立成份分析 7
2.4 分類迴歸樹 9
第三章 研究方法 11
3.1 資料前處理 12
3.2 空間性及時間性獨立成份分析 13
3.3 分類迴歸樹 15
3.4 交易策略的制定與模式正確率計算 16
第四章 研究結果 17
4.1 資料來源及前處理 17
4.2 特徵萃取 21
4.3 實證結果 23
第五章 結論與建議 30
參考文獻 31
附錄A:CART最小成本規則範例 36
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論文全文使用權限:同意授權於2011-07-07起公開