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研究生:黃明輝
研究生(外文):Ming-Hui Huang
論文名稱:資料探勘在財務領域的運用-以債券型基金之績效評估為例
論文名稱(外文):Mining The Performance Of Mutual Funds Using Neural Networks And Multivariate Adaptive Regression Splines
指導教授:邵曰仁邵曰仁引用關係
指導教授(外文):Yueh-Jen Shao
學位類別:碩士
校院名稱:輔仁大學
系所名稱:金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:55
中文關鍵詞:資料探勘MARS鑑別分析羅吉斯迴歸類神經網路
外文關鍵詞:Data MiningMutual FundNeural NetworksClassificationMultivariate Adaptive Regression Splines
相關次數:
  • 被引用被引用:39
  • 點閱點閱:941
  • 評分評分:
  • 下載下載:194
  • 收藏至我的研究室書目清單書目收藏:12
資料探勘(Data Mining)主要是從資料或資料庫中,運用相關的分析技術發掘出新的、未知的樣式或規則,並且透過資料探勘的應用,發掘出超越歸納範圍外的資料間關係型態。而隨著資料探勘技術的越受重視,其所應用的範圍也越來越廣泛,直至目前為止,資料探勘技術的範圍涵蓋了金融、保健、行銷、通訊、科學、教育、甚至新產品的研發等,而主要的應用則包含分類問題(Classification)、趨勢分析(Trend Analysis)、分群模式(Clustering)、關聯分析(Associations)及順序型樣(Sequential Analysis)等幾大類。
有別於國內基金績效之研究多以傳統統計方法進行分析,本研究將以另一種方式進行探討。實際的作法是以文獻中提及可能影響基金報酬與擇時能力之債券持有比率、附買回交易持有比率、基金成立時間長短、基金規模變動大小等變數以資料探勘常用方法如類神經網路以及多元適應性雲形迴歸(Multivariate Adaptive Regression Splines, MARS)等分析工具“探勘”該基金是否具有擇時能力。研究結果除可篩選出影響基金績效表現之重要因素外,更可以比較不同工具之預測精準度。而為了驗證本研究所提出之模式之可行性及評估建構模式之診斷能力,將以1999年7月到2001年6月間,34家投資國內債券之債券型基金進行實證研究,實證結果顯示MARS相較於類神經網路有較好的判別能力。
Data mining is useful tools to discovery valid, novel, useful, new patterns or unknown relations in the data set or database. We also use data mining technology for finding out rules within database over the induction. Due to its applications to information systems, decision making, fraud detection, business failure prediction, database marketing, and lots of other applications, it has drawn serious attention from both academic researchers and practitioners.
The purpose of this research is to investigate the performance of mutual fund bond using two commonly used data mining tools, artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS). Several variables that may affect the performance mentioned in the literature, like the age of the bond fund, the portfolio of the bond fund, the scale of the bond fund, will be used to “predict” whether a particular bond will have the timing ability or not. In order to evaluate the classification capability of ANNs and MARS in mining the timing capability of mutual funds, historical date of 34 Taiwan bond funds from July 1999 to June 2001 will be used in this study. Analytic results demonstrate that MARS has better out-of-sample forecasts than ANNS in terms of average correct classification rates.
第壹章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 .4
第貳章 文獻探討 6
第一節 資料探勘 6
第二節 資料探勘方法 8
第三節 基金績效評估 12
第四節 債券型基金相關問題探討 18
第參章 研究方法 22
第一節 MARS 22
第二節 類神經網路 26
第三節 我國債券型基金擇時能力檢測模型 31
第肆章 實證研究 33
第一節 債券型基金之擇時能力分析 34
第二節 類神經網路之資料分析 36
第三節 MARS之資料分析 42
第四節 綜合比較 45
第伍章 結論與建議 46
參考文獻 47
附錄一 基金基本資料 54
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