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題名:結合ART2與K平均數集群分析於基金績效分類及持續性之研究
書刊名:醒吾學報
作者:陳明琪 引用關係林逾先 引用關係張有中 引用關係
作者(外文):Chen, Ming-chiLin, Edward Yu-hsienChang, Yu-chung
出版日期:2007
卷期:33
頁次:頁33-75
主題關鍵詞:自適應共振理論II神經網路共同基金績效分類績效持續性ART2Adaptive resonance theory IIMutual fundsPerformance clusteringPerformance persistence
原始連結:連回原系統網址new window
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  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
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  • 共同引用共同引用:5
  • 點閱點閱:39
本研究提出一個結合自適應共振理論Ⅱ(Adaptive Resonance Theory Ⅱ;ART2)與K-平均數集群(General K-Means網Method)的改良式二階段集群分析法,利用ART2判斷分群組數並搜尋初始群心代入K-means中進行國內基金績效的分類(本研究定義為ART2+K),並對國內基金績效的持續性進行Spearman等級相關檢定。經實證研究後發現:ART2+K對基金績效分群的結果優於單獨使用ART2或K-means的分群結果。ART2+K對國內基金績效的分群相當完善,並且可以根據其他相關的變數:報酬率、β係數、Sharpe指標、Jensen指標與Treynor指標將所分類的群組予以分別命名為「高報酬高績效--股票型基金」、「穩定收益型基金」、「中報酬中績效--股票型基金」、「中報酬高績效--跨國投資型基金」、「低報酬低績效--債券股票平衡型基金」。國內基金績效經Spearman等級相關檢定後,不論是針對年度報酬率或Sharpe績效指標而言,短期內均具績效持續性,長期則不具績效持續性。
This research supply a new two-stage clustering method which integration of adaptive resonance theory II (ART2) and K-means method. By using ART2 neural network to determine the number of clusters and the staring points and then employing the K-means method to find the final solution, can provide very good solution of data clust4ering. We apply this two-stage clustering method ART2+K to cluster the performance of mutual funds and use Spearman rank-order correlation to study the performance persistence of mutual funds. Data is collected from January 2001 to May 2006, and the evaluation indexes of mutual funds include return. Beta coefficient, Sharp Index, Jensen index, and Treynor index. This research obtains the following conclusion after empirical study: For classification, this two-stage clustering method ART2+K is better than ART2 or K-means method in the performance clustering of mutual funds. For persistence, in a short term, there is persistency for performance of mutual funds by Spearman rank-order correlation. But there is not persistency for performance of mutual funds in a long term.
期刊論文
1.Fayyad, Usama、Stolorz, P.(1997)。Data mining and KDD: promise and challenges。Future Generation Computer Systems,13,99-115。  new window
2.陳明琪、張有中(20031200)。類神經網路應用於臺灣股票市場獲利率預測之研究。臺灣銀行季刊,54(4),314-333。new window  延伸查詢new window
3.Blake, C. R.、Elton, J. E.、Gruber, M. J.(1995)。Fundamental Economic Variabels Expected Returns and Bond Fund Performance。Journal of Finance,4,1229-1256。  new window
4.Chen, M. S.、Han, J.、Yu, P. S.(1987)。Data Mining: An Overview from Database Perspective。IEEE Transaction on Knowledge and Data Engineering,8(6)。  new window
5.Chu, C.-H.、Widjaja, D.(1994)。Neural Network System for Forecasting Method Selection。Decision Support System,12(1),13-24。  new window
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7.Treynor, J. L.、Mazuy, K. K.(1966)。Can Mutual Fund Outguess the Market。Harvard Business Review,43,63-75。  new window
8.Kuo, R. J.、Liao, J. L.、Tu, C.(2005)。Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce。Decision Support Systems,40(2),355-374。  new window
9.Kuo, R. J.、Chang, K.、Chien, S. Y.(2002)。Integration of Self-Organizing Feature Map and Genetic Algorithm Based Clustering Method for Market Segmentation。Journal of Organizational Computing and Electronic Commerce,14(1)。  new window
10.Kuo, R. J.、Ho, L. M.、Hu, C. M.(2002)。Integration of self-organizing feature map and k-means algorithm for market segmentation。Computers & Operations Research,29(11),1475-1493。  new window
11.Elton, E. J.、Gruber, M. J.、Blake, C. R.(1993)。The Performance of Bond Mutual Funds。The Journal of Business,66(3),371-403。  new window
12.Chang, Eric C.、Lewellen, Wilbur G.(1984)。Market timing and mutual fund investment performance。Journal of Business,57(1),57-72。  new window
13.Henriksson, R. D.(1984)。Market Timing and Mutual Fund Performance: An Empirical Investigation。Journal of Business,57(1),73-96。  new window
14.De Veaux, R. D.、Gordon, A. L.、Comiso, J. C.、Bacherer, N. E.(1993)。Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines。Journal of Geophysical Research,98(C11),20307-20319。  new window
15.Treynor, Jack L.(1965)。How to rate management of investment funds?。Harvard Business Review,43(1),63-75。  new window
16.Craven, M. W.、Shavlik, J. W.(1997)。Using neural networks for data mining。Future Generation Computer Systems,13(2/3),221-229。  new window
17.Desai, V. S.、Crook, J. N.、Overstreet, G. A. Jr.(1996)。A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment。European Journal of Operational Research,95(1),24-37。  new window
18.Sharpe, William F.(1966)。Mutual fund performance。Journal of Business,39(1),119-138。  new window
19.Fabozzi, Frank J.、Francis, Jack C.(1979)。Mutual Fund Systematic Risk for Bull and Bear Markets: An Empirical Examination。Journal of Finance,34(5),1243-1250。  new window
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會議論文
1.Kuo, R. J.、Chung, W. J.(2002)。Integration of Self-Organizing Map and Genetic K-Means Algorithm for Data Mining。30th International Conference of Computer and Industrial Engineering,(會議日期: 2002/06/29-07/02)。Tinos Island, Aggean, Sea。  new window
2.Dutta, S.、Shekhar, S.(1988)。Bond-Rating: A Non-Conversation Application of Neural Network。IEEE International Conferenceon Neural Networks,443-450。  new window
學位論文
1.吳梁傑(2003)。台灣地區共同基金績效之衡量--生產邊界法之應用(碩士論文)。中國文化大學。  延伸查詢new window
2.李明仁(1996)。臺灣開放式債券型基金之績效研究(碩士論文)。國立政治大學。  延伸查詢new window
3.李鳳美(2000)。基金流量與績效評估之實證研究--以國內開放型股票基金為例(碩士論文)。輔仁大學。  延伸查詢new window
4.張瑞芬(2000)。國內債券型共同基金報酬之影響因素(碩士論文)。國立臺灣大學。  延伸查詢new window
5.彭文俊(1995)。債券指數之建構與債券型基金績效之研究(碩士論文)。國立中山大學。  延伸查詢new window
6.鄭淑娥(2001)。台灣開放型共同基金績效分類之研究(碩士論文)。國立成功大學。  延伸查詢new window
7.蔡依玲(2001)。台灣股票市場報酬率之研究(碩士論文)。國立成功大學。  延伸查詢new window
8.楊朝舜(1993)。臺灣共同基金選股能力與時機掌握能力之研究(碩士論文)。國立臺灣大學。  延伸查詢new window
9.許峻源(2001)。類神經網路與MARS於資料探勘分類模式之應用(碩士論文)。輔仁大學。  延伸查詢new window
圖書
1.Berry, M. J. A.、Linoff, G. S.(1997)。Data Mining Technique: For Marketing, Sale, and Customer Support。New York:Wiley。  new window
2.葉怡成(1992)。類神經網路模式應用與實作。臺北:儒林書局。  延伸查詢new window
3.Adriaans, Peter、Zantinge, Dolf(1996)。Data Mining。Addison-Wesley。  new window
4.葉怡成(1999)。類神經網路模式應用與實作。臺北市:儒林圖書。  延伸查詢new window
 
 
 
 
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