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研究生:黃思瑋
研究生(外文):Szu-Wei Huang
論文名稱:運用FANNC與BPN評估共同基金績效
論文名稱(外文):Evaluating Mutual Fund Performance through FANNC and BPN
指導教授:王克陸王克陸引用關係
指導教授(外文):Keh-Luh Wang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:管理科學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:42
中文關鍵詞:共同基金績效類神經網路
外文關鍵詞:Mutual Fund PerformanceNeural NetworkFANNCBPN
相關次數:
  • 被引用被引用:3
  • 點閱點閱:429
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:9
本論文主要是比較FANNC與BPN兩種類神經網路模型運用於共同基金績效評估時的表現。FANNC為一新提出的類神經網路模型,它結合了ART與Field theory模型的特性。在我們評估共同基金績效的實證研究中,FANNC運算的速度較BPN迅速得多,RMS亦以FANNC的結果較為優異。無論於共同基金績效分類問題或績效預測問題,其結果都以FANNC較為優異。
The purpose of this paper is to compare two different approaches, FANNC and BPN, in mutual fund performance evaluation. FANNC is a newly developed neural network which combines the features in ART and field theory. In our experiment, mutual fund performance can be evaluated much faster in FANNC approach than that in BPN approach. RMS is also superior for FANNC. These results hold for both classification problems and for prediction problems.
Chinese Abstract i
English Abstract ii
Contents iii
Figures v
Tables vi
1. Introduction 1
2. The standard for mutual fund performance evaluation 2
2.1. Sharpe Index 2
2.2. Treynor Index 2
2.3. Jensen Index 3
2.4. Treynor and Mazuy 4
3. Models for Evaluating Mutual fund performance 4
3.1. Statistical Methods 5
3.2. Backpropagation Neural Network 6
3.3. FANNC method 6
3.3.1 Adaptive Resonance Theory 7
3.3.2 Field Theory 8
3.3.3 Architecture of FANNC 8
3.3.4 Algorithm of FANNC 9
4. Factors affecting mutual fund performance 16
4.1. Momentum investment strategies 16
4.1.1 The momentum Measures 16
4.1.2 Extensions of the Measures 16
4.2. Herding behavior 17
4.2.1 The Measurement of Herding Behavior 17
5. Preparing the Input and output instances 18
5.1. Raw data selection 19
5.2. Computation of Investment Momentum 19
5.3. Computation of Herding Behavior 20
5.4. Sharpe Index 20
6. Training and the Result 21
6.1. Backpropagation Neural Network 21
6.2. FANNC 22
6.3. Result Comparison 23
7. Conclusion and Future Research 24
8. Reference 25
Appendix A Variables Table 28
Appendix B Scatter Diagrams 34
1. Brown SJ, Goetzmann WN (1995),”Preformance Persistence”, Journal of Finance, 4, pp.141-166.
2. B. S. Ahn, S.S. Cho, C.Y. Kim (2000), “The integrated methodology of rough set theory and artificial neural network for business failure prediction”, Expert Systems with Applications, 18, pp. 65-74.
3. Carpenter, G. A. and S. Grossberg (1987), “A massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine”, Computer Vision, Graphics, and Image Processing, 37, pp.54-115.
4. Carhart MM (1997),”On Persistence in Mutual Fund Performance”, Journal of Finance, 52, pp.57-82.
5. Carlos Serrano-Cinca (1996), “Self Organization Neural Networks for Financial diagnosis”, Decision Support System, 17, pp227-238.
6. D.C. Indro, C.X. Jiang, B.E. Patuwo, G.P. Zhang, (1999) “Predicting mutual fund performance using artificial neural networks”, The International Journal of Management Science, Vol.27, pp.373-380
7. Jensen, M. C. (1968), “The performance of Mutual Funds in the Period”, Journal of Finance, 23, pp.389-416.
8. Kate A. Smith and Jatinder N.D. Gupta (2000), “Neural Network in Bussiness: techniques and applications for the operation researcher”, Computers and operation research, 27, pp1023-1044
9. Kohonen, T. (1989), “Self-organization and Associative Memory 3rd “, Springer-Verlag Press, Brelin..
10. Lakonishok Josef, Shieifer Andrei and Vishny Robert W. (1992), “The impact of institutional Trading on Stock Price”, Journal of Financial Economics, 32 pp.23-43.
11. Mark Grinblatt, Sheridan Titman and Russ Wermers (1995), “Momentum Investment Strategies, Portfolio Performance, and Herding: A Study of Mutual Fund Behavior”, The American Economic Review, 85(5), pp.1088-1105
12. Rumelhart, D. E., G. E. Hinton, & R. J. Williams. (1986), “Learning Internal Representations by Error propagation”, Parallel Distributed Processing, Vol. 1 chapter 8. reprinted in Anderson & Rosenfeld, 1988, pp.675-695.
13. Sharpe, W. F (1966), “Mutual Fund Performance”, Journal of Business, 39,pp. 119-138.
14. Stern, H. S. (1996), “Neural Networks in Applied Statistics (with discussion)”, Technometrics, (38), pp.205-220.
15. Sergio Davalos, Richard D. Gritta, Garland Chow, (1999), “The application of neural network approach to predicting bankruptcy risks facing major US air carriers: 1979-1996”, Journal of Air Transport Management, 5, pp.81-86.
16. Surkan, A. J., and Singleton, J. C. (1989), “Neural Networks for Bond Rating Improved by Multiple Hidden layers”, IJCNN-89, pp.157-162.
17. Treynor, J. L. (1965), “How to Rate Management of Investment Funds”, Harvard Bussiness Revies, (13), pp.63-75.
18. Udo G., (1993), “Neural network performance on bankruptcy classification problem.”, Computer and Industrial Engineering, 25, pp.377-380.
19. Zhihua Zhou, Shifu Chen and Zhaoquan Chen, (2000), “FANNC: Fast Adaptive Neural Network Classifier”, Knowledge and information System, 2, pp.115-129.
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