
系統識別號 
U00020806201112314700 
中文論文名稱

適應性類神經模糊推論系統在動態交易決策的應用台灣股票指數期貨的實證研究 
英文論文名稱

The Application of Adaptive NeuroFuzzy Inference System(ANFIS) for Dynamic Trading Decision Support SystemEvidence from TAIEX Stock Index Futures 
校院名稱 
淡江大學 
系所名稱(中) 
財務金融學系碩士班 
系所名稱(英) 
Department of Banking and Finance 
學年度 
99 
學期 
2 
出版年 
100 
研究生中文姓名 
何東翰 
研究生英文姓名 
TungHan Ho 
學號 
698530564 
學位類別 
碩士 
語文別 
英文 
口試日期 
20110506 
論文頁數 
53頁 
口試委員 
指導教授李沃牆 共同指導教授吳典明 委員李沃牆 委員吳典明 委員沈大白 委員池秉聰 委員顧廣平

中文關鍵字 
類神經網路
適應性模糊推論系統(ANFIS)
基因演算法

英文關鍵字 
Adaptive NeuroFuzzy Inference System(ANFIS)
Neural Networks
Genetic Algorithms

學科別分類 
學科別＞社會科學＞商學

中文摘要 
股票市場的預測非常重要，因為成功的預測能帶來相當大的獲利。然而，能夠成功的預測是非常複雜而且困難的。
本研究擴展了適應性類神經模糊推論系統（ANFIS），創建一個交易決策支援系統，能夠利用模糊推論結合類神經網路的模型識別能力用於預測與交易台灣加權股價指數期貨。
本研究結果，提出了人工智能的方法結合模糊理論與類神經網絡來實現最佳化的交易規則。結果表明，結合模糊理論和類神經網絡產生的交易決策支援系統，能夠使交易員或是投資專家能夠克服眾多交易決策上資訊分析上的限制，提高投資效益，ANFIS能夠在預測未來的股市期貨指數上成為一個有用的工具，增加交易的獲利。

英文摘要 
Stock market prediction is important because successful prediction of stock prices may promise attractive benefits. Yet, these tasks are highly complicated and very difficult.
This thesis extends the Adaptive NeuroFuzzy Inference System (ANFIS), to create a trading decision support system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in forecasting and trading the futures of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).
This study, as a result, proposes an approach of artificial intelligence by integrating fuzzy theory with neural networks to achieve the optimization of trading rules. The result indicates that integrating fuzzy theory with neural networks has produced a trading decision support system which overcomes the physical limitations of human experts and traders in taking decisions of trading and improve the investment performance. The experimental results indicate that ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock index future price and increase the returns of a trader's portfolio.

論文目次 
Contents
Chapter Content
Abstract…………………………………………………………………………………………I
Chapter Content III
Table Content IV
Figure Content V
Chapter 1: Introduction 1
1.1 Motivation 1
1.2 Research Objectives 5
1.3 The Flow Chart 5
Chapter 2: Theories and Literature 7
2.1 Brief review of fuzzy 7
2.2 Review of NeuroFuzzy Systems 8
2.3 Review of ANFIS 13
Chapter 3: Methodologies 17
3.1 Fuzzy Clustering 17
3.2 ANFIS 18
3.3 Learning Algorithm of ANFIS 23
Chapter 4: Empirical Design and Results Analysis 25
4.1 Data description 27
4.2 Experiment Steps 33
4.3 Analysis’ result 43
Chapter 5: Conclusion and Suggestions 46
5.1. Conclusion 46
5.2. Suggestions 47
References ……………………………………………………………………………………48
Table Content
Table 1. Summary of training algorithm 24
Table 2. The TAIEX Futures 26
Table 3. Summary of input variables for training 32
Table 4. Transaction costs for example 40
Table 5. Calculate the profit for example 41
Table 6. The accuracy of initial training for onemonth expiration contract (model 1). 43
Table 7. The accuracy of initial training for oneyear expiration contract (model 2) 44
Table 8. The accuracy between model 1 and model 2 44
Table 9. The trading profits 45
Figure Content
Fig. 1.1: the Flow Chart 6
Fig. 2.1: Connectionist fuzzy logic control/decision system (Lin and Lee, 1991) 10
Fig. 2.2: Network representation of fuzzy systems without linguistic rules 11
Fig. 2.3 The architecture of ANFIS. (Jang, 1993) 12
Fig. 3.1: An ANFIS architecture for a two rule Sugeno system 21
Fig. 4.1: Flowchart of proposed procedure 34
Fig. 4.2: The FIS structure 37
Fig. 4.4: Moving windows training 42 
參考文獻 
1. Abbasi, E., and A. Abouec, (2008), “Stock price forecast by using neurofuzzy inference system”. Proceedings of World Academy of Science, Engineering and Technology, vol.36, pp.320–323.
2. Afolabi, M., and O. Olatoyosi, (2007), “Predicting stock prices using a hybrid Kohonen selforganizing map (SOM),” Annual Hawaii international conference on system sciences, 40th, pp. 1–8.
3. Altrock, C. V.(1995), “Fuzzy logic & neuro fuzzy applications explained,” PrenticeHall International, Inc..
4. Armano, G., M. Marchesi, and A. Murru, (2005), “A hybrid geneticneural architecture for stock indexes forecasting, ” Information Sciences, vol . 170, pp. 3–33.
5. Atsalakis, G. S., and K. P. Valavanis, (2009a), “Surveying stock market forecasting techniques – part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941.
6. Atsalakis, G. S., and K. P. Valavanis,(2009b), “Forecasting stock market shortterm trends using a neurofuzzy based methodology,” Expert Systems with Applications, vol. 36, no. 3, pp. 10696–10707.
7. Avcı, E. (2007), “Forecasting daily and sessional returns of the ISE100 index with neural network models,” Journal of Dogus University, vol. 8, no. 2, pp. 128–142.
8. Avcı, E. (2008), “Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system,” Applied Soft Computing, vol. 8, no. 1, pp. 225–231.
9. Avcı, E., and Z. H. Akpolat,(2006), “Speech recognition using a wavelet packet adaptive network based fuzzy inference system,” Expert Systems with Applications, vol. 31, no. 3, pp. 495–503.
10. Avcı, E., D. Hanbay, and A. Varol, (2007), “An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition,” Expert Systems with Applications, vol. 33, no.3, pp.582–589.
11. Avcı, E., I. Turkoglu, and M. Poyraz, (2005), “Intelligent target recognition based on wavelet adaptive network based fuzzy inference system,” Lecture Notes in Computer Science, vol. 3522, pp. 594–601.
12. Avcı, E., I. Turkoglu, and M. Poyraz,(2006), “The performance analysis of STFTANFIS classification method on pulsed radar target categorization,” Istanbul University – Journal of Electrical and Electronics Engineering, vol.6, no. 1, pp. 97–105.
13. Bezdec, J. C. (1981), “Pattern recognition with fuzzy objective function algorithms”, Plenum Press, New York.
14. Chang, P.C., and C. H. Liu, (2008), “A TSK type fuzzy rule based system for stock price prediction,” Expert Systems with Applications, vol. 34, no, 1, pp. 135–144.
15. Chu, H.H.,T. L. Chen, C. H. Cheng,and C. .C. Huang, (2009), “Fuzzy dualfactor timeseries for stock index forecasting,” Expert Systems with Applications, vol. 36, no. 1, pp. 165–171.
16. Egeli, B., M. Ozturan, and B. Badur, (2003), “Stock market prediction using artificial neural networks,” In Proceedings of the 3rd Hawaii international conference on business, Honolulu, Hawaii.
17. Gencay, R. (1998), “The predictability of security returns with simple technical trading rules,” Journal of Empirical Finance, vol. 5, pp. 47–359
18. Grosan, C., A. Abraham, V. Ramos,and S. Y. Han,(2005), “Stock market prediction using multi expression programming,” In Proceedings of Portuguese conference of artificial intelligence, workshop on artificial life and evolutionary algorithms, Portuguese: IEEE Press, pp. 73–78
19. Hiemstra, Y. (1995). “Modeling structured nonlinear knowledge to predict stock market returns,” Chaos and nonlinear dynamics in the financial markets: Theory, evidence and applications. Chicago, IL: Irwin, pp. 163–175.
20. Hua, S., and Z. Sun, (2001), “Support vector machine approach for protein subcellular localization prediction,” Bioinformatics, vol. 17, pp. 8, pp. 721–728.
21. Huang, C. L., and C. Y. Tsai,(2009), “A hybrid SOFMSVR with a filterbased feature selection for stock market forecasting,” Expert Systems with Applications, vol. 36, no, 2, pp.1529–1539.
22. Jang, J.S. Roger (1993), “ANFIS : AdaptiveNetworkBased Fuzzy Inference System,” IEEE Trans. On Systems, Man, and Cybernetics, vol. 23, no. 3, pp.665685.
23. Jang, J.S. Roger, C. T. Sun, and E. Mizutani,(1997), “Neurofuzzy and soft computing,” PrenticeHall International, Inc..
24. Kablan, A. (2009), “Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting.” Proceedings of The Third International Conference on Advanced Engineering Computing and Applications in Sciences.
25. Karaatli, M., I. Gungor, Y. Demir, and S. Kalayci, (2005), “Estimating stock market movements with neural network approach,” Journal of Balikesir University, vol. 2, no. 1, pp. 22–48.
26. Kim, S. H., and S. H. Chun,(1998), “Graded forecasting using an array of bipolar predictions: Application of probabilistic neural networks to a stock market index,” International Journal of Forecasting, vol. 14, pp.323–337.
27. Kim, K., and I. Han, (2000), “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert Systems with Applications, vol. 19, pp. 125–132.
28. Kimoto, T., K. Asakawa, M. Yoda, and M. Takeoka,(1990), “Stock market prediction system with modular neural networks,” In Proceedings of the international joint conference on neural networks, San Diego, California, pp. 1–6.
29. Klir, G. J., and B. Yuan,(1995), “Fuzzy sets and fuzzy logic, theory and applications,” PrenticeHall International, Inc..
30. Kosko B. (1992), “Neural networks and fuzzy systems,” PrenticeHall International, Inc..
31. Kosko, B. (1997), “Fuzzy engineering”, PrenticeHall International, Inc..
32. Wang, L. X. (1994), “Adaptive fuzzy systems and control”, PrenticeHall International, Inc..
33. Wang, L. X. (1997), “A course in fuzzy systems and control,” PrenticeHall International, Inc..
34. Lin, C. T. and C.S. Lee, (1991), “Neuralnetworkbased fuzzy logic control and decision system,” IEEE Trans. On Computers, vol. 40, no. 12, pp. 13201336.
35. Lin, C. T. and C. S. Lee, (1996), “Neural fuzzy systems: a neurofuzzy synergism to intelligent systems,” Upper Saddle River, NJ: PrenticeHall.
36. Su, M. C. (1997), “Identification of singleton fuzzy models via fuzzy hyperrectangular composite NN,” Fuzzy Model Identification: Selected Approaches, H. Hellen doorn and D. Driankov, Eds. pp. 215250.
37. Manish, K., and M. Thenmozhi, (2006), “Support vector machines approach to predict the S&P CNX NIFTY index returns,” In Proceedings of 10th Indian institute of capital markets conference. Available from http://ssrn.com/abstract=962833.
38. Olson, D., and C. Mossman, (2003), “Neural network forecasts of Canadian stock returns using accounting ratios,” International Journal of Forecasting, vol. 19, no. 3, pp. 453–465.
39. Pai, P. F., and C. S. Lin,(2005), “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, pp. 497–505.
40. Saad, E. W., D. C. Prokhorov, and D. C. Wunsch, (1998), “Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks,” IEEE Transactions on Neural Networks, vol. 9, pp. 1456–1470.
41. Takahashi, T., R. Tamada, and K. Nagasaka, (1998), “Multiple linesegments regression for stock prices and longrange forecasting system by neural networks,” In Proceedings of the 37th SICE annual conference, pp. 1127–1132.
42. Tan, T. Z., C. Quek, and G. Ng, (2007), “Biological braininspired genetic complementary learning for stock market and bank failure prediction,” Computational Intelligence, vol. 23, no.2, pp.236–261.
43. Tay, F. E. H., and L. J. Cao, (2001), “Application of support vector machines in financial time series forecasting,” Omega, vol. 29, pp. 309–317.
44. Tay, F. E. H., and L. J. Cao, (2002), “Modified support vector machines in financial time series forecasting,” Neurocomputing, vol. 48, pp. 847–861.
45. Trinkle, B. S. (2006). “Forecasting annual excess stock returns via an adaptive networkbased fuzzy inference system,” Intelligent Systems in Accounting, Finance and Management, vol. 13, no. 3, pp. 165–177.
46. Yao, J., L. T. Chew, and H. L. Poh, (1999), “Neural networks for technical analysis: A study on KLCI,” International Journal of Theoretical and Applied Finance, vol. 2, no. 2, pp. 221–241.
47. Yoon, Y., T. Guimaraes, and G. Swales,(1994), “Integrating neural networks with rulebased expert systems”, Decision Support Systems, vol. 11, pp. 497507.
48. Yoon, Y., and G. Swales, (1991), “Predicting stock price performance: A neural network approach”. In Proceedings of the 24th annual Hawaii international conference on systems sciences, Honolulu, Hawaii, pp. 156–162.
49. Yudong, Z., and W. Lenan, (2009), “Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network”. Expert Systems with Applications, vol. 36, no. 5, pp. 8849–8854.
50. Yunos, Z. M., and S. M. Shamsuddin, and R. Sallehuddin, (2008), “Data modeling for Kuala Lumpur Composite Index with ANFIS. In second Asia international conference on modeling and simulation,” AICMS 08, Kuala Lumpur, pp. 609–614.
51. Quek, C. (2005), “Predicting the impact of anticipator action on US stock market – An event study using ANFIS (a neural fuzzy model),” Computational Intelligence, vol. 23, pp. 117–141.
52. Wang, L. X. and J. H. Mendel, (1992), “Backpropagation fuzzy systems as nonlinear dynamic system identifiers,” Proc. IEEE Int. Conf. On Fuzzy Systems, San Diego, pp. 11631 170.
53. Zadeh L.A. (1965), “Fuzzy sets,” Information Control, vol. 8, pp.338–353

論文使用權限 
同意紙本無償授權給館內讀者為學術之目的重製使用，於20160629公開。不同意授權瀏覽/列印電子全文服務。 


