||The Application of Adaptive Neuro-Fuzzy Inference System(ANFIS) for Dynamic Trading Decision Support System-Evidence from TAIEX Stock Index Futures
||Department of Banking and Finance
Adaptive Neuro-Fuzzy Inference System(ANFIS)
||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 Neuro-Fuzzy 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.
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 Neuro-Fuzzy 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
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 one-month expiration contract (model 1). 43
Table 7. The accuracy of initial training for one-year expiration contract (model 2) 44
Table 8. The accuracy between model 1 and model 2 44
Table 9. The trading profits 45
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
||1. Abbasi, E., and A. Abouec, (2008), “Stock price forecast by using neuro-fuzzy 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 self-organizing map (SOM),” Annual Hawaii international conference on system sciences, 40th, pp. 1–8.
3. Altrock, C. V.(1995), “Fuzzy logic & neuro fuzzy applications explained,” Prentice-Hall International, Inc..
4. Armano, G., M. Marchesi, and A. Murru, (2005), “A hybrid genetic-neural 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 short-term trends using a neuro-fuzzy based methodology,” Expert Systems with Applications, vol. 36, no. 3, pp. 10696–10707.
7. Avcı, E. (2007), “Forecasting daily and sessional returns of the ISE-100 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 STFT-ANFIS 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 dual-factor time-series 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 SOFM-SVR with a filter-based feature selection for stock market forecasting,” Expert Systems with Applications, vol. 36, no, 2, pp.1529–1539.
22. Jang, J.S. Roger (1993), “ANFIS : Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. On Systems, Man, and Cybernetics, vol. 23, no. 3, pp.665-685.
23. Jang, J.S. Roger, C. T. Sun, and E. Mizutani,(1997), “Neuro-fuzzy and soft computing,” Prentice-Hall 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,” Prentice-Hall International, Inc..
30. Kosko B. (1992), “Neural networks and fuzzy systems,” Prentice-Hall International, Inc..
31. Kosko, B. (1997), “Fuzzy engineering”, Prentice-Hall International, Inc..
32. Wang, L. X. (1994), “Adaptive fuzzy systems and control”, Prentice-Hall International, Inc..
33. Wang, L. X. (1997), “A course in fuzzy systems and control,” Prentice-Hall International, Inc..
34. Lin, C. T. and C.S. Lee, (1991), “Neural-network-based fuzzy logic control and decision system,” IEEE Trans. On Computers, vol. 40, no. 12, pp. 1320-1336.
35. Lin, C. T. and C. S. Lee, (1996), “Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems,” Upper Saddle River, NJ: Prentice-Hall.
36. Su, M. C. (1997), “Identification of singleton fuzzy models via fuzzy hyper-rectangular composite NN,” Fuzzy Model Identification: Selected Approaches, H. Hellen doorn and D. Driankov, Eds. pp. 215-250.
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 line-segments regression for stock prices and long-range 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 brain-inspired 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 network-based 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 rule-based expert systems”, Decision Support Systems, vol. 11, pp. 497-507.
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), “Back-propagation fuzzy systems as nonlinear dynamic system identifiers,” Proc. IEEE Int. Conf. On Fuzzy Systems, San Diego, pp. 1163-1 170.
53. Zadeh L.A. (1965), “Fuzzy sets,” Information Control, vol. 8, pp.338–353