A type-2 fuzzy model for stock market analysis

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2007, Pages

  Zarandi, M H Fazel ^a   Neshat, E ^a   Turksen, I B ^b   Rezaee, B ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; COMMERCE; DECISION SUPPORT SYSTEMS; EXPERT SYSTEMS; FUZZY INFERENCE; FUZZY RULES; FUZZY SETS; FUZZY SYSTEMS; INVENTORY CONTROL; LAWS AND LEGISLATION; MANAGEMENT INFORMATION SYSTEMS;

FUZZY MODEL; INPUT VARIABLES; INTERNATIONAL CONFERENCES; RULE BASED; RULE BASED EXPERT SYSTEM; STOCK MARKET ANALYSIS; STOCK MARKETS; STOCK PRICES; TYPE-2 FUZZY LOGIC SYSTEMS;

FUZZY LOGIC;

EID: 50249095850     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2007.4295378     Document Type: Conference Paper

References (32)

1. Abraham, A., Baikunth, N., and Mahanti, P. K. (2001) Hybrid intelligent systems for stock market analysis, Lecture Notes in Computer Science, 2074, pp. 337-345.
2. Abraham, A., Philip, N. S., and Saratchandran, F. (2003) Modeling chaotic behavior of stock indices using intelligent paradigms, Neural Parallel and Scientific Computations, 11, pp. 143-160.
3. Aiken, M., and Bsat, M. (1999) Forecasting market trends with neural networks, Information Systems Management, 16(4), pp. 42-48.
4. Baba, N., Inoue, N., and Asakawa, H. (2000). Utilization of Neural Networks and GAs for Constructing Reliable Decision Support Systems to Deal Stocks, IEEE-INNS-ENNS International Joint Conf. on Neural Networks (IJCNN'00) 5, pp. 3111-3116.
5. Baduska R., (1998) Fuzzy Modeling for Control, Kluwer Academic Publishers.
6. Baduska, R. (1993) New approach to constructing fuzzy relational models from data, Profc. European Control Conf., Rome, pp. 1207-1212.
7. Bezdek J.C, (1975) Mathematical models for systematic and taxonomy, in Proc. 8th Int. Conf., numerical taxonomy, G. Estabrook, Ed., Freeman, San Franscisco, CA, pp. 143-166.
8. Chang, P. C., and Wang, Y. W. (2006) Fuzzy delphi and back-propagation model for sales forecasting in PCB Industry, Expert Systems with Applications, 30(4), pp. 715-726.
9. Chang, P. C., Wang, Y. W., and Yang, W. N. (2004) An investigation of the hybrid forecasting models for stock price variation in Taiwan, Journal of the Chinese Institute of Industrial Engineering, 21(4), pp. 358-368.
10. Chang, P.C., Liu, C.H. (2006) A TSK type fuzzy rule based system for stock price prediction, Expert Systems with Applications. On-line available.
11. Chang, P.C., Liu, C.H., and Wang, Y.W. (2006) A hybrid model by clustering and evolving fuzzy rules for sale forecasting in printed circuit board industry, Decision Support Systems. Vol. 42, Issue 3, pp. 1254-1269
12. Chi, S. C., Chen, H. P., and Cheng, C. H. (1999) A forecasting approach for stock index future using Grey theory and neural networks, IEEE International Joint Conf on Neural Networks, pp. 3850-3855.
13. Fazel Zarandi M.H. (1998). Aggregate System Analysis for Prediction of Tardiness and Mixed Zones of Continuous Casting with Fuzzy Methodology, Ph.D. Thesis.
14. Fukuyama Y., Sugeno M., (1989) A new methodof choosing the number of clusters for the fuzzy c-means method, Proc. of the Fifth Fuzzy Systems Symposium, pp. 247-250.
15. Gath I. and Geva A.B., (1989) Unsupervised optimal fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) pp. 773-781.
16. Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
17. Kim D., Lee K.H., Lee D., (2003) Fuzzy cluster validation index based on inter-cluster proximity, Pattern Recognition Lett. 24 (15), pp. 2561-2574.
18. Kim, K. J., and Han, I. (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, pp. 125-132.
19. Kimoto, T., and Asakawa, K. (1990) Stock market, prediction system with modular neural network. IEEE International Joint Conf. on Neural Network, pp. 1-6.
20. Kuo, R. J., Chen, C. H., and Hwang, Y. C. (2001) An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, pp. 21-24.
21. Kwon S.H. (1998) Cluster validity index for fuzzy clustering, Electron. Lett. 34(22), pp. 2176-2177.
22. Lee, J.W. (2001) Stock Price Prediction Using Reinforcement Learning. IEEE International Joint Conf. on Neural Networks, pp. 690-695.
23. Mendel, J. M. (2001), Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper-Saddle River, NJ.
24. Mendel, J. M. (2007) Advances in type-2 fuzzy sets and systems, Information Sciences, Vol. 177, Issue 1. pp. 84-110.
25. Mendel, J. M. and John, R. I. (2002) Type-2 Fuzzy Sets Made Simple, IEEE Trans. on Fuzzy Systems, Vol. 10, pp. 117-127.
26. Mendel, J. M. John, R. I. Liu, F. (2006) Interval Type-2 Fuzzy Logic Systems Made Simple, IEEE Trans. on Fuzzy Systems, Vol. 14, Issue. 6, pp. 808-821.
27. Sakawa, M., (2002) Genetic Algorithms and Fuzzy Multiobjective Optimization. Kluwer Academic, Boston.
28. Sugeno M. and Yasukawa, T. (1993) A Fuzzy Logic Based Approach to Qualitative Modeling, IEEE Trans. on Fuzzy Systems Vol. 1, pp 7-31.
29. Wu D. and Tan, W. W. (2004) A Type-2 Fuzzy Logic Controller for the liquid-Level process, Proc. of IEEE International Conf. on Fuzzy Systems, Budapesi, Hungary, July 2004, pp. 953-958.
30. Xie X.L. and Beni G., (1991) A validity measure for fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell. 13(8), pp. 841-847.
31. Yao, J., and Poh, H. L. (1995) Forecasting the KLSE index using neural networks. IEEE International Conf. on Neural Networks, pp. 1012-1017.
32. Yoon, Y., and Swales, J. (1991) Prediction stock price performance: a neural network approach. Proc. of Twenty-Fourth Annual Hawaii International Conf. on System Science, pp. 156-162.