An ANFIS model for stock price prediction: The case of Tehran stock exchange

INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications

Volumn , Issue , 2011, Pages 44-49

  Esfahanipour, Akbar ^a   Mardani, Parvin ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

ANFIS; Artificial Neural Network; fuzzy subtractive clustering; Stock price prediction; Tehran Stock Exchange

Indexed keywords

ANFIS; ARTIFICIAL NEURAL NETWORK; FUZZY SUBTRACTIVE CLUSTERING; STOCK PRICE PREDICTION; TEHRAN STOCK EXCHANGES;

C (PROGRAMMING LANGUAGE); COSTS; INTELLIGENT SYSTEMS; NEURAL NETWORKS; RISK PERCEPTION;

FORECASTING;

EID: 79961203318     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INISTA.2011.5946124     Document Type: Conference Paper

References (19)

1. G. S. Atsalakis, and P. K. Valavanis, "Surveying stock market forecasting techniques- part II: soft computing methods," Journal of Expert System with Applications, vol.36, 2009, pp.5932-5941.
2. F. Karray, and C. D. Silva, Soft Computing and Intelligent System Design, Addison Weseley, 2004.
3. J. R. Jang, "ANFIS: Adaptive network based fuzzy inference system," IEEE Trans. On Systems, Man and Cybernetics vol. 23, no. 3, 1993, pp. 665-685.
4. H. Khalouzadeh, and A. Khaki, "Evaluating stock price prediction methods and representing a nonlinear model based on neural networks," (In persian), Economic Researches (published by Tehran University Faculty of Economics), no. 64 , 2003, pp. 43-85.
5. Kh. Nasrollahi, S. Samadi, and R. Saghafi, "An appraisal of predictability in TEPIX," (In persian), Quarterly Journal of Tehran Securities& Exchange Organization, vol. 2, no. 6 , 2009, pp. 5-30.
6. A. Abraham, N. S. Philip, and P. Saratchandran, "Modeling chaotic behavior of stock indices using intelligent paradigms," 2004, available at http://arxiv.org/abs/cs.
7. R. J. Li, and Zh. B. Xiong, "Forecasting stock market with fuzzy neural networks," IEEE, [4th International Conf. on Machine Learning and Cybernetics, pp. 3475-3479, 2005].
8. G. S. Atsalakis, and K. P. Valavanis, "Forecasting stock market short term trends using a neuro-fuzzy based methodology," Journal of Expert Systems with Applications, vol. 36, no. 7, 2009, pp.10696-10707.
9. Ch. H. Cheng, L. Y. Wei, and Y. Sh. Chen, "Fusion ANFIS models based on multi- stock volatility causality for TAIEX forecasting," Journal of Neurocomputing, vol. 72, no. 16-18, 2009, pp. 3462-3468.
10. A. Esfahanipour, and W. Aghamiri, "Adapted neuro-fuzzy inference system on indirict approach TSK fuzzy rule base for stock market analysis," Journal of Expert Systems with Applications, vol. 37, 2010, pp. 4742-4748.
11. M. A. Boyacioglu, and D. Avci. "An adaptive Network Based Fuzzy Inference System (ANFIS) for the prediction of Stock Market Return: The Case of Istanbul Stock Exchange." Journal of Expert Systems with Applications 37, no. 12, 2010, pp. 7908-7912.
12. F. Zhai, Q. Wen, Z. Yang, and Y. Song. "Hybrid forecasting model research on stock data mining,"IEEE, [4th International Conf. on New Trends in Inf. Science and Service Science (NISS), pp. 630-633, 2010].
13. J. I. Collazo Cuevas, M. A. Aceves Fernandez, E. Gorrostieta Hurtado, J. C. Pedraza Ortega, A. Sotomayor Olmedo, and M. Delgado Rosas. "Comparison between fuzzy c-mean clustering and fuzzy clustering subtractive in urban air pollution," IEEE, [20th international conference on Electronics, Communications and Computer (CONIELECOMP), pp. 174-179, 2010].
14. C. Pereira, and A. Dourado, "Neuro-fuzzy systems complexity reduction by subtractive clustering and support vector learning for nonlinear process modeling," eunite, 2001.
15. A. Ghodsi, "Efficient parameter selection for system identification," IEEE, [Annual Meeting of the Fuzzy Information (NAFIPS'04), pp. 750- 755, 2004].
16. M. b. Pang, and X. P. Zhao, "Traffic flow prediction of chaos time series by using subtractive clustering for fuzzy neural network modeling," IEEE, [Second International Symposium on Intelligent Information Technology Application, pp. 23-27, 2008].
17. T. Sh. Mahmoud, M. H. Marhaban, and T. S. Hong, "ANFIS controller with fuzzy subtractive clustering method to reduce coupling effects in twin rotor MIMO systems (TRMS) with less memory at time usage," IEEE, [International Conf. on Advanced Computer Control, pp. 19-23, 2008].
18. S. Chopra, R. Mitra, and V. Kumar, "Identification of rules using subtractive clustering with application to fuzzy controllers," IEEE, [The Third International Conf. on Machine Learning and Cybernetics Shanghai, 4125-4130, 2004].
19. S. Chiu, "Method and software for extracting fuzzy classification rules by subtractive clustering," IEEE, 1996, pp. 461-465.