A simple neuro fuzzy model for ISE 100 index prediction;Yalin sinirsel bulanik bir model ile imkb 100 endeksi tahmini

Journal of the Faculty of Engineering and Architecture of Gazi University

Volumn 26, Issue 4, 2011, Pages 897-904

  Ok, Yeşim ^a   Atak, Mehmet ^b   Akçayol, M Ali ^b  

^a ATATÜRK UNIVERSITY   (Turkey)

^b GAZI UNIVERSITY   (Turkey)

Author keywords

ANFIS; ISE 100 index prediction; Neuro fuzzy systems

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; COMPLEX MODEL; EXCHANGE RATES; INPUT DATAS; INPUT VARIABLES; INTEREST RATES; ISTANBUL STOCK EXCHANGE; NEURO-FUZZY MODEL; NEUROFUZZY SYSTEM; PREDICTION PERFORMANCE; PREDICTIVE FACTORS; STOCK MARKET MODEL; STOCK MARKET PREDICTION; TRADING VOLUMES;

COMMERCE; FINANCE; FORECASTING; FUZZY INFERENCE;

FUZZY SYSTEMS;

EID: 84855362245     PISSN: 13001884     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Kutlu, B., Badur, B., "Yapay Sinir Aǧlari Ile Borsa Endeksi Tahmini", Yönetim, 63:25-40, (2009)
2. Işeri, M., Çaǧlar, H., Çaǧlar N., "A model proposal for the chaotic structure of Istanbul stock exchange", Chaos, Solitons and Fractals 36:1392-1398, 2008. (Pubitemid 351172790)
3. Murphy, J.J, "Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications" New York Institute of Finance, 1999.
4. Diler, A. I., "IMKB Ulusal 100 Endeksinin Yönünün Yapay Sinir Aǧlari: Hata Geriye Yayma Yöntemi ile Tahmin Edilmesi", IMKB Dergisi, 7:65-81, 2003.
5. Yumlu, S., Gürgen, F., Okay, N., "A Comparison of Global, Recurrent and Smoothed-Piecewise Neural Models for Istanbul Stock Exchange Prediction", Pattern Recognition Letters, 26:2903-2103, 2004.
6. Altay, E., Satman, M. H., "Stock Market Forecasting: Artificial Neural Networks and Linear Regression Comparison in an Emerging Market", Journal of Financial Management and Analysis, 18(2):18-33, 2005.
7. Karaatli, M., Güngör, I., Demir Y. Kalayci, Ş., "Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Aǧlari Yöntemi ile Tahmin Edilmesi", Balikesir Üniversitesi IIBF Dergisi, 2(1), 22-48, 2005.
8. Karaçor, Z., Alptekin, V. "Finansal Krizlerin Önceden Tahmin Yoluyla Deǧerlendirilmesi: Türkiye Örneǧi", Yönetim ve Ekonomi, 13 (2), 237-256, 2006.
9. Avci, E., "Forecasting daily and sessional returns of the ISE-100 index with neural network models", Journal of Dogus University, 8(2):128-142, 2007.
10. Hamzaçebi, C., Bayramoǧlu, M. F., "Yapay Sinir Aǧlari ile IMKB 100 Endeksinin Tahmini", YAEM 27.Ulusal Kongresi, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Endüstri Mühendisliǧi Bölümü ve Yöneylem Araştirmasi Derneǧi, 2007.
11. Yildiz, B., Yalama A., Coşkun M., "Forecasting the Istanbul Stock Exchange National 100 Index Using an Artificial Neural Network", World Academy of Science, Engineering and Technology 46:36-39, 2008.
12. Öǧüt, H., Doǧanay M. , Aktaş, R., "Detecting stock-price manipulation in an emerging market: The case of Turkey", Expert Systems with Applications 36 :11944-11949, 2009.
13. Quek, C. Predicting the impact of anticipator action on US stock market-An event study using ANFIS (a neural fuzzy model). Computational Intelligence, 23, 117-141, 2005.
14. Trinkle, B. S., "Forecasting annual excess stock returns via an adaptive network-based fuzzy inference system", Intelligent Systems in Accounting, Finance and Management, 13(3), 165-177, 2006.
15. Atsalakis, G., Valavanis, K., "Forecasting stock market short-term trends using a neuro-fuzzy based methodology" Expert Systems with Applications, 36(3) : 10696-10707, 2009.
16. Akbar E., Werya A. , "Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis", Expert Systems with Applications 37:4742-4748, 2010.
17. Boyacioglu, M., A., Avci, D., "An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange" , Expert Systems with Applications 37 : 7908-7912, 2010.
18. Atsalakis G., Valavanis K., "Surveying stock market forecasting techniques-Part II: Soft computing methods", Expert Systems with Applications 36(3) : 5932-5941 2009.
19. Baykal, N., Beyan T., "Bulanik mantik uzman sistemler ve denetleyiciler", Biçaklar Kitabevi, Ankara 2004.
20. Abraham, A., "Neuro-fuzzy systems: state-ofthe-art modeling techniques, connectionist models of neurons, learning processes and artificial intelligence", Lecture Notes in Computer Science, 2084: 269-276, 2001.
21. Takagi, T., Sugeno, M., "Fuzzy identification of systems and its applications to modelling and control", IEEE Transactions on Systems, Man, and Cybernetics, 15: 116-132, 1985.
22. Jang, J. S. R., "ANFIS: Adaptive-network based fuzzy inference systems", IEEE Trans. On Systems, Man, and Cybernetics, 23 (03): 665-685, 1993.
23. Demuth, H., Beale, M., "Anfis and the ANFIS Editor GUI" Fuzzy logic toolbox for use with MATLAB, User's Guide Version 4, 2:104-130, MA, 2000.
24. Izumi, K., & Ueda, KAnalysis of exchange rate scenarios using an artificial market approach. Proceedings of the international conference on artificial intelligence, 2:360-366, 1999.
25. Akel, V., Bayramoǧlu, M., F., "Kriz Dönemlerinde Yapay Sinir Aǧlari ile Finansal Öngörüde Bulunma: Imkb 100 Endeksi Örneǧi", International Symposium on International Capital Flows and Emerging Markets, Balikesir, 2008.