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Financial Risk and Management Reviews

June 2015, Volume 1, 2, pp 53-67

Stock Market Index Prediction with Neural Network during Financial Crises: A Review on Bist-100

Åžakir SAKARYA

Mehmet YAVUZ

Aslan Deniz KARAOÄžLAN

Necati Ã–ZDEMÄ°R

Åžakir SAKARYA¹
Mehmet YAVUZ²

Aslan Deniz KARAOÄžLAN³
Necati Ã–ZDEMÄ°R⁴

Balikesir University, Department of Business Administration, CaÄŸiÅŸ Campus, BalÄ±kesir, Turkey ¹

Necmettin Erbakan University, Fakulty of Science, Department of Mathematics-Computer Sciences, Meram, Konya, Turkey ²

Balikesir University, Department of Industrial Engineering, CaÄŸiÅŸ Campus, BalÄ±kesir, Turkey ³

Balikesir University, Department of Mathematics, CaÄŸiÅŸ Campus, BalÄ±kesir, Turkey ⁴

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Pages: 53-67

DOI: 10.18488/journal.89/2015.1.2/89.2.53.67

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Abstract:

Predetermining the future value of a variable is both quite important and rather difficult process in financial markets. In this context, especially in the last 15 years, Artificial Neural Networks (ANNs) are widely used in order to resolve various kinds of financial problems such as performing portfolio construction, stock index, and bankruptcy prediction. This study examines the predictability of daily and weekly returns of Borsa İstanbul (BIST)-100 Index during global crisis period (July 2007-December 2009) by using ANN. It differs from other similar studies in the literature as it: i) covers global crisis period, ii) predicts index value of the next day and next week and finally iii) uses seven different economic parameters (variables) as input. The results obtained suggest that ANN can be used quite successfully in this area and foresee correctly the value for next day and next week with an accuracy margin error of less than 5% even for unknown samples. The ANN model in this study is developed using MATLAB R2008b.

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Aghababaeyan, R., K. Najeeb Ahmad and S. Tamanna, 2011. Forecasting the Tehran stock market by artificial neural network. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence: 13-17. Available from http://dx.doi.org/10.14569/SpecialIssue.2011.010303#sthash.YWRIBxW0.dpuf.
Akel, V. and M.F. Bayramo?lu, 2008. Kriz dönemlerinde yapay sinir a?lar? ile finansal ongörüde bulunma: ?MKB-100 endeksi Örne?i. In International Symposium on International Capital Flows and Emerging Markets, Turkey: 24-27.
Demirtas, M. and K. Aslan Deniz, 2012. Optimization of PI parameters for DSP-based permanent magnet brushless motor drive using response surface methodology. Energy Conversion and Management, 56: 104-111.
Desai, J., A. Trivedi and N.A. Joshi, 2012. Forecasting of stock market indices using artificial neural network. Shri Chimanbhai Patel Institutes, Ahmedabad Working Paper No. CPI/MBA/2013/0003.
Diler, A.?., 2003. ?MKB Ulusal 100 endeksinin yönünün Yapay Sinir A?lar?: Hata Geriye Yayma Yöntemi ile Tahmin Edilmesi. Türkiye'de Banklar, Sermaye Piyasas? ve Ekonomik Büyüme: Koentegrasyon ve Nedensellik Analizi (1989-2000), 81.
Gençtürk, M., 2009. Finansal Kriz dönemlerinde makroekonomik faktörlerin hisse senedi fiyatlar?na Etkisi. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(1): 127-136.
Guresen, E., G. Kayakutlu and T.U. Daim, 2011. Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8): 10389-10397.
Kara, Y., M.A. Boyacioglu and Ö.K. Baykan, 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange. Expert Systems with Applications, 38(5): 5311-5319.
Karaoglan, A.D., 2011. An integrated neural network structure for recognizing autocorrelated and trending processes. Mathematical & Computational Applications, 16(2): 514-523.
Kimoto, T., K. Asakawa, M. Yoda and M. Takeoka, 1990. In: Stock market prediction system with modular neural network. Proceedings of the International Joint Conference on Neural Networks. pp: 1-6.
Kutlu, B. and B. Bertan, 2009. Yapay Sinir A?lar? ile Borsa Endeksi Tahmini. Yönetim, ?stanbul Üniversitesi ??letme Fakültesi ??letme ?ktisadi Enstitüsü Dergisi, 20(63): 25-40.
Li, F. and C. Liu, 2009. Application study of BP neural network on stock market prediction. Ninth International Conference on Hybrid Intelligent Systems. IEEE, pp: 174-178.
Mallaris, M. and S. Linda, 1996. Using neural networks to forecast the S&P 100 implied volatility. Neurocomputing, 10(2): 83-195.
Mizuno, H., M. Kosaka, H. Yajima and N. Komoda, 1998. Application of neural network to technical analysis of stock market prediction. Studies in Informatic and Control, 7(3): 111-120.
O’connor, N. and G.M. Michael, 2005. A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems, 19(5): 371-378.
Oh, K.J., T.Y. Kim and C. Kim, 2006. An early warning system for detection of financial crisis using financial market volatility. Expert Systems, 23(2): 83-98.
Öztemel, E., 2003. Yapay sinir a?lar?. ?stanbul: Papatya Yay?nc?l?k.
Phua, P.K.H., D. Ming and W. Lin, 2000. Neural network with genetic algorithms for stocks prediction. Fifth Conference of the Association of Asian-Pacific Operations Research Societies Proceedings, 5th - 7th July, Singapore.
Vural Bar??, B., 2007. Yapay sinir a?lar? ?le finansal tahmin. Yüksek Lisans Tezi, Ankara Üniversitesi Sosyal Bilimler Enstitüsü, ??letme Anabilim Dal?, Ankara.
Wang, J.Z., J.J. Wang, Z.G. Zhang and S.P. Guo, 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11): 14346-14355.
Y?ld?z, B., 2001. Finansal Ba?ar?s?zl???n Öngörülmesinde Yapay Sinir A?? Kullan?m? ve Halka Aç?k ?irketlerde Ampirik Bir Uygulama. ?MKB Dergisi, 5(17): 51-67.
Yildiz, B., A. Yalama and M. Coskun, 2008. Forecasting the Istanbul stock exchange national 100 index using an artificial neural network. World Academy of Science, Engineering and Technology, 46: 36-39.
Yoon, Y. and S. George, 1991. Predicting stock price performance: A neural network approach. Proceedings of the 24th Annual Hawaii International Conference on System Sciences, 4: 156-162.
Yoon, Y., G. Swales and T.M. Margvio, 1993. A comparison of discriminant analysis versus artificial neural networks. Journal of the Operational Research Society, 44(1): 51-60.