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Forecasting the Istanbul Stock Exchange National 100 Index Using an Artificial Neural Network

Authors: Birol Yildiz, Abdullah Yalama, Metin Coskun

Abstract:

Many studies have shown that Artificial Neural Networks (ANN) have been widely used for forecasting financial markets, because of many financial and economic variables are nonlinear, and an ANN can model flexible linear or non-linear relationship among variables. The purpose of the study was to employ an ANN models to predict the direction of the Istanbul Stock Exchange National 100 Indices (ISE National-100). As a result of this study, the model forecast the direction of the ISE National-100 to an accuracy of 74, 51%.

Keywords: Artificial Neural Networks, Istanbul StockExchange, Non-linear Modeling.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077187

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References:


[1] E. Y. Li,"Artificial Neural Networks and Their Business Applications," Information and Management, vol. 27, no. 5, pp. 303-313, 1994.
[2] F. Zadehi, "A Meta-analysis of Financial Applications of Neural Networks," International Journal of Computational Intelligence and Organization, vol. 1, no. 3, pp. 164-178, 1996.
[3] D. West, "Neural Network Credit Scoring Models," Computers and Operational Research, vol. 27, no. 11/12, pp. 1131-1152, 2000.
[4] Y. Yoon, G. Swales Jr., and T.M. Margavio "A Comparison of Discriminant Analysis versus Artificial Neural Networks," Journal of the Operational Research Society, vol. 44, no. 1, pp. 51-60, January 1993.
[5] A. Kanas, "Non-linear forecasts of stock returns ," Journal of Forecasting, vol. 22, no.4, pp. 299-315, July 2003.
[6] R.L.Wilson, and R. Sharda, "Bankruptcy Prediction Using Neural Networks," Decision Support System, vol. 11, no.5, pp. 545-557, June 1994.
[7] R.S. Ludwig and M.J. Piovoso, "A Comparison of Machine-Learning Classifiers For Selecting Money Managers," Intelligent Systems in Accounting, Finance and Management, Chichester, vol. 13, no. 3, p. 151-164, July 2005.
[8] K. Kumar and S. Bhattacharya, "Artificial Neural Network vs Linear Discriminant Analysis in Credit Ratings Forecast: A Comparative Study of Prediction Performances," Review of Accounting and Finance, vol. 5, no. 3, 217-227, 2006.
[9] P.R. Burell and B.O. Florin, "The Impact of Neural Networks in Finance," Neural Computing and Applications, vol. 6, no. 4, pp. 193- 200, December 1997.
[10] S.V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications, Wiley IEEE Press, New York, August 1995.
[11] R.Stein and V. Dhar, Intelligent Decision Support Methods: The Science of Knowledge Work, Prentice Hall Business Publishing, N.J., 1996
[12] S. Ward and M. Sherald, The Neural Network Financial Wizards, Technical Analysis of Stocks and Commodities, Reprinted, Technical Analyses Inc., Seattle, Washington 1995.
[13] P.K.H. Pahua, D. Ming and W. Lin, "Neural Network with Genetically Evolved Algorithms for Stocks Prediction," Asia-Pacific Journal of Operational Research, vol. 18, pp. 103-107, 2001.
[14] A.I. Diler, "IMKB-100 Endeksinin Yön├╝n├╝n Yapay Sinira─ƒlar─▒ Hata Geriye Yayma Yöntemiyle Tahmin Edilmesi," IMKB Dergisi, vol.7, pp. 25-26, 2003.
[15] A. Erdinç and M.H. Satman, "Stock Market Forecasting: Artificial Neural Network and Linear Regression Comparison in An Emerging Market," Journal of Financial Management and Analysis, vol.18, no. 2, pp. 18-34, 2005.