| Paper title: |
A Feed-Forward Neural Network Approach to Istanbul Stock Exchange |
| DOI: | https://doi.org/10.4316/JACSM.201802005 |
| Published in: | Issue 2, (Vol. 12) / 2018 |
| Publishing date: | 2018-10-15 |
| Pages: | 31-36 |
| Author(s): | YAVUZ Mehmet, ÖZDEMIR Necati |
| Abstract. | In this study the trend estimation of the participation indices (PARTI) in the Istanbul Stock Exchange (BIST) using artificial neural network (ANN) theory. PARTI can be regarded as the Participation 50 Index (KAT50) and the Participation 30 Index (KATLM). Since KAT50 has only been calculated since 9th July 2014, there are only a few studies on this index. On the other hand, PARTI indices are growing more and more important in global economies, especially in Turkey, England, etc. Therefore, in this study, firstly, we have used ANN method, using 1410 daily closing values of KATLM, between 1st August 2012 and 30th June 2016. For the KAT50 index, we used 720 daily closing values between 9th July 2014 and 29th July 2016. We created a feed-forward back-propagation neural network model in order to predict the trends of these indices and we applied the previously mentioned daily closing values of these participation indices to this model. The results obtained using the ANN method are compared in the figures and tables. It can be concluded that the results of this study are very helpful for individual and institutional investors’ investment decisions within global economies |
| Keywords: | BIST, Participation Index, Artificial Neural Network, Trend Prediction |
| References: | 1. Ertuğrul, Ö. F. and M. E. Tağluk, Forecasting Financial Indicators by Generalized Behavioral Learning Method, Soft Computing, 1-14, (2017). 2. Yao, J. and H.-L. Poh, Forecasting the Klse Index Using Neural Networks, Proceedings, Neural Networks, 1995. Proceedings., IEEE International Conference on: IEEE, 1012-1017, (1995). 3. Avcı, E., Stock Return Forecasts with Artificial Neural Network Models, İktisadi ve İdari Bilimler Dergisi, 26(1), 443-461, (2009). 4. Dixit, G., et al., Predicting India Volatility Index: An Application of Artificial Neural Network, International Journal of Computer Applications, 70(4), (2013). 5. Monfared, S. A. and D. Enke, Volatility Forecasting Using a Hybrid Gjr-Garch Neural Network Model, Procedia Computer Science, 36, 246-253, (2014). 6. Yavuz, M., et al., Yapay Sinir Ağları Ile Risk-Getiri Tahmini Ve Portföy Analizi, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(4), 87-107, (2015). 7. Siddiqui, T. A. and Y. Abdullah, Developing a Nonlinear Model to Predict Stock Prices in India: An Artificial Neural Networks Approach, IUP Journal of Applied Finance, 21(3), 36, (2015). 8. Sakarya, S., et al., Stock Market Index Prediction with Neural Network During Financial Crises: A Review on Bist-100, Financial Risk and Management Reviews, 1(2), 53-67, (2015). 9. Telli, Ş. and M. Coşkun, Forecasting the Bist 100 Index Using Artificial Neural Networks with Consideration of the Economic Calendar, International Review of Economics and Management, 4(3), (2016). 10. Moghaddam, A. H., et al., Stock Market Index Prediction Using Artificial Neural Network, Journal of Economics, Finance and Administrative Science, 21(41), 89-93, (2016). 11. Yusoff, Y., et al., Potential Ann Prediction Model for Multiperformances Wedm on Inconel 718, Neural Computing and Applications, 1-15, (2016). 12. Kara, Y., et al., 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, (2011). 13. Ibrahim, M. H., et al., Determining Optimum Structure for Artificial Neural Network and Comparison between Back-Propagation and Levenberg-Marquardt Training Algorithms, International Journal of Engineering Science, 7(9), 14887-14890, (2017). 14. Guresen, E., et al., Using Artificial Neural Network Models in Stock Market Index Prediction, Expert Systems with Applications, 38(8), 10389-10397, (2011). 15. Qiu, M., et al., Application of Artificial Neural Network for the Prediction of Stock Market Returns: The Case of the Japanese Stock Market, Chaos, Solitons & Fractals, 85, 1-7, (2016). 16. Masoud, N., Predicting Direction of Stock Prices Index Movement Using Artificial Neural Networks: The Case of Libyan Financial Market, British Journal of Economics, Management & Trade, 4(4), 597-619, (2014). 17. Cheng, C.-J., et al., Customer Lifetime Value Prediction by a Markov Chain Based Data Mining Model: Application to an Auto Repair and Maintenance Company in Taiwan, Scientia Iranica, 19(3), 849-855, (2012). 18. Sattar, A. M., et al., Extreme Learning Machine Model for Water Network Management, Neural Computing and Applications, 1-13, (2017). |
| Back to the journal content | |
