bilturk BİLTÜRK Ekonomi ve İlişkili Çalışmalar Dergisi 2667-5927 Fatih DEYNELİ 10.47103/bilturk.1039669 Economics Business Administration Ekonomi İşletme Forecasting BIST 100 Index with Artificial Neural Networks and Regression Analysis Forecasting BIST 100 Index with Artificial Neural Networks and Regression Analysis https://orcid.org/0000-0002-0983-1455 Ünvan Yüksel Akay Ankara Yıldırım Beyazıt Üniversitesi https://orcid.org/0000-0002-4722-0911 Ergenç Cansu ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ 01 31 2022 4 1 20 32 12 22 2021 01 10 2022 Copyright © 2019, BİLTÜRK Ekonomi ve İlişkili Çalışmalar Dergisi 2019 BİLTÜRK Ekonomi ve İlişkili Çalışmalar Dergisi

Making reliable forecasts is very important for financial analysis. For this reason, financial analysts make analyzes using different models. Financial analysts try to make the most accurate estimation in these analyzes. The artificial neural network model is a widely used method in the field of finance. In this study, BIST 100 index was estimated using artificial neural networks and regression model. By using the closing prices of the BIST 100 index between 2010 and 2020, the closing values of the BIST 100 index for 2021 were estimated. Moreover, the regression model and artificial neural network model predictions were obtained. The mean square error of the neural networks and regression model was also found. Finally, according to the result of the mean of error squares, the performance of the models was compared and seen that the artificial neural network model was better.

Making reliable forecasts is very important for financial analysis. For this reason, financial analysts make analyzes using different models. Financial analysts try to make the most accurate estimation in these analyzes. The artificial neural network model is a widely used method in the field of finance. In this study, BIST 100 index was estimated using artificial neural networks and regression model. By using the closing prices of the BIST 100 index between 2010 and 2020, the closing values of the BIST 100 index for 2021 were estimated. Moreover, the regression model and artificial neural network model predictions were obtained. The mean square error of the neural networks and regression model was also found. Finally, according to the result of the mean of error squares, the performance of the models was compared and seen that the artificial neural network model was better.

 Artificial Neural Network Regression BIST 100 Forecasts Artificial Neural Network Regression BIST 100 Forecasts Aiken, L. S., & West, S. G. Reno., RR (1991). Multiple regression: Testing and interpreting interactions. Altan, A., & Karasu, S. (2019). The effect of kernel values in support vector machine to forecasting performance of financial time series. The Journal of Cognitive Systems, 4(1), 17-21. Benrhmach, G., Namir, K., Namir, A., & Bouyaghroumni, J. (2020). Nonlinear autoregressive neural network and extended Kalman filters for prediction of financial time series. Journal of Applied Mathematics, 2020. Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and its Applications, 519, 127-139. Cao, Q., Parry, M. E., & Leggio, K. B. (2011). The three-factor model and artificial neural networks: predicting stock price movement in China. Annals of Operations Research, 185(1), 25-44. Carvalhal, A., & Ribeiro, T. (2008). Do artificial neural networks provide better forecasts? Evidence from Latin American stock indexes. Latin American Business Review, 8(3), 92-110. Diaz, J. F., & Nguyen, T. T. (2021). Application of grey relational analysis and artificial neural networks on corporate social responsibility (CSR) indices. Journal of Sustainable Finance & Investment, 1-19. Elliott, G., & Timmermann, A. (2008). Economic forecasting. Journal of Economic Literature, 46(1), 3-56. KangaraniFarahani, M., & Mehralian, S. (2013, August). Comparison between artificial neural network and neuro-fuzzy for gold price prediction. In 2013 13th Iranian Conference on Fuzzy Systems (IFSC) (pp. 1-5). IEEE. Fisher, R. A. (1922). The goodness of fit of regression formulae, and the distribution of regression coefficients. Journal of the Royal Statistical Society, 85(4), 597-612. Gauss, C. F. (1809). Theoria motus corporum coelestium in sectionibus conicis solem ambientium auctore Carolo Friderico Gauss. sumtibus Frid. Perthes et IH Besser. Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba, 333, 2006-2013. Lenel, L., Köster, R., & Fritsche, U. (2020). Introduction (Futures Past. Economic Forecasting in the 20th and 21st Century). Futures Past. Economic Forecasting in the 20th and 21st Century. Maciel, L. S., & Ballini, R. (2008). Design a neural network for time series financial forecasting: Accuracy and robustness analysis. Anales do 9º Encontro Brasileiro de Finanças, Sao Pablo, Brazil. Maind, S. B., & Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96-100. Molla, B., Cagil, G., & Uyaroglu, Y. (2021). Chaotic analysis of BIST 100 return time series and short-term predictability with ANFIS. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. Ozbey, F., & Paksoy, S. (2020). GARCH Ailesi Modelleri ve ANN Entegrasyonu ile BİST 100 Endeks Getirisinin Volatilite Tahmini 1. Business and Economics Research Journal, 11(2), 385-396. Patel, L., & Gaurav, K. A. (2020). Introduction to Machine Learning and Its Application. In Applications of Artificial Intelligence in Electrical Engineering (pp. 262-290). IGI Global. Pradeepkumar, D., & Ravi, V. (2017). Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Applied Soft Computing, 58, 35-52. Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (2016). Understanding regression analysis: An introductory guide (Vol. 57). Sage Publications. Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons. Siami-Namini, S., & Namin, A. S. (2018). Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803.06386. Wieland, V., & Wolters, M. (2013). Forecasting and policy making. In Handbook of economic forecasting (Vol. 2, pp. 239-325). Elsevier. Xiong, T., Bao, Y., & Hu, Z. (2014). Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge-Based Systems, 55, 87-100. Yule, G. U. (1897). On the theory of correlation. Journal of the Royal Statistical Society, 60(4), 812-854. Zhang, G. P., & Berardi, V. L. (2001). Time series forecasting with neural network ensembles: an application for exchange rate prediction. Journal of the operational research society, 52(6), 652-664.