ESTIMATING THE MOVEMENT OF BELEX15 INDEX VALUES USING THE ARIMA MODEL
Keywords:
ARIMA, Belex15
Abstract
In this paper the author tried to find a suitable model for forecasting the movement of the Belex15 index on the Belgrade Stock Exchange, using one of the most used methodologies of the last decades - the ARIMA method. Through method application in four steps, the author has come to the conclusion that ARIMA (0,1,11) is the optimal model for forecasting, using the given stock index data series. After evaluating the statistical appropriateness of the model and its application to short-term forecasts, the results were compared with the results of the previous work of the author in which he made the forecast using the linear and nonlinear methods (linear regression and neural networks) to the same data. The selected ARIMA model showed good statistical suitability and the possibility of a satisfactory short-term forecast of the Belgrade Stock Exchange index, although it showed a higher RMSE (root mean square error) compared to the nonlinear model applied to the same data.
References
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Clement, E., P. (2014). Using Normalized Bayesian Information Criterion (Bic) to Improve Box - Jenkins Model Building, American Journal of Mathematics and Statistics 2014, 4 (5): 214-221 DOI: 10.5923/j.ajms.20140405.02
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Fatima, S., & Hussain, G. (2006). Statistical models of KSE100 index using Hybrid Financial Systems, Neurocomputing 71(13-15): 2742-2746. DOI:10.1109/ICEIS.2006.1703193
Huang, C.-J., Yang, D.-X. & Chuang, Y.-T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction, Expert Systems with Applications 34 (2008) 2870–2878
Jarrett, J., E. & Kyper, E. (2011). ARIMA Modeling with Intervention to Forecast and Analyze Chinese Stoc Prices, International journal of engineering bussines management, 2011, Vol. 3, No. 3, 53-58
Jiang, X.-Q. & Zhang, L.-C. (2019). Stock Price Fluctuation Prediction Method Based on Time Series Analysis, Discrete and Continuous Dynamical Systems Series S, Volume 12, Number 4&5, pp. 915-927, doi:10.3934/dcdss.2019061
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Khashei, M., & Hajirahimi, Z. (2018). A comparative study of series arima/mlp hybrid models for stock price forecasting, Communications in Statistics—Simulation and Computation, 2018, Vol. 0, No. 0, 1-16
Makridakis, S., & Le Hibon, M. (1997). ARMA Models and the Box-Jenkins Methodology, Journal of Forecasting, Vol. 16, 147-163 (1997)
Merh, N., Saxena, V., P. & Pardasani, K., R., (2010), A Comparison Between Hybrid Approaches of Ann and Arima for Indian Stock Trend Forecasting, Business Intelligence Journal, 2010 Vol.3 No.2
Mondal, P., Shit, L. & Goswami, S., (2014), Study of Effectiveness of Time Series Modeling (ARIMA) in Forecasting Stock Prices, International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.2
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Pai, P.-F. & Lin, C.-S., (2005), AhybridARIMAand support vector machines model in stock price forecasting, Omega (The International Journal of Management Science) 33 (2005) 497 – 505
Petrović, M. (2019). Assessment of Movement of the Belex15 Index Using Linear Regression and Neural Network, JPMNT – Journal of Process Management New Technologies International, Vol. 7 (4)
Ralević, N., M., Glišović, N., S., Đaković, V., Đ., & Anđelić, G., B. (2014). The Performance of the Investment Return Prediction models: Theory and Evidence, SISY 2014 • IEEE 12th International Symposium on Intelligent Systems and Informatics • September 11–13, 2014, Subotica, Serbia
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Yousif, A., & Elfaki, F. (2017). Artificial Neural Network versus Linear Model Forecasting Doha Stock Market, IOP Conf. Series: Journal of Physics: Conf. Series 949 (2017) 012014, doi :10.1088/1742-6596/949/1/012014
Wang, L., Zou, H., Su, J., Li, L., & Chaudhry, S. (2013). An ARIMA-ANN Hybrid Model for Time Series Forecasting, Systems Research and Behavioral Science Syst. Res. 30, 244–259 (2013), DOI: 10.1002/sres.2179
Xiong, L. & Lu, Y., (2017), Hybrid ARIMA-BPNN Model for Time Series Prediction of the Chinese Stock Market, 2017 3rd International Conference on Information Management
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https://www.ibm.com/support/knowledgecenter/bs/SS3RA7_17.0.0/clementine/ex_bb_fcast_exmod.html, Accessed February 23, 2020.
Published
2020/04/29
Section
Original Scientific Paper