Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye

Year 2022, , 439 - 454, 29.07.2022

Tamerlan Mashadihasanli

https://doi.org/10.26650/JEPR1056771

Abstract

Because of its critical position in open economies and its extremely high
volatility, the stock market price index has been a popular subject of
market research. In modern financial markets, traders and practitioners
have had trouble predicting the stock market price index. In order to
solve this problem, some methods have been researched by researchers
and suitable methods have been found. To analyze and forecast monthly
stock market price index, a variety of statistical and econometric models
are extensively used. Thus, this study aims to investigate the application
of autoregressive integrated moving averages (ARIMA) for forecasting
monthly stock market price index in Istanbul for the period from 2009-
M01 to 2021-M03. As compared to all other tentative models, the
research showed that the ARIMA (3,1,5) model is the best fit model for
predicting the stock market price index. Forecasting is conducted by
using the developed model ARIMA (3,1,5) and the results indicated that
the forecasted values are very similar to the actual ones, reducing forecast
errors. In general, the stock market price index in Istanbul; showed a
downwards trend over the forecasted period. The results of the study
can set an example for researchers and practitioners working in the stock
market and can be a guide for economic decision units and investors in
the stock market. 

Keywords

ARIMA, forecasting, stock market price index, time series, Turkiye

Borsa İstanbul Fiyatlarının Arima Modeli İle Tahmin Edilmesi

Abstract

Açık ekonomilerdeki kritik konumu ve son derece yüksek oynaklığı
nedeniyle borsa fiyat endeksi, piyasa araştırmalarının popüler bir konusu
olmuştur. Modern finans piyasalarında, tüccarlar ve uygulayıcılar borsa fiyat endeksini tahmin etmekte zorlanıyorlar. Bu soruna çözüm getirmek için araştırmacılar tarafından bazı yöntemler
araştırılmış ve uygun yöntemler bulunmuştur. Aylık borsa fiyat endeksini analiz etmek ve tahmin etmek için çeşitli
istatistiksel ve ekonometrik modeller yaygın olarak kullanılmaktadır. Bu nedenle, bu çalışma, 2009-M01 ile 2021-M03
arasındaki dönem için İstanbul'da aylık borsa fiyat endeksini tahmin etmek için otoregresif entegre hareketli ortalamalar
(ARIMA) uygulamasını araştırmayı amaçlamaktadır. Araştırma, diğer tüm geçici modellerle karşılaştırıldığında, ARIMA
(3,1,5) modelinin borsa fiyat endeksini tahmin etmek için en uygun model olduğunu göstermiştir. Tahmin, geliştirilen
ARIMA (3,1,5) modeli kullanılarak yapılmıştır ve sonuçlar, tahmin edilen değerlerin gerçek değerlere çok benzer olduğunu
ve tahmin hatalarını azalttığını göstermiştir. Genel olarak İstanbul'da borsa fiyat endeksi; tahmin edilen dönemde aşağı
yönlü bir eğilim göstermiştir. Çalışmanın sonuçları borsada çalışan araştırmacı ve uygulayıcılara örnek teşkil edebileceği
gibi borsada ekonomik karar birimlerine ve yatırımcılara yol gösterici olabilir.

Keywords

ARIMA, tahminleme, borsa fiyat endeksi, zaman serisi, Türkiye

References

• Al-Zeaud, H. A. (2011). Modelling and forecasting volatility using ARIMA Model. European Journal of Economics, Finance & Administrative Science, 35, 109-125. google scholar
• Atsalakis, G. S., & Valavanis, K. P. (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert systems with Applications, 36(7), 10696-10707. google scholar
• Atsalakis, G. S., Dimitrakakis, E. M., & Zopounidis, C. D. (2011). Elliott wave theory and neuro-fuzzy systems, in stock market prediction: The WASP system. Expert Systems with Applications, 38(8), 9196-9206. google scholar
• Baba, N., & Kozaki, M. (1992). An intelligent forecasting system of stock price using neural networks. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks (Vol. 1, pp. 371-377). google scholar
• Bircan, H., & Karagöz, Y. (2003). Box-Jenkins modelleri ile aylık döviz kuru tahmini üzerine bir uygulama. Kocaeli Üniversitesi Sosyal Bilimler Dergisi, (6), 49-62. google scholar
• Çevik, O (2002). İMKB endeksinin Box-Jenkins yöntemi ile modellenmesi. Afyon Kocatepe Üniversitesi İİBF Dergisi, (C.IV, S.1), 17-31. google scholar
• Chang, C. L., Sriboonchitta, S., & Wiboonpongse, A. (2009). Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation. Mathematics and computers in simulation, 79(5), 17301744. google scholar
• Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE transactions on power systems, 18(3), 1014-1020. google scholar
• Datta, K. (2011). ARIMA forecasting of inflation in the Bangladesh Economy. IUP Journal of Bank Management, 10(4), 7-15. google scholar
• Etuk, H. E., Uchendu, B., & Udo, E. O. (2012). Box-Jenkins modeling of Nigerian stock prices data. Greener Journal of Science Engineering and Technological Research, 2(2), 32-38. google scholar
• Gupta, S., & Kashyap, S. (2015). Box Jenkins approach to forecast exchange rate in India. Prestige International Journal of Management and Research, 8(1), 1-11. google scholar
• Kamijo, K. I., & Tanigawa, T. (1990). Stock price pattern recognition-a recurrent neural network approach. In 1990 IJCNN international joint conference on neural networks (pp. 215-221). google scholar
• Kharimah, F., Usman, M., Widiarti, W., & Elfaki, F. A. (2015). Time series modeling and forecasting of the consumer price index Bandar Lampung. Science International, 27(5 (B)), 4619-4624. google scholar
• Kihoro, J. M., & Okango, E. L. (2014). Stock market price prediction using artificial neural network: an application to the Kenyan equity bank share prices. Journal of Agriculture, Science and Technology, 16(1), 160-171. google scholar
• Kock, A. B., & Terasvirta, T. (2013). Forecasting the Finnish consumer price inflation using artificial neural network models and three automated model selection techniques. Finnish Economic Papers, 26(1), 13-24. google scholar
• Kumar, K., Yadav, A. K., Singh, M. P., Hassan, H., & Jain, V. K. (2004). Forecasting Daily Maximum Surface Ozone. Journal of the Air & Waste Management Association, 54(7), 80-814 google scholar
• Kumar, M., & Thenmozhi, M. (2014). Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. International Journal of Banking, Accounting and Finance, 5(3), 284-308. google scholar
• Liu, Q., Liu, X., Jiang, B., & Yang, W. (2011). Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC infectious diseases, 11(1), 1-7. google scholar
• 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, 3(2), 23-43. google scholar
• Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. google scholar
• Mitra, S. K. (2009). Optimal combination of trading rules using neural networks. International business research, 2(1), 86-99. google scholar
• Mostafa, M. M. (2010). Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert systems with applications, 37(9), 6302-6309. google scholar
• Nandakumar, R., Uttamraj, K. R., Vishal, R., & Lokeswari, Y. V. (2018). Stock price prediction using long short term memory. International Research Journal of Engineering and Technology, 5(03), 1-9. google scholar
• Nyoni, T. (2018). Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis. Dimorian Review, 5(6), 16-40. google scholar
• Nyoni, T., & Nathaniel, S. P. (2018). Modeling rates of inflation in Nigeria: An application of ARMA, ARIMA and GARCH models. MPRA Paper No. 91351, 1-29. google scholar
• Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505. google scholar
• Saibu, O. (2015). Determining optimal crude oil price benchmark in Nigeria: An empirical approach. Romanian Economic Journal Year XVIII no, 58. google scholar
• Sekreter, A., & Gursoy, A. (2014). Combining forecasting method vs. individual forecasting methods: Evidence from Istanbul Stock Exchange National 100 Index. The Empirical Economics Letters, 13(7), 735-743. google scholar
• Shah, D., Campbell, W., & Zulkernine, F. H. (2018). A comparative study of LSTM and DNN for stock market forecasting. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4148-4155). google scholar
• Sterba, J., & Hilovska, K. (2010). The implementation of hybrid ARIMA neural network prediction model for aggregate water consumption prediction. Aplimat—Journal of Applied Mathematics, 3(3), 123-131. google scholar
• Subing, H. J. T., & Kusumah, R. W. R. (2017). An empirical analysis of internal and external factors of stock pricing: Evidence from Indonesia. Problems and Perspectives in Management, 15(4), 178-87. google scholar
• Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics. Boston, MA: Pearson. google scholar
• Takahashi, T., Tamada, R., & Nagasaka, K. (1998). Multiple line-segments regression for stock prices and long-range forecasting system by neural network. In Proceedings of the 37th SICE Annual Conference. International Session Papers (pp. 1127-1132). google scholar
• Tsitsika, E. V., Maravelias, C. D., & Haralabous, J. (2007). Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models. Fisheries science, 73(5), 979-988. google scholar
• Uko, A. K., & Nkoro, E. (2012). Inflation forecasts with ARIMA, vector autoregressive and error correction models in Nigeria. European Journal of Economics, Finance & Administrative Science, 50, 71-87. google scholar
• Yaziz, S. R., Ahmad, M. H., Nian, L. C., & Muhammad, N. (2011). A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices. Journal of applied sciences, 11(7), 1129-1135. google scholar
• Yoo, S. (2007). Neural Network Model vs. SARIMA Model in Forecasting Korean Stock Price Index (KOSPI). Issues in Information Systems, 8(3), 372-378. google scholar
• Yoon, Y., & Swales, G. (1991). Predicting stock price performance: A neural network approach. In Proceedings of the twenty-fourth annual Hawaii international conference on system sciences (Vol. 4, pp. 156-162). google scholar
• Zhang, G. P. (2003). Time Series Forecasting using A Hybrid ARIMA and Neural Network Model. Neurocomputing, 50, 159-175. google scholar