AN APPLICATION ON BIST30 STOCKS: FORECASTING WITH ETS, MLP, BATS AND TBATS METHODS

Abstract

Forecasting future economic conditions is a primary objective for economists and policymakers. Econometric modeling uses statistical and mathematical methods to define economic relationships and forecast future economic variables. These models generally rely on historical data analysis, aiming to predict future trends and price movements by examining past price changes and volume data. Today, with the rapid advancement of technology, the development of software capable of processing big data, and the assistance of artificial intelligence, forecasting has become more efficient, resulting in reduced error rates in predictions. This study performs time series analyses on the closing values of selected banks from the BIST30 index using ETS, MLP, BATS, and TBATS models, calculating 24-month forecasts. Various performance criteria were applied to assess the predictive accuracy of these models. Based on these criteria, the model with the lowest error value was determined to be more successful than the others.

Keywords

• Ets
• Mlp
• Bats
• Tbats
• Tıme Series.

BIST30 HİSSE SENETLERİ ÜZERİNE BİR UYGULAMA: ETS, MLP, BATS VE TBATS YÖNTEMLERİ İLE ÖNGÖRÜDE BULUNMA

Abstract

Ekonomide geleceği tahmin etmek ve öngörüde bulunmak, ekonomistlerin ve politika yapıcıların en önemli amaçlarından biridir. Bunun için yapılan ekonometrik modellemelerde, istatistiksel ve matematiksel yöntemleri kullanarak ekonomik ilişkileri tanımlamaya ve gelecekteki ekonomik değişkenleri tahmin etmeye çalışılır. Bundan dolayı bu modeller genellikle geçmiş verilerin analizine dayanır yani geçmiş fiyat hareketlerini ve hacim verilerini inceleyerek gelecekteki trendleri ve fiyat hareketlerini tahmin etmeyi amaçlar. Günümüzde teknolojinin hızlı bir şekilde ilerlemesi, büyük verileri işleyebilecek paket programlarının gelişmesi ve yapay zekanın yardımı ile öngörüde bulunmak daha kolaylaşmış ve bunun sonucunda yapılan tahminlerin hata oranları azalmıştır. Bu çalışmada, BIST30 hisse senetlerinden bazı bankaların kapanış değerlerinin ETS, MLP, BATS ve TBATS modelleri kullanılarak zaman serisi analizleri yapılmış ve 24 aylık öngörüleri hesaplanmıştır. Bu modellerin tahmin doğruluklarını karşılaştırmak için çeşitli performans ölçüt kriterleri uygulanmıştır. Bu performans ölçüt kriterlerine göre en düşük hata değerini veren model diğer modellere göre daha başarılı olduğu sonucuna varılmıştır.

Keywords

• Ets Mlp
• Bats
• Tbats
• Zaman Serileri.

References

1. Akşehir, Z. D., & Kılıç, E. (2019). Makine öğrenmesi teknikleri ile banka hisse senetlerinin fiyat tahmini. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 12(2), 30-39.
2. Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69-80. https://doi.org/10.1016/0169-2070(92)90008-W
3. Arslankaya, S., & Toprak, Ş. (2021). Makine öğrenmesi ve derin öğrenme algoritmalarını kullanarak hisse senedi fiyat tahmini. International Journal of Engineering Research and Development, 13(1), 178-192.
4. Badr, A., Makarovskikh, T., Mishra, P., Abotaleb, M., Al Khatib, A. M. G., Karakaya, K., ... & Attal, E. (2021). Modelling and forecasting of web traffic using Holt's linear, BATS and TBATS models. J. Math. Comput. Sci., 11(4), 3887-3915.
5. De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
6. Du, Y. (2018). Application and analysis of forecasting stock price index based on combination of ARIMA model and BP neural network. In 2018 Chinese Control and Decision Conference (CCDC) (pp. 2854-2857). IEEE. https://doi.org/10.1109/CCDC.2018.8407611
7. Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1-28. https://doi.org/10.1002/for.3980040103
8. Hamilton, D. J. (1994). Time Series Analysis. Princeton University Press.

9. Hargrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994). Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21(3), 249-263. https://doi.org/10.1016/0305-0548(94)90088-4
10. Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: Short-run persistence of relative performance. The Journal of Finance, 48(1), 93-130. https://doi.org/10.1111/j.1540-6261.1993.tb04703.x
11. Holt, C. C. (1957). Forecasting trends and seasonals by exponentially weighted averages. ONR Memorandum No. 52. Carnegie Institute of Technology, Pittsburgh, USA. (Published in International Journal of Forecasting, 2004, 20, 5-13). https://doi.org/10.1016/j.ijforecast.2003.09.017
12. Huffman, G. J. (1997). Estimates of root-mean-square random error for finite samples of estimated precipitation. Journal of Applied Meteorology and Climatology, 36(9), 1191-1201. https://doi.org/10.1175/1520-0450(1997)036<1191>2.0.CO;2
13. Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1-22. https://doi.org/10.18637/jss.v027.i03
14. Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
15. Indro, D. C., Jiang, C. X., Patuwo, B. E., & Zhang, G. P. (1999). Predicting mutual fund performance using artificial neural networks. Omega, 27(3), 373-380. https://doi.org/10.1016/S0305-0483(98)00048-6
16. Iwok, I. A., & Udoh, G. M. (2016). A comparative study between the ARIMA-Fourier model and the Wavelet model. American Journal of Scientific and Industrial Research, 7(6), 137-144.
17. İclal, G. (2016). Çok katmanlı algılayıcı yapay sinir ağı ile lineer diferansiyel denklem sisteminin çözümü. 18. Akademik Bilişim Konferansı, Aydın, 3-5 Şubat, 738-745.
18. Jeong, K., Koo, C., & Hong, T. (2014). An estimation model for determining the annual energy cost budget in educational facilities using SARIMA and ANN. Energy, 71, 71-79. https://doi.org/10.1016/j.energy.2014.04.027
19. Kalteh, A. M. (2008). Rainfall-runoff modelling using artificial neural networks: Modelling and understanding.
20. Karan, M. B. (2020). Yatırım analizi ve portföy yönetimi. Gazi Kitapevi.
21. Kayakuş, M., & Terzioğlu, M. (2021). Yapay sinir ağları ve çoklu doğrusal regresyon kullanarak emeklilik fonu net varlık değerlerinin tahmin edilmesi. Bilişim Teknolojileri Dergisi, 14(1), 95-103. https://doi.org/10.17671/gazibtd.742995
22. Kourentzes, N. (2019). nnfor: Time series forecasting with neural networks. R package version 0.9.6. https://CRAN.R-project.org/package=nnfor
23. Lima, M. V. M. D., & Laporta, G. Z. (2020). Evaluation of the models for forecasting dengue in Brazil from 2000 to 2017: An ecological time-series study. Insects, 11(11), 794.
24. Lin, J. C., Singh, A. K., & Yu, W. (2009). Stock splits, trading continuity, and the cost of equity capital. Journal of Financial Economics, 93(3), 474-489. https://doi.org/10.1016/j.jfineco.2008.09.008
25. Öğücü, M. O. (2006). Yapay sinir ağları ile sistem tanıma (Yayımlanmamış Doktora Tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü.
26. Naim, I., Mahara, T., & Idrisi, A. R. (2018). Effective short-term forecasting for daily time series with complex seasonal patterns. Procedia Computer Science, 132, 1832-1841. https://doi.org/10.1016/j.procs.2018.05.136
27. Oukhouya, H., & El Himdi, K. (2023). Comparing machine learning methods—svr, xgboost, lstm, and mlp—for forecasting the Moroccan stock market. Computer Sciences & Mathematics Forum, 7(1), 39. https://doi.org/10.3390/IOCMA2023-14409
28. Pegels, C. C. (1969). Exponential forecasting: Some new variations. Management Science, 15(5), 311-315. https://doi.org/10.1287/mnsc.15.5.311
29. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
30. Riise, T., & Tjozstheim, D. (1984). Theory and practice of multivariate ARMA forecasting. Journal of Forecasting, 3(3), 309-317. https://doi.org/10.1002/for.3980030308
31. Sakia, R. M. (1992). The Box-Cox transformation technique: A review. Journal of the Royal Statistical Society: Series D (The Statistician), 41(2), 169-178. https://doi.org/10.2307/2348250
32. Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715-725. https://doi.org/10.1016/S0169-2070(03)00003-7
33. Tektaş, A., & Karataş, A. (2004). Yapay sinir ağları ve finans alanına uygulanması: Hisse senedi fiyat tahminlemesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3-4).
34. Yavuz, S., & Deveci, M. (2012). İstatiksel normalizasyon tekniklerinin yapay sinir ağin performansina etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (40), 167-187.
35. Webb, G. I., & Sammut, C. (Eds.). (2010). Encyclopedia of Machine Learning. Springer.
36. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324-342. https://doi.org/10.1287/mnsc.6.3.324
37. Wiri, L., & Essi, I. D. (2018). Seasonal autoregressive integrated moving average (SARIMA) modelling and forecasting of inflation rates in Nigeria (2003-2016). International Journal of Applied Science and Mathematical Theory, 4(1), 2-4.