Fourier Serileri ile Modifiye Edilmiş Modelleri Kullanarak Borsa İstanbul (BİST) Üzerine Bir Uygulama

Cebeli İnan; Erkan Oktay

EN TR

An Application on Borsa Istanbul (BIST) Using Models Modified with Fourier Series

Abstract

Predicting the future accurately is of vital importance in all disciplines, as well as in the field of social sciences. Especially today, due to the development of technology and the presence of package programs that can process huge data, it is a very important development for all fields, including econometrics, that we can reach more accurate estimates. As a result, it is a situation that is directly related to the decrease in the error rates of the forecasts and more accurate planning for the future. In this study, time series analyzes were made over the proportional changes of the closing values of the Borsa Istanbul (BIST30) index, that is, the monthly average closing values, and 24-month forecasts were calculated. For this purpose, in order to increase the accuracy of the prediction results of the classical ARIMA models and the models based on the Box-Cox transform, new prediction results were obtained with the models modified with the Fourier series. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE) performance criteria were used to evaluate the success of the established models. It was concluded that the model with the lowest value for MSE, RMSE, MAE and MAPE performance criteria was more successful than the other models. As a result, more successful results were obtained with more accurate estimation results, that is, with lower error rates.

Keywords

Kaynakça

Badr, A., Makarovskikh, T., Mishra, P., Abotaleb, M., Al Khatib, A. M. G., Karakaya, K., ... ve 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.
Naim, I., Mahara, T., ve Idrisi, A. R. (2018). Effective short-term forecasting for daily time series with complex seasonal patterns. Procedia computer science, 132, 1832-1841.
Kulkarni, M., Jadha, A., ve Dhingra, D. (2020, March). Time Series Data Analysis for Stock Market Prediction. In Proceedings of the International Conference on Innovative Computing ve Communications (ICICC). (March 28, 2020). http://dx.doi.org/10.2139/ssrn.3563111
Hyndman, R. J., ve Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of statistical software, 27(1), 1-22.
Kourentzes, N. (2019). nnfor: Time Series Forecasting with Neural Networks. R package version 0.9.6. https://CRAN.R-project.org/package=nnfor.
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Ollech D. (2021). seastests: Seasonality Tests. R package version 0.15.4. https://CRAN.R-project.org/package=seastests.
De Livera, A. M., Hyndman, R. J., ve 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

Iwok, I. A., ve 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.
Walker, J. S. (1991). Fourier Series, Oxford University Press, New York.
Nachane, D., ve Clavel, J. G. (2008). “Forecasting interest rates: a comparative assessment of some second-generation nonlinear models.” Journal of Applied Statistics, 35(5), 493-514. https://doi.org/10.1080/02664760701835243
Hannan, E. J., Terrell, R. D., ve Tuckwell, N. E. (1970). “The seasonal adjustment of economic time series.” International Economic Review, 11(1), 24-52. https://doi.org/10.2307/2525336
Saeed, W. (2022). “Frequency-based ensemble forecasting model for time series forecasting.” Computational and Applied Mathematics, 41(2), 1-17. https://doi.org/10.1007/s40314-022-01765-x
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
Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge Press. https://doi.org/10.1017/CBO9781107049994
Grinblatt, M., ve Keloharju, M. (2000). “The investment behavior and performance of various investor types: a study of Finland's unique data set.” Journal of financial economics, 55(1), 43-67. https://doi.org/10.1016/S0304-405X(99)00044-6
Cavanaugh, J. E. (1997). “Unifying the derivations for the Akaike and corrected Akaike information criteria.” Statistics ve Probability Letters, 33(2), 201-208. https://doi.org/10.1016/S0167-7152(96)00128-9
West, M., ve Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science ve Business Media. Harvey, A. C. (1990). The econometric analysis of time series. Mit Press.
Akaike, H. (1974). “A new look at the statistical model identification.” IEEE transactions on automatic control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
Konarasinghe, W. G. S., Abeynayake, N. R., ve Gunaratne, L. H. P. (2015). “ARIMA models on forecasting Sri Lankan share market returns.” International journal of novel research in Physics Chemistry and Mathematics, 2(1), 6-12.
Hassani, H., Silva, E. S., Gupta, R., ve Segnon, M. K. (2015). “Forecasting the price of gold.” Applied Economics, 47(39), 4141-4152. https://doi.org/10.1080/00036846.2015.1026580
Asante-Darko, D., Adabor, E., ve Amponsah, S. K. (2016). “A Fourier series model for forecasting solid waste generation in the Kumasi metropolis of Ghana.” WIT Transactions on Ecology and the Environment, 202, 173-185. https://doi.org/10.2495/WM160161
Shu, M. H., Hung, W. J., Nguyen, T. L., Hsu, B. M., ve Lu, C. H. U. N. W. E. I. (2014). “Forecasting with Fourier residual modified ARIMA model-An empirical case of inbound tourism demand in New Zealand.” WSEAS Transactions on Mathematics, 13(1), 12-21.
Mijinyawa, M., Mbaga, Y. V., Amdzaranda, M., ve Akinrefon, A. A. (2019). “Pairs Determination for Sine and Cosine Function in Modeling Nigerian Gross Domestic Product.” Direct Research Journal of Social Science and Educational Studies. 6 (6), 90-94.
Phumchusri, N., ve Ungtrakul, P. (2020). “Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand.” Journal of Revenue and Pricing Management, 19(1), 8-25. https://doi.org/10.1057/s41272-019-00221-6
Boudrioua, M. S., ve Boudrioua, A. (2020). “Modeling and forecasting the algerian stock exchange using the Box-Jenkins methodology.” Journal of Economics, Finance and Accounting Studies, 2(1), 1-15.
Leneenadogo, W., ve Pius, U. S. (2020). “A Comparative Study of Fourier Series Models and Seasonal-Autoregressive Integrated Moving Average Model of Rainfall Data in Port Harcourt.” Asian Journal of Probability and Statistics, 10(3): 36-46. https://doi.org/10.9734/ajpas/2020/v10i330249
Son, H. G., Kim, Y., ve Kim, S. (2020). “Time series clustering of electricity demand for industrial areas on smart grid.” Energies, 13(9), 2377. https://doi.org/10.3390/en13092377
Bağcı, B. (2020). “Hareketli ortalamalar ve üssel düzeltme yöntemlerinin tahmin gücünün artırılması: Türkiye’de döviz kuru tahmini” Turkuaz Uluslararası Sosyo-Ekonomik Stratejik Araştırmalar Dergisi, 2(2).
De Livera, A. ve Hyndman, R. (2009). “Forecasting time series with complex seasonal patterns using exponential smoothing.” Department of Econometrics ve Business Statistics, Monash University. Working paper 15/09

Ayrıntılar

Birincil Dil

Türkçe

Konular

Ekonomi

Bölüm

Araştırma Makalesi

Yazarlar

Cebeli İnan ^*
0000-0002-7924-9911
Türkiye

Erkan Oktay
0000-0002-1739-3184
Türkiye

Yayımlanma Tarihi

11 Ocak 2023

Gönderilme Tarihi

16 Ağustos 2022

Kabul Tarihi

6 Eylül 2022

Yayımlandığı Sayı

Yıl 1970 Cilt: 5 Sayı: 2

IZ

https://izlik.org/JA63JK57BA

Kaynak Göster

RIS / Bibtex

APA

İnan, C., & Oktay, E. (2023). Fourier Serileri ile Modifiye Edilmiş Modelleri Kullanarak Borsa İstanbul (BİST) Üzerine Bir Uygulama. Turkish Research Journal of Academic Social Science, 5(2), 81-91. https://izlik.org/JA63JK57BA

Fourier Serileri ile Modifiye Edilmiş Modelleri Kullanarak Borsa İstanbul (BİST) Üzerine Bir Uygulama

An Application on Borsa Istanbul (BIST) Using Models Modified with Fourier Series

Abstract

Keywords