Hybrid Approaches in Financial Time Series Forecasting: A Stock Market Application

Authors : Canberk BULUT, Burcu HUDAVERDİ

Pages : 53-68

Doi:10.26650/ekoist.2022.37.1108411

View : 22 | Download : 10

Publication Date : 2022-12-29

Article Type : Research Paper

Abstract :The hybrid approach in time series forecasting is one of the key methodologies in selecting the most accurate model when compared to the single models. Applications of machine learning algorithms in hybrid modeling for stock market forecasting have been developing rapidly. In this paper, we propose hybrid modeling through machine learning approach for four stock market data; two from the developed stock markets insert ignore into journalissuearticles values(NASDAQ and DAX); and the other two from the emerging stock markets insert ignore into journalissuearticles values(NSE and BIST);. A stock market is known with its volatile structure and has an unstable nature, so we propose several combinations for the hybrid models considering volatility to reach the most accurate time series forecasting model. In hybrid modeling, first ARIMA insert ignore into journalissuearticles values(Autoregressive Integrated Moving Average); models combined with GARCH models insert ignore into journalissuearticles values(Generalized Autoregressive Conditional Heteroscedasticity); are used for modeling of time series, then intelligent models such as SVM insert ignore into journalissuearticles values(support vector machine); and LSTM insert ignore into journalissuearticles values(Long-Short term memory); are used for nonlinear modeling of error series. We also compare their performances with single models. The proposed hybrid methodology markedly improves the prediction performances of time series models by combining several models which reflect the time series data characteristics best.
Keywords : Hibrit Yaklaşımlar, Makine Öğrenimi, Hisse Senedi Piyasası, ARIMA, GARCH, SVM, LSTM

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