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

Year 2022, Issue: 37, 53 - 68, 29.12.2022

Canberk Bulut Burcu Hudaverdi

https://doi.org/10.26650/ekoist.2022.37.1108411

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 (NASDAQ and DAX) and the other two from the emerging stock markets (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 (Autoregressive Integrated Moving Average) models combined with GARCH models (Generalized Autoregressive Conditional Heteroscedasticity) are used for modeling of time series, then intelligent models such as SVM (support vector machine) and LSTM (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

Hybrid time series, machine learning, ARIMA, GARCH, SVM, LSTM, stock market, Hibrit Yaklaşımlar, Makine Öğrenimi, Hisse Senedi Piyasası

Supporting Institution

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Project Number

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Thanks

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Finansal Zaman Serileri Tahmininde Hibrit Yaklaşımlar: Bir Hisse Senedi Piyasası Uygulaması

Abstract

Zaman serisi tahmininde hibrit yaklaşım, tekli modellerle karşılaştırıldığında en doğru modeli seçmede anahtar metodolojilerden biridir. Hisse senedi piyasası tahmini için hibrit modellemede makine öğrenmesi algoritmalarının uygulanması hızla gelişmektedir. Bu çalışmada, ikisi gelişmiş hisse senedi piyasasından (NASDAQ ve DAX) ve diğer ikisi yükselen hisse senedi piyasasından (NSE ve BIST) olmak üzere dört hisse senedi verisi için makine öğrenimi yaklaşımıyla hibrit modellemesi uygulanmıştır. Bir hisse senedi piyasası, değişken yapısıyla bilinir ve istikrarsız bir yapıya sahiptir, bu nedenle, bu çalışmada, en doğru zaman serisi tahmin modeline ulaşmak için oynaklığı dikkate alan çeşitli hibrit modeller önerilmektedir. Hibrid modellemede, öncelikle GARCH (Generalized Autoregressive Conditional Hetereoscedastic) ile birleştirilen ARIMA (Autoregressive Integrated Moving Average) modelleri zaman serilerinin modellemesinde, ardından SVM (support vector machine) ve LSTM (Long-Short term memory) gibi zeki modeller hata serilerinin doğrusal olmayan modellemesinde kullanılmaktadır. Ayrıca, hibrit modellerin performansları mevcut metodolijiler kullanılarak tekli modeller ile karşılaştırılmaktadır. Önerilen hibrit metodoloji, zaman serisi verisinin özelliklerini en iyi yansıtan birkaç modeli birleştirerek tahmin performanslarını önemli ölçüde iyileştirmektedir.

Keywords

Hibrit Yaklaşımlar, Makine Öğrenimi, Hisse Senedi Piyasası, ARIMA, GARCH, SVM, LSTM

Project Number

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