Performance Comparisons of Deep Learning and ARIMA: A Borsa Istanbul Stock Example

Year 2023, , 419 - 438, 14.09.2023

Caner Erden

https://doi.org/10.18657/yonveek.1208807

Cited By: 1

Abstract

Financial time-series data are nonlinear, complex, influenced by many economic factors, and are difficult to predict. Several traditional statistical methods have been developed for financial time series modeling. However, because it is now easier to record, analyze, and transform big data into meaningful information, the use of machine learning algorithms in financial forecast development has increased in recent years. In this study, the data of EREGL stocks, which are among the stocks traded in the main metal market in the Borsa İstanbul index, are analyzed using time series methods and then modeled using ARIMA and deep models. In the developed deep learning method, the prediction performance improved with data preprocessing stages, feature extraction studies, and different time windows. For deep learning algorithms to be used in time-series studies, a framework of time delays must be used. In this study, scenarios for different time delays and performance comparisons are performed between ARIMA models and deep learning models using long-short term memory (LSTM), gated repeating unit (GRU), and recursive neural network (RNN) algorithms. Experimental studies demonstrate that the RNN algorithm has a better prediction performance than the others and predicts with an average accuracy of 93% on the test dataset.
Key Words: ARIMA, BIST, Deep Learning, GRU, LSTM, RNN, Stock Price Prediction
JEL Classification: E47, G17, E37

Keywords

BIST, LSTM, RNN, GRU, ARIMA, Stock Price Prediction, Deep Learning

Derin Öğrenme ve ARIMA Yöntemlerinin Tahmin Performanslarının Kıyaslanması: Bir Borsa İstanbul Hissesi Örneği

Abstract

Finansal zaman serisi verileri doğrusal olmayan, karmaşık, birçok ekonomik faktörden etkilenen ve tahmin edilmesi zor verilerdir. Çok boyutlu ilişkilerin tahminini gerektiren finansal zaman serisi modelleri için çeşitli istatistiksel yöntemler geliştirilmiştir. Ancak günümüzde büyük verilerin kaydedilmesi, analiz edilmesi ve anlamlı bilgiye dönüştürülmesi kolaylaştığından dolayı finansal tahmin geliştirmede makine öğrenmesi algoritmalarının kullanımı özellikle son yıllarda artmıştır. Bu çalışmada, Borsa İstanbul endeksinde metal ana pazarında işlem gören EREGL hissesine ait veriler zaman serisi yöntemleri ile analiz edilmiş ardından ARIMA ve derin öğrenme modelleri ile tahmin edilmiştir. Geliştirilen derin öğrenme yönteminde veri ön işleme aşamaları, özellik çıkarımı çalışmaları ve farklı zaman çerçeveleri ile tahmin performansı iyileştirilmiştir. Derin öğrenme algoritmalarının zaman serisi çalışmalarında kullanılabilmesi için zaman gecikmelerinden oluşan bir çerçeve kullanılmalıdır. Bu çalışmada, farklı zaman gecikmeleri için senaryolar denenmiş ve performans kıyaslaması ARIMA modelleri ve uzun-kısa vadeli bellek (LSTM), geçitli tekrarlayan ünite (GRU) ve özyineli sinir ağları (RNN) algoritmalarını kullanan derin öğrenme modelleri arasında gerçekleştirilmiştir. Deneysel çalıştırmalar ile RNN algoritmasının diğerlerine göre daha iyi tahmin performansına sahip olduğu ve ele alınan test veri seti üzerinde ortalama %93’lük doğrulukla tahmin ettiği ortaya konulmuştur.
Anahtar Kelimeler: ARIMA, BIST, Derin Öğrenme, GRU, Hisse Senedi Tahmini, LSTM, RNN
JEL Sınıflandırması: E47, G17, E37

Keywords

Hisse Senedi Tahmini, BIST, LSTM, RNN, GRU, ARIMA, Derin Öğrenme

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