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

Yıl 2023, , 419 - 438, 14.09.2023

Caner Erden

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

Cited By: 1

Öz

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

Anahtar Kelimeler

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

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

Öz

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

Anahtar Kelimeler

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

Kaynakça

• Aktas, O. U., Kryzanowski, L., & Zhang, J. (2022). Price-limit effectiveness: Evidence from the Borsa Istanbul (BIST). International Journal of Islamic and Middle Eastern Finance and Management, 15(3), 527-568. https://doi.org/10.1108/IMEFM-04-2020-0151
• Alacahan, N. D., & Akarsu, Y. (2019). Döviz kuru riskinin Borsa İstanbul 100 endeksi üzerindeki etkisi zaman serisi analizi: Türkiye örneği. Journal of Life Economics, 6(2), 133-150. https://doi.org/10.15637/jlecon.6.009
• Aslan, B., & Erdur, R. C. (2020). Stock Market Prediction with Deep Learning Using Public Disclosure Platform Data. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 1-5.
• Baykut, E., & Veysel, K. (2018). Borsa İstanbul pay endekslerinin volatilite yapısı: BİST-50 örneği (2007-2016 yılları). Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 20(1), 279-303. https://doi.org/10.5578/jss.66770
• Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems. https://doi.org/10.1108/imds-08-2018-0368
• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127. https://doi.org/10.1561/2200000006
• Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2006). Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19. Beverungen, A. (2019). Algorithmic trading, artificial intelligence and the politics of cognition. transcript.
• Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2012). Joint learning of words and meaning representations for open-text semantic parsing. Artificial intelligence and statistics, 127-135.
• Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
• Chan, E. P. (2021). Quantitative trading: How to build your own algorithmic trading business. John Wiley & Sons.
• Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018). Which artificial intelligence algorithm better predicts the Chinese stock market? IEEE Access, 6, 48625-48633. https://doi.org/10.1109/ACCESS.2018.2859809
• Chen, X.-W., & Lin, X. (2014). Big data deep learning: Challenges and perspectives. IEEE access, 2, 514-525. https://doi.org/10.1109/ACCESS.2014.2325029
• Chen, Y.-J., Chen, Y.-M., Tsao, S.-T., & Hsieh, S.-F. (2018). A novel technical analysis-based method for stock market forecasting. Soft Computing, 22(4), 1295-1312. https://doi.org/10.1007/s00500-016-2417-2
• Choi, D., & Lee, K. (2018). An artificial intelligence approach to financial fraud detection under IoT environment: A survey and implementation. Security and Communication Networks, 2018. https://doi.org/10.1155/2018/5483472
• Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1), 30-42. https://doi.org/10.1109/TASL.2011.2134090
• Dahl, G., Ranzato, M., Mohamed, A., & Hinton, G. E. (2010). Phone recognition with the mean-covariance restricted Boltzmann machine. Advances in neural information processing systems, 23.
• Day, M.-Y., & Lin, J.-T. (2019). Artificial intelligence for ETF market prediction and portfolio optimization. Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 1026-1033. https://doi.org/10.1145/3341161.3344822
• Deoras, A., & Kombrink, S. (2011). Empirical evaluation and combination of advanced language modeling techniques. 12th Annual Conference of the International Speech Communication Association.
• Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), 1597-1600. https://doi.org/10.1109/MWSCAS.2017.8053243
• DiPietro, R., & Hager, G. D. (2020). Deep learning: RNNs and LSTM. İçinde Handbook of medical image computing and computer assisted intervention (ss. 503-519). Elsevier.
• Fang, B., & Zhang, P. (2016). Big data in finance. Big data concepts, theories, and applications, 391-412. https://doi.org/10.1007/978-3-319-27763-9_11
• Feng, W., & Han, C. (2015). A novel approach for trajectory feature representation and anomalous trajectory detection. 2015 18th International Conference on Information Fusion (Fusion), 1093-1099. Ferreira, F. G., Gandomi, A. H., & Cardoso, R. T. (2021). Artificial intelligence applied to stock market trading: A review. IEEE Access, 9, 30898-30917. https://doi.org/10.1109/ACCESS.2021.3058133
• Fontanills, G. A., & Gentile, T. (2002). The volatility course (C. 137). John Wiley & Sons.
• Gamboa, J. C. B. (2017). Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887. https://doi.org/10.48550/arXiv.1701.01887
• Gasparin, A., Lukovic, S., & Alippi, C. (2022). Deep learning for time series forecasting: The electric load case. CAAI Transactions on Intelligence Technology, 7(1), 1-25. https://doi.org/10.1049/cit2.12060
• Giudici, P. (2018). Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1, 1. https://doi.org/10.3389/frai.2018.00001
• Goh, R. Y., Lee, L. S., Seow, H.-V., & Gopal, K. (2020). Hybrid harmony search–artificial intelligence models in credit scoring. Entropy, 22(9), 989. https://doi.org/10.3390/e22090989
• Gündüz, H., Yaslan, Y., & Çataltepe, Z. (2018). Stock market prediction with deep learning using financial news. 2018 26th Signal Processing and Communications Applications Conference (SIU), 1-4. https://doi.org/10.1109/SIU.2018.8404616
• Hasan, A., Kalıpsız, O., & Akyokuş, S. (2020). Modeling traders’ behavior with deep learning and machine learning methods: Evidence from BIST 100 index. Complexity, 2020. https://doi.org/10.1155/2020/8285149
• Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep learning in finance. arXiv preprint arXiv:1602.06561. https://doi.org/10.48550/arXiv.1602.06561
• Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
• Hipel, K. W., McLeod, A. I., & Lennox, W. C. (1977). Advances in Box-Jenkins modeling: 1. Model construction. Water Resources Research, 13(3), 567-575. https://doi.org/10.1029/WR013i003p00567
• Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 9. https://doi.org/10.3390/asi4010009
• Hyndman, R. J. (2020). A brief history of forecasting competitions. International Journal of Forecasting, 36(1), 7-14. https://doi.org/10.1016/j.ijforecast.2019.03.015
• Ince, H., & Aktan, B. (2009). A comparison of data mining techniques for credit scoring in banking: A managerial perspective. Journal of Business Economics and Management, 10(3), 233-240. https://doi.org/10.3846/1611-1699.2009.10.233-240
• Jakšič, M., & Marinč, M. (2019). Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management, 21(1), 1-18. https://doi.org/10.1057/s41283-018-0039-y
• Ji, X., Wang, J., & Yan, Z. (2021). A stock price prediction method based on deep learning technology. International Journal of Crowd Science, 5(1), 55-72. https://doi.org/10.1108/IJCS-05-2020-0012
• Khisamova, Z. I., Begishev, I. R., & Sidorenko, E. L. (2019). Artificial intelligence and problems of ensuring cyber security. International Journal of Cyber Criminology, 13(2), 564-577.
• Kihoro, J., Otieno, R. O., & Wafula, C. (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386 Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47-56. https://doi.org/10.1016/j.neucom.2013.03.047
• Kurt, F. E., & Senal, S. (2018). Borsa İstanbul’da Kote Bireysel Emeklilik, Hayat Ve Hayat-Dışı Sigorta Şirketlerinin Hisse Senedi Fiyat Tahmininde Box-Jenkıns Yöntemi. Muhasebe ve Finansman Dergisi, 80, 233-252. https://doi.org/10.25095/mufad.465942
• Längkvist, M. (2014). Modeling time-series with deep networks [PhD Thesis]. Örebro university.
• Li, J. (2018). Cyber security meets artificial intelligence: A survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462-1474. https://doi.org/10.1631/FITEE.1800573
• Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1-11.
• Mashadihasanli, T. (2022). Stock Market Price Forecasting Using the Arima Model: An Application to Istanbul, Turkiye. Journal of Economic Policy Researches, 9(2), 439-454.
• Mehrmolaei, S., & Keyvanpour, M. R. (2016). Time series forecasting using improved ARIMA. 2016 Artificial Intelligence and Robotics (IRANOPEN), 92-97. https://doi.org/10.1109/RIOS.2016.7529496
• Mushtaq, R. (2011). Augmented dickey fuller test. https://doi.org/10.2139/ssrn.1911068 Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2(1), 1-21. https://doi.org/10.1186/s40537-014-0007-7
• Navale, G. S., Dudhwala, N., Jadhav, K., Gabda, P., & Vihangam, B. K. (2016). Prediction of stock market using data mining and artificial intelligence. International Journal of Computer Applications, 134(12), 9-11. https://doi.org/10.5120/ijca2016907635
• Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision support systems, 50(3), 559-569. https://doi.org/10.1016/j.dss.2010.08.006
• Peter, Ď., & Silvia, P. (2012). ARIMA vs. ARIMAX–which approach is better to analyze and forecast macroeconomic time series. Proceedings of 30th international conference mathematical methods in economics, 2, 136-140.
• Qiu, X., Zhang, L., Ren, Y., Suganthan, P. N., & Amaratunga, G. (2014). Ensemble deep learning for regression and time series forecasting. 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL), 1-6. https://doi.org/10.1109/CIEL.2014.7015739
• Santur, Y. (2020). Deep learning based regression approach for algorithmic stock trading: A case study of the Bist30. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(4), 1195-1211.
• Sewell, M. (2011). Characterization of financial time series. Rn, 11(01), 01.
• Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
• Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
• Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM–a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586.
• Sun, Y., Shi, Y., & Zhang, Z. (2019). Finance big data: Management, analysis, and applications. İçinde International Journal of Electronic Commerce (C. 23, Sayı 1, ss. 9-11). Taylor & Francis.
• Tekin, S., & Çanakoğlu, E. (2019). Analysis of price models in istanbul stock exchange. 2019 27th Signal Processing and Communications Applications Conference (SIU), 1-4.
• Turner, J. T. (2014). Time series analysis using deep feed forward neural networks. University of Maryland, Baltimore County.
• Vergil, H., & Ozkan, F. (2007). Purchasing Power Parity and ARIMA Models in Forecasting Exchange Rates: The Case of Turkey. Istanbul Stock Exchange Review, 9(35), 37-50. Wangdi, K., Singhasivanon, P., Silawan, T., Lawpoolsri, S., White, N. J., & Kaewkungwal, J. (2010). Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan. Malaria Journal, 9(1), 1-9. https://doi.org/10.1186/1475-2875-9-251
• Xie, M. (2019). Development of artificial intelligence and effects on financial system. Journal of Physics: Conference Series, 1187(3), 032084. https://doi.org/10.1088/1742-6596/1187/3/032084 Yalçın Kayacan, E. (2019). Deep learning for time series forecasting.
• Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0
• Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146-157. https://doi.org/10.1016/j.inffus.2017.10.006
• Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014). Time series classification using multi-channels deep convolutional neural networks. International conference on web-age information management, 298-310.