PREDICTING BANKING STOCK PRICES USING RNN, LSTM, AND GRU APPROACH
Dias Satria
dias.satria@ub.ac.idUniversitas Brawijaya (Indonesia)
https://orcid.org/0000-0002-4068-6807
Abstract
In recent years, the implementation of machine learning applications started to apply in other possible fields, such as economics, especially investment. But, many methods and modeling are used without knowing the most suitable one for predicting particular data. This study aims to find the most suitable model for predicting stock prices using statistical learning with RNN, LSTM, and GRU deep learning methods using stock price data for 4 (four) major banks in Indonesia, namely BRI, BNI, BCA, and Mandiri, from 2013 to 2022. The result showed that the ARIMA Box-Jenkins modeling is unsuitable for predicting BRI, BNI, BCA, and Bank Mandiri stock prices. In comparison, GRU presented the best performance in the case of predicting the stock prices of BRI, BNI, BCA, and Bank Mandiri.
Keywords:
GRU, Indonesia Stock Price Prediction, Machine LearningReferences
Acheampong, P., Agalega, E., & Shibu, A. K. (2014). The effect of financial leverage and market size on stock returns on the ghana stock exchange: Evidence from Selected Stocks in the Manufacturing Sector. International Journal of Financial Research, 5(1), 125-134. https://doi.org/10.5430/ijfr.v5n1p125
DOI: https://doi.org/10.5430/ijfr.v5n1p125
Google Scholar
Ahmad, G. I., Singla, J., Ali, A., Reshi, A. A., & Salameh, A. A. (2022). Machine learning techniques for sentiment analysis of code-mixed and switched indian social media text corpus: A comprehensive review.
Google Scholar
International Journal of Advanced Computer Science and Applications, 13(2), 455–467. https://doi.org/10.14569/IJACSA.2022.0130254
DOI: https://doi.org/10.14569/IJACSA.2022.0130254
Google Scholar
Almalaq, A., & Edwards, G. (2017). A review of deep learning methods applied on load forecasting. Proceedings - 16th IEEE International Conference on Machine Learning and Applications (pp. 511–516). IEEE. https://doi.org/10.1109/ICMLA.2017.0-110
DOI: https://doi.org/10.1109/ICMLA.2017.0-110
Google Scholar
Bank Indonesia. (2022). Policy Synergy and Innovation to Maintain Financial System Stability and Support National Economic Growth.
Google Scholar
Bhatt, G., Bansal, H., Singh, R., & Agarwal, S. (2020). How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? Proceedings of the 58th Annual Meeting of the Association for
Google Scholar
Computational Linguistics: Student Research Workshop (pp. 244–254). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-srw.33
DOI: https://doi.org/10.18653/v1/2020.acl-srw.33
Google Scholar
Bibi, I., Akhunzada, A., Malik, J., Iqbal, J., Mussaddiq, A., & Kim, S. (2020). A dynamic DL-driven architecture to combat sophisticated android malware. IEEE Access, 8, 129600–129612. https://doi.org/10.1109/ACCESS.2020.3009819
DOI: https://doi.org/10.1109/ACCESS.2020.3009819
Google Scholar
Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307–1317. https://doi.org/10.1007/s13042-019-01041-1
DOI: https://doi.org/10.1007/s13042-019-01041-1
Google Scholar
Ghenimi, A., Chaibi, H., & Omri, M. A. B. (2021). Liquidity risk determinants: Islamic vs conventional banks. International Journal of Law and Management, 63(1), 65–95. https://doi.org/10.1108/IJLMA-03-2018-
DOI: https://doi.org/10.1108/IJLMA-03-2018-0060
Google Scholar
Gupta, U., Bhattacharjee, V., & Bishnu, P. S. (2022). StockNet—GRU based stock index prediction. Expert Systems with Applications, 207(March 2021), 117986. https://doi.org/10.1016/j.eswa.2022.117986
DOI: https://doi.org/10.1016/j.eswa.2022.117986
Google Scholar
IDX (2023). https://www.idx.co.id/id. Retrieved March, 18 2023.
Google Scholar
Jahan, I., & Sajal, S. (2018). Stock price prediction using recurrent neural network (RNN) algorithm on timeseries data. In 2018 Midwest Instruction and Computing Symposium. The College of St Scholastica.
Google Scholar
Jarrah, M., & Salim, N. (2019). A recurrent neural network and a discrete wavelet transform to predict the Saudi stock price trends. International Journal of Advanced Computer Science and Applications, 10(4), 155–162. https://doi.org/10.14569/ijacsa.2019.0100418
DOI: https://doi.org/10.14569/IJACSA.2019.0100418
Google Scholar
Khan, M., Wang, H., Riaz, A., Elfatyany, A., & Karim, S. (2021). Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification. Journal of Supercomputing, 77(7), 7021–
DOI: https://doi.org/10.1007/s11227-020-03560-z
Google Scholar
Le, T. H., Chuc, A. T., & Taghizadeh-Hesary, F. (2019). Financial inclusion and its impact on financial efficiency and sustainability: Empirical evidence from Asia. Borsa Istanbul Review, 19(4), 310–322. https://doi.org/10.1016/j.bir.2019.07.002
DOI: https://doi.org/10.1016/j.bir.2019.07.002
Google Scholar
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018
DOI: https://doi.org/10.3390/e23010018
Google Scholar
Ludwig, S. A. (2019). Comparison of Time Series Approaches applied to Greenhouse Gas Analysis: ANFIS, RNN, and LSTM. IEEE International Conference on Fuzzy Systems, (pp. 1–6). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8859013
DOI: https://doi.org/10.1109/FUZZ-IEEE.2019.8859013
Google Scholar
Madge, S., & Bhatt, S. (2015). Predicting Stock Price Direction using Support Vector Machines. https://github.com/SaahilMadge/Spring-2015-IW
Google Scholar
Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168–1173. https://doi.org/10.1016/j.procs.2020.03.049
DOI: https://doi.org/10.1016/j.procs.2020.03.049
Google Scholar
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
DOI: https://doi.org/10.1186/s40537-014-0007-7
Google Scholar
Qin, H. (2019). Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction. ArXiv, arXiv:1911.08414. http://arxiv.org/abs/1911.08414
Google Scholar
Ringmu, H. S., & Oumar, S. B. (2022). Forecasting stock prices in the New York stock exchange. Journal of Economics Bibliography, 9(1), 1–20. https://doi.org/10.1453/jeb.v9i1.2269
Google Scholar
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 Journal, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
DOI: https://doi.org/10.1016/j.asoc.2020.106181
Google Scholar
Shahi, T. B., Shrestha, A., Neupane, A., & Guo, W. (2020). Stock price forecasting with deep learning: A comparative study. Mathematics, 8(9), 1–15. https://doi.org/10.3390/math8091441
DOI: https://doi.org/10.3390/math8091441
Google Scholar
Shumway, R. H., & Stoffer, D. S. (2019). Time Series: A Data Analysis Approach Using R. CRC Press.
DOI: https://doi.org/10.1201/9780429273285
Google Scholar
Tembhurne, J. V., & Diwan, T. (2021). Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks. Multimedia Tools and Applications, 80(5), 6871–6910. https://doi.org/10.1007/s11042-020-10037-x
DOI: https://doi.org/10.1007/s11042-020-10037-x
Google Scholar
Taud, H., & Mas, J. F. (2018). Multilayer Perceptron (MLP) BT. Geomatic Approaches for Modeling Land Change Scenarios (pp. 451–455). Springer.
DOI: https://doi.org/10.1007/978-3-319-60801-3_27
Google Scholar
Tsai, Y. T., Zeng, Y. R., & Chang, Y. S. (2018). Air pollution forecasting using rnn with lstm. Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th
Google Scholar
International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASCPICom-DataCom-CyberSciTec 2018, (pp. 1068–1073). https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00178
DOI: https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00178
Google Scholar
Utomo, D. (2017). Stock price prediction using back propagation neural network based on gradient descent with momentum and adaptive learning rate. Journal of Internet Banking and Commerce, 22(3), 16.
Google Scholar
Wei, W. W. S. (2006). Time series analysis: univariate and multivariate methods. Journal of the American Statistical Association, 86(413), 245-246. https://doi.org/10.2307/2289741
DOI: https://doi.org/10.2307/2289741
Google Scholar
Wei, X., Zhang, L., Yang, H. Q., Zhang, L., & Yao, Y. P. (2021). Machine learning for pore-water pressure timeseries prediction: Application of recurrent neural networks. Geoscience Frontiers, 12(1), 453–467. https://doi.org/10.1016/j.gsf.2020.04.011
DOI: https://doi.org/10.1016/j.gsf.2020.04.011
Google Scholar
Wibowo, J. M. (2020). Lockdown Generation: Pengangguran di Masa COVID-19. Pusat Riset Kependudukan.
Google Scholar
Wu, C. H., Lu, C. C., Ma, Y. F., & Lu, R. S. (2019). A new forecasting framework for bitcoin price with LSTM. IEEE International Conference on Data Mining Workshops (pp. 168–175). IEEE. https://doi.org/10.1109/ICDMW.2018.00032
DOI: https://doi.org/10.1109/ICDMW.2018.00032
Google Scholar
Yadav, O., Cynara, G., Abhishek, K., & Abhishek, Y. (2019). Inflation prediction model using machine learning. International Journal of Information and Computing Science, 6(5), 121–128.
Google Scholar
Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091–2100. https://doi.org/10.1016/j.procs.2020.03.257
DOI: https://doi.org/10.1016/j.procs.2020.03.257
Google Scholar
Yang, C., & Guo, S. (2021). Inflation prediction method based on deep learning. Computational Intelligence and Neuroscience, 2021, 1071145. https://doi.org/10.1155/2021/1071145
DOI: https://doi.org/10.1155/2021/1071145
Google Scholar
Zainab, M., Usmani, A. R., Mehrban, S., & Hussain, M. (2019). FPGA Based Implementations of RNN and CNN: A Brief Analysis. 3rd International Conference on Innovative Computing (pp. 1-8). IEEE. https://doi.org/10.1109/ICIC48496.2019.8966676
DOI: https://doi.org/10.1109/ICIC48496.2019.8966676
Google Scholar
Authors
Dias Satriadias.satria@ub.ac.id
Universitas Brawijaya Indonesia
https://orcid.org/0000-0002-4068-6807
Statistics
Abstract views: 637PDF downloads: 363
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Elmehdi BENMALEK, Jamal EL MHAMDI, Abdelilah JILBAB, Atman JBARI, A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Pascal Krutz, Matthias Rehm, Holger Schlegel, Martin Dix, RECOGNITION OF SPORTS EXERCISES USING INERTIAL SENSOR TECHNOLOGY , Applied Computer Science: Vol. 19 No. 1 (2023)
- Archana Gunakala, Afzal Hussain Shahid, A COMPARATIVE STUDY ON PERFORMANCE OF BASIC AND ENSEMBLE CLASSIFIERS WITH VARIOUS DATASETS , Applied Computer Science: Vol. 19 No. 1 (2023)
- Robert KARPIŃSKI, KNEE JOINT OSTEOARTHRITIS DIAGNOSIS BASED ON SELECTED ACOUSTIC SIGNAL DISCRIMINANTS USING MACHINE LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Dilek AYDOGAN-KILIC, Deniz Kenan KILIC, Izabela Ewa NIELSEN, EXAMINATION OF SUMMARIZED MEDICAL RECORDS FOR ICD CODE CLASSIFICATION VIA BERT , Applied Computer Science: Vol. 20 No. 2 (2024)
- Jolanta Brzozowska, Arkadiusz Gola, COMPUTER AIDED ASSEMBLY PLANNING USING MS EXCEL SOFTWARE – A CASE STUDY , Applied Computer Science: Vol. 17 No. 2 (2021)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Sahar ZAMANI KHANGHAH, Keivan MAGHOOLI, EMOTION RECOGNITION FROM HEART RATE VARIABILITY WITH A HYBRID SYSTEM COMBINED HIDDEN MARKOV MODEL AND POINCARE PLOT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Saheed ADEWUYI, Segun AINA, Aderonke LAWAL, Adeniran OLUWARANTI, Moses UZUNUIGBE, AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING , Applied Computer Science: Vol. 15 No. 4 (2019)
You may also start an advanced similarity search for this article.