Comparison of Long Short-Term Memory and Recurrent Neural Network For Stock Market Price Movement Classification in Islamic Bank Finance
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
This study addresses the importance of accurate stock price prediction in the Islamic finance sector, where reliable forecasting supports better investment decisions and market stability. Despite the growing use of deep learning methods, comparative studies on sequential models in this domain remain limited. Therefore, this research compares the performance of Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) models for classifying stock price movement direction of Islamic banks in Indonesia. The dataset was sourced from two Islamic banks in Indonesia, covering the period from 2022 to mid-2024, with features such as Open, High, Low, Close, Adjusted Close, and Volume. The CRISP-DM method was applied for data processing, and testing was performed with data splits of 60:40, 70:30, and 80:20, as well as epoch variations (30, 50, 80). Results indicate that RNN outperforms LSTM, with the highest accuracy of 58% for RNN and 53% for LSTM. Evaluation metrics also included precision, recall, and F1-score. In conclusion, RNN performs better for stock movement classification direction, while LSTM is more effective for minimizing prediction error.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish articles in CoreID Journal agree to the following terms:
- Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution–ShareAlike License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
S. U. Nurbaidah, R. Hidayat, and D. Saharuddin, “The Dynamics of Indonesia’s Sharia Capital Market Development 2014–2024: Opportunities, Challenges, and Strategic Futures,” CASHFLOW CURRENT ADVANCED RESEARCH ON SHARIA FINANCE AND ECONOMIC WORLDWIDE, vol. 4, no. 3, pp. 187–202, Aug. 2025, doi: 10.55047/cashflow.v4i3.1888.
D. U. A. Absari and I. Kholili, “Ziyadah from an Islamic Perspective (Case study at BMT Al-Rifa’ie),” Journal of Economics and Business Letters, vol. 4, no. 2, pp. 78–84, Apr. 2024, doi: 10.55942/jebl.v4i2.314.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
K. Sivaraman, R. M. V Krishnan, B. Sundarraj, and S. S. Gowthem, “Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.
M. Waqas and U. W. Humphries, “A critical review of RNN and LSTM variants in hydrological time series predictions,” MethodsX, vol. 13, p. 102946, Dec. 2024, doi: 10.1016/J.MEX.2024.102946.
F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.
M. Diqi, “StockTM: Accurate Stock Price Prediction Model Using LSTM,” International Journal of Informatics and Computation (IJICOM), vol. 4, no. 1, Aug. 2022, doi: 10.35842/ijicom.
S. Kumar and M. Singh, “Big data analytics for healthcare industry: Impact, applications, and tools,” Big Data Mining and Analytics, vol. 2, no. 1, pp. 48–57, 2019, doi: 10.26599/BDMA.2018.9020031.
S. A. S. A. Mohammed, “Artificial Neural Networks (ANN) for Stock Price Prediction: A Financial Machine Learning Analysis,” Augmenting Retail Reality, Part B: Blockchain, AR, VR, and AI, pp. 131–143, Dec. 2024, doi: 10.1108/978-1-83608-708-320241019.
M. Khan and Y. Hossni, “A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition,” Sci. Rep., vol. 15, no. 1, p. 3858, Dec. 2025, doi: 10.1038/S41598-025-88378-6.
Y. Wu and others, “Large scale incremental learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 374–382. doi: 10.1109/CVPR.2019.00046.
A. Mosavi, S. Shamshirband, E. Salwana, K. W. Chau, and J. H. M. Tah, “Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning,” Engineering Applications of Computational Fluid Mechanics, vol. 13, no. 1, pp. 482–492, 2019, doi: 10.1080/19942060.2019.1613448.
V. Palanisamy and R. Thirunavukarasu, “Implications of big data analytics in developing healthcare frameworks – A review,” Journal of King Saud University - Computer and Information Sciences, vol. 31, no. 4, pp. 415–425, 2019, doi: 10.1016/j.jksuci.2017.12.007.
J. Sadowski, “When data is capital: Datafication, accumulation, and extraction,” Big Data Soc., vol. 6, no. 1, pp. 1–12, 2019, doi: 10.1177/2053951718820549.
J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, “Comparing a traditional approach for financial brand communication analysis with a big data analytics technique,” IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301.
M. K. Pasha, A. R. Atmadja, and M. D. Firdaus, “Intelligent Traffic Management System Using Mask Regions-Convolutional Neural Network,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 10, no. 3, pp. 329–338, Aug. 2025, doi: 10.22219/KINETIK.V10I3.2233.
C. Aulia, A. F. Dinah, D. N. Zahratunnisa, and R. Efendi, “Short Message Spam Classification using Decision Tree, Naive Bayes, and Logistic Regression,” CoreID Journal, vol. 3, no. 3, pp. 114–123, Nov. 2025, doi: 10.60005/COREID.V3I3.146.
M. Irfan, T. V. Riyadi, A. R. Atmadja, R. S. Fuadi, and A. Muin, “Application of Convolutional Neural Network Algorithm for Analyzing Sentiments on the Kampus Merdeka Policy,” Proceeding of 2024 the 10th International Conference on Wireless and Telematics, ICWT 2024, 2024, doi: 10.1109/ICWT62080.2024.10674724.
R. DiPietro and G. D. Hager, “Deep learning: RNNs and LSTM,” Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 503–519, Jan. 2020, doi: 10.1016/B978-0-12-816176-0.00026-0.
X. Kong et al., “Deep learning for time series forecasting: a survey,” International Journal of Machine Learning and Cybernetics 2025 16:7, vol. 16, no. 7, pp. 5079–5112, Feb. 2025, doi: 10.1007/S13042-025-02560-W.
I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications,” Information 2024, Vol. 15, Page 517, vol. 15, no. 9, p. 517, Aug. 2024, doi: 10.3390/INFO15090517.
S. R. Chowdhury, Y. Khare, and S. Mazumdar, “Classification of diseases from CT images using LSTM-based CNN,” Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods, pp. 235–249, Jan. 2023, doi: 10.1016/B978-0-323-96129-5.00008-1.
C. Zhou et al., “Using long short-term memory networks to predict energy consumption of air-conditioning systems,” Sustain. Cities Soc., vol. 55, p. 102000, Apr. 2020, doi: 10.1016/J.SCS.2019.102000.
A. Tashakkori, N. Erfanibehrouz, S. Mirshekari, A. Sodagartojgi, and V. Gupta, “Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index”, doi: 10.5281/ZENODO.14810580.
D. Gupta, N. Dhanda, and K. K. Gupta, “A Comparative Study of Various LSTM Models for Stock Market Time Series Classification,” Proceedings of 8th International Conference on Computing Methodologies and Communication, ICCMC 2025, pp. 1024–1029, 2025, doi: 10.1109/ICCMC65190.2025.11140645.
S. K. Sahu, A. S. Mokahde, J. Chakole, S. N. Ajani, and M. M. Goswami, “Performance Analysis of LSTM, Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional LSTM (ConvLSTM) and LSTM with Attention in Stock Market Prediction,” International Journal of Basic and Applied Sciences, vol. 14, no. SI-2, pp. 131–140, Jul. 2025, doi: 10.14419/6wpxv706.
X. Cui, “Stock Market Prediction Using Recurrent Neural Network and LSTM,” Finance & Economics, vol. 1, no. 2, Apr. 2025, doi: 10.61173/QB8N8V02.