Machine learning algorithm-based decision support system for prime bank stock trend prediction
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Published:
Mar 30, 2024
Keywords:
Artificial Neural Network (ANN), Machine Learning Algorithms, Predictive Analytics, Stock Trend Prediction, Support Vector Machine (SVM)
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Abstract
In the complex landscape of financial markets, predicting bank stock trends is a critical aspect that supports more accurate investment decision-making. This study aims to develop and evaluate machine learning algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—for predicting the trends of major bank stocks in Indonesia using the IDX-PEFINDO dataset from January 1, 2020, to December 31, 2023. The adopted methodology includes collecting historical data, initial processing, feature selection, and training and validating models using evaluation metrics such as Accuracy, Precision, Recall, F1-Score, MAE, and RMSE. Results indicate that although no single algorithm is dominant, SVM and ANN perform better within the given data context. This research underscores the importance of a tailored approach to maximize the potential of machine learning algorithms in stock prediction, providing new insights into developing decision support systems for bank stock investments. This study implies that it recommends the integration of broader economic indicators and the exploration of advanced machine-learning techniques to enhance stock prediction accuracy in the future.
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Gunawan, G., Budiono, W., Andriani, W., & Naja, N. N. P. W. . (2024). Machine learning algorithm-based decision support system for prime bank stock trend prediction. Journal of Intelligent Decision Support System (IDSS), 7(1), 1-9. https://doi.org/10.35335/idss.v7i1.207
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Deng, C., Huang, Y., Hasan, N., & Bao, Y. (2022). Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition. Information Sciences, 607, 297–321.
Dhivyaa, C. R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., & Johnpaul, P. (2020). Skin lesion classification using decision trees and random forest algorithms. Journal of Ambient Intelligence and Humanized Computing, 1–13.
Ellis, S., Sharma, S., & Brzeszczyński, J. (2022). Systemic risk measures and regulatory challenges. Journal of Financial Stability, 61, 100960.
Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data, 8, 1–37.
Jun, M.-J. (2021). A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: The case of the Seoul metropolitan area. International Journal of Geographical Information Science, 35(11), 2149–2167.
Kim, S. Y., & Upneja, A. (2021). Majority voting ensemble with a decision trees for business failure prediction during economic downturns. Journal of Innovation & Knowledge, 6(2), 112–123.
Kumar, P. S., Behera, H. S., Kumari, A., Nayak, J., & Naik, B. (2020). Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades. Computer Science Review, 38, 100288.
Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., & Kalidindi, S. R. (2020). Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology, 294(2), 421–431.
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data, a comparative analysis. Ieee Access, 8, 150199–150212.
Oleghe, O. (2020). A predictive noise correction methodology for manufacturing process datasets. Journal of Big Data, 7(1), 89.
Ramos, D., Faria, P., Vale, Z., Mourinho, J., & Correia, R. (2020). Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning. In Energies (Vol. 13, Issue 18). https://doi.org/10.3390/en13184774
Razzaq, A., Sharif, A., An, H., & Aloui, C. (2022). Testing the directional predictability between carbon trading and sectoral stocks in China: New insights using cross-quantilogram and rolling window causality approaches. Technological Forecasting and Social Change, 182, 121846.
Salazar, J. J., Garland, L., Ochoa, J., & Pyrcz, M. J. (2022). Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy. Journal of Petroleum Science and Engineering, 209, 109885.
Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of Big Data, 7, 1–33.
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021). COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164–1183.
Taneja, K., Arora, V., & Verma, K. (2023). Classifying the heart sound signals using textural‐based features for an efficient decision support system. Expert Systems, 40(6), e13246.
Thudumu, S., Branch, P., Jin, J., & Singh, J. (Jack). (2020). A comprehensive survey of anomaly detection techniques for high dimensional big data. Journal of Big Data, 7(1), 42. https://doi.org/10.1186/s40537-020-00320-x
Yang, D., Alessandrini, S., Antonanzas, J., Antonanzas-Torres, F., Badescu, V., Beyer, H. G., Blaga, R., Boland, J., Bright, J. M., & Coimbra, C. F. M. (2020). Verification of deterministic solar forecasts. Solar Energy, 210, 20–37.
Yao, Z., Wang, H., Yan, W., Wang, Z., Zhang, W., Wang, Z., & Zhang, G. (2023). Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images. European Journal of Radiology, 110934.
Yates, L. A., Aandahl, Z., Richards, S. A., & Brook, B. W. (2023). Cross validation for model selection: a review with examples from ecology. Ecological Monographs, 93(1), e1557.
Zhao, J., Han, X., Ouyang, M., & Burke, A. F. (2023). Specialized deep neural networks for battery health prognostics: Opportunities and challenges. Journal of Energy Chemistry.
Zhou, X., Lu, P., Zheng, Z., Tolliver, D., & Keramati, A. (2020). Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliability Engineering & System Safety, 200, 106931.
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