Predicting the Financial Performance of Banks in GCC Countries Using Data Mining Techniques
Keywords:
Artificial Neural Network, K-Nearest Neighbor, Machine Learning, RMSE, MBE, MAPEAbstract
The study seeks to evaluate data mining techniques for predicting the financial performance of banks in the Gulf Cooperation Council (GCC) region using the primary exogenous variables (ROA and ROE) of profitability. The study applied some widely used techniques K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) to predict financial performance using annual data from banks in the GCC region from 2010 to 2021. Our findings showed that the KNN and ANN are appropriate techniques for predicting a bank’s financial performance. The lower statistical errors (RMSE, MBE, and MAPE) of the trained model confirm that both these techniques can be used to predict the financial performance of the GCC region with the highest accuracy; however, the models can only predict the return on assets with the lowest statistical errors comparatively. This experimental venture addresses a gap in the banking industry's performance prediction caused by a lack of data mining techniques. The results of this study validate that data mining techniques may be used to predict bank financial performance with higher accuracy. Financial researchers and decision-makers can use the accurate prediction from the rising use of data mining techniques to generate accurate predictions and develop strategies accordingly. These strategies may be used to examine and extract useful information to make valuable decisions.
References
Adhikari, B. K., & Agrawal, A. (2016). Does local religiosity matter for bank risk-taking? Journal of Corporate Finance, 38, 272–293. https://doi.org/10.1016/j.jcorpfin.2016.01.009
Alejandrino, J. C., P. Bolacoy, J. Jr., & Murcia, J. V. B. (2023). Supervised and unsupervised data mining approaches in loan default prediction. International Journal of Electrical and Computer Engineering (IJECE), 13(2), 1837. https://doi.org/10.11591/ijece.v13i2.pp1837-1847
Alharthi, M. (2017). Financial performance and stability in Islamic banks: Evidence from GCC countries. Corporate Ownership and Control, 14, 103–113. https://doi.org/10.22495/cocv14i4art9
Alkhatib, K., Najadat, H., Hmeidi, I., & Shatnawi, M. (2013). Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm. https://www.semanticscholar.org/paper/Stock-Price-Prediction-Using-K-Nearest-Neighbor-Alkhatib-Najadat/1507329f5382a1550430657ebae7f70507f63410
Anwar, S., & Hasan, Md. M. (2018). ANNs-Based Early Warning System for Indonesian Islamic Banks. Buletin Ekonomi Moneter Dan Perbankan, 20. https://doi.org/10.21098/bemp.v20i3.856
Atiku, S. O., & Obagbuwa, I. C. (2021). Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance. Applied Computational Intelligence and Soft Computing, 2021, e7747907. https://doi.org/10.1155/2021/7747907
Brighi, P., & Venturelli, V. (2016). How functional and geographic diversification affect bank profitability during the crisis. Finance Research Letters, 16, 1–10.
Chitra, K., & Subashini, B. (2013). Data mining techniques and its applications in banking sector. International Journal of Emerging Technology and Advanced Engineering, 3(8), 219–226.
Chua, Z. (2013). Determinants of Islamic Banks Profitability in Malaysia (SSRN Scholarly Paper No. 2276277). https://doi.org/10.2139/ssrn.2276277
Dhenuvakonda, P., Anandan, R., & Kumar, N. (2020). Stock price prediction using artificial neural networks. Journal of Critical Reviews, 7(11), 846–850.
Feng, J., & Lu, S. (2019). Performance Analysis of Various Activation Functions in Artificial Neural Networks. Journal of Physics: Conference Series, 1237(2), 022030. https://doi.org/10.1088/1742-6596/1237/2/022030
Gržeta, I., Žiković, S., & Tomas Žiković, I. (2023). Size matters: Analyzing bank profitability and efficiency under the Basel III framework. Financial Innovation, 9(1), 43. https://doi.org/10.1186/s40854-022-00412-y
Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351–1362.
Imandoust, S. B., & Bolandraftar, M. (2013). Application of K-nearest neighbor (KNN) approach for predicting economic events theoretical background. Int J Eng Res Appl, 3, 605–610.
Kassani, S. H., Kassani, P. H., & Najafi, S. E. (2018). Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches. ArXiv Preprint ArXiv:1810.05524.
Khan, M. A., Abbas, K., Su’ud, M. M., Salameh, A. A., Alam, M. M., Aman, N., Mehreen, M., Jan, A., Hashim, N. A. A. B. N., & Aziz, R. C. (2022). Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach. Sustainability, 14(16), Article 16. https://doi.org/10.3390/su14169964
Kumar, V., Thrikawala, S., & Acharya, S. (2022). Financial inclusion and bank profitability: Evidence from a developed market. Global Finance Journal, 53, 100609. https://doi.org/10.1016/j.gfj.2021.100609
Ledhem, M. A. (2021a). Data mining techniques for predicting the financial performance of Islamic banking in Indonesia. Journal of Modelling in Management, 17(3), 896–915. https://doi.org/10.1108/JM2-10-2020-0286
Ledhem, M. A. (2021b). Data mining techniques for predicting the financial performance of Islamic banking in Indonesia. Journal of Modelling in Management, 17(3), 896–915. https://doi.org/10.1108/JM2-10-2020-0286
Ledhem, M. A. (2022). Data mining techniques for predicting the financial performance of Islamic banking in Indonesia. Journal of Modelling in Management, 17(3), 896–915.
Ledhem, M. A., & Mekidiche, M. (2020). Economic growth and financial performance of Islamic banks: A CAMELS approach. Islamic Economic Studies, 28(1), 47–62. https://doi.org/10.1108/IES-05-2020-0016
Moradi, M., Salehi, M., Ghorgani, M. E., & Sadoghi Yazdi, H. (2013). Financial distress prediction of Iranian companies using data mining techniques. Organizacija, 46(1).
Moradi, S., & Mokhatab Rafiei, F. (2019a). A dynamic credit risk assessment model with data mining techniques: Evidence from Iranian banks. Financial Innovation, 5(1), 15. https://doi.org/10.1186/s40854-019-0121-9
Moradi, S., & Mokhatab Rafiei, F. (2019b). A dynamic credit risk assessment model with data mining techniques: Evidence from Iranian banks. Financial Innovation, 5(1), 15. https://doi.org/10.1186/s40854-019-0121-9
O’Connell, M. (2022). Bank-specific, industry-specific and macroeconomic determinants of bank profitability: Evidence from the UK. Studies in Economics and Finance, 40(1), 155–174. https://doi.org/10.1108/SEF-10-2021-0413
Patel, N., & Wang, J. T. L. (2015). Semi-supervised prediction of gene regulatory networks using machine learning algorithms. Journal of Biosciences, 40(4), 731–740. https://doi.org/10.1007/s12038-015-9558-9
Riana, I. G., Suparna, G., Suwandana, I. G. M., Kot, S., & Rajiani, I. (2021). IN PROMOTING INNOVATION AND ORGANIzATIONAL PERFORMANCE.
Sapuan, N. M., Bakar, S., & Ramlan, H. (2017). Predicting the Performance and Survival of Islamic Banks in Malaysia to Achieve Growth Sustainability. SHS Web of Conferences, 36, 00016. https://doi.org/10.1051/shsconf/20173600016
Shahvaroughi Farahani, M., & Razavi Hajiagha, S. H. (2021). Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models. Soft Computing, 25(13), 8483–8513. https://doi.org/10.1007/s00500-021-05775-5
Uludağ, O., & Gürsoy, A. (2020). On the Financial Situation Analysis with KNN and Naive Bayes Classification Algorithms. Journal of the Institute of Science and Technology, 2881–2888. https://doi.org/10.21597/jist.703004
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 167, 599–606. https://doi.org/10.1016/j.procs.2020.03.326
Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485–498. https://doi.org/10.1016/j.ribaf.2015.10.002
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