a2ec0a3e-f777-4336-9e5d-25f5442443fd20210211101751477wseamdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS1109-952610.37394/23207http://wseas.org/wseas/cms.action?id=4016211202021120201710.37394/23207.2020.17http://wseas.org/wseas/cms.action?id=23182YazedAlsaawyDepartment of Information Technology, Faculty of Computer Science and Information System, Islamic University of Madinah, Medina, SAUDI ARABIAAhmadAlkhodreDepartment of Information Technology, Faculty of Computer Science and Information System, Islamic University of Madinah, Medina, SAUDI ARABIAMohammedBenaidaDepartment of Information Technology, Faculty of Computer Science and Information System, Islamic University of Madinah, Medina, SAUDI ARABIARafi AhmadKhanDepartment of Management Studies, University of Kashmir, INDIAThe main motivation of this study is to forecast the performance of Indian banks using multiple regression analysis and artificial neural network and to compare these two methods for the accuracy. To achieve this goal, financial data spread over 10 years from 2010 to 2019 was collected from 19 Indian public sector banks. The data consists of 17 financial ratios collected from financial statements and other publications of the sample organizations. Capital Adequacy Ratio (CRAR) has been chosen as dependent variable for measuring and predicting the financial strength of banks. Identification of significant ratios that are determinants of CRAR are identified by using regression technique then these identified ratios are used as input for a developing a neural network model. The findings of multi-linear regression analysis identified 7 financial ratios that have a positive relationship between with dependent variable (CRAR). These 7 dependent variables were used to predict the financial strength (CRAR) of the Banks. Then a feed forward back propagation neural network was developed with these 7 dependent variables to predict the CRAR. Finally, the performances of these two methods were measured by using the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE). The results indicate that ANN model scores an improvement of 55.67% in MSE over regression model. In RMSE which re-scales the errors in order to keep the errors’ dimension as the predicted value, ANN model scores an improvement of 33.425% over regression model. It also indicates that ANN model scores an improvement of 99.32% over regression model in MAPE which measures the magnitude of absolute errors in relative terms. Results show that ANN model outperforms the regression model and is superior technique of forecasting CRAR of Indian Banks.61620206162020627637https://www.wseas.org/multimedia/journals/economics/2020/b225107-978.pdf10.37394/23207.2020.17.60https://www.wseas.org/multimedia/journals/economics/2020/b225107-978.pdf10.21799/frbp.wp.2008.01J. P. Hughes and L. J. Mester, "Efficiency in Banking: Theory, Practice, and Evidence," Federal Reserve Bank of Philadelphia Working Paper 08-1, 2008.10.1016/j.intfin.2006.07.001P. Athansasoglou, S. Brissimis and M. Delis, "Bank-Specific, Industry-Specific and Macroeconomic Determinants of Bank Profitability," Journal of International Financial Markets , Institutions and Money, pp. 121-136, 2006.10.5089/9781451871623.001V. Flamini, C. McDonald andL. Schumacher, "The Determinants of Commercial Bank Profitability in Sub-Saharan Africa," 2009 2009. [Online]. Available: www.imf.org/external/pubs/ft/wp/2009/wp0915.pdf. [Accessed 26 may 2010].S. Naceur, "The Determinants of the Tunisian Banking Industry Profitability: Panel Evidence," 2003. [Online]. Available: www.mafhoum.com/press6/ 174E11. pdf. [Accessed 29 May 2010].F. Sufian and R. R. Chong, "Determinants of Bank Profitability in a Developing Economy: Empirical Evidence from Philippines," Asian Academy of Management Journal of Accounting and Finance, vol. 4, no. 2, pp. 91-112 , 2008.N. A. Sanusi and N. Mohammed, "Profitability of an Islamic Bank: Panel Evidence from Malaysia," in Islamic Economics & Finance, 2007, pp. 97-116.S. Haron, "Determinants of Islamic Bank Profitability," Global Journal of Finance & Economics, vol. 1, no. 1, pp. 11-33, 2004. 10.2139/ssrn.1595751O. Uzhegova, "The Relative Importance of Bank-specific Factors for Bank Profitability in Developing Economies," 4 2010. [Online]. Available: http://ssrn.com/abstract=1595751. [Accessed 10 6 2010].S. Elyor, "Factors Affecting the Performance of Foreign Banks in Malaysia," Universiti Utara Malaysia, 2009.10.3126/jnbs.v2i1.55K. J. Baral, "Health Check-up of Commercial Banks in the Framework of CAMEL: A Case Study of Joint Venture Banks in Nepal," The Journal of Nepalese Business Studies, vol. 2, no. 1, 2005. 10.2307/2229425F. Brechling and G. Clayton, "Commercial Banks' Portfolio Behaviour l," The Economic Journa, vol. 75, no. 298, pp. 290-316, 1965. 10.1086/259597S. Peltzman, "Capital Investment in Commercial Banking and Its Relation to Portfolio Regulation," Journal of Political Economy, vol. 78, pp. 1-26, 1970.C. Christian, J. S. Moffitt and Subberly, "Fundamental Analysis for Evaluating Bank Performance," Bank Accounting & Finance, vol. 21, no. 6, pp. 17-24, 2008.10.2139/ssrn.253849G. Gorton and A. Winton, "Liquidity Provision, Bank Capital, and the Macroeconomy," SSR NeLibrary DOI: 10.2139/ssrn.253849., 2000.10.1016/s0957-4174(99)00016-0A. Vellido, "Neural Networks in Business: A Survey of Applications (1992-1998)," Expert Systems with Applications, vol. 17, p. 51 –70, 1999.10.1108/02635579910291984M. Aiken, "Using a Neural Network to Forecast Inflation," Industrial Management & Data Systems, vol. 99, no. 7, pp. 296-301, 1999.N. Nguyen and A. Cripps, "Predicting Housing Value: A Comparison of Multiple Linear Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, vol. 22, no. 3, pp. 313-336, 2001.N. Arulsudar, N. Subramaniam and R. Murthy, "Comparison of Artificial Neural Network and Multiple Linear Regression in the Optimization of Formulation Parameters of Leuprolide Acetate Loaded Liposomes," Journal ofPharmacy and Pharmaceutical Sciences, vol. 8, no. 2, pp. 243-258, 2005.10.1016/0925-2312(94)00060-3M. Leshno and Y. Spector, "Neural Network Prediction Analysis: The Bankruptcy Case," Neurocomputing, pp. 125-147, 1996.10.1016/j.artmed.2004.07.002D. Delen, G. Walker and A. Kadam, "Predicting Breast Cancer Survivability: A Comparison of Three Data Mining Methods," Artificial Intelligence in Medicine, vol. 34, pp. 113-117, 2005. 10.1016/j.jhydrol.2007.11.024K. T. Chokmani, J. V. Quarda, S. Hamilton, G. M. Hosni and G. Hugo, "Comparison of Ice-Affected Streamflow Estimates Computed Using Artificial Neural Networks and Multiple Regression Techniques," Journal of Hydrology, vol. 349, pp. 383-396, 2008. 10.1016/s0377-2217(98)00051-4G. Zhang, M. Y. Hu, B. E. Patuwo and D. C. Indro, "Artificial Neural Networks in Bankruptcy Prediction:General Framework and Cross-Validation Analysis," European Journal of Operational Research, pp. 16-32, 1999. G. P. Naidu and K. Govinda, in 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India , 19-20 Jan. 2018 .Y. Tang, "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, vol. 13, no. 2, 2019. 10.1016/0957-4174(95)00020-8J. E. Boritz and D. B. Kennedy, "Effectiveness of Neural Network Types for Prediction of Business Failure," Expert Systems with Applications, vol. 9, no. 4, pp. 503-512, 1995. 10.1002/(sici)1099-1174(199709)6:3<177::aid-isaf134>3.0.co;2-dR. Barniv, A. Agarwal and R. Leach, "Predicting the outcome following bankruptcy filing: A three-state classification using neural networks," International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 6, no. 3, p. 177–194, 1997.10.1002/j.1099-1174.1994.tb00068.xK. Fanning and K. Cogger, "A comparative analysis of artificial neural networks using financial distress prediction," International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 3, no. 3, p. 241–252, 1994. T. Bell, G. Ribar and J. Verchio, "Neural nets vs. logistic regression: A comparison of each model’s ability to predict commercial bank failures," in Deloitte and Touche/University of Kansas Symposium on Auditing Problems, 1990.10.2307/2584215S. P. Curram and J. Mingers, "Neural networks, decision tree induction and discriminant analysis: An empirical comparison," Journal of Operational Research Society, vol. 45, no. 4, p. 440–450, 1994. 10.1016/s0957-4174(97)00011-0H. Lee, H. Jo and I. Han, "Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis," Expert Systems With Applications, vol. 13, p. 97–108, 1997.D. Fletcher and E. Goss, "Forecasting with neural networks and application using bankruptcy data," Information and Management, vol. 24, p. 159–167, 1993.10.1016/s0957-4174(96)00056-5H. Jo and I. Han, "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction," Expert Systems with Applications, vol. 11, p. 415–422, 1996. 10.2307/3665934P. Coates and L. Fant, "Recognizing financial distress patterns usinga neural network tool,"Financial Management, vol. 22, no. 3, p. 142–155, 1993.10.1109/ijcnn.1990.137710M. Odom and R. Sharda, "A neural network model for bankruptcy prediction," in Proceedings of the international joint conference on neural networks, 1990.10.1016/0167-9236(94)90024-8R. Wilson and R. Sharda, "Bankruptcy prediction using neural networks," Decision Support Systems, vol. 11, p. 545–557, 1994. 10.1287/mnsc.38.7.926K. Y. Tam and M. Y. Kiang, "Managerial applications of neural networks: The case of bank failure predictions," Management Science, vol. 38, no. 7, p. 926–947, 1992. 10.1016/0377-2217(95)00246-4V. S. Desai, J. N. Crook and G. A. Overstreet, "A comparison of neural networks andlinear scoring models in the credit union environment," European Journal of Operational Research, vol. 95, p. 24–37, 1996.S. Sarojini Devi and Y. Radhika, "An analytical approach for bankruptcy prediction using big data and machine learning technique," Journal of Theoretical and Applied Information Technology , vol. 97, no. 6, pp. 1823-32, 2019.J. O. Rawlings, Applied Regression Analysis, 1st & 2nd ed ed., Belmont, CA: Wadsworth, 1988,1998. A. Fadlalla and C.-H. Lin, "An Analysis of the Applications of Neural Networks in Finance," Interfaces, vol. 31, no. 4, pp. 112-122, 2001.