Applying Multiple Linear Regression and Neural Network to Predict Bank Performance

Abstract

<p class="MsoNormal" style="margin: 0cm 0cm 4pt; line-height: 12pt; mso-line-height-rule: exactly; mso-pagination: widow-orphan; tab-stops: 387.0pt;"><span style="font-size: 10pt; mso-font-kerning: 0pt; mso-fareast-language: EN-US; mso-ansi-language: EN-GB;" lang="EN-GB"><span style="font-family: Times New Roman;">Globalization and technological advancement has created a highly competitive market in the banking and finance industry. Performance of the industry depends heavily on the accuracy of the decisions made at managerial level. This study uses multiple linear regression technique and feed forward artificial neural network in predicting bank performance. The study aims to predict bank performance using multiple linear regression and neural network. The study then evaluates the performance of the two techniques with a goal to find a powerful tool in predicting the bank performance. Data of thirteen banks for the period 2001-2006 was used in the study. ROA was used as a measure of bank performance, and hence is a dependent variable for the multiple linear regressions. Seven variables including liquidity, credit risk, cost to income ratio, size, concentration ratio, inflation and GDP were used as independent variables. Under supervised learning, the dependent variable, ROA was used as the target output for the artificial neural network. Seven inputs corresponding to seven predictor variables were used for pattern recognition at the training phase. Experimental results from the multiple linear regression show that two variables: credit risk and cost to income ratio are significant in determining the bank performance.<span style="mso-spacerun: yes;"> </span>Two variables were found to explain about 60.9 percent of the total variation in the data with a mean square error (MSE) of 0.330. The artificial neural network was found to give optimal results by using thirteen hidden neurons. Testing results show that the seven inputs explain about 66.9 percent of the total variation in the data with a very low MSE of 0.00687. Performance of both methods is measured by mean square prediction error (MSPR) at the validation stage. The MSPR value for neural network is lower than the MPSR value for multiple linear regression (0.0061 against 0.6190). The study concludes that artificial neural network is the more powerful tool in predicting bank performance.</span></span></p

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