Effect of Credit Risk on Bank Performance - Research Paper Example

Effect of credit risk on bank performance Introduction Credit risk has in frequent years been a concern not only for financial institution but the whole business environment globally due to risk of trading partner who doesn’t fulfill their commitment as a whole on time. The research will focus on appraising the impact of credit risk control on financial performance of commercial banks. Research design The research design employed in the research was descriptive research design which is significant due to the fact that the study entail a comprehensive research on credit risk control as well as the link between the two variables {credit risk control as well as the financial performance of the financial institution (Duffie 2008). Data collection The secondary data was employed for the reasons of research as well as the data was derived from the financial statement of the banks. First hand data was collected by using the normal questionnaire as well as entire respondent was completed. The questionnaires had open as well as closed ended questions created for the writing .this entail the detailed statement of income as well as statement of financial position of financial institution (Cheng-Few Lee 2010). The variables employed were return on equity worked out as annual net income after tax divided by shareholder’s equity as an appraisal of financial performance. Data analysis The section entails the manner to which the data is analyzed. There are many approaches through which data is analyzed such as the use of graph, statistical percentage, tables as well as pie charts. The data collected from the questionnaires handed out to the sample population comprehended the questions as well as confirmed for accuracy. The data was then checked for any errors as well as rectified. The descriptive statistics was run. The researcher then proceeded to interpret and present the data. Result This entails the assessment of data gathered as well as discuses the findings of the impact of credit risk control on performance of financial institution in Australia .of the 43 financial institutions sampled for research, full data was obtained from the 26 bank and consequently focused on these banks. The outcome of the regression model are presented and examined as well as effect of the credit risk control on [profitability is explained. Descriptive association of variables with Non-performing loan and capital adequacy ratio as independent Variables in 2015 Mean standard deviation number return on equity 17 9 26 Non-performing loan 7 8 15 loan to total loan 0 0 0 capital ratio 26 15 41 It can be observed that the return on equity is examined against the non-performing loan as well as capital ratio. The roe depicted a value of 26 as at 2015 with a mean of 17 and a standard deviation of 9 while Non-performing loan to total loans had a mean of 7 and capital ratio had a mean of 26 and standard deviation of 15. Regression results with Non-performing loan and capital adequacy ratio as independent variables in 2015 Table 4.2: coefficient summary table year 2015 Correlation table return on equity Non-performing loan Capital ratio Pearson correlation Return on equity 1.0 -.3 -.07 Non-performing loan -.31 1.000 .05 Capital ratio -.07 .05 1.000 sig. (1-tailed) Return on equity . .06 .36 Non-performing loan .06 . .41 loans to total loans .36 .41 . Return on equity 26 26 26 Non-performing loan s to total loans 26 26 26 capital ratio 26 26 26 From the above, it can be observed that the outcome of 2015 profitability where the return on equity is an independent variable. The table depict that the Non-performing loan affects roe unconstructively. Non-performing loan β coefficient is -0.3 implying that a unit growth in Non-performing loan reduces the return on equity by 0.3 while the capital adequacy ratio is fixed. The statistical significance of Non-performing loan on roe is 0.059 implying that the Non-performing loan forecast impact of 0.07 and this it point out that the unit growth in capital adequacy ratio will lead to a reduction in return on equity by 0.07 units, holding the Non-performing loan fixed. The statistical significance of capital adequacy ratio is 0.4 implying that the significance is low and thus the roe will be 64% likely. The outcome of the assessment implies that both the Non-performing loan and capital adequacy ratio depict a negative as well as significant impact on roe, with Non-performing loan depicting a higher significant impact on roe in relation to capital adequacy ratio (Elena Beccalli 2013). 4.2.3 Linearity of the variables in 2015 Table 4.3: model summary table year 2015 change statistics model r r square adjusted r Square std. error of the estimate R square change % of change df1 df2 Significance change 1 .319a .1 .023 8.9 .1 1.3 2 23 .3 A. predictors: (constant), capital ratio, Non-performing loan loans to total loans Source: research finding From the table above, it can be observed that the correlation coefficient of 0.32(r=.32) point out that the linearity of the variable examined for roe, Non-performing loan and capital adequacy ratio is a weak. This means that the points of the variables along the line of best fit are far disposed from the line of best fit. The adjusted r, also point out the outcome prior to the error is reduced. the table as well present the figure for the entire equitation , r squared depict the forecast level of variation in roe by Non-performing loan as well as the capital adequacy ratio .it implies therefore that r,10% of roe mat be forested from Non-performing loan and capital adequacy ratio (John B. Caouette 2008). Between the two independent variables Non-performing loan morel dependably forecast ROE. in table 4.3, the statistic value of f is 1.3 with p-values of 0.3 which as well implies that the roe is forecasted with 98% probability by Non-performing loan and capital adequacy ratio depicting a statically significance link among them. the f- value linked with p-values confirm that there is a significance link between the profitability appraised as roe as well as credit control appraised as Non-performing loan and capital adequacy ratio . Table 4.4: anova table year 2015 anovab model sum of squares df mean square F test sig. 1 Regression 205.9 2 2 103.0 13 .275a residual 183581 24 79l.9 Total 2041.6 24 a. predictors: (constant), capital ratio, Non-performing loan loans to total loans b. dependent variable: return on equity source: research findings it can be observed that the sum of squares due to regression is 205.9 with 2(df) degrees of freedom at the same time as the sum of squares residual due to 24 degrees of freedom is 1835.8.the means square provides a more precise level of association and authority with the 2 variables {Non-performing loan and capital adequacy ratio} having better results than the remaining 23 due to residual effect. 4.3 the relationship between credit risk control and financial performance in 2012 to 2015 4.3.1 measurement of correlation between variables table 4.5: coefficient summary table year 2012-2015 for 26 banks correlations ROE Non-performing loan capital adequacy ratio Pearson correlation Return on equity 1.000 -.47 -.046 Non-performing loan -.47 1.000 .087 capital adequacy ratio -.045 .086 1.000 sig. (1-tailed) ROE . .000 .33 Non-performing loan .000 .0 .195 capital adequacy ratio .325 .194 .0 N ROE 105 105 105 Non-performing loan 105 105 105 capital adequacy ratio 105 105 105 source: research findings it can be depicted that there is a negative association between roe and the Non-performing loan loans ratio as pointed out by the coefficient summary table. Pearson coefficient of -0.5 implies that the two are in opposite direction while the Pearson coefficient of -.05 between roe and capital adequacy ratio depict no link between the two since it is close to zero. nevertheless, there exist an inverse relationship between Non-performing loan loans and capital adequacy ratio with a Pearson coefficient of 0.88 and 0.19 for the case of 1 tail. Conclusion and recommendation Credit risk control in financial institution is growing but much still need to be done. the research required to examine the impact of credit control on performance of banks. It was observed therefore that there is a link between financial performance and credit risk in term of Non-performing loan s and capital adequacy ratio. The outcome from the regression model depict that there is an impact of credit risk control on bank’s profitability on a justifiable level with 23% likelihood of Non-performing loan and capital adequacy ratio in forecasting the variation in return on equity. It therefore implies that the 235 of ROE may be forecasted from Non-performing loan capital adequacy ratio. Between the two independent variable, Non-performing loan is more dependably in forecasting the ROE. As a result, the credit risk control plan defines the likelihood to a significant level. Reference list Cheng-Few Lee, ‎L 2010, Handbook of Quantitative Finance and Risk Management, John Wiley $ Son's, New York. Duffie, D 2008, Credit Risk: Pricing, Measurement And Management, Springer, Sydney. Elena Beccalli, ‎A‎G 2013, Retail Credit Risk Management, Cingage Learning, London. John B. Caouette, ‎IA‎N 2008, Managing Credit Risk: The Next Great Financial Challenge, John Wiley & sons, New Yorl. Longest, KC 2014, Using Stata for Quantitative Analysis, Cingage, London. Read More