Medición del Clima

We ran both Random and Fixed effect models and did the Hausman test to decide as to which model to choose. The choice of these two models is based on the assumption that the banks are heterogeneous, thus, ignoring the pooled ordinary least squares (OLS) regression model that denies the heterogeneity or individuality that may exist between these five banks (Podesta, 2000; Studenmund, 2011; Baltagi, Econometric Analysis of Panel Data, 2013).

Table 4.3 Fixed Effect Model Credit Risk

DEPENDENT VARIABLE: ROE

* ** *** 10%, 5% and 1% level of significance respectively.

“Variable Coefficient Std. Error t-Statistic Prob.”

C 28.46362*** 4.390752 6.482631 0.0000 LA_TD 0.031766** 0.015764 2.015050 0.0505 NPL_LA -1.033009*** 0.196884 -5.246779 0.0000 LR 0.081217*** 0.017297 4.695473 0.0000 LOGLOG_TA -0.541132* 0.302782 -1.787203 0.0813 Effects Specification “Cross-section fixed (dummy variables)”

R-squared 0.815442 Mean dependent var 19.74600 Adjusted R-

squared 0.779431 S.D. dependent var 5.592678 S.E. of

regression 2.626590 Akaike info criterion 4.930799 Sum squared

resid 282.8580 Schwarz criterion 5.274963 Log likelihood -114.2700 Hannan-Quinn criter. 5.061859

F-statistic 22.64405 Durbin-Watson stat 1.945546 Prob(F-

statistic) 0.000000

To choose the most accurate method to interpret our data for objective results we employed both FEM and REM and performed the Hausman test to decide the best approach.

Table 4.4 Random Effect Model Credit Risk

DEPENDENT VARIABLE: ROE

* ** *** 10%, 5% and 1% level of significance respectively.

Variable Coefficient Std. Error t-Statistic Prob.

C 47.70950*** 3.136574 15.21070 0.0000 LA_TD -0.041920*** 0.009325 -4.495410 0.0000 NPL_LA -0.282262** 0.137880 -2.047164 0.0465 LR 0.070429*** 0.016620 4.237645 0.0001 LOGLOG_TA -1.962601*** 0.195671 -10.03012 0.0000 Effects Specification S.D. Rho

Cross-section random 1.76E-06 0.0000

Idiosyncratic random 2.626590 1.0000

Weighted Statistics

R-squared 0.609283 Mean dependent var 19.74600 Adjusted R-squared 0.574553 S.D. dependent var 5.592678 S.E. of regression 3.647897 Sum squared resid 598.8219 F-statistic 17.54324 Durbin-Watson stat 1.399496 Prob(F-statistic) 0.000000

Unweighted Statistics

R-squared 0.609283 Mean dependent var 19.74600 Sum squared resid 598.8219 Durbin-Watson stat 1.399496

Model Choice

In order to choose as to which model was superior between the above estimated models (fixed/random effects models); the Hausman Test was conducted.

Hausman Test

H0: Random Effect Model is Appropriate

H1: Fixed Effect Model is Appropriate.

Table 4.5 Hausman Test Credit Risk

CORRELATED RANDOM EFFECTS: HAUSMAN TEST

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 45.798649 4 0.0000

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

LA_TD 0.031766 -0.041920 0.000162 0.0000

NPL_LA -1.033009 -0.282262 0.019753 0.0000

LR 0.081217 0.070429 0.000023 0.0244

LOGLOG_TA -0.541132 -1.962601 0.053390 0.0000

Results: the probability value, p-value was 0.0000 and 100% significant. Meaning we rejected the null hypothesis that random effect model was suitable in favour of the alternative hypothesis.

Consequently, the fixed effect model was then used to analyse the effect of credit risk management on South African banks’ profitability.

Fixed Effect Model: The study shows that loans and “advances to total deposit, non- performing loans to loans and advances, leverage ratio, and log of total asset were key credit risk factors that influenced the performance of sampled banks in South Africa. Non-performing loan to loans and advances, and leverage ratio were significant at 1%, loans and advances significant at 5%, and logarithm of total assets was significant at 10% significant level.

Loans and Advances to Total Deposit: The results showed a positive and a significant relationship between loans and advances to total deposit and return on equity, at 5% significance level in accordance with the expected sign. The findings were in-line with the findings by Funso, Kolado and Ojo (2012). Both loans and deposits were equally important in the banking operation like two sides of the same coin. In general, the main source of income for the banks was interest from loans and advances. The primary function of the bank was to lend money to the borrowers in order to mobilise the interest revenue; this was the ultimate source of revenue for the banks. Normally all the banks try to increase the amount of loans to the borrowers for aggregate interest revenue in the financial statement. It is understandable that the banks offer more loans the more it goes on to generate high revenue and profit, Abreu and

Mendes (2002).” This means a 1% increase in loans and advances to total deposit increased the profitability of South African banks by (0.031766%).

Non-Performing Loans to Loans and Advances: The result showed a negative relationship between non-performing loans to loans and advances and return on equity, at 1%significance level in accordance with the expected sign. The findings were also in-line with the findings by

Funso, Kolado and Ojo (2012); and Mwangi (2012). This meant that as the number of non- performing loans decreased the banks’ profitability increased; therefore a converse relationship. Bank performance was dependent on the management practices pertaining to non- performing loan. This signified that the best practices in non-performing loan management had the prospect of improving the financial performance of that institution. Nonperforming loans can bring down investors’ confidence in the banking sector, piling up unproductive economic resources even though depreciations are taken care of, and impeding the resource allocation

process.” Nevertheless, our findings showed that with a 1% increase in non-performing loans there was a (-1.033009%) in return on equity.

Leverage Ratio: The results showed that leverage ratio and ROE were positively related, at1% significance level in accordance with the expected sign. Banks that had huge amounts of

debt were better capable to improve their firm value or profitability compared to banks with less; the primary reason for this being the additional discipline and interest tax shield that was brought by the high debt. This notion was in agreement with Modigliani and Miller’s (1958) second proposition, which summarised that a company’s worth was to a greater extent heavily dependent on its capital base. Nevertheless, our findings showed that a 1% increase in a banks’ leverage ratio, return on equity increased by (0.081217%). These findings were in-line with the findings by Boahene (2012) and Lane (2009).

Log of Total Asset: The result showed a negative relationship between firm size (log of total assets) and return on equity at a 10% significance level. The prior expectation was of a positive relationship; indicating economies of scale. However, in this case South African banks seemed to have experienced diseconomies of scale. The findings were in-line with the findings by Becker (2010) and Mesut (2013). This meant, for a 1% increase in firm total assets, the banks’ profitability would decrease by (-0.541132%). Nevertheless, this would not be surprising because much of the banks’ assets were loans which were risky assets. An increase in risky asset may decrease profitability.

Suitability of the Research Model

H0: “The model is not appropriate; when the independent variable doesn’t affect the

dependent variable.”

H1: “The model is appropriate; when the independent variable affects the dependant

variable.” The Decision Rule

Accept H0 If (Sig. F) > 5% Accept H1 If (Sig. F) < 5%

From the “analysis output, the value of (Sig. F) was equal to (0.00000). Therefore, we accepted the alternative hypothesis and the model used was appropriate; meaning credit risk management had an effect on banks’ financial performance.

The divergence of the dependent variables was explained by the independent variable (R-

squared). R2 _{suggested that 83% of the total variation in ROE across the banking firms was}

explained by joint variations in the four variables.”