Stationarity of Data The panel data stationarity test has a severe size distortion inconsistent with the null hypothesis. Stationary is vague since the mean and variance of the data is not constant. The most appropriate resolution in this case is to merge ailing banks or let strongly financed banks purchase bad debts in accordance with market mechanisms and securitization in Portugal. In view of that, weak banks should be merged or be acquired by stronger banks that appear in the panel data.

Causality Test

The influence of NPL on technical competence is close to zero and not significant. On the other hand, the effect of technical efficiency on loan Loss is positive and significant at 5% level, once more indicating that the causality would run from bank efficiency to non-performing loans. Turning to the allocative efficiency case, the performance is poor and there is no significant response to variation in NPL. Consequently, the loan losses react negatively to transformations in this bank efficiency, with a significance level of 10%. Finally, looking at the economic efficiency, it appears to have a negative impact on problem loans and to be despicably affected by the credit risk variable. Nonetheless, none of the coefficients are significant.

Co-integration test

The results of co-integration between Bank Capital, Loan and GDP at 5% significance level for all commercial banks in Portugal suggest the presence of co-movements among the variables, indicating long-run stationarity. The Error Correction Model (ECM) corrected the deviation...

This may be because of high inflation and instability in economy especially during 2005 to 2011.Thus; short-term transactions have a strong influence in financial markets.
This situation increases fund costs and affects investors negatively. Similarly, unstable economy causes small investors withdraws from financial markets by increasing their risk.

Eigenvalue

Likelihood ratio (Qmax)

Critical Value (5%)

0 . 2 8

4 0 . 3 6

2 9 . 6 8

0 . 2 4

1 9 . 6 0

1 5 . 4 1

0 . 0 4

2 . 4 0

3 . 7 6

Data Envelopment Analysis

DEA compares each bank with all other service units, and identifies those units that are operating inefficiently compared with other units. It accomplishes this by locating the best practice or relatively efficient units. It also measures the magnitude of inefficiency of units compared to the best practice units. The best practice units are relatively efficient and are identified by a DEA efficiency rating. The inefficient units are identified by an efficiency rating of less than or equal to 1. The upper limit is set as 1 or 100% to reflect the view that a unit cannot be more than 100% efficient.

The large number of commercial banks in Portugal, the high branch density, the slow technological…