of management.
However, a major weakness of ratio analysis is that there is a lack of agreement in the
literature on the relative importance of various types of indicators. If a particular study
wishes to incorporate the related financial indicators to measure technical efficiency in
banks, it will lead to an issue of the weight assignment to each indicator. In addition,
financial ratio analysis, each single ratio must be com-pared with some benchmark ratios
one at a time while one assumes that other factors are fixed and the benchmarks chosen
are suitable for comparison
The financial ratio method can be an appropriate method when firms use a single input
or produce a single output. However, as in many organizations, banks employ various
inputs to provide various services (outputs). Which ratio should be selected becomes an
issue of evaluators when a great number of related financial indicators are involved. One
of the solving methods is to aggregate average among all indicators in order to integrate a
single measurement.
2.1.1.2 DEA Model
DEA approach can be employed to solve the issue of weight assignment which is the
major limitation of the ratio analysis. This approach uses a mathematical programming
method to generate a set of weights for each indicator. It considers how much efficiency
in the banking sector could be improved, and ranks the efficiency scores of individual
banks. Charnes et a (1985) was first to describe the DEA model, employing a
mathematical programming model to determine the efficiency frontier based on the
concept of the Pareto optimum when more than one measure is used.
9|
P
age

DEA is a mathematical programming methodology that can be applied to assess the
'relative' efficiency of a variety of institutions using a variety of input and output data.
The term 'relative' is rather important here since an institution identified by DEA as an
efficient unit in a given data set may be deemed inefficient when compared to another
set of data. One starts using DEA by building a relative ratio consisting of total
weighted outputs to total weighted inputs for each institution. The relatively 'most efficient'
units become the 'efficient frontier', and the degree of the inefficiencies of the other
units relative to the efficient frontier are then determined using a mathematical method.
An advantage of DEA is that it uses actual sample data to derive the efficiency frontier
against which each unit in the sample is evaluated with no a priori information regarding
which inputs and outputs are most important in the evaluation procedure. Instead, the
efficient frontier is generated when a mathematical algorithm is used to calculate the
DEA efficiency score for each unit.
However, DEA as an evaluation tool has also some limitations. Firstly the traditional
DEA framework is handicapped by its implicit distribution assumption that all
input-output variables are specified accurately. Stochastic-tic disturbances, such as
measurement error, random noise, outlier observations or external effects, may violate
this assumption.