Of management however a major weakness of ratio

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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.

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