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Efficiency is a very widely used concept in economics. In general, the basic concept of efficiency in economics is referring to the relationship between input and output or costs and benefits. When the concept of efficiency applied to a single enterprise, efficiency is mainly pointing at whether the enterprise makes the maximum output of its resources, or whether a certain amount of output achieves a minimum cost. Capital efficiency can be defined as the enterprise output from a unit of capital input. Traditionally, people generally believe EVA is one of the best indicators to evaluate capital efficiency, is that so? Can capital efficiency be better evaluated by means of Data Envelopment Analysis? This paper gives the corresponding discussion through five steps. Firstly, the calculation of the EVA of sample companies is given. Secondly, the basic principle on how DEA reflects capital efficiency is illustrated. Thirdly, the input and output variables are determined in the calculation of DEA by the PCA approach. Fourthly, capital efficiency values of sample companies obtained from the EVA and the DEA respectively are compared. Finally, the main conclusion attached to the outlook of this research is drawn.

1 Calculation of the EVA of Sample Companies EVA, as an indicator for evaluating capital efficiency developed in 1990s in the western counties, is getting more and more attention and favor in business. It is Stern Stewart(1991) who made pioneering research on EVA, they discussed the relationship between EVA and MVA by using the U.S. listed companies from 1984 to 1988 as samples. Their research demonstrated EVA and MVA were significant correlated. Their research also showed that EVA was the indicator to test whether the company substantially made profit. If the difference between net profit after tax for the year and the total cost of capital was positive, then the economic added value happened, the company created added value; Otherwise, the value of the company shrink down. Tully, Shawn’s study (1993) showed that EVA and H. Tan (Ed.): Knowledge Discovery and Data Mining, AISC 135, pp. 15–23. © Springer-Verlag Berlin Heidelberg 2012 springerlink.com

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stock price were closely related; Uyemura, Kantor, Pettit (1996) used 100 banks between 1986-1995 as samples, MVA as explained variables, ROA, ROE, EPS and NI calculated according to financial statements and EVA as explanatory variables, to do regression analysis with MVA, found that EPS had the lowest explanatory power was: 6%; and EVA had the highest: 40%. O 'Byrne (1996) used 6551 U.S. companies during 1985-1993 as study samples, the “market value / total invested capital” as explained variable, the EVA, NOPAT and free cash flow FCF as explanatory variables, to do regression analysis with the beginning capital standards and the “market value / invested capital”, found the explanatory power of each variable was: EVA: 31%, NOPAT: 33%, FCF: 0%. After eliminating scale effect and adding industrial-related indicators, the explanatory capability of EVA increase