Estimating Efficiency Performance of Decision-Making Unit by using SFA and DEA Method: A Cross-Sectional Data Approach


  • Roslah Arsad
  • Zaidi Isa
  • Siti Nabilah Mohd Shaari





Efficiency, Frontier Analysis, Non-Parametric, Parametric, Productivity.


In this paper, a cross-sectional samples data of 115 Malaysian stocks have been employed to compare both Data Envelopment Analysis (DEA) method and Stochastic Frontier Analysis (SFA) method. These approaches are used to provide a review of frontier conceptual measurement, strength and limitation of the parametric and non-parametric models. Stochastic frontier production function of Cobb-Douglas type was utilized for the estimation. The function was estimated using the maximum likelihood estimation technique. Two models in DEA, DEA-CCR and DEA-BCC are applied in this study and the ranking correlation between SFA method and both models DEA are determined by using the Spearman rank method. The result revealed using SFA, the mean technical efficiency of sample consumer product companies is 37.5% and implies that companies operating at means level of technical efficiency could produce 80.1% more output for given level of inputs if they become technically more efficient. From empirical results of the SFA method, we determined that the deviations from the efficient frontiers of production functions are largely attributed to inefficiency effects (technical inefficiency). Finally, the findings also showed that the difference in ranking stocks performance using DEA-CCR, DEA-BCC and SFA methods. The main contribution of the paper is showing the comparative performance based on both model, DEA and SFA method using financial ratio.




[1] Coelli TJ, Rao DSP, O’Donnell CJ & Battes GE (2005), An introduction to efficiency and productivity analysis. Springer Sciences.

[2] Hasan MZ, Kamil AA, Mustafa A & Baten MA (2012), A Cobb Douglas stochastic frontier model on measuring domestic bank efficiency in Malaysia. PLoS One 7(8), 1–5.

[3] Flubacher M (2015), Comparison of the economic performance between organic and conventional dairy farms in the Swiss mountain region using matching and stochastic frontier analysis. Journal Socio-Economics Agriculture 8, 76–84.

[4] Bin Zheng X & Park NK (2016), A study on the efficiency of container terminals in Korea and China. Asian Journal of Shipping and Logistics 32(4), 213–220.

[5] Ling OP & Kamil AA (2010), data envelopment analysis for stocks selection on Bursa Malaysia. Archives of Applied Sciences Research 2(5), 11–35.

[6] Zamani L, Beegam R & Borzoian S (2014), Portfolio selection using data envelopment analysis (DEA): A case of select indian investment companies. International Journal of Current Research and Academic Review 2(4), 50–55.

[7] Nektarios M, Xenos P, Nektarios G, Poulakis K & Chouzouris M (2015), Efficiency analysis of Lloyd’s syndicates: A comparison of DEA and SFA approaches. Spoudai-Journal of Economics and Business 65(1–2), 27–46.

[8] Koopmans TC (1951), Activity analysis of production and allocation. John Wiley Sons.

[9] Debreu G (1951), The coefficient of resource utilization. Econometrica-Journal of Econometric Society 19(3), 273-292.

[10] Farrell MJ (1957), The measurement of productive efficiency. Journal of the Royal Statistical Society 120(3), 253–290.

[11] Bogetoft P & Otto L (2011), Benchmarking with DEA, SFA, and R. Springer Sciences.

[12] Aigner DJ & Chu SF (1968), On estimating the industry production function. American Economic Review 58(4), 826–839.

[13] Aigner D, Lovell CAK & Schmidt P (1977), Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6(1), 21–37.

[14] Charnes A, Cooper WW & Rhodes E (1978), Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444.

[15] Banker RD, Charnes A & Cooper WW (1984), Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30(9), 1078–1092.

[16] Mokhtar M, Shuib A & Mohamad D (2014), Identifying the critical financial ratios for stocks evaluation: A fuzzy Delphi approach. Proceedings of the 3rd International Conference on Quantitative Sciences and Its Applications: Fostering Innovation, Streamlining Development, pp. 348-354.

[17] Bos JWB & Koetter M (2011), Handling losses in translog profit models. Applied Economics 43(3), 307–312.

[18] Afsharian M & Alirezaee MR (2007), A complete ranking of DMUs using restriction in DEA models. Applied Mathematics and Computation 189, 1550–1559.

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