Satisficing data envelopment analysis: a Bayesian approach for peer mining in the banking sector

Vincent Charles, Ioannis Tsolas, Tatiana Gherman

Research output: Contribution to JournalArticlepeer-review


Over the past few decades, the banking sectors in Latin America have undergone rapid structural changes to improve the efficiency and resilience of their financial systems. The up-to-date literature shows that all the research studies conducted to analyze the above-mentioned efficiency are based on a deterministic data envelopment analysis (DEA) model or econometric frontier approach. Nevertheless, the deterministic DEA model suffers from a possible lack of statistical power, especially in a small sample. As such, the current research paper develops the technique of satisficing DEA to examine the still less explored case of Peru. We propose a Satisficing DEA model applied to 14 banks operating in Peru to evaluate the bank-level efficiency under a stochastic environment, which is free from any theoretical distributional assumption. The proposed model does not only report the bank efficiency, but also proposes a new framework for peer mining based on the Bayesian analysis and potential improvements with the bias-corrected and accelerated confidence interval. Our study is the first of its kind in the literature to perform a peer analysis based on a probabilistic approach.
Original languageEnglish
Pages (from-to)81-102
Number of pages22
JournalAnnals of Operations Research
Early online date17 Jun 2017
Publication statusE-pub ahead of print - 17 Jun 2017
Externally publishedYes


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