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

Vincent Charles, Ioannis Tsolas, Tatiana Gherman

Research output: Contribution to JournalArticle

Abstract

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
JournalAnnals of Operations Research
Volume269
Early online date17 Jun 2017
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

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