Measuring Bank Performance: From Static Black Box to Dynamic Network Models

Hirofumi Fukuyama () and William L. Weber ()
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William L. Weber: Southeast Missouri State University

Chapter Chapter 10 in Handbook of Operations Analytics Using Data Envelopment Analysis, 2016, pp 241-266 from Springer

Abstract: Abstract This chapter presents the recently developed dynamic-network bank technology and performance measures of Fukuyama and Weber (Efficiency and productivity growth: Modelling in the financial services industry. Wiley, London, pp. 193–213, 2013; J Product Anal 44(3):249–264, 2015a; Ann Oper Res, in press, 2015b; Japanese bank productivity, 2007-2012: A dynamic network approach. Mimeo, 2016). The method uses DEA to represent the production technology and directional distance functions to measure bank performance. A two stage bank technology where an intermediate product is produced in a first stage and then used to produce final outputs in a second stage is extended over time. The performance measure allows the researcher to compare observed inputs and outputs, including undesirable outputs, with the outputs and inputs that might be produced if a producer were able to optimally choose production plans relative to a dynamic benchmark technology. Although Fukuyama and Weber’s studies apply the dynamic network technology to measure the performance of Japanese banks, the method can be applied to banks in other countries and to other types of financial institutions.

Keywords: Data envelopment analysis (DEA); Network DEA model; Dynamic DEA model; Dynamic-network DEA model; Bad outputs; Nonperforming loans; Productivity (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-1-4899-7705-2_10

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