A Study of the Relationship between Bank Survival and CostEfficiency

Abstract

This study aims to investigate the relationship between a bank's survival and its cost efficiency by examining47 commercial banks in Taiwan between the years 2000 and 2008. Based on the CAMELS model, wefirst use logistic regression to extract the key factors which might affect bank survival. Then, according to Battese and Coelli (1995), we simultaneously estimate the stochastic cost frontier function and the inefficiency functionto evaluate the bank's cost efficiency. Our main empirical findings are as follows: (1) Four key factors that cause bank survival or failure are debt ratio, non-performing loans (NPLs) ratio, growth rate of assets and bank's ownership. (2) The higher the debt ratio and the NPL ratio, the worse is the efficiency of banks. (3) The cost efficiency of state owned banks are better than that of private banks. (4) The averagecost efficiency of failed banks is worse than that of survived banks.

Competitive Differences in Input-Factor Utilization among Brazilian Financial Institutions

“…al, 2019), transportation (Ripplinger & Bitzan, 2018), energy (Ouyang & Sun, 2015), and economic growth in developing countries (Ramasamy, et al, 2017), among others. Within finance, the approach has recently been employed to illustrate topics such as the economic content of cost efficiency and bank survival in developing countries (Liang, et al, 2015), competition in banking industries (Rezitis, 2010;Barbosa, et al, 2015), the impact of information technology on bank profitability (Gupta, et al, 2018). Its popularity in modeling the economic production technology of firms emanates from its flexibility with no a priori assumptions.…”

Ownership Structure, Capital Structure, and Firm Survival

“…The study also shows that partnerships with third-party firms with supplementary assets reduce the survival rate of new firms as business model innovation intensifies. Liang et al (2015) examine the relationship between bank survival and cost-efficiency in 47 commercial banks in Taiwan between 2000 and 2008. Using a logistic regression model, the study extracts the four key factors that may affect bank survival.…”