A fuzzy-set qualitative comparative analysis model to predict bank bailouts: a study of the Spanish financial system

Abstract

This paper examines the restructuring of the Spanish financial system. This study is justified by the massive economic and social impact of this process in Spain. Based on the annual accounts and the annual reports of Spanish credit institutions, a model was created to predict the possibility of bank failure or bailout. The variables were selected following a review of the literature. They included the legal form of the credit institution (savings bank versus bank), leverage, real estate investment, gross operating margin, staff costs and non-performing loans. Two variables that had not previously been used in studies of this type were also included in the model: risk-weighted assets and coverage (as part of the provisions for non-performing loans). Fuzzy-set qualitative comparative analysis (fsQCA) was used for the analysis. Use of fsQCA is another novel aspect of this study. This technique has never been used to predict corporate bankruptcy or the failure of credit institutions. Proposals were made and tested using data on Spanish banks and savings banks for the year 2010, a critical year for the Spanish financial system. The results yield several interesting conclusions. First, greater leverage increases the probability of survival, contrary to the opinion of most scholars. This result shows that the problem facing Spanish credit institutions was the closure of international markets, not the level of debt. Another key conclusion is that financial institutions with higher staff costs have a greater chance of survival. Therefore, the widespread policy of reducing human resource costs should be reconsidered. Finally, the risk-weighted assets measure offers an excellent predictor of failure of credit institutions, as well as providing other benefits, which substantially increase the value of this measure.

Leveraging SMEs technologies adoption in the Covid-19 pandemic: a case study on Twitter-based user-generated content

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