Prediction of financial distress in the Spanish banking system

Jessica Paule-Vianez, Milagros Gutiérrez-Fernández and José Luis Coca-Pérez

Applied Economic Analysis, 2019, vol. 28, issue 82, 69-87

Abstract: Purpose - The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector. Design/methodology/approach - The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies. Findings - The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems. Originality/value - This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

Keywords: Financial distress; Artificial neural networks; Banking sector; CAMELS; Spain (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aeapps:aea-10-2019-0039

DOI: 10.1108/AEA-10-2019-0039

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