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.
Highlights
IntroductionThe financial crisis that began in the summer of 2007 with the bursting of the property market bubble had multiple consequences on the global economy, showing, among other issues, that the financial problems of credit institutions is a social and economic problem that affects companies around the world (Halteh et al, 2018)
The financial crisis that began in the summer of 2007 with the bursting of the property market bubble had multiple consequences on the global economy, showing, among other issues, that the financial problems of credit institutions is a social and economic problem that affects companies around the world (Halteh et al, 2018).In the study of the financial problems suffered by these entities, commonly known as financial distress, the capacity to predict and anticipate the consequences is essential
Given that the objective of this study is to predict short-term financial distress, the prediction model is constructed by selecting the explanatory variables (CAMELS variables and macroeconomic variables) on December 31st of the previous year to that in which the entity was in a situation of financial distress
Summary
The financial crisis that began in the summer of 2007 with the bursting of the property market bubble had multiple consequences on the global economy, showing, among other issues, that the financial problems of credit institutions is a social and economic problem that affects companies around the world (Halteh et al, 2018). In the study of the financial problems suffered by these entities, commonly known as financial distress, the capacity to predict and anticipate the consequences is essential. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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