Articlediamond2019

Prediction of financial distress in the Spanish banking system

Jéssica Paule-Vianez · Milagros Gutiérrez Fernández · José Luís Coca-Pérez

58 citations0/0 files storedApplied Economic Analysis

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Financial Distress and Bankruptcy Prediction Banking stability, regulation, efficiency Corporate Insolvency and Governance

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 1972A Discriminant Analysis of Predictors of Business Failure 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 1994Bankruptcy prediction using neural networks 2014Prediction of financial distress: An empirical study of listed Chinese companies using data mining

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