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Prediction of financial distress in the Spanish banking system

Author

Listed:
  • Jessica Paule-Vianez
  • Milagros Gutiérrez-Fernández
  • José Luis Coca-Pérez

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.

Suggested Citation

  • Jessica Paule-Vianez & Milagros Gutiérrez-Fernández & José Luis Coca-Pérez, 2019. "Prediction of financial distress in the Spanish banking system," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 28(82), pages 69-87, December.
    • Handle: RePEc:eme:aeapps:aea-10-2019-0039
    DOI: 10.1108/AEA-10-2019-0039
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