Prediction of financial distress in the Spanish banking system

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.

Sustainability in the Banking Sector: A Predictive Model for the European Banking Union in the Aftermath of the Financial Crisis

“…In general terms, these results support the proposed hypotheses (except for sensitivity to market risk) and confirm that variables in a CAMEL model can predict the stressed capital tier 1 ratio. These findings are in line with previous evidence supporting that bank solvency positively correlates with capital [32,42,44,46,48,49] and profitability [44,49,51,52,65], while it correlates negatively with assets quality when NPL and losses are significant [49,50,65]. Inefficiency is also significant and influences solvency in a negative way, as previous papers sustained [42,49,50,52], while liquidity according to buffers in line with the Basel rules reinforces soundness.…”

“…Banking soundness or, alternatively, banking distress, has been profoundly analyzed in research papers, mainly using the so-called CAMELS methodology (which stands for Capital, Assets quality, Management, Earnings, Liquidity and Sensitivity to market risk). This model based on accounting variables has been used on numerous occasions to explain and predict banking failures, such as the cases of Argentina [33], Australia [34], Brazil [35], Croatia [36], the US [37][38][39], Jamaica [40], South-East Asia [41], Spain [42] or Venezuela [43], and its capabilities as an early-warning system or tool for anticipating distress have been pointed out [44].…”

“…Management: according to the literature, efficiency is inversely related to banking risk and therefore has a positive influence on bank solvency [44,46,[48][49][50]52]. Moreover, efficient management is closely related to earnings and assets quality, because better-managed institutions keep an appropriate earnings capacity and usually have a lower NPL ratio, as management quality is also reflected in loan quality [42]. However, most of the research uses a cost-to-income ratio as a proxy, which actually reflects inefficiency and considerable high expenses as an undesirable input.…”

“…Sensitivity: the most controversial variable is sensitivity to market risk (S). According to [57], it is reflected in total assets (size) and positively affects solvency as such investments somehow impede default risk, but [42] introduces earnings from commercial transactions/operating revenue as a proxy and [58] uses the level of other interest-bearing liabilities to total assets, both expecting that the higher this ratio, the higher the sensitivity of financial institutions to the impact of liquidity issues on interest expense and hence on firms' profitability, with the subsequent negative effects on soundness. These arguments lead us to propose the subsequent hypothesis: Hypothesis 6 (H6).…”

Financial Distress Phenomenon of Non-financial Companies in Indonesia

“…Giannopoulos & Sigbjørnsen (2019) mendefinisikan financial distress sebagai kegagalan ketika perusahaan tidak mampu memenuhi kewajibannya kepada pemberi pinjaman, pemegang saham preferen maupun pemasok, tagihan yang cukup tinggi atau dinyatakan bangkrut secara hukum. Definisi lain diungkapkan oleh Paule-Vianez et al(2019) yang menyatakan bahwa financial distress merupakan situasi ketika perusahaan memiliki masalah solvabilitas pada berbagai level yang menyebabkan perusahaan tidak dapat menjalankan usaha tanpa bantuan dari pihak eksternal serta menurunkan nilai perusahaan hingga mencapai fase kebangkrutan bahkan keluar dari pasar. Dengan demikian, tujuan pemegang saham untuk memaksimalkan nilai perusahaan tidak dapat tercapai.…”