Forecasting distress in cooperative banks: The role of asset quality

Forgione, Antonio Fabio; Migliardo, Carlo

doi:10.1016/j.ijforecast.2018.04.008

Cited by 19 publications

(16 citation statements)

References 46 publications

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“…The Uniform Financial Rating System, proposed since November 1979 by US regulators and known as CAMELS, is a framework to forecast the bank default and adopt proxies of the following corporate profile: capital adequacy, asset quality, management, earnings, liquidity and sensitivity to market risk. In the CAMELS framework, several asset quality proxies are exploited to bring back bank soundness to their lending activities (Forgione and Migliardo, 2018).…”

Section: Final Remarksmentioning

confidence: 99%

Forecasting the macro determinants of bank credit quality: a non-linear perspective

Fallanca

Forgione

Otranto

2020

JRF

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Purpose This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability. Design/methodology/approach Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach. Findings Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017. Research limitations/implications The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique. Practical implications The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans. Originality/value The authors show a clear outperformance of the MS model concerning the linear one.

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Section: Final Remarksmentioning

confidence: 99%

Forecasting the macro determinants of bank credit quality: a non-linear perspective

Fallanca

Forgione

Otranto

2020

JRF

Self Cite

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“…Mare (2015) finds that the interstate deposit rate and the unemployment rate are significant variables to predict Italian cooperative banks' default. Applying their model to forecast the distress of Italian cooperative banks based on CAMEL ratios, Forgione and Migliardo (2018) find that banks with high loan to deposit ratio and located in the south are most vulnerable.…”

Section: Cooperative Banks In Austria Germany and Italymentioning

confidence: 99%

Risk exposures of European cooperative banks: a comparative analysis

Mare

Gramlich

2020

Rev Quant Finan Acc

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This study sheds light on risk exposures of cooperative banks in Austria, Germany and Italy. We investigate how major risk elements of banks in these countries have evolved over time, across countries and institutions. Cooperative banks’ exposure to risk is analyzed looking at aggregate risk categories. In detail, we address the questions of (a) how single risk categories can be assessed in a consistent way, (b) how the different risk categories behaved over time, (c) what factors drive the diverse risks and (d) if there are similarities of risk characteristics for specific clusters of cooperative banks. We find that credit, interest rate and residual risk have a high degree of commonality. Liquidity risk is somehow dissociated from the other categories. Nevertheless, the risk behavior appears to vary over time and for different countries. This feature is relevant for the prudent management of cooperative banks and for the assessment of systemic financial risk.

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“…Third, forecasting methods proposed by recent literature can be divided into two categories-individual models and integrated models. Individual statistic methods are widely employed to forecast corporate failure, such as discriminant analysis and its expansions [35], logistic regression and its expansions [21], a proportional hazards model and its expansions [36], etc. It is easy to analyze and explain the impact of each variable on corporate failure using individual statistic models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is well known that the character of corporates in different sectors is quite different [20]. Recently, researchers have paid more attention to the corporate failure forecasting in a specific sector, such as commercial banks distress prediction [21], bankruptcy forecasting in the agribusiness sector [22], manufacturing firms financial distress forecasting [23], hospitality firm failure prediction [11,24], etc. Up to date, to the best of our knowledge, only Doumpos et al [2] has explored corporate failure forecasting in the energy sector.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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Forecasting distress in cooperative banks: The role of asset quality

Cited by 19 publications

References 46 publications

Forecasting the macro determinants of bank credit quality: a non-linear perspective

Forecasting the macro determinants of bank credit quality: a non-linear perspective

Risk exposures of European cooperative banks: a comparative analysis

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

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