Machine learning in bank merger prediction: A text-based approach

Katsafados, Apostolos G.; Leledakis, George N.; Pyrgiotakis, Emmanouil G.; Androutsopoulos, Ion; Fergadiotis, Manos

doi:10.1016/j.ejor.2023.07.039

European Journal of Operational Research

2024

DOI: 10.1016/j.ejor.2023.07.039

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Machine learning in bank merger prediction: A text-based approach

Apostolos G. Katsafados

George N. Leledakis

Emmanouil G. Pyrgiotakis

et al.

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2024

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Cited by 7 publications

References 90 publications

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Credit risk modelling within the euro area in the COVID‐19 period: Evidence from an ICAS framework

Chortareas,

Katsafados,

Pelagidis

et al. 2024

Int. J Fin Econ

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This paper develops a logistic regression model in an in‐house credit assessment system (ICAS) framework for predicting corporate defaults in the Greek economy. We consider the impact of the COVID‐19 pandemic and the associated government financial support schemes, aiming to protect against financial vulnerabilities, on the probability of default of non‐financial firms, as well as the relevant sectoral and firm‐size effects. In developing the ICAS framework, we address methodological issues such as the predictive performance of statistical versus machine learning approaches and the imbalanced dataset problem, indicating ways to evaluate such models with strong predictive power. Our findings suggest that the effect of the financial support measures dominates the pandemic shocks, thus substantially reducing the probability of firms' default, while the size‐ and industry‐based models show that firms in the micro and services sectors benefited the most. Furthermore, using a random forest model, our findings highlight the trade‐off between the transparency of traditional statistical models and the predictive value of machine learning models.

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Credit risk modelling within the euro area in the COVID‐19 period: Evidence from an ICAS framework

Chortareas,

Katsafados,

Pelagidis

et al. 2024

Int. J Fin Econ

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Short-term prediction of bank deposit flows: do textual features matter?

Katsafados,

Anastasiou

2024

Ann Oper Res

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The purpose of this study is twofold. First, to construct short-term prediction models for bank deposit flows in the Euro area peripheral countries, employing machine learning techniques. Second, to examine whether textual features enhance the predictive ability of our models. We find that Random Forest models including both textual features and macroeconomic variables outperform those that include only macro factors or textual features. Monetary policy authorities or macroprudential regulators could adopt our approach to timely predict potential excessive bank deposit outflows and assess the resilience of the whole banking sector in the Euro area peripheral countries.

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Industry-sensitive language modeling for business

Borchert,

Coussement,

De Weerdt

et al. 2024

European Journal of Operational Research

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Machine learning in bank merger prediction: A text-based approach

Cited by 7 publications

References 90 publications

Credit risk modelling within the euro area in the COVID‐19 period: Evidence from an ICAS framework

Credit risk modelling within the euro area in the COVID‐19 period: Evidence from an ICAS framework

Short-term prediction of bank deposit flows: do textual features matter?

Industry-sensitive language modeling for business

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