Predicting Bankruptcy Using Neural Networks in the Current Financial Crisis: A Study of U.S. Commercial Banks

López‐Iturriaga, Félix J.; López‐de‐Foronda, Óscar; Pastor-Sanz, Iván

doi:10.2139/ssrn.1716204

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…One of the early studies adopting neural network was that of Tam (1991), who examined failed banks in in the period of 1985-1987. López-Iturriaga et al (2010 applied the neural network method, studying U.S. commercial banks during the financial crisis period.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Liu

Sathye

2021

JRFM

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Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Liu

Sathye

2021

JRFM

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“…Beyond binary choice models, Jordan et al (2010) use proxies for CAMELS and the multiple discriminant analysis methodology to predict US bank failures during the global financial crisis, while López-Iturriaga et al (2010) use proxies of CAMELS and an artificial neural network for the same purpose. Both studies find a high degree of predictability of US bank failures during the global financial crisis.…”

Section: Related Literaturementioning

confidence: 99%

Predicting distress in European banks

Betz

Opricǎ

Peltonen

et al. 2014

Journal of Banking & Finance

245

252

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In 2013 all ECB publications feature a motif taken from the €5 banknote.note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. All rights reserved. ISSN 1725-2806 (online) EU Catalogue NoQB-AR-13-094-EN-N (online)Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors. The refereeing process of this paper has been coordinated by a team composed of Gerhard Rünstler, Kalin Nikolov and Bernd Schwaab (all ECB).The paper is released in order to make the research of MaRs generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect those of the ECB or of the ESCB. AcknowledgementsThe authors want to thank Riina Vesanen for excellent research assistance and colleagues at the ECB, Non-technical summaryThe global financial crisis has had a significant impact on the health of the European banking system and on the soundness of individual banks. Beyond the direct bailout costs and output losses, the interplay of fiscally strained sovereigns and weak banking systems that characterize the ongoing sovereign debt crisis show the importance of the euro area banking sector for the stability of the entire European Monetary Union. The motivation for an early-warning model for European banks is thus clear.To derive an early-warning model for European banks, this paper introduces a novel dataset of bank distress events. As bank defaults are rare in Europe, the data set complements bankruptcies, liquidations and defaults by also taking into account state interventions, and mergers in distress. State interventions comprise capital injections and asset reliefs (asset protection and guarantees). A distressed merger occurs if (i) a parent receives state aid within 12 months after the merger or (ii) if a merged entity has a coverage ratio (capital equity and loan reserves minus non-performing loans to total assets) smaller than 0 within 12 months before the merger.The outbreak of a financial crisis is known to be difficult to predict (e.g. Rose and Spiegel, 2011). Recently, the early-warning literature has therefore focused on detecting underlying vulnerabilities, and finding common patterns preceding financial crises (e.g. Reinhart and Rogoff, The models are estimated to derive probabilities of banks being in vulnerable states, but a policy maker needs to know when to act. Following Sarlin (2013), the signals of the model are evaluated taking into account the policymaker's preferences between type I and type II errors, the uneven frequency of tranquil and distress events, and the systemic relevance of each bank.This paper presents the first application of the evaluation framework t...

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“…Compared with traditional statistical methods such as univariate and multivariable discrimination and regression, neural network does not need to preset standardized function formulas and give hypotheses of statistical distribution characteristics of variables in the model, and it can be used for identification and prediction of variables and models changing over time [12]. At present, in addition to the aforementioned application of neural network in enterprise internal control, many scholars have used neural network methods to explore financial crisis, enterprise bankruptcy, financial market trend prediction [13][14][15], and bank performance evaluation [16]. However, the NN-based model is easy to overfit.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Identification of Deficiencies in Enterprise Internal Control

Huang

2022

Wireless Communications and Mobile Computing

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Enterprise management and internal control are used to prevent and control risks and promote enterprises to achieve development strategy. This paper adopts comprehensive multidisciplinary research method to study the basic theory of prediction of major defects in enterprise internal control. Firstly, this paper proposes the prediction index system and sample selection standard of internal control major defects. Totally, 630 listed company and 12 indicators are collected and then use the random forest classification method based on principal component analysis. The parameters of random forest are optimized by genetic algorithm. Finally, the prediction model of major internal control defects of listed companies is established. The experimental results show that the average score of PCA-RF model in TPR value reaches 85%, which is nearly 20% higher than the 65% of RF model, proving that the PCA and GA can significantly improve the classification accuracy of ST Company and has important practical significance. Therefore, the proposed method system can reasonably solve the prediction problem of major defects in internal control.

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Predicting Bankruptcy Using Neural Networks in the Current Financial Crisis: A Study of U.S. Commercial Banks

Cited by 14 publications

References 26 publications

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Predicting distress in European banks

A Hybrid Approach for Identification of Deficiencies in Enterprise Internal Control

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