The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques.
Until the Great Recession, rescuing banks with taxpayers’ money had been the preferred way to deal with banking crises. The dramatic effects of these practices on the real economy highlighted that bailouts are not a sustainable method to resolve troubled banks going forward. As a result, a new regulatory framework has been proposed, forcing the financial industry to move from “bailout” to “bail-in.” Understanding the implications of such a change is key to ensuring the success of these new banking rules. This article aims to build up a comprehensive and unbiased set of research articles in order to draw conclusions about the current status of the academic literature in the field of capital and loss absorption requirements. A research agenda on the topic is also proposed. The methodological approach undertaken is based on ProKnow-C (Knowledge Development Process-Constructivist). We also contribute to the development of Proknow-C methodology by adding a cross-reference extension to the original framework. The results of our analysis point out that further research has to be undertaken on the subject of loss absorption requirements.
The introduction of Central Bank Digital Currency (CBDC) could represent a deep structural change to the financial sector, and in particular to the banking sector. This paper proposes a Deep Neural Network (DNN) design to model the introduction of CBDC and its potential impact on commercial banks’ deposits. The model proposed forecasts the likelihood of the occurrence of bank runs as a function of the system characteristics and of the intrinsic features of CBDC. The success rate of CBDC and the impact on the banking sector is highly dependent on its design. Whether CBDC should carry any form of interest, if the amount of CBDC should be capped by account or if convertibility from banks’ deposits should be guaranteed by commercial banks are important features to consider. Further, the design of CBDC needs to contribute to enhancing the sustainability of the financial system, hence a CBDC design that promotes financial inclusion is paramount. The model is initially calibrated with Euro area system data. Results show that an increase in the financial system risk perception would trigger a significant transfer of wealth from bank deposits to CBDC, while the wealth transfer to CBDC is to a lesser extent also sensitive to its interest rate.
This work uses fuzzy set theory and qualitative comparative analysis (QCA) to determine the causal configurations leading to interbank contagion in a resolution event. This study pioneers the introduction of fsQCA methodology in banking crisis analyses. The event providing the necessary data for this study is the resolution of the Spanish bank Banco Popular. We develop sufficient and necessary condition analyses to find the key metrics that lead to interbank contagion. The results demonstrate that weak solvency metrics, low asset quality and belonging to the same country where the resolution has been triggered tend to lead to higher contagion.
The Editorial Office did not detect errors in the affiliations and two callouts for Figure 4 on page 11 [...]
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