Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method

Pompella, Maurizio; Dicanio, Antonio

doi:10.1016/j.econmod.2016.08.023

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…Chakraborty et al 2017 An overview of the applications of machine learning to financial problems, the most popular modelling approaches, and three case studies of relevant works for central banks. This study also establishes that machine learning models usually outperform tradi Pompella et al 2017 An EWS is proposed to detect likely-to-fail banks. This method is compared with risk agencies' rating and detects possibly wrongly rated banks.…”

Section: Abellan Et Al 2017mentioning

confidence: 57%

“…Prompted by the 2008 Global Financial Crisis and the need to foresee signals of financial instability, Italian authors Pompella and Dicanio (2017) developed an Early Warning System (EWS) to help uncover distress signs for banks. This credit risk model allows users to discriminate stable from likely-to-fail banks and might be useful in adjusting rating assignments by Rating Agencies.…”

Section: -2018mentioning

confidence: 99%

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Machine Learning Applied to Banking Supervision a Literature Review

Guerra

Castelli

2021

Risks

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Machine learning (ML) has revolutionised data analysis over the past decade. Like innumerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a comprehensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neighbours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.

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Section: Abellan Et Al 2017mentioning

confidence: 57%

Section: -2018mentioning

confidence: 99%

Machine Learning Applied to Banking Supervision a Literature Review

Guerra

Castelli

2021

Risks

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“…Once isolated, the individual stocks valuations for the observation period were averaged out, and standardized to allow for comparability, and processed in order to carry out a Principal Component Analysis (PCA). The Exhibits below (2.a / 2.b and 3. from three different perspectives) provide a visual evolution of the stocks baking the ETFs (28 of them) in the Principal Components 3D space, obtained by applying the PC-Mahalanobis approach (Pompella M., Dicanio A. 2016) When isolating the individual stocks by category, the trends provide a clear connotating difference in the growth paths within the fintech industry.…”

Section: Before and During Covid-19mentioning

confidence: 99%

Financial Innovation and Technology after COVID-19: a few Directions for Policy Makers and Regulators in the View of Old and New Disruptors

Pompella

Costantino²

2021

ECO

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Innovation and technology have led to the redefinition of business models and development of new ones in many bricks and mortar sectors. Similarly, blockchain and fintech have impacted the finance and banking industries and are expected to further affect them in the future, leading some media to coin the expression “Uberization of banking”. The authors extrapolate from sharing economy models to conclude that while blockchain and fintech are poised to advance finance and banking, there are no disruptive features that corroborate the term. By analogy and successive approximations, this article identifies the limitations of the arguments for disruption in finance and banking. Besides, hinging upon stylized facts, the article establishes similarities with sharing economy models to identify potential threats stemming from financial innovations such as Tokenomics, tagged as “no-ABSs”. Eventually, the authors identify entry points and ways forward arising from the COVID-19 pandemic for policy makers and regulators to regain their pivotal role in policing the market and ensuring transparency while driving innovation.

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“…The sample is structured as follows: 43 US banks, 56 EU banks, and the other 147 from the rest of the world (Table No. 1). in order to choose the best bank indicators that could represent banks' vulnerability, we follow the methodology discussed in Pompella & Dicanio (2016) 5 and the framework applied by Regulatory Authorities 6 . So, the four indicators selected are: i-Tier 1 ratio (T1) calculated as the ratio between the Tier 1 Capital (in practice the shareholders' equity adjusted with intangible assets) and the Risk Weighted Assets.…”

Section: Datasetmentioning

confidence: 99%

“…The PCM analysis (Pompella & Dicanio, 2016), on the other hand, allows us to discriminate the US from EU banks in a very clear and impressive way (Fig. No.…”

mentioning

confidence: 99%