Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

López‐Iturriaga, Félix J.; Sanz, I.

doi:10.1016/j.eswa.2014.11.025

Cited by 166 publications

(81 citation statements)

References 72 publications

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“…These include expert systems based on self-organising maps ( Jagric, Bojnec, & Jagric, 2015 ), neural network models ( Celik & Karatepe, 2007;Iturriaga & Sanz, 2015;Nag & Mitra, 1999;Sevim, Oztekin, Bali, Gumus, & Guresen, 2014 ), regression trees ( Manasse & Roubini, 2009 ), binary classification trees ( Duttagupta & Cashin, 2008 ), hybrid models ( Lin, Khan, Chang, & Wang, 2008;Lin, 2009 ), grey rational analysis ( Lin & Wu, 2011 ), support vector machines ( Ahn, Oh, Kim, & Kim, 2011;Feki, Ishak, & Feki, 2012 ), random forests ( Alessi & Detken, 2014 ), and the multiple-indicator-multiple-cause method ( Rose & Spiegel, 2012 ). Comparisons between such methods have been conducted by Boyacioglu, Kara, and Baykan (2009) .…”

Section: Related Workmentioning

confidence: 98%

Systemic banking crisis early warning systems using dynamic Bayesian networks

Dabrowski

Beyers

Villiers

2016

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 98%

Systemic banking crisis early warning systems using dynamic Bayesian networks

Dabrowski

Beyers

Villiers

2016

Expert Systems with Applications

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“…For instance, in the context of bankruptcy prediction (Lopez-Iturriaga & Pastor-Sanz, 2015), and when clustering countries (Jagric et al, 2015) and handwritten digits (Lacerda & Mello, 2013). Furthermore, Vesanto (1997) suggested that self-organizing maps could be used for clustering purposes to enable the creation of local regressions for each node.…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Initial stage clustering when estimating accounting quality measures with self-organizing maps

Haga

Siekkinen

Sundvik

2015

Expert Systems with Applications

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“…2002; Cole, White, 2012;Shaffer, 2012;Cox, Wang, 2014;López Iturriaga, Sanz, 2015). There is a limited number of studies of bank failures from other countries due to the fact that the number of failures is rather low.…”

Section: Literature Review and Research Hypothesismentioning

confidence: 99%

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

Iwanicz-Drozdowska

Ptak-Chmielewska

2019

Folia Oeconomica

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In this study we focus on distress events of European banks over the period of 1990-2015, using unbalanced panel of 3,691 banks. We identify 132 distress events, which include actual bankruptcies as well as bailout cases. We apply CAMEL-like bank-level variables and control macroeconomic variables (GDP, inflation, unemployment rate). The analysis is based on traditional logistic regression and k-means clustering. We find, that the probability of distress is connected with macroeconomic conditions via regional grouping (clustering). Bank-level variables that were stable predictors of distress from 1 to 4 years prior to event are equity to total assets ratio (leverage) and loans to funding (liquidity). From macroeconomic factors, the GDP growth is a reasonable variable, however with differentiated impact: for 1 year distance high distress probability is connected with low GDP growth, but for 2, 3 and 4 year distance: high distress probability is conversely connected with high GDP growth. This shows the changing role of macroeconomic environment and indicates the potential impact of favorable macroeconomic conditions on building-up systemic problems in the banking sector.FOE 6(345) 2019 www.czasopisma.uni.lodz.pl/foe/ ale dla 2, 3 i 4 lat przed bankructwem wysokie ryzyko bankructwa jest związane z wysoką dynamika PKB, czyli jest to zależność odwrotna. Pokazuje to zmienną rolę otoczenia makroekonomicznego i wskazuje na potencjalny wpływ sprzyjających warunków makroekonomicznych na powstawanie problemu systemowego w sektorze bankowym.Słowa kluczowe: bankructwo, CAMEL, predykcja logistyczna, zmienne makroekonomiczne JEL: G20, G21

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Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

Cited by 166 publications

References 72 publications

Systemic banking crisis early warning systems using dynamic Bayesian networks

Systemic banking crisis early warning systems using dynamic Bayesian networks

Initial stage clustering when estimating accounting quality measures with self-organizing maps

Prediction of Banks Distress – Regional Differences and Macroeconomic Conditions

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