Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises

Slavici, Titus; Maris, Simina; Pirtea, Marilen

doi:10.1007/s11135-014-0154-0

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…These authors show how artificial neural networks presented a more accurate performance than the other classifiers. Slavici et al (2016) used artificial neural networks to project the financial distress in eastern European companies by claiming that artificial neural networks are more productive for predicting bankruptcy and more accurate than traditional methods. Inam et al (2018) compared multivariate discriminant analysis, logarithmic regression and artificial neural networks for bankruptcy prediction by demonstrating how artificial neural networks were more appropriate than predictive techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this study, we try to predict the financial distress in the Spanish banking system with artificial neural networks because of their greater effectiveness in predicting stress situations (Bell et al , 1990; Odom and Sharda, 1990; Coats and Fant, 1992; Fletcher and Goss, 1993; Serrano and Martín, 1993; Wilson and Sharda, 1994; Rafiei et al , 2011; Geng et al , 2015; Slavici et al , 2016; Inam et al , 2018; Lahmiri and Bekiros, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of financial distress in the Spanish banking system

Paule-Vianez

Gutiérrez-Fernández

Coca-Pérez

2019

AEA

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Purpose The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector. Design/methodology/approach The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies. Findings The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems. Originality/value This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of financial distress in the Spanish banking system

Paule-Vianez

Gutiérrez-Fernández

Coca-Pérez

2019

AEA

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“…Their research included 55 samples, 18 financial indicators, and 3 hidden layers. The model had an accuracy of 98% (Slavici et al, 2015). Compared to the international datasets, in Romania, research on bankruptcy is limited due to the low availability of official financial data and fiscal behaviour.…”

Section: Literature Review On Bankruptcy Predictionmentioning

confidence: 99%

“…In one of his later researches, Goleţ used more logistic regression models and tested their differences on a sample of 5,908 companies (of these, 354 were bankrupt) between 2008 and 2012 (Goleţ, 2014). The use of neural network modelling in Romania was carried out by Slavici et al (2015). Their research included 55 samples, 18 financial indicators, and 3 hidden layers.…”

Section: Literature Review On Bankruptcy Predictionmentioning

confidence: 99%

Modelling the Financial Failure of Romanian Stock Companies

Fejér-Király

Ágoston

Varga³

2019

Acta Universitatis Sapientiae, Economics and Business

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The aim of the present article is to model and predict the financial failure of non-financial companies listed on the Bucharest Stock Exchange. The prediction models are based on the companies’ financial reports. The paper emphasizes the importance of processing outlier data and the significance of categorical independent variables. The paper contributes to bankruptcy and corporate financial failure research by presenting a Romanian situation. Results show that the model that uses 3-year financial data prior to the failure has a better accuracy. Several models have been compared, and it was found that using categorical independent variables as explanatory variables increased the accuracy of the models against those that used numerical regressors.

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“…Instead, second order methods like Newton, quasi-Newton, Levenberg Marquardt, conjugate gradient variations and similar methods are preferred (Slavici et al, 2016), which are more efficient while training in non-linear cases (Fletcher, 2013;Shepherd, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance comparison of second order conjugate algorithms in neural networks for predictive data mining

Sehgal

2017

Int. j. adv. appl. sci.

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In this paper, a performance comparison of several variations of the nonlinear conjugate gradient method has been investigated. Neural Networkbased prediction models for life insurance sector have been developed and their training has been done with a variety of first and second order algorithms to find an efficient training algorithm, but keeping the focus on conjugate gradient based methods. Traditional second order methods require computation of second order derivatives and need to compute hessian for quadratic termination; which is a tedious and memory consuming task. Here we employ conjugate gradient methods which bypass the computation of hessian, but still achieve quadratic termination and thus prove to be memory efficient.

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Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises

Cited by 10 publications

References 5 publications

Prediction of financial distress in the Spanish banking system

Prediction of financial distress in the Spanish banking system

Modelling the Financial Failure of Romanian Stock Companies

Performance comparison of second order conjugate algorithms in neural networks for predictive data mining

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