Dinamikus pénzügyi mutatószámok alkalmazása a csődelőrejelzésben

Nyitrai, Tamás

doi:10.14267/phd.2016045

Cited by 1 publication

(4 citation statements)

References 49 publications

(64 reference statements)

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“…Results also revealed that certain financial ratios rather give early warning indication to potential bankruptcy in the short-run, whereas others in the long-run. The author performed empirical research in the same year using CHAID decision trees and arrived at similar conclusions (Nyitrai 2015a).…”

Section: Machine Learning and Data Miningmentioning

confidence: 73%

“…From a methodological point of view, bankruptcy prediction is a binary classification problem with the aim to differentiate between solvent and insolvent groups of companies as good as possible (Virág 2004). Bankruptcy prediction is regarded as a boundary discipline between corporate finance and statistics (data mining), which attempts to predict the future solvency of companies using financial ratios as explanatory variables applying multivariate methods (Nyitrai 2015a).…”

Section: Methodological Development In the International Literature Omentioning

confidence: 99%

“…Since the 1970s the development of the field has been dominated by the modernisation of mathematical-statistical classification methods and the IT solutions supporting them (Nyitrai 2015a). Passing through the distribution and variance assumptions of DA, logistic regression (logit) has become a more and more popular bankruptcy prediction method, which was first applied by Chesser (1974) on a credit risk database.…”

Section: Methodological Development In the International Literature Omentioning

confidence: 99%

“…The classification power of the final model is the average of that of the individual models, usually outperforming the classification power without using ensemble methods. The most frequently applied ensemble methods are boosting, bagging, random subspace, random forest, Gauss-processes and autoencoder belonging to the family of machine learning procedures (Nyitrai 2015a;Wang 2017). Today's bankruptcy prediction researches are unambiguously dominated by machine learning, data mining, artificial intelligence and hybrid modelling through creatively combining different new methods (Barboza et al 2017).…”

Section: Methodological Development In the International Literature Omentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary

Kristóf

Virág

2020

JRFM

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The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared to the western countries, however, based on the historical development of corporate bankruptcy prediction after the political system change it can be argued that it has already caught up to the level of international best practice. Throughout the development history of Hungarian bankruptcy prediction, it can be tracked how the initial, small, cross-sectional sample and classic methodology-based bankruptcy prediction has evolved to today’s corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning, and hybrid modelling. On the basis of empirical results, the article draws several normative proposals how to assemble a bankruptcy prediction database and select the right classification method(s) to accomplish efficient corporate bankruptcy prediction.

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Section: Machine Learning and Data Miningmentioning

confidence: 73%

Section: Methodological Development In the International Literature Omentioning

confidence: 99%

Section: Methodological Development In the International Literature Omentioning

confidence: 99%