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Ein Vergleich des binären Logit-Modells mit künstlichen neuronalen Netzen zur Insolvenzprognose anhand relativer Bilanzkennzahlen
Franken, Ronald (contributor) - 2007
Die Prognose der Insolvenzgefährdung von Unternehmen anhand statistischer Methodik war und ist eine bedeutende Aufgabe … die sog. externe Bilanzanalyse anhand verschiedener relativer Kennzahlen(-systeme) dar, welche aus den veröffentlichten … Jahresabschlüssen von Kapitalgesellschaften abgeleitet werden können. In der aktuellen Praxis der empirischen Insolvenz- und …
Persistent link: https://www.econbiz.de/10003634014
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Kombinierte Liquiditäts- und Solvenzkennzahlen und ein darauf basierendes Insolvenzprognosemodell für deutsche GmbHs
Perederiy, Volodymyr (contributor) - 2007
. -- Logistische Regression ; Varablenauswahl ; Insolvenzprognose ; Bilanzanalyse ; bilanzielle Kennzahl ; Liquidität ; Solvenz …
Persistent link: https://www.econbiz.de/10003635001
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Graphical data representation in bankruptcy analysis
Härdle, Wolfgang (contributor);  … - 2006
Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...
Persistent link: https://www.econbiz.de/10003324316
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Estimating probabilities of default with support vector machines
Härdle, Wolfgang (contributor); Moro, Rouslan (contributor) - 2007
This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of...
Persistent link: https://www.econbiz.de/10003633940
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Unveiling the diverse efficacy of artificial neural networks and logistic regression : a comparative analysis in predicting financial distress
Sabek, Amine - In: Croatian review of economic, business and social … 9 (2023) 1, pp. 16-32
The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict...
Persistent link: https://www.econbiz.de/10014372938
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In Research of Optimal Model for Predicting Bankruptcy
Abdipoor, Sajad - 2014
In this study we tried to compare two models in order to identify optimal neural networks models in predicting bankruptcy. Multi-layered perceptron (MLP) because of easy training and high efficiency and also integrated multi-layered perceptron (most used neural network in predicting bankruptcy)...
Persistent link: https://www.econbiz.de/10013051202
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Graphical Data Representation in Bankruptcy Analysis
Härdle, Wolfgang K. - 2017
Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...
Persistent link: https://www.econbiz.de/10012966231
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Estimating Corporate Bankruptcy Forecasting Models by Maximizing Discriminatory Power
Charalambous, Christakis; Martzoukos, Spiros; … - 2021
In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional...
Persistent link: https://www.econbiz.de/10013225542
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Machine Learning, Corporate Bankruptcies and Variable Selection
Rossi, Ludovico - 2021
I employ a variety of machine learning techniques to predict corporate bankruptcies. I compare machine learning techniques' predictions with the ones of reduced-form regressions and structural models. To assess the performances of different models, I compute a range of scores both in-sample and...
Persistent link: https://www.econbiz.de/10013216689
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On Modelling IPO Failure Risk
Colak, Gonul; Fu, Mengchuan; Hasan, Iftekhar - 2022
This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals...
Persistent link: https://www.econbiz.de/10013296828