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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

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

Forecasting corporate distress in the Asian and Pacific region

Moro, Russ; Härdle, Wolfgang; Aliakbari, Saeideh; … - 2011

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...

Persistent link: https://www.econbiz.de/10009125559

Learning machines supporting bankruptcy prediction

Härdle, Wolfgang; Moro, Rouslan; Hoffmann, Linda - 2010

In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification...

Persistent link: https://www.econbiz.de/10003973650

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

The prediction of corporate bankruptcy and Czech economy's financial stability through logit analysis

Jakubík, Petr (contributor); Teplý, Petr (contributor) - 2008

This article presents a financial scoring model estimated on Czech corporate accounting data. Seven financial indicators capable of explaining business failure at a 1-year prediction horizon are identified. Using the model estimated in this way, an aggregate indicator of the creditworthiness of...

Persistent link: https://www.econbiz.de/10003755238

Bankruptcy and the COVID-19 crisis

Wang, Jialan; Yang, Jeyul; Iverson, Benjamin; Kluender, … - 2020

We examine the impact of the COVID-19 economic crisis on business and consumer bankruptcies in the United States using real-time data on the universe of filings. Historically, bankruptcies have closely tracked the business cycle and contemporaneous unemployment rates. However, this relationship...

Persistent link: https://www.econbiz.de/10012511297

Estimation of default probabilities with Support Vector Machines

Chen, Shiyi (contributor); Härdle, Wolfgang (contributor); … - 2006

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to...

Persistent link: https://www.econbiz.de/10003402291

Global macro-financial shocks and expected default frequencies in the euro area

Castrén, Olli; Dées, Stéphane; Zaher, Fadi - 2008

Modelling the link between the global macro-financial factors and firms’ default probabilities constitutes an elementary part of financial sector stress-testing frameworks. Using the Global Vector Autoregressive(GVAR) model and constructing a linking satellite equation for the firm-level...

Persistent link: https://www.econbiz.de/10011604921

Bankruptcy prediction : a comparison of some statistical and machine learning techniques

Peña, Tonatiuh; Martínez, Serafín; Abudu, Bolanle - 2009

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...

Persistent link: https://www.econbiz.de/10003928976

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