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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

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

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

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

Localising forward intensities for multiperiod corporate default

Prastyo, Dedy Dwi; Härdle, Wolfgang - 2014

Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model...

Persistent link: https://www.econbiz.de/10010403045

Stochastic mortality, macroeconomic risks, and life insurer solvency

Hanewald, Katja; Post, Thomas; Gründl, Helmut - 2009

stochastic process. Our results show that insolvency probabilities are significantly higher when the reaction of mortality rates …

Persistent link: https://www.econbiz.de/10003814526

Support vector machines with evolutionary feature selection for default prediction

Härdle, Wolfgang; Prastyo, Dedy Dwi; Hafner, Christian M. - 2012

Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform...

Persistent link: https://www.econbiz.de/10009526609

The default risk of firms examined with smooth support vector machines

Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - 2008

decision task of loan officers. -- Insolvency Prognosis ; SVMs ; Statistical Learning Theory ; Non-parametric Classification …

Persistent link: https://www.econbiz.de/10003636001

A meta-learning framework for bankruptcy prediction

Tsai, Chih-fong; Hsu, Yu-feng - In: Journal of forecasting 32 (2013) 2, pp. 167-179

Persistent link: https://www.econbiz.de/10009758667