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Ein Vergleich des binären Logit-Modells mit künstlichen neuronalen Netzen zur Insolvenzprognose anhand relativer Bilanzkennzahlen
Franken, Ronald - 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/10010263695
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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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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
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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
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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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Development of a financial health indicator based on companies' annual accounts
Vivet, David - 2011
This document describes the development of a financial health indicator based on companies' financial statements. This indicator is conceived as a weighted combination of variables, which is obtained through a model discriminating between failing firms and non-failing firms. The definition of...
Persistent link: https://www.econbiz.de/10011596313
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Corporate failure and the business cycle : measuring systematic risk
Ogneva, Maria; Piotroski, Joseph D.; Zakolyukina, … - 2016
In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to...
Persistent link: https://www.econbiz.de/10011524470
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A unified model of distress risk puzzles
Chen, Zhiyao; Hackbarth, Dirk; Strebulaev, Ilya A. - 2019
We build a dynamic model to link two empirical patterns:\ the negative failure probability-return relation (Campbell, Hilscher, and Szilagyi, 2008) and the positive distress risk premium-return relation (Friewald, Wagner, and Zechner, 2014). We show analytically and quantitatively that (i)...
Persistent link: https://www.econbiz.de/10012065129