Narrow search
Showing 1 - 10 of 21,515
Cover Image
Estimation of Default Probabilities with Support Vector Machines
Shiyi, Chen; Härdle, Wolfgang K.; Moro, Rouslan Arthur - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 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/10005861245
Cover Image
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
Cover Image
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
Cover Image
Financial Statements as Sole Predictors of Financial Distress : The Need for New Financial Ratios
Assaad Khalil, Karim Wagih - 2011
Four new ratios, that capture firms' Stability, Downside Risk and Audit Quality, are significant predictors of financial distress as evidenced by bankruptcy. Moreover, they improve substantially a logit based credit metric when combined with other classic ratios. A credit metric that comprises a...
Persistent link: https://www.econbiz.de/10013127905
Cover Image
The Default Risk of Firms Examined with Smooth Support Vector Machines
Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...
Persistent link: https://www.econbiz.de/10005860752
Cover Image
Estimating Probabilities of Default With Support Vector Machines
Härdle, Wolfgang; Moro, Rouslan; Schäfer, Dorothea - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 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/10005861009
Cover Image
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
Cover Image
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
Cover Image
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
Cover Image
Predicting Bankruptcy Using Neural Networks in the Current Financial Crisis : A Study of U.S. Commercial Banks
López-Iturriaga, Félix J. - 2010
We develop a model of neural networks to study the bankruptcy of U.S. banks. We provide a new model to predict bank defaults some time before the bankruptcy occurs, taking into account the specific features of the current financial crisis. Based on data from the Federal Deposit Insurance...
Persistent link: https://www.econbiz.de/10013135648