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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

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

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

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

Predicting distresses using deep learning of text segments in annual reports

Hansen, Caspar; Hansen, Christian; Matin, Rastin; … - 2018

Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional...

Persistent link: https://www.econbiz.de/10011930209

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

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

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