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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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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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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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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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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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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
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Integrating Neural Network and Colonial Competitive Algorithm : A New Approach for Predicting Bankruptcy in Tehran Security Exchange
Abdipoor, Sajad - 2016
Bankruptcy filings are as high today as ever, calling into question the efficacy of existing bankruptcy prediction models. This paper tries to provide an alternative for bankruptcy prediction by integrated Multi Layered Perceptron with Imperialist Competitive Algorithm (MLP-ICA) and Kohonen self...
Persistent link: https://www.econbiz.de/10013006207
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In Research of Optimal Model for Predicting Bankruptcy
Abdipoor, Sajad - 2014
In this study we tried to compare two models in order to identify optimal neural networks models in predicting bankruptcy. Multi-layered perceptron (MLP) because of easy training and high efficiency and also integrated multi-layered perceptron (most used neural network in predicting bankruptcy)...
Persistent link: https://www.econbiz.de/10013051202
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Network Centrality, Failure Prediction and Systemic Risk
Zareei, Abalfazl - 2015
A financial market can be expressed in a network structure where the stocks resides as nodes and the links account for returns correlation. Centrality measure in the financial network structure captures firms' embeddedness and connectivity in the capital market structure. This paper investigates...
Persistent link: https://www.econbiz.de/10013021792
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Modelling cross-sectional tabular data using convolutional neural networks : prediction of corporate bankruptcy in Poland
Dzik-Walczak, Aneta; Odziemczyk, Maciej - In: Central European economic journal 8 (2021) 55, pp. 352-377
The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for...
Persistent link: https://www.econbiz.de/10012799240