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PREDICTION OF INSOLVENCY IN NON-LIFE INSURANCE COMPANIES USING SUPPORT VECTOR MACHINES, GENETIC ALGORITHMS AND SIMULATED ANNEALING
Segovia-Vargas, María Jesús; Salcedo-Sanz, Sancho; … - In: Fuzzy Economic Review IX (2004) 1, pp. 79-94
In this paper we propose an approach to predict insolvency of non-life insurance companies based on the application of … also other specific ratios which have been proposed for evaluating insolvency of insurance sector. In the simulations … techniques show that the proposed algorithm can be a useful tool for parties interested in evaluating insolvency of non …
Persistent link: https://www.econbiz.de/10004992708
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The influence of variable selection methods on the accuracy of bankruptcy prediction models
du Jardin, Philippe - Volkswirtschaftliche Fakultät, … - 2012
Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the...
Persistent link: https://www.econbiz.de/10011107955
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Bankruptcy prediction and neural networks: The contribution of variable selection methods
du Jardin, Philippe - Volkswirtschaftliche Fakultät, … - 2008
Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks are among the most challenging. Despite the characteristics of neural networks, most of the research done until now has not taken them into consideration for building financial failure models, nor...
Persistent link: https://www.econbiz.de/10011110766
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A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről
Kristóf, Tamás - In: Közgazdasági Szemle (Economic Review - monthly of the … LV (2008) 5, pp. 441-461
A Bázel-2 tőkeegyezmény magyarországi bevezetése új lendületet adott a sokváltozós csőd-előrejelzési módszerek alkalmazásnak és továbbfejlődésének. A cikk a nemzetközi szakirodalomban és pénzintézeti gyakorlatban leggyakrabban alkalmazott négy csőd-előrejelzési...
Persistent link: https://www.econbiz.de/10010963104
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Exploratory Graphics of a Financial Dataset
Unwin, Antony; Theus, Martin; Härdle, Wolfgang - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2006
Persistent link: https://www.econbiz.de/10005207928
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Estimating Probabilities of Default With Support Vector Machines
Härdle, Wolfgang; Moro, Rouslan; Schäfer, Dorothea - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 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/10005207929
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The Default Risk of Firms Examined with Smooth Support Vector Machines
Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2008
In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitabil- ity of Smooth...
Persistent link: https://www.econbiz.de/10005207945
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Estimating probabilities of default with support vector machines
Härdle, Wolfgang Karl; Moro, Rouslan A.; Schäfer, Dorothea - Deutsche Bundesbank - 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/10005082805
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The Application of Neural Networks to the Pricing of Credit Derivatives
Ludovici, Alessandro - In: Rivista di Politica Economica 96 (2006) 6, pp. 187-221
The present paper deals with a new approach to the pricing of credit derivatives, which are innovative financial instruments able to immunize a securities portfolio from the default risk of the issuers, using neural networks. After an essential analysis of the most important topics inherent to...
Persistent link: https://www.econbiz.de/10005015533
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Support Vector Machines with Evolutionary Feature Selection for Default Prediction
Härdle, Wolfgang Karl; Prastyo, Dedy Dwi; Hafner, Christian - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 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 rms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database....
Persistent link: https://www.econbiz.de/10010543377