Chapter2014

Support Vector Machines with Evolutionary Model Selection for Default Prediction

Jeffrey Scott Racine · Liangjun Su · Wolfgang Karl Härdle · Dedy Dwi Prastyo · Christian Matthias Hafner

33 citations0/0 files storedOxford University Press eBooks

No readable copy stored yet — this record is metadata only.

Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

This chapter describes a support vector machine (SVM) for default prediction that features evolutionary model selection. It explains that a genetic algorithm was used as an evolutionary algorithm to optimize the SVM parameters and discusses the importance of the discriminative power of classification methods in the quality of default prediction. It reviews the support vector machine methodology in classification, focusing on classical linear and nonlinear classification for linearly separable and nonseparable scenarios. This chapter also evaluates the application of the SVM on the CreditReform database consisting of 20,000 solvent and 1000 insolvent German companies in the period from 1996 to 2002.

Citations in this library

Stored only when both papers are in the corpus — 33 citations exist upstream.

References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1974ON THE PRICING OF CORPORATE DEBT: THE RISK STRUCTURE OF INTEREST RATES*1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2004Default Risk in Equity Returns 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1994Bankruptcy prediction using neural networks 2000Benchmarking Quantitative Default Risk Models: A Validation Methodology

Imported from OpenAlex (CC0) — nothing here is generated.