Article2008

Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies

Wolfgang Karl Härdle · Yuh‐Jye Lee · Dorothea Schäfer · Yi‐Ren Yeh

88 citations0/0 files storedJournal of Forecasting

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Imbalanced Data Classification Techniques

Abstract

Abstract 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 objectives regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of smooth support vector machines (SSVM), and investigate how important factors such as the selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Moreover, we show that oversampling can be employed to control the trade‐off between error types, and we compare SSVM with both logistic and discriminant analysis. Finally, we illustrate graphically how different models can be used jointly to support the decision‐making process of loan officers. Copyright © 2008 John Wiley & Sons, Ltd.

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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 1966Financial Ratios As Predictors of Failure 1995A Simple Approach to Valuing Risky Fixed and Floating Rate Debt 1996Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads 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)

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2014Forecasting the U.S. real house price index 2020Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison 2012Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation 2015Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques 2011

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