A New Corporate Credit Scoring System Using Semi-Supervised Discriminant Analysis

African Journal of Business Management Vol. 5(22), pp. 9355-9362, 30 September, 2011
Available online at http://www.academicjournals.org/AJBM
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
A new corporate credit scoring system using semi-supervised discriminant analysis
Shian-Chang Huang
Department of Business Administration, National Changhua University of Education, No.2, Shi-Da Rd., Changhua,
Taiwan. E-mail: [e-mail]. Tel: [phone].
Accepted 18 July, 2011
Corporate credit scoring is important for investors and banks in risk management. However, the high dimensional data available from public financial statements make credit analysis difficult. To address the problem, dimensionality reduction is a key step to enhance scoring accuracy. By using semi-supervised discriminant analysis (SSDA) and support vector machines (SVMs), this study develops a novel system for credit scoring, where SSDA transforms high dimensional data space (over
50 financial variables) to a perfect low dimensional representative subspace with maximal discriminating power. Constructing SVM classifier in the new space effectively reduces overfitting and enhances classification accuracy. Empirical results indicate that SSDA is better than traditional dimensionality reduction schemes, and it significant improves SVM performance. More importantly, the new classification system substantially outperforms conventional classifiers. The new decision support system can help corporate bond investors make good assessments on their risks and substantially reduce their losses.
Key words: Semi-supervised discriminant analysis, dimensionality reduction, credit scoring, support vector machine, risk management.
INTRODUCTION
Recent financial crisis in 2007-2008 mainly results from credit risk. Corporate credit scoring is important in contemporary risk management. It determines the risk premiums requested by corporate bond investors. The

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