By Topic

A Multiclass Machine Learning Approach to Credit Rating Prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yun Ye ; Dept. of Comput. Sci. & Technol., Jilin Univ., Changchun ; Shufen Liu ; Jinyu Li

Corporate credit ratings are important financial indicators of investment risks. Traditional credit rating models employ classical econometrics methods with heteroscedasticity adjustments across various industries. In this paper, we propose using machine learning techniques in predicting corporate ratings and demonstrate, empirically, that multiclass machine learning algorithms outperform traditional econometrics models in exact, 1-notch, or 2-notch away rating predictions. We use three years of CompuStat data from four very different industries and compare corporate credit rating prediction tasks across linear regression, ordered probit model, bagged decision tree with Laplace smoothing, multiclass support vector machines (SVM), and multiclass proximal support vector machines (PSVM). Our findings show that with the proper multiclass and heteroscedasticity adjustments, the computationally inexpensive multiclass PSVM can be utilized in making viable automated corporate credit rating systems for todaypsilas vast marketplace.

Published in:

Information Processing (ISIP), 2008 International Symposiums on

Date of Conference:

23-25 May 2008