A hybrid KMV model, random forests and rough set theory approach for credit rating

Authors:
Ching-Chiang Yeh;Fengyi Lin;Chih-Yu Hsu
Affiliations:
Department of Business Administration, National Taipei College of Business, No. 321, Sec. 1, Ji-Nan Rd., Zhongzheng District, Taipei 10051, Taiwan;Department of Business Management, National Taipei University of Technology, No. 1, Sec. 3, Chung-hsiao E. Rd., Taipei 10608, Taiwan;Graduate Institute of Commerce Automation and Management, National Taipei University of Technology, No. 1, Sec. 3, Chung-hsiao E. Rd., Taipei 10608, Taiwan
Venue:
Knowledge-Based Systems
Year:
2012

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Abstract

In current credit ratings models, various accounting-based information are usually selected as prediction variables, based on historical information rather than the market's assessment for future. In the study, we propose credit rating prediction model using market-based information as a predictive variable. In the proposed method, Moody's KMV (KMV) is employed as a tool to evaluate the market-based information of each corporation. To verify the proposed method, using the hybrid model, which combine random forests (RF) and rough set theory (RST) to extract useful information for credit rating. The results show that market-based information does provide valuable information in credit rating predictions. Moreover, the proposed approach provides better classification results and generates meaningful rules for credit ratings.