Academic Journal of Business & Management, 2023, 5(26); doi: 10.25236/AJBM.2023.052602.
Tian Zhou1, Weiming Lu2
1School of Business, Nanjing University, Nanjing, China
2School of Accounting, Nanjing University of Finance and Economics, Nanjing, China
The bond credit rating model in the world is still dominated by the traditional credit rating model of European and American countries, and investors' judgment of bond credit risk almost comes from the credit rating report given by credit rating agencies. However, such a credit rating model may lead to problems such as different rating methods, rating pandering, adverse selection, and the lag of rating results. With the development of artificial intelligence, the bond credit rating model based on machine learning has a good development prospect, which can make up for the shortcomings of the traditional credit rating with strong subjectivity and the uneven level of the corresponding assessors. It can also constantly learn to optimize and automatically adjust the weight to adapt to the new environment, so as to adjust the bond credit rating timely and accurately. The accuracy is much higher than that of the classical statistical regression analysis model.
Machine Learning; Bond Credit Rating; BP Neural Network
Tian Zhou, Weiming Lu. Bond Credit Rating Based on Machine Learning Model. Academic Journal of Business & Management (2023) Vol. 5, Issue 26: 9-14. https://doi.org/10.25236/AJBM.2023.052602.
[1] Coats P.K. and Fant L.F. Recognizing Financial Distress Patterns Using a Neural Network Tool. Financial Management, 1993,22(3):142~155
[2] Lee Y.C. Application of Support Vector Machines to Corporate Credit Rating Prediction. Expert Systems with Applications, 2007, 33(1): 67~74.
[3] Shoumo S.Z.H., Dhruba M.I.M., Hossain S., Ghani N.H., Arif H. and Islam S. Application of Machine Learning in Credit Risk Assessment: A Prelude to Smart Banking. New Jersey: Proceedings of the 2019 Ieee Region 10 Conference, 2019. 2023~2028
[4] Kim K.J. and Ahn H. A Corporate Credit Rating Model Using Multi-Class Support Vector Machines with an Ordinal Pairwise Partitioning Approach. Computers & Operations Research, 2012, 39(8): 1800~1811
[5] Zhong H.M., Miao C.Y., Shen Z.Q. and Feng Y.H. Comparing the Learning Effectiveness of BP, ELM, IELM, and SVM for Corporate Credit Ratings. Neurocomputing, 2014(128):285~295
[6] Dastile X., Celik T. and Potsane M. Statistical and Machine Learning Models in Credit Scoring: A Systematic Literature Survey. Applied Soft Computing, 2020(91):1~21
[7] Luo C.C. A Comprehensive Decision Support Approach for Credit Scoring. Industrial Management & Data Systems, 2020, 120(2): 280~290
[8] Choi J., Suh Y. and Jung N. Predicting Corporate Credit Rating Based on Qualitative Information of MD&A Transformed Using Document Vectorization Techniques. Data Technologies and Applications, 2020, 54(2): 151~168