Hagiwara Motohiro    Department   Undergraduate School  , School of Commerce    Position   Professor
Date	2011/08/24
Presentation Theme	Did the Lehman Shock have an impact on consistency of rating information?
Conference	ICAOR'11 International Conference on Applied Operational Research
Presentation Type	Speech (General)
Contribution Type	Individual
Details	The distribution of ratings changes plays a crucial role in many credit risk models. As is well-known, these distributions vary across time and different issuer types. Ignoring such dependencies may lead to inaccurate assessments of credit risk. We introduce a new approach to improve the performance of rating prediction models for multinational corporations. In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. It is apparent that a better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real-world problems. The objectives of this research is to verify the effectiveness of artificial neural networks (ANNs) and examine and compare the stability of rating structures of rating agencies in the United States and Japan. Method to predict corporate ratings by public quantitative information in a inter-temporally stable manner would be useful from the perspective of cost-benefit performance especially in recent rapidly changing economic situation. We find that ANNs has more explanatory power in many cases than models by previous research and that R&I and Moody’s changed rating structure significantly in 2006 and 2007 respectively.