报 告: A "Public Good" Approach to Credit Rating Reform

–the NUS-RMI Default Prediction System for Listed Corporates

汇报人：Jin-Chuan Duan （National University of Singapore）

时 间：5月24日（周二）14:00-15:30pm

地 点：拉斯维加斯9888新楼216教室

Credit rating agencies’ failure to uphold their gatekeeper role figured prominently in the 2008-09 financial crisis. In the aftermath, tighter regulation and oversight of credit rating agencies have been proposed and implemented in some jurisdictions. The US Dodd-Frank Act went as far as requiring the removal of regulatory references to credit ratings when appropriate. As an alternative and complementary route to credit rating reform, the Risk Management Institute (RMI) of National University of Singapore embarked on a “public good” approach to credit ratings in March 2009. Being a not-for-profit undertaking, the RMI credit rating initiative was designed to tap into the global talent pool and to function in a Wiki-style by offering alternative credit information based on an organically evolving and cutting-edge rating methodology. A fully functioning RMI default prediction system was launched in July 2010 with an initial coverage of over 17,000 listed corporates in 12 Asian economies. The system was further expanded to include North America and Europe with a total coverage standing at about 28,000 listed firms in 30 economies.

The RMI default prediction system employs a quantitative model that factors in financial ratios and stock prices. The default predictions are updated daily for all firms over time horizons ranging from one month to two years. RMI welcomes challenges and suggestions to its default prediction model. Challenges and suggestions are viewed as an integral part of its Wiki-style undertaking. Professor Duan will provide the background to and discuss the conceptual foundation of the RMI credit rating initiative. He will describe the research team and the credit research infrastructure being built up at RMI, and how researchers from around the world can take part in this collective effort. A brief demonstration of the default prediction system will follow.

迎接感兴致的教员和同学参与！