Greifenberg Digital, a member of the IMTE Group, today launched Credit AI, a groundbreaking suite of risk analytics for corporate bonds with an initial coverage universe of nearly 30,000 Chinese local-currency corporate bonds. The analytics are delivered via Internet on an interactive website.
“We’ve done comprehensive back testing, including rigorous out-of-sample tests,” said Uwe Parpart, Greifenberg’s managing partner. “Our system has provable predictive value for China’s corporate bond market. And it performed particularly well in forecasting distress in China’s property bond sector.”
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“The volatility in China’s high yield bond market during the past year underscores the need for better navigational tools,” Parpart added. “We set out to create the state of the art in credit risk management for China and, eventually, many other corporate bond markets.”
The new system includes an innovative financial scoring model, a machine-learning algorithm that detects default risk in corporate financial reporting, an Artificial Intelligence model to assess the probability of misstatements in corporate balance sheets and income statements, and a Contingent Claims Analysis model that derives default risk from real-time equity and option market data.
In addition, Greifenberg uses Natural Language Processing, a form of Artificial Intelligence analysis of news and social media to gauge changes in sentiment about corporate bond issuers. The NLP system presently covers the whole spectrum of Chinese-language public sources, and will be available to website users by January 1.
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Greifenberg also offers matrix pricing of infrequently-traded bonds. This methodology allows investors to identify profit opportunities in the less liquid part of the Chinese corporate bond universe. Greifenberg’s proprietary matrix pricing model identifies reference bonds from the liquid universe and estimates the fair value of illiquid bonds by comparing the risk characteristics of illiquid bonds to the characteristics of comparable liquid instruments. The matrix pricing system provides a reference point for relative value for a large part of China’s onshore bond universe.
In addition, Credit AI provides a portfolio management system that calculates Value at Risk and expected loss from default for corporate bond portfolios. The portfolio module translates the risk measurements of each bond into a default probability and uses the correlation of bond performance to calculate risk at the portfolio level.
“The whole of our analytic suite is greater than the sum of the parts,” said Parpart explained. “We have introduced some true innovations, especially in the application of machine learning to credit analysis and in Natural Language Processing. But what makes the system so robust is the combination of diagnostic tools. We process the entire spectrum of market signals, from balance sheet anomalies to equity market volatility to social media commentary, and extract risk and relative value signals that portfolio managers can use in a timely fashion.”
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The Greifenberg website is now available to institutional investors. Along with the inauguration of the analytics website, Greifenberg released a White Paper detailing the performance of the analytics suite during the Chinese property bond crisis of August-October 2021. “To a great extent, the shakeout in China’s corporate bond market was predictable. We examine in this White Paper the case histories of defaults in the Chinese corporate bond market that prompted the distress in property and other credit. In most cases, default events were forecast accurately by Greifenberg’s combination of credit valuation tools,” the White Paper states.
Greifenberg’s team includes Uwe Parpart, former head of research at Reorient Group and of Asia strategy at Bank of America (Hong Kong); David Goldman, former head of fixed income research at Bank of America and developer of widely-used quantitative credit models; Jerry Lucas, former chief interest-rate strategist at Bank of America and Deutsche Bank; and Michael Peng, who has built credit models for J.P. Morgan, Bank of America Merrill Lynch, and Boston Consulting Group.
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