S&P’s latest risk pricing tool looks to put a new spin on collected data.
Recently S&P launched a reconfigured analytical tool on its Global Credit Portal to help investors in corporate bonds determine if they are being adequately compensated for the risk of the bond—i.e., does it yield enough. Using a “Risk-to-Price” scoring methodology, the tool allows users to rank securities on a risk basis (both market and credit risk) relative to one another and bucketing them into one of four quadrants. Bonds falling in Quartile 1 are projected to offer the best risk-adjusted yield and those in Quartile 4, the worst.
Spinning existing data anew
“This isn’t really all that new,” said a ratings advisor for a global bank. The rating agencies have all been offering tools for issuing firms and investors to go DIY in response to flagging confidence in traditional ratings and bank regulations that allowed for internal risk assessments in determining capital needs. “The agencies have been doing this for a couple years. They have a lot of information they can provide and in a number of ways,” he noted, so they are trying out new ways for customers to interface with the data. “The Moody’s and S&P default studies are the things that everyone uses to develop their own models,” he said, so they try to add value by offering their own tools too.
“With Basel II, pretty much any major bank has to have its own internal model,” the rating advisor reminded us. “What your model is trying to do is predict both probability of default and loss and event of default,” he noted, which is similar to what the new S&P benchmark aims to do, “and you have to get experience [data] on that.” The question is can you just rely on something like this S&P tool, or do you need to build your own model and feed it with the rating agencies’ data?.
Another rating agency dependency?
For S&P its recent Valuation and Risk Strategies Risk-to-Price (R2P) group, which is actually a recast of its Market, Credit and Risk Strategies group, claims to provide “a different way of looking at risk,” but it looks more like just a new means to attract buyers of its data. Thus, the long-term question for credit markets and purveyors of risk analysis tools is if issuers stop paying for ratings, and investors are not buying the rating agency tools, can the rating agencies still afford to collect and distribute the experience data? Is selling data enough of a business for them?
If the market is forced to look for alternative offerings to rating agencies to provide this data for their credit risk models they will find they lack the depth. This is a dilemma that will need to be sorted out at some point before internal credit risk assessments drain more of the rating agencies’ revenue.