Ratings Agencies: Accepting Accountability

Credit rating agencies including Standard & Poor’s, Moody’s and Fitch have drawn regulatory scrutiny and suggestions of conflicts of interest from industry participants noting that these agencies are paid by the very banks whose investment products they rate. The agencies’ credibility has also taken a hit as many mortgage-backed securities they initially deemed high-grade turned out to be toxic to investors’ bottom lines. Their work in terms of improving methodologies and boosting transparency is clearly cut out for them.

But regardless of what is in store for the ratings agencies going forward, the investment managers heavily reliant on those agencies for credit-related analysis have their own houses to get in order by adopting more hands-on, proactive approaches when it comes to assessing creditworthiness.

What exactly that means in terms of technology and operations depends on various factors—what, if any, internal capabilities a manager has already established for credit research; how vanilla or complex its investment strategy is; and whether a manager has the resources to set up its own credit-related infrastructure or seek out tools from third-party providers. Fundamentally, though, investment managers can no longer let rating agency data stand in for more robust internal evaluation and analysis of credit factors. Such undertakings can require significant time and resources, but will also go a long way toward improving performance, understanding risk exposures and satisfying clients.

Ratings Repurposing

Buy-side managers appear already headed in the direction of taking on more responsibility for their credit research and analysis, according to industry sources. Predicating that move is an appreciation, or re-appreciation, of what ratings agency data reveals about a security and what it does not.

So says Deven Sharma, president of Standard & Poor’s, who explains that as many of S&P’s buy-side clients improve their own credit assessment capabilities, the agency must also revamp its offerings to meet those clients’ more self-directed needs.

“The role of ratings is really that of a credit risk benchmark that speaks to the creditworthiness of an entity or security,” Sharma says. “That’s what it is, period.”

That risk benchmark represents an important data point for an investor making a decision, Sharma explains, but does not provide a whole picture.

“Credit risk does not speak to the valuation or volatility or other important issues,” he contends.

A recent investor survey Standard & Poor’s conducted showed that a large group of clients has increased their use of the agency’s ratings-related research tools rather than just its ratings information, according to Sharma.

“They are telling us to provide more details regarding our thinking and rationale, and that is what they are finding valuable. There are also some investors saying they are doing a lot more of their own work now, and that creates other opportunities for us because if we can provide them more insight via our research and tools, they can use us for validation purposes.”

Smaller Funds, More Work

Sharma describes three key types of investment manager clients using Standard & Poor’s ratings data in different—not always ideal—ways.

“Different investors also have different needs,” Sharma says. “There are investment managers who use credit risk to assess or screen where they should invest—typically these are the smaller managers.”

A second group of managers, according to Sharma, constitutes larger institutional managers using ratings to validate their own investment decisions, or to identify arbitrage opportunities.

“Then there are other managers that have frankly used ratings in the past to make their investment decisions,” he says. “We have been pushing a lot of investor education to caution institutional and small players not to make investment decisions just using that rating information. They should be looking at pricing, volatility and other factors as well.”

Given that smaller managers have the most work to do setting up more robust credit analysis tools than their larger colleagues, costs of doing so invariably impact how they go about these efforts.

John Jay, senior analyst at Aite Group, notes that although the largest institutional and mutual fund managers have had internal credit analysis operations in place since before the 2008 market meltdown, every buy-side shop should reconsider how it handles credit research regardless of how heavily or lightly reliant it is upon the embattled rating agencies.

“Some larger managers already have these infrastructures in place, but one could argue that pre-crisis versus post-crisis, there could be improvements,” argues the analyst. “It never hurts to revisit your own process and the assumptions you are making on an ongoing basis, as well as the models you are using.

“The mid- to small-tier managers have the most work to do here,” Jay continues, adding that adopting capabilities similar to those of the top-tier managers would prove a strain on smaller funds, especially fixed-income managers.

“To the extent that some of these smaller funds may not be able to set up their own robust credit analysis departments, they can still look outward beyond the big three agencies,” Jay advises, suggesting other less well-known rating providers, credit sites widely used by bond funds, and third-party analysis and services. The degree of third-party support, however, depends heavily on the type of instrument under assessment.

Degrees of Difficulty

Jay identifies various challenges associated with evaluating the three major credit sectors—unsecured corporate debt, sovereign and municipal debt, and structured products. Unsurprisingly, structured products continue to present the greatest challenge to managers trying to establish more hands-on assessment processes.

“The structured world is more involved, especially in the mortgage arena,” Jay says. “There are firms that can help investors get information not only on performance but also the collateral attributes, and from there they can build credit models with which to infer the creditworthiness of the particular bundles they are looking at.”

But idiosyncratic risks endemic to each structured vehicle require a deeper level of credit analysis; not only must a manager discern the creditworthiness of the “visible” pool of collateral, but also its potential impact on the security features at each tranche of the instrument.

“It is a multi-level analysis, and again, you are dealing with collecting data and building a model with your own assumptions,” says the analyst.

“There are many more moving parts in the structured arena. In order to make your own infrastructure and use the big three agencies for guidance, it’s a lot of heavy lifting. But if you’re the investor, you have to bear more of this responsibility.”

David Kelly, director of credit product development at credit derivatives modeling and analytics technology provider Quantifi Solutions, also emphasizes the differences between evaluating vanilla and complex credit instruments.

“For securitizations, if you’re going to play with these instruments it’s critical that you have the tools to look at not only each asset within a pool, but also be able to simulate them all together in order to get a read on how losses can eat through tranches, and what assumptions are being made by your model,” Kelly says, adding that assumptions are never static.

“They move, and when they move, they move very quickly,” he argues. “It’s not just a matter of looking at the underlying assets and making a determination on cash flows—it’s then taking that together with a correlation assumption and simulating them. If you are going to play in this stuff, you are asking for trouble unless you have the tools to do that,” he warns.

“Unless you are only playing with the most liquid names in the most vanilla markets, you really need some capability to evaluate correlation, and I don’t mean just a rating agency or something on a screen—I mean a technology that allows you to do some real scenario analysis,” he says.

Along these lines, Sharma at Standard & Poor’s says S&P has undertaken efforts to improve its analytical coverage of structured instruments.

“We have always done recovery analytics as part of our corporate ratings, and have now launched recovery analytics for structured finance, and also started efforts to make sure our ratings are comparable across asset classes and also very forward-looking,” Sharma says. “We are testing our ratings against different scenarios and assumptions, and have made those efforts transparent to investors.”

The Data Element

Of course, establishing a sound internal credit analytics capability necessitates a foundation of accurate and well-filtered data.

Even for simpler credit instruments, the challenge lies less in the availability of data to feed analysis than in weeding through immense volumes of information from multiple sources in order to make informed assessments.

“Post-crisis and following the credibility gap that has emerged around rating agencies, I think the buy side clearly needs to make sure that they do the necessary due diligence themselves around the credit component of the risks they are taking,” says Lance Uggla, CEO at Markit.

“The one common problem that all of Markit’s accounts have is that there is just so much information to digest,” Uggla continues. A well-covered buy-side client, he explains, receives research from more than 20 sell-side providers, as well as news coming in from multiple major sources as well as specialized press, plus data from the major rating agencies.

“Historically, financial information services firms have focused on delivery speed, empowering the user with the fastest possible information,” Uggla says. “What they haven’t spent much time on is the ability to sort that workflow in order to make jobs easier.  A lot of this is about getting taxonomy right for accurate indexing and single sign-on.”

Now that speed no longer presents a challenge for data providers, ensuring proper filtering of that data will allow managers to get a “single snapshot” view of a particular credit, says Uggla.

“When a trader draws a picture of a marketplace, they are combining many facets including trade price, research feeds, credit ratings and news. So yes, buy-side managers have to pay closer attention when it comes to credit, and at the same time companies like Markit have to focus on helping those firms get through that abundance of information that is currently not all available in one place.”