Barbie and her pals no doubt threw a huge party when the company that makes the iconic doll, Mattel, saw its debt upgraded from junk to investment grade by the three major credit rating firms earlier this year.
Had they spoken with some market watchers, however, the Barbieland party could have started months – maybe years – earlier.
Traders were already attuned to the corporate improvements that had spurred the California-based company’s upgrade when it happened in February, even before the box-office success of the film based on the doll’s adventures had elevated Mattel’s prospects. By watching the company’s bond prices and data on its fundamentals and a range of other indicators, they knew that the sub-investment grade rating under which it had languished for years was now out of date.
Those in the know traded accordingly and cleaned up. Those who didn’t have the capabilities to properly assess the company had no such luck.
According to Bloomberg global head of risk and investment analytics products, Zane Van Dusen, the reason that more firms didn’t profit was because not all traders can effectively feed the corporate-specific information to which they are privy into their firm’s operational knowledge banks.
“It took many months for the rating agencies to come to a consensus that Mattel belonged in the investment-grade sector,” Van Dusen told Data Management Insight. “But if you look at market sentiment you can see from bond market activity that Mattel’s yields decreased pretty significantly all the way back in 2022, giving an early signal that the back office might need to prepare for this name becoming part of their investable universe.”
Bridging the Gap
This disconnect between the front and back office is potentially costing firms dearly as they try, and often fail, to forecast the market. Traders and their colleagues are privy to information through their day-to-day activities that isn’t always easily transferred back to the firm’s risk, research and other teams that help shape investment strategy. That gulf of information was part of the drive behind Bloomberg’s recent upgrade to its market-implied probability default (MIPD) solution, a key forecasting data tool.
Launched in 2021, MIPD offers visibility into fixed-income markets by incorporating data from the company’s evaluated pricing service (BVAL). MIPD provide early-warning indicators that quantifies the impact of news and events on a company and quickly reacts to changing market and issuer-level conditions. It builds par yield curves on more than 30,000 issuers to offer default probabilities and implied credit default swaps spreads, among other metrics.
In the summer, MIPD was upgraded with the addition of five years of history that enables back-testing of early-warning indicators. Its functions were also enhanced to provide deeper insights into the data and to improve user friendliness with new “easy button” calculations, like how an issuer’s current default probability compares to its historical levels, so risk managers in the back office can quickly survey hundreds to thousands of names at once.
Van Dusen explained **that MIPD, when used with other Bloomberg tools, can help firms bridge the information gap between front and back offices.
“On the front-office side, you are covering maybe a specific sector or even a subset of names within that sector, so you know those names really, really well and what exactly is going on with them even if it’s not front-page news,” he said. “If you’re a second- or third-line risk manager, however, you’re looking at the names everyone in the front office covers. This could be hundreds, if not thousands of companies. As a result, the back office doesn’t have the capacity to do the same kind of manual analysis.”
Multiple Sources
Bloomberg relies primarily on market data like bond prices to provide those signals for MIPD and converts the data into standardised risk indicators for different companies. Bond prices are regarded as a better gauge of corporate performance and sentiment because only institutions tend to trade in corporate debt and yields are less prone to the sort of irrational trading frenzies that can affect equities. Additionally, they are closely correlated to news sentiment, which is difficult to quantify.
Those risk forecasts are reassessed programmatically daily in MIPD to accurately reflect changes in sentiment that the rating agencies might take longer to show in their ratings, Van Dusen said.
“My goal is to convert your front-office intuition into a data feed so that you are speaking the same language as the back-office guys,” he said. “Because there’s knowledge there that can be converted into data. What we are doing, essentially, is our best approximation of taking that front-office perception and market sentiment and turning it into numbers.”
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