From the outside, syndicated loans can look like a data black hole. Capital goes into them, but very little information on the performance of those investments comes out.
That may be about to change. As investors diversify their portfolios to hedge against volatility amid tense markets, asset classes once considered too exotic for generalists have come into play. And that means demand for information on these markets has risen.
Enter Bloomberg’s latest offering, its US Leveraged Loan Index, which has been created to provide transparency into one of the most opaque corners of capital markets. The US-based data behemoth has harnessed its huge financial data troves and fixed-income expertise to build the index, which covers 1,300 leveraged loans with a market value of US$1.3 trillion.
Custom Indices
The gauge, part of a new Syndicated Loans Data Solutions offering that covers 74,000 active global loan tranches, is intended to help clients benchmark loan-only funds, enable them to create custom indices and construct passive products such as exchange-traded funds, said Nick Gendron, global head of fixed income index product management at Bloomberg.
“With many clients investing in this asset class within core bond portfolios and using Bloomberg’s indices to measure many of their other fixed-income asset classes, there has been growing interest in Bloomberg launching an offering to cover the leveraged loans space,” Gendron told Data Management Insight.
“We feel this will bring more transparency to the market via access on the Bloomberg Terminal and many market participants will use the data for research purposes, as well.”
Growing Market
Bloomberg has beefed up its loans data provisions as returns on the asset class continue to grow and outperform other fixed-income assets. It attributes this growth largely to their inclusion in ETFs, which Bloomberg said have grown to more than US$9 trillion in assets.
Of the $21tn of syndicated loans issued, investors can access the $2trillion of them that are labelled “broadly syndicated”, data on most of which is captured in the new syndicated loans index.
“Interestingly, loans have had a low correlation with most other core fixed income asset classes making them an important choice for clients seeking diversification,” Gendron said.
Data Challenge
Despite the growing market for syndicated loans, data on the market remains difficult to access. The first that investors know about many deals is when they are announced in media releases. And because they are not required to be reported to the US regulators’ Trade Reporting and Compliance Engine (TRACE), issuers are under no obligation to issue standardised data on the loans’ performance.
Bloomberg is betting that its position within the data market and its financial technology heft will enable it to scale the collection of the scant data that is available.
“We have really focused on quickly sourcing the right data and incorporating updates as quickly as possible,” Leila Sadiq, global head of enterprise data content at Bloomberg told Data Management Insight.
The company’s loans data is sourced from 12,000 loan-specific documents filed with the Security and Exchange Commission’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, Sadiq said. It is then reviewed and updated throughout the life of the loan, augmented with information received from Bloomberg’s “direct relationships with more than 600 unique banking entities globally”.
Sub-Indices
Bloomberg has enabled clients to access the data through a variety of its proprietary tools including the Bloomberg Terminal, Data License for scalable enterprise-wide use, B-PIPE for a real-time New Issues feed, and DL+ for managed access. Further, BVAL, Bloomberg’s evaluated pricing service provides pricing on these loans multiple times a day.
The index has also been used to create sub-indices that aggregate loans by size, sector and quality.
“Bloomberg offers the same level of transparency with loans as it does with other fixed-income asset classes on the Bloomberg Terminal with access to constituent level data, analytics and pricing,” said Gendron.
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