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Welcome to Data Spotlight, our series showcasing insights derived from Bloomberg’s 8,000+ enterprise datasets available on data.bloomberg.com via Data License.
In this edition we take a look at transaction data, and credit metrics and how they can help with investment thesis development.
Our previous editions of Data Spotlight can be found here:
1. Gaining an early read on industry trends
When inflation rises, restaurant spending is often one of the first expenses consumers look to trim. How was that reflected during the period of high inflation in 2023? Research Data products, such as Company Financials Point-in-Time and Bloomberg Second Measure transaction data, can help users get an early read on a company’s performance.
To do so, as illustrated in Chart 1, necessary data can be queried throughout the latest tool available: a python UDM* API to simplify data query for a set of US restaurant companies. One important feature is auto-completion of fields helping researchers and data scientists with data discoverability.
*Unified Data Model, a Bloomberg proprietary data model helping its clients with an advanced data model to link all datasets into Entities, Instruments, Markets and Pricing.
On an individual company-level, investors can get an early read on the potential sales performance of companies in the quarter compared to the consensus estimates by using alternative data, such as transaction data.
The divergence between observed sales and consensus can help to get an early read on company earnings ahead of the company public announcement. Chart 2 shows the reported earnings of the most recent 15 quarters for Starbucks, 80% of the time Bloomberg Second Measure pointed in the right direction, while consensus was either over or under estimating earnings.
Bloomberg Second Measure transaction data analytics available via Bloomberg Data License is delivered daily with a 3-day lag to enable faster decision-making. Additionally, Bloomberg Second Measure data analytics are available via Bloomberg Terminal ALTD <GO> function on a 7-day lag.
Theme: Systematic Investing
Roles: Quant Portfolio Managers, Traders, Systematic Investors
Bloomberg Datasets: Company Financials, Estimates and Pricing Point-in-Time, Bloomberg Second Measure Transaction Data
2. Using credit metrics backtesting in bonds selection
Systematic investment in corporate credit typically involves strategies to choose a set of bonds to buy based on an investor’s desired risk-return profile. In recent years, this area has experienced significant growth as an increasing percentage of corporate bond trades are being executed electronically.
Investment strategies in this space traditionally rely on credit ratings typically published by third-party rating agencies. However, as more reliable company financials and bond market data become available, more quantitative credit risk measures are increasingly used in this space. Bloomberg’s credit analysis tools product provides access to both model-based Default Risk (DRSK) and Market Implied Probability of Default (MIPD) measures which can investors develop systematic trading strategies for corporate bonds.
Both DRSK and MIPD data solutions calculate the probability of default for a bond issuer over a specific time horizon (such as six months or five years into the future) but are derived differently.
DRSK uses the Bloomberg corporate default database to fit a proprietary quantitative model pointing to such events based on company’s characteristics, performance measures and, for public companies, stock price.
MIPD, on the other hand, directly implies default probabilities using BVAL’s evaluated bond prices. MIPD metrics offer a daily snapshot of market sentiment for an issuer’s creditworthiness.They can quickly capture the impact of issuer-specific news and broader market events for the 36,000+ companies and sovereigns globally that issue bonds.
As a result, MIPD is typically significantly higher than DRSK reflecting the market risk premium embedded in bond prices. Essentially, DRSK offers a risk profile driven by both equity market dynamics and fundamentals, whereas MIPD provides a primarily bond market-driven outlook.
Chart 1 shows the time evolution of the average overall issuers present in the US Corporate High Yield Index for these two measures.
Note the sharp increase in market-implied credit risk after Silicon Valley Bank collapse in March 2023, which was not reflected to the same degree in the more fundamentals-driven DRSK measure suggesting that a potential trading opportunity might have existed at that time.
We analyzed a systematic strategy of avoiding high DRSK values (to reduce fundamental default risk) and buying the highest MIPD values in the remaining universe. The DRSK and MIPD thresholds were chosen in a way that corresponded to distressed and low grade bonds, respectively.
The result of this exercise can be seen on Chart 2. Over the period of study (2022-2024) this simple strategy (the purple line on Chart 2) beats the overall index (dark blue line).
Looking to further enhance the analysis by adding another fundamental dimension we analyzed a second strategy by incorporating Company’s data from Bloomberg Company Financials, Estimates and Pricing Point-in-Time (CoFi) product which provides a wealth of measures of company reported and analyst-estimated performance.
To illustrate this, we added a filter on top of the strategy described above to buy only the lower half of the bonds according to Net Debt to EBITDA ratio over the last twelve months. It would seem that a lower debt-to-earnings ratio would be a positive indicator for a company’s ability to repay its debt. Indeed, adding this filter appears to noticeably improve strategy returns (light blue line).
There are several simplifying assumptions used in this example, which may significantly affect the results in practice, such as ignoring transaction costs by assuming all bonds can be traded at BVAL mid-market prices. However, this demonstrates powerful ways these data solutions can be used for comprehensive, proactive credit risk analysis.
Theme: Systematic Investing
Roles: Credit Portfolio Managers, Traders, Systematic Investors
Bloomberg Datasets: Credit Risk Indicators, Company Financials, Estimates and Pricing Point-in-Time
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