Bloomberg Professional Services
This article was written by the Bloomberg Enterprise Investment Research Data team: Michael Beal, Jerome Barkate, Michael Ashikhmin, Frances Shi.
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 focus on how investors can use Industry Specific data to run deep sector analysis in both discretionary and systematic ways. We also look at how supply chain data can help with understanding the impact of specific countries or regions on operational risk.
Looking for our other data-related findings? Explore these recent articles from the Data Spotlight series:
- Supply chain data & AI-based news insights
- Intraday trading & market volatility
- Transaction data & credit backtesting
For more articles in this series click here.
1. Why did US bank stocks outperform their European counterparts?
Since the global financial crisis in 2008, the stock prices of US banks have significantly outperformed European banks. To understand why, we used the recently released Industry Specific Company KPIs dataset for deeper insights into this sector.
In our analysis, we aggregated measures for both regions based on relevant indicators reported by the banks. The outperformance of US banks can be attributed in part to U.S. banks exhibiting generally stronger profitability metrics than their European counterparts. This is illustrated by a key indicator of banks’ profitability: the net interest margin (Chart 1, left side), which measures the difference between the amount of money a bank earns on its assets and the amount it pays in interest on its liabilities.
Additionally, US banks have, on average, better asset quality compared to European banks, as evidenced by their lower non-performing loan (NPL) ratios. Higher NPL ratios usually correlate with greater credit risk and reduced loan quality, adversely affecting profitability and capital adequacy (Chart 1, right side).
Industry Specific Company KPIs and Estimates PiT dataset provides ~1,200 unique company reported key performance indicators across the 11 BICS Level 1 Sectors and 44 BICS Level 3 industries for the global public company universe covered by Bloomberg (Industry Specific Company Financials History starting from 2006 with relevant Estimates history from 2016).
The data is updated as companies release new information enabling deep sector and industry research allowing clients to build a data driven view of companies, sectors and industries worldwide over time. Below chart shows how many performance indicators users can get for the 11 BICS Level 1 sectors.
Theme: Equity Fundamentals
Roles: Equity Analysts, Portfolio Managers, Researchers
Bloomberg Dataset: Industry Specific Company KPIs and Estimates PiT
2. Companies’ KPIs: A systematic approach to finding industry drivers
Researchers have long aimed to uncover hidden patterns in specific industries through systematic sector analysis. However, to effectively implement this type of approach, industry-specific data is essential. In the past, accessing and analyzing granular data for specific sectors was a labor-intensive and time-consuming process, requiring manual collection and cleaning of data from various sources with data coverage and quality issue. This limited the ability to explore systematic patterns and create sector-specific models at scale.
With the advent of research-ready dataset investors can easily access and analyze large amounts of point-in-time industry-specific data. This opens up new possibilities for quantitative sector investing, enabling the creation of models that can identify trends and inform investment decisions.
In this short study, we conducted a simple correlation analysis between industry-specific indicators and equity returns over the past year to determine which metrics have been rewarded by the equity market in each industry. Chart 1 illustrates the top 10 results for two industries (Retail – Consumer Staples and Software), showing that different metrics are rewarded depending on the sector analyzed.
Though this analysis is simplified – advanced researchers would likely take a more detailed approach by examining trends over time or surprise versus consensus estimates – it is interesting to note that the correlation coefficient shows that relationship between industry specific metrics and equity markets can be significant (Chart 2).
Theme: Systematic Investing
Roles: Quant Portfolio Managers, Traders, Systematic Investors
Bloomberg Datasets: Industry Specific Company KPIs and Estimates PiT
3. Have companies with U.S. centric supply chains outperformed their peers since the U.S. elections?
We recently explored how supply chain data reveals biodiversity and sector risks. In this study we examine the impact of the supply chain as a systematic investment factor in the U.S. Equity and Credit markets by comparing the performance of companies with varying degrees of supply chain exposure to the U.S.
In an increasingly interconnected global economy, understanding the impact of specific countries or regions on operational risk is crucial for investors. This study investigates how a company’s operational exposure to China and the US, through its supply chain and facility locations, can affect its market performance.
Our analysis focuses on companies listed in the Russell 1000 index in November 2024. Utilizing Bloomberg’s Enterprise Data products to identify the suppliers, customers, and facility locations associated with each company. We filter the point-in-time universe for companies with at least one supplier, customer, or facility (depending on the study) reported on the day of trading decision. For access to the full study, please contact your Bloomberg representative or request a demo here.
In the example provided in Chart 1, we group companies of the Russell 1000 Index based on their indirect exposure to the US, as defined by the percentage of their suppliers domiciled in the US (using country information from Bloomberg’s Corporate Structure product). It is interesting to note that, in the two weeks following the US elections, companies with US-centric suppliers consistently outperformed those with globally diverse supply chains – possibly highlighting equity investors’ expectations of future US policies.
Chart 2 shows a similar strategy, but this time companies of the Russel 1000 Index are grouped based on their indirect exposure to China, by measuring the percentage of their suppliers domiciled in China. Looking at the bond market, we measure the change in z-spreads – a measure of credit risk – for each group of companies. Similarly to what we observed in the equity market, we see that issuers of corporate bonds for companies with high supplier concentration in China tend to underperform their peers in the days following the U.S. elections: their z-spread compressed less than for their peers.
This type of analysis shows how investors can use Bloomberg Supply Chain data to broaden company analysis and get additional insights.
Themes: Alpha generation, Risk management
Roles: Portfolio Managers, Analysts, Risk Managers
Bloomberg Datasets: Company Financials, Estimates and Pricing Point-in-Time, Supply Chain
Interested in learning more about our data offering? Bloomberg’s Enterprise Investment Research Data product suite provides end-to-end solutions to power research workflows. All of these data solutions are interoperable and can be seamlessly connected with other datasets, including alternative data, and are available through a number of delivery mechanisms, including in the Cloud and via API. More information on these solutions can be found here.
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