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Welcome to the second edition of 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 insights related to alternative data, in particular—Bloomberg Second Measure’s transaction data analytics, as well as Bloomberg’s Company Financials, Estimates and Pricing Point-in-Time dataset.
Interested in the previous edition of our Data Spotlight? You can see it here.
1. Getting an early read on macro insights with transaction data analytics
Powered by billions of U.S. consumer credit card and debit card transactions, the Bloomberg Second Measure data analytics feeds provide early insight into the performance of consumer companies and greater depth of analysis. The transaction data comes from a subset of a U.S. consumer panel that includes 20+ million consumers, and covers 3,000+ public and private companies and 4,000+ brands across industries, with over 7 years of consumer spending history.
Additionally, by aggregating data from companies within the Consumer Discretionary sector (as defined by BICS Level 1 Sector), users can derive valuable insights into U.S. retail sales trends. The observed sales data from these companies closely correlates with the U.S. Retail Sales Index (RSTUTOTL Index), offering a powerful tool for understanding consumer trends and market performance.
Bloomberg Second Measure transaction data analytics are now available via Bloomberg Data Licence. This data is delivered daily with a 3-day lag to enable faster decision-making. The US Retail Sales Index is typically released in the middle of the following month, but with the Bloomberg Second Measure dataset, users can access early insights before such numbers are released. Additionally, Bloomberg Second Measure data analytics are available via Bloomberg Terminal ALTD <GO> function on a 7-day lag.
Theme: Macro Investing
Roles: Global Macro Portfolio Managers, Hedge Funds, Strategists
Bloomberg Datasets: Bloomberg Second Measure Transaction Data, BICS
2. Monitoring company financial performance
Transaction data can be a powerful tool for assessing a company’s financial performance relative to its peers on a daily basis. By analyzing transaction data, we can gain insights into a company’s revenue and customer analytics, and get a read on the company’s overall financial health.
The observed data can be compared to that of competitors or industry averages to identify areas of strength and weakness. For example, investors can look at a company’s revenue growth rate and compare it to that of its industry peers to determine if it is keeping up with or outpacing the competition. Similarly, users can examine observed sales per customer ratios to see if a company is able to improve how much clients spend relative to its industry peers.
Chart 1 below shows how two companies (Dollar Tree and Five Below) had a different trajectory when it comes to evolution of their observed sales growth, as well as the change in observed sales per customer – with Five Below managing to make significant progress in observed sales growth while improving its observed average sales per customer over the last few quarters.
This simplified chart does not represent all key metrics in assessing the relative performance of those two companies, but provides an illustration of the possibilities of analysis offered by Bloomberg Second Measure transaction data analytics.
This type of analysis can be automated and integrated into a daily workflow, providing timely and actionable insights for decision-making. Notably, This is made possible thanks to Bloomberg Second Measure’s data coverage of 3,000+ companies across 23 sectors (BICS level 2 – Chart 2).
Theme: Equity Investing
Roles: Equity Portfolio Managers, Equity Analysts, Strategists
Bloomberg Datasets: Bloomberg Second Measure Transaction Data Analytics
3. Equity factor backtesting with Point-in-Time data
Factor investing is a key concept for equity investors as it provides a structured framework to understand the sources of returns and build more robust and diversified portfolios. Factors represent common characteristics of a group of stocks, such as value, momentum, quality, and growth, which have historically driven stock performance. By targeting specific factors, investors can enhance their returns, manage risk, and improve portfolio diversification.
Backtesting is a key tool in factor investing as it allows investors to evaluate the historical performance of different factors and determine which factors have consistently delivered superior risk-adjusted returns.
This helps investors make more informed decisions about which factors to include in their investment strategies and provides a quantitative basis for portfolio construction. Accurate and reliable data ensures the validity and reliability of backtest results. High-quality data includes accurate stock prices, financial statements, and relevant metadata, free from errors, biases, and outliers. Especially, using only known information at a given date (“Point-in-Time”) is key to avoid forward-looking or survivorship biases.
To illustrate the possibilities offered by Bloomberg’s Company Financials, Estimates and Pricing Point-in-Time solution, we ran a backtest of three factors, as well as a combined version of those factors* (Chart 1). Among the hundred of indicators available in the dataset (Chart 2), this backtesting utilizes:
- Current Ratio (Current Asset to Current Liabilities), which is a measure of Working Capital providing an assessment of the liquidity of a company.
- Cash Flow to Interest Ratio, which provides a measure of the Leverage of a company by measuring its ability to quickly cover its interest expense
- The Asset Turnover Ratio, which measures the efficiency of a company’s assets in generating revenue or sales giving a picture of its Profitability.
Based on that, we can see from Chart 1 that taking into account those three factors while screening for companies would have generated positive returns during 2013-2024.
Please note, information included here is not intended as an investment recommendation but rather as an illustration of how to perform a factor backtest using Bloomberg’s Company Financials, Estimates and Pricing Point-in-Time.
Theme: Factor Investing
Roles: Systemic & Quant Investors
Bloomberg Datasets: Company Financials, Estimates and Pricing Point-in-Time
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