Bloomberg has launched Company Financials, Estimates and Pricing Point-in-Time, a new offering aimed at tackling challenges linked to sourcing research data from multiple data providers.
The new offering plans to equip investors with the data and insights they need to get a competitive edge, by connecting and integrating a wide range of datasets from various sources, providing historical point-in-time data, as well as enabling the linkage of traditional company data to alternative data sets.
“Infinite computing power, data-friendly programming languages, machine learning tools, advances in AI and easy access to financial analytics has unlocked a vast and abundant set of new data sources for investors,” said Tony McManus, global head of enterprise data at Bloomberg.
“Managing the amount of data that is available today — and gleaning insights not already discovered by the market — has become a massive undertaking. By pre-ingesting, mapping and linking many different data sources together, Bloomberg allows customers to significantly reduce the time needed to generate signals or insights.”
The launch aims to meet customer demand for differentiated, value-adding data with standardised company-level fundamentals, estimates and industry-specific metrics, as well as macro information.
According to Bloomberg, the offering will enable customers to perform deep single company and cross-company analysis, deriving insights into the key performance drivers of a company or sector.
The new data offering’s key features include: historical point-in-time company actuals, consensus estimates, company guidance, historical identifiers and pricing data for 58,000 public companies; clearly marked information when company revisions and/or corrections are made by Bloomberg to company actuals; and financial ratios calculated daily and adjusted as needed.
“A critical component of Bloomberg’s offering is its inclusion of true, historical point-in-time data, which is essential for accurate backtesting,” said Angana Jacob, head of research data, enterprise data at Bloomberg.
“Without historical point-in-time data, models can overestimate returns due to survivorship bias and look ahead bias. What sets Bloomberg’s new data solution apart is that it empowers quants and research analysts with the insights they need to build accurate models that allow them to forecast as precisely as possible a security’s performance.”