London Stock Exchange Group (LSEG) today announced the launch of Historical Analytics available through Snowflake.

The Historical Analytics offering combines LSEG’s Pricing Services with Yield Book Analytics to provide pricing information for over 2.9 million bonds with over 20 years lookback.

LSEG customers will be able to access the Historical Analytics offering via Snowflake which as a delivery channel offers LSEG customers flexibility in accessing the solution. This allows customers to move away from multiple services to one fully integrated powerful analytics service.

Emily Prince, Group Head of Analytics at LSEG commented:

“The combination of Yield Book’s trusted, in-depth analytics and LSEG’s expertise in evaluated pricing delivers a robust and comprehensive suite of tools to empower clients. These tools allow customers to backtest portfolios, optimise strategies, and manage risk effectively. By offering this through Snowflake, we ensure seamless access to advanced analytics, enabling more efficient, scalable, and integrated solutions for our clients.”

Historical Analytics can provide solutions for various customers use cases. Some examples include:

• Regulatory Reporting and Risk Management: a versatile framework to calculate a comprehensive set of analytics with a view on the current state of risk and/or historical analytics, allowing for accurate regulatory reporting and ongoing risk management.
• Security Valuation and Portfolio Analysis: market-leading analytics can be generated for historical periods to calculate relative value to assess trade ideas and portfolio strategies across diverse securities, spanning basic bonds, complex structured mortgages, and derivative instruments. In addition, the historical valuations and analytical measures can be leveraged to evaluate performance, conduct attribution analysis, assess portfolio/trading strategies and hedge risk.
• Research, Index Strategy and Historical Simulations: underpinned by market research, customers can run historical simulations to back test index strategies in the creation of custom-tailored benchmarks for either efficient passive index replication or alpha-designed indices.