Bloomberg has unveiled a new liquidity risk assessment tool (LQA), which aggregates its comprehensive data capabilities with machine learning techniques in a bid to ascertain what factors directly influence liquidity, according to a Bloomberg statement.
LQA is derived from two pillars: Bloomberg’s data streams and quantitative models, as well as the application of machine learning techniques. The group’s foray into liquidity management is important for a host of venues and both retail and institutional players, given the sometimes less than transparent nature of certain markets, i.e. fixed income, among others.
In particular, LQA utilizes such methods as machine learning techniques and cluster analysis to help identify and leverage transaction data as well as assist in liquidity risk analyses. As opposed to other liquidity assessment tools, LQA’s reliance on machine learning garners a more extensive scope and analysis by which liquidity can be measured. Bloomberg’s LQA is also compatible across all asset classes.
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The new tool caters to risk managers, portfolio managers, traders, and compliance officers. The genesis for the launch of the device has its roots in the US financial crisis in 2008, in which a panel of regulators scrutinized the quality and effectiveness of risk assessment processes and liquidity standards.
According to Ilaria Vigano, Head of the Regulatory and Accounting Products Group at Bloomberg, in a recent statement on the launch: “Assessing liquidity risk is an essential business process for both buy-side and sell-side institutions because they need to assess the cost of capital for any asset they want to hold in their portfolio or on their balance sheet.”
“Bloomberg LQA provides a consistent data-driven approach to measuring liquidity that helps our clients make more informed investment decisions, as well as simplify their regulatory reporting and risk management processes,” Vigano added.