Bloomberg announced the release of its Liquidity Assessment Tool (LQA), a new solution for risk analysis of bond-trading liquidity that utilizes machine learning.
The LQA tool, which was developed over a six-year period and is the first system of its kind to use machine learning, according to Bloomberg, is aimed at providing bond risk managers, portfolio managers, traders, and compliance officers a quantitative, consistent approach to liquidity risk through a standard definition of liquidity and expected cost of liquidation for a specific volume of securities at desired time horizons.
Bloomberg LQA utilizes machine-learning techniques, including cluster analytics for the identification and leverage of transaction data for comparable securities, designed to tackle the opaque nature of fixed-income trading and liquidity risk measurement.
"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," says Ilaria Vigano, head of the regulatory and accounting products group at Bloomberg. "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."
WatersTechnology US editor Anthony Malakian looked at machine learning in his March feature. You can read more about it here.
Bill Murphy, CTO of Blackstone, once again joins the podcast to discuss the private equity firm's new offices, designed to house its innovations team.Subscribe to Weekly Wrap emails