Bloomberg has launched the Liquidity Assessment Tool (Bloomberg LQA), a new functionality pioneering the use of machine learning to estimate liquidity risk in response to global regulatory requirements for institutional investors to factor liquidity into risk and pricing models.
Bloomberg LQA, which gives institutional investors a quantitative approach to calculating liquidity risk consistently across asset classes, is the first liquidity estimation tool to combine Bloomberg's financial data and machine learning techniques to calculate 'the multitude of relevant factors influencing liquidity'.
"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," said Ilaria Vigano (pictured), head of the regulatory and accounting products group at Bloomberg, in a statement. "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."
Bloomberg LQA is targeted at risk managers, portfolio managers, traders, and compliance officers and provides a standard definition of liquidity and a consistent approach to measuring the expected cost of liquidation for a specific volume of securities, and a desired time horizon. The new tool also provides a score designed to indicate security-level liquidity with respect to liquidation cost and its distributions across different volumes.
The launch responds to new regulatory requirements emerging from the financial crisis, around the quality and comprehensiveness of existing risk assessment processes and new standards for liquidity management on buy-and sell-side institutions. According to Vigano, establishing a systematic approach to measuring and reporting liquidity risk demonstrates a firm's commitment to robust risk management practices, comprehensive pre-and-post trade analysis and efficient regulatory reporting that meet new industry standards.
Bloomberg Professional service subscribers can access Bloomberg LQA data for more than 130,000 global government and corporate securities. The same data can also be provided for enterprise use, allowing clients to override Bloomberg's default inputs with their own assumptions so the model considers their unique perspective on the market.
While Bloomberg LQA covers government and corporate securities, the methodology that underpins the tool can be applied across asset classes, to assess liquidity risk at the portfolio-level.
Bloomberg LQA uses machine learning techniques, such as cluster analysis, to dynamically identify and leverage transaction data for comparable securities. The application of machine learning enables Bloomberg LQA to transcend the inherent limitations of the widely used bid/ask approach to measure liquidity risk, which can overlook important variables. By leveraging a larger set of factors, Bloomberg LQA can better assess liquidity across instruments and across time.
Bloomberg LQA is part of Bloomberg's Regulatory and Accounting Products suite, which helps senior compliance officers at banks, asset managers, insurers and other financial organizations navigate an increasingly complex regulatory and accounting compliance environment.
Bloomberg's LQA follows the launch of a new functionality to provide exchanges, data providers, custodians and other organizations direct access to multiple tools for identifying, mapping and requesting the Financial Instrument Global Identifier (Figi), and the expansion of the technology integration between Lexifi and Bloomberg to support the new Bloomberg Derivatives Library (DLIB) on the Bloomberg Professional service, the company's financial data and information platform.
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