Market liquidity risk product of the year: Bloomberg

Key points Summary

• Bloomberg’s Liquidity Assessment (LQA) won Market liquidity risk product of the year for improving transparency and cross‑asset consistency.
• LQA uses deep multi‑source market data, daily quotes and trades, and machine learning to estimate liquidity where trade data are sparse.
• Recent enhancements include uncapping Trace‑reported sizes, improved SEC Rule 22e‑4 classification for emerging markets and a predefined ‘Tariff 2025’ stress scenario.
• Clients said LQA held up during the tariff‑related volatility in early 2025, and the service is available via Bloomberg Data License, the Terminal, APIs and major cloud providers.

These summaries are generated by an LLM, but edited by humans.

Liquidity risk has become one of the most challenging dimensions of modern risk management. With global regulators sharpening their expectations for liquidity classifications, stress-testing and monitoring frameworks – and with firms facing structurally thinner fixed income markets – the need for precise, defendable and data-driven liquidity assessment has never been greater. The winner of this year’s Market liquidity risk product of the year, Bloomberg’s Liquidity Assessment (LQA) solution, stood out for its ability to bring analytical discipline, transparency and cross-asset consistency to a notoriously opaque field.

LQA is built on a straightforward premise: liquidity cannot be modelled effectively without genuine market data, continuous recalibration and a framework that adjusts dynamically to changing conditions. Bloomberg’s solution delivers this through a combination of deep multi-source data coverage, machine learning techniques designed to fill the gaps where data does not exist and a cross-asset architecture that allows portfolio-level liquidity risk to be assessed with a single, coherent methodology.

What impressed judges was not only the sophistication of the underlying models, but also Bloomberg’s consistent demonstration that LQA performs reliably across extreme market environments – including the shocks of 2020, 2022 and 2023, and the tariff-driven volatility of 2025.

A data-driven model built for today’s market structure

Liquidity risk modelling is fundamentally a data problem. Fixed income markets in particular are characterised by incomplete transparency, highly fragmented execution venues and a long tail of instruments that trade either infrequently or not at all. Bloomberg’s position in the market gives it access to an unusually rich set of trading data, spanning exchanges, Trade Reporting and Compliance Engine (Trace), clearing houses and large volumes of anonymised client contributions. The LQA team has built extensive validation, cleansing and outlier-removal processes around these datasets to ensure that the resulting liquidity metrics are genuinely reflective of current market conditions.

Where instruments lack sufficient trading history for empirical measures, Bloomberg uses machine learning to estimate liquidity characteristics in a way that respects the nuances of each asset class. The firm’s quantitative research team has developed asset-specific methodologies that avoid the pitfalls of applying models designed for limit-order-book assets to fixed income markets where price formation behaves differently. This ensures that liquidity cost, liquidation horizon and volume metrics are comparable across equities, corporate bonds, municipals, high-yield debt and other asset classes, enabling firms to establish a unified view of liquidity at the portfolio level.

This cross-asset consistency has become increasingly important as regulators and investors demand portfolio-level liquidity reporting and stress-testing.

Extending transparency through new enhancements

Bloomberg’s development over the past year has focused on expanding transparency and improving regulatory alignment across global markets. One of the most impactful enhancements is Bloomberg’s work to uncap Trace data. By cross-referencing Trace-reported transactions with additional datasets, the firm has been able to identify true trade sizes for a substantial share of investment-grade and high-yield bonds that exceed Trace reporting caps. This improvement supports more accurate liquidity modelling, helps with price discovery and is especially meaningful for newly issued bonds where early-stage liquidity is critical to portfolio decision-making.

Bloomberg has also updated its US Securities and Exchange Commission Rule 22e-4 classification logic for emerging markets. Working with clients and conducting settlement timing research, the firm has improved the model’s ability to capture risks linked to converting non-USD securities into dollars.

These enhancements reflect a product philosophy rooted in continuous learning from market dynamics and client workflows. As requirements for liquidity stress-testing expand, Bloomberg has also invested in expanding the set of predefined historical scenarios. For example, a ‘Tariff 2025’ scenario was developed in response to client demand, reflecting the atypical dynamics observed during that period, including the significant impacts on traditionally ‘safe-haven’ assets.

Performance through volatility

The early months of 2025 brought tariff-related volatility and a temporary liquidity crunch in certain fixed income segments. While many risk models require reactive recalibration during periods of heightened uncertainty, Bloomberg’s LQA did not. The system’s daily integration of quotes and trades means liquidity metrics adjust automatically, maintaining accuracy without manual intervention. Bloomberg’s revamped backtesting framework, introduced in 2024, provided further reassurance that LQA’s liquidation cost and horizon measures would remain robust during periods of instability.

Clients reported that LQA’s outputs were reflective of real conditions during the brief dislocation in April 2025. Bloomberg’s ability to distinguish between volatility and genuine liquidity stress – especially in high-yield credit – proved critical as firms sought clarity during a period of uncertainty.

Supporting a wide range of use cases

Although liquidity risk measurement is a regulatory requirement, Bloomberg’s clients increasingly use LQA for portfolio construction, pre-trade decision-making, exchange-traded fund management, dealer inventory oversight and investor reporting.

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LQA is delivered for use across the enterprise via Bloomberg Data License, enabling access through secure file transfer protocol or representational state transfer application programming interface (API), and providing native availability within all major cloud providers. The solution’s flexibility also stems from Bloomberg’s broad integration options, which span the Terminal, Bloomberg Query Language, the Excel API, programmatic APIs and daily batch data feeds. This allows firms to incorporate liquidity analytics directly into front-office systems or risk platforms and to co-ordinate liquidity oversight across risk, investment and compliance teams.

Bloomberg LQA’s ability to support hypothetical and historical liquidity stress scenarios provides firms with the tools to test resilience under bespoke and regulatory frameworks. By capturing asset-class-specific behaviour and enabling granular scenario analysis down to the instrument or transaction level, Bloomberg continues to extend LQA beyond compliance into proactive, forward-looking liquidity risk management.

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