Can the Data Collected for CAT RFQ Reporting Be Used to Analyze Liquidity Provider Performance?

Concept

The question of whether Consolidated Audit Trail (CAT) data for Request for Quote (RFQ) events can be used to analyze liquidity provider (LP) performance cuts to the core of a fundamental tension in modern market structure. It places the immense analytical power of a comprehensive regulatory dataset directly against the foundational principles of data privacy and intended use. The data collected under the CAT NMS Plan offers an unprecedented, granular view of the entire lifecycle of an order, including the nuanced, off-exchange interactions of bilateral price discovery protocols. From a purely technical standpoint, the data is not just suitable for such analysis; it is arguably the most complete and unbiased source in existence for that purpose.

However, the system was not designed to be a commercial tool. Its purpose is regulatory oversight. The architects of the system and its governing bodies have been explicit in their prohibition of using CAT data for commercial purposes. This creates a critical distinction between what is technically possible and what is operationally permissible.

The data stream contains the precise timestamps of quote requests, the identities of the responding market makers, the specifics of their quotes, and the final execution details. This information would permit a firm to construct a highly detailed, empirical scorecard of its liquidity providers’ speed, pricing competitiveness, and reliability. Yet, engaging in this activity would constitute a direct violation of the rules governing the system’s use.

This reality forces a shift in perspective. The inquiry evolves from a technical question about data utility to a strategic one about data governance. The core challenge for any trading desk is not how to illicitly leverage regulatory data, but how to architect an internal data capture and analysis framework that replicates the insights one might theoretically glean from CAT. The existence of CAT data as a regulatory tool validates the importance of collecting such metrics.

It implicitly confirms that analyzing response times, quote spreads, and fill rates is a critical component of understanding market dynamics and ensuring best execution. The task for the institution, therefore, is to build a proprietary system that achieves these same analytical ends using its own order and execution data, which is both permissible and essential for maintaining a competitive edge.

Strategy

Developing a strategy to analyze liquidity provider performance requires navigating the boundary between regulatory data and proprietary execution data. While CAT provides the most exhaustive dataset, its use for commercial analysis is forbidden. The effective strategy, therefore, is to use the concept of CAT as a blueprint for building a robust internal analytics framework based on data a firm is legally entitled to use ▴ primarily from its own Order Management System (OMS) and Execution Management System (EMS).

Architecting an Internal Analytics Framework

The strategic objective is to systematically measure and compare the performance of liquidity providers to optimize execution quality, reduce costs, and strengthen trading relationships. This involves defining key performance indicators (KPIs) that mirror the kind of analysis CAT data would allow, but are populated entirely with the firm’s own transactional data. The process begins by ensuring that all RFQ-related events are captured with high-fidelity timestamps and metadata within the firm’s internal systems.

A firm’s internal data is the appropriate and sanctioned source for constructing a detailed analysis of its liquidity providers.

This data serves as the raw material for a transaction cost analysis (TCA) program specifically tailored to the RFQ workflow. The analysis can be segmented into several key dimensions of liquidity provider performance.

• Responsiveness and Reliability This dimension assesses the consistency and speed of an LP’s engagement. Key metrics include the response rate to RFQs, the average time to respond, and the frequency of actionable quotes versus simple acknowledgements.
• Pricing Competitiveness This is the core evaluation of an LP’s value proposition. It involves measuring the quoted spread against the prevailing market midpoint at the time of the request, comparing the quote to those of other responding LPs, and tracking the frequency with which an LP provides the best price.
• Execution Quality This dimension moves beyond the quote to the actual execution. Metrics include the fill rate (the percentage of quotes that result in a trade), the magnitude of any price improvement received relative to the initial quote, and the market impact of the trade.

Key Performance Indicators for Liquidity Providers

To implement this strategy, a firm must establish a clear set of quantifiable metrics. The following table outlines a practical framework for this analysis, detailing the KPI, the required internal data points, and the strategic insight each metric provides.

How Does This Differ from Using CAT Data?

What are the primary distinctions between this internal strategy and a hypothetical analysis using CAT data? The most significant difference is scope. An internal analysis is, by definition, limited to the firm’s own order flow. It cannot provide insight into how an LP interacts with other market participants.

CAT data, conversely, would offer a market-wide view of an LP’s activity. This limitation is also a feature from a regulatory perspective. It prevents a firm from gaining an unfair informational advantage over its competitors or its LPs. The regulatory framework effectively mandates that firms focus on optimizing their own execution outcomes with their counterparties, rather than surveilling the entire market for commercial gain.

Execution

Executing a robust liquidity provider analysis program using internal data requires a disciplined, multi-stage process that transforms raw transactional data into actionable intelligence. This operational playbook details the necessary steps, from data capture and normalization to quantitative modeling and strategic review. The entire process operates under the foundational principle that all analysis must be derived from the firm’s proprietary OMS and EMS records, in full compliance with the regulatory prohibitions on the commercial use of CAT data.

The Operational Playbook

The successful implementation of an LP analysis framework can be broken down into a series of distinct, sequential phases. This process ensures that the resulting analytics are accurate, consistent, and directly applicable to strategic decision-making regarding counterparty management.

1. Data Systematization and Capture The first step is to ensure that all relevant data points from the RFQ lifecycle are being systematically captured and stored in a structured format. This involves configuring the firm’s EMS and OMS to log every event with precise, synchronized timestamps. This includes the initial RFQ issuance, each individual LP response (including price, size, and time), any modifications to the RFQ, and the final execution report.
2. Data Normalization and Aggregation Raw data from trading systems must be cleaned and normalized. This involves mapping different LP symbologies to a common standard, adjusting for any time zone discrepancies, and creating a unified “RFQ event” record that links a parent request to all its child responses and the eventual fill. This clean dataset forms the foundation for all subsequent analysis.
3. Metric Calculation and Attribution With a normalized dataset, the firm can compute the KPIs defined in the strategy phase. This involves running automated scripts that calculate response times, quoted spreads against a reference benchmark (like the consolidated book midpoint), and price improvement statistics for every execution. Each calculated metric is then attributed to the specific LP involved.
4. Performance Reporting and Visualization The calculated metrics are aggregated into performance dashboards and reports. These tools should allow traders and managers to view LP performance across different timeframes, asset classes, and market conditions. Visualization tools like time-series charts for response speed or bar charts for spread competitiveness can make the data more intuitive.
5. Strategic Review and Action The final and most critical phase is the regular, systematic review of the performance reports. This involves periodic meetings between the trading team and relationship managers to discuss the findings. The insights from this analysis should directly inform decisions about which LPs receive more or less order flow and serve as the basis for constructive, data-driven conversations with the LPs themselves.

Quantitative Modeling and Data Analysis

A core component of the execution phase is the quantitative analysis of liquidity provider performance. The following table provides a hypothetical example of an aggregated performance report. This type of analysis allows for a direct, evidence-based comparison of counterparties.

This quantitative comparison reveals a nuanced performance landscape where the fastest provider is not the most competitive on price.

In this hypothetical analysis, LP Delta is the most reliable in terms of providing competitive quotes that lead to executions and deliver the highest price improvement, despite being the slowest to respond. In contrast, LP Gamma is the fastest but offers wider spreads and lower value. This data allows a trading desk to make informed, quantitative trade-offs when routing orders.

What Are the Practical Barriers to This Analysis?

Why can firms not simply use CAT for this purpose? The primary barrier is regulatory prohibition. The SEC has been clear that CAT data is for regulatory surveillance and market reconstruction, not for providing commercial advantages. The system’s security protocols are designed to prevent the type of bulk data downloading and analysis that would be required for this kind of performance monitoring.

Attempting to circumvent these rules would expose a firm to significant legal and financial penalties. Therefore, the execution of an LP analysis strategy is fundamentally an exercise in internal data discipline. It requires investment in technology and processes to capture and analyze a firm’s own trading data, a practice that aligns with the principles of best execution and effective risk management.

Reflection

The delineation between regulatory and proprietary data architectures prompts a deeper consideration of a firm’s internal systems. The knowledge that a comprehensive record of all market activity exists, yet remains inaccessible for commercial use, should motivate an institution to elevate its own data infrastructure. How does your current data capture strategy measure up to the theoretical benchmark set by CAT?

Does your firm possess a sufficiently granular and synchronized dataset to conduct the kind of rigorous performance analysis that ensures true best execution? The ultimate strategic advantage lies not in seeking access to a restricted regulatory database, but in building an internal intelligence layer that is equally powerful, entirely proprietary, and fully aligned with your operational objectives.