How Can a Firm Leverage Rfq Platform Data for Its Annual Rts 28 Reporting Obligations?

Concept

An institution’s approach to regulatory obligations reveals its core operational philosophy. The annual RTS 28 reporting requirement, born from the second Markets in Financial Instruments Directive, is currently undergoing a significant evolution. In early 2024, European regulators signaled a deprioritization of enforcement for these specific reports, acknowledging their limited utility to end clients and paving the way for their eventual removal from the statute. A superficial reading of this development suggests a simple reduction in administrative burden.

A more sophisticated interpretation, however, recognizes that the operational discipline imposed by RTS 28 provides a valuable, non-negotiable blueprint for constructing a resilient and evidence-based execution architecture. The mandate may be fading, but the methodology it enforces is timeless.

The central purpose of the RTS 28 report was to compel investment firms to publicly disclose their top five execution venues and provide a quantitative summary of the execution quality achieved. For firms that heavily utilize Request for Quote (RFQ) platforms, this presented a unique challenge and opportunity. RFQ systems, by their nature, are decentralized and bilateral. They represent a distinct form of liquidity discovery compared to lit central limit order books.

The data generated within these platforms ▴ a rich log of counterparty interactions, response times, quote competitiveness, and execution success ▴ is the raw material for a profound analysis of a firm’s trading efficacy. Leveraging this data for RTS 28, or for the enduring best execution analysis that supersedes it, is an exercise in transforming a compliance task into a strategic intelligence function.

What Is the True Value of Regulatory Data Frameworks?

The value of a framework like RTS 28 lies in its structure. It forces a firm to move beyond anecdotal evidence and establish a rigorous, data-driven process for evaluating its execution counterparties. The core of market microstructure theory is the study of how trading mechanisms translate latent investor demand into transactions, a process invariably subject to frictions like information asymmetry and transaction costs.

RFQ protocols are a specific mechanism designed to manage these frictions for large or less liquid orders, allowing a firm to discreetly solicit competitive prices from a select group of liquidity providers. Without a systematic analysis of the resulting data, a firm is effectively navigating this complex environment blind.

RFQ platform data provides the objective, granular evidence required to validate a firm’s execution policy and systematically refine its counterparty relationships.

The data from an RFQ platform is a complete record of a competitive process. Each request initiates a micro-auction, and the data log captures every participant’s behavior. This includes not only who provided the best price but also the speed of their response, their willingness to quote, and the frequency with which their quotes lead to successful execution.

This information is the ground truth of a firm’s execution quality. By systematizing its collection and analysis, a firm builds an empirical foundation for its venue selection policy, which is the ultimate objective of the best execution mandate that underpins RTS 28.

Therefore, the operational question evolves. It shifts from “How do we comply with RTS 28?” to “How do we build a perpetual, data-centric execution quality monitoring system using our RFQ platform data as the primary input?” This reframing transforms the exercise from a retrospective reporting task into a proactive, continuous loop of performance analysis and optimization. The principles of RTS 28 serve as the architectural schematic for this system, ensuring that the analysis is comprehensive, quantifiable, and aligned with the fiduciary duty to achieve the best possible outcome for clients.

Strategy

A strategic framework for leveraging RFQ data begins with the recognition that this data is a high-fidelity digital exhaust of a firm’s liquidity sourcing process. The objective is to architect a system that captures, normalizes, and analyzes this exhaust to produce actionable intelligence for both internal oversight and, where still required, regulatory disclosure. The strategy is built on a direct mapping of the granular data points available from RFQ platforms to the qualitative execution factors defined under MiFID II. These factors ▴ price, costs, speed, likelihood of execution, size, and nature of the order ▴ are the pillars of any robust best execution policy.

The first step is establishing a data governance protocol. This involves defining a canonical data model for all RFQ interactions, regardless of the specific platform used. The model must capture the full lifecycle of a quote request, from initiation to execution or expiration. Key data fields must be identified and standardized.

This creates a clean, reliable dataset that can be fed into an analytical engine. This process of data structuring is the foundational layer upon which the entire analytical framework is built. Without it, any subsequent analysis would be inconsistent and unreliable.

How Does RFQ Data Substantiate Execution Quality?

The core of the strategy involves translating raw data points into metrics that directly measure the MiFID II execution factors. This translation is what gives the data meaning and allows for a systematic comparison of execution venues (in the RFQ context, the liquidity providers). An effective system connects each piece of data to a specific performance indicator, providing a multi-dimensional view of counterparty performance.

The following table illustrates the direct mapping between common RFQ platform data fields and the analytical factors required for a comprehensive execution quality assessment.

This mapping forms the logic of the analytical engine. By processing trade data through this lens, a firm can move from a simple ranking of venues based on volume to a sophisticated, multi-factor scoring system. This system can then be used to objectively identify the top counterparties who consistently deliver the best results across the most important execution factors for the firm’s specific trading style and client base.

A systematic data strategy transforms compliance from a historical reporting exercise into a forward-looking tool for optimizing execution performance.

Building the Internal Reporting Architecture

With a data governance model and an analytical framework in place, the final strategic component is the reporting architecture. This involves designing standardized internal dashboards and reports that present the findings of the analysis in a clear and concise manner. These reports are the primary tool for the firm’s Best Execution Committee or equivalent oversight body. They should provide a periodic, evidence-based review of counterparty performance, highlighting trends and identifying areas for improvement.

The structure of these internal reports can mirror the format of the original RTS 28 disclosures. This provides a familiar and regulatorily-tested structure for presenting the information. The key difference is the audience and purpose.

These reports are not for public consumption; they are for internal strategic decision-making. They are the mechanism by which the firm demonstrates to itself, its auditors, and its regulators that it has a robust, repeatable, and effective process for fulfilling its best execution obligations, regardless of the status of a specific reporting mandate.

Execution

The execution phase translates the strategic framework into a concrete operational workflow. This is the engineering of the analytical system, a step-by-step process for transforming raw RFQ data into the definitive evidence required for rigorous best execution analysis. This operational playbook is designed to be implemented by a firm’s technology and compliance teams, creating a repeatable and auditable system for performance monitoring.

The Operational Playbook a Data Analysis Workflow

Implementing a robust analysis system requires a clear, sequential process. This workflow ensures that data is handled consistently and the resulting analytics are sound. The process can be broken down into four distinct stages, from raw data ingestion to final report generation.

1. Data Aggregation and Normalization This initial stage involves collecting RFQ data from all relevant platforms. Many platforms provide this data via API endpoints or scheduled flat-file deliveries. The primary task is to transform these disparate data sources into a single, normalized format as defined by the data governance protocol. This involves mapping platform-specific field names to the firm’s canonical model and ensuring consistent data types and formats, especially for timestamps and pricing information.
2. Data Enrichment Once normalized, the raw data should be enriched with additional market context. This is a critical step for meaningful analysis. For each RFQ, the dataset should be appended with a baseline “arrival price” for the instrument. This could be the prevailing mid-point of the primary market’s best bid and offer (BBO) at the time of the quote request. This enrichment allows for a true measure of price improvement and slippage.
3. Quantitative Metric Calculation With a clean, enriched dataset, the analytical engine can compute the key performance indicators for each liquidity provider. This involves running calculations on a trade-by-trade basis and then aggregating the results by counterparty over the reporting period. The goal is to build a comprehensive scorecard for each liquidity provider.
4. Reporting and Visualization The final stage is to present the aggregated metrics in a format suitable for review by the Best Execution Committee. This involves generating summary tables and visualizations that highlight key performance trends and allow for easy comparison between counterparties. The output should be clear enough to support strategic decisions about the firm’s liquidity provider relationships.

Quantitative Modeling and Data Analysis

The heart of the execution phase is the quantitative analysis of counterparty performance. The following table provides a detailed example of a quantitative scorecard for a set of hypothetical liquidity providers over an annual reporting period. This type of analysis provides the objective evidence needed to rank venues based on actual performance.

The ‘Composite Score’ is a firm-specific weighted average of the other metrics, reflecting the firm’s unique priorities. For example, a firm prioritizing speed of execution might assign a higher weight to the ‘Avg. Response Time’ metric. This quantitative approach removes subjectivity from the venue selection process.

Objective data analysis is the mechanism that transforms a best execution policy from a static document into a dynamic and effective operational control.

What Is the Optimal Workflow for Annual Reporting?

Even with the deprioritization of public RTS 28 reports, producing a similar summary for internal use is a critical governance practice. This annual summary provides a definitive record of the firm’s execution quality analysis. The workflow should be structured to produce a clear, auditable output.

• Finalize Annual Dataset At the end of the reporting period, a final, immutable dataset of all RFQ transactions should be created. This dataset is the source of truth for the annual report.
• Generate Final Scorecards Run the quantitative analysis on the final dataset to generate the definitive performance scorecards for all liquidity providers.
• Rank and Select Top Venues Based on the composite scores and total volume executed, rank the liquidity providers to identify the top five for each class of financial instrument.
• Compile Summary Report Assemble the final report, including the top-five venue list and a summary of the analysis that was used to arrive at the selection. This summary should explain the relative importance of the different execution factors and how the data supports the firm’s conclusions.

This internal report becomes the primary evidence that the firm is actively and systematically monitoring the quality of its execution, fulfilling its overarching fiduciary and regulatory obligations.

Reflection

From Mandate to Mechanism

The transition away from mandatory RTS 28 reporting presents a pivotal moment for an institution. It prompts a fundamental question about internal motivation. When the external compliance pressure recedes, does the internal commitment to rigorous, evidence-based analysis remain? A firm’s answer to this question defines its operational maturity.

The systems and processes built to satisfy the regulation should not be dismantled; they should be enhanced. They are the core components of a sophisticated execution management system.

The data harvested from RFQ platforms, when analyzed through the disciplined lens of the RTS 28 framework, provides more than a compliance artifact. It offers a clear, unbiased view into the complex network of relationships a firm maintains with its liquidity providers. It quantifies trust, measures performance, and reveals the true cost and quality of execution.

The challenge now is to fully integrate this analytical mechanism into the firm’s decision-making fabric, using it not just to justify past choices but to proactively architect a superior execution strategy for the future. How will your firm use this operational inheritance?