What Are the Specific Data Reporting Requirements under RTS 28 for RFQ Trades? ▴ Question

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Concept

The operational mandate known as Regulatory Technical Standard 28, or RTS 28, represents a fundamental architectural blueprint for quantifying and disclosing execution quality. Its recent deprioritization by European regulators ahead of a formal legislative repeal signals a shift in the mechanism of oversight. This development does not dissolve the underlying principle of best execution; it transfers the locus of responsibility, demanding that firms possess an even more robust internal framework for analyzing their execution pathways.

The original purpose of the regulation was to provide a transparent, standardized data set for investors to compare how and where their orders were executed. For sophisticated market participants, its value was always in the data architecture it prescribed.

This prescribed architecture is especially potent when applied to bilateral trading protocols like the Request for Quote system. RFQ mechanisms, by their nature, operate with a degree of opacity. A firm solicits quotes from a select group of liquidity providers, creating a private auction for a specific instrument. While this is highly effective for executing large or illiquid blocks, it generates fragmented data across multiple counterparties.

The RTS 28 framework provides a coherent model for aggregating this disparate information into a unified analytical structure. It compels a systematic evaluation of the entire execution stack, from the choice of counterparty to the final settlement price, transforming a series of discrete trades into a coherent data narrative.

The core of RTS 28 is a data-centric model for systematically proving best execution, a principle that persists beyond the formal reporting requirement.

Viewing RTS 28 as a system schematic reveals its enduring utility. It provides the field definitions and data points necessary to build an internal execution quality analysis engine. This engine becomes the definitive tool for validating counterparty selection, assessing the true cost of execution, and optimizing the firm’s overall liquidity sourcing strategy.

The end of the formal reporting mandate is an opportunity to internalize its logic, moving from a compliance-driven exercise to a source of genuine competitive and operational intelligence. The data points it specifies are the building blocks for a powerful, evidence-based understanding of a firm’s market interaction, particularly within the strategically vital, yet inherently complex, world of RFQ-based trading.

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Strategy

Strategically, the RTS 28 framework is a powerful tool for deconstructing and optimizing execution policy. Its application extends far beyond regulatory compliance, offering a clear methodology for enhancing capital efficiency and minimizing signaling risk in RFQ trades. The central strategic function of this data architecture is to impose empirical rigor on the process of venue and counterparty selection.

For RFQ-based liquidity sourcing, where the choice of dealers to include in a query is a critical decision, a systematic approach is paramount. By continuously capturing and analyzing execution data along the lines prescribed by RTS 28, a trading desk can move from relationship-based assumptions to a quantitative, evidence-based selection process.

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What Is the Strategic Value in Counterparty Analysis?

The strategic value is unlocked by analyzing the four pillars of execution quality defined within the RTS 28 framework ▴ price, cost, speed, and likelihood of execution. For an RFQ trade, these pillars translate into a precise set of performance indicators for each liquidity provider. Price improvement, or the difference between the quoted price and the final execution price, becomes a measurable metric. Costs, including any fees or commissions, are made explicit.

Speed, while less of a variable in a session-based RFQ, can be measured in terms of response time and settlement efficiency. Likelihood of execution measures the reliability of a counterparty’s quotes, tracking the frequency with which they provide competitive pricing versus declining to quote.

This systematic analysis allows a firm to build a quantitative profile of each counterparty. This profile informs the RFQ process itself, enabling the system to dynamically select the most appropriate dealers for a given instrument, size, and market condition. It answers critical strategic questions ▴ Which counterparties provide the tightest spreads for 5-year interest rate swaps? Which are most reliable during periods of high volatility?

How does execution quality vary between lit markets and direct counterparty engagement via RFQ? The framework provides the tools to answer these questions with data, refining the firm’s liquidity map and enhancing its ability to achieve best execution.

Leveraging the RTS 28 data model internally allows a firm to transform its RFQ process from a simple price solicitation protocol into a dynamic, self-optimizing liquidity sourcing engine.

The following table illustrates how these factors can be structured for a strategic review of execution venues, including direct counterparties engaged via RFQ, which are considered a type of execution venue in this context.

Execution Quality Factor Analysis
Execution Factor	Application to RFQ Counterparty Analysis	Key Performance Indicator (KPI)
Price	Analysis of the competitiveness of the quote relative to the market’s prevailing price at the time of the request. Measures the value provided by the counterparty’s pricing engine.	Price Improvement vs. Arrival Price (in basis points)
Costs	Quantification of all explicit costs associated with the trade, including any commissions or fees embedded in the spread.	Total Explicit Cost per Million Traded
Speed	Evaluation of the counterparty’s response time to the RFQ and the efficiency of the post-trade settlement process.	Average Quote Response Time (in milliseconds)
Likelihood of Execution	Assessment of the counterparty’s reliability, measured by the ratio of quotes provided to quotes requested.	Quote Fill Ratio (%)

This structured analysis also addresses the critical issue of information leakage. By identifying which counterparties offer consistently high-quality execution, a firm can reduce the number of dealers in its RFQ panel for any given trade. A smaller, more targeted panel minimizes the risk of signaling the firm’s intentions to the broader market, preserving the value of the alpha it seeks to capture.

Execution

While the mandatory public disclosure of RTS 28 reports is ending, the technical specifications of the regulation provide a definitive operational playbook for constructing a best execution analysis system. For a firm utilizing RFQ protocols, implementing an internal data capture and analysis framework based on these specifications is a masterclass in operational discipline. It ensures that every aspect of the execution process is quantified, auditable, and available for performance optimization. The execution of this strategy requires meticulous data hygiene and a commitment to capturing the specific data fields outlined in the original regulation.

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How Should a Firm Structure Its RFQ Data?

The core of the execution lies in structuring the data capture around the two main components of the RTS 28 report ▴ a quantitative summary of execution venues and a qualitative assessment of execution quality. For RFQ trades, the “execution venue” is the liquidity provider that wins the auction. A firm must log every RFQ sent, the responses received, and the characteristics of the winning quote.

The following procedural list outlines the data aggregation process that a firm should implement to align with the RTS 28 architecture:

Order and RFQ Logging ▴ For every client order, the system must generate a unique identifier that links the parent order to all child RFQs sent to different liquidity providers.
Counterparty Response Capture ▴ The system must capture the full details of each response from every queried counterparty, including the quoted price, time of quote, and any associated conditions. This must be done even for counterparties that do not win the trade.
Execution Data Recording ▴ For the winning quote, the system must record the final execution price, size, time of execution, and all explicit costs.
Data Classification ▴ Each executed trade must be classified according to the financial instrument classes defined in Annex I of the regulation. This allows for apples-to-apples comparisons of execution quality.
Quarterly Analysis and Review ▴ The aggregated data should be analyzed quarterly to review counterparty performance, update execution policies, and identify any degradation in execution quality from specific providers.

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Quantitative Data Fields for Execution Analysis

The quantitative heart of the RTS 28 framework is the table detailing the top five execution venues. For a firm using RFQs, this translates to a report on the top five liquidity providers. The table below is a representation of the data fields required under the original regulation, repurposed here as a blueprint for an internal counterparty performance dashboard.

Internal Counterparty Performance Report (Based on RTS 28, Annex II, Table 1)
Financial Instrument Class	Top 5 Liquidity Providers (Execution Venues)	Volume of Executed Orders (% of Total)	Number of Executed Orders (% of Total)	Percentage of Passive Orders	Percentage of Aggressive Orders	Percentage of Directed Orders
Interest Rate Derivatives – Swaps				N/A for RFQ	100%
Bonds – Government				N/A for RFQ	100%

For RFQ trades, the order is always “aggressive” as it “takes” the liquidity offered by the quoting dealer. The “passive” order concept of providing liquidity to an order book does not apply in the same way. This is a key distinction when applying the framework.

A disciplined implementation of the RTS 28 data schema provides an unassailable, evidence-based audit trail for every execution decision.

In addition to the quantitative table, the firm must produce a qualitative summary of its execution analysis. This involves a narrative explanation of the monitoring process, detailing how the data is used to ensure best execution is achieved. This summary should cover:

A summary of the relative importance of the execution factors ▴ An explanation of how the firm prioritizes price, cost, speed, and likelihood of execution for different instrument types and market conditions.
A description of any close links with execution venues ▴ Disclosure of any specific relationships with liquidity providers that could affect impartiality.
An explanation of how execution venue selection is reviewed ▴ Detailing the process for adding or removing counterparties from the RFQ panel based on performance data.

By building an internal system that mirrors this regulatory architecture, a firm gives itself a permanent, sophisticated mechanism for managing and optimizing one of its most critical functions ▴ the sourcing of liquidity and the execution of trades.

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References

European Securities and Markets Authority. “Public Statement ▴ ESMA clarifies certain best execution reporting requirements under MiFID II.” ESMA, 13 Feb. 2024.
European Commission. “Commission Delegated Regulation (EU) 2017/576 of 8 June 2016 supplementing Directive 2014/65/EU.” Official Journal of the European Union, L 87/166, 31 Mar. 2017.
Financial Conduct Authority. “RTS 28 ▴ Best execution reporting.” FCA Handbook, 2021.
Norton Rose Fulbright. “MiFID II / MiFIR Delegated Acts, Regulatory and Implementing Technical Standards update.” 2023.
DLA Piper. “ESMA publishes statement on reporting requirements under RTS 28 of MiFID II.” 20 Feb. 2024.
Anagnostidis, Georgios, et al. “Best Execution under MiFID II ▴ A Study on the RTS 27 & 28 Reports.” European Capital Markets Institute, no. 16, 2020.
Comerton-Forde, Carole, and James Rydge. “Best Execution ▴ A Guide for Trustees.” The Centre for International Finance and Regulation, 2015.

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Reflection

The transition away from mandatory RTS 28 reporting presents a pivotal moment. It compels a shift in perspective, from viewing execution data analysis as an externally imposed obligation to recognizing it as an integral component of a firm’s core operational intelligence. The architecture of the regulation, though no longer enforced through public filings, provides a robust and tested model for achieving systemic integrity in the execution process. The essential question for any institutional participant is no longer “How do we comply?” but rather “How do we architect our data infrastructure to deliver a persistent, verifiable execution advantage?”

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Is Your Data Architecture a Strategic Asset?

Consider your firm’s current data systems. Do they merely record transactions, or do they provide a dynamic, analytical view of your interaction with the market? The framework laid out by RTS 28 offers a blueprint for transforming post-trade data from a static record into a predictive tool. This tool can be used to refine counterparty selection, optimize execution strategies, and provide a quantifiable defense of the firm’s execution quality.

The end of the reporting mandate is an invitation to build a superior internal system, one that is driven by the pursuit of operational excellence. The ultimate measure of success will be found in the quality and efficiency of every trade executed.