How Can Firms Use RTS 27 and RTS 28 Data to Improve Their RFQ Best Execution Policies?

Concept

The operational challenge of optimizing a Request for Quote (RFQ) system is rooted in information asymmetry. A firm soliciting quotes for a block trade operates with a limited view of a counterparty’s true capacity and recent performance. Regulatory Technical Standards 27 and 28, born from MiFID II, provide a publicly available, structured dataset that directly addresses this asymmetry.

These are not mere compliance artifacts; they are architectural components for building a superior execution policy. They represent a stream of raw data on execution quality, waiting to be refined into strategic intelligence.

The system functions through a duality. RTS 27 reports are published by execution venues, including systematic internalisers and market makers who are the typical recipients of RFQs. These documents provide granular, standardized metrics on their execution performance, such as prices, costs, and likelihood of execution for various financial instruments.

They are a public declaration of a venue’s capabilities, offering a quantitative baseline that was previously unavailable. A firm can systematically harvest this data to understand how a potential counterparty performs for specific asset classes and order sizes.

Viewing RTS 27 and RTS 28 reports as interconnected data feeds is the first step toward transforming a compliance necessity into a competitive advantage in execution.

Complementing this is the RTS 28 report, which is published by the investment firm itself. This report details the top five execution venues used for each class of financial instrument and provides a qualitative summary of how the firm achieved best execution. When analyzed correctly, a firm’s own RTS 28 data becomes a mirror, reflecting its historical execution patterns and dependencies.

It reveals which counterparties are relied upon, exposing potential concentration risks or suboptimal routing habits that have become ingrained over time. The synthesis of these two data sources ▴ the external view from RTS 27 and the internal reflection from RTS 28 ▴ creates a powerful foundation for building a dynamic and evidence-based RFQ best execution policy.

Strategy

A robust strategy for leveraging RTS data moves beyond passive compliance and implements an active intelligence framework. The objective is to transform the static, quarterly reports into a dynamic, pre-trade decision support system for the firm’s traders. This can be conceptualized as an “Execution Quality Intelligence Cycle,” a continuous loop of data ingestion, analysis, action, and review that refines the firm’s RFQ process over time.

The Execution Quality Intelligence Cycle

This cycle consists of four distinct but interconnected stages, designed to systematically integrate RTS data into the operational fabric of the firm’s trading desk.

1. Data Aggregation and Normalization ▴ The initial and most technically demanding stage is the automated collection of RTS 27 reports from all relevant execution venues. These reports are often published in varied formats, making direct comparison difficult. A strategic system must ingest this data, parse it, and normalize it into a unified internal database. This creates a single source of truth for counterparty performance metrics across the market.
2. Quantitative Counterparty Scorecarding ▴ With normalized data, the firm can build a proprietary scoring system. This involves assigning weights to the execution factors most important to the firm’s policy ▴ such as price improvement, response speed, and execution likelihood. Each potential RFQ counterparty is then scored for specific asset classes and instrument types based on their RTS 27 disclosures. This replaces subjective, relationship-based counterparty selection with an objective, data-driven hierarchy.
3. Dynamic RFQ Protocol Integration ▴ The counterparty scorecards are integrated directly into the pre-trade workflow. When a trader initiates an RFQ, the system should automatically present a ranked list of counterparties best suited for that specific order. The protocol can be configured to require a certain number of quotes from top-tier counterparties, while still allowing trader discretion with a mandatory justification for any deviations. This ensures policy is enforced at the point of execution.
4. Performance Review and Feedback Loop ▴ After an RFQ is completed, the execution details ▴ winning counterparty, price achieved, response times ▴ are captured and compared against the pre-trade scorecard predictions. This post-trade analysis is crucial. It validates the accuracy of the scorecards and identifies counterparties who may be outperforming or underperforming their public disclosures. This feedback is then used to adjust the scoring weights and data, ensuring the system adapts and improves with every trade.

What Is the Strategic Value of Each Report?

RTS 27 and RTS 28 serve different strategic purposes within this cycle. Understanding their distinct roles is essential for effective implementation.

The strategic goal is to create a system where every RFQ is informed by a comprehensive, data-driven view of the market’s execution landscape.

By treating RTS 27 as a forward-looking indicator of potential performance and RTS 28 as a backward-looking validation of internal policy, a firm can construct a comprehensive governance framework. This approach transforms the RFQ process from a simple price discovery mechanism into a sophisticated, evidence-based system for sourcing liquidity and achieving demonstrably better execution outcomes.

Execution

The execution of this strategy requires a disciplined, quantitative approach to building the underlying analytical tools. The centerpiece of this operational architecture is the Quantitative Counterparty Scorecard, a granular system that translates raw RTS 27 data into an actionable, pre-trade signal. This is complemented by a rigorous post-trade review process that ensures the system remains adaptive and accurate.

Building the Quantitative Counterparty Scorecard

The scorecard is a multi-factor model that evaluates potential RFQ counterparties based on the firm’s specific best execution priorities. The process begins with extracting specific fields from the RTS 27 reports of relevant venues.

• Price Improvement ▴ Data from Table 3 of RTS 27, showing the average price improvement per share/unit against the market reference price. This is a direct measure of the value added by the counterparty.
• Likelihood of Execution ▴ Information from Table 4 of RTS 27, which details the probability of an order being executed. This is a critical factor for ensuring certainty, especially in less liquid instruments.
• Speed of Execution ▴ Metrics on the average time from order receipt to execution. For certain strategies, speed is a primary determinant of quality.
• Cost Analysis ▴ A breakdown of explicit costs, including fees and commissions, as disclosed in the reports.

These factors are then weighted according to the firm’s execution policy. For instance, a policy for large, illiquid blocks might heavily weight “Likelihood of Execution,” while a policy for liquid, standard-size trades might prioritize “Price Improvement.”

Hypothetical Counterparty Scorecard for Corporate Bonds

The following table illustrates how this data can be synthesized into a practical tool for a trader looking to execute a corporate bond RFQ.

How Does the Post Trade Analysis Refine the System?

The execution cycle is incomplete without a robust post-trade analysis component. This stage serves to validate and refine the predictive accuracy of the counterparty scorecards. It is a continuous feedback loop that ensures the best execution policy is a living system, not a static document.

An effective execution framework uses post-trade data to continuously refine its pre-trade intelligence, creating a self-improving system.

The process involves capturing the outcome of every RFQ and comparing it to the metrics that informed the counterparty selection. The key questions to answer are:

1. Did the winning counterparty’s execution quality align with their RTS 27-derived scorecard?
2. Did the counterparties who were not selected have a high probability of offering a better price, based on their scores?
3. Are there persistent deviations between a counterparty’s reported data and their actual performance in our RFQs?

This analysis provides concrete evidence for the firm’s Best Execution Committee. It allows the firm to challenge counterparties with data, demonstrating where their provided liquidity fails to meet their public disclosures. It also allows the firm to adjust the weightings in its scorecard model, making it a more accurate predictor of future performance. This data-driven governance is the ultimate goal of leveraging RTS 27 and RTS 28, turning regulatory reports into a powerful tool for optimizing execution and managing counterparty relationships.

Reflection

The integration of RTS 27 and RTS 28 data transforms a firm’s best execution policy from a statement of intent into a quantifiable, operational system. The framework outlined here provides the architecture for this transformation. It compels a shift in perspective, where regulatory data is viewed as a strategic asset for refining execution logic. The ultimate value is found in the creation of a feedback loop, where every trade executed informs the intelligence for the next one.

Considering Your Own Operational Framework

This prompts a critical examination of your current RFQ protocols. Does your counterparty selection process rely on static lists and historical relationships, or is it informed by dynamic, market-wide data? Is your best execution policy a document reviewed annually, or is it an adaptive system that is tested and refined with every order? The answers to these questions will determine whether your firm is merely complying with regulation or truly weaponizing it to build a durable competitive edge in execution quality.