How Can Firms Use RTS 27 and RTS 28 Data to Inform Their RFQ Counterparty Strategy?

Concept

The operational challenge of optimizing a Request for Quote (RFQ) counterparty list is an exercise in navigating a complex information landscape. The regulatory framework of MiFID II presented a potential solution in the form of Regulatory Technical Standards (RTS) 27 and RTS 28. These standards were engineered to inject a current of transparency into the traditionally opaque mechanics of execution quality. The core design is a feedback loop ▴ execution venues publish granular performance metrics under RTS 27, and investment firms, in turn, disclose their top execution venues in RTS 28 reports.

This architecture was intended to provide the raw material for firms to systematically evaluate and select their trading partners. A firm’s ability to harness this data translates directly into a more robust and empirically grounded counterparty strategy, moving the selection process from a purely relationship-based model to one underpinned by quantitative evidence.

At its core, RTS 27 is a detailed quarterly report from execution venues, including Systematic Internalisers (SIs), Multilateral Trading Facilities (MTFs), and other liquidity providers. It offers a granular breakdown of execution quality metrics for each financial instrument. These metrics include data points on price, costs, speed, and the likelihood of execution. For an institution focused on bilateral price discovery, this data represents a foundational layer of intelligence.

It provides a standardized dataset to begin comparing the execution efficacy of different liquidity sources. The information allows a firm to move beyond anecdotal evidence and begin constructing a quantitative baseline for counterparty performance.

RTS 27 and RTS 28 reports provide the standardized data necessary for firms to build a quantitative foundation for their RFQ counterparty selection process.

RTS 28 complements this by functioning as a public disclosure from investment firms themselves. Annually, firms must publish a report detailing the top five execution venues they utilized for each class of financial instrument. This report also requires a summary of the analysis and conclusions the firm has drawn from monitoring its execution quality.

For a firm formulating its RFQ strategy, analyzing the RTS 28 reports of its peers can reveal the dominant liquidity pools and counterparty relationships within the market. It offers a map of the established execution pathways, providing valuable context for a firm’s own counterparty selection and diversification efforts.

The intended synthesis of these two reporting streams is a powerful one. A firm can use RTS 27 data to build detailed performance profiles of potential RFQ counterparties. It can then cross-reference these findings with RTS 28 reports to understand prevailing market practices and identify potential new counterparties. This data-driven approach allows for a more sophisticated counterparty risk management framework, where selection is based on a verifiable track record of execution quality.

The challenge, however, lies in the practical application. The data, while comprehensive, is not always perfectly tailored to the specific needs of an RFQ workflow, requiring a dedicated effort to extract actionable intelligence from the raw reporting.

Strategy

A sophisticated RFQ counterparty strategy moves beyond simple compliance and transforms regulatory data into a competitive advantage. The strategic objective is to construct a dynamic and evidence-based framework for selecting counterparties, one that optimizes for execution quality while managing risk. This involves a multi-stage process that begins with data acquisition and culminates in a tiered and segmented counterparty list tailored to specific trading needs.

Developing a Quantitative Counterparty Scorecard

The first step is to systematize the analysis of RTS 27 data from potential counterparties. This requires the development of a proprietary counterparty scorecard. This scorecard translates the raw data from RTS 27 reports into a set of key performance indicators (KPIs) that are directly relevant to an RFQ workflow.

The process involves assigning weights to different execution factors based on the firm’s own execution policy and trading objectives. For example, a firm focused on minimizing market impact for large, illiquid orders might assign a higher weight to price improvement metrics, while a firm executing more standardized orders might prioritize speed and certainty of execution.

The scorecard should be designed to provide a clear, at-a-glance assessment of a counterparty’s historical performance. It becomes the central analytical tool for the trading desk, enabling informed, data-driven decisions in the pre-trade phase. This quantitative foundation is what elevates the selection process from intuition to a repeatable, auditable system.

How Do You Integrate Qualitative Factors?

A purely quantitative approach, while robust, is incomplete. The RFQ process is inherently a high-touch, relationship-driven protocol. Therefore, the quantitative scorecard must be augmented with a structured assessment of qualitative factors. These are the elements of a counterparty relationship that cannot be gleaned from a regulatory report but are critical to successful execution.

• Relationship Strength ▴ This captures the reliability and responsiveness of the counterparty’s trading desk. A strong relationship can be invaluable during volatile market conditions or when executing complex, multi-leg orders.
• Willingness to Quote ▴ Certain counterparties may specialize in specific asset classes or be more willing to provide competitive quotes for illiquid or large-sized orders. Tracking this willingness provides a crucial overlay to the quantitative data.
• Information Leakage Risk ▴ This is a critical consideration in the RFQ process. A firm must assess the likelihood that a counterparty’s quoting activity could signal the firm’s trading intentions to the broader market. This assessment is often based on past experience and market reputation.

By combining the quantitative scorecard with these qualitative assessments, a firm can create a holistic view of each counterparty. This integrated approach allows for a more nuanced and effective segmentation of the counterparty list.

Counterparty Segmentation Framework

The final stage of the strategy is to use the integrated analysis to segment counterparties into tiers. This segmentation allows the trading desk to quickly identify the most appropriate counterparties for a given RFQ based on the specific characteristics of the order.

1. Tier 1 Counterparties ▴ These are the firm’s primary liquidity providers. They consistently score high on both quantitative and qualitative assessments and are the first port of call for large, sensitive, or complex orders.
2. Tier 2 Counterparties ▴ These are reliable counterparties that provide competitive pricing for more standardized orders. They may have lower scores in specific areas, such as willingness to quote for very large sizes, but are valuable partners for the bulk of the firm’s daily trading activity.
3. Tier 3 Counterparties ▴ This tier may include specialist or niche providers that are used for specific, hard-to-source instruments. They may also include newer counterparties that are being evaluated for promotion to a higher tier.

Effective counterparty segmentation allows a trading desk to match the specific needs of an order to the demonstrated strengths of a liquidity provider.

This tiered structure provides a clear and actionable framework for the trading desk. It ensures that every RFQ is directed to the counterparties most likely to provide optimal execution, based on a rigorous and data-driven strategic process.

Execution

The execution phase translates the strategic framework into a concrete, operational workflow. This involves the practical steps of data ingestion, analysis, and integration into the pre-trade decision-making process. The objective is to create a seamless system where RTS 27 and RTS 28 data actively informs every RFQ sent from the trading desk. This requires both a disciplined process and the right technological infrastructure.

A Procedural Guide to Data-Informed RFQ

Implementing a data-driven RFQ process follows a clear, sequential path. This procedure ensures that the insights derived from RTS data are consistently applied, creating a repeatable and auditable workflow that continuously refines the firm’s execution strategy.

1. Data Aggregation ▴ The first operational step is to establish an automated process for collecting the RTS 27 and RTS 28 reports from all current and potential counterparties. This typically involves building scrapers or leveraging third-party data vendors to pull these reports as they are published. The data should be stored in a centralized database for analysis.
2. Data Normalization and Analysis ▴ The raw, machine-readable data from the reports must be cleaned and normalized into a consistent format. A dedicated quantitative analyst or team then applies the firm’s proprietary scorecard model to this data, calculating the KPIs for each counterparty and updating their scores.
3. Integration with EMS/OMS ▴ The output of the analysis, specifically the counterparty tiers and scores, must be integrated directly into the firm’s Execution Management System (EMS) or Order Management System (OMS). This provides the trader with actionable intelligence at the point of trade, displaying the recommended counterparty list for a given instrument and order size.
4. Pre-Trade Workflow ▴ When a trader initiates an RFQ, the EMS should automatically present the tiered list of counterparties. The trader can then select the appropriate counterparties for the request, with the system logging the rationale for the selection. This creates a clear audit trail for best execution purposes.
5. Post-Trade Analysis and Feedback Loop ▴ After the trade is executed, the results are fed into the firm’s Transaction Cost Analysis (TCA) system. The TCA data, which measures the actual execution quality against various benchmarks, is then used to validate and refine the counterparty scorecard. This creates a continuous feedback loop, ensuring the counterparty rankings remain accurate and reflective of the most recent performance.

What Is the Role of Quantitative Modeling?

Quantitative modeling is the engine of this entire process. It transforms the vast and often noisy data from RTS reports into clear, actionable signals. The core of this is the counterparty scorecard, but it extends to more sophisticated models that can predict the likelihood of receiving a competitive quote from a given counterparty based on order size, time of day, and prevailing market volatility.

The following table provides a simplified example of how raw data points from an RTS 27 report for a specific instrument can be transformed into a meaningful score within a firm’s internal system.

Systematic data analysis transforms regulatory reporting from a compliance burden into a source of strategic insight for execution optimization.

System Integration and Technological Architecture

The successful execution of this strategy is contingent on a robust technological architecture. The system must be capable of handling large volumes of data, performing complex calculations in a timely manner, and presenting the results to traders in an intuitive format.

• Data Warehouse ▴ A centralized data warehouse is required to store the historical RTS 27/28 data, as well as the firm’s own post-trade TCA data. This becomes the single source of truth for all counterparty performance analysis.
• Analytical Engine ▴ A powerful analytical engine, likely using Python or R with data science libraries, is needed to run the quantitative models and generate the counterparty scores.
• API Integration ▴ The system must have robust APIs to connect the analytical engine with the firm’s EMS/OMS. This allows for the seamless flow of data and ensures that traders are always working with the most up-to-date counterparty intelligence.

Ultimately, the goal is to build a cohesive system where regulatory data is not just collected, but is actively processed, analyzed, and deployed to enhance every aspect of the RFQ workflow. This transforms the trading desk from a passive consumer of liquidity into an active, data-driven manager of its counterparty relationships.

Reflection

The architecture of a data-driven counterparty strategy, built upon the foundations of RTS 27 and RTS 28, represents a significant evolution in execution management. It provides a systematic and defensible methodology for what has historically been a qualitative art. Yet, the implementation of such a system raises a fundamental question for any trading institution. How does a firm calibrate the balance between the quantitative rigor of data analysis and the qualitative strength of its established counterparty relationships?

The data offers a powerful lens on past performance, but it cannot fully capture the forward-looking value of a trusted partner, especially in moments of market stress. The ultimate expression of an advanced execution framework is one that integrates these two domains. It uses data to inform and challenge intuition, creating a system where human expertise is augmented, not replaced, by machine intelligence. The challenge is not simply to build the model, but to cultivate a trading culture that embraces this synthesis, viewing every execution as an opportunity to refine the firm’s collective intelligence.