What Are the Core Data Points Required for an RTS 28 Report for RFQ-Based Trades? ▴ Question

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Concept

An RTS 28 report functions as a quantitative ledger of execution quality. For trades initiated through a Request for Quote (RFQ) protocol, this regulatory instrument moves beyond simple trade confirmation, creating a structured data set that reveals the decision-making process behind large or illiquid block transactions. It is a framework for transparency, compelling investment firms to systematically record and disclose not just where they executed a trade, but how the final execution price was determined within a competitive environment.

The core of the RTS 28 mandate for RFQ-based flow is the documentation of choice. It provides a mechanism to analyze the spectrum of quotes received, comparing the winning bid or offer against its counterparts, and thereby substantiating the claim of having achieved the best possible outcome for the client under the prevailing market conditions.

The system operates by classifying financial instruments and then demanding a consistent set of disclosures for each class. For an RFQ, the critical data points are those that reconstruct the competitive landscape at the moment of execution. This includes the identities of the counterparties that were invited to quote, the quotes they provided, and the timestamps associated with each step of this bilateral price discovery process. This information creates an empirical basis for evaluating the effectiveness of a firm’s execution policy.

It transforms the abstract principle of “best execution” into a series of verifiable data points, allowing for objective analysis by clients, regulators, and internal oversight functions. The process itself generates a powerful feedback loop, enabling firms to refine their counterparty lists and execution protocols based on historical performance data.

The RTS 28 framework for RFQ trades provides a structured methodology for documenting and verifying execution quality through detailed data reporting.

Understanding this reporting obligation requires a shift in perspective. It is a data-driven defense of execution strategy. The regulation presumes that for any given trade, a range of outcomes was possible, and it places the onus on the investment firm to prove its process for navigating those possibilities was robust and geared towards the client’s best interest.

For RFQ-based trades, which are by nature conducted away from the continuous order books of lit markets, this formalized disclosure is the primary mechanism for achieving post-trade transparency. It illuminates the opaque process of sourcing liquidity for difficult-to-trade instruments, providing a structured window into the over-the-counter (OTC) market landscape.

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Strategy

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A Framework for Execution Intelligence

Strategically, the RTS 28 reporting obligation for RFQ-based trades is a powerful tool for enhancing a firm’s execution intelligence. The data collected serves as the foundation for a rigorous, quantitative approach to counterparty management and execution protocol optimization. By systematically analyzing the required data points, a firm can move beyond subjective assessments of its trading partners and build a scorecard based on empirical evidence.

This allows for a more strategic selection of liquidity providers for specific asset classes and market conditions, ultimately improving the quality of execution. The process of compiling the report becomes a periodic, structured review of a firm’s execution apparatus, revealing patterns in quote competitiveness, response times, and fill rates that would otherwise remain obscured.

The effective use of RTS 28 data involves its integration into a firm’s broader Transaction Cost Analysis (TCA) framework. While the report itself is a regulatory requirement, the underlying data provides the raw material for much deeper analysis. A firm can compare the winning quote not only to other quotes received but also to prevailing market benchmarks at the time of the request. This “quote shortfall” analysis provides a more nuanced measure of execution quality than simply looking at the final trade price.

It quantifies the value generated by the firm’s specific RFQ process and its choice of counterparties, providing a clear metric for demonstrating best execution to clients. This strategic application of the data transforms a compliance exercise into a source of competitive advantage.

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Data Segmentation and Analysis

A critical component of an effective RTS 28 strategy is the proper segmentation of data by financial instrument class. The regulation requires separate reporting for distinct classes, recognizing that execution quality is context-dependent. The table below outlines a typical classification scheme, which forms the basis for the detailed analysis that follows.

Financial Instrument Class	Key Characteristics for RFQ	Primary Execution Factors
Equities ▴ Shares & Depository Receipts (Illiquid)	Large block sizes relative to average daily volume; high price impact risk.	Minimizing information leakage; sourcing natural counterparties.
Bonds (Corporate & Sovereign)	Fragmented liquidity across many individual ISINs; sensitivity to credit and interest rate risk.	Access to dealer inventories; speed of execution in volatile markets.
Equity Derivatives (Options & Futures)	Multi-leg structures; dependence on underlying volatility and liquidity.	Competitive pricing on the entire spread; certainty of execution.
Other Instruments (e.g. Structured Products)	Bespoke, highly illiquid instruments with complex payoffs.	Access to specialized desks; ability to price complex risk factors.

For each of these classes, a firm must develop a tailored strategy for collecting and analyzing RFQ data. The following list outlines the key analytical steps:

Counterparty Performance Measurement ▴ Systematically track the competitiveness of quotes from each liquidity provider. This includes calculating the frequency with which a counterparty provides the best quote and the average spread of their quotes relative to the winning price.
Response Time Analysis ▴ Monitor the time it takes for counterparties to respond to RFQs. Slow response times can be a significant source of slippage in fast-moving markets.
Fill Rate Monitoring ▴ Analyze the frequency with which counterparties honor their quotes. A high rate of “last look” rejections can indicate a problem with a counterparty’s pricing engine or risk management.
Execution Venue Analysis ▴ For firms using multi-dealer RFQ platforms, the platform itself is considered the execution venue. The RTS 28 report must identify these platforms as the top venues for executing RFQ-based trades.

Leveraging RTS 28 data for internal analysis transforms a regulatory requirement into a continuous process for refining execution strategy and counterparty relationships.

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Execution

The Operational Playbook

Implementing a robust data collection and reporting framework for RTS 28 is a systematic process that bridges trading, compliance, and technology functions. The objective is to create a repeatable, auditable workflow that ensures the accuracy and completeness of the final report. This process can be broken down into a series of distinct operational steps, forming a playbook for any firm handling RFQ-based orders.

Data Point Identification and Mapping ▴ The initial step involves a comprehensive mapping of every required data field in the RTS 28 report to its source system within the firm’s technology stack. This requires close collaboration between the trading desk, which understands the RFQ workflow, and the IT department, which manages the underlying systems (e.g. EMS, OMS).
Automated Data Capture ▴ Wherever possible, the capture of RTS 28 data should be automated. For RFQ-based trades, this means configuring the firm’s EMS or RFQ platform to log all relevant events, including the timestamp of the initial request, the identity of each counterparty invited to quote, the full details of each quote received (price, quantity, and timestamp), and the final execution details. Manual data entry should be minimized to reduce the risk of errors.
Data Enrichment and Validation ▴ Once the raw data is captured, it often needs to be enriched with additional information. This might include mapping counterparty identifiers to their legal entity identifiers (LEIs) or classifying the trade according to the correct financial instrument class. A validation layer should be implemented to check for missing or anomalous data points before they are stored.
Centralized Data Warehousing ▴ All RTS 28-related data should be stored in a centralized repository. This creates a single source of truth for all best execution reporting and analysis, simplifying the process of generating the final report and enabling deeper, ad-hoc analysis by the firm’s quantitative teams.
Report Generation and Review ▴ The final step is the generation of the report in the prescribed machine-readable format (typically XML). This process should be automated, pulling data from the central warehouse and formatting it according to the RTS 28 technical specifications. Before publication, the report should be reviewed and signed off by the firm’s compliance officer and relevant business heads.

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Quantitative Modeling and Data Analysis

The core of the RTS 28 report is the quantitative data that substantiates a firm’s best execution claims. For RFQ-based trades, this data must reconstruct the competitive environment of the trade. The following table details the essential data points required for a comprehensive report, along with their typical source and significance in the context of an RFQ workflow.

Data Point	Regulatory Definition	Data Type	Typical Source System	Application to RFQ-Based Trades
Execution Venue (Top 5)	The platform or entity where the trade was executed.	LEI	EMS/RFQ Platform	For RFQs, this is typically the multi-dealer platform used, or the firm itself if acting as a systematic internaliser.
Volume of Orders	Total number of orders executed on the venue.	Integer	OMS/Trade Blotter	Provides a measure of how frequently a particular RFQ venue is used.
Value of Orders	Total value of orders executed on the venue.	Decimal	OMS/Trade Blotter	Indicates the economic significance of a venue to the firm’s RFQ flow.
Passive Orders	Orders that provided liquidity.	Percentage	EMS/TCA System	In an RFQ context, the winning quote provider is considered to have provided liquidity.
Aggressive Orders	Orders that took liquidity.	Percentage	EMS/TCA System	The firm initiating the RFQ is the aggressive party in the trade.
Directed Orders	Orders where the client specified the execution venue.	Percentage	OMS	Less common for RFQ-based trades, but must be tracked if applicable.
Best Execution Factors	A summary of how the firm prioritized execution factors (e.g. price, speed, likelihood of execution).	Text	Compliance/Policy Docs	A qualitative summary that must be supported by the quantitative data.

The granular data collected for RTS 28 provides the empirical foundation for a dynamic and quantitatively rigorous best execution policy.

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Predictive Scenario Analysis

Consider the case of a portfolio manager at a long-only asset management firm who needs to sell a large, illiquid position in a small-cap European stock. The order size is equivalent to five times the average daily trading volume, meaning that executing on a lit exchange would cause significant market impact and price deterioration. The firm’s execution policy dictates that such trades should be handled via an RFQ to a select group of trusted liquidity providers who specialize in this type of block trade.

The trader initiates an RFQ through the firm’s EMS, inviting five dealers to provide a quote for the full size of the order. The EMS automatically logs the timestamp of the request and the LEIs of the five dealers. Over the next few minutes, four of the five dealers respond with quotes. The EMS records the price, quantity, and timestamp for each response.

The fifth dealer declines to quote, an event that is also logged. The trader analyzes the four quotes received and executes against the one offering the highest price. The final execution details, including the winning counterparty’s LEI, the execution price, and the timestamp, are all captured automatically.

When the firm compiles its annual RTS 28 report, the data from this single trade provides a rich narrative of best execution. The report will show that for this class of financial instrument (illiquid equities), the firm used a specific RFQ platform as one of its top five execution venues. The quantitative data will demonstrate that the firm solicited competitive quotes from multiple providers and executed at the best available price.

The qualitative summary of the firm’s execution policy will explain why the RFQ protocol was chosen for this trade, highlighting the importance of minimizing market impact. This single, well-documented trade becomes a powerful piece of evidence in the firm’s broader case for having a robust and effective best execution framework.

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System Integration and Technological Architecture

A successful RTS 28 reporting process depends on a well-designed technological architecture that ensures the seamless flow of data from the point of execution to the final report. The central component of this architecture is typically the firm’s Order or Execution Management System (OMS/EMS). These systems must be configured to capture the full lifecycle of an RFQ trade, from the initial request to the final fill.

For firms that rely on the Financial Information eXchange (FIX) protocol for their trading communications, specific FIX tags are essential for capturing the necessary data. Key tags include:

QuoteReqID (131) ▴ A unique identifier for the RFQ, allowing all related messages to be linked.
NoRelatedSym (146) / Symbol (55) ▴ To identify the financial instrument being traded.
NoQuoteEntries (295) / QuoteEntryID (299) ▴ To track the individual quotes received from different counterparties.
BidPx (132) / OfferPx (133) ▴ The prices quoted by the counterparties.
TransactTime (60) ▴ The timestamp of the final execution.

Beyond the trading desk, the architecture must include a data pipeline that extracts this information from the EMS/OMS, transforms it into a consistent format, and loads it into a central data warehouse. This warehouse becomes the definitive source for all regulatory reporting. Modern reporting solutions often use APIs to connect directly to this warehouse, allowing for the automated generation of the RTS 28 report in the required XML format. This level of system integration minimizes the need for manual intervention, reducing operational risk and ensuring the accuracy and timeliness of the firm’s regulatory disclosures.

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References

Commission Delegated Regulation (EU) 2017/576 of 8 June 2016 supplementing Directive 2014/65/EU of the European Parliament and of the Council with regard to regulatory technical standards for the annual publication by investment firms of information on the identity of execution venues and on the quality of execution.
European Securities and Markets Authority. (2024). ESMA public statement on reporting requirements under RTS 28. ESMA35-42-1793.
Harris, L. (2003). Trading and Exchanges ▴ Market Microstructure for Practitioners. Oxford University Press.
O’Hara, M. (1995). Market Microstructure Theory. Blackwell Publishing.
Financial Conduct Authority. (2021). Changes to UK MiFID’s conduct and organisational requirements. Policy Statement PS21/21.

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Reflection

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From Mandate to Mechanism

The framework of RTS 28, particularly as it applies to the nuanced environment of RFQ-based trading, presents a unique opportunity. It is a regulatory mandate that, when approached with strategic intent, becomes a powerful internal mechanism for systematic improvement. The data points required are not arbitrary; they are the fundamental components of execution quality, the building blocks of a transparent and defensible trading process. Viewing this data collection as a mere compliance task is a profound miscalculation of its potential value.

The true purpose of this information extends far beyond the submission of an annual report. It is the raw material for a perpetual, data-driven dialogue about execution strategy. How can we refine our counterparty lists? At what point does a request for a fifth quote yield diminishing returns?

How do our execution outcomes in volatile periods compare to those in stable markets? The answers to these questions lie within the data that RTS 28 compels firms to collect. By building an operational architecture that not only captures this data but also facilitates its analysis, a firm transforms a regulatory obligation into a core component of its institutional intelligence, creating a durable, data-driven edge in the pursuit of superior execution.