What Are the Core Data Points Required to Prove RFQ Competitiveness under MiFID II? ▴ Question

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Concept

The mandate to demonstrate Request for Quote (RFQ) competitiveness under the second Markets in Financial Instruments Directive (MiFID II) is an exercise in data architecture. It requires the systematic assembly of evidence to construct a defensible narrative of best execution. At its core, this regulatory obligation compels a firm to prove, with granular data, that every step taken to execute a client’s order was sufficient to achieve the optimal result.

The focus shifts from simply achieving a good price to validating the entire execution process through a rigorous, evidence-based framework. This framework is built upon a foundation of specific, interlocking data points that, when aggregated, provide a complete picture of the execution lifecycle.

Understanding this requirement begins with the directive’s elevation of the best execution standard itself. The previous “reasonable steps” criterion was replaced with the more demanding “all sufficient steps.” This linguistic alteration represents a profound shift in regulatory expectation, moving from a procedural checklist to a results-oriented, evidence-based justification. For the RFQ protocol, which operates bilaterally and often in less transparent over-the-the-counter (OTC) markets, this sufficiency must be demonstrated empirically.

The core data points, therefore, are the raw materials for this demonstration. They serve as the quantitative proof points in the argument that a firm’s execution methodology is not only sound in theory but also effective in practice.

The core challenge is transforming discrete data events from an RFQ workflow into a coherent, auditable record of execution quality.

The entire system rests on several key execution factors that must be monitored and recorded. These factors, outlined in the regulation, are price, costs, speed, and the likelihood of execution and settlement. For each RFQ, a firm must capture data corresponding to each of these factors to justify its execution choices.

This includes not only the winning quote but all competing quotes received, the timestamps of each event in the process, the direct and indirect costs associated with the transaction, and metrics that speak to the reliability of the chosen counterparties. The data must collectively answer the fundamental question ▴ considering all relevant factors, was the outcome achieved the best possible result for the client on a consistent basis?

This process is formalized through two primary reporting mechanisms ▴ the RTS 27 and RTS 28 reports. RTS 27 reports are published quarterly by execution venues (including Organised Trading Facilities, or OTFs, and Systematic Internalisers, or SIs) and provide detailed data on execution quality. RTS 28 reports are published annually by investment firms and detail their top five execution venues for each class of financial instrument, along with a qualitative assessment of the execution quality achieved.

The data points required to prove RFQ competitiveness are those needed to populate these reports accurately and, more importantly, to build the internal audit trail that substantiates the firm’s execution policy and choices. The process is a continuous loop of data capture, analysis, and reporting, all designed to make the invisible mechanics of OTC trading visible and justifiable to regulators and clients.

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Strategy

A robust strategy for demonstrating RFQ competitiveness under MiFID II is centered on building a defensible data ecosystem. This ecosystem must do more than simply capture the required data points; it must structure them in a way that provides a clear, logical narrative of best execution. The strategy involves a multi-layered approach, combining pre-trade analytics, real-time monitoring, and post-trade analysis to create a comprehensive and auditable record of every RFQ transaction. This approach transforms the regulatory requirement from a compliance burden into a strategic advantage, using data to refine execution processes, optimize counterparty selection, and ultimately improve client outcomes.

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The Strategic Value of Execution Factors

The core execution factors ▴ price, costs, speed, and likelihood of execution ▴ form the strategic pillars of the data narrative. Each factor requires a distinct data collection and analysis strategy to be effectively proven.

Price Justification ▴ For OTC instruments traded via RFQ, proving a “fair” price is a primary challenge. The strategy here is to gather external market data to benchmark the quotes received. This involves capturing reference prices from comparable or similar products at the time of the RFQ. Pre-trade Transaction Cost Analysis (TCA) tools become essential, providing an estimated fair value against which the received quotes can be measured. The data strategy must ensure that these benchmark prices are captured and stored alongside the RFQ data.
Total Cost Transparency ▴ The strategic focus on costs extends beyond the transaction price to encompass all explicit and implicit expenses. This includes execution venue fees, clearing and settlement fees, and any other third-party charges. A comprehensive data model is required to itemize these costs and attribute them to specific trades. This allows the firm to calculate the “total consideration” for the client, which is the ultimate measure of best execution for retail clients.
Speed and Information Leakage ▴ In the context of an RFQ, the speed of the process is a critical data point. The strategy is to timestamp every event in the RFQ lifecycle, from the initial request to the final execution. Analyzing these timestamps can reveal inefficiencies in the process and potential information leakage. For instance, prolonged quote response times from certain counterparties might indicate they are using the information from the RFQ to trade ahead of the client’s order.
Likelihood of Execution and Settlement ▴ This factor addresses the reliability of counterparties and execution venues. The strategy involves tracking metrics such as quote rejection rates, settlement failures, and the frequency of “last look” holds by liquidity providers. By analyzing this data over time, a firm can build a quantitative profile of each counterparty’s reliability, providing a data-driven justification for its counterparty selection decisions.

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What Is the Role of Pre-Trade and Post-Trade Analytics?

A comprehensive strategy for demonstrating RFQ competitiveness relies heavily on the integration of pre-trade and post-trade analytics. These two phases of analysis provide a continuous feedback loop for improving execution quality.

Pre-trade TCA is a proactive measure that sets the stage for best execution. By analyzing historical data and real-time market conditions, pre-trade TCA models can provide an expected cost for a given transaction. This allows the trader to set realistic execution targets and to assess the fairness of quotes received in real time. The data points captured during this phase, such as the expected spread and market impact, become part of the audit trail, demonstrating that the firm took proactive steps to secure a good outcome.

Effective execution strategy under MiFID II transforms regulatory data into a continuous feedback loop for performance optimization.

Post-trade TCA, on the other hand, is a reactive measure that validates the execution outcome. It compares the actual execution results against the pre-trade estimates and other relevant benchmarks. This analysis provides the quantitative evidence needed to prove that best execution was achieved. The table below outlines the strategic data requirements for both pre-trade and post-trade analysis in the context of RFQs.

Analysis Phase	Strategic Objective	Core Data Points	Analytical Output
Pre-Trade Analysis	Establish a fair value benchmark and anticipate execution costs.	Instrument characteristics (ISIN, asset class) Real-time market data (bid/ask spread) Historical volatility Estimated liquidity for the instrument	Expected price range Anticipated market impact Optimal counterparty list based on historical data
Post-Trade Analysis	Verify the execution outcome and measure performance against benchmarks.	All RFQ timestamps (request, quote, execution) All quotes received (price and size) Winning quote details Actual execution price and costs Post-trade market data for slippage calculation	Price improvement vs. benchmark Total cost of execution (in basis points) Counterparty performance scorecard Slippage analysis

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Counterparty Management as a Data-Driven Discipline

Under MiFID II, the selection of counterparties for an RFQ can no longer be based solely on relationships or qualitative assessments. It must be a data-driven process. The strategy is to create a comprehensive counterparty management system that continuously tracks and evaluates the performance of each liquidity provider. This system should be designed to capture and analyze the data points that reflect a counterparty’s contribution to best execution.

This provides a clear, defensible rationale for why certain counterparties were included in an RFQ and why a particular quote was chosen. The result is a more systematic and objective approach to liquidity sourcing, which is at the heart of the MiFID II best execution requirements.

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Execution

The execution of a MiFID II-compliant data strategy for RFQs requires a granular and systematic approach to data capture and management. It involves building a detailed audit trail for every RFQ transaction, integrating data from multiple sources, and structuring it in a way that facilitates both internal analysis and regulatory reporting. The focus is on creating a single, immutable record of truth for each trade, capturing every relevant data point with high precision and accuracy. This operational framework is the ultimate proof of a firm’s commitment to achieving the best possible results for its clients.

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Constructing the RFQ Audit Trail

The foundation of a compliant execution framework is a comprehensive audit trail for every RFQ. This audit trail must capture all the data points necessary to reconstruct the entire lifecycle of the trade, from the initial client order to the final settlement. The following table details the essential data fields that must be captured for each RFQ. The precision of timestamps, typically to the microsecond or nanosecond level, is a critical component of this process, as it allows for a detailed analysis of latency and speed of execution.

Data Category	Specific Data Point	Purpose in Proving Competitiveness
Order and Request Data	Client Identifier Order Timestamp (arrival at the firm) Instrument Identifier (e.g. ISIN) Order Size and Direction (Buy/Sell) RFQ Request Timestamp	Establishes the initial conditions and timing of the client’s instruction.
Counterparty and Quote Data	List of Solicited Counterparties Timestamp for each quote request Timestamp for each quote response Price and Size of each quote received Quote Rejection Timestamps and Reasons	Provides a complete record of the competitive landscape for the trade.
Execution Data	Winning Counterparty Identifier Winning Quote Details (Price, Size) Execution Timestamp Execution Venue (e.g. SI, OTF) Total Consideration (Price + all costs)	Pinpoints the exact details of the final transaction and its total cost to the client.
Market Context Data	Reference Price at time of RFQ Reference Price at time of Execution Market Volatility Metric Market Spread at time of RFQ	Provides the necessary market context to assess the fairness of the execution price.

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How Do You Integrate Data from Disparate Systems?

A significant operational challenge in executing this data strategy is the integration of data from various systems. RFQs, particularly for certain asset classes, may be conducted over multiple channels, including electronic platforms, chat messages, and voice calls. Each of these channels presents unique data capture challenges.

For electronic RFQ platforms, data capture can often be automated through APIs that feed directly into the firm’s data warehouse. This allows for the systematic collection of structured data with high-precision timestamps. The process becomes more complex for less structured channels like chat and voice.

For chat-based RFQs, firms must employ natural language processing (NLP) tools to parse the conversations and extract the key data points, such as the instrument, size, price, and timestamps. This extracted data must then be normalized and integrated into the structured audit trail.

A complete RFQ audit trail must systematically capture and integrate data from all communication channels, including electronic platforms, chat, and voice.

Voice trading represents the most difficult data capture problem. Firms must use voice-to-text transcription services to convert the audio recordings of trader-broker conversations into a machine-readable format. These transcriptions can then be analyzed using NLP tools, similar to the process for chat data.

The key is to establish a rigorous operational process for linking these unstructured data sources back to the specific RFQ transaction they relate to. This often requires traders to manually tag or confirm the data, adding an element of human oversight to the process.

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Leveraging RTS 27 and Preparing for RTS 28

The execution of a MiFID II data strategy involves both consuming external data and producing internal reports. Firms must have the operational capability to ingest and analyze the quarterly RTS 27 reports published by execution venues. These reports provide a wealth of data on execution quality, including detailed information on prices, costs, and likelihood of execution for various financial instruments. By analyzing this data, a firm can benchmark its own RFQ execution performance against the broader market, identify the top-performing venues, and refine its execution policies accordingly.

This analysis directly feeds into the preparation of the firm’s own annual RTS 28 report. This report requires the firm to disclose its top five execution venues for each class of financial instrument and to provide a summary of the analysis and conclusions drawn from its monitoring of execution quality. The data captured in the RFQ audit trail provides the raw material for this report.

The operational process must ensure that this data is accurately aggregated and summarized to meet the RTS 28 requirements. This includes not only quantitative data on volumes and costs but also a qualitative assessment of the execution quality provided by each venue, supported by the detailed data captured throughout the year.

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References

Tradeweb. “Best Execution Under MiFID II and the Role of Transaction Cost Analysis in the Fixed Income Markets.” 2017.
“Best Execution Under MiFID II.” 2018. This appears to be a presentation slide deck, specific author and publisher are not cited in the source.
“Guide for drafting/review of Execution Policy under MiFID II.” 2018. The specific authoring body is not explicitly named in the provided search snippet, but it appears to be a guidance document from a regulatory or consulting body.
Khwaja, Amir. “MiFID II and Best Execution for Derivatives.” Clarus Financial Technology, 2015.
Hogan Lovells. “Achieving best execution under MiFID II.” 2017.

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Reflection

The assembly of data points to prove RFQ competitiveness under MiFID II is a foundational requirement. A truly superior operational framework, however, views this data as more than an instrument of compliance. It sees it as a strategic asset.

The systems you build to capture and analyze these data points should form the core of your execution intelligence. They provide the feedback loop necessary to refine your strategies, optimize your counterparty relationships, and ultimately, enhance your ability to deliver superior results.

Consider your current data architecture. Does it merely record what has happened, or does it actively inform what should happen next? Does it function as a static repository for regulatory reporting, or is it a dynamic system that generates actionable insights? The directive provides the blueprint for the data you must collect.

The strategic potential lies in how you choose to use it. The ultimate goal is to construct a system where the evidence of best execution is a natural byproduct of a process designed for optimal performance.