How Does MiFID II Impact RFQ Workflows for Illiquid Securities?

Concept

The Markets in Financial Instruments Directive II (MiFID II) represents a fundamental re-architecting of European financial markets. Its arrival was not a mere incremental update but a systemic intervention designed to rebuild the market’s operating system around the principles of transparency, investor protection, and verifiable fairness. For those of us who operate within the intricate workflows of institutional trading, particularly in the challenging domain of illiquid securities, the directive’s impact on the Request for Quote (RFQ) protocol is a subject of immense operational significance. You have likely experienced the friction firsthand ▴ the old ways of sourcing liquidity, reliant on established relationships and voice-brokering, now sit uneasily within a framework that demands a provable, data-driven approach to best execution.

At its core, the challenge stems from a collision of two realities. On one side, you have the very nature of illiquid instruments. These are securities characterized by infrequent trading, wide bid-ask spreads, and a general lack of public price data. Their value is not continuously discovered on a lit exchange but is instead negotiated in discrete moments of engagement.

On the other side, you have MiFID II’s mandate for “all sufficient steps” to obtain the best possible result for a client. The directive explicitly moves beyond the singular focus on price to include a wider set of execution factors ▴ costs, speed, likelihood of execution and settlement, size, and any other relevant consideration. The central question for any trading desk becomes ▴ how do you systematically prove you have met this multi-faceted obligation for an instrument where reliable data is, by definition, scarce?

The directive compels a shift from a relationship-based execution model to a data-centric, auditable process, even in markets where data is inherently limited.

This is where the traditional RFQ workflow comes under intense scrutiny. The act of soliciting quotes from a select group of liquidity providers, a cornerstone of trading in illiquid bonds and complex derivatives, must now be embedded within a much more rigorous governance structure. It is no longer sufficient to simply contact the usual three to five dealers.

The system must now justify why those specific dealers were chosen, how the fairness of their quotes was assessed against available market data, and how the final execution decision aligns with the firm’s documented best execution policy. This transforms the RFQ from a simple price discovery tool into a formal, auditable event within the trading lifecycle.

The regulation effectively forces a professionalization of the liquidity sourcing process. It demands that firms build an internal system capable of capturing, analyzing, and storing the data necessary to defend their execution choices. This system must be able to demonstrate a consistent and intelligent approach to navigating the unique challenges of illiquid markets, proving that the firm is not merely defaulting to habit but is actively seeking the best possible outcome for its clients within the constraints of the available liquidity landscape.

Strategy

Navigating the complexities of MiFID II’s impact on RFQ workflows for illiquid assets requires a deliberate strategic overhaul. The directive fundamentally alters the required mindset, moving from a qualitative, relationship-driven approach to a quantitative, evidence-based framework. The core strategic objective is to construct and maintain a defensible execution process that satisfies regulatory obligations while preserving access to vital liquidity in thin markets. This involves building a system that is both intelligent in its application and robust in its auditability.

From Best Efforts to Demonstrable Proof

Prior to MiFID II, best execution in illiquid markets was often a “best efforts” undertaking. A trader’s experience and network were paramount. The strategy was to leverage trusted relationships to find a counterparty willing to provide a price. While effective, this process was opaque and difficult to validate externally.

MiFID II systematically dismantles this paradigm. The strategic imperative now is to create a demonstrable record of the steps taken to achieve the best possible result. This is not about achieving a perfect outcome every time, which is impossible in illiquid markets. It is about proving that the process itself was sound, consistent, and designed to protect the client’s interests.

This strategic shift is encapsulated in the concept of taking “all sufficient steps.” It compels firms to formalize their decision-making process. The selection of counterparties for an RFQ, for example, can no longer be based solely on habit. The strategy must involve a periodic and data-informed assessment of liquidity providers, considering factors like their historical responsiveness, pricing competitiveness, and settlement reliability for specific asset classes. The firm’s execution policy becomes a central strategic document, clearly articulating how these factors are weighed and how the choice of execution venue or counterparty is made.

What Is the Role of the Legitimate Reliance Test?

A key strategic consideration, particularly for firms dealing on their own account, is the “legitimate reliance test.” This concept, carried over from MiFID I, helps determine when the full weight of best execution applies to RFQ scenarios. Best execution obligations are triggered when a client has a legitimate expectation that the firm will protect their interests regarding the price and other aspects of the transaction. The four-fold test considers:

• Who initiates the transaction ▴ If the firm approaches the client with a trade idea, the client’s reliance is considered higher.
• Market practice and conventions ▴ In markets where it is standard practice for clients to “shop around” for quotes, the reliance on any single firm is lower.
• Relative levels of price transparency ▴ In opaque markets, the client naturally relies more on the firm’s expertise to determine a fair price.
• Information provided by the firm ▴ The way a firm presents its services and relationship with the client is a determining factor.

The strategy here is not to avoid the best execution obligation, but to understand precisely when and how it applies. For illiquid securities, where transparency is low, the client’s reliance is almost always presumed to be high. Therefore, the default strategic posture must be to assume the obligation applies and to build workflows accordingly. This means that even when acting as a principal, the firm must have a system to check the fairness of its proposed price against available market data.

A Multi-Factor Execution Framework

The most significant strategic departure is the move away from a price-only evaluation. MiFID II mandates a holistic assessment. The strategy must be to integrate these factors into the RFQ workflow systematically.

The following table illustrates the strategic shift in evaluating execution quality for an illiquid bond RFQ:

Data as a Strategic Asset

Underpinning the entire strategy is the elevation of data. In the MiFID II world, data is not just a byproduct of trading; it is the raw material for compliance and competitive advantage. The strategy must focus on three key areas of data management:

1. Pre-Trade Data Collection ▴ The system must be able to gather all relevant data points before the RFQ is sent. This includes not just indicative prices from data vendors, but also information on recent trades in similar instruments and the firm’s own historical trading data. For illiquid assets, this might involve looking at a bond from the same issuer with a different maturity, or a derivative with similar underlying risk factors.
2. At-Trade Data Capture ▴ The RFQ process itself must be meticulously recorded. Every quote requested, every response received (including declines to quote), the time of each event, and the identity of the counterparties must be captured in an immutable audit trail.
3. Post-Trade Analysis ▴ The strategy must include a feedback loop. Data from executed trades must be used to refine the execution policy and the counterparty selection process. This involves analyzing execution quality against the firm’s stated objectives and reporting on it as required by RTS 27 (for execution venues) and RTS 28 (for investment firms).

By treating data as a strategic asset, a firm can move beyond a purely compliance-driven approach. It can use the insights generated from its execution data to build more intelligent RFQ models, identify the most reliable liquidity providers for specific asset classes, and ultimately achieve better outcomes for clients in a manner that is fully compliant with the directive’s requirements.

Execution

The execution of an RFQ for an illiquid security under MiFID II is a high-fidelity process that transforms a traditionally informal inquiry into a structured, auditable, and data-intensive operational workflow. The focus shifts from merely finding a price to constructing a complete evidentiary record that justifies the final execution decision. This requires a robust technological and procedural architecture capable of navigating the directive’s requirements with precision.

The Operational Playbook for a Compliant RFQ Workflow

Executing a trade in an illiquid instrument is no longer a simple two-step process of “call and trade.” It is a multi-stage procedure where each step must be logged and justified. The following playbook outlines the critical phases of a compliant RFQ workflow from an operational standpoint.

1. Pre-Trade Analysis and Price Fairness Assessment ▴ Before any RFQ is initiated, the system must perform a thorough pre-trade analysis. The objective is to establish a “fair value” benchmark against which incoming quotes can be assessed. Given the absence of continuous pricing for illiquid assets, this is a complex task that relies on a hierarchy of data sources. The system must automatically gather and record this information.
   • Data Gathering ▴ The process involves querying multiple data sources to build a price picture. This includes evaluated pricing services (e.g. from Bloomberg, Refinitiv), data from consolidated tape providers where available, and prices of “similar” or “comparable” products. For a specific illiquid corporate bond, this could mean looking at bonds from the same issuer with adjacent maturities, or bonds from different issuers in the same sector and with a similar credit rating.
   • Benchmark Creation ▴ The gathered data is used to construct a pre-trade price benchmark or a fair value range. This is not a single price but a reasoned estimate. The methodology for creating this benchmark must be documented in the firm’s execution policy.
   • Audit Trail Initiation ▴ The system must log all the data points used in this pre-trade assessment, creating the first entry in the order’s audit trail. This demonstrates that the firm took steps to understand the potential fair value before seeking liquidity.
2. Intelligent Counterparty Selection ▴ The choice of which liquidity providers to include in the RFQ is a critical decision that must be actively managed and justified. The process must be governed by the firm’s execution policy and supported by quantitative data.
   • Counterparty Scoring ▴ The system should maintain a dynamic scorecard for all potential liquidity providers. This scorecard is updated based on historical performance data across several key metrics as outlined in the table below.
   • RFQ Roster Generation ▴ When an order is received, the system uses the counterparty scores, in conjunction with the specific characteristics of the instrument (asset class, size, currency), to generate a recommended list of dealers for the RFQ. A human trader may have the discretion to override this list, but any such deviation must be explicitly justified and logged by the system.
   • Documentation of Rationale ▴ The rationale for selecting the final set of counterparties for the RFQ is recorded. For example ▴ “Selected LPs A, B, and D based on top-quartile pricing competitiveness and settlement reliability for EUR-denominated corporate bonds of this size over the past six months. LP C was excluded due to a low response rate on similar inquiries.”
3. Systematic RFQ Dissemination and Monitoring ▴ The RFQ is sent electronically to the selected counterparties. The system must capture every aspect of this interaction with precise timestamps.
   • Electronic Submission ▴ RFQs are sent via dedicated platforms or FIX-based connections to ensure all communications are logged. Voice-based RFQs must be minimized, and if used, must be recorded and transcribed to be included in the electronic audit trail.
   • Response Tracking ▴ The system tracks all responses in real-time. This includes not only the prices quoted but also any “decline to quote” messages and the time taken for each response. This data is vital for updating the counterparty scorecards.
4. Execution Decision and Justification ▴ The final execution decision must be made by weighing the received quotes against the pre-trade benchmark and the non-price execution factors.
   • Holistic Evaluation ▴ The trader evaluates the quotes. While the best price is a primary consideration, the system must allow the trader to factor in other elements. For example, a slightly worse price from a counterparty with a perfect settlement record might be preferable to the best price from a counterparty known for frequent settlement fails, as this aligns with the “likelihood of settlement” factor.
   • Justification Capture ▴ If the trader does not select the best price, the system must require them to provide a clear, documented reason for their decision. For example ▴ “Executed with LP B at 100.26, despite a better price of 100.28 from LP A, due to the large size of the order and LP B’s superior demonstrated capacity to handle block trades in this asset class, minimizing the risk of partial execution.”
   • Final Record ▴ The system logs the final execution details, including the chosen counterparty, the execution price, the time of execution, and the justification.

Quantitative Modeling and Data Analysis

A compliant MiFID II workflow is built upon a foundation of robust data analysis. The following table details the kind of quantitative data that a firm’s execution management system should be capturing and using to inform the RFQ process for illiquid securities.

How Should a Firm Structure Its Execution Policy?

The execution policy is the constitutional document that governs all trading activity. For illiquid securities, it must be particularly detailed in its description of the RFQ process. It should clearly explain how the firm defines “illiquid,” the methodology for establishing fair value, the criteria for selecting and reviewing liquidity providers, and the process for handling situations where very few or no quotes are received.

This document is not static; it must be reviewed at least annually and updated based on the results of the firm’s post-trade execution quality analysis. It serves as the primary piece of evidence for regulators that the firm has a systematic and thoughtful approach to achieving best execution for its clients.

Reflection

The integration of MiFID II’s principles into the RFQ workflow for illiquid assets has established a new operational baseline. The systems and procedures detailed here represent a significant architectural shift, moving the trading desk from an environment of intuition to one of structured, evidence-based decision making. The framework provides a pathway to compliance, but its true potential lies beyond regulatory adherence. Consider how this data-centric architecture can be leveraged not just as a defensive shield, but as a strategic tool.

How can the granular data captured from every RFQ be used to build predictive models of liquidity? How can the systematic analysis of counterparty performance inform more sophisticated and dynamic liquidity sourcing strategies? The directive has mandated the construction of a powerful data infrastructure. The ultimate competitive advantage will be realized by those who view this infrastructure not as a finished product, but as the foundation for the next generation of trading intelligence.