How Does MiFID II’s Best Execution Standard Alter Dealer Selection in RFQs?

Concept

The Mandate for Provable Fairness

The Markets in Financial Instruments Directive II (MiFID II) fundamentally recalibrated the operational calculus for institutional trading, extending its reach deep into the mechanics of over-the-counter (OTC) markets and bilateral trading protocols like the Request for Quote (RFQ). Before the directive’s implementation, dealer selection within an RFQ process often operated on a framework heavily weighted toward established relationships and qualitative judgments. The directive introduced a regulatory requirement for investment firms to take “all sufficient steps” to obtain the best possible result for their clients, a mandate that transformed best execution from a guiding principle into a legally enforceable, data-driven discipline.

This shift necessitates a profound architectural change within a firm’s trading apparatus. The core of MiFID II’s best execution standard is the move away from relying solely on the final price of a transaction as the measure of success. Instead, it mandates a holistic evaluation based on a series of “execution factors.” These factors include not only price and costs but also speed, likelihood of execution and settlement, size, and any other relevant consideration. For the RFQ process, this means that the simple act of sending a query to a handful of trusted counterparties and selecting the best price is no longer sufficient.

A firm must now be able to systematically prove, with verifiable data, why a particular set of dealers was chosen for the RFQ, why the winning dealer was selected, and how that selection consistently leads to the best possible outcome for the end client. This creates an auditable trail of logic, data, and outcomes.

From Reasonable Steps to Sufficient Steps

A subtle but critical change in language from MiFID I to MiFID II reveals the directive’s core intent. The original text required firms to take all “reasonable steps,” a standard that allowed for a degree of subjectivity in its interpretation. MiFID II elevated this to “all sufficient steps,” a change that signals a higher bar for diligence and a demand for a more structured, systematic, and evidence-based process. This sufficiency must be demonstrated through a firm’s execution policy, a formal document that outlines how the firm will achieve best execution for its clients across different financial instruments and client types.

In the context of RFQs, this heightened standard has several direct consequences:

• Systematic Counterparty Evaluation ▴ Firms can no longer rely on anecdotal evidence or historical relationships to justify their dealer lists. They must implement a formal process for evaluating and selecting counterparties based on their performance against the prescribed execution factors.
• Data-Driven Justification ▴ The selection of dealers for any given RFQ, and the choice of the winning bid, must be justifiable with quantitative data. This includes pre-trade analysis of which dealers are likely to provide the best outcome and post-trade Transaction Cost Analysis (TCA) to verify the results.
• Dynamic Monitoring ▴ The obligation is continuous. Firms must regularly monitor the effectiveness of their execution arrangements and policies to ensure they are consistently delivering the best possible results. This involves ongoing analysis of counterparty performance and market conditions.

The directive effectively compels firms to build an internal system of record and analysis that can withstand regulatory scrutiny. The focus shifts from the outcome of a single trade to the robustness and integrity of the process that produces those outcomes over time. This architectural requirement forces a re-evaluation of how technology, data, and human oversight interact within the trading lifecycle.

The transition from “reasonable” to “sufficient” steps under MiFID II transformed best execution from a subjective goal into a quantifiable and auditable engineering challenge.

Strategy

Developing a Defensible Execution Policy

Under MiFID II, an investment firm’s execution policy is the central strategic document that governs its approach to dealer selection and trade execution. This policy is a formal declaration of how the firm will satisfy its obligation to achieve the best possible result for its clients. For RFQ-centric workflows, particularly in less liquid markets like corporate bonds or OTC derivatives, the policy must articulate a clear and defensible strategy for selecting counterparties. This strategy represents a move from an informal, relationship-based model to a structured, evidence-based framework.

The first step in this strategic shift is defining the relative importance of the execution factors for different types of instruments and trades. While price is a primary consideration, MiFID II explicitly states that it is not the only one. For a large, illiquid block trade, the likelihood of execution and minimizing market impact may be more important than achieving a marginal price improvement. Conversely, for a small, liquid trade, price and speed might be the dominant factors.

The execution policy must codify this logic, creating a decision-making matrix that guides traders and can be explained to both clients and regulators. This requires a deep understanding of market microstructure and the specific characteristics of the assets being traded.

Table 1 ▴ Comparison of Dealer Selection Models

The Central Role of Transaction Cost Analysis

Transaction Cost Analysis (TCA) becomes the strategic engine for fulfilling MiFID II’s best execution requirements. Previously often seen as a post-trade, backward-looking exercise, TCA is now a critical component of the entire trading lifecycle. A robust TCA framework allows a firm to measure, manage, and demonstrate the quality of its execution, providing the quantitative evidence needed to support its dealer selection strategy.

The strategic implementation of TCA involves several layers:

1. Pre-Trade Analysis ▴ Before an RFQ is sent, TCA data can help determine the optimal strategy. This includes identifying which dealers have historically performed well for a specific instrument, estimating potential market impact, and deciding on the optimal number of counterparties to include in the RFQ to balance the need for competitive tension against the risk of information leakage.
2. At-Trade Analysis ▴ During the execution process, real-time data and analytics can provide context for the quotes received. A trader can compare incoming prices against a benchmark, such as the arrival price or a volume-weighted average price (VWAP), to make a more informed decision.
3. Post-Trade Analysis ▴ This is the verification stage. After the trade is complete, a detailed TCA report measures the execution quality against various benchmarks. This analysis feeds back into the pre-trade process, creating a continuous improvement loop. The data gathered here is essential for the periodic review of execution venues and counterparties as required by the directive.

By embedding TCA into its strategic framework, a firm can move beyond simple compliance. It can use the data to optimize its trading strategies, improve its counterparty relationships based on performance, and ultimately deliver better outcomes for its clients. The strategic goal is to create a virtuous cycle where data informs strategy, strategy guides execution, and execution generates new data for further refinement.

MiFID II elevates TCA from a simple measurement tool to the core strategic engine driving an auditable and continuously improving execution framework.

Execution

Building the Data-Driven Infrastructure

The execution of a MiFID II-compliant dealer selection process is fundamentally an exercise in data engineering and systems architecture. The directive’s mandate for “all sufficient steps” requires a technological framework capable of capturing, storing, analyzing, and reporting on a vast amount of trading data. This infrastructure forms the backbone of the evidence-based approach, translating regulatory requirements into operational reality.

The starting point is data capture. For every RFQ, the system must log critical data points, including:

• Timestamps ▴ The precise time the RFQ was sent, when each response was received, and when the final trade was executed.
• Counterparty Information ▴ Which dealers were included in the RFQ and which one ultimately won the trade.
• Quote Data ▴ The price and size of every quote received from every dealer, even the losing ones. This is crucial for demonstrating that the best available quote was taken.
• Trader Rationale ▴ In cases where the best-priced quote is not selected, the system must allow the trader to log a clear and concise justification, referencing the firm’s execution policy (e.g. “Selected Dealer B due to higher certainty of execution for this size, in line with our policy for illiquid instruments”).

This data must then be stored in a structured and accessible manner, often in a dedicated data warehouse. This repository becomes the single source of truth for all execution analysis, feeding into TCA platforms and internal reporting dashboards. The ability to query this data effectively is paramount for both internal oversight and regulatory requests. An auditor should be able to select any trade and see a complete, time-stamped record of the decision-making process, supported by the underlying data.

The Counterparty Scorecard a Practical Implementation

A key operational tool that emerges from this data-driven infrastructure is the counterparty scorecard. This is a dynamic, quantitative assessment of each dealer’s performance against the firm’s defined execution factors. The scorecard provides an objective basis for deciding which dealers to include in an RFQ and for conducting the formal periodic reviews required by MiFID II.

Constructing a meaningful scorecard requires a systematic approach to weighting the various execution factors according to the firm’s policy. The output is a clear, data-backed ranking system that moves the evaluation of dealers from the realm of subjective opinion to objective measurement.

Table 2 ▴ Sample Counterparty Scorecard Metrics

This scorecard system must be integrated into the trader’s workflow, often through the firm’s Execution Management System (EMS) or Order Management System (OMS). When a trader initiates an RFQ, the system can automatically suggest a list of counterparties based on their current scores for that specific asset class and trade size. This provides a defensible, data-driven starting point for the execution process. The results of each new trade then feed back into the system, continuously updating the scores and ensuring the process remains dynamic and reflective of the most recent performance.

This operational framework does not eliminate the role of the human trader. Instead, it empowers the trader with better tools. The trader’s expertise is still needed to handle complex orders, navigate volatile markets, and manage the nuances of counterparty relationships. However, their decisions are now supported by a robust quantitative framework, ensuring that the firm’s best execution obligations are met in a consistent, demonstrable, and ultimately more effective manner.

A compliant execution framework under MiFID II is built on a foundation of systematic data capture and quantitative counterparty analysis.

Reflection

From Mandate to Mechanism

The integration of MiFID II’s principles into the RFQ workflow represents a significant operational evolution. The regulatory language, centered on “all sufficient steps,” compels a firm to look inward at its own processes and systems. The challenge is to construct an internal architecture where best execution is an emergent property of the system itself, rather than the result of discretionary effort on a trade-by-trade basis. This involves creating a feedback loop where data from every execution informs the strategy for the next, turning a regulatory requirement into a source of continuous operational improvement.

The Future of Dealer Relationships

The shift towards a quantitative, evidence-based framework does not render dealer relationships obsolete. Instead, it redefines them. The conversations between traders and dealers can evolve, moving beyond pricing to a more strategic dialogue about liquidity, market structure, and mutual performance.

A dealer’s value is no longer measured solely by the sharpness of their pencil, but by their consistency, reliability, and contribution to the client’s overall execution quality, as demonstrated by the data. This fosters a more transparent and performance-oriented partnership, where both sides are aligned around the goal of achieving the best possible outcome.