How Can Firms Quantitatively Prove Best Execution for Illiquid Corporate Bonds under MiFID II?

Concept

The central challenge in satisfying the Markets in Financial Instruments Directive II (MiFID II) for illiquid corporate bonds is demonstrating a quantitative proof of best execution for an asset class fundamentally defined by a scarcity of quantitative data. The regulatory mandate requires firms to take “all sufficient steps” to obtain the best possible result for their clients. This obligation moves the analytical focus beyond a singular pursuit of the best price.

It establishes a holistic framework where price is one critical component among many. For fixed-income instruments that trade infrequently, a successful execution framework is built upon a qualitative judgment that is rigorously supported by quantitative evidence, however fragmented that evidence may be.

The architecture of equity market regulation, which often relies on a National Best Bid and Offer (NBBO) derived from a consolidated, real-time data feed, provides a useful contrast. That model is structurally incompatible with the over-the-counter (OTC), dealer-centric nature of corporate bond markets. The universe of corporate bonds is vastly larger and more heterogeneous than that of equities. A specific bond may not trade for days, weeks, or even months, rendering the concept of a continuous, market-wide best price moot.

The European Commission itself has acknowledged the profound illiquidity of the market, finding only a small fraction of corporate bonds in Europe meet the criteria for being ‘liquid’. Consequently, the MiFID II framework was designed with this reality in mind. It compels firms to construct their own evidentiary process, shifting the burden of proof from observing a public benchmark to demonstrating a robust internal methodology.

Proving best execution for illiquid bonds involves documenting a systematic process of inquiry and decision-making, using available data to justify the final execution outcome.

The core of this process lies in understanding and balancing the prescribed execution factors ▴ price, costs, speed, likelihood of execution, likelihood of settlement, and the size and nature of the order. In the context of an illiquid bond, the “likelihood of execution” and the ability to transact in the desired “size” often become the dominant factors. Sourcing a sufficient quantity of a specific bond to meet a portfolio mandate at a workable price is the primary challenge.

A firm’s ability to quantitatively prove best execution, therefore, rests on its capacity to systematically record its search for liquidity, document the prices and sizes quoted by counterparties, and justify its ultimate execution decision within this multi-faceted context. The proof is the audit trail of a well-defined, repeatable, and defensible process.

What Is the Regulatory Standard for Execution?

The regulatory standard under MiFID II is “all sufficient steps,” a deliberate evolution from the previous “all reasonable steps” language. This linguistic shift signifies a higher and more proactive standard of care. It requires investment firms to move beyond passive compliance and actively design and implement policies and procedures that can consistently deliver the best possible results. The framework acknowledges that the relative importance of execution factors varies depending on the client’s objectives, the instrument’s characteristics, and prevailing market conditions.

For a retail client, total consideration (price and costs) might be paramount. For an institutional client executing a large order in an illiquid bond, the certainty of completion (likelihood of execution) may be the primary driver of the trading decision. A firm’s execution policy must articulate how it weighs these factors for different client types and asset classes, creating a clear intellectual framework that guides the actions of its traders.

Strategy

A firm’s strategy for quantitatively proving best execution for illiquid bonds is built upon a three-pillar architecture ▴ comprehensive data aggregation, systematic venue and counterparty analysis, and a sophisticated multi-benchmark approach to Transaction Cost Analysis (TCA). This strategy accepts the inherent imperfections of bond market data and focuses on constructing a defensible process. The objective is to create a complete narrative of each trade, supported by the best available quantitative reference points, from the pre-trade decision to the post-trade assessment.

A Multi-Layered Data Aggregation Framework

The absence of a centralized consolidated tape for bonds in Europe necessitates that firms build their own data infrastructure. This is a strategic imperative. The goal is to capture a wide spectrum of data points that can collectively form a picture of the available liquidity and pricing at the moment of execution. This involves integrating several distinct data streams.

• Pre-Trade Information ▴ This layer includes indicative pricing from data vendors, executable quotes from trading platforms like Multilateral Trading Facilities (MTFs) or Organised Trading Facilities (OTFs), and the direct quotes solicited from market makers through Request for Quote (RFQ) protocols. The system must log not just the prices but the associated sizes and the timestamps of their validity.
• At-Trade Information ▴ This is the real-time capture of the execution process itself. For an RFQ, it means logging every single quote returned by counterparties, even those that are far from the eventual execution level. This data is the primary evidence of a firm’s effort to survey the market.
• Post-Trade Information ▴ This involves capturing the firm’s own execution data (price, size, venue, counterparty, timestamp) and enriching it with public post-trade data where available from Approved Publication Arrangements (APAs). While this public data is often delayed and may lack completeness, it provides a valuable contextual overlay for internal analysis.

Systematic Venue and Counterparty Analysis

MiFID II requires firms to monitor the effectiveness of their execution arrangements and to publicly disclose their top five execution venues annually for each class of financial instrument. This regulatory requirement forces a data-driven strategy for venue and counterparty selection. It is insufficient to rely on historical relationships or qualitative assumptions. Firms must quantitatively assess where they achieve the best results.

This analysis hinges on segmenting execution data by venue type, such as OTFs, MTFs, and Systematic Internalisers (SIs). The challenge is that identifying whether a counterparty is acting as an SI for a specific trade can be difficult. A robust strategy involves maintaining an internal database of SI designations and continuously tracking execution quality metrics for each counterparty and venue. These metrics should extend beyond simple price improvement and include factors like quote response rates, quote competitiveness (how often they are at or near the best price), and execution reliability (fill rates).

A defensible execution strategy relies on demonstrating that the choice of venue and counterparty was based on a consistent, quantitative evaluation of past performance.

How Should Firms Benchmark Illiquid Trades?

Choosing the correct benchmark is the most critical element of the quantitative proof. Given the lack of a universal reference price, a single-benchmark approach is inadequate. A multi-benchmark strategy provides a more resilient and credible assessment of execution quality. The selection of benchmarks should be tailored to the nature of the illiquid bond market.

The following table outlines several benchmark types, their application, and their inherent limitations in the context of illiquid corporate bonds.

Execution

Executing the strategy to prove best execution requires a disciplined, technology-driven operational playbook. The core of this playbook is a Transaction Cost Analysis (TCA) process that is deeply integrated into the trading workflow. It is a system designed to create a comprehensive audit trail for every order, transforming the qualitative judgments of a trader into a structured, data-rich record. This process is not merely a post-trade reporting exercise; it is an active, full-lifecycle system for managing and evidencing execution quality.

The Operational Playbook a TCA Workflow

A robust TCA workflow for illiquid bonds can be broken down into four distinct stages, each generating critical data for the final proof. This system ensures that from the moment an order is conceived to its final settlement, every decision is framed by the “all sufficient steps” doctrine.

1. Pre-Trade Intelligence ▴ Before any RFQ is sent, the system must arm the trader with contextual data. This involves displaying the latest available evaluated price for the bond, any recent trade prints from APAs, and the firm’s own historical trade data in the instrument or in similar securities from the same issuer. Crucially, it should also present historical performance metrics for potential counterparties for this specific type of bond, such as their historical quote competitiveness and response rates. The trader’s rationale for selecting a specific execution strategy (e.g. a targeted RFQ to three dealers versus a broader inquiry) must be logged at this stage.
2. At-Trade Capture ▴ This is the evidentiary heart of the process. The execution management system (EMS) must automatically capture every aspect of the RFQ process. This includes the list of counterparties queried, the precise timestamp of the request, and every quote received (price and size), again with a precise timestamp. If a trader executes at a price that is not the best quote received, the system must require a justification to be entered at that moment. Reasons might include the best-priced dealer being unable to provide the full required size or having known settlement issues. This contemporaneous record is far more powerful than a post-hoc explanation.
3. Post-Trade Measurement ▴ Immediately following execution, the TCA system performs its initial calculations. The executed price is compared against the key benchmarks ▴ the best quote received in the RFQ, the other quotes from the RFQ, and the evaluated price at the time of the trade. The resulting slippage figures, measured in basis points and currency terms, form the primary quantitative output for that trade.
4. Periodic Review and Reporting ▴ Individual trade data is aggregated over time, typically on a quarterly basis. This allows for a higher-level analysis of execution quality. Reports are generated to compare performance across different execution venues, counterparties, traders, and bond sub-asset classes (e.g. Investment Grade vs. High Yield). This aggregated data provides the quantitative foundation for the annual RTS 28 (Top 5 Venues) report and gives compliance and oversight committees the evidence needed to confirm that the firm’s execution policy is effective.

Quantitative Modeling and Data Analysis

The aggregated data from the TCA workflow allows for sophisticated quantitative analysis that goes beyond single-trade metrics. The goal is to identify patterns and systematically evaluate the effectiveness of the firm’s execution arrangements. This analysis is central to demonstrating that the firm is taking sufficient steps to improve its processes over time.

The following table provides an example of a quarterly execution quality review dashboard. This dashboard synthesizes data across thousands of trades to provide a strategic overview of counterparty performance, fulfilling the analytical requirements of MiFID II.

In this analysis, ‘Slippage vs. BVAL’ measures the difference between the execution price and the third-party evaluated price, with a negative value indicating favorable execution. ‘Quote Hit Rate’ shows the percentage of RFQs where that counterparty provided the best price. This data allows the firm to quantitatively prove why it directs more flow to Dealer A, as they consistently provide more competitive pricing across different asset classes, thereby fulfilling the obligation to achieve the best possible result.

Why Is a Defensible RFQ Protocol the Ultimate Proof?

For illiquid instruments, the RFQ protocol is the market. The ability to demonstrate a robust, fair, and systematically logged RFQ process is the most compelling form of quantitative proof. The data generated by the RFQ ▴ the list of dealers queried, the prices they returned, the time it took them to respond ▴ creates a self-contained, trade-specific benchmark. It is a record of a live market sounding.

A firm that can produce a complete log for every trade, showing a competitive process where multiple dealers were solicited, has a powerful defense against any challenge to its execution quality. This documented process proves the firm took sufficient steps to survey the available liquidity and pricing, which is the foundational requirement of the best execution obligation for these instruments.

Reflection

The architecture of compliance for illiquid instruments reveals a fundamental truth about market intelligence. The systems built to satisfy regulatory mandates are the very same systems that create a durable competitive advantage. The process of gathering data to prove best execution is the process of building a superior map of a fragmented market. The discipline required to log and analyze every trade decision creates a feedback loop that sharpens a firm’s execution strategy over time.

The question then becomes, how is your operational framework transforming the burden of proof into a source of proprietary insight? Is your data architecture merely a defensive measure, or is it the engine of your execution alpha?