How Do Firms Quantify Best Execution for Illiquid Assets without a Tape?

Concept

The challenge of quantifying best execution for illiquid assets originates in a fundamental architectural difference when compared to equity markets. For equities, a consolidated tape acts as a public, synchronized, and continuous data feed ▴ a universal clock against which every transaction can be measured with sub-second precision. This system provides an objective, verifiable price at any given moment. In the world of illiquid assets, such as esoteric bonds, certain derivatives, or private placements, this central nervous system of data does not exist.

The market structure is fragmented, decentralized, and operates on bilateral relationships. Consequently, the very concept of a single, definitive “market price” is an abstraction. The task for the firm is to construct a defensible, data-driven proxy for a truth that is never fully observable.

This absence of a universal price tape forces a systemic shift in the definition of best execution itself. It moves from a simple, price-centric measurement to a multi-faceted, process-oriented evaluation. The core of the problem becomes one of data scarcity and asymmetry. Where an equity trader looks outward to a public utility for a benchmark, the illiquid asset trader must look inward, to their own process and proprietary data, to build one.

The quantification of best execution becomes an exercise in demonstrating a rigorous, repeatable, and auditable process designed to achieve the optimal outcome in an environment of inherent uncertainty. The focus is on validating the quality of the decision-making process given the information that could be reasonably gathered at the time of the trade.

The absence of a consolidated tape transforms best execution from a price verification exercise into a process validation discipline.

This reality demands a framework where the firm’s execution policy is the primary analytical tool. This policy must codify the procedures for price discovery, liquidity sourcing, and risk management in a fragmented environment. It defines the “sufficient steps” a firm must take to meet its fiduciary duty. Quantifying best execution, therefore, is the systematic measurement of performance against this internal, pre-defined process.

It involves a qualitative assessment of the execution strategy and a quantitative analysis of the results against bespoke, constructed benchmarks. The goal is to create a evidentiary record that proves the firm navigated the fragmented liquidity landscape in a way that consistently protected the client’s interests, even without a public map.

Strategy

Developing a strategy to quantify best execution for illiquid assets requires building an internal system of record and analysis that compensates for the lack of a public one. The overarching strategy is to shift the burden of proof from a simple price comparison to a comprehensive demonstration of procedural diligence. This framework is built on three pillars ▴ robust pre-trade analysis, disciplined execution methodology, and forensic post-trade review.

Pre-Trade Intelligence the Foundation of Defensible Execution

Before an order is even placed, the strategic framework must be activated. The goal of the pre-trade phase is to document the market conditions and justify the chosen execution path. This is not a passive observation; it is an active intelligence-gathering process.

Firms build a “liquidity profile” for the specific asset in question. This involves answering a series of structured questions:

• Recent Activity What is the volume and frequency of trading in this instrument or highly similar ones? This often involves querying proprietary databases, dealer indications, and historical trade data purchased from vendors.
• Counterparty Landscape Which dealers have historically shown an axe (an interest) in this or similar securities? The system must maintain a database of counterparty strengths and specialties.
• Market Depth What is the likely market impact of the order? Pre-trade analytics models estimate the potential price slippage based on order size and historical liquidity patterns. For large orders, this analysis dictates whether to break the order into smaller pieces or seek a single block execution.
• Benchmark Selection What is the most appropriate benchmark for this trade? The strategy must define a hierarchy of benchmarks. This could be a recent trade price, a composite quote from multiple dealers (a “quote-centric” benchmark), or the price of a correlated, more liquid asset. The choice of benchmark and the rationale for its selection are documented at this stage.

Execution Methodology a Disciplined Approach

The execution strategy itself is a critical component of the overall framework. The choice of how to execute the trade is a direct result of the pre-trade analysis. The firm’s strategy must define clear rules of engagement for different scenarios.

For instance, a small order in a moderately illiquid bond might be handled via a Request for Quote (RFQ) process sent to a targeted list of three to five dealers known to be active in that sector. A large block trade in a highly illiquid private credit instrument, conversely, might necessitate a high-touch, single-dealer negotiation to minimize information leakage. The key is that the policy dictates the methodology. The trader’s discretion is exercised within the bounds of this pre-defined strategic playbook, and any deviations are documented with a clear rationale.

A firm’s execution strategy for illiquid assets is a playbook that maps asset characteristics to a specific, pre-defined execution protocol.

The table below illustrates how different asset characteristics might map to different execution strategies under a well-defined policy.

Post-Trade Review Transaction Cost Analysis (TCA)

The post-trade review is where the quantification happens. This is the forensic analysis that validates the entire process. For illiquid assets, TCA is less about comparing to a single price point and more about reconstructing the “best possible” outcome based on available data. The analysis measures the execution against the benchmark selected in the pre-trade phase.

Key metrics in illiquid TCA include:

1. Quote Composite Arrival Price The primary benchmark is often a composite price created from the indicative quotes received from dealers at the time of the order. The execution price is then compared to the best potential price available at that moment.
2. Spread Analysis How did the execution price compare to the bid-ask spread offered by the winning dealer and the range of spreads from all participating dealers? A narrow spread relative to peers is an indicator of quality.
3. Reversion Analysis After the trade, did the price of the asset revert? Significant reversion might suggest the trade had a large market impact, a cost that needs to be quantified.
4. Peer Comparison How did this execution compare to other trades the firm has done in similar securities under similar market conditions? This internal benchmarking is critical for identifying trends and improving the execution process over time.

This strategic framework creates a closed loop. Pre-trade analysis informs the execution, and post-trade analysis reviews the entire process, providing data that refines the pre-trade models for the future. It is a system designed to learn and adapt, continuously improving the firm’s ability to navigate markets that lack a central source of truth.

Execution

The execution of a best execution framework for illiquid assets is a detailed, data-intensive process. It moves beyond theory and strategy into the realm of operational protocols, quantitative modeling, and auditable record-keeping. This is where the firm builds the evidentiary case for its adherence to its fiduciary duty on a trade-by-trade basis.

The Operational Playbook for an Illiquid Trade

A trader executing an order for an illiquid asset follows a precise, multi-step playbook. This process is designed to be systematic and repeatable, ensuring that every trade is handled with the same level of diligence. The goal is to create a comprehensive audit trail that can be reviewed internally by compliance and externally by regulators or clients.

1. Order Receipt and Pre-Trade Documentation
   • The trader receives the order from the portfolio manager into the Order Management System (OMS).
   • The OMS automatically enriches the order ticket with pre-trade data ▴ historical price information (if any), data on similar securities, and a list of potential counterparties based on past activity.
   • The trader formally documents the initial assessment of the asset’s liquidity and the intended execution strategy (e.g. “Targeted RFQ to 5 dealers due to block size and specialized nature of the bond”).
2. Price Discovery Protocol
   • The trader initiates the price discovery process as defined by the execution strategy. For an RFQ, this means sending the request to the selected dealers simultaneously through an electronic platform or via recorded chat/email messages.
   • All responses ▴ prices, quantities, and timestamps ▴ are automatically captured and logged. This includes dealers who decline to quote, as that is also a valuable piece of market information.
3. Execution and Rationale Capture
   • The trader executes against the best response. The definition of “best” is governed by the Best Execution Factors Matrix. While price is paramount, factors like settlement risk or the ability to execute the full size may lead to choosing a quote that is not the absolute best price.
   • If the chosen execution is not at the best price, the trader must provide a structured justification at the time of the trade (e.g. “Executed with Counterparty B at a slightly lower price to ensure full fill and avoid slicing the order, which would have incurred higher total cost and market risk”). This is a critical step for the audit trail.
4. Post-Trade Data Aggregation and Analysis
   • The execution details are fed into the Transaction Cost Analysis (TCA) system.
   • The TCA system automatically compares the execution to the relevant benchmarks defined in the pre-trade phase (e.g. quote composite arrival price, spread analysis).
   • The system generates a preliminary TCA report for the trade, which is reviewed by the trader and flagged for any anomalies.

Quantitative Modeling the Constructed Benchmark

The cornerstone of quantifying execution for illiquid assets is the creation of a reliable benchmark where none exists publicly. The most common approach is the development of an internal “evaluated price” or “quote composite” benchmark. This model synthesizes various data points to create a fair value estimate at a specific point in time.

The model typically incorporates:

• Direct Dealer Quotes The bids and offers received during the RFQ process for the specific trade.
• Indicative Dealer Runs Daily or intra-day price indications sent by dealers for a range of securities.
• Data from Evaluated Pricing Services Subscriptions to services like Bloomberg’s BVAL or ICE Data Services, which use their own models to price millions of fixed-income securities.
• Matrix Pricing For truly obscure securities, the model may use “matrix pricing.” This involves finding a more liquid security with similar characteristics (e.g. same issuer, similar maturity, similar credit rating) and using its price or yield as a starting point, adjusting for the differences.

The quote composite benchmark is an algorithmically generated fair value estimate, creating a synthetic “tape price” for a specific moment in time.

The table below provides a granular example of a TCA report for a hypothetical corporate bond trade. This demonstrates how multiple quantitative metrics are used to build a comprehensive picture of execution quality.

How Is the Best Execution Factors Matrix Applied in Practice?

The Best Execution Factors Matrix is a formal tool used to ensure that the trading decision considers all relevant aspects of execution, as required by regulations like MiFID II. It forces a holistic view. For an illiquid asset, “Likelihood of Execution” and “Minimizing Market Impact” often receive a higher weighting than they would for a liquid stock. For example, a trader might accept a price that is 5 basis points lower than the best quote if that counterparty is offering to take down the entire block, whereas the best-priced quote is only for a small fraction of the order.

Breaking up the order could lead to information leakage and cause the price of the remaining pieces to deteriorate significantly, resulting in a worse all-in price. The matrix provides a structured way to document this type of complex, multi-factor decision, proving that the firm acted in the client’s best interest by optimizing for total cost rather than just the headline price of the first execution.

Reflection

Is Your Execution Framework an Evidence Locker or a Learning System?

The architecture described provides a robust system for quantifying execution quality in the absence of a tape. It creates a defensible, auditable record. Yet, its ultimate value is determined by its function. A framework used merely to populate compliance reports is a static defense mechanism ▴ an evidence locker to be opened during an audit.

A superior framework functions as a dynamic learning system. The data gathered from every trade, every quote, and every dealer interaction should be fed back to refine the pre-trade analytics, sharpen counterparty selection, and evolve the execution playbook. The objective moves from proving diligence to improving performance. Consider your own operational architecture.

Does it simply record the past, or does it actively inform the future? The answer separates a firm that merely complies with its fiduciary duty from one that masters it.