How Do You Quantify Best Execution for Assets without a Public Tape?

Concept

The quantification of best execution for assets traded outside the continuous, observable stream of a public tape presents a foundational challenge in institutional finance. It is an exercise in navigating an information vacuum. For equities, a consolidated tape provides a universal, millisecond-by-millisecond record of price and volume, a ground truth against which any single transaction can be measured. For the vast universe of over-the-counter (OTC) derivatives, structured products, and many fixed-income instruments, this ground truth is absent.

The market is a decentralized network of bilateral conversations, not a centralized forum. Price discovery occurs in discrete, private negotiations, leaving a fragmented data trail that is difficult to assemble and interpret.

Therefore, the task of quantifying execution quality in these markets is fundamentally an act of construction. It requires the institution to build its own analytical framework, a synthetic tape assembled from the fragments of available data. This is an engineering problem before it is a measurement problem. The objective is to design a system that captures, normalizes, and analyzes every available data point to create a reliable proxy for the market at the moment of execution.

This system becomes the lens through which execution quality is viewed, assessed, and improved. The absence of a public tape is not a barrier to analysis; it is the primary design parameter for the analytical system itself. The rigor of this system, its architecture, and its processes become the bedrock of a firm’s fiduciary duty.

Quantifying best execution without a public tape is an engineering challenge to construct a synthetic, private benchmark from fragmented data.

This perspective shifts the focus from a passive search for a non-existent benchmark to the active creation of an internal one. The core components of this constructed reality are the records of interactions with liquidity providers. Every Request for Quote (RFQ), every quote received, every trade executed, and every market data point from ancillary sources must be captured with precision. Timestamps, dealer identities, quantities, and the full context of the inquiry are the raw materials.

The quality of the subsequent analysis is a direct function of the quality and completeness of this initial data capture. Without a robust data foundation, any attempt at quantification is an exercise in speculation. The system must treat this internal data with the same reverence that an equity trading system treats the public tape.

The ultimate goal is to create a defensible, repeatable, and auditable process that demonstrates that all sufficient steps were taken to achieve the best possible outcome for the client. This outcome is a composite of multiple factors, with price being only one, albeit significant, component. Other critical variables include the speed of execution, the certainty of settlement, minimizing information leakage, and accessing sufficient liquidity to complete the desired size without adverse market impact. In the OTC space, these factors are often in tension.

A wider RFQ to more dealers might improve the probability of finding the best price, but it also increases the risk of information leakage, which can cause the market to move away from the trader. Quantifying best execution, therefore, involves evaluating these trade-offs within a structured, data-driven framework. It is the architecture of this framework that provides the foundation for fiduciary compliance and competitive advantage.

Strategy

Developing a strategy to quantify best execution in opaque markets requires a multi-layered approach that spans the entire lifecycle of a trade. The strategy is predicated on the creation of a proprietary data ecosystem and the analytical models to interpret it. This process can be divided into three distinct phases ▴ pre-trade analysis, at-trade execution, and post-trade verification.

Each phase relies on the systematic collection and evaluation of data to inform decisions and measure outcomes. The overarching objective is to build a feedback loop where the results of post-trade analysis continuously refine the pre-trade and at-trade strategies.

Pre-Trade Intelligence the Foundation of Execution

The pre-trade phase is about establishing a benchmark before the order is exposed to the market. Without a public tape, this benchmark cannot be a single point; it must be a calculated expectation of fair value. This involves synthesizing data from multiple sources:

• Historical Trade Data ▴ The institution’s own history of similar trades provides a powerful starting point. By analyzing past executions of the same or similar instruments, traders can establish a baseline for expected costs and identify which liquidity providers have historically offered the most competitive quotes.
• Evaluated Pricing Services ▴ Third-party services, such as those provided by S&P Global or Bloomberg, offer independent, model-driven valuations for a vast range of OTC instruments. These evaluated prices, or “marks,” serve as an objective, unbiased reference point for the expected value of an asset at a specific point in time.
• Indicative Quotes and Market Color ▴ Traders gather continuous intelligence from sales coverage and electronic platforms. This qualitative data, while not firm, provides context on market sentiment, liquidity conditions, and potential axes of interest from dealers.

The synthesis of these inputs allows the trading desk to construct a pre-trade “fair value corridor.” An order is expected to be executed within this range. Any deviation from this corridor during the execution process becomes a measurable event requiring documentation and analysis.

At-Trade Execution the Protocol for Price Discovery

The at-trade phase is the active process of price discovery. For OTC markets, this is most commonly managed through the Request for Quote (RFQ) protocol. A successful at-trade strategy depends on a disciplined and structured approach to the RFQ process:

1. Strategic Dealer Selection ▴ The choice of which dealers to include in an RFQ is a critical decision. Pre-trade analysis informs this choice. A trader might select a small group of dealers for a large, sensitive order to minimize information leakage, or a broader group for a more liquid instrument to maximize price competition. The rationale for this selection must be documented.
2. Systematic Quote Capture ▴ All quotes received in response to an RFQ must be captured electronically. This includes the price, the quantity for which the quote is firm, the time of response, and the identity of the dealer. This data forms the core of the at-trade analysis.
3. Benchmark-Referenced Execution ▴ The trader executes the order against the best responding quote, while simultaneously comparing all quotes to the pre-trade fair value corridor. The “winning” quote is not the only data point of interest; the entire distribution of quotes provides a snapshot of the market’s depth and competitiveness at that moment.

A structured RFQ process transforms a private negotiation into a competitive auction, creating a measurable dataset for analysis.

The table below illustrates a comparison of different strategic approaches to the RFQ process, highlighting the trade-offs inherent in each.

Post-Trade Verification the Feedback Loop

Post-trade analysis, or Transaction Cost Analysis (TCA), is where the quantification of best execution truly crystallizes. It involves a forensic examination of the executed trade against the data collected in the pre-trade and at-trade phases. The goal is to calculate “slippage,” which is the difference between the final execution price and various benchmarks.

Key TCA metrics include:

• Arrival Price Slippage ▴ The difference between the execution price and the pre-trade evaluated price. This measures the cost of execution against the initial fair value expectation.
• Quote Spread Analysis ▴ The difference between the best quote received and the average or median quote. This measures the value of the competitive RFQ process. A wide spread suggests the process added significant value by identifying an outlier price.
• Dealer Performance Metrics ▴ Over time, TCA data is aggregated to build scorecards for each liquidity provider. These scorecards track metrics like response rates, quote competitiveness, and fade rates (where a dealer withdraws a quote). This data-driven approach informs future dealer selection in the pre-trade phase, thus closing the feedback loop.

This three-phase strategy transforms the abstract concept of best execution into a concrete, measurable, and continuously improving operational process. It provides a defensible framework that can be demonstrated to clients and regulators, proving that a systematic and intelligent process was used to maximize value in the absence of a public tape.

Execution

The execution of a best execution framework for non-taped assets is a complex undertaking that merges policy, technology, and quantitative analysis into a single, cohesive system. It moves beyond theoretical strategies to the granular details of implementation. This is where the architectural plans developed in the concept and strategy phases are translated into a functioning operational reality. The success of the entire endeavor hinges on the precision and rigor applied at this stage.

It requires a firm-wide commitment to data integrity and a culture of analytical discipline. The following subsections provide a detailed playbook for constructing such a system, from the operational procedures to the underlying technological and quantitative models.

The Operational Playbook

Building a robust best execution framework requires a clear and comprehensive operational playbook that governs the actions of traders, compliance officers, and technology teams. This playbook is a living document that codifies the firm’s policies and procedures.

1. Establish a Best Execution Committee ▴ This cross-functional group, comprising senior members from trading, compliance, risk, and technology, is responsible for overseeing the entire framework. Their mandate includes:
   • Defining and approving the best execution policy.
   • Reviewing quarterly TCA reports and challenging anomalous results.
   • Authorizing changes to the quantitative models or technology stack.
   • Acting as the final arbiter on complex execution quality questions.
2. Codify the Best Execution Policy ▴ This formal document is the cornerstone of the framework. It must articulate the firm’s approach to achieving best execution and be provided to clients. Key sections should include:
   • Execution Factors ▴ A clear definition of the factors considered in execution, such as price, costs, speed, likelihood of execution, and any other relevant considerations.
   • Venue and Dealer Selection ▴ The criteria used to select execution venues and liquidity providers, including the process for ongoing performance review.
   • RFQ Protocols ▴ Specific guidelines on when to use competitive, targeted, or non-comp RFQs, including documentation requirements for each.
   • TCA Methodology ▴ A description of the benchmarks and analytical methods used in post-trade analysis.
3. Implement a Data Governance Framework ▴ The integrity of the entire system depends on the quality of the data. A strict data governance policy is essential. This involves:
   • Automated Data Capture ▴ Ensuring that all relevant trade data (RFQs, quotes, timestamps, execution details) is captured automatically from the EMS/OMS to eliminate manual entry errors.
   • Data Validation ▴ Implementing automated checks to validate the integrity of the captured data (e.g. checking for missing timestamps, nonsensical prices).
   • Data Enrichment ▴ Integrating third-party data sources (e.g. evaluated pricing) into the internal dataset to provide necessary context for analysis.
   • Secure Data Storage ▴ Maintaining a centralized, immutable data warehouse where all execution data is stored for analysis and auditing purposes.
4. Define the Trader Workflow ▴ The playbook must provide clear, actionable guidance for traders. This includes checklists and required documentation steps within the EMS/OMS for different types of trades. For example, for a large, illiquid block trade, the trader might be required to complete a pre-trade analysis form, documenting the rationale for their chosen dealer list and their expectation for the execution price before sending the first RFQ.
5. Schedule Regular Reporting and Review ▴ The process must include a regular cadence of reporting and review. This typically involves:
   • Daily Exception Reports ▴ Automated reports that flag trades executed outside of pre-defined tolerance bands.
   • Monthly Dealer Scorecards ▴ Quantitative reports that rank dealer performance across various metrics.
   • Quarterly Committee Reviews ▴ In-depth reviews of overall execution quality, dealer performance, and the effectiveness of the current policy.

An operational playbook codifies the firm’s execution philosophy into a set of auditable, repeatable procedures that guide daily activity.

Quantitative Modeling and Data Analysis

The heart of the best execution framework is the quantitative engine that transforms raw data into actionable insights. This involves a series of models and analytical techniques designed to measure performance and identify patterns. The following table provides a hypothetical example of the data captured during an RFQ for a corporate bond, which forms the input for the quantitative models.

From this raw data, a series of TCA metrics can be calculated. The formulas for these metrics are critical to understanding the analysis:

• Arrival Price Slippage ▴ This measures the cost relative to the pre-trade expectation. Formula ▴ (Execution Price – Pre-Trade Mark) Nominal Value Example ▴ (98.57 – 98.50) 10,000,000 = $70,000 cost vs. mark.
• Best Quote Slippage ▴ This measures whether the trader achieved the best price available in the RFQ. Formula ▴ (Execution Price – Best Offer Price) Nominal Value Example ▴ (98.57 – 98.57) 10,000,000 = $0. This indicates perfect execution against the best available quote.
• Mean Quote Slippage ▴ This measures the execution price against the average of all quotes received, indicating the value of selecting the best quote. Formula ▴ (Execution Price – Average Offer Price) Nominal Value Average Offer ▴ (98.58 + 98.60 + 98.57 + 98.62) / 4 = 98.5925 Example ▴ (98.57 – 98.5925) 10,000,000 = -$22,500. The negative result indicates a saving of $22,500 compared to trading at the average price.
• Dealer Response Time ▴ This is a key metric for dealer performance. Formula ▴ Quote Received Time – RFQ Sent Time Example (Dealer C) ▴ 14:30:04.987 – 14:30:00.000 = 4.987 seconds.

These metrics are then aggregated over time to build a comprehensive picture of execution quality and dealer performance, allowing the Best Execution Committee to make data-driven decisions about policies and relationships.

Predictive Scenario Analysis

To illustrate the entire framework in action, consider the case of a portfolio manager, Anna, at a mid-sized asset manager. She needs to execute a $25 million position in a complex, 5-year credit default swap (CDS) on a single, non-benchmark corporate name. The instrument is notoriously illiquid, and there is no public data stream.

Anna’s firm has implemented the robust best execution framework detailed above. The process unfolds over several hours.

The first step is pre-trade analysis. Anna opens the order in her firm’s EMS. The system automatically pulls in the latest evaluated mark for this specific CDS from their third-party data provider, which is 110 basis points (bps). The system also scans the firm’s historical trade database.

It finds only two trades in the same CDS over the past year, one executed at 105 bps and another at 112 bps, both for smaller sizes. The system flags the instrument’s high execution risk. Based on this data, the EMS calculates a pre-trade fair value corridor of 107-113 bps. Anna’s goal is to pay a spread no wider than 113 bps.

She then moves to dealer selection. Her firm’s TCA database provides a scorecard for all approved credit derivatives dealers. For this specific corporate sector, the data shows that Dealer A and Dealer B have historically provided the most competitive quotes and have the highest response rates. Dealer C is a new entrant but has been aggressive in the space.

Dealers D and E have shown a tendency to “fade” on illiquid names, often not quoting or providing very wide markets. Based on this data, Anna constructs a targeted RFQ list ▴ Dealer A, Dealer B, and Dealer C. She consciously excludes D and E to minimize information leakage, documenting this rationale in the EMS. This is a critical, auditable step.

At 10:00 AM, Anna launches the RFQ. The requests are sent simultaneously through the EMS. The system’s clock is synchronized to NIST standards, ensuring the accuracy of all timestamps. At 10:01:15, Dealer C responds with an offer of 112 bps.

At 10:01:45, Dealer A responds with 111.5 bps. At 10:02:30, Dealer B responds with 112.5 bps. All three quotes are for the full $25 million size. The EMS displays the quotes in a grid, highlighting the best offer of 111.5 bps from Dealer A. The system also shows that this best offer is comfortably within the pre-trade fair value corridor of 107-113 bps.

Anna has 30 seconds to execute before the quotes expire. She clicks to execute with Dealer A at 111.5 bps. The trade is filled, and an electronic confirmation is received moments later. The entire at-trade process, from RFQ launch to execution, is captured in the system’s database.

The post-trade analysis begins immediately. The system automatically generates a TCA report for the trade. The Arrival Price Slippage is calculated as (111.5 bps – 110 bps), which is 1.5 bps of “cost” against the third-party mark. This is considered well within tolerance for such an illiquid instrument.

The Best Quote Slippage is zero, as Anna executed at the best price offered. The Mean Quote Slippage is calculated against the average of the three quotes (111.5, 112, 112.5), which is 112 bps. The slippage is (111.5 – 112), or -0.5 bps, representing a saving of $1,250 per year for the life of the swap compared to trading at the average price. The report also logs the response times for each dealer.

This single trade’s data is then rolled up into the firm’s aggregate TCA database. At the end of the month, the dealer scorecards are updated. Dealer A’s competitiveness score improves slightly. Dealer C’s response time is noted as being the fastest. Dealer B’s quote is recorded as the least competitive in this instance.

Three months later, the firm’s Best Execution Committee convenes for its quarterly review. The TCA report for the credit desk is presented. The committee reviews a summary of all trades, including Anna’s CDS execution. The report highlights that for illiquid CDS, the targeted RFQ strategy has consistently resulted in lower arrival price slippage compared to broader RFQs, validating the firm’s policy.

They note the strong performance of Dealer A and the emerging competitiveness of Dealer C. Based on this aggregate data, they decide to slightly increase the firm’s trading limits with Dealer C. The committee signs off on the quarterly report, creating a formal record that they have reviewed and approved the firm’s execution quality. Anna’s single trade, governed by the firm’s systematic framework, has not only achieved a good outcome for her client but has also contributed valuable data that refines the firm’s strategy for the future. This is the virtuous cycle of a well-executed best execution system.

System Integration and Technological Architecture

The operational and quantitative elements of the framework can only function if supported by a robust and integrated technological architecture. This architecture is the central nervous system that collects, processes, and stores the necessary data.

The core components of this architecture include:

• Execution Management System (EMS) / Order Management System (OMS) ▴ This is the primary interface for the trader. A modern EMS/OMS for OTC markets must have a highly functional, integrated RFQ module. This module should allow for the creation of custom dealer lists, simultaneous or sequential RFQ submission, and the real-time display of incoming quotes in a structured format. Crucially, it must automatically capture every data point in the process ▴ timestamps, dealer IDs, prices, quantities, and trader actions ▴ without requiring manual input.
• Data Warehouse ▴ A centralized repository is needed to store all execution-related data. This database must be designed for analytical queries. It should store not only the raw trade data but also the enriched data, such as the associated pre-trade marks and the calculated TCA metrics. Using a time-series database can be particularly effective for this purpose.
• API Integration Layer ▴ The system must be able to communicate with external services via Application Programming Interfaces (APIs). This allows for the automated ingestion of third-party data, such as the evaluated pricing data from vendors needed for pre-trade benchmarks and post-trade analysis. It also enables connectivity to various electronic trading venues.
• FIX Protocol ▴ The Financial Information eXchange (FIX) protocol is the lingua franca for electronic trading. The firm’s systems should use FIX messaging to standardize communication with liquidity providers. Key message types include FIX 4.4 Quote Request (R) to send RFQs, Quote (S) to receive quotes, and Execution Report (8) to confirm trades. Using FIX ensures that data is captured in a structured, consistent, and universally understood format, which is vital for data integrity.
• TCA Engine ▴ This can be a proprietary or third-party application. The TCA engine connects to the data warehouse, runs the quantitative models described previously, and generates the reports for traders, compliance, and the Best Execution Committee. It should offer flexible reporting tools that allow for slicing and dicing the data by asset class, trader, dealer, or any other relevant dimension.

This integrated architecture ensures a seamless flow of data from the moment an order is created to the final review by the oversight committee. It eliminates manual processes, reduces the risk of errors, and creates a single source of truth for all execution quality analysis. This technological foundation is the essential scaffolding that supports the entire best execution framework.

Reflection

The construction of a best execution framework for assets without a public tape is a profound exercise in institutional self-awareness. It forces a firm to move beyond reliance on external benchmarks and to create its own, internally consistent definition of value. The system that emerges is more than a compliance tool; it is a mirror that reflects the firm’s market intelligence, its technological capabilities, and its commitment to its fiduciary duties. The data it generates tells a continuous story about the firm’s interactions with the market, revealing its strengths and its weaknesses with impartial clarity.

The process is dynamic. The models are refined, the technology is upgraded, and the policies evolve. The framework is not a static edifice but a living system designed to adapt to changing market conditions and new analytical techniques. The ultimate question for any institution, therefore, is not whether its framework is perfect, but whether it is designed to learn.

How does the system process anomalies? How does it incorporate new sources of data? How does it challenge the assumptions of its own operators? The resilience and adaptability of this analytical architecture are the true measures of its strength and the ultimate foundation of a lasting competitive edge.