How Does Best Execution Documentation Differ between RFQ and Lit Markets?

Concept

The fundamental distinction in best execution documentation between Request for Quote (RFQ) and lit markets originates from their core architectural designs. One is a system of public, continuous price discovery, while the other is a private, discrete negotiation. Consequently, the evidentiary requirements to prove best execution diverge, reflecting the unique data generated by each process. Documenting an execution on a lit exchange is an exercise in demonstrating adherence to a visible, system-wide benchmark.

The process centers on capturing the state of the public order book at the moment of execution, primarily the National Best Bid and Offer (NBBO), and proving the trade was filled at or better than that price. The documentation is a snapshot of a public event.

In stark contrast, documenting an RFQ execution is an exercise in reconstructing a competitive, private auction. Since no public, continuous benchmark exists for the instrument at the precise moment of the request, the obligation shifts. The firm must create its own benchmark through the act of soliciting quotes. The documentation, therefore, must provide a complete, auditable history of this manufactured competitive process.

It details who was invited to quote, their responses, the timing of each response, and the final rationale for selecting the winning counterparty. This record serves as the evidence of a rigorous and fair price discovery process where one did not previously exist.

The core difference lies in proving adherence to a public benchmark versus reconstructing a private, competitive process.

This structural difference dictates the entire data collection and reporting paradigm. Lit market documentation is reactive; it records what happened against a public yardstick. RFQ documentation is proactive; it builds the yardstick and then records the outcome against it. The regulatory mandate, such as that outlined by FINRA Rule 5310 or MiFID II, applies to both, but its application adapts to the character of the market.

For lit markets, the data is abundant and standardized. For RFQ markets, the data is bespoke to the specific trading event and must be meticulously curated by the executing firm to tell a complete story of competitive tension and diligent counterparty selection.

Strategy

Developing a robust strategy for best execution documentation requires a firm to architect distinct data capture and analysis frameworks for its lit and RFQ workflows. The strategic objective is to create an evidentiary trail that satisfies regulatory obligations while also providing internal feedback for improving execution quality. The two workflows demand different data points, analytical metrics, and review processes, reflecting their intrinsic differences in liquidity sourcing and price discovery.

Data Architecture for Lit Markets

For trades executed on transparent, continuous order books, the documentation strategy centers on capturing a high-fidelity snapshot of the market at the instant of execution. The core of this strategy is to prove that the execution price was favorable relative to the prevailing public quotes. The data points are standardized and widely available through market data feeds.

• Core Data Points ▴ The essential elements include the precise timestamp of the order routing and execution, the execution venue, the price, the size, and the NBBO at the time of the order.
• Analytical Metrics ▴ The primary metric is Price Improvement (PI), which measures the difference between the execution price and the NBBO. A positive PI demonstrates a superior execution. Other metrics include effective spread capture and execution speed.
• Review Process ▴ The review is often quantitative and can be highly automated. The firm’s compliance systems can systematically compare execution records against historical market data to flag any deviations from the firm’s best execution policy, such as executions outside the NBBO without proper justification.

Data Architecture for RFQ Systems

The strategy for RFQ documentation is fundamentally a narrative construction. The firm must build a compelling case that it created a competitive environment to achieve the best possible outcome for the client. This requires capturing the full lifecycle of the quote solicitation process. The data points are generated internally through the firm’s own actions.

The documentation must meticulously record each stage of the bilateral or multi-dealer negotiation. This includes not just the winning quote, but all solicited quotes, to demonstrate that a competitive field was established. This process is particularly critical for instruments like certain derivatives or fixed-income securities that are not traded on a continuous lit venue.

How Does the Documentation Process Differ in Practice?

The practical application of these strategies reveals a significant divergence in operational workload and technological requirements. Lit market data capture can be largely commoditized, relying on established protocols and third-party data providers. RFQ data capture is proprietary to the firm’s execution system and requires purpose-built logging and reporting capabilities within the Order Management System (OMS) or Execution Management System (EMS).

The following table illustrates the strategic differences in the data points that must be documented for each market type.

Strategic Policy and Review

A firm’s execution policy must explicitly address these different workflows. The policy should define the criteria for when an RFQ is appropriate (e.g. for block trades or illiquid instruments) and the minimum number of dealers that should be included in a solicitation to ensure adequate competition. The regular and rigorous review process mandated by regulators must also be bifurcated.

Lit market reviews can focus on aggregate statistical analysis of execution quality. RFQ reviews must be more qualitative, examining individual RFQ events to ensure the process was fair and the dealer selection was justified, especially in cases where the best-priced quote was not chosen.

Execution

The execution of a best execution documentation policy translates strategic principles into concrete operational protocols and technological systems. The objective is to ensure that for every trade, regardless of market structure, a complete and defensible audit trail is generated automatically. This requires deep integration between trading platforms, data repositories, and compliance oversight tools.

Constructing the RFQ Audit File

For an institutional desk executing a large, multi-leg options spread via an RFQ, the documentation process is a critical, concurrent part of the trade lifecycle. The system must be designed to capture a rich dataset that goes far beyond the simple fill report of a lit market trade. The goal is to create a self-contained narrative of the competitive process.

A defensible RFQ audit trail is built on a foundation of comprehensive, timestamped data from every stage of the private negotiation.

The following table provides a granular view of the data fields required to construct a robust audit file for a single RFQ event. This is the raw material from which a best execution report is built.

What Is the Role of Technology in This Process?

Technology is the central nervous system of this documentation process. The firm’s EMS must be configured to log every message related to the RFQ workflow, from the initial request to each dealer’s response, including declines to quote. These logs, often using the FIX protocol, are the primary source of evidence.

The system should also provide an interface for traders to annotate trades with their rationale, especially in complex scenarios where the best-priced quote might be passed over for reasons of size or settlement certainty. This annotation is a critical piece of qualitative evidence that complements the quantitative data.

Comparative Transaction Cost Analysis (TCA)

After the execution, the documentation process extends to Transaction Cost Analysis (TCA). The metrics used for TCA differ significantly between the two market types, reflecting the different definitions of a “good” execution.

• Lit Market TCA ▴ The analysis is benchmarked against public data.
  • Primary Metric ▴ Price Improvement vs. NBBO. This measures how much better the execution price was compared to the best available public quote.
  • Secondary Metrics ▴ Slippage vs. Arrival Price (the price at the time the order was received), Percent of Volume, and Reversion (adverse price movement after the trade).
• RFQ Market TCA ▴ The analysis is benchmarked against the private auction data.
  • Primary Metric ▴ Quote Spread Capture. This measures where the execution occurred relative to the best bid and best offer received from the solicited dealers. A price at or near the best received quote is considered a good execution.
  • Secondary Metrics ▴ Quote Response Time (how quickly dealers respond), Hit Rate (how often a dealer’s quote is selected), and Winner’s Curse Analysis (examining if the winning dealer consistently provides quotes far from the pack, which could indicate stale pricing).

Ultimately, the execution of a best execution documentation policy is about creating a system that can withstand regulatory scrutiny and provide actionable insights. For lit markets, this means proving compliance with a public standard. For RFQ markets, it means proving that the firm created its own valid competitive standard and then met it. The technological and procedural overhead for the latter is substantially higher, but it is a necessary component of accessing the unique liquidity and execution benefits that RFQ systems provide for large and complex trades.

Reflection

The architecture of your firm’s best execution documentation is more than a compliance necessity; it is a reflection of your operational philosophy. It reveals how you define and pursue execution quality across different market structures. The data you collect, the benchmarks you use, and the reports you generate are the tangible artifacts of your firm’s commitment to its clients.

Viewing this documentation not as a static record but as a dynamic intelligence system is the first step toward transforming a regulatory burden into a strategic asset. What does your current data architecture reveal about your firm’s approach to execution, and what capabilities must be built to create a more complete, defensible, and insightful system?