What Are the Primary Challenges in Reporting Non-Actionable RFQ Responses to CAT?

Concept

The mandate to report non-actionable Request for Quote (RFQ) responses to the Consolidated Audit Trail (CAT) creates a fundamental architectural conflict. This issue originates at the intersection of bespoke pre-trade negotiation and the standardized data structures of market surveillance. An institution’s use of a bilateral price discovery protocol is an exercise in sourcing liquidity with discretion and precision. The CAT system’s objective is to create a comprehensive, linear record of market activity.

The primary challenge arises because a non-actionable RFQ response is, by its nature, an incomplete data object. It is a preliminary signal, an invitation to continue a dialogue, which lacks the finality of an executable order that CAT’s framework is built to ingest.

This dissonance is most apparent in the definitional ambiguity surrounding what constitutes a “bid or offer” under existing regulations like SEC Rule 613. A non-executable quotation, often transmitted via FIX protocol with an indicative flag or through an informal chat, functions as a form of price exploration. It allows market participants to gauge liquidity and potential cost without committing capital or creating a firm, legally binding obligation.

Forcing this indicative data into a reporting field designed for firm commitments introduces systemic risk. The core of the problem is a mismatch in statefulness ▴ the RFQ process is an iterative, evolving dialogue, while CAT reporting requires discrete, unambiguous events.

A surveillance architecture’s integrity depends on its ability to accurately represent the legal and economic substance of a market event.

The operational reality of institutional trading involves complex workflows that do not map cleanly onto a simple event-driven model. A multi-leg options strategy, for instance, may be priced through a series of back-and-forth messages, with pegged prices and spread-based adjustments that are computationally dependent on other market variables. These are not static quotes. They are dynamic calculations.

Attempting to capture this fluid process as a series of distinct “quote” events in CAT results in a representation that is both technically cumbersome and substantively misleading. The system is being asked to record a conversation as a series of declarations, losing the essential context of negotiation in the process.

What Is the Core Regulatory Conflict?

The central regulatory issue is the classification of data. Rule 613 and the architecture of the CAT NMS Plan were constructed around the concept of tangible, reportable events such as orders, routes, and trades. Non-actionable RFQ responses occupy a gray area. They are more than general conversation but less than a firm offer.

The Financial Information Forum (FIF) has argued that these communications are functionally equivalent to indications of interest, which are explicitly not reportable to CAT. The requirement to report them creates a paradox where firms may be compelled to label a non-binding price check as a formal “quote,” potentially prejudicing their legal standing in other regulatory contexts. This forces a firm’s system to apply a definitive label to an inherently non-definitive communication.

Strategy

An institution’s strategy for addressing the challenges of reporting non-actionable RFQ responses must be built on a precise understanding of the data’s lifecycle and its associated risks. The temporary SEC exemption until July 2026 provides a window to develop robust internal systems, yet the underlying definitional problems persist. A sound strategy involves classifying internal data streams with precision, building technological systems that can adapt to regulatory ambiguity, and advocating for a reporting framework that reflects the economic reality of off-book liquidity sourcing.

The first step is a rigorous internal data audit to classify all forms of RFQ communication. This process moves beyond simple electronic flags to analyze the context and intent of each message type. This classification dictates the reporting logic.

The strategic objective is to create an internal system of record that is more granular than what CAT currently requires, allowing the firm to respond to future regulatory shifts with agility. This involves creating a clear distinction between communications that are firm offers and those that are merely part of a price discovery process.

Deconstructing Reporting Obligations

The distinction between an immediately actionable response and a non-actionable one is the central pivot for any reporting strategy. An actionable response is effectively an order waiting for acceptance; a non-actionable response is a piece of market intelligence. The table below outlines the systemic differences that must inform a firm’s strategic approach to compliance and risk management.

The Systemic Risks of Misclassification

The primary strategic risk in this domain is the mischaracterization of a firm’s intent. When a non-binding price indication is logged into a permanent regulatory database as a “quote,” it creates a record that can be misinterpreted. This data could be used in future surveillance analyses or enforcement actions to imply a level of market-making intent or firmness that did not exist. For a principal trading firm, this represents a significant liability.

The firm’s strategy must therefore prioritize the preservation of context, ensuring that any data reported to CAT is accompanied by internal documentation that clarifies its non-binding nature. This defensive data strategy is essential for mitigating long-term regulatory risk.

A compliance framework must not only report data but also preserve the strategic intent behind the communication.

Furthermore, the operational costs associated with building and maintaining these reporting systems are substantial. A strategic assessment must weigh the cost of compliance against the potential penalties for non-compliance or incorrect reporting. This involves a cost-benefit analysis of developing sophisticated parsing tools for chat communications, modifying order management systems to capture new data fields, and training personnel to correctly classify RFQ responses.

The absence of clear guidance from regulators means that firms are investing resources to solve a problem whose final parameters are still undefined. This uncertainty is a strategic challenge in itself, requiring firms to build flexible systems capable of adapting to the final implementation plan due in 2025.

Execution

The execution of a reporting solution for non-actionable RFQ responses reveals deep technical deficiencies in the current Consolidated Audit Trail framework. The core challenge in execution is that the existing CAT event structures, particularly the Quote event, are fundamentally unsuited for the complexity and nuance of indicative pricing workflows. Firms are faced with the task of mapping a multi-dimensional negotiation process onto a flat, rigid data schema, a task that is fraught with complexity and potential for error.

Successfully executing a reporting protocol requires a multi-layered approach. First, firms must develop sophisticated internal data capture and classification engines. These systems need to parse various communication formats, from structured FIX messages to unstructured chats, and apply a consistent logic to determine if a response is actionable.

Second, this classified data must be mapped to the appropriate CAT event fields, a process that is currently problematic. Third, firms must establish robust validation and reconciliation processes to prevent linkage errors between their reports and those of their counterparties, a significant challenge given the lack of clear, universal guidance.

The Inadequacy of Existing CAT Event Types

A detailed analysis of the current CAT Quote event for equities (MENQ) shows its unsuitability for non-actionable responses. As highlighted by the Financial Information Forum, nearly every significant field in the event is either irrelevant or requires modification to accommodate this new data type. For example, fields like firmDesignatedID or accountHolderType are often inapplicable, as an indicative quote is not typically generated from a specific account. The entire event is predicated on the existence of a firm bid and offer, which is absent in many RFQ scenarios.

This forces firms into a position of making interpretive leaps. The execution challenge is to build a translation layer that can populate these fields in a way that is compliant yet does not misrepresent the underlying activity. The most effective long-term solution, as advocated by industry bodies, is the creation of new, purpose-built CAT events, such as a NonExecutablePriceIndication event. Such an event would include fields that accurately describe the nature of the communication, solving the root problem of semantic mismatch.

How Do Firms Handle Complex RFQ Protocols?

The diversity of modern RFQ workflows presents a significant execution hurdle. A robust reporting system must be designed to handle these specific, complex scenarios with precision.

• Pegged and Spread-Based Prices These prices are not fixed values but are calculated based on a reference benchmark (e.g. NBBO, VWAP) or a differential between instruments. The current CAT Quote event lacks fields to specify the pegging methodology or the components of a spread, making accurate reporting impossible without significant data transformation that could lose essential information.
• High-Frequency Updates and Modifications In many systems, a non-actionable RFQ response is not a single event but a stream of continuous updates, sometimes changing multiple times per second. A critical execution question is how to report this stream. Reporting every update would create a flood of data with little value. The CAT framework lacks specific Quote Cancel or Quote Modify events for options, creating ambiguity about how to handle the lifecycle of an indicative quote.
• Communications via Unstructured Formats When price discovery occurs over a Bloomberg chat or similar system, the data is unstructured. Executing a reporting solution requires sophisticated natural language processing (NLP) tools to parse these conversations, identify key data points like symbol, side, price, and quantity, and determine the point at which an indicative price becomes firm. This introduces a layer of inferential logic that is a source of potential reporting errors.

A Proposed Technical Framework

To overcome these execution challenges, a new data structure is required. The following table outlines a conceptual design for a new CAT event tailored to non-actionable responses, addressing the deficiencies of the current system.

The path to full implementation remains complex. The SEC’s temporary relief acknowledges these deep-seated technical issues. The deadline for submitting a written implementation plan by July 31, 2025, and the final reporting deadline of July 31, 2026, establish a clear timeline for the industry and regulators to collaborate on developing a functional and accurate reporting architecture. Firms must use this period to build flexible, intelligent systems that can adapt to the eventual technical specifications.

Reflection

The challenges inherent in reporting non-actionable RFQ responses to CAT are more than a technical compliance exercise. They compel a deeper consideration of a fundamental principle ▴ how does a centralized surveillance system adapt to the evolving, decentralized nature of modern finance? The friction between the fluid logic of bilateral negotiation and the rigid structure of a regulatory database highlights a critical design question for the future of market oversight.

As your institution refines its operational framework, consider how your internal data architecture functions not just as a compliance tool, but as a system of intelligence. The ability to capture, classify, and analyze the full context of your pre-trade communications is a source of strategic advantage. This situation presents an opportunity to build systems that provide a higher fidelity view of your own liquidity sourcing activities. The ultimate goal is an operational framework where regulatory reporting is a natural byproduct of a superior internal data model, one that provides clarity for both internal strategy and external oversight without compromising the integrity of either.