What Are the Key Differences in Best Execution Obligations for Equities versus Non-Equities under the SI Regime?

Concept

The application of best execution principles under the Systematic Internaliser regime reveals a fundamental divergence in market architecture. An SI’s obligation is uniform in its mandate ▴ to achieve the best possible result for a client. The operational reality of fulfilling that mandate for a centrally cleared, liquid equity versus a bilaterally settled, illiquid corporate bond represents a study in contrasts. The core of the issue resides in the structural nature of the instruments themselves and the markets in which they transact.

Equity markets are characterized by high levels of transparency, standardized instrument structures, and a concentration of liquidity on lit venues. Non-equity markets, conversely, are frequently opaque, bespoke, and fragmented across numerous over-the-counter (OTC) relationships.

A Systematic Internaliser, by definition, is an investment firm which, on an organised, frequent, systematic and substantial basis, deals on own account when executing client orders outside a regulated market, an MTF or an OTF. This definition forces the firm to internalize flow, creating a unique execution venue. For equities, the SI operates in a world of abundant public data. The SI’s quoted price can be directly and immediately compared against a consolidated tape and the live order books of multiple competing venues.

The challenge is one of micro-optimization within a data-rich environment. The firm must demonstrate that its internalized price was superior to what was publicly available, factoring in both explicit and implicit costs.

The best execution mandate is consistent across asset classes, but its practical application is dictated by the inherent differences in market structure and transparency.

For non-equities, the SI’s role shifts from a price taker or improver in a transparent market to a primary source of liquidity and price discovery in an opaque one. When an SI provides a quote for a non-equity instrument, it is often creating a price point where none may be publicly visible. The concept of a “best” price becomes inherently more qualitative and dependent on the process of price formation itself.

The obligation moves from evidencing the best outcome against a public benchmark to demonstrating a consistently fair and diligent process for arriving at a price in the absence of one. This distinction is the critical starting point for any analysis of an SI’s operational build-out for best execution compliance.

What Is the Core Regulatory Expectation

The regulatory framework, specifically MiFID II, acknowledges this structural dichotomy. While the overarching principle of taking “all sufficient steps” to obtain the best possible result applies universally, the supporting technical standards and supervisory expectations diverge significantly. For equities, the regime is highly prescriptive, focusing on quantitative comparison against public reference prices. The availability of post-trade data underpins this approach, allowing for rigorous, data-driven analysis of execution quality by the firm, its clients, and regulators.

In the non-equity space, the regulatory expectation is more principles-based. Regulators understand that comparing a bespoke OTC derivative or an illiquid bond to a non-existent public benchmark is a futile exercise. The focus therefore shifts to the integrity of the SI’s internal processes.

The firm must demonstrate that its pricing models are fair, its choice of pricing inputs is sound, and its handling of client requests for quotes (RFQs) is consistent and unbiased. The evidence of best execution is found within the firm’s own operational logic and governance framework, a stark contrast to the externalized, data-centric validation process for equities.

Strategy

Developing a compliant best execution strategy as a Systematic Internaliser requires two distinct architectural blueprints, one for equities and one for non-equities. The unifying strategic goal is the creation of a defensible and repeatable process that evidences optimal client outcomes. The pathways to achieving this goal, however, are dictated by the unique liquidity and data landscapes of each asset class.

For equities, the strategy is fundamentally quantitative and comparative. The SI must construct a system that continuously ingests market data from all relevant lit venues. The core of the strategy is the firm’s smart order router (SOR) and pricing engine, which work in concert to determine the SI’s own quote.

The strategic imperative is to prove, on a trade-by-trade basis, that the internalized execution was superior to the European Best Bid and Offer (EBBO) at the moment of execution. This involves a sophisticated model of total cost analysis (TCA), which accounts for not only the price but also exchange fees, clearing costs, and the implicit cost of information leakage that might occur if the order were routed to a lit market.

A successful SI strategy for equities is built on superior data processing and quantitative comparison, while the non-equity strategy relies on robust internal governance and qualitative price discovery protocols.

How Does Market Transparency Shape Strategy

The level of market transparency is the single most significant factor shaping an SI’s best execution strategy. For equities, the strategy is one of reaction and optimization within a transparent system. The SI is reacting to publicly available prices and optimizing its internalization process to provide a marginal, yet demonstrable, improvement.

This leads to investment in low-latency data feeds, high-speed processing hardware, and sophisticated TCA analytics platforms. The firm’s competitive advantage is derived from its technological capacity to process public information faster and more intelligently than its competitors.

For non-equities, the strategy is one of price formation and justification within an opaque system. Lacking a constant stream of public price data, the SI must build a framework that creates fair prices. This strategy involves several key components:

• Pricing Models ▴ Developing and maintaining robust, validated pricing models for different classes of non-equity instruments. These models must be fed with reliable data inputs, which may include prices from similar instruments, dealer-run pricing services, or relevant underlying benchmarks.
• Counterparty Management ▴ Establishing a broad and diverse set of liquidity providers for instruments where the SI may need to hedge its risk. The ability to poll multiple sources for a price, even if those prices are not public, is a key component of demonstrating a diligent process.
• RFQ Protocols ▴ Designing a systematic and fair request-for-quote system. The SI must ensure that its responses to client inquiries are consistent and that its pricing methodology does not discriminate between clients without a justifiable reason.

The following table illustrates the strategic divergence in approach for an SI across these two asset classes.

Execution

The execution of a best execution policy within a Systematic Internaliser framework translates strategic design into operational reality. The core difference in execution lies in the nature of the data that must be captured, analyzed, and reported. For equities, execution is a high-frequency data processing challenge.

For non-equities, it is a qualitative data and governance challenge. This distinction permeates every aspect of the operational workflow, from pre-trade analytics to post-trade reporting.

An SI’s compliance with its obligations is ultimately evidenced through the data it publishes, primarily through reports mandated by Regulatory Technical Standard 27 (RTS 27). While the mandate to report exists for both asset classes, the content and meaning of these reports are vastly different. For an equity SI, the RTS 27 report is a granular, quantitative account of its performance against the public market. It includes detailed metrics on price, costs, and speed, often measured in microseconds, compared against the prevailing market conditions at the time of each trade.

Operationalizing best execution for an SI requires building two parallel but distinct monitoring and reporting systems, one grounded in high-frequency quantitative analysis for equities and the other in the qualitative integrity of the price formation process for non-equities.

For a non-equity SI, the RTS 27 report serves a different purpose. Given the lack of continuous public pricing, the report focuses on providing transparency into the SI’s activity and pricing behavior. It contains information on the number of orders executed, the volume traded, and indicators of the prices quoted.

It is a disclosure of the SI’s activity rather than a direct comparison against a benchmark that does not exist. The operational burden shifts from proving superiority to proving consistency and fairness.

What Are the Practical Reporting Differences

The practical differences in executing a reporting framework are substantial. The table below provides a comparative view of the key data fields an SI must prepare for an RTS 27 report for a liquid equity versus a bespoke interest rate swap, illustrating the divergent operational focus.

Operational Monitoring Workflow

The day-to-day monitoring process also diverges. An SI’s compliance and trading functions must execute distinct workflows for each asset class to ensure the firm’s policies are being followed.

1. Equity Monitoring ▴
   • Pre-Trade ▴ The system automatically checks the availability of liquidity and pricing on lit venues before an SI quote is generated. Alerts are triggered if the SI’s potential price is outside a defined tolerance from the EBBO.
   • At-Trade ▴ Real-time transaction cost analysis is performed. The execution engine captures a snapshot of the market at the microsecond of the trade to create an evidentiary record.
   • Post-Trade ▴ Daily reports are generated comparing all SI executions against the public market benchmarks. Any deviations are flagged for review by the compliance team, who must investigate and document the reason for the deviation (e.g. better size improvement, lower overall cost).
2. Non-Equity Monitoring ▴
   • Pre-Trade ▴ The process is initiated by a client RFQ. The system logs the request and the data inputs used by the pricing model to generate the quote (e.g. benchmark rates, volatility surfaces).
   • At-Trade ▴ The system records the quote provided to the client, the client’s response time, and the final execution details. For voice-traded instruments, key details of the negotiation must be logged.
   • Post-Trade ▴ Periodic reviews are conducted to assess the fairness and consistency of pricing. This involves analyzing the spreads quoted to different clients for similar instruments and reviewing the performance of the underlying pricing models. The focus is on the integrity of the process, not on comparison to a non-existent external price.

This dual-track approach is resource-intensive, requiring specialized technology and personnel for each asset class. The team overseeing equity best execution needs expertise in market microstructure and high-frequency data analysis. The team responsible for non-equities requires deep knowledge of derivatives pricing models, OTC market conventions, and qualitative governance frameworks.

Reflection

Calibrating the Execution Architecture

The examination of best execution obligations for equities and non-equities under the Systematic Internaliser regime moves beyond a simple compliance checklist. It compels a deeper inquiry into the core design of a firm’s trading architecture. The fundamental divergence in market structure necessitates two distinct operational philosophies running in parallel within a single legal entity. This is not a matter of tweaking a unified system but of engineering two separate, purpose-built engines governed by a common principle.

The critical question for any institutional operator is how this duality is reflected in their own technological and governance frameworks. Is the distinction between quantitative, comparative evidence for equities and qualitative, process-driven evidence for non-equities truly embedded in the firm’s operational DNA? Or is there a risk of applying a single, equity-centric model of oversight to asset classes where it is ill-suited, creating both compliance vulnerabilities and missed opportunities for genuine execution quality improvement? The architecture of compliance must mirror the architecture of the market itself.