How Can an Asset Manager Quantitatively Prove Best Execution When Using a Systematic Internalisers?

Concept

An asset manager’s directive to secure best execution is an immutable principle of fiduciary duty. When engaging with a Systematic Internaliser (SI), a firm that uses its own capital to execute client orders outside of a traditional lit exchange, the process of proving this principle becomes a rigorous quantitative exercise. The architecture of your proof rests upon a foundation of data, defensible benchmarks, and a transparent analytical framework.

It is the methodical construction of this evidence chain that transforms the abstract obligation of best execution into a tangible, measurable, and auditable reality. The core challenge lies in demonstrating that the price received from the SI was the superior outcome available for that specific order, at that precise moment, considering the full spectrum of execution factors.

Systematic Internalisers operate within a specific regulatory definition, engaging in organized, frequent, and substantial own-account trading when executing client orders. This structure presents both opportunities and analytical hurdles. The opportunity is access to a deep pool of proprietary liquidity, which can reduce market impact and offer price improvement. The hurdle is the inherent opacity of this bilateral engagement compared to the centralized limit order books of public exchanges.

Your quantitative proof, therefore, must illuminate this process, using data to bridge the gap between the private quote and the public market consensus. This requires a shift in perspective from simply receiving a fill to actively deconstructing the quality of that fill relative to all viable alternatives.

The fundamental task is to build a verifiable and data-driven narrative demonstrating that routing an order to a Systematic Internaliser was the optimal decision for the client.

The regulatory framework, particularly MiFID II, mandates that firms take “all sufficient steps” to obtain the best possible result for their clients. This is a holistic standard that encompasses price, costs, speed, likelihood of execution, and any other relevant consideration. For professional clients, these factors must be weighed to determine the superior outcome. For retail clients, the focus is sharper, centered on the total consideration, which combines the price of the instrument with all explicit costs.

Your quantitative framework must be sophisticated enough to capture and correctly weight these factors according to the client type and the specific context of the order. This is the operational mandate ▴ to design and implement a system of measurement that is as dynamic and multifaceted as the execution obligation itself.

Strategy

A robust strategy for demonstrating best execution with Systematic Internalisers is built on three pillars ▴ a comprehensive data architecture, the selection of appropriate and defensible benchmarks, and a rigorous post-trade analysis engine. This strategy moves beyond mere compliance to create a feedback loop for continuously refining execution quality. The objective is to construct a system that not only proves past performance but also informs future routing decisions. It is an exercise in building an internal intelligence layer that governs the firm’s interaction with all liquidity sources, including SIs.

Constructing the Data and Benchmarking Framework

The foundation of any quantitative analysis is the quality and granularity of the underlying data. To evaluate SI performance, an asset manager must systematically capture a wide array of data points for every single order. This is a non-negotiable prerequisite for any credible analysis.

The required data includes not just the execution details but also the state of the market at the moment the routing decision was made. This allows for a complete reconstruction of the trading environment.

Key data points to capture include:

• Order Timestamps ▴ Millisecond-level timestamps for order creation, routing to the SI, quote reception, and final execution are essential for accurate latency and slippage calculations.
• SI Quote Data ▴ The full quote provided by the Systematic Internaliser, including price and available size, must be logged.
• Synchronous Market Data ▴ At the moment the SI quote is received, the system must capture the state of the relevant public market data, including the National Best Bid and Offer (NBBO) or equivalent European Best Bid and Offer (EBBO).
• Execution Details ▴ The final execution price, size, and any associated fees or commissions must be recorded with precision.
• Alternative Venue Data ▴ For a complete analysis, data from alternative execution venues, such as lit markets or other liquidity providers, should also be captured to understand the full range of available options at the time of the trade.

The selection of a benchmark determines the lens through which execution quality is viewed, making its appropriateness to the order’s intent paramount.

With a complete dataset, the next strategic decision is the selection of benchmarks. A single benchmark is insufficient; a suite of benchmarks should be used to analyze different facets of execution quality. The choice of the primary benchmark should align with the order’s underlying instruction and intent.

What Is the Role of Transaction Cost Analysis?

Transaction Cost Analysis (TCA) is the engine that drives the entire strategy. It is the application of the chosen benchmarks to the captured data to produce quantitative metrics of execution quality. A sophisticated TCA framework provides a multi-dimensional view of performance.

The analysis should be segmented by asset class, order size, market conditions, and by the SI itself. This allows the asset manager to identify patterns in execution quality and make data-driven decisions about future order routing.

The core output of the TCA process is a set of metrics that form the basis of the best execution proof. These metrics should be calculated for every relevant trade and then aggregated to provide a comprehensive picture of SI performance. This analytical output is the definitive evidence required to satisfy both internal governance and external regulatory scrutiny.

Execution

The execution of a quantitative best execution framework involves the operational implementation of the data, benchmarking, and analysis strategy. This is where the theoretical framework is translated into a concrete, repeatable, and auditable process. The goal is to create a system that automatically captures the necessary data, calculates the key performance metrics, and generates reports that can be reviewed by compliance, management, and regulators. This process must be systematic and integrated directly into the firm’s trading workflow through its Order Management System (OMS) and Execution Management System (EMS).

The Operational Playbook for Quantitative Proof

Implementing a defensible proof of best execution follows a clear, multi-step process. This playbook ensures that every order executed with a Systematic Internaliser is subject to the same level of rigorous, quantitative scrutiny.

1. Pre-Trade Analysis ▴ Before an order is routed, the system should perform a snapshot analysis. This involves capturing the current market conditions, including the EBBO/NBBO and available liquidity on lit venues. This pre-trade snapshot serves as the baseline against which the SI’s execution will be measured.
2. At-Trade Data Capture ▴ When the order is sent to the SI and a quote is returned, the system must log all relevant data points with high-precision timestamps. This includes the SI’s quoted price and size, alongside a refreshed snapshot of the public market data. This is the critical point of comparison.
3. Post-Trade Metric Calculation ▴ Immediately following execution, the TCA engine calculates a suite of performance metrics. These calculations are performed automatically and stored in a dedicated database for analysis and reporting.
4. Regular Review and Reporting ▴ On a periodic basis (e.g. monthly or quarterly), the aggregated TCA data is compiled into comprehensive reports. These reports should provide summary statistics, trend analysis, and detailed breakdowns of execution quality by SI, asset class, and order type.
5. Feedback Loop and Policy Refinement ▴ The conclusions drawn from the analysis must be fed back into the firm’s execution policy. If the data shows that a particular SI is consistently underperforming for certain types of orders, the routing logic should be adjusted accordingly. This creates a dynamic and self-improving execution process.

Quantitative Modeling and Data Analysis

The core of the execution process is the quantitative analysis itself. The primary metric for evaluating an SI is Price Improvement. This measures the value added by executing with the SI compared to the prevailing public market price. Price Improvement can be calculated in several ways, each providing a different perspective on performance.

The fundamental calculation is against the public market quote at the time of execution:

• For a buy order ▴ Price Improvement = (Reference Price – Execution Price) Quantity
• For a sell order ▴ Price Improvement = (Execution Price – Reference Price) Quantity

The ‘Reference Price’ is typically the Best Offer for a buy order and the Best Bid for a sell order. A positive result indicates that the client received a better price than was publicly available.

Consistent, automated data capture and analysis are the bedrock of a defensible best execution policy.

Let’s consider a hypothetical analysis of a series of trades with an SI.

This data provides a clear, quantitative record of performance. For trades A101 and A102, the SI provided tangible price improvement. For trades A103 and A104, the execution was at the public market price, which is still a valid execution, but demonstrates no additional price benefit. Aggregating this data over thousands of trades allows the asset manager to build a statistically significant profile of the SI’s performance and prove, on a quantitative basis, the quality of execution obtained for its clients.

How Should Latency Be Factored into the Analysis?

Another critical quantitative metric is execution latency. This can be measured as the time difference between sending the order to the SI and receiving the execution confirmation. High latency can be a significant cost, especially in fast-moving markets, as the market price can move away from the expected execution price.

This ‘slippage’ due to latency should be tracked and analyzed as part of the overall TCA process. By correlating latency with price improvement, an asset manager can determine if an SI’s slower execution times are justified by superior pricing, or if they represent an unacceptable level of opportunity cost.

Reflection

The construction of a quantitative proof for best execution is an exercise in building a more intelligent and responsive trading infrastructure. The data, metrics, and reports are the components of a system designed to achieve a singular goal ▴ delivering a superior, defensible outcome for the client. The process forces a firm to look inward, to examine its own decision-making architecture and its technological capabilities. Does your current framework provide this level of granular insight?

Can it automatically capture the necessary data points with the required precision? The answers to these questions reveal the maturity of your execution process and highlight the path toward a more robust and data-driven operational model. The ultimate advantage lies in transforming a regulatory obligation into a source of competitive and operational strength.