How Can Buy-Side Firms Quantitatively Measure the Benefits of Using Systematic Internalisers?

Concept

A buy-side firm’s decision to route order flow to a Systematic Internaliser (SI) is a calculated move within the complex architecture of modern financial markets. The central question is one of verifiable, quantitative benefit. The value proposition of an SI is rooted in its unique structural position, operating as a private liquidity source that executes client orders against its own capital.

This model, formalized under MiFID II, creates a distinct execution pathway, separate from the public auction dynamics of a lit exchange. For the institutional trader, this is not merely an alternative venue; it is a strategic tool for managing transaction costs and information leakage.

The core mechanism involves the buy-side firm interacting directly with the SI, which is obligated to provide firm quotes for liquid instruments up to a Standard Market Size (SMS). This interaction is bilateral, a direct engagement with a known counterparty’s principal liquidity. The quantitative measurement of its benefits, therefore, begins with a precise understanding of what is being measured.

It is an assessment of execution quality within this specific structural context, weighed against the outcomes achievable on transparent, multilateral trading venues like exchanges or MTFs. The analysis must quantify the trade-offs between the potential for price improvement and reduced market impact offered by the SI, and the explicit and implicit costs of this access.

The fundamental analysis compares the execution quality of bilateral, principal-based liquidity against the multilateral, anonymous order flow of public exchanges.

Understanding this requires a systems-level view. The European market is a fragmented landscape of liquidity pools. An SI represents a significant node in this network, concentrating a specific type of liquidity. For a buy-side desk, the challenge is to build a data-driven framework that can dynamically assess when routing to this node produces a superior outcome.

This framework moves beyond simple post-trade analysis to a predictive capability, evaluating which orders, under which market conditions, are best suited for internalisation. The quantitative journey starts by acknowledging the SI’s dual nature ▴ it is both a counterparty and a regulated execution venue, a status that brings both opportunities for enhanced execution and obligations for transparency and reporting.

Strategy

Developing a strategy to quantify the benefits of Systematic Internalisers requires a robust and multi-faceted approach to Transaction Cost Analysis (TCA). A traditional TCA framework, while useful, provides an incomplete picture. A comprehensive strategy must be built around a core philosophy of comparative analytics, benchmarking SI performance not just against a single point of reference, but across a spectrum of available execution alternatives.

A Multi-Benchmark Analytical Framework

The cornerstone of a successful measurement strategy is the selection of appropriate benchmarks. A single benchmark, such as the Volume-Weighted Average Price (VWAP), is insufficient as it fails to capture the nuances of SI execution. A superior strategy employs a hierarchy of benchmarks to isolate different aspects of performance.

• Arrival Price ▴ This benchmark, representing the market midpoint at the time the order is sent to the SI, is the purest measure of implementation shortfall. It captures the full cost of execution from the moment the investment decision is made.
• European Best Bid and Offer (EBBO) ▴ For any given trade, the execution price from an SI can be directly compared to the prevailing EBBO at the moment of execution. This provides a clear, quantifiable measure of price improvement (PI). A consistent pattern of execution at prices better than the EBBO is a primary indicator of SI value.
• Contemporaneous Exchange Trades ▴ The analysis should compare the execution costs of SI trades with those of similar trades executed on lit markets during the same period. This provides a direct, like-for-like comparison of venue performance, controlling for market conditions. Research indicates that while SI trades may have a higher liquidity cost, they often exhibit a lower price impact, a crucial benefit for larger orders.

What Is the True Cost of Information Leakage?

A critical, yet difficult to quantify, benefit of using an SI is the reduction of information leakage. Exposing a large order to a lit market can signal intent, leading to adverse price movements as other participants react. SIs, by their bilateral nature, offer a degree of discretion. The strategy to measure this involves analyzing post-trade price reversion.

Price reversion analysis examines the behavior of the stock’s price in the minutes and hours after a trade is executed. If a large buy order is executed and the price subsequently reverts downwards, it suggests the initial trade had a temporary, impact-driven effect. Conversely, if the price continues to rise, it may indicate that the trade was part of a larger, informed strategy that was not fully leaked to the market. By comparing the price reversion patterns of SI trades against those of lit market trades, a firm can quantify the implicit cost savings from reduced market impact.

Effective measurement hinges on comparing SI execution outcomes against a dynamic set of benchmarks, including lit market prices and post-trade price behavior.

The following table outlines a strategic framework for organizing this comparative analysis:

This structured approach allows a buy-side firm to move from simple cost reporting to a sophisticated, evidence-based strategy for venue selection. It provides the data necessary to justify routing decisions, satisfy best execution requirements, and ultimately, build an execution architecture that optimizes performance by intelligently allocating order flow to the most suitable venue.

Execution

The execution of a quantitative framework to measure SI benefits is a detailed, data-intensive process. It requires the systematic collection, normalization, and analysis of trade data from multiple sources. The goal is to build a living analytical model that provides actionable intelligence to the trading desk and satisfies rigorous best execution oversight.

The Operational Playbook for Quantitative Analysis

Implementing a robust measurement system follows a clear, multi-step process. This operational playbook ensures consistency, accuracy, and depth in the analysis.

1. Data Aggregation and Normalization ▴ The first step is to consolidate all relevant data. This includes the firm’s own order management system (OMS) records, execution data from the SI (including the precise time of execution), and high-frequency market data from a reliable vendor. Market data must include the full order book to reconstruct the EBBO accurately at the microsecond level. All timestamps must be synchronized to a common standard, typically UTC.
2. Benchmark Calculation ▴ For each execution on an SI, a series of benchmarks must be calculated. This involves querying the market data to find the EBBO midpoint, bid, and ask at the time of the trade. For impact analysis, the price trajectory before and after the trade needs to be recorded at set intervals (e.g. 1 second, 1 minute, 5 minutes, 30 minutes).
3. Metric Computation ▴ With the raw data and benchmarks in place, the core metrics can be computed. Price Improvement (PI) is calculated as the difference between the execution price and the relevant EBBO price (offer for a buy, bid for a sell), multiplied by the number of shares. Price reversion is calculated as the percentage change in price from the execution time to a future point in time.
4. Segmentation and Peer Group Analysis ▴ The analysis becomes powerful when data is segmented. Trades should be grouped by factors like order size, stock liquidity, time of day, and market volatility. This allows the firm to answer specific questions, such as “Does this SI provide better execution for large-cap stocks in volatile markets?” Performance should be compared against a peer group of other SIs and lit venues.
5. Reporting and Feedback Loop ▴ The final step is to translate the quantitative findings into actionable reports for traders and compliance officers. These reports should visualize trends in PI, market impact, and other key metrics. This creates a feedback loop, allowing the trading desk to refine its routing logic based on empirical evidence.

Quantitative Modeling and Data Analysis

The core of the execution phase lies in the detailed analysis of the trade data. The following table presents a hypothetical analysis of a series of trades routed to a Systematic Internaliser, demonstrating how the key metrics are calculated and interpreted.

How Should Adverse Selection Be Modeled?

A sophisticated analysis must also model for adverse selection. This involves assessing whether the SI is disproportionately providing liquidity for “non-toxic” order flow while rejecting orders that are more informed. One way to model this is to analyze the “information ratio” of the trades executed. This can be proxied by the magnitude of post-trade price movement in the direction of the trade.

If the SI’s executed trades consistently precede smaller market movements than trades executed on lit venues, it may indicate the SI is successfully avoiding informed flow. A high rate of rejection for orders that are subsequently executed on a lit market with significant market impact is also a red flag for adverse selection.

A granular, data-driven execution framework transforms TCA from a compliance exercise into a source of competitive advantage in venue selection.

By executing this detailed playbook, a buy-side firm can build a comprehensive and dynamic picture of the value provided by each of its SI counterparties. This quantitative rigor is essential for optimizing execution strategies, fulfilling regulatory obligations, and ultimately, protecting and enhancing portfolio returns. It provides the empirical foundation needed to navigate the complexities of a fragmented liquidity landscape with confidence and precision.

Reflection

Calibrating the Execution Architecture

The framework for quantifying the value of a Systematic Internaliser is more than an analytical model; it is a critical component of a firm’s overall execution architecture. The data and metrics derived from this process provide the intelligence needed to build smarter, more adaptive order routing systems. The insights gained allow a firm to move beyond static routing rules to a dynamic system that considers not only the instrument and order size but also the prevailing market conditions and the historical performance of each potential counterparty.

This process prompts a deeper consideration of the firm’s relationship with liquidity. It forces a quantitative definition of execution quality and a systematic evaluation of every available pathway. Assembling this capability is an investment in the firm’s own intelligence layer, creating a durable competitive advantage.

The ultimate goal is an execution system that is not merely compliant, but optimally efficient, leveraging every structural feature of the market to achieve the best possible outcome for the end investor. The question then becomes how this calibrated system can be evolved to anticipate changes in market structure and liquidity provision.