How Can a Firm Quantitatively Measure the Price Improvement Gained from Using Systematic Internalisers?

Concept

The quantitative measurement of price improvement from a Systematic Internaliser (SI) is an exercise in architectural validation. It is the process of defining, with empirical rigor, the economic value generated by a firm’s decision to internalize order flow. A firm constructs or engages an SI as a deliberate piece of market infrastructure, a private mechanism for liquidity provision operating in parallel to public exchanges.

The fundamental purpose of this measurement is to move beyond anecdotal evidence of “good fills” and establish a data-driven, auditable proof of the system’s efficacy. This requires a framework that treats price improvement as a core output of a sophisticated risk-management and execution system.

At its heart, a Systematic Internaliser is an investment firm that uses its own capital to execute client orders bilaterally. When a client sends an order, the SI can choose to fill it directly, becoming the counterparty to the trade. This process occurs outside the ecosystem of traditional lit venues like a stock exchange. The capacity to offer price improvement stems directly from this structure.

By internalizing a trade, the SI can circumvent exchange fees, clearing costs, and the need to cross the bid-ask spread present on the public market. The SI captures a portion of this spread for itself while passing a fraction of it back to the client in the form of a better price. This is the foundational economic exchange that the measurement process seeks to quantify.

The core challenge lies in constructing a counterfactual ▴ proving the value of the path taken against the value of the paths available in the public market at the precise moment of execution.

The mechanics of this value transfer are governed by the market’s microstructure and regulatory frameworks, principally the Markets in Financial Instruments Directive (MiFID II) in Europe. MiFID II formalizes the SI regime, establishing thresholds for activity and imposing transparency obligations. One critical constraint is the tick size regime, which standardizes the minimum price increments for quoting on public venues.

An SI, in certain circumstances, could offer price improvements at a granularity finer than the public tick size, providing a distinct and measurable advantage. However, regulations also mandate that SI quotes must reflect prevailing market conditions, tethering their pricing to the public market’s benchmark, the European Best Bid and Offer (EBBO).

Therefore, measuring price improvement is a two-fold analytical problem. First, it requires the establishment of a high-fidelity, time-synchronized benchmark representing the best available public market price at the instant of internalization. Second, it demands a sophisticated understanding of the risks absorbed by the SI in providing this improved price. The SI is taking on inventory and market risk.

A complete quantitative picture must account for this risk, ensuring the measured “improvement” is a true economic gain and not a temporary benefit that is later eroded by adverse price movements. The entire process is a testament to a firm’s ability to manage its own liquidity, risk, and technology stack to produce a superior outcome for its clients.

Strategy

Developing a strategy to quantify SI price improvement is about building a robust and defensible measurement architecture. The strategic objective is to create a system of record that can withstand internal scrutiny and regulatory examination, proving unequivocally the value delivered by internalization. This moves the analysis from a simple calculation to a comprehensive Transaction Cost Analysis (TCA) framework tailored specifically to the principal-trading nature of a Systematic Internaliser.

A Multi-Benchmark Approach

A credible strategy cannot rely on a single point of comparison. The market is dynamic, and the quality of an execution can be viewed through multiple lenses. A multi-benchmark framework provides a more complete and resilient assessment of performance.

The primary and most critical benchmark is the European Best Bid and Offer (EBBO) at the time of the trade. This represents the best available price on any lit European venue. For a client purchase, the price improvement is measured against the EBBO Ask; for a client sale, it is measured against the EBBO Bid. This is the most direct and intuitive measure of the immediate financial benefit passed to the client.

Secondary benchmarks provide deeper context about the execution’s quality relative to broader market activity:

• Arrival Price ▴ This is the midpoint of the EBBO at the moment the parent order arrives at the firm’s trading desk. Comparing the final execution price to the arrival price is a core component of Implementation Shortfall analysis. It captures not only the price improvement relative to the spread but also the price drift, or market impact, that may have occurred between the order’s arrival and its execution.
• Volume-Weighted Average Price (VWAP) ▴ For large orders that are filled in multiple tranches over a period, comparing the average execution price against the market’s VWAP for the same period is a standard industry practice. It assesses how the execution performed relative to the average price paid by all market participants during that time.
• Quote Midpoint ▴ A trade executed at the midpoint of the EBBO is often considered a “perfect” trade, as it equally splits the benefit of crossing the spread between the two counterparties. Measuring the frequency and volume of midpoint executions is a powerful indicator of execution quality.

What Is the Necessary Data Architecture?

The foundation of any quantitative strategy is the quality and granularity of its data. To perform these calculations accurately, a firm must maintain a sophisticated data infrastructure capable of capturing and synchronizing multiple data streams with high-precision timestamps, ideally at the microsecond or nanosecond level.

An effective measurement strategy transforms raw execution data into strategic intelligence, revealing the interplay between price improvement and absorbed risk.

The following table outlines the essential data fields required for each trade record in the analysis database. The integrity of this data is paramount; any inconsistencies in timestamps or price data can invalidate the entire analysis.

Integrating Risk Adjustment

A sophisticated strategy recognizes that gross price improvement is only half the story. When an SI provides liquidity, it takes the other side of the client’s trade onto its own book, absorbing inventory risk. The most significant of these risks is adverse selection ▴ the possibility that the client is trading based on information the SI does not possess. If the market consistently moves against the SI’s position immediately after a trade, the initial price improvement offered can be quickly eroded by trading losses.

Therefore, the strategy must incorporate a risk-adjustment layer. This is typically achieved through markout analysis, which measures the profitability of each trade from the SI’s perspective over short time horizons following the execution. By subtracting the average cost of adverse selection (as measured by markouts) from the gross price improvement, a firm can calculate a “net economic value.” This net figure represents the true, risk-adjusted benefit generated by the Systematic Internaliser, providing a far more honest and strategically useful measure of its performance.

Execution

The execution of a quantitative measurement framework for SI price improvement is a detailed, multi-stage process. It requires the systematic application of financial logic to granular trade and market data. This operational protocol transforms abstract strategic goals into concrete, quantifiable results, forming the bedrock of a firm’s ability to assess and optimize its internalization activities.

The Operational Playbook for Measurement

The core of the execution process can be structured as a formal operational playbook, typically run on a periodic basis (e.g. monthly or quarterly) to assess performance. This playbook ensures consistency, repeatability, and auditability of the results.

1. Data Aggregation and Cleansing ▴ The first step is to gather all necessary data for the analysis period. This involves pulling trade logs from the firm’s Execution Management System (EMS) and Order Management System (OMS). Simultaneously, it requires retrieving synchronized, high-frequency market data from a dedicated provider. This market data must contain the consolidated European order book to accurately reconstruct the EBBO for any given nanosecond. The two datasets are then merged, aligning each SI execution with the precise market state at the time of the trade. Any records with missing data or timestamp mismatches must be flagged and investigated.
2. Gross Price Improvement Calculation ▴ With a clean dataset, the initial calculation can proceed. For every single trade, the gross price improvement is computed. This calculation is the foundational layer of the entire analysis.
3. Adverse Selection Costing via Markout Analysis ▴ The next stage introduces the risk adjustment. Markout analysis is performed on the same set of trades to quantify the cost of providing liquidity. This reveals the average profit or loss experienced by the SI in the immediate aftermath of the trade.
4. Net Economic Value Synthesis ▴ The outputs of the previous two stages are then combined. The total cost of adverse selection is subtracted from the total gross price improvement. This provides the Net Economic Value, a holistic metric that balances the client benefit with the risk and cost borne by the firm.
5. Reporting and Dissemination ▴ The final results are compiled into reports. These reports are tailored to different audiences ▴ detailed, trade-level analysis for the trading desk to optimize its quoting logic; summary-level dashboards for senior management to assess the SI’s overall contribution; and specific formats required for regulatory reporting, such as the data points for MiFID II’s RTS 27 reports on execution quality.

Quantitative Modeling and Data Analysis

The heart of the execution lies in the precise application of quantitative models. The following tables provide a granular, realistic view of the data and calculations involved.

Table 1 ▴ Gross Price Improvement Calculation

This table demonstrates the foundational calculation. The reference benchmark is the EBBO. Price Improvement (PI) is calculated in both currency terms per share and as a basis point (BPS) measure for standardized comparison.

Formula for PI (Buy) ▴ (EBBO Ask – Execution Price) Quantity

Formula for PI (Sell) ▴ (Execution Price – EBBO Bid) Quantity

Table 2 ▴ Markout Analysis for Adverse Selection Costing

This table illustrates how to measure the SI’s post-trade performance. A negative markout from the SI’s perspective (i.e. the price moves against the SI’s new position) represents a cost. The markout is calculated as the change in the market midpoint price at various time intervals after the trade.

Formula for Markout (Buy) ▴ (Midpoint_t+N – Midpoint_t) Quantity

Formula for Markout (Sell) ▴ (Midpoint_t – Midpoint_t+N) Quantity

How Do These Metrics Inform Strategy?

The analysis of this data directly informs trading strategy. For example, if the markout analysis for a particular client consistently shows high adverse selection costs (large negative markouts), the SI’s quoting algorithm may be adjusted to offer slightly less aggressive price improvement or wider spreads for that client’s orders. Conversely, for flow that proves to be benign or “uninformed” (consistently positive or zero markouts), the SI can confidently offer more competitive pricing to attract greater volume. This creates a dynamic feedback loop where quantitative measurement directly refines execution strategy, enhancing the overall profitability and value proposition of the Systematic Internaliser.

Reflection

The establishment of a robust quantitative framework transforms the perception of a Systematic Internaliser. It ceases to be a simple routing destination and becomes a dynamic, measurable component of a firm’s core market-facing architecture. The data generated provides more than a report card; it offers a detailed schematic of the firm’s interaction with client order flow and the broader market ecosystem.

With this level of empirical evidence, how does the internal conversation about execution quality change? When the trading desk can articulate its value not just in basis points of price improvement, but in the net economic value after accounting for absorbed risk, the dialogue with portfolio management and compliance becomes one of shared intelligence. The data provides a common language to discuss the complex trade-offs between immediate execution cost and longer-term market impact.

Ultimately, this framework prompts a deeper strategic question. If the value of internalization can be so precisely measured and optimized, what other aspects of the firm’s trading and investment process could benefit from a similar level of architectural scrutiny? The ability to quantify this specific edge is a gateway to viewing the entire operational structure as a system to be engineered for superior performance.