How Does the Emergence of Systematic Internalisers Affect Best Execution Analysis for Institutional Investors? ▴ Question

Q: How Does SI Interaction Reshape Best Execution Factors?

The traditional factors of best execution—price, cost, speed, and likelihood of execution—are all re-contextualized by the presence of SIs. The "price" factor is no longer a single data point from a lit exchange. It becomes a composite variable that must include the potential for price improvement offered by an SI, weighed against the public quote. The "cost" element must also be reassessed. While explicit commission costs may be lower, the implicit costs of information leakage or potential adverse selection if an SI rejects an order must be modeled. An effective strategy involves a pre-trade analytical process that estimates the market impact of executing on a lit venue versus the probability of a successful, non-impactful execution at an SI.

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Concept

The emergence of the Systematic Internaliser (SI) within the European market architecture fundamentally re-calibrates the analytical framework for best execution. For an institutional investor, viewing the SI as merely another destination for order flow is a critical miscalculation. It represents a structural shift in how liquidity is accessed and priced, moving a significant volume of principal-desk capital into a more transparent, quasi-public framework.

An SI is an investment firm that executes client orders on its own account in an organized, frequent, and substantial manner. This mechanism effectively creates a hybrid venue, one that possesses the bilateral characteristics of Over-the-Counter (OTC) trading while simultaneously adhering to the pre-trade transparency obligations typically associated with lit markets.

The core operational principle of an SI is dealing on own account. When an institutional order is routed to an SI, the firm is the direct counterparty. This process internalizes the trade, matching the client’s order against the firm’s own inventory and risk book. The regulatory framework of MiFID II mandates that this internalization, when performed at scale, must be governed by specific rules.

SIs are required to publish firm quotes for liquid instruments, making their pricing accessible and comparable. This requirement injects a new layer of competitive tension into the market, compelling these large dealers to compete on price in a way that was previously confined to their private client relationships or the anonymity of a central limit order book.

Systematic Internalisers introduce a formalized, regulated channel for accessing principal liquidity, thereby altering the very topology of the institutional execution landscape.

This structural alteration has profound implications. Best execution analysis can no longer be a simple comparison between lit exchange prices and the final execution price. It must now incorporate a more sophisticated evaluation of a fragmented liquidity landscape where SIs operate as major nodes.

The analysis must account for the unique benefits and drawbacks of this channel, including the potential for price improvement against public benchmarks, the reduction of market impact for large orders, and the specific counterparty risk associated with the SI itself. The institutional desk’s challenge is to build a decision-making and analytical matrix that correctly weighs these factors, treating the SI not as an endpoint, but as a dynamic component within a holistic execution strategy.

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Strategy

Integrating Systematic Internalisers into an institutional execution strategy requires a move beyond legacy analytical models. The process demands a dynamic framework that evaluates SIs as a distinct liquidity source with a unique risk-return profile. The primary strategic adaptation involves the evolution of Smart Order Routers (SORs) and other execution algorithms. These systems must be recalibrated to recognize SIs as a primary venue class, capable of offering competitive pricing, particularly for orders that fall below the Large-in-Scale (LiS) threshold but are substantial enough to cause impact on lit markets.

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Recalibrating Liquidity Sourcing

An institution’s liquidity sourcing strategy must be re-architected to intelligently query SIs. This involves more than simply adding SI destinations to an SOR’s routing table. It requires a sophisticated understanding of which SIs specialize in which asset classes and under what market conditions they are most likely to offer competitive quotes.

The strategy is to use SIs as a tool for minimizing information leakage. By routing a significant order to an SI, an investor can potentially execute the trade without signaling their intent to the broader market, thereby reducing the adverse price movement that often accompanies large transactions on lit exchanges.

A sophisticated SI strategy is defined by its ability to dynamically route orders based on real-time analysis of quote quality and the probability of price improvement.

The table below outlines a comparative framework for evaluating execution venues, highlighting the specific strategic position of SIs.

Venue Type	Primary Mechanism	Transparency Profile	Key Strategic Advantage	Primary Consideration
Lit Exchange	Central Limit Order Book (CLOB)	Full Pre- and Post-Trade	Centralized Price Discovery	Potential for Market Impact
Multilateral Trading Facility (MTF)	CLOB or Quote-Driven	Full Pre- and Post-Trade	Competition and Lower Fees	Fragmented Liquidity
Systematic Internaliser (SI)	Principal Dealing (Own Account)	Quote Disclosure / Post-Trade	Reduced Market Impact; Potential Price Improvement	Counterparty Risk; Quote Quality
Dark Pool	Anonymous Matching	Post-Trade Only	Minimal Market Impact; Size Discovery	Adverse Selection Risk; Lack of Pre-Trade Transparency

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How Does SI Interaction Reshape Best Execution Factors?

The traditional factors of best execution ▴ price, cost, speed, and likelihood of execution ▴ are all re-contextualized by the presence of SIs. The “price” factor is no longer a single data point from a lit exchange. It becomes a composite variable that must include the potential for price improvement offered by an SI, weighed against the public quote. The “cost” element must also be reassessed.

While explicit commission costs may be lower, the implicit costs of information leakage or potential adverse selection if an SI rejects an order must be modeled. An effective strategy involves a pre-trade analytical process that estimates the market impact of executing on a lit venue versus the probability of a successful, non-impactful execution at an SI.

Pre-Trade Analysis ▴ The process begins with an assessment of the order’s characteristics. Is the instrument liquid? Is the order size likely to move the market? This analysis determines the viability of using an SI.
Venue Selection Logic ▴ The SOR logic is configured to query available SIs for a firm quote, comparing it in real-time to the European Best Bid and Offer (EBBO). The decision to route to an SI is based on this comparison, along with historical data on the SI’s fill rates and quality of execution.
Post-Trade Review ▴ Transaction Cost Analysis (TCA) is enhanced to specifically measure SI performance. This includes tracking fill rates, the magnitude of price improvement versus the EBBO at the time of the query, and any signaling risk if the order is subsequently routed to a lit market.

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Execution

The operational execution of a trading strategy that incorporates Systematic Internalisers is a data-intensive process. It requires robust technological infrastructure and a quantitative approach to performance measurement. The objective is to move from a qualitative assumption of best execution to a provable, data-driven validation process that satisfies regulatory obligations and achieves superior outcomes for the end client. This involves a granular analysis of execution quality and the systematic integration of SI performance metrics into the firm’s overarching best execution policy.

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A Procedural Guide for SI Integration

Integrating SIs into the execution workflow is a multi-stage process that touches technology, compliance, and trading functions. The following steps provide a high-level operational playbook for an institutional desk.

Technology Stack Audit ▴ The first step is to ensure the firm’s Execution Management System (EMS) and Smart Order Router (SOR) are equipped with the necessary connectivity and logic to handle SIs. This includes the ability to process SI-specific quote formats and to route orders based on complex, multi-factor rules.
Update of Best Execution Policy ▴ The firm’s formal Best Execution Policy must be amended to explicitly recognize SIs as a distinct execution venue class. This documentation should detail the factors that will be considered when directing orders to an SI and how their performance will be evaluated relative to other venues.
Development of a Quantitative Analytics Framework ▴ A dedicated analytics framework must be developed to measure SI execution quality. This framework is the core of the execution process, providing the data needed for the SOR to make intelligent routing decisions and for the compliance team to produce regulatory reports.
Trader Training and Protocol Development ▴ Traders must be trained on the new protocols for interacting with SIs. This includes understanding when to manually direct an order to an SI and how to interpret the data coming from the quantitative analytics framework.

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What Quantitative Metrics Define SI Performance?

Effective execution requires a rigorous, quantitative approach to measuring the value provided by each SI relationship. The table below details the key data points and metrics required for a comprehensive Transaction Cost Analysis (TCA) that properly incorporates SIs. This data must be captured for every order routed to an SI, whether executed or not.

Metric Category	Specific Data Point	Analytical Purpose	Formula/Definition
Price Improvement	Effective Spread Capture	Measures the price improvement relative to the public market spread.	((Execution Price – Midpoint) / (Ask – Bid)) 2
Price Improvement	Price Improvement per Share	Quantifies the absolute monetary value of the price improvement.	(Reference Price – Execution Price) Quantity
Execution Quality	Fill Rate	Assesses the reliability of the SI’s quotes.	(Number of Orders Executed / Number of Orders Routed)
Execution Quality	Rejection Rate	Identifies potential signaling risk from failed executions.	(Number of Orders Rejected / Number of Orders Routed)
Market Impact	Post-Trade Price Reversion	Measures if the execution had a temporary or permanent market impact.	Price movement in the minutes following the execution.

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Predictive Scenario Analysis a Case Study

Consider a portfolio manager needing to sell a 200,000-share position in a moderately liquid European stock. The European Best Bid and Offer (EBBO) is €10.00 / €10.02. A traditional execution algorithm might begin by placing small “iceberg” orders on the lit market to minimize impact. However, this approach still risks signaling the large selling interest, potentially causing the bid to drop.

An advanced SOR, configured for SI interaction, would operate differently. It would simultaneously query several SIs known for providing liquidity in this stock. Suppose SI ‘A’ responds with a firm bid of €10.005 for the full 200,000 shares. The SOR’s logic evaluates this quote.

It offers €0.005 per share of price improvement over the best bid, a total of €1,000 for the block. It also offers the significant benefit of executing the entire order in a single transaction with zero market impact or information leakage. The decision is clear. The SOR routes the entire order to SI ‘A’.

The execution is completed instantly at €10.005, capturing price improvement and avoiding the implicit costs of working the order on a lit exchange.

The post-trade TCA report would validate this decision. It would show a positive value for “Price Improvement per Share” and a zero value for market impact metrics. By contrast, a simulation of a lit market execution might show an average execution price of €9.99 due to slippage, representing a €2,000 opportunity cost.

This data-driven feedback loop is essential. It allows the firm to continuously refine its SOR logic, favoring SIs that consistently provide high-quality, impactful executions and justifying its best execution methodology to both clients and regulators.

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References

Bank of Montreal Europe plc. “MiFID II Order Execution Policy.” BMO Europe, 2023.
Deloitte. “Best Execution Under MiFID II.” Deloitte, 2017.
Ganado Advocates. “MiFID II ▴ Are you a systematic internaliser?” 5 February 2024.
BaFin. “Systematic internalisers ▴ Main points of the new supervisory regime under MiFID II.” 2 May 2017.
International Capital Market Association. “MiFID II SI Regime Workshops ▴ A summary report.” ICMA, 2017.
Lehalle, Charles-Albert, and Sophie Laruelle, eds. Market Microstructure in Practice. World Scientific Publishing, 2018.
O’Hara, Maureen. Market Microstructure Theory. Blackwell Publishers, 1995.

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Calibrating the Execution Architecture

The integration of Systematic Internalisers into the market fabric is more than an incremental update. It is a test of an institution’s operational adaptability. The data and frameworks presented here provide the schematics for analysis, but the ultimate execution quality depends on the intelligence of the system that wields them. The true strategic question is not whether to connect to SIs, but how to build an analytical and technological architecture that can dynamically assess their value in real-time.

This requires a commitment to viewing execution as a science of probabilities and implicit costs. The firms that achieve a decisive edge will be those that construct a holistic, data-centric feedback loop, where every execution, successful or not, serves as a data point to refine the model for the next one. The ultimate goal is an execution system that is predictive, adaptive, and demonstrably aligned with the client’s best interests.