How Does the Rise of Systematic Internalisers Change the Strategic Considerations for Best Execution in Europe?

Concept

The formalization of the Systematic Internaliser regime under the second Markets in Financial Instruments Directive (MiFID II) represents a fundamental re-architecting of European market structure. It moves a significant volume of over-the-counter (OTC) activity into a defined, regulated framework, compelling a strategic reassessment of best execution for all institutional participants. An SI is an investment firm that, on an organized, frequent, systematic, and substantial basis, deals on its own account when executing client orders outside a regulated market, a Multilateral Trading Facility (MTF), or an Organised Trading Facility (OTF). This codifies a practice that has long existed but places it under new obligations of transparency and reporting, transforming principal liquidity from a purely bilateral arrangement into a recognized component of the broader market ecosystem.

Understanding this shift requires seeing the market not as a single, monolithic entity but as a network of interconnected liquidity venues, each with distinct protocols and characteristics. The SI regime was designed to increase transparency in previously opaque corners of the market, particularly in non-equity asset classes like bonds and derivatives. Regulators sought to ensure that the internalization of order flow by large dealers does not undermine the public price formation process that occurs on lit exchanges. Consequently, SIs are subject to specific pre-trade and post-trade transparency requirements.

For liquid instruments, they must make firm quotes public to their clients, creating a new, accessible stream of price information. This creates a powerful new dynamic for the buy-side, which now has a regulatory mandate to demonstrate that it has taken all sufficient steps to achieve the best possible result for its clients, considering factors far beyond simple price, such as cost, speed, and likelihood of execution.

The rise of Systematic Internalisers reframes European best execution from a public market quest to a sophisticated process of sourcing private, principal liquidity with demonstrable price improvement.

The implications are profound. For an asset manager or executing broker, the universe of potential counterparties has been re-categorized. A large investment bank, once viewed simply as a broker or counterparty, might now also be a formal execution venue for specific instruments.

This dual role necessitates a more granular and data-driven approach to venue analysis. The strategic question is no longer just “who is the best broker for this trade?” but “what is the optimal execution venue for this order, given its specific characteristics, and how does interacting with an SI fit into that calculus?” The answer changes the entire strategic framework, moving it from a reliance on traditional agency brokerage to a more complex, hybrid model where principal liquidity from SIs is a primary and quantifiable source of execution quality.

The New European Liquidity Map

The introduction of the SI regime effectively redrew the map of European financial liquidity. Before MiFID II, the landscape was broadly divided between lit exchanges and a vast, heterogeneous OTC space, which included broker-crossing networks (BCNs) and other off-exchange trading arrangements. MiFID II sought to bring order to this environment by creating a clearer taxonomy of execution venues. SIs now exist alongside Regulated Markets (RMs), MTFs, and OTFs, each governed by a specific rule set.

SIs are unique in this quadrumvirate because they are the only venue type where the operator is exclusively dealing on a principal basis, putting its own capital at risk to fill client orders. This fundamental characteristic drives both the opportunities and the challenges they present.

A Taxonomy of Execution Venues

To navigate this environment, a firm’s execution strategy must be predicated on a deep understanding of how these venues differ. The choice of where to route an order is a critical determinant of the final execution outcome, influencing everything from the explicit cost to the implicit cost of market impact.

• Regulated Markets (RMs) ▴ These are the traditional stock exchanges. They operate a central limit order book (CLOB) with full pre-trade and post-trade transparency. Price discovery is their primary function.
• Multilateral Trading Facilities (MTFs) ▴ MTFs are also typically order-driven systems that bring together multiple third-party buying and selling interests. They can operate lit or dark order books but, unlike RMs, have more flexibility in their rulebooks.
• Organised Trading Facilities (OTFs) ▴ A category introduced by MiFID II primarily for non-equity instruments like bonds and derivatives. An OTF allows for a greater degree of discretion in execution compared to RMs and MTFs, accommodating voice and RFQ-based trading models.
• Systematic Internalisers (SIs) ▴ These are investment firms trading bilaterally with clients using their own capital. They do not bring together third-party interests but instead act as the sole counterparty to every trade. Their rise has created a substantial source of non-displayed liquidity that is accessible without the potential for information leakage associated with resting large orders on a lit book.

The strategic imperative is to build a system, both technological and procedural, that can intelligently access these disparate pools of liquidity. A modern Smart Order Router (SOR) must be calibrated to understand the specific quoting behavior of different SIs, the likelihood of receiving price improvement, and the potential market impact of ignoring this significant liquidity source. The best execution policy of an asset manager must, in turn, be able to justify the selection of an SI over a lit venue, using robust data to prove that the outcome served the client’s best interest. This marks a definitive shift from a process-oriented compliance exercise to a data-driven, quantitative discipline.

Strategy

The integration of Systematic Internalisers into the European market framework necessitates a complete strategic overhaul of the execution process. The core challenge for asset managers and brokers is to evolve their best execution policies from a static, compliance-driven document into a dynamic, data-centric operational strategy. This strategy must recognize SIs as a distinct and valuable liquidity source, requiring specific protocols for engagement, measurement, and optimization. The overarching goal is to construct a framework that systematically leverages SI liquidity to improve execution quality across multiple dimensions, including price, cost, and market impact, while rigorously documenting these benefits to satisfy MiFID II’s stringent requirements.

A successful strategy begins with the acknowledgment that not all liquidity is equal. The bilateral, principal-based liquidity offered by an SI presents a different set of trade-offs compared to the anonymous, multilateral liquidity of a central limit order book. Interacting with an SI can provide significant price improvement over the prevailing European Best Bid and Offer (EBBO) and can reduce the market impact associated with large orders. However, it also introduces the need to manage counterparty relationships and analyze potential information leakage in a different context.

Therefore, the strategy cannot be one-size-fits-all; it must be segmented by asset class, order size, and the specific characteristics of the instrument being traded. A sophisticated execution strategy will involve a multi-pronged approach, incorporating SIs into Smart Order Routers, developing robust RFQ protocols, and enhancing Transaction Cost Analysis (TCA) to capture the unique value proposition of SI interaction.

Recalibrating Venue Selection and Order Routing

The cornerstone of a modern execution strategy is the Smart Order Router (SOR). In a post-MiFID II world, an SOR’s logic must be far more sophisticated than simply seeking the best displayed price on lit markets. It must be programmed to intelligently interact with SIs, treating them as a primary destination for certain types of order flow. This requires a significant investment in data and analytics to understand the behavior of individual SIs.

A Decision Framework for SOR Logic

The SOR must be configured with a decision matrix that weighs multiple factors before routing an order. This logic moves beyond a simple price-time priority to a more holistic assessment of execution quality.

• For Small, Liquid Orders ▴ The primary objective is often to achieve price improvement. The SOR should be configured to ping multiple SIs via a competitive RFQ process simultaneously with checking lit market prices. The routing decision should be based on the venue that offers the best net price after considering fees.
• For Large, Illiquid Orders ▴ The main concern shifts to minimizing market impact. Placing a large order on a lit exchange can signal intent and lead to adverse price movements. Routing the order, or portions of it, to an SI can prevent this information leakage. The SI absorbs the trade onto its own book, internalizing the risk and shielding the market from the full size of the order.
• For Multi-Leg Spreads ▴ Executing complex options or derivatives spreads requires precise timing and coordination. SIs, particularly the large bank-dealers, often have sophisticated capabilities to price and execute these strategies as a single package, mitigating the legging risk inherent in executing each component separately on different venues.

This recalibration requires a continuous feedback loop. The performance of each SI must be constantly monitored and fed back into the SOR’s logic. Fill rates, the magnitude of price improvement, and the speed of quote responses are all critical data points that must be captured and analyzed. A firm’s strategy should involve ranking SIs based on their performance in specific instruments or asset classes, creating a dynamic and evidence-based approach to venue selection.

The following table provides a comparative analysis of the strategic considerations for choosing an execution venue, highlighting the distinct role of SIs.

The Centrality of the Request for Quote Protocol

The Request for Quote (RFQ) protocol is the primary mechanism for interacting with Systematic Internalisers. While RFQ has been a staple of OTC trading for decades, MiFID II has formalized its role within the electronic trading workflow. A modern execution strategy must treat the RFQ process not as a simple messaging standard, but as a tool for creating competition and extracting the best possible price from SIs.

When a firm sends an RFQ to multiple SIs for the same instrument, it creates a competitive auction for its order flow. This process is central to proving that the firm has taken sufficient steps to achieve best execution.

A firm’s best execution policy must evolve to justify SI selection with robust data, transforming a compliance task into a quantitative discipline.

The strategy should define clear protocols for the RFQ process. For instance, it should specify the number of SIs to include in each RFQ based on the instrument’s liquidity and the order’s size. It should also establish time-outs for quote responses to ensure timely execution. Furthermore, the analysis of RFQ responses must be automated.

The system should be able to instantly compare the quoted prices from multiple SIs against the prevailing EBBO and calculate the potential price improvement in real-time. This data is not only crucial for making the immediate execution decision but also for the post-trade TCA process that will be used to justify the choice of venue.

The operational benefits of this formalized interaction are significant. When trading with an SI, the reporting obligation for the trade typically falls on the SI itself. This removes a significant administrative and operational burden from the buy-side firm, reducing costs and complexity associated with MiFID II’s post-trade transparency requirements. A comprehensive strategy will factor this operational efficiency into its overall cost analysis of different execution channels.

Execution

The execution of a strategy that incorporates Systematic Internalisers is a discipline rooted in technological integration, quantitative analysis, and rigorous operational procedure. It moves beyond high-level strategic concepts to the granular details of system architecture, data modeling, and real-world trading decisions. For a trading desk to effectively leverage the SI ecosystem, it must possess the technological infrastructure to connect and communicate, the analytical tools to measure and compare, and the operational playbook to ensure consistency and compliance.

This is where the theoretical advantages of SI liquidity are converted into tangible, measurable improvements in execution quality. The focus shifts from what is possible to how it is achieved, day in and day out, on the trading floor.

At its core, successful execution is about building a closed-loop system. This system begins with pre-trade analysis to identify the right SIs for a given order, moves to the live execution phase where orders are routed and quotes are analyzed in real-time, and concludes with a post-trade analysis phase where the results are meticulously measured and fed back to refine the pre-trade assumptions. Each stage requires a specific set of tools and procedures.

The technological backbone for this is often the firm’s Execution Management System (EMS), which must be seamlessly integrated with both internal Order Management Systems (OMS) and the external connectivity protocols of the various SIs. The quality of this integration directly impacts the firm’s ability to execute its strategy efficiently and effectively.

The Operational Playbook for SI Engagement

A robust operational playbook provides the trading desk with a clear, repeatable process for engaging with SIs. This playbook ensures that all traders are following a consistent methodology that is aligned with the firm’s best execution policy. It breaks down the trading lifecycle into distinct stages, each with its own set of procedures and required outputs.

1. Pre-Trade Analysis and SI Selection
   • Maintain a Universe Map ▴ The first step is to maintain a comprehensive, internal directory of all relevant SIs. This map should be enriched with data on which SIs are most active and competitive in which specific instruments or asset classes. This is not a static list; it must be updated regularly based on performance data from post-trade analysis.
   • Order-Specific Filtering ▴ Before sending an RFQ, the system should apply a set of filters based on the characteristics of the order. For a large block trade in a FTSE 100 stock, the RFQ list will be different than for a small, illiquid corporate bond. The playbook should define the criteria for this filtering process.
2. Live Execution via RFQ
   • Competitive Auction Dynamics ▴ The playbook must specify the standard operating procedure for RFQs. This includes the minimum number of SIs to be included in the request (e.g. at least five for any liquid equity). This creates a competitive environment designed to elicit the best possible price.
   • Real-Time Benchmarking ▴ As quotes are received from SIs, the EMS must display them alongside the real-time EBBO from lit markets. The system should automatically calculate and highlight the price improvement offered by each SI in both absolute terms and basis points. This provides the trader with immediate, actionable intelligence.
   • Automated Execution Logic ▴ For certain types of flow, the playbook may allow for automated execution. For example, any quote that represents more than a specified amount of price improvement could be automatically accepted, reducing latency and freeing up trader time for more complex orders.
3. Post-Trade Analysis and Feedback Loop
   • Data Capture ▴ The system must capture a rich set of data for every SI execution. This includes not just the price, but also the time the RFQ was sent, the time the quote was received, the quote’s duration, and the identity of all SIs that were queried but did not respond or were not competitive.
   • Performance Reporting ▴ This data feeds into detailed TCA reports that compare the performance of SIs against executions on other venues. These reports are the primary tool for proving best execution to clients and regulators.
   • Strategy Refinement ▴ The findings from the TCA reports are used to update the pre-trade SI Universe Map. Underperforming SIs can be downgraded or removed from the standard RFQ list for certain instruments, while top performers can be prioritized. This creates the closed-loop system that drives continuous improvement.

Quantitative Modeling and Data Analysis

The credibility of an SI-inclusive execution strategy rests on the quality of its data analysis. Transaction Cost Analysis must be adapted to capture the specific nuances of SI trading. A simple comparison to the arrival price is insufficient.

A proper TCA model will incorporate metrics that specifically measure the benefits of SI interaction, such as the level of price improvement achieved. The following table presents a hypothetical TCA report for a series of buy orders in Vodafone (VOD.L), comparing executions across a lit market (LSE), a dark pool (MTF), and two different Systematic Internalisers.

This data-driven approach provides objective evidence of execution quality. In this example, SI-A consistently provides price improvement and is capable of absorbing large blocks with minimal negative market impact, making it a valuable liquidity partner. Conversely, SI-B’s performance on this occasion was subpar, and continued results like this would lead to its de-prioritization in the routing logic. This quantitative rigor is non-negotiable for meeting the best execution obligations under MiFID II.

Successful execution is a closed-loop system where post-trade data continuously refines pre-trade intelligence.

Predictive Scenario Analysis a Case Study

Consider a portfolio manager at a mid-sized European asset manager who needs to purchase 2.5 million shares of a DAX-listed German manufacturing company, representing approximately 35% of the average daily volume (ADV). The order must be completed within a single trading day. The head trader is tasked with devising an execution strategy that minimizes both market impact and implementation shortfall. The trader has several options ▴ a pure algorithmic execution on the lit markets (Xetra), a high-touch approach working the order through a broker’s desk, or a strategy that heavily incorporates the firm’s network of Systematic Internalisers.

The trader begins by consulting the firm’s pre-trade analytics. The system projects that placing the entire order on the lit market using a standard Volume-Weighted Average Price (VWAP) algorithm would likely result in significant market impact, estimated at around 8-10 basis points of slippage against the arrival price. The sheer size of the order relative to ADV means the algorithm would be a highly visible and predictable buyer, attracting adverse selection from high-frequency traders. A high-touch approach could potentially find block liquidity, but it is slower and relies on the broker’s individual network, with less systematic data to prove that the final price was optimal.

The chosen strategy is a hybrid model. The trader decides to allocate 60% of the order (1.5 million shares) to be sourced via the firm’s automated RFQ system, targeting a pre-vetted list of seven SIs known for their competitiveness in German equities. The remaining 40% (1 million shares) will be worked concurrently via a passive, liquidity-seeking algorithm on both Xetra and several MTF dark pools. This dual approach allows the firm to absorb size quietly through its SI relationships while simultaneously participating in the natural flow of the public markets.

The execution begins. The RFQ system starts by sending out requests for 100,000-share blocks. The first few auctions yield positive results. Three different SIs provide quotes that are, on average, 1.5 basis points better than the prevailing EBBO on Xetra.

The system automatically executes these, and within the first hour, 600,000 shares are purchased with positive price improvement and, critically, zero information leakage to the public market. Concurrently, the passive algorithm has managed to buy 150,000 shares at the midpoint in the dark pools.

As the day progresses, the trader notices the SI quote quality beginning to fade slightly. The price improvement shrinks as the SIs adjust their internal risk models, sensing the persistent buying interest. The trader’s playbook dictates a change in tactics. The RFQ size is increased to 250,000 shares to signal a more urgent need for a larger block.

This prompts two of the largest SIs to provide aggressive quotes to internalize a significant piece of the order. One SI fills the entire 250,000-share block at the ask price, resulting in zero price improvement but also zero negative market impact, a crucial trade-off for such a large chunk. The other provides a further 250,000 shares with a modest 0.5 basis points of improvement.

By the end of the day, the full 2.5 million shares have been acquired. The final TCA report is compiled. The 1.5 million shares executed via SIs were purchased with an average slippage of just +0.2 basis points against the arrival price, with an average price improvement of 0.9 basis points versus the EBBO. The 1 million shares worked via the algorithm experienced -1.5 basis points of slippage, as the persistent buying pressure inevitably pushed up the price in the lit market.

The blended result is a total execution cost far lower than the initial projection for a pure algorithmic strategy. The detailed, time-stamped log of all RFQs and their responses provides a complete audit trail, forming an unassailable body of evidence that the firm took all sufficient steps to achieve the best possible result for its client. This case study demonstrates how a systematic, data-driven engagement with SIs transforms best execution from a qualitative goal into a quantifiable outcome.

System Integration and Technological Architecture

The execution of this strategy is contingent on a robust and flexible technological architecture. The central nervous system is the firm’s EMS, which must have native support for SI connectivity and RFQ workflows. This is not simply a matter of routing orders; it requires specific functionality.

• FIX Protocol Fluency ▴ While the Financial Information eXchange (FIX) protocol is the industry standard, interactions with SIs often require specific custom tags or message flows. The EMS must be able to handle these variations seamlessly. For instance, the FIX messages for submitting a multi-leg options spread to an SI for a packaged quote are more complex than those for a simple equity order.
• API Integration ▴ Some SIs are moving beyond FIX to offer more modern Application Programming Interfaces (APIs) for quoting and trading. A forward-looking execution architecture will have the flexibility to integrate with these APIs to access the full spectrum of available liquidity.
• Data Normalization ▴ Each SI may provide its quote data in a slightly different format. The EMS has the critical job of normalizing this data in real-time so that quotes can be compared on a true like-for-like basis. This includes adjusting for different fee schedules or settlement conventions.
• OMS Write-Back ▴ Once an execution occurs with an SI, the details of the trade must be written back to the firm’s OMS instantly and accurately. This ensures that portfolio managers have a real-time view of their positions and that the firm’s risk and compliance systems are updated. Any latency in this process can lead to significant operational risk.

Building and maintaining this technological stack is a significant undertaking. It requires ongoing investment and a close partnership between the trading desk, the firm’s technology team, and its external vendors. However, in the modern European market, it is an essential component of any institutional-grade execution capability.

Reflection

The integration of Systematic Internalisers into the fabric of European markets is more than a regulatory footnote; it is a catalyst for institutional evolution. The frameworks and strategies discussed here provide a blueprint for navigating this new terrain, but the true determinant of success lies within a firm’s own operational culture. The ability to harness the full potential of the SI regime is directly proportional to an organization’s commitment to a data-centric mindset. It requires moving beyond legacy workflows and embracing a process of continuous, quantitative improvement.

The essential question for every institution is whether its current architecture ▴ of technology, of strategy, of human capital ▴ is sufficiently agile to capitalize on this fragmented, competitive liquidity landscape. Is your firm’s Transaction Cost Analysis capable of not just measuring outcomes, but of driving future strategy? Is your execution system a passive conduit for orders, or an active intelligence layer that optimizes every single trade?

The rise of Systematic Internalisers has raised the bar for best execution. It offers a pathway to superior results, but only for those who are willing to build the sophisticated operational capabilities required to seize it.