What Are the Compliance Challenges in Proving Best Execution across SIs and MTFs? ▴ Question

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Concept

The mandate to deliver best execution is an absolute. For an institutional trader, the operational challenge is substantiating this mandate with empirical data across a fragmented landscape of execution venues, specifically Systematic Internalisers (SIs) and Multilateral Trading Facilities (MTFs). The core difficulty resides in the structural differences between these two venue types and the resulting complexities in creating a unified, evidence-based execution quality assessment. This is a problem of data, of measurement, and ultimately, of system architecture.

An SI operates as a bilateral trading venue, where an investment firm executes client orders against its own account. This environment offers the potential for significant size and reduced market impact, as the liquidity is internalized. The pricing, however, is determined by the SI, and while it must be “firm, fair, and reasonable,” it is inherently less transparent than a public order book.

Proving best execution in this context requires a rigorous comparison of the SI’s offered price against a broader market benchmark at the moment of execution. The compliance challenge is to capture a reliable, time-stamped snapshot of the wider market to validate the SI’s quote, a task complicated by the sheer volume and velocity of market data.

The fundamental compliance challenge in proving best execution across SIs and MTFs lies in reconciling structurally different liquidity pools and data outputs into a single, coherent analytical framework.

In contrast, an MTF is a multilateral system that brings together multiple third-party buying and selling interests in financial instruments. It operates with a non-discretionary rulebook, creating a more transparent price discovery process through a central limit order book (CLOB) or other trading systems. While the price discovery process on an MTF is more transparent, the liquidity can be fragmented across multiple MTFs and other lit venues.

The challenge here shifts to demonstrating that the chosen MTF, or combination of venues, provided the optimal outcome considering not just price, but also factors like likelihood of execution, settlement, and associated costs. The data from MTFs is more structured, yet the universe of potential execution pathways is far larger, demanding a sophisticated analytical capability to model and compare the different options.

The Markets in Financial Instruments Directive II (MiFID II) has intensified these challenges by expanding the scope of best execution to all asset classes and demanding more granular reporting. Firms are required to develop comprehensive order execution policies that detail how they will achieve the best possible result for their clients. This policy must be more than a static document; it needs to be a dynamic framework that is regularly monitored, reviewed, and evidenced with data. The core of the compliance problem is this evidentiary burden.

It requires firms to build or acquire a technology stack capable of capturing, normalizing, and analyzing vast quantities of data from disparate sources, each with its own format and structure. The system must be able to reconstruct the state of the market at any given point in time to justify execution decisions, a task that is both computationally intensive and operationally complex.

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Strategy

A robust strategy for proving best execution across SIs and MTFs is predicated on a unified data architecture and a sophisticated Transaction Cost Analysis (TCA) framework. The objective is to create a single, coherent view of execution quality that transcends the structural differences between these venues. This requires a move beyond simple price comparisons to a multi-faceted analysis that incorporates all relevant execution factors.

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A Unified Data Architecture

The foundational element of a successful best execution strategy is a data architecture that can ingest, normalize, and store data from all relevant execution venues. This includes not only SIs and MTFs but also regulated markets, exchanges, and other liquidity providers. The system must be able to handle different data formats, from the structured FIX protocol messages of MTFs to the proprietary API data feeds of SIs.

The goal is to create a single, time-stamped repository of all order and execution data, enriched with market data from multiple sources. This unified data set becomes the “single source of truth” for all subsequent analysis.

Effective best execution strategy hinges on a unified data architecture that can normalize and analyze disparate data streams from both bilateral and multilateral trading venues.

This data architecture must also be designed for performance and scalability. The volume of data generated by modern financial markets is immense, and the ability to process and analyze this data in a timely manner is critical. This often requires the use of advanced data technologies, such as distributed databases and parallel processing frameworks. The architecture should also be flexible enough to accommodate new execution venues and data sources as the market evolves.

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Advanced Transaction Cost Analysis

With a unified data architecture in place, the next step is to implement an advanced TCA framework. This framework should go beyond the traditional focus on price and incorporate a wider range of execution factors, including:

Costs ▴ Explicit costs, such as fees and commissions, as well as implicit costs, such as market impact and spread.
Speed of Execution ▴ The time taken to execute an order, from placement to confirmation.
Likelihood of Execution ▴ The probability of an order being filled, particularly for large or illiquid orders.
Size and Nature of the Order ▴ The specific characteristics of the order, which will influence the choice of execution venue.
Settlement and Counterparty Risk ▴ The risks associated with the settlement process and the creditworthiness of the counterparty.

The TCA framework should use this data to generate a range of metrics that provide a holistic view of execution quality. These metrics can be used to compare the performance of different execution venues, identify areas for improvement, and demonstrate compliance with best execution obligations. The analysis should be conducted on a pre-trade, intra-trade, and post-trade basis to provide a complete picture of the execution process.

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How Do You Compare SI and MTF Performance?

A key challenge is the direct comparison of SI and MTF performance. The following table outlines a framework for such a comparison:

Execution Factor	Systematic Internaliser (SI)	Multilateral Trading Facility (MTF)
Price Improvement	Comparison of execution price against the European Best Bid and Offer (EBBO) at the time of the trade.	Analysis of the execution price relative to the volume-weighted average price (VWAP) or other relevant benchmarks on the MTF.
Market Impact	Measurement of price movement on lit markets following the SI execution.	Analysis of the order’s impact on the MTF’s order book and subsequent price formation.
Likelihood of Execution	Analysis of the SI’s fill rates for different order sizes and types.	Measurement of fill rates and the depth of the order book on the MTF.
Latency	Time from order submission to execution confirmation from the SI.	Time from order submission to execution on the MTF’s matching engine.

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The Role of the Order Execution Policy

The order execution policy is the strategic document that ties together the data architecture and the TCA framework. It should clearly articulate the firm’s approach to best execution, including the factors it considers when selecting an execution venue and the relative importance of these factors. The policy should be a living document, regularly reviewed and updated in light of changes in the market and the firm’s own execution performance. It should also be transparent and easily understood by clients.

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Execution

The execution of a best execution compliance framework requires a significant investment in technology, processes, and expertise. It is a continuous cycle of data capture, analysis, reporting, and process improvement. The goal is to create a system that not only meets regulatory requirements but also provides a tangible competitive advantage through superior execution quality.

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Building the Technology Stack

The technology stack is the backbone of the best execution compliance framework. It must be capable of handling the volume, velocity, and variety of data generated by modern financial markets. The key components of the technology stack include:

Data Capture and Normalization ▴ This layer is responsible for ingesting data from all relevant sources, including SIs, MTFs, regulated markets, and market data providers. It must be able to handle a wide range of protocols and formats, and to normalize the data into a consistent, time-stamped format.
Data Storage and Management ▴ This layer provides a scalable and performant repository for all order, execution, and market data. It should be designed for fast query and retrieval, and to support complex analytical workloads.
Analytical Engine ▴ This is the core of the TCA framework. It should be able to perform a wide range of calculations and generate a comprehensive set of execution quality metrics. The engine should be flexible enough to accommodate new analytical models and techniques as they emerge.
Reporting and Visualization ▴ This layer provides the tools for generating regulatory reports, internal management dashboards, and client-facing execution quality analysis. The reports should be clear, concise, and easily understood by a range of audiences.

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What Are the Key Data Points for Analysis?

A granular approach to data is essential for robust analysis. The following table details the critical data points that must be captured:

Data Category	Specific Data Points	Purpose
Order Data	Timestamp (nanosecond precision), Instrument ID (ISIN), Order Type, Size, Price, Venue	To reconstruct the order lifecycle and intent.
Execution Data	Execution Timestamp, Venue, Price, Size, Counterparty	To analyze the outcome of the order.
Market Data	Level 2 Order Book Data, Trade Prints, EBBO	To provide a benchmark for execution quality.
Venue Data	Venue Rules, Fee Schedules, Latency Statistics	To model the total cost of execution.

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Implementing the Compliance Workflow

With the technology stack in place, the next step is to implement a robust compliance workflow. This workflow should be designed to ensure that best execution is monitored on an ongoing basis and that any issues are identified and addressed in a timely manner. The key steps in the workflow include:

Daily Monitoring ▴ A daily review of execution quality metrics to identify any outliers or exceptions. This should be an automated process, with alerts generated for any trades that fall outside of pre-defined tolerance levels.
Quarterly Review ▴ A more in-depth review of execution quality on a quarterly basis. This should include a comparison of the performance of different execution venues and a review of the firm’s order execution policy.
Annual Reporting ▴ The preparation and publication of the annual RTS 28 report, which provides a detailed summary of the firm’s execution practices.
Ad-Hoc Analysis ▴ The ability to conduct ad-hoc analysis in response to client inquiries, regulatory requests, or internal investigations.

A well-defined compliance workflow, supported by a robust technology stack, is the key to demonstrating a consistent and evidence-based approach to best execution.

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The Human Element

Technology alone is not enough to ensure best execution compliance. A team of skilled and experienced professionals is also required to oversee the process, interpret the results of the analysis, and make informed decisions about how to improve execution quality. This team should include representatives from compliance, trading, and technology, and should have a clear mandate to drive continuous improvement in the firm’s execution practices. Regular training and development are essential to ensure that the team stays up-to-date with the latest regulatory requirements and market developments.

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References

Linedata. “Tackling the Challenges of MiFID II ▴ Best Execution.” 2016.
Novatus Global. “Best Execution ▴ MiFID II & SEC Compliance Essentials Explained.” 2020.
SteelEye. “Best Execution Challenges & Best Practices.” 2021.
“Best Execution Under MiFID II.” 2018.
“Best execution compliance in a global context.” 2025.

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Reflection

The challenge of proving best execution across a fragmented landscape of SIs and MTFs is a microcosm of the broader evolution of financial markets. It is a challenge that demands a shift in thinking, from a siloed, venue-specific approach to a holistic, data-driven perspective. The systems and processes that are built to solve this problem will not only ensure regulatory compliance but will also create a more efficient, transparent, and resilient market for all participants. The ultimate question for any firm is not whether they can afford to build such a system, but whether they can afford not to.

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How Will Your Firm Evolve Its Execution Strategy?

As market structures continue to evolve and regulatory expectations increase, a static approach to best execution will no longer be viable. The ability to adapt, to innovate, and to continuously improve will be the defining characteristic of the firms that succeed in this new environment. The journey towards a truly data-driven approach to best execution is a continuous one, and it is a journey that every firm must embark on.