How Does the SI Regime Alter the Demonstration of Best Execution for Bonds? ▴ Question

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Concept

The introduction of the Systematic Internaliser (SI) regime under MiFID II represents a fundamental architectural shift in the fixed income markets. It imposes a data-centric, quasi-exchange structure onto a market that has historically operated on relationships, voice brokerage, and bilateral negotiation. The core challenge for any institution trading bonds is that the regulatory mandate to demonstrate best execution now collides with the inherent nature of fixed income instruments, which are frequently illiquid, fragmented across thousands of unique ISINs, and lack a persistent, centralized source of real-time pricing. The SI regime does not merely add a new rule; it alters the very physics of how execution quality is measured and proven for bonds.

At its heart, the directive moves the demonstration of best execution from a qualitative, policy-based exercise to a quantitative, evidence-based discipline. Before the SI framework, proving best execution for a corporate bond trade often relied on documenting a sound process, such as polling a reasonable number of dealers. With the formalization of the SI, a new category of execution venue was created. An SI is an investment firm dealing on its own account by executing client orders outside a regulated market (RM), multilateral trading facility (MTF), or organised trading facility (OTF).

This designation forces a new level of transparency and accountability. SIs are obligated to provide firm quotes for liquid instruments up to a certain size, effectively creating a new stream of structured, pre-trade data that did not formally exist in the over-the-counter (OTC) space.

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What Is the Core Architectural Change for Bond Markets?

The primary architectural change is the injection of mandatory, machine-readable data into the OTC workflow. The SI regime compels certain high-volume dealers to publish pre-trade quotes and detailed post-trade reports (under RTS 27) on their execution quality. This includes data on price, costs, speed, and likelihood of execution. For the first time, this provides a public, standardized benchmark for an SI’s performance.

Consequently, a firm can no longer justify its execution choice solely on the basis of a dealer relationship or a qualitative assessment of market conditions. It must now incorporate this newly available, quantifiable data into its decision-making process and, crucially, into its audit trail.

The SI regime transforms the abstract duty of best execution into a concrete obligation of data capture, analysis, and verifiable reporting for bond trading.

This creates a systemic challenge and an opportunity. The challenge lies in building the technological and procedural infrastructure to capture, store, and analyze this data. The opportunity resides in leveraging this data to create a more sophisticated and defensible execution policy.

The demonstration of best execution for bonds is now a function of how well a firm integrates these new data streams from SIs and other venues into a coherent, repeatable, and auditable process. It shifts the focus from simply getting a good price to proving, with data, that the chosen execution pathway was the optimal one when considering all relevant execution factors.

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Strategy

Strategically, adapting to the SI regime requires a fundamental rewiring of a firm’s approach to bond execution. The operating model must evolve from a decentralized, trader-discretion model to a centralized, system-driven framework. The objective is to construct an execution architecture that not only achieves the best outcome for the client but also generates a complete, contemporaneous audit trail that satisfies the “all sufficient steps” standard mandated by MiFID II. This is a higher threshold than the previous “all reasonable steps” and implies a more exhaustive and proactive process.

A core component of this strategy involves redefining the Request for Quote (RFQ) process. In the pre-SI world, an RFQ was often a less formal process. Under the new framework, every RFQ process, especially when interacting with an SI, becomes a data-gathering event that feeds the best execution demonstration.

The firm must be able to prove why it chose to solicit quotes from a specific set of counterparties, including SIs, and why it ultimately transacted on a particular quote. This requires a systematic approach to counterparty selection based on historical performance data, which SIs are now required to provide.

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Developing a Data-Driven Venue Selection Matrix

A sophisticated strategy involves creating a dynamic venue selection matrix. This is not a static policy document but a living framework embedded within the firm’s Order Management System (OMS) or Execution Management System (EMS). This matrix should rank and select potential execution venues ▴ SIs, MTFs, OTFs, and traditional OTC dealers ▴ based on a weighted score of the MiFID II execution factors. The weighting must be appropriate for the specific characteristics of the bond in question (e.g. liquidity, size of the order, credit quality).

For instance, for a large order in an illiquid high-yield bond, the likelihood of execution and minimization of market impact may be weighted more heavily than marginal price improvement. For a small order in a liquid sovereign bond, price and speed will be paramount. The SI’s published RTS 27 data provides direct inputs for this model, allowing a firm to quantitatively justify its choice of venue. The strategy is to turn the regulatory requirement for data publication into a proprietary source of competitive advantage in execution routing.

Firms must transition from merely consuming quotes to systematically evaluating the quality of the quote providers themselves using the mandated data streams.

The following table illustrates how a firm might strategically evaluate different execution venues for bonds, incorporating the new realities of the SI regime.

Table 1 ▴ Strategic Comparison of Bond Execution Venues
Venue Type	Primary Interaction Model	Pre-Trade Transparency	Data for Best Execution Proof	Strategic Consideration
Systematic Internaliser (SI)	Bilateral RFQ	Firm quotes on liquid instruments up to SSTI.	High (Published RTS 27 reports, firm quotes).	Provides reliable, firm liquidity source with rich data for audit. Risk of information leakage is contained but dealer dependency is a factor.
Multilateral Trading Facility (MTF)	All-to-all RFQ, Central Limit Order Book (CLOB)	Varies by protocol; can be high for CLOBs.	High (Full audit trail of all responses, market depth data).	Access to a wider pool of liquidity, potentially better price discovery. Can be less effective for very large or illiquid trades.
Organised Trading Facility (OTF)	Discretionary voice/electronic hybrid	Indicative quotes; operator has discretion.	Moderate (Execution records exist but pre-trade comparison is less structured).	Useful for complex, illiquid instruments where human negotiation is valuable. Demonstrating best execution requires more qualitative documentation.
Traditional OTC (Non-SI)	Bilateral RFQ (Voice/Chat)	No obligation to provide firm quotes.	Low (Requires manual capture of quotes; difficult to benchmark).	Maintains access to specialist dealers. Justifying execution requires robust benchmarking against other available data (e.g. SI quotes, MTF prices).

Ultimately, the strategy is one of integration. It is about building a single, coherent system that can intelligently route orders based on data, execute them efficiently, and, most importantly, produce a defensible narrative of why every decision was made. The SI regime provides a critical new set of data points that must be at the core of this integrated strategy.

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Execution

The execution of a compliant best execution policy in the SI era is a matter of technological architecture and procedural discipline. It requires the implementation of systems capable of capturing all relevant data points before, during, and after the trade. The demonstration is no longer a post-trade narrative constructed from memory and disparate chat logs; it is a granular, time-stamped reconstruction of the execution decision, supported by a rich dataset. The focus shifts from what the trader knew to what the firm’s system recorded.

A firm’s execution framework must be built around a centralized system, typically an OMS or EMS, that serves as the single source of truth. This system must be configured to:

Log all client orders with a precise timestamp and all relevant parameters (ISIN, size, side, any specific client instructions).
Automate the RFQ process to a pre-defined list of execution venues, including SIs and MTFs, based on the instrument’s characteristics.
Capture all quote responses in a structured format, including the price, the time of the quote, its duration, and the identity of the provider. This includes capturing the firm quotes provided by SIs.
Snapshot market data from multiple sources (e.g. composite pricing feeds, MTF order books, SI quotes) at the moment of execution to create a comprehensive picture of the available liquidity landscape.
Record the final execution details, including the venue, price, costs, and timestamp, linking it directly back to the parent order and the associated RFQ process.

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Quantifying the Best Execution Factors

The core of the execution process is the systematic evaluation of the best execution factors. While price and cost are critical, the SI regime, with its impact on market structure, elevates the importance of other, more qualitative factors. A robust execution process translates these qualitative factors into quantifiable metrics wherever possible. The following table provides a detailed breakdown of how these factors are analyzed and documented in the context of a bond trade executed with an SI.

Table 2 ▴ Documenting Best Execution Factors for a Bond Trade
Execution Factor	Analysis and Documentation in an SI Context	Required Data Points
Price	The executed price is compared against all other quotes received (from other SIs, MTFs) and against a benchmark price (e.g. a composite bond price like BVAL or CBBT) at the time of execution. Any deviation must be justified.	Executed price, all competing quotes, benchmark price at execution time (T0).
Costs	Includes explicit commission and implicit costs (e.g. the bid-ask spread). The SI’s spread is compared against spreads available on other venues. Total cost of execution is calculated and documented.	Execution price, mid-price at T0, any explicit fees.
Speed of Execution	The time elapsed from sending the RFQ to receiving a firm quote from the SI is measured. This is compared to the SI’s published RTS 27 data on execution speed and against other responders.	RFQ sent timestamp, quote received timestamp, execution timestamp.
Likelihood of Execution	Analysis of the SI’s historical fill rates and quote firmness, derived from their RTS 27 reports. For large or illiquid bonds, the certainty of execution provided by an SI’s firm quote is a significant positive factor.	SI’s published RTS 27 data, internal records of past trade success with the SI.
Size and Nature of the Order	The decision to use an SI is justified for orders that might cause market impact on a lit venue. The SI’s ability to absorb a large block trade without price slippage is a key justification.	Order size vs. average daily volume, SI’s published Large-in-Scale (LIS) thresholds.
Information Leakage	Executing bilaterally with a trusted SI minimizes the risk of information leakage compared to an all-to-all RFQ on an MTF. This is a qualitative but critical justification, especially for sensitive orders.	Documentation of the rationale for choosing a bilateral trading protocol.

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How Does This Framework Function in Practice?

Imagine a Best Execution Committee reviewing a trade. The trader’s report would no longer be a simple statement like “I traded with SI ‘A’ because they showed the best price.” Instead, the system would generate a detailed report showing that for this specific ISIN and order size, the firm’s policy dictated an RFQ to three SIs and one MTF. The report would show the quotes received from all four, benchmarked against a composite price. It would show that while MTF ‘X’ had a slightly better mid-price, SI ‘A’ offered a firmer quote for the full size, had a historically higher likelihood of execution based on its RTS 27 data, and presented a lower risk of information leakage.

This data-rich, multi-faceted justification is the new standard for demonstrating best execution. The SI regime, by forcing the publication of execution quality data, provides the raw material for this robust, defensible process.

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References

Bovill. “Best Execution Under MiFID II.” 2017.
International Capital Market Association (ICMA). “MiFID II/MiFIR ▴ Transparency & Best Execution requirements in respect of bonds.” 2016.
The Investment Association. “Fixed Income Best Execution ▴ Not Just a Number.” 2018.
European Securities and Markets Authority (ESMA). “MiFID II Best Execution Questions and Answers.” 2017.
Bank of America. “Order Execution Policy.” 2020.
AFME. “Guide for drafting/review of Execution Policy under MiFID II.” 2017.
Lehalle, Charles-Albert, and Sophie Moinas. “Market Microstructure in Practice.” World Scientific Publishing, 2021.
Harris, Larry. “Trading and Exchanges ▴ Market Microstructure for Practitioners.” Oxford University Press, 2003.

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Reflection

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Calibrating the Execution Architecture

The integration of the Systematic Internaliser regime into the bond market provides a new set of architectural components for constructing a superior execution framework. The data streams mandated by the regulation are not merely a compliance burden; they are feeds for a more intelligent system. The task now is to calibrate your firm’s internal systems to process these signals effectively.

How does your current operational workflow ingest, analyze, and act upon the RTS 27 data published by your SI counterparties? Is this data used to dynamically adjust your venue selection logic, or is it reviewed retrospectively as a historical artifact?

Viewing this regulatory evolution through an architectural lens reveals its true implication. It provides the tools to build a system that can make demonstrably better decisions. The ultimate advantage lies in designing a framework that not only satisfies the regulator’s demand for proof but also provides your portfolio managers with a more precise, efficient, and reliable path to liquidity, transforming a regulatory mandate into a tangible execution edge.