How Does the SI Quoting Obligation Impact an Institution's Best Execution Analysis? ▴ Question

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Concept

An institution’s framework for analyzing best execution is fundamentally reshaped by the Systematic Internaliser (SI) quoting obligation. This regulatory requirement under MiFID II is a structural market component that introduces a distinct, bilateral liquidity source with specific data and transparency characteristics. Your execution analysis must therefore evolve from a simple comparison of public venues to a sophisticated evaluation of a hybrid market structure.

The SI regime compels certain investment firms, based on their trading volumes, to provide firm quotes when executing client orders on their own account. This creates a parallel liquidity channel outside of traditional lit markets or Multilateral Trading Facilities (MTFs), one that your systems must be architected to access, measure, and compare.

The core of the issue resides in transforming a regulatory mandate into a quantifiable input for your execution quality assessment. The SI obligation is a mechanism designed to bring a degree of transparency to what was previously opaque over-the-counter (OTC) activity. For liquid instruments, SIs must make these quotes public, providing a new data stream for pre-trade analysis.

For illiquid instruments, quotes are provided upon request, creating a discreet, relationship-driven execution pathway. Your best execution analysis must possess the technical and procedural sophistication to correctly weigh the benefits of this private liquidity channel ▴ potential price improvement, reduced market impact ▴ against the broad, anonymous liquidity available on public exchanges.

The SI quoting obligation transforms a compliance requirement into a distinct liquidity source that must be systematically integrated into any credible best execution analysis.

Understanding this dynamic is the first principle. The analysis ceases to be a passive, post-trade reporting exercise. It becomes an active, pre-trade strategic decision support system. The presence of SIs in a particular instrument means your firm has more than just a choice of where to trade; it has a choice of how to trade.

You can interact with a public order book or engage in a bilateral negotiation with a principal dealer who is legally obligated to provide a firm price. This distinction is the foundational element upon which a modern, robust best execution framework is built. It requires an architecture that can handle fragmented liquidity and diverse execution protocols, ensuring that every decision is defensible, data-driven, and aligned with the ultimate goal of achieving the optimal outcome for the end client.

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Strategy

Integrating the SI quoting obligation into a best execution strategy requires a shift in perspective. You are architecting a system that treats SI liquidity as a managed resource, not an incidental trading destination. The strategy is to systematically leverage the unique properties of SI quoting ▴ its bilateral nature, the firmness of its quotes, and its data output ▴ to enhance overall execution quality. This involves developing a dynamic venue selection logic that can intelligently route orders based on their specific characteristics, such as size, liquidity profile, and market sensitivity.

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Architecting the Venue Selection Framework

A sophisticated strategy begins with a formal classification of all available execution venues, including SIs. This is not a static list; it is a dynamic map of the liquidity landscape. Your internal systems must be able to identify which counterparties are designated SIs for which specific financial instruments. This knowledge is a strategic asset.

It allows your order routing systems to make informed decisions. For a large, potentially market-moving block order, the optimal strategy might be to solicit quotes directly from multiple SIs, leveraging their obligation to quote to access principal liquidity without signaling intent to the broader market. This pathway minimizes information leakage, a critical component of best execution for institutional-sized orders.

Conversely, for small, liquid orders, the strategy might prioritize speed and the certainty of execution on a lit market. The key is building a rules-based engine that can weigh these factors. The SI quoting obligation provides a valuable alternative, and the strategy lies in knowing precisely when and how to use it. This requires pre-trade analytics that can estimate the potential for price improvement and market impact across different venue types.

A successful strategy treats the SI network as a specialized liquidity pool, to be accessed based on the specific risk and execution profile of each individual order.

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How Does SI Data Enhance Execution Analysis?

The quoting obligation is a powerful source of structured data for post-trade analysis. Each quote requested from an SI, whether executed or not, is a data point. It provides a timestamped, firm price for a specific size. This data is invaluable for Transaction Cost Analysis (TCA).

It allows for a more rigorous and evidence-based assessment of execution quality. Your analysis can directly compare the executed price against the firm quotes that were available from SIs at the time of the trade. This creates a robust benchmark for measuring price improvement.

The table below illustrates a comparative framework for evaluating execution venues, incorporating SIs as a distinct category. This data-driven approach is the core of a defensible best execution policy.

Table 1 ▴ Comparative Execution Venue Analysis
Execution Factor	Lit Market (e.g. LSE)	MTF (e.g. Cboe BXE)	Systematic Internaliser
Pre-Trade Transparency	High (Full Order Book)	High (Full Order Book)	Conditional (Public for liquid, on-request for illiquid)
Liquidity Type	Anonymous, Central Limit Order Book	Anonymous, Central Limit Order Book	Bilateral, Principal-to-Client
Market Impact Risk	High for large orders	High for large orders	Low (Contained, off-book)
Potential for Price Improvement	Possible via spread crossing	Possible via spread crossing	High (Quotes can be better than EBBO)
Trade Reporting Obligation	Venue Reports	Venue Reports	SI Reports

Furthermore, the strategic use of SIs can simplify an institution’s own operational overhead. When trading with an SI, the obligation to make the trade public post-trade falls upon the SI. For a buy-side firm, routing orders to SIs can therefore be a component of a strategy to reduce its own compliance and reporting burden, provided the execution quality remains paramount. A truly strategic framework balances execution performance with operational efficiency, and the SI regime provides a direct mechanism to achieve both.

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Execution

The execution of a best execution analysis that properly incorporates the SI quoting obligation is a matter of precise data architecture and rigorous, repeatable process. It moves beyond policy into the realm of operational engineering. The objective is to build a system that not only satisfies regulatory requirements like RTS 27 and RTS 28 but also generates actionable intelligence to refine trading strategies continuously.

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A Procedural Guide for Integrating SI Analysis

Implementing a robust analysis framework requires a clear, multi-stage process. This procedure ensures that SI performance is measured, benchmarked, and reviewed consistently, forming a feedback loop for the trading desk and compliance functions.

Counterparty Classification Your firm must maintain a continuously updated internal registry of all trading counterparties, flagging those who have SI status for specific asset classes. This is a foundational data layer. This process involves monitoring regulatory notifications and direct disclosures from the counterparties themselves.
Smart Order Router (SOR) Configuration The logic within your SOR must be explicitly configured to recognize and query SIs. This involves setting up rules that determine when to poll SIs for quotes. For example, orders exceeding a certain percentage of the average daily volume might automatically trigger a Request for Quote (RFQ) to a panel of relevant SIs alongside checking lit market prices.
Standardized Data Capture For every order, a standardized set of data points must be captured, regardless of the execution venue. When an SI is queried, this must include the full quote ladder received, not just the executed price. This creates the necessary dataset for a fair comparison.
Quarterly Execution Quality Review A formal review process must be established. This involves generating quantitative reports that compare execution performance across all venues, including SIs. The review committee should include representatives from trading, compliance, and risk management.
Policy Adjustment The findings of the quarterly review must lead to actionable outcomes. If an SI consistently provides superior price improvement for a certain type of trade, the SOR logic should be adjusted to favor that venue more heavily. Conversely, underperforming venues should be down-weighted.

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What Are the Key Data Points for SI Analysis?

To perform a granular analysis, specific data fields must be captured from the interaction with an SI. This data forms the bedrock of any quantitative comparison and is essential for defending execution decisions to regulators and clients. The table below details the critical data architecture required.

Table 2 ▴ Essential Data Capture for SI Execution Analysis
Data Field	Description	Analytical Purpose
Quote Request Timestamp	High-precision timestamp of when the quote was requested.	Synchronization with lit market data for arrival price benchmarks.
Quote Response Timestamp	High-precision timestamp of when the SI’s quote was received.	Measures latency and speed of execution.
SI Quoted Price(s) and Size(s)	The firm bid/offer prices and corresponding quantities provided by the SI.	Core data for calculating price improvement vs. EBBO.
European Best Bid and Offer (EBBO)	The best bid and offer available on lit markets at the time of quote request.	Primary benchmark for assessing price quality.
Execution Price and Timestamp	The final price and time of the execution, if it occurred.	The outcome variable for all performance metrics.
Rejection/Decline Reason	A code or message if the SI declined to quote or the quote was not taken.	Measures likelihood of execution and SI reliability.

The quality of your execution analysis is a direct function of the granularity and integrity of your captured data.

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Quantitative Modeling of Execution Quality

With this data architecture in place, an institution can build a quantitative model to score each execution. A composite Best Execution Score (BES) can be calculated for each trade, incorporating multiple factors. A simplified model could be:

BES = (w1 PriceImprovement) + (w2 SpeedScore) + (w3 LikelihoodScore)

Where:

PriceImprovement is measured in basis points relative to the EBBO at the time of execution. A positive value indicates a better price.
SpeedScore is an inverted measure of the time from order routing to execution confirmation. Faster executions receive a higher score.
LikelihoodScore is derived from historical data, representing the probability that a particular venue will provide a firm quote and execute for a given order type.
w1, w2, w3 are the weights assigned to each factor, reflecting the institution’s specific execution policy priorities. For a high-touch desk, price improvement might have the highest weight, while an algorithmic desk might prioritize speed.

By calculating this score for every trade across every venue, the institution creates a powerful, objective dataset. This allows the firm to prove to regulators that it is not merely connecting to SIs but is actively monitoring their performance and using that data to fulfill its overarching duty of best execution in a systematic and defensible manner.

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References

Gomber, P. et al. “High-frequency trading.” Goethe University, House of Finance, 2011.
Autorité des Marchés Financiers. “The French regulator’s initial thoughts on the impact of MiFID 2 on market structure.” AMF News Release, 2018.
ICMA. “MiFID II implementation ▴ the Systematic Internaliser regime.” ICMA Publication, 2017.
SmartStream Technologies. “Systematic Internalisation under MiFID II ▴ What’s Needed Now.” SmartStream White Paper, 2018.
European Securities and Markets Authority. “MiFID II and MiFIR investor protection and intermediaries topics.” ESMA Q&A Document, 2021.
CFA Institute. “Market Microstructure ▴ The Practitioner’s Guide.” CFA Institute Publication, 2020.

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Reflection

The integration of the Systematic Internaliser regime into your operational framework is a test of your institution’s architectural intelligence. The data streams and liquidity pathways created by this regulation are now permanent features of the market landscape. Viewing them as mere compliance hurdles is a strategic failure. The real question is how you engineer your systems to process these inputs, converting regulatory mandates into execution alpha.

Does your current data capture and analysis framework possess the required granularity to not only prove compliance but to actively discover superior execution pathways? The answer to that question defines your competitive standing in the current market structure.