How Has the Rise of Systematic Internalizers Affected RFQ Strategies Post-MiFID II?

Concept

The introduction of the Markets in Financial Instruments Directive II (MiFID II) fundamentally re-architected the European liquidity landscape. A central component of this new regulatory framework was the formalization of the Systematic Internaliser (SI) regime, which brought a significant portion of over-the-counter (OTC) trading into a more transparent and structured environment. For buy-side institutions, this shift necessitated a profound re-evaluation of their liquidity sourcing and execution strategies, particularly concerning the Request for Quote (RFQ) protocol. The core challenge became how to integrate this newly defined liquidity channel ▴ the SI ▴ into existing workflows to satisfy the stringent best execution requirements mandated by the regulation.

Before MiFID II, the process of sourcing liquidity for large or less liquid trades often relied on informal, bilateral relationships. An RFQ was a discretionary tool used to solicit prices from a known set of counterparties. The process, while effective, operated with minimal formal oversight and its data footprint was limited. MiFID II changed this by creating a quantitative, rules-based definition for SIs ▴ investment firms dealing on their own account by executing client orders outside of a regulated trading venue on an organized, frequent, systematic, and substantial basis.

This compelled a large volume of principal trading activity, previously conducted in the dark, into a registered and observable framework. Consequently, the universe of potential counterparties for an RFQ expanded, but it also became more complex to navigate.

The formalization of the SI regime under MiFID II transformed a relationship-based liquidity model into a system-based one, demanding a more sophisticated and data-driven approach to execution.

The SI regime introduced a dual obligation and opportunity for buy-side firms. The obligation was to demonstrably achieve and evidence best execution, considering a wider array of factors beyond just price, such as speed and likelihood of execution. The opportunity was the ability to tap into a new, formalized stream of principal liquidity from these SIs, who were now required to provide firm quotes under certain conditions.

This altered the very nature of the RFQ. It evolved from a simple price-seeking message into a critical component of a firm’s best execution architecture, requiring a systematic process for counterparty selection, data capture, and post-trade analysis.

What Is the Core Function of a Systematic Internaliser?

A Systematic Internaliser functions as a liquidity provider that uses its own capital to execute client orders. Unlike a traditional exchange, which matches buyers and sellers, an SI acts as the counterparty to the trade. The MiFID II framework established specific thresholds for trading activity that, once crossed, require a firm to register as an SI for particular instruments. This registration carries with it specific obligations, including the requirement to provide firm quotes to clients upon request and to adhere to post-trade transparency rules by reporting trades to an Approved Publication Arrangement (APA).

The primary purpose of this regime was to increase transparency in the vast OTC market and ensure that the internalization of order flow does not detract from efficient price discovery on public venues. For the buy-side, interacting with an SI offers potential benefits like price improvement over lit market prices and the assumption of trade reporting duties by the SI, which reduces the buy-side’s operational burden.

Strategy

The emergence of the SI regime prompted a necessary evolution in buy-side RFQ strategies. A passive or purely relationship-driven approach to liquidity sourcing became untenable under the new best execution mandates. A sophisticated, data-driven strategy was required to effectively harness the liquidity offered by SIs while mitigating the risks, such as information leakage. The strategic imperative shifted from merely finding a counterparty to designing a systematic process for interacting with a diverse and fragmented liquidity landscape.

A primary strategic adjustment involved the intelligent segmentation of order flow. Not all orders are suitable for an RFQ, and not all RFQs should be sent to every available counterparty. The rise of SIs meant that buy-side desks had to develop a more nuanced understanding of which orders would benefit from being shown to an SI. For example, trades that are large in scale but within the standard market size for which SIs are obligated to quote became prime candidates for this channel.

Conversely, very large or illiquid orders might still be better suited for high-touch execution to avoid signaling risk. This required the integration of pre-trade analytics into the workflow, allowing traders to classify orders based on their characteristics and route them to the most appropriate liquidity sources, including SIs, lit markets, or dark pools.

Effective RFQ strategy in the post-MiFID II world is defined by the systematic integration of SIs into a holistic liquidity discovery process, governed by data and tailored to the specific characteristics of each order.

Adapting RFQ Workflows for a Multi-Venue Environment

The post-MiFID II environment is characterized by a multiplicity of liquidity pools. An effective RFQ strategy must account for this fragmentation. This involves moving from a static list of counterparties to a dynamic and intelligent counterparty selection process.

Modern Execution Management Systems (EMS) play a vital role here, as they can be configured to automatically select the most relevant SIs and other liquidity providers for a given RFQ based on historical performance, instrument specialization, and the specific attributes of the order. This automation helps to reduce the “noise” and potential for information leakage associated with broadcasting RFQs too widely.

Furthermore, a key strategic decision for buy-side firms is how to balance the use of RFQs with other execution methods. An over-reliance on RFQs, even when directed at SIs, can lead to adverse market impact if not managed carefully. A robust strategy involves creating a feedback loop where the outcomes of RFQs are continuously analyzed.

This Transaction Cost Analysis (TCA) should extend beyond simple price comparison to include metrics on response times, fill rates, and any observed market impact post-trade. This data can then be used to refine the counterparty selection logic and the decision of when to use an RFQ versus an algorithmic strategy or a direct-to-market order.

Comparative Analysis of RFQ Strategies

The table below outlines the key differences between pre- and post-MiFID II RFQ strategies, highlighting the shift towards a more systematic and data-centric approach.

How Do Firms Mitigate Information Leakage?

Mitigating information leakage in an SI-driven RFQ environment requires a disciplined and technologically-enabled approach. The core principle is to limit the “blast radius” of the RFQ. Instead of sending an inquiry to a wide group of potential counterparties, firms now use more sophisticated methods:

• Tiered Counterparty Lists ▴ Firms create tiered lists of liquidity providers. The initial RFQ may go to a small, trusted group of top-tier SIs. If a satisfactory quote is not received, the inquiry can be escalated to a wider group.
• Conditional RFQs ▴ Some platforms allow for conditional RFQs, where the inquiry is only revealed to counterparties who meet certain pre-defined criteria, such as having recently traded the instrument.
• Use of Aggregators ▴ Buy-side firms can use aggregation platforms that anonymize their initial inquiry, only revealing their identity once a counterparty has committed to providing a firm quote.

Execution

The execution of RFQ strategies in the post-MiFID II era is a matter of operational precision and technological integration. The strategic decisions about when and how to use RFQs must be translated into a concrete, repeatable, and auditable workflow within the firm’s trading infrastructure. This requires a seamless connection between the firm’s Order Management System (OMS), which houses the portfolio manager’s initial order, and its Execution Management System (EMS), where the trader designs and implements the execution strategy.

A critical execution protocol is the “sweep-to-SI” model. In this workflow, before an order is sent to a lit market or placed into an algorithmic trading strategy, the EMS automatically sends a targeted RFQ to a pre-defined list of SIs for that specific instrument. This process is automated and occurs in a fraction of a second. The system then compares the quotes received from the SIs against the prevailing price on the lit market (e.g. the European Best Bid and Offer, or EBBO).

If an SI offers a price that is better than the lit market, the system can automatically execute against the SI quote, capturing valuable price improvement. If the SI quotes are not competitive, the order can then proceed to the next step in the execution logic, such as an algorithmic strategy.

Optimal execution is achieved when the RFQ process is fully integrated into an automated workflow that systematically checks for price improvement opportunities from SIs before accessing other liquidity pools.

Operational Playbook for SI-Integrated RFQ

Implementing a robust, SI-aware RFQ process involves several distinct operational steps. This playbook outlines a best-practice approach for a buy-side trading desk.

1. Order Staging and Classification ▴ Upon receiving an order from the OMS, the EMS should automatically classify it based on pre-defined criteria (e.g. security type, order size, liquidity profile). This classification determines the appropriate execution pathway.
2. Automated SI Counterparty Selection ▴ For orders deemed suitable for an RFQ, the system should consult a dynamic database of SIs. This database must be regularly updated to reflect which firms are registered SIs for which specific instruments. The selection should be based on historical performance data, focusing on SIs that have consistently provided competitive quotes and high fill rates for similar orders.
3. Targeted RFQ Dissemination ▴ The RFQ is sent electronically and simultaneously to the selected SIs. The number of counterparties should be kept to a minimum (typically 3-5) to minimize information leakage.
4. Quote Aggregation and Evaluation ▴ The EMS aggregates the responses in real-time. The system must have a clear logic for evaluating the quotes, which includes not only the price but also the size and any other conditions attached to the quote. This evaluation must also happen in the context of the live, lit market price.
5. Execution and Post-Trade Processing ▴ Once the best quote is identified, execution is automated. The system then captures all relevant data points for the trade, including the winning and losing quotes, the time of execution, and the prevailing lit market price at the moment of execution. This data is critical for TCA and demonstrating best execution. The SI is responsible for the post-trade reporting, but the buy-side firm must ensure its records reflect this.

Quantitative Analysis of RFQ Execution Pathways

The following table provides a simplified quantitative comparison of different execution outcomes for a hypothetical order to buy 10,000 shares of a stock, illustrating the potential benefits of an SI-integrated RFQ workflow.

Reflection

The integration of Systematic Internalisers into the trading ecosystem represents more than a regulatory compliance exercise. It marks a structural evolution in market mechanics. The frameworks and protocols developed to interact with SIs ▴ the data-driven counterparty selection, the automated execution logic, the rigorous post-trade analysis ▴ are components of a larger operational intelligence system.

As you assess your own firm’s execution architecture, consider how these components function in concert. The true strategic advantage lies not in any single tool, but in the coherence of the entire system and its ability to adapt to the market’s continuous evolution.