What Are the Primary Conflicts between Last Look Practices and MiFID II's Best Execution Mandate? ▴ Question

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Concept

The operational tension between last look liquidity protocols and the principles of MiFID II’s best execution mandate originates in their fundamentally different objectives. One is a risk mitigation mechanism for the market maker, while the other is a comprehensive regulatory framework designed to protect the end client. Understanding this divergence is the first step in designing an execution policy that navigates the complexities of modern, fragmented liquidity landscapes, particularly in over-the-counter (OTC) markets like foreign exchange.

Last look is a practice where a liquidity provider (LP) receives a trade request and takes a final, brief moment to accept or reject the trade at the quoted price. This mechanism functions as a final check against stale pricing or rapid, adverse market moves. For the LP, it is a critical tool to manage the risks of providing liquidity, especially in high-velocity electronic markets.

The practice allows LPs to show tighter spreads than they might otherwise offer, contributing to the appearance of deep liquidity. The core components of this practice involve a hold time, the period during which the LP decides, and the potential for a rejection, where the trade is not filled at the submitted price.

The essential conflict arises because the final decision to execute rests with the liquidity provider after the client has already committed to the trade based on a given price.

Conversely, the Markets in Financial Instruments Directive II (MiFID II) imposes a stringent obligation on investment firms to secure the most favorable terms for their clients. This “best execution” mandate is multifaceted, extending beyond just the headline price. The regulation compels firms to consider a range of execution factors, including cost, speed, likelihood of execution and settlement, size, and any other relevant consideration.

Firms must establish a systematic process, documented in an order execution policy, to ensure they are taking all sufficient steps to achieve the best possible result for their clients consistently. This requires not just post-trade analysis but a proactive approach to venue selection, counterparty assessment, and execution methodology.

The conflict is therefore systemic. A buy-side firm’s duty under MiFID II is to maximize the probability of a successful execution on the most advantageous terms for the client. The use of a last look protocol by an LP introduces an element of uncertainty into this process. The “likelihood of execution,” a key factor under MiFID II, is directly impacted.

A trade request sent to a last look venue is not a firm trade; it is a request that can be denied, leading to potential slippage and opportunity cost for the end client as the market may move while the firm seeks an alternative execution path. This dynamic creates a structural challenge for firms obligated to prove they are acting in their clients’ best interests.

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Strategy

Developing a robust strategy to manage the friction between last look practices and MiFID II compliance requires a shift from a price-centric view to a holistic assessment of execution quality. An effective execution policy treats liquidity sources not as equals, but as distinct pools with varying characteristics that must be quantified and continuously monitored. The strategic objective is to build a framework that can justify execution choices based on a broad set of metrics, aligning the firm’s actions with the client’s best interests as defined by the regulation.

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A Multi-Factor Approach to Venue Analysis

MiFID II mandates that firms consider several “execution factors” beyond price. A sophisticated strategy operationalizes this requirement by systematically scoring liquidity venues, including those that use last look. This involves moving beyond simple Transaction Cost Analysis (TCA) and incorporating a wider array of metrics. The goal is to create an internal, data-driven hierarchy of liquidity that informs routing decisions in real-time.

Key factors for this analysis include:

Fill Rates and Rejection Analysis ▴ This is the most direct measure of the impact of last look. A high rejection rate from a particular LP indicates a lower likelihood of execution. This data must be tracked granularly, differentiating between “soft” rejections (e.g. due to technical issues) and “hard” rejections (likely due to price moves).
Hold Time Latency ▴ The time an LP holds a trade request before confirming or rejecting it is a cost to the client. This “hold time” introduces uncertainty and exposes the client to market risk. Measuring this latency for each LP allows a firm to quantify this implicit cost.
Post-Trade Market Impact ▴ Analyzing price movement immediately after a trade is executed (or rejected) can reveal patterns. Consistent adverse price movement following interactions with a specific LP may suggest information leakage, a critical consideration for best execution.
Quoted vs. Effective Spread ▴ The quoted spread from a last look venue may be attractive, but if rejections are frequent, the “effective” spread (the cost of eventually getting the trade done elsewhere) is much wider. A strategic approach calculates this effective spread to make more informed comparisons between last look and firm liquidity pools.

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Constructing a Compliant Order Execution Policy

The firm’s Order Execution Policy is the central document outlining its approach to best execution. To address the challenges of last look, this policy must be explicit about how the firm evaluates and interacts with such liquidity. It should detail the quantitative and qualitative factors used in its venue analysis and explain the circumstances under which it might route an order to a last look venue.

A defensible execution policy does not necessarily prohibit the use of last look venues but provides a clear, evidence-based rationale for when and why they are used.

The following table illustrates a simplified comparison of execution quality metrics between a firm (no last look) liquidity pool and a last look pool. An asset manager would use a more detailed version of this analysis to justify its routing decisions.

Metric	Firm Liquidity (No Last Look)	Last Look Liquidity	MiFID II Relevance
Quoted Spread	0.5 pips	0.3 pips	Price
Fill Rate	99.9%	92.0%	Likelihood of Execution
Average Hold Time	~2 ms	~80 ms	Speed, Implicit Cost
Rejection Rate	0.1%	8.0%	Likelihood of Execution
Effective Spread (Post-Rejection)	N/A	0.7 pips	Total Cost

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The Role of Technology and Aggregation

A successful strategy relies heavily on technology. Smart order routers (SORs) and aggregation systems are essential tools for implementing a sophisticated execution policy. These systems can be configured with the firm’s quantitative venue analysis, allowing for dynamic and intelligent routing of orders.

For example, an SOR could be programmed to favor firm liquidity for time-sensitive orders while allowing for the use of last look venues for less urgent trades, but only if their rejection rates and hold times are below a certain threshold. This technological layer translates the firm’s strategic policy into automated, repeatable, and auditable execution logic, which is fundamental to demonstrating compliance with MiFID II.

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Execution

The execution framework for managing last look liquidity under MiFID II is a detailed, data-intensive process. It moves beyond high-level strategy to the granular, operational level of data capture, analysis, and reporting. The objective is to create a closed-loop system where execution data continuously informs and refines the firm’s routing logic and policies, creating a demonstrable and auditable trail of adherence to best execution principles.

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Systematic Data Capture and Analysis

At the core of a compliant execution framework is the systematic capture of every stage of the order lifecycle. For interactions with last look venues, this means logging not just filled trades, but all rejected quotes as well. This data forms the foundation of the firm’s quantitative venue analysis.

A typical data capture process should include:

Request Timestamp ▴ The precise time a trade request is sent to the LP.
Response Timestamp ▴ The time a response (fill or reject) is received.
LP Identification ▴ The specific counterparty providing the quote.
Instrument and Size ▴ The details of the requested trade.
Quoted Price ▴ The price at which the trade was requested.
Status ▴ Filled or Rejected. If rejected, a reason code, if provided.
Market Data Snapshot ▴ A snapshot of the consolidated market price at the time of the request and at the time of the response. This is crucial for calculating slippage on rejected trades.

Demonstrating “all sufficient steps” under MiFID II is impossible without a comprehensive and granular data collection architecture.

This data feeds into a TCA system that is specifically designed to handle the nuances of last look. The following table provides an example of the kind of granular analysis a firm must perform to evaluate its LPs.

LP Name	Total Requests	Fill Ratio (%)	Avg. Hold Time (ms)	Rejection Slippage (pips)	Internal Quality Score
LP Alpha	10,000	98.5%	35	-0.05	9.2/10
LP Beta	15,000	91.0%	150	-0.25	6.5/10
LP Gamma (Firm)	12,000	99.9%	5	N/A	9.8/10
LP Delta	8,000	88.0%	120	-0.30	5.1/10

In this example, “Rejection Slippage” measures the average market movement against the client between the trade request and the rejection message. A high negative value for this metric at LP Beta and LP Delta indicates that these LPs tend to reject trades when the market has moved in their favor, a key red flag for a best execution committee.

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Integrating Analysis into the Execution Workflow

The output of this analysis must be fed back into the firm’s execution systems. The “Internal Quality Score” is a composite metric that can be used by a smart order router to prioritize liquidity. The SOR’s logic can be programmed to penalize LPs with high hold times and significant rejection slippage. For example, an order might only be routed to LP Beta if the price improvement it offers over LP Alpha is greater than a predefined threshold that compensates for its lower fill ratio and higher implicit costs.

This creates a dynamic and self-optimizing execution process. The firm is not just passively analyzing past trades; it is actively using that analysis to improve future execution outcomes. This proactive approach is central to the spirit of MiFID II.

The ability to demonstrate this feedback loop to regulators, showing that the firm systematically identifies and addresses poor execution outcomes, is a powerful defense of its execution policy. This process also allows the firm to have informed, data-driven conversations with its LPs about their execution quality, potentially leading to improved performance.

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References

FCA Handbook, COBS 11.2A Best execution ▴ MiFID provisions. Financial Conduct Authority, 2017.
European Parliament and Council. “Directive 2014/65/EU of the European Parliament and of the Council of 15 May 2014 on markets in financial instruments and amending Directive 2002/92/EC and Directive 2011/61/EU.” Official Journal of the European Union, 2014.
Lehalle, Charles-Albert, and Sophie Laruelle, eds. Market Microstructure in Practice. World Scientific, 2018.
Harris, Larry. Trading and Exchanges ▴ Market Microstructure for Practitioners. Oxford University Press, 2003.
O’Hara, Maureen. Market Microstructure Theory. Blackwell Publishers, 1995.
Global Foreign Exchange Committee. “FX Global Code ▴ Principles of Good Practice.” Bank for International Settlements, 2017.
Johnson, Barry. Algorithmic Trading and DMA ▴ An introduction to direct access trading strategies. 4Myeloma Press, 2010.
Financial Industry Regulatory Authority (FINRA). “Rule 5310. Best Execution and Interpositioning.” FINRA Manual, 2023.

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From Compliance to Competitive Advantage

The regulatory requirements of MiFID II, particularly concerning best execution, present a significant operational challenge. The inherent conflict with practices like last look demands a sophisticated, data-driven response. Firms that approach this challenge as a mere compliance exercise miss the underlying opportunity. The process of building a robust, evidence-based execution framework does more than satisfy regulators; it cultivates a deeper understanding of market mechanics and liquidity behavior.

This journey transforms the firm’s execution desk from a cost center into a source of strategic advantage. The granular data collected for compliance becomes the raw material for optimizing trading strategies, reducing implicit costs, and ultimately, delivering superior returns for clients. The question for investment firms is not simply “Are we compliant?” but rather “How can we leverage this regulatory framework to build a more intelligent and efficient execution system?” The answer lies in viewing the market not as a series of disconnected venues, but as a complex system to be navigated with precision, data, and a relentless focus on the client’s ultimate outcome.