How Can a Company Effectively Isolate the Impact of Pre-RFP Engagement from Other Market Factors? ▴ Question

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The Attribution Imperative

In the architecture of institutional trading, every action seeks a justifiable outcome. The decision to engage with a counterparty before a formal Request-for-Quote (RFQ) is initiated represents a significant input into this system. This pre-RFG engagement, a subtle and often unrecorded dialogue, can range from informal liquidity checks to more structured indications of interest. The fundamental challenge for any trading desk is determining the precise value of this activity.

A company must possess a rigorous method to distinguish the alpha generated by this preliminary communication from the beta of general market movements. This is the attribution imperative ▴ a mandate to deconstruct execution outcomes, assigning value to specific, deliberate actions while accounting for the pervasive influence of external market conditions.

The core of the problem lies in disentangling two concurrent data streams. The first is the specific, targeted signal generated by pre-RFQ engagement. This could manifest as tighter spreads, larger available size, or reduced slippage from a counterparty who has been primed for the trade. The second stream is the ambient noise of the market.

This encompasses a wide spectrum of factors, including macroeconomic data releases, shifts in broad market sentiment, sector-specific news, and the latent trading intentions of thousands of other anonymous participants. Without a formal system of analysis, a positive execution outcome could be wrongly attributed to skillful engagement when it was merely the result of a favorable market tide. Conversely, a poor outcome might be blamed on market volatility when it was, in fact, due to information leakage from the pre-RFQ dialogue itself.

Isolating the effect of pre-trade engagement requires treating it as a measurable input within a comprehensive execution analysis framework.

Achieving this separation is a systems design challenge. It requires moving beyond anecdotal evidence and trader intuition, though both have their place, toward a quantitative framework. Such a system must capture and timestamp not only the formal stages of an RFQ but also the informal interactions that precede it. It must simultaneously ingest a wide array of market state variables.

The goal is to build a model that can predict a baseline execution cost for a given trade, assuming no pre-RFQ engagement occurred. The deviation of the actual execution cost from this modeled baseline represents the tangible impact, positive or negative, of the preliminary communication. This process transforms the abstract concept of “good relationships” or “market feel” into a quantifiable performance metric, allowing for systematic improvement and strategic allocation of resources.

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Strategy

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A Multi-Factor Analytical Framework

To effectively isolate the impact of pre-RFQ engagement, a company must adopt a multi-factor analytical framework that treats the problem as a scientific experiment. The core strategy is to establish a control group against which the “treatment” ▴ the pre-RFQ engagement ▴ can be measured. This involves a combination of rigorous data structuring, the application of econometric models, and the integration of qualitative, structured feedback. The objective is to create a system that can answer the counterfactual question ▴ “What would our execution quality have been for this specific trade, at this exact moment, had we not engaged with the counterparty beforehand?” Answering this requires a deliberate and systematic approach to data collection and analysis.

The foundation of this strategy is the creation of comparable datasets. The “treatment group” consists of all RFQs where pre-engagement occurred. The “control group” comprises RFQs for similar instruments, of similar size, executed under comparable market conditions, but without any prior engagement. Creating a valid control group can be achieved in several ways.

A “matched sampling” approach involves finding a historical trade in a different but highly correlated instrument that closely matches the characteristics of the treatment trade. A more robust method is “propensity score matching” (PSM), where a statistical model calculates the probability (propensity score) of any given trade receiving pre-engagement based on its characteristics (size, instrument type, time of day, etc.). Trades with similar propensity scores can then be compared, even if they are not identical, effectively controlling for the observable factors that lead a trader to engage in the first place.

A robust strategy combines quantitative modeling with structured qualitative inputs to build a complete picture of execution outcomes.

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Designing the Quantitative Architecture

The quantitative core of the strategy relies on multiple regression analysis. This statistical technique models the relationship between a dependent variable (the execution outcome, e.g. slippage in basis points) and multiple independent variables. These independent variables must include a binary flag for pre-RFQ engagement (1 if it occurred, 0 if not) alongside a comprehensive set of market factors.

The coefficient calculated for the engagement flag represents its isolated impact on the execution outcome, holding all other included factors constant. The challenge lies in selecting the right variables to include in the model to avoid omitted variable bias, where an unobserved factor that correlates with both pre-engagement and execution quality can skew the results.

Execution Outcome Metrics (Dependent Variables) ▴ These are the key performance indicators to be measured. Examples include slippage vs. arrival price, spread paid, and the fill ratio of the request.
Market State Variables (Control Variables) ▴ A wide range of data is needed to capture the market environment. This includes realized volatility of the underlying asset, the bid-ask spread of the instrument at the time of the RFQ, order book depth, and market-wide volume figures.
Trade-Specific Variables (Control Variables) ▴ These account for the unique characteristics of each trade, such as the notional value, the type of instrument (e.g. option, future), and the time of day to account for intraday liquidity patterns.

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Integrating Qualitative Overlays

Quantitative data alone may not capture the full context of pre-RFQ engagement. The strategy must incorporate a system for collecting structured qualitative data from traders. Immediately following an execution, the trader responsible should be prompted to log key details of any pre-RFQ communication. This is not a free-form text box but a standardized input form.

This qualitative data can then be coded and used in the quantitative analysis, either as additional variables or as a way to segment the results. For example, the analysis could differentiate between the impact of “passive” engagement (e.g. a simple liquidity check) and “active” engagement (e.g. negotiating a specific size or spread). This fusion of man and machine, of quantitative rigor and human insight, provides a far more granular and accurate understanding of the value created through these preliminary interactions.

Table 1 ▴ Comparison of Impact Analysis Methodologies
Methodology	Description	Strengths	Weaknesses
Simple A/B Testing	Direct comparison of the average execution quality for trades with and without pre-RFQ engagement.	Easy to implement and understand.	Highly susceptible to confounding variables; does not control for market conditions or trade characteristics.
Multiple Regression Analysis	A statistical model that estimates the effect of pre-RFQ engagement while controlling for a range of other factors.	Provides a quantitative measure of impact (the regression coefficient); controls for multiple confounding variables simultaneously.	Requires clean, comprehensive data; results can be misleading if important variables are omitted.
Propensity Score Matching (PSM)	Matches each “treatment” trade with a “control” trade that had a similar likelihood of receiving the treatment.	Creates a more robust control group, reducing selection bias.	Can only control for observable variables; requires significant data and statistical expertise.
Difference-in-Differences (DiD)	Compares the change in execution quality for the treatment group before and after a policy change (e.g. a new engagement protocol) to the change for a control group.	Excellent for measuring the impact of a specific change in strategy; controls for time-invariant unobserved factors.	Requires a specific event or policy change to analyze; assumes parallel trends between groups.

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Execution

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The Implementation Protocol for Attribution

Executing a robust attribution analysis for pre-RFQ engagement requires a disciplined, multi-stage protocol. This process moves from raw data acquisition to sophisticated modeling and, finally, to actionable reporting. It is an operational workflow designed to transform transaction data into strategic intelligence.

The success of this protocol hinges on the quality and granularity of the data collected at each stage. The ultimate goal is to create a feedback loop where the analysis of past trades informs the strategy for future engagements, systematically enhancing execution quality across the firm.

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A Step-by-Step Implementation Guide

The practical application of this analytical framework can be broken down into a clear, sequential process. Each step builds upon the last, ensuring that the final analysis is grounded in a solid foundation of high-quality, well-structured data.

Data Aggregation and Timestamping ▴ The foundational step is to create a unified data warehouse that captures all relevant information with high-precision timestamps. This system must log every stage of the trade lifecycle, from the first informal message with a potential counterparty to the final execution confirmation. This includes data from communication platforms (e.g. chat logs, voice transcripts converted to text), the Order Management System (OMS), and the Execution Management System (EMS).
Structured Data Entry for Qualitative Factors ▴ Implement a mandatory post-trade data entry module for traders. This module should use standardized fields to capture the nature of any pre-RFQ engagement. Key fields would include ‘Engagement Type’ (e.g. ‘Liquidity Check’, ‘Size Discovery’, ‘Spread Indication’), ‘Counterparty’, and a ‘Confidence Score’ from the trader on the perceived impact.
Control Variable Ingestion ▴ Concurrently, the system must ingest and align a wide array of market data. This includes tick-by-tick data for the instrument and its underlying, market-wide volume metrics, volatility indices (like the VIX), and data on order book depth from the primary exchanges.
Model Execution and Calibration ▴ With the dataset constructed, the core regression model is executed. This should not be a one-time event. The model should be recalibrated periodically (e.g. quarterly) to adapt to changing market regimes. The output of the model provides the key metric ▴ the coefficient for the ‘Pre-RFQ Engagement’ variable, which quantifies its average impact on execution slippage in basis points.
Reporting and Visualization ▴ The results must be presented in a clear, intuitive dashboard. This should allow desk heads and strategists to view the impact of engagement broken down by counterparty, instrument type, market cap, and trader. This visualization is critical for translating complex statistical output into actionable business intelligence.
Strategic Feedback Loop ▴ The final step is to close the loop. The findings from the analysis are fed back to the trading desk in regular performance reviews. This allows traders to understand which types of engagement with which counterparties are most effective, and to adjust their behavior accordingly.

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Quantitative Modeling and Data Analysis

The heart of the execution protocol is the data and the model that analyzes it. The following tables provide a blueprint for the necessary data architecture and a simplified example of how to interpret the model’s output. This level of detail is essential for building a system that can produce credible and reliable results.

Table 2 ▴ Pre-RFQ Impact Analysis Data Schema
Field Name	Data Type	Description	Source System
Trade_ID	String	Unique identifier for each RFQ.	OMS/EMS
Execution_Slippage_BPS	Float	The execution price slippage in basis points relative to the arrival price. (This is the dependent variable).	TCA System
Pre_RFQ_Engagement	Boolean	Flag indicating if pre-RFQ engagement occurred (1 for Yes, 0 for No). (The primary independent variable).	Trader Input Module
Notional_Value_USD	Integer	The total value of the trade in USD.	OMS/EMS
Market_Vol_30D	Float	The 30-day realized volatility of the underlying asset at the time of the RFQ.	Market Data Feed
Spread_at_RFQ_BPS	Float	The quoted bid-ask spread in basis points at the moment the RFQ was sent.	Market Data Feed
Counterparty_ID	String	An identifier for the winning counterparty.	OMS/EMS
Hour_of_Day	Integer	The hour of the day (0-23) to control for intraday liquidity patterns.	EMS

Once this data is collected for a large sample of trades, a multiple regression analysis can be performed. The goal is to estimate the coefficients (β) in an equation like the following:

Execution_Slippage_BPS = β₀ + β₁(Pre_RFQ_Engagement) + β₂(Notional_Value_USD) + β₃(Market_Vol_30D) + β₄(Spread_at_RFQ_BPS) + ε

The key output is the value and statistical significance of β₁, the coefficient for the Pre_RFQ_Engagement variable. A negative and statistically significant β₁ would indicate that, on average, pre-RFQ engagement reduces execution slippage, after accounting for the effects of trade size, market volatility, and liquidity.

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References

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Campigli, F. Bormetti, G. & Lillo, F. (2023). Measuring price impact and information content of trades in a time-varying setting. arXiv preprint arXiv:2307.05030.
Hautsch, N. & Podolskij, M. (2013). Pre-averaging based estimation of quadratic variation in the presence of noise and jumps ▴ theory, implementation, and empirical evidence. Journal of Econometrics, 176(1), 165-189.
Madhavan, A. (2000). Market microstructure ▴ A survey. Journal of Financial Markets, 3(3), 205-258.
Rosenbaum, P. R. & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Abadie, A. & Imbens, G. W. (2011). Matching on the estimated propensity score. Econometrica, 79(1), 231-267.
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335.
O’Hara, M. (1995). Market Microstructure Theory. Blackwell Publishers.
Cont, R. & Kukanov, A. (2017). Optimal order placement in high-frequency trading. Quantitative Finance, 17(1), 21-39.
Bouchaud, J. P. Farmer, J. D. & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Handbook of financial markets ▴ dynamics and evolution (pp. 57-160). North-Holland.

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From Attribution to Systemic Intelligence

The capacity to precisely attribute execution outcomes to specific actions is a powerful operational tool. It transforms the trading desk from a reactive cost center into a proactive hub of strategic execution. The framework detailed here provides a methodology for dissecting the impact of pre-RFQ engagement, but its true value lies in its application as a continuous learning mechanism. The insights generated by this system should not be static reports, but dynamic inputs that refine the firm’s entire approach to liquidity sourcing.

This analytical rigor fosters a culture of accountability and empirical decision-making. It allows a firm to systematically identify which counterparties are true partners, providing tangible value in the form of improved pricing and size, and which are merely passive participants. The process itself, by requiring the structured capture of previously ephemeral data, hardens the firm’s operational discipline. Ultimately, mastering this level of attribution is a foundational component of a larger system of intelligence, one that provides a durable, information-based advantage in the complex, ever-evolving landscape of institutional finance.