What Are the Key Metrics for a Dealer Scorecard in an RFQ System? ▴ Question

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Concept

The construction of a dealer scorecard within a Request for Quote system is the architectural backbone of sophisticated liquidity sourcing. It represents a fundamental shift from relationship-based intuition to a data-driven, quantitative framework for managing counterparty performance. For the institutional desk, the RFQ protocol is a primary mechanism for accessing deep liquidity, particularly for large or complex orders where minimal market impact is paramount.

The scorecard, in this context, functions as the system’s core intelligence layer, a perpetual audit of the efficacy of each liquidity provider. It is the mechanism that transforms raw transactional data ▴ every quote, every response time, every fill ▴ into a strategic asset.

Its primary function is to provide a transparent, objective, and continuously updated performance record for every dealer participating in the RFQ workflow. This record is built upon a foundation of key metrics designed to quantify every dimension of a dealer’s service. The system moves the evaluation process beyond the simple metric of “best price” to a holistic assessment of a counterparty’s value.

This includes their reliability, the quality of their pricing, their capacity for size, and the subtle yet critical element of their market impact. The scorecard becomes the definitive ledger of which dealers provide true liquidity versus those who merely provide quotes.

A robust dealer scorecard serves as the central nervous system for an institutional RFQ protocol, translating performance data into actionable execution strategy.

Understanding this system requires viewing the RFQ process as a series of structured auctions. In each auction, the buy-side institution solicits bids from a select group of dealers. The scorecard is the tool that governs the selection of those dealers. A poorly performing dealer, one who consistently provides wide quotes, responds slowly, or backs away from their price, represents a systemic inefficiency.

By systematically tracking these behaviors, the scorecard provides the empirical basis for optimizing the dealer list, ensuring that requests are directed only to those counterparties who consistently add value to the price discovery process. This disciplined approach enhances the competitiveness of every auction, directly improving execution quality and reducing implicit trading costs.

The ultimate purpose of this system is to create a feedback loop that drives continuous improvement. Dealers are made aware that their performance is being measured across a spectrum of objective criteria. This creates a powerful incentive for them to tighten their spreads, improve their response times, and provide firm, reliable liquidity.

The scorecard, therefore, is an instrument of behavioral alignment, ensuring that the objectives of the liquidity provider are in sync with the execution goals of the institutional client. It is the architectural solution to the inherent information asymmetries of bilateral trading, providing a clear, quantitative lens through which to manage and optimize the most critical relationships in the trading lifecycle.

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Strategy

Developing a strategic framework for a dealer scorecard requires a precise definition of performance. The architecture of the scorecard must be built around a set of core metric categories that, in aggregate, provide a complete and multi-dimensional view of a dealer’s contribution. These categories serve as the pillars of the evaluation system, each one measuring a distinct aspect of the dealer’s function within the RFQ ecosystem. The strategic selection and weighting of these metrics will determine the scorecard’s effectiveness as a tool for optimizing execution.

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Core Metric Categories

The scorecard’s design should be organized around four primary strategic pillars. Each pillar addresses a critical question about the dealer’s performance, moving from basic participation to the most sophisticated measures of execution quality.

Participation and Reliability This category forms the baseline of performance. It measures the dealer’s fundamental willingness and ability to engage in the RFQ process. A dealer who is unresponsive or unreliable introduces friction and uncertainty into the execution workflow. Key metrics in this pillar quantify consistency and dependability.
Price Competitiveness This is the most visible dimension of performance, assessing the quality of the prices a dealer provides. A sophisticated analysis in this area moves beyond simply tracking the winning bid. It seeks to understand how consistently a dealer contributes to price improvement and the overall competitiveness of the auction.
Execution and Fulfillment Quality A quote is only valuable if it is actionable. This pillar measures the dealer’s reliability at the point of trade, assessing the firmness of their quotes and their ability to execute at the quoted price and size. It separates dealers who provide firm liquidity from those who provide indicative quotes.
Capacity and Market Impact This advanced category evaluates the dealer’s ability to handle institutional size without causing adverse market movements. It also addresses the critical, yet difficult to measure, concept of information leakage ▴ the degree to which a dealer’s trading activity signals the institution’s intentions to the broader market.

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What Are the Strategic Metrics within Each Category?

Within each of these strategic pillars, specific and quantifiable metrics must be defined. The selection of these metrics should be tailored to the specific asset class and trading objectives of the institution. The following provides a granular breakdown of the key performance indicators (KPIs) that form the core of a robust dealer scorecard system.

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Metrics for Participation and Reliability

Response Rate This is the most fundamental metric. It is calculated as the percentage of RFQs to which a dealer provides a quote, out of the total number of RFQs they were invited to. A low response rate indicates a lack of engagement and is a primary filter for dealer list optimization.
Average Response Time This measures the elapsed time, typically in milliseconds, from when the RFQ is sent to when the dealer’s quote is received. Faster response times allow the institution to make quicker trading decisions and react to market movements. Tracking the median and standard deviation of response times is also valuable to identify outliers and inconsistency.
Auto-Quoting Rate This metric identifies the percentage of a dealer’s responses that are generated by an automated pricing engine versus those that require manual intervention. A high auto-quoting rate generally correlates with faster and more consistent responses, especially for standard trades.

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Metrics for Price Competitiveness

Price competitiveness is the central element of the scorecard, directly impacting the cost of execution. A comprehensive assessment requires looking at several facets of a dealer’s pricing behavior.

The true measure of a dealer’s pricing is not just their ability to win an auction, but their consistent contribution to the tightening of the competitive spread.

The following table outlines the key metrics used to evaluate the quality and competitiveness of a dealer’s pricing.

Metric	Description	Strategic Implication
Win Rate	The percentage of RFQs where the dealer provided the winning bid (best price).	A primary indicator of overall price leadership. It should be analyzed in conjunction with other metrics to avoid rewarding overly aggressive pricing on small, non-representative trades.
Cover	The difference in price (e.g. in basis points or ticks) between the winning bid and the second-best bid in an auction. When the dealer wins, this measures their margin of victory. When they lose, it measures how far off the winning price they were.	A consistently small cover when winning suggests sharp, competitive pricing. A large cover when losing indicates the dealer is not a significant factor in the auction.
Price Improvement	The difference between the winning quote and a pre-defined benchmark price at the time of the RFQ. Common benchmarks include the arrival price (mid-market at the time the order is received), the last traded price, or a composite feed price.	This is a direct measure of the value a dealer is providing relative to the prevailing market. It is one of the most important metrics for quantifying execution cost savings.
Top 3 Rate	The percentage of RFQs where the dealer’s quote was among the top three most competitive prices.	This metric provides a broader view of competitiveness than the simple win rate. A dealer with a high Top 3 Rate is a consistent contributor to the competitive tension of the auction, even if they do not always win.

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Metrics for Execution and Fulfillment Quality

Fill Rate This crucial metric measures the percentage of winning quotes that are successfully executed without issue. A fill rate below 100% indicates that the dealer is “backing away” from their quotes, a significant red flag. This is sometimes referred to as a “firmness” metric.
Rejection Rate This is the inverse of the fill rate, tracking how often a dealer rejects a trade after winning the auction. Any rejection should be investigated, as it undermines the integrity of the RFQ process.
Size Improvement This metric tracks instances where a dealer is willing to execute a larger size at the winning price than was initially requested in the RFQ. This indicates a strong appetite for the trade and provides valuable information about the dealer’s risk-taking capacity.

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Execution

The execution of a dealer scorecard system translates the strategic framework into an operational reality. This process involves the systematic capture of data, the rigorous calculation of metrics, and the integration of the scorecard’s output into the daily trading workflow. It is an engineering challenge that requires a robust technological architecture and a disciplined, repeatable process. The goal is to create a closed-loop system where performance is continuously measured, evaluated, and used to inform future trading decisions.

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The Data Capture Architecture

The foundation of any scorecard is the raw data. A comprehensive logging mechanism must be in place to capture every relevant event in the lifecycle of an RFQ. This data must be time-stamped with high precision (ideally to the millisecond) and stored in a structured format that facilitates analysis. The following table details the essential data points that must be captured for each RFQ sent to each dealer.

Data Field	Description	Data Type	Example
RFQ ID	A unique identifier for each individual request for quote.	String/UUID	“RFQ-20250804-A7B3”
Instrument ID	A unique identifier for the security being traded (e.g. ISIN, CUSIP).	String	“US912828U699”
Request Timestamp	The precise time the RFQ was sent from the institution’s system.	Timestamp (ms)	“2025-08-04T14:39:01.105Z”
Dealer ID	A unique identifier for the dealer receiving the RFQ.	String	“DEALER-08”
Requested Size	The quantity of the instrument requested in the RFQ.	Integer	10000000
Response Timestamp	The precise time the dealer’s quote was received. Null if no response.	Timestamp (ms)	“2025-08-04T14:39:01.852Z”
Quoted Price	The price quoted by the dealer.	Decimal	99.875
Quoted Size	The size the dealer is willing to trade at the quoted price.	Integer	10000000
Status	The outcome of the quote (e.g. Win, Loss, No Quote, Timed Out).	Enum	“Win”
Execution Timestamp	The time the trade was executed with the winning dealer.	Timestamp (ms)	“2025-08-04T14:39:02.115Z”
Execution Status	The final status of the trade (e.g. Filled, Rejected).	Enum	“Filled”
Benchmark Price	The reference market price at the time of the RFQ.	Decimal	99.872

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How Should Quantitative Metrics Be Calculated?

With the raw data captured, the next step is to process it into the performance metrics defined in the strategy. This typically involves running a series of queries or calculations over the data log on a regular basis (e.g. daily, weekly, or monthly). The output of this process is the dealer scorecard itself. The following is a procedural guide for calculating some of the key quantitative metrics.

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Calculating Response and Reliability Metrics

Response Rate Calculation For each dealer, count the total number of RFQs where Response Timestamp is not null. Divide this by the total number of RFQs sent to that dealer. Response Rate = (Count(Responded RFQs) / Count(Total RFQs)) 100.
Average Response Time Calculation For each dealer, calculate the difference between Response Timestamp and Request Timestamp for all responded RFQs. The average of these differences is the Average Response Time. It is critical to exclude outliers that could skew the average, so calculating the median and 95th percentile is also highly recommended.

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Calculating Price Competitiveness Metrics

Win Rate Calculation For each dealer, count the number of RFQs where their Status was “Win”. Divide this by the total number of RFQs to which they responded. Win Rate = (Count(Won RFQs) / Count(Responded RFQs)) 100.
Price Improvement Calculation For each RFQ that was won by a dealer, calculate the difference between their Quoted Price and the Benchmark Price. The average of these values, typically expressed in basis points, represents the Average Price Improvement. Price Improvement (bps) = ((Quoted Price / Benchmark Price) – 1) 10000.

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The Operational Scorecard

The final output is a consolidated scorecard that presents a clear, comparative view of all dealers across all key metrics. This scorecard becomes the primary tool for the trading desk to make informed decisions about dealer selection and to conduct periodic performance reviews.

The operational scorecard transforms abstract data into a concrete decision-making matrix, guiding the daily allocation of trading opportunities.

The following is a sample scorecard illustrating how these metrics can be presented. The data is hypothetical and for illustrative purposes only.

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Sample Dealer Scorecard Quarterly Review Q3 2025

Dealer ID	Response Rate (%)	Avg. Response Time (ms)	Win Rate (%)	Avg. Price Improvement (bps)	Fill Rate (%)	Qualitative Score (1-5)
DEALER-01	98.5	450	22.1	0.75	100.0	4.5
DEALER-02	85.2	1250	15.8	0.60	100.0	3.0
DEALER-03	99.1	310	8.5	0.25	100.0	4.0
DEALER-04	95.0	780	18.9	0.81	99.5	3.5
DEALER-05	72.0	950	5.2	0.15	100.0	2.5

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Integrating the Scorecard into the Trading Workflow

The scorecard’s value is realized when it is actively used to drive decisions. This integration can take several forms:

Automated Dealer Ranking The scorecard data can be fed into the Order Management System (OMS) or Execution Management System (EMS) to create a dynamic ranking of dealers for any given trade. The system can automatically suggest a list of the top-tier dealers to include in an RFQ based on their historical performance for similar instruments.
Dealer Review Meetings The scorecard provides the objective data needed for structured, productive conversations with dealers. Quarterly business reviews can be held where the scorecard is presented to the dealer, highlighting areas of strength and identifying areas for improvement. This creates a transparent and data-driven relationship.
Dynamic Dealer Lists For certain automated trading strategies, the system can be configured to dynamically adjust the dealer list based on real-time performance. A dealer whose performance drops below a certain threshold (e.g. fill rate drops, or response times spike) could be automatically and temporarily removed from certain RFQ lists until their performance stabilizes.

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References

Bessembinder, Hendrik, et al. “Capital commitment and illiquidity in corporate bonds.” The Journal of Finance, vol. 71, no. 4, 2016, pp. 1715-1762.
Di Maggio, Marco, et al. “The value of intermediation in the stock market.” Journal of Financial Economics, vol. 138, no. 1, 2020, pp. 1-22.
Hendershott, Terrence, et al. “The rise of electronic trading in corporate bonds.” The Review of Asset Pricing Studies, vol. 10, no. 3, 2020, pp. 433-473.
O’Hara, Maureen, and Gideon Saar. “The ‘make or take’ decision in an electronic market.” Journal of Financial Economics, vol. 101, no. 2, 2011, pp. 293-311.
Schürhoff, Norman, and Gábor Orosz. “Dealer Networks and the Cost of Immediacy.” Swiss Finance Institute Research Paper Series, no. 21-43, 2021.
Weill, Pierre-Olivier. “Liquidity in financial networks.” The Review of Economic Studies, vol. 87, no. 5, 2020, pp. 2417-2453.
Madhavan, Ananth. “Market microstructure ▴ A survey.” Journal of Financial Markets, vol. 3, no. 3, 2000, pp. 205-258.

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Reflection

The implementation of a dealer scorecard is the formalization of an institution’s execution policy. It elevates the process of counterparty management from an art based on relationships to a science grounded in data. The metrics and frameworks discussed here provide the architectural plans for such a system. Yet, the true potential of the scorecard is realized when it is viewed as a dynamic component within a larger, integrated trading apparatus.

Consider the data generated by the scorecard. This is a rich, proprietary dataset detailing the behavior of your counterparties in response to your specific flow. How can this data be used beyond simple ranking?

Could it be used to predict which dealers are most likely to provide the best price for a specific type of instrument, under specific market conditions? Could it inform the optimal number of dealers to include in an RFQ to maximize competitive tension without risking information leakage?

The scorecard is a system for looking backward, for auditing past performance. The ultimate objective is to use that insight to build a system that looks forward, one that anticipates liquidity and intelligently routes orders to the counterparties best equipped to handle them. The framework is a starting point. The final architecture is one you must build, continuously refining it to reflect the unique character of your order flow and your strategic objectives in the market.