What Is the Non-Monotone Relationship between Dealer Network Size and Execution Costs? ▴ Question

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Concept

The operational objective of institutional trading is achieving high-fidelity execution at the lowest possible cost. A central component of this process, particularly in over-the-counter (OTC) or block markets, is the dealer network engaged through a Request for Quote (RFQ) protocol. An inquiry into the relationship between the size of this network and the resultant execution costs reveals a complex, non-monotone system.

The architecture of this system dictates that expanding the dealer network does not produce a linear reduction in costs. Instead, the relationship follows a distinct curve where costs initially decrease with the addition of competitive dealers, only to increase past a critical inflection point.

This phenomenon arises from the fundamental tension between two opposing forces inherent in the market’s structure ▴ competitive pricing and information leakage. On one hand, soliciting quotes from a larger set of dealers introduces greater competition. This pressure compels dealers to tighten their bid-ask spreads to win the trade, directly reducing the most explicit component of execution cost.

The initial expansion of a dealer network from a very small number, for instance from one to three or five participants, almost invariably yields superior pricing for the initiator. The probability of encountering the dealer with the best axe (a natural inclination to take the other side of a trade due to inventory or risk positions) increases, and the direct competitive dynamic provides a powerful incentive for price improvement.

The total cost of execution is a function of both the price improvement from competition and the market impact from information leakage.

On the other hand, every dealer included in an RFQ is a potential channel for information leakage. An RFQ for a significant size, particularly in a less liquid asset, is a potent piece of information. It signals a specific trading intent from a large institution. Dealers who receive the RFQ but do not win the auction are still privy to this information.

They can use it to inform their own trading strategies, potentially trading ahead of the institutional order in the public markets or adjusting their own quotes on other venues. This activity, known as adverse selection from the perspective of the initiator, moves the market price away from the institution’s favor before the full order can be executed. This price movement is a direct, and often substantial, component of implicit execution costs, frequently termed market impact. As the dealer network size grows, the probability and potential magnitude of this information leakage increase systematically. The signal is broadcast more widely, and the risk of one or more participants acting on that information rises in tandem.

The non-monotone relationship is the direct result of these two competing effects. The total execution cost is the sum of the spread cost and the information leakage cost. Initially, the benefits of spread compression from adding more dealers outweigh the costs of marginal information leakage. Execution costs fall.

However, a point of diminishing returns is reached where the price improvement from adding one more dealer becomes negligible. Beyond this point, the cost of broadcasting the trade intention to an even wider audience becomes the dominant factor. Each additional dealer represents a significant new potential source of information leakage, causing the market impact component of costs to rise sharply. This dynamic forces total execution costs to increase, creating the second half of the U-shaped cost curve. Understanding this inflection point is a core principle of sophisticated execution management.

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Strategy

Strategically navigating the non-monotone cost curve requires a shift from a simplistic “more is better” approach to a sophisticated, data-driven dealer selection framework. The objective is to identify and operate at the optimal point of the cost curve for any given trade, balancing the benefits of competition against the perils of information leakage. This involves developing a system for calibrating the size of the dealer network based on the specific characteristics of the order and the historical performance of the dealers.

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Deconstructing the Cost Curve

The strategic implementation begins with a granular understanding of the two primary vectors influencing total execution cost. The initial phase of network expansion focuses on maximizing competitive pressure, while the secondary phase requires actively mitigating information decay.

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Phase 1 the Benefits of Competitive Pressure

When an institution expands its RFQ panel from a very limited set (e.g. 1-2 dealers) to a moderately sized group (e.g. 3-7 dealers), the primary effect is a reduction in the bid-ask spread. This is the most direct and measurable benefit.

A larger pool of liquidity providers increases the statistical likelihood of capturing a quote from a dealer whose current inventory or risk profile makes them a natural counterparty. This dealer can offer a tighter price while still meeting their own profitability targets. The table below illustrates this initial benefit.

Number of Dealers Queried	Average Spread Quoted (bps)	Probability of Price Improvement	Competitive Benefit Index
2	10.5	Low	100
4	7.2	Moderate	145
6	6.1	High	172
8	5.9	Very High	178

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Phase 2 the Acceleration of Information Leakage

What is the tipping point for execution costs? The strategic challenge materializes as the network continues to grow. Each additional dealer included in an RFQ represents another node through which sensitive trade information can disseminate. This leakage leads to adverse selection, where other market participants adjust their prices or trade ahead of the institution, causing slippage against the arrival price.

This cost is implicit but critically important. The risk of leakage is not linear; adding the 15th dealer to an RFQ presents a much greater marginal risk than adding the 5th, as the signal of a very large order is being confirmed by a wider, more diverse set of market participants.

An optimal execution strategy identifies the point where the marginal benefit of spread compression equals the marginal cost of information leakage.

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Developing a Tiered Dealer System

A core strategy for managing this trade-off is the implementation of a tiered dealer system. This framework categorizes dealers based on their historical performance, reliability, and perceived risk of information leakage. This allows the trading desk to dynamically adjust the RFQ panel based on the sensitivity of the order.

Tier 1 Dealers ▴ This is a small, core group of the most trusted and competitive liquidity providers. They consistently offer tight spreads and have a proven track record of discretion, showing minimal post-trade price reversion (a sign of low information leakage). These dealers are used for the largest, most sensitive, and least liquid block trades. The network size is deliberately kept small (e.g. 3-5 dealers) to minimize the information footprint.
Tier 2 Dealers ▴ This is a broader group of reliable dealers who provide consistent liquidity but may not have the same level of discretion or competitiveness as Tier 1. They are included in RFQs for medium-sized orders in more liquid assets where the risk of market impact is lower. The network size here might range from 5 to 10 dealers.
Tier 3 Dealers ▴ This includes the widest possible network of dealers. This tier is used for small, routine orders in highly liquid assets. In this context, information leakage is of minimal concern, and the primary goal is to maximize the probability of capturing the best possible price at that moment. The network size can be much larger, potentially 10-20 or more dealers, depending on the technological capacity of the execution platform.

This tiered approach allows the institution to move along the non-monotone cost curve intelligently. Instead of using a single, static dealer network for all trades, the trading desk selects a network size and composition that is appropriate for the specific risk-return profile of each order. This dynamic calibration is the essence of a sophisticated execution strategy, transforming a structural market problem into a source of competitive advantage.

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Execution

The execution of a strategy to manage the non-monotone cost relationship moves beyond theory and into the domain of quantitative analysis and operational protocol. It requires building a robust system for Transaction Cost Analysis (TCA) that can measure dealer performance across multiple dimensions and an Execution Management System (EMS) capable of deploying this analysis in real-time. The goal is to create a feedback loop where post-trade data continuously refines pre-trade decisions.

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The Operational Playbook for Dealer Management

Implementing a dynamic dealer selection process involves a clear, repeatable set of operational steps. This playbook ensures that the tiered strategy is applied consistently and that the underlying data is constantly refreshed to reflect current market realities.

Data Capture ▴ The foundation of the system is comprehensive data collection for every RFQ sent. This includes the asset, size, timestamp, all dealers queried, all quotes received (both winning and losing), the winning quote, and the execution timestamp.
Post-Trade Data Integration ▴ The system must also capture post-trade market data. This includes the market price evolution immediately following the execution. This is critical for estimating the implicit costs associated with information leakage.
Performance Metric Calculation ▴ A set of key performance indicators (KPIs) is calculated for each dealer. These metrics form the basis of the quantitative dealer scorecard and are updated on a rolling basis (e.g. monthly or quarterly).
Dealer Scorecard Review ▴ The trading desk holds periodic reviews of the dealer scorecards. These reviews are used to make decisions about dealer tiering, including promoting high-performing dealers and demoting or removing underperforming ones.
EMS Protocol Adjustment ▴ The insights from the scorecard review are then translated into updated protocols within the EMS. This could involve adjusting the default dealer lists for certain asset classes or order sizes, or even implementing automated rules that suggest an optimal number of dealers based on real-time order characteristics.

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Quantitative Modeling and Data Analysis

How can one measure dealer performance effectively? A quantitative scorecard is the centerpiece of the execution strategy. It moves dealer evaluation from a qualitative, relationship-based assessment to an objective, data-driven process.

The table below presents a sample TCA scorecard designed to dissect dealer performance and inform the tiering system. The metrics are designed to capture both the competitive pricing provided by the dealer and their impact on the market.

Dealer ID	RFQs Received	Win Rate (%)	Spread vs Mid (bps)	Slippage vs Arrival (bps)	Post-Trade Reversion (bps)	Overall Score
Dealer A	500	25%	-2.5	-1.0	+0.8	9.2
Dealer B	480	15%	-3.1	-1.5	-1.2	6.5
Dealer C	510	18%	-2.8	-1.2	+0.5	8.1
Dealer D	350	5%	-4.5	-3.0	-2.5	3.4
Dealer E	490	22%	-2.6	-1.1	+0.7	8.9

In this model:

Spread vs Mid ▴ Measures the competitiveness of the dealer’s quotes relative to the prevailing market midpoint. A more negative number is better.
Slippage vs Arrival ▴ Measures the execution price against the market price at the moment the order was initiated. It captures the immediate market impact. A smaller negative number is better.
Post-Trade Reversion ▴ This is a crucial metric for estimating information leakage. It measures the degree to which the price moves back in the opposite direction after the trade is completed. A positive reversion (for a buy order, the price falls after the trade) suggests the execution price was pushed up by temporary pressure, a hallmark of information leakage. Dealers with consistently high negative reversion (the price continues to move against the initiator) are likely leaking information. Dealer B and Dealer D in the example show signs of negative reversion, indicating potential information leakage.

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System Integration and Technological Architecture

The effective execution of this strategy is heavily dependent on the technological architecture of the trading platform. Modern Execution Management Systems are designed to facilitate this level of analysis and automation. The system must be able to integrate pre-trade, real-time, and post-trade data into a unified analytical framework. Key technological components include:

API Integration ▴ The EMS must have robust APIs for connecting to various liquidity providers (dealers) as well as market data feeds. This allows for the seamless transmission of RFQs and the ingestion of quote and trade data.
TCA Engine ▴ A powerful TCA engine is required to perform the calculations for the dealer scorecard. This engine should be able to process large volumes of data and run complex queries to derive the necessary performance metrics.
Rules-Based Routing ▴ The EMS should support rules-based order routing. This allows the trading desk to program the logic of the tiered dealer system directly into the platform. For example, a rule could be created to automatically send any order over a certain size in a specific asset class to the designated Tier 1 dealer list.

By integrating these technological components with a rigorous quantitative framework, an institutional trading desk can move beyond a static approach to dealer management. It can build a dynamic, adaptive system that actively seeks the optimal point on the execution cost curve for every trade, thereby creating a sustainable source of execution alpha.

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References

Lehar, Alfred, Christine A. Parlour, and Marius Zoican. “Liquidity fragmentation on decentralized exchanges.” Journal of Financial Economics, vol. 153, 2024, pp. 103780.
O’Hara, Maureen. Market Microstructure Theory. Blackwell Publishers, 1995.
Harris, Larry. Trading and Exchanges ▴ Market Microstructure for Practitioners. Oxford University Press, 2003.
Kyle, Albert S. “Continuous Auctions and Insider Trading.” Econometrica, vol. 53, no. 6, 1985, pp. 1315-35.
Bessembinder, Hendrik, and Herbert M. Spanjers. “Price Discovery and Post-trade Transparency in the U.S. Treasury Market.” The Journal of Finance, vol. 77, no. 2, 2022, pp. 1095-1144.
Foucault, Thierry, and Albert J. Menkveld. “Competition for Order Flow and Smart Order Routing.” The Journal of Finance, vol. 63, no. 1, 2008, pp. 119-58.
Easley, David, and Maureen O’Hara. “Price, Trade Size, and Information in Securities Markets.” Journal of Financial Economics, vol. 19, no. 1, 1987, pp. 69-90.

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Reflection

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Calibrating the Execution System

The analysis of the dealer network’s impact on cost is a component of a much larger operational system. Viewing this relationship not as a static problem to be solved but as a dynamic state to be managed is the critical insight. The optimal number of dealers is not a fixed integer; it is a variable that depends on the asset’s liquidity, the order’s size, the prevailing market volatility, and the institution’s own strategic objectives.

Your execution framework must therefore be a learning system, one that continuously ingests data and refines its parameters. The true operational advantage is found in the architecture of this adaptive system, which translates market structure knowledge into repeatable, superior execution outcomes.