Concept
The determination of an optimal dealer count for a Request for Quote (RFQ) in an illiquid market is a function of managing a fundamental tension. The process involves balancing the pursuit of competitive pricing against the imperative to control information leakage. In markets characterized by infrequent trading and significant size, the very act of soliciting a price is a potent market signal. Each dealer added to an RFQ introduces a potential improvement in the quoted price through competition.
Simultaneously, each additional recipient expands the surface area for information leakage, risking adverse price movement before the transaction is complete. The central challenge, therefore, is one of calibration, not of maximization.
The Illusion of Maximal Competition
A common assumption is that a wider competitive field invariably leads to a better outcome. In the context of illiquid RFQs, this premise is flawed. The structure of these markets introduces dynamics where dealer participation is strategic and discretionary. As the number of competitors in a single RFQ increases, the perceived probability of winning for any individual dealer diminishes.
This can lead to a counterintuitive outcome where dealers offer less aggressive pricing or decline to respond altogether, anticipating a “winner’s curse” scenario where the winning bid is likely underpriced relative to the instrument’s true market value following the trade’s disclosure. Research into multi-dealer platforms suggests that, in equilibrium, a client may achieve a better result by contacting as few as two dealers, as this maximizes the incentive for those dealers to provide a serious, competitive quote.
The optimal number of dealers is the point at which the marginal benefit of price competition is precisely offset by the marginal cost of information leakage.
RFQ as a Surgical Instrument
The RFQ protocol is specifically suited for illiquid instruments because it allows the initiator to control the flow of information with precision. Unlike a central limit order book (CLOB), where orders are displayed to the entire market, an RFQ is a private inquiry directed to a select group of liquidity providers. This surgical approach is essential for assets where a large, publicly visible order would immediately trigger adverse selection and cause other market participants to adjust their prices. The effectiveness of the protocol hinges on the initiator’s ability to select the right number of dealers, transforming the RFQ from a blunt instrument of mass inquiry into a finely tuned tool for price discovery.
Strategy
Crafting an effective RFQ strategy in illiquid markets requires a systematic approach to dealer selection. The objective is to build a dynamic framework that adapts to the specific characteristics of each trade, rather than applying a static, one-size-fits-all number. This framework must be built upon a deep understanding of the interplay between trade parameters, market conditions, and dealer behavior.
A Framework for Dealer Selection
The decision of how many dealers to include in an RFQ is a multi-variable problem. An effective strategy involves evaluating each trade against a consistent set of criteria to determine the appropriate level of competition and disclosure. The core factors are the trade’s size and the underlying instrument’s liquidity profile.
Large trades in highly illiquid assets carry the greatest risk of market impact, demanding the most constrained and targeted RFQs. Conversely, smaller trades in more frequently traded instruments can support a wider inquiry.
The following table outlines the key strategic variables and their implications for the number of dealers to engage:
| Strategic Variable | Implication for Dealer Count | Rationale |
|---|---|---|
| Trade Size | Inversely correlated | Larger sizes amplify the cost of information leakage, as the market impact of a revealed trading intention is more significant. |
| Asset Liquidity | Inversely correlated | For highly illiquid assets, fewer dealers possess the inventory or risk appetite. A narrow RFQ targets specialists and minimizes leakage. |
| Market Volatility | Inversely correlated | In volatile markets, the risk of adverse price movement between the RFQ and execution is elevated. A smaller dealer set accelerates the pricing process. |
| Trade Complexity | Inversely correlated | Multi-leg or exotic structures require specialized pricing capabilities. A targeted RFQ to a few expert dealers yields higher quality quotes than a broad, generic inquiry. |
| Dealer Relationship | Positively correlated with trust | Strong relationships with trusted dealers may allow for a slightly larger RFQ, as there is a higher degree of confidence that the information will be handled discreetly. |
The Dynamics of Dealer Response
Understanding the motivations of the liquidity providers is fundamental to a successful RFQ strategy. Dealers are not passive price providers; they are strategic participants managing their own inventory and risk. When presented with an RFQ, a dealer implicitly assesses the probability of winning the trade against the cost of pricing it and the risk of holding the position.
If the number of dealers in the RFQ is disclosed and is high, a dealer may widen their price to compensate for the lower win probability or choose not to quote at all. This strategic withdrawal can undermine the very competition the initiator sought to create.
A successful RFQ strategy prioritizes the quality of quotes over the quantity of bidders.
An effective strategy often involves segmenting dealers into tiers. A primary tier of trusted, high-capacity dealers may be approached for the most sensitive and significant trades, often in an RFQ of just two or three participants. A secondary tier can be engaged for less sensitive trades, allowing for a broader inquiry of perhaps four to six dealers. This tiered approach ensures that the most critical trades are protected by a layer of discretion, while still allowing for healthy competition on more routine business.
Execution
The transition from a strategic framework to operational execution requires the implementation of a disciplined, data-informed process. An institutional-grade execution protocol for RFQs in illiquid markets codifies the strategic variables into a repeatable workflow, ensuring consistency and enabling post-trade analysis for continuous improvement.
An Operational Protocol for RFQ Sizing
A robust execution protocol moves beyond intuition and establishes a quantitative basis for determining the dealer count. This can be achieved by developing an internal scoring system that evaluates each prospective trade against the critical risk factors. By assigning numerical values to variables such as trade size, asset liquidity, and market volatility, a firm can generate a composite risk score that maps to a specific, predefined execution protocol.
The following table provides a model for such a quantitative framework. In this system, each trade is scored, and the total score dictates the number of dealers to be included in the RFQ. The weights assigned to each factor should be calibrated based on the firm’s specific risk tolerance and historical trading data.
| Factor | Metric | Score (1-5) | Weight | Weighted Score |
|---|---|---|---|---|
| Trade Size | As % of Average Daily Volume | 5 (High %) | 40% | 2.0 |
| Asset Liquidity | Internal Liquidity Score | 4 (Very Illiquid) | 30% | 1.2 |
| Market Volatility | VIX or Asset-Specific Volatility | 3 (Moderate) | 20% | 0.6 |
| Urgency | Required Execution Window | 2 (Standard) | 10% | 0.2 |
| Total Risk Score | 4.0 |
Protocol Mapping
- Risk Score > 4.0 ▴ Constrained RFQ. Engage 2-3 specialist dealers. High-touch monitoring required.
- Risk Score 2.5 – 4.0 ▴ Standard RFQ. Engage 3-5 dealers. Standard execution protocol.
- Risk Score < 2.5 ▴ Broad RFQ. Engage 4-6+ dealers. Low-touch, automated execution possible.
The Role of Technology and Post-Trade Analytics
Modern execution platforms are critical components of this protocol. They provide the tools to manage dealer lists, execute RFQs efficiently, and, most importantly, capture the data necessary for post-trade analysis. The platform’s configuration, particularly regarding the disclosure of the number of competing dealers, is a key tactical choice. Some platforms may allow for “blind” RFQs where dealers are unaware of the competition, which can alter their pricing behavior.
Systematic execution transforms RFQ management from an art into a science, creating a feedback loop for perpetual refinement.
Following execution, a rigorous Transaction Cost Analysis (TCA) is essential. The analysis should compare the execution price not only to market benchmarks but also across different RFQ sizes and dealer cohorts. Over time, this data will reveal which dealers provide the best pricing in specific scenarios and what the firm’s optimal dealer count is for different types of trades.
This data-driven feedback loop allows the firm to refine the weights in its scoring model, adjust its dealer tiers, and continuously improve its execution quality. The ultimate goal is to build a proprietary data set that provides a persistent competitive edge in sourcing liquidity.
References
- Wang, J. (2022). The Limits of Multi-Dealer Platforms. Wharton Finance, University of Pennsylvania.
- O’Hara, M. & Zhou, X. A. (2021). The Electronic RFQ Market for Corporate Bonds. Johnson School of Management Research Paper Series.
- Di Maggio, M. Franzoni, F. & Kermani, A. (2019). The relevance of information leakage in electronic markets. National Bureau of Economic Research.
- Hendershott, T. & Madhavan, A. (2015). Click or Call? The Role of Intermediaries in Over-the-Counter Markets. The Journal of Finance.
- Riggs, L. M. Cimon, D. & Garriott, C. (2020). Liquidity and competition in the era of dealer platforms. Bank of Canada Staff Working Paper.
Reflection
From Optimal Number to Optimal System
The inquiry into the optimal number of dealers, while valid, directs focus toward a single variable within a larger, more complex system. The true objective is the design of a superior operational framework for liquidity sourcing. The number of dealers is but one parameter in this system. A more profound question is how an institution orchestrates its technology, relationships, and data to consistently achieve high-fidelity execution.
The knowledge gained about dealer selection is a component of this larger intelligence system. Viewing the challenge through this systemic lens transforms the goal from finding a number to building a durable, adaptive execution capability.
Glossary
Information Leakage
Illiquid Market
Rfq
Price Discovery
Execution Protocol
Dealer Count
Transaction Cost Analysis
