How Does Research Unbundling Affect Best Execution Obligations for Illiquid Assets?

Concept

The mandate to unbundle research from execution services introduces a profound architectural shift in the asset management operating system. This is not a superficial accounting change. It is a fundamental redesign of the protocols governing how investment decisions are sourced and how they are implemented in the market. At its core, this separation forces a clear, auditable distinction between the cost of generating an idea and the cost of executing that idea.

For liquid, high-volume securities, this distinction introduces welcome transparency and cost discipline. For illiquid assets, however, this same mandate creates a significant operational tension. The very nature of an illiquid asset is defined by information scarcity and high execution friction. Historically, the brokers who possessed the specialized capacity to navigate these opaque markets were also the primary sources of the scarce information, or research, about them. The bundled commission model, for all its inherent conflicts, was a system that organically coupled information discovery with execution capability.

Research unbundling systematically dismantles this coupling. The direct consequence is that the obligation of best execution for illiquid assets is placed under a more intense and unforgiving analytical lens. Best execution in this context expands far beyond a simple measure of price. It becomes a multi-variate optimization problem where the primary factors are the minimization of market impact and the probability of successful execution.

When a portfolio manager must transact in an asset characterized by wide spreads, low volume, and high information asymmetry, the act of execution itself is a significant source of alpha erosion. The process of finding a counterparty without signaling intent to the wider market is the paramount challenge. The unbundling rules compel firms to prove that their choice of execution counterparty is based solely on that counterparty’s demonstrable ability to manage this friction, completely divorced from any research services that may have been consumed.

The unbundling of research payments forces a structural separation between idea generation and trade implementation, placing an explicit analytical burden on proving execution quality, especially in opaque, illiquid markets.

This creates a new operational imperative. Asset managers must now architect two distinct and independent procurement systems. One system is for sourcing intellectual property ▴ the research, data, and analysis that drives investment theses. The other is for sourcing market access and liquidity ▴ the specialized execution services required to transact without undue cost.

For illiquid assets, this dual-sourcing model presents a challenge because the pool of providers for both services is inherently small. The brokers with the deepest liquidity pools and most sophisticated execution protocols for a niche credit instrument or a small-cap equity are few. Likewise, the number of analysts providing credible, in-depth coverage of these assets is limited. The unbundling directive means a firm can no longer rely on a single relationship to solve both problems.

Instead, it must build a robust, evidence-based framework to select the optimal execution partner for every trade, while simultaneously constructing a separate, deliberate strategy for acquiring the necessary research to inform its investment decisions. This elevates the importance of quantitative Transaction Cost Analysis (TCA) from a post-trade reporting function to a critical pre-trade decision support tool. The data from TCA becomes the primary evidence that the best execution obligation has been met, a task made more complex by the unique execution profile of each illiquid asset.

Strategy

The strategic response to research unbundling’s effect on illiquid assets is rooted in the principle of decoupling. Asset managers must pivot from a relationship-driven, bundled procurement model to a function-driven, specialized sourcing strategy. This involves architecting separate, optimized workflows for research acquisition and execution services, recognizing that the best provider of one is rarely the best provider of the other, particularly in the challenging environment of illiquid markets.

The core conflict of interest that MiFID II sought to eliminate was the incentive to direct trades to a broker based on the value of their research rather than their execution quality. This strategic decoupling directly addresses that conflict and forms the foundation for a more robust and defensible best execution framework.

Redefining Broker Selection Protocols

A primary strategic shift is the formalization of broker selection based exclusively on execution quality metrics. For illiquid assets, these metrics are substantially different from those used for liquid securities. While price is a factor, the strategic priorities are minimizing market impact and maximizing the certainty of execution. This requires a quantitative and qualitative evaluation framework.

Firms must develop scorecards that rank execution partners based on their historical performance in specific types of illiquid assets. This data is derived from sophisticated Transaction Cost Analysis (TCA) systems. The strategic objective is to move from a static list of approved brokers to a dynamic, data-driven hierarchy where the choice of broker is tailored to the specific characteristics of the asset and the trade.

Table of Execution Venue Selection Criteria

The following table outlines a strategic framework for selecting an execution venue for an illiquid asset, reflecting the priorities dictated by a post-unbundling environment.

Architecting a Research Sourcing Framework

The second pillar of the strategy is to address the potential for a “research desert” in illiquid markets. As asset managers’ research budgets have fallen post-MiFID II, sell-side providers have rationally reduced coverage of less profitable areas, which often includes small and mid-sized enterprises (SMEs) and esoteric fixed-income instruments. A passive approach to research sourcing is therefore no longer viable. The strategy must be proactive and multi-pronged.

A successful strategy requires building a proactive and diversified research procurement function, actively seeking out specialized independent providers to counteract the decline in sell-side coverage for illiquid assets.

• Independent Research Providers (IRPs) ▴ A core strategic element is to build relationships with and a budget for IRPs. These firms operate on a subscription-based model, free from the conflicts of execution services. The challenge is to identify IRPs with genuine expertise in the required niche.
• Internal Capability Enhancement ▴ Firms may need to invest in their own internal analyst teams. This represents a significant cost but provides a proprietary information edge and insulates the firm from the volatility of the external research market.
• Expert Networks and Primary Research ▴ For truly opaque assets, the strategy may involve leveraging expert networks or commissioning primary research. This provides highly valuable, non-public information but requires rigorous compliance and control frameworks.
• Systematic Data Acquisition ▴ The strategy should also include sourcing alternative data sets (e.g. satellite imagery, supply chain data) that can provide an analytical edge in the absence of traditional sell-side reports.

By implementing these dual strategies, an asset manager transforms the challenge of unbundling into a structural advantage. The firm develops a superior, data-driven execution process that demonstrably fulfills best execution obligations. Simultaneously, it builds a more resilient and diversified research function that is tailored to its specific investment philosophy, rather than being dependent on the offerings of its execution partners.

Execution

The execution of a best execution policy for illiquid assets in a post-unbundling world is a matter of high-fidelity operational design. It requires the integration of technology, quantitative analysis, and rigorous procedure to create a defensible and repeatable process. The theoretical separation of research and execution costs must be translated into a tangible set of actions and systems that guide the trader from the point of receiving an order to its final settlement and analysis. This is where the architectural principles of the new framework are tested.

The Operational Playbook

A trading desk must operate under a clear, multi-stage playbook for every illiquid asset transaction. This playbook is not a set of loose guidelines; it is a sequence of mandatory steps embedded within the firm’s Order Management System (OMS) and Execution Management System (EMS).

1. Pre-Trade Analytics and Liquidity Assessment ▴ Upon receiving an order for an illiquid asset, the first step is a systematic pre-trade analysis. The EMS should automatically query historical data and third-party analytics to generate a liquidity profile for the specific instrument. This profile includes estimated daily volume, average bid-ask spread, and historical volatility. The system must then recommend a primary execution strategy (e.g. High-Touch, RFQ, Algorithmic) based on the order’s size relative to the asset’s liquidity profile.
2. Evidence-Based Broker Selection ▴ The trader, guided by the system’s recommendation, proceeds to select an execution partner. This selection is governed by a quantitative broker scorecard, which is updated quarterly with TCA data. The scorecard ranks brokers on metrics relevant to illiquid assets ▴ fill rates, price improvement versus benchmark, and measured market impact. The trader must select from the top-ranked brokers for that specific asset class and justify, in writing within the OMS, any deviation from this recommendation. This creates a clear audit trail.
3. Protocol-Specific Execution Workflow ▴ The execution itself follows a strict protocol. If an RFQ strategy is chosen, the EMS must manage the process of sending discreet inquiries to a pre-defined list of market makers. The system logs all quotes received and the final execution price, providing a complete record of the competitive bidding process. If a high-touch strategy is selected, the trader is required to log all communications with the broker’s sales trader in the OMS, noting key instructions and price targets.
4. Real-Time Monitoring and Post-Trade Analysis ▴ During the execution, the trader monitors progress against the pre-trade benchmark (e.g. arrival price). After the trade is complete, the TCA system automatically generates a detailed report. This report compares the execution quality against both the pre-trade estimates and the performance of other brokers. This report is attached to the order record in the OMS and is reviewed by a compliance or oversight committee on a regular basis.

Quantitative Modeling and Data Analysis

The entire operational playbook is underpinned by robust data analysis. The unbundling of costs allows for a much cleaner signal in TCA, as execution costs are no longer conflated with research payments. This enables more precise modeling.

How Can We Quantify Execution Quality?

The following table presents a sample TCA report for the sale of 100,000 shares of an illiquid small-cap stock. This level of granular data is essential for proving best execution and refining the broker selection process.

Predictive Scenario Analysis

Consider a portfolio manager at an institutional asset management firm who needs to liquidate a 500,000-share position in “Innovatech Corp,” a small-cap technology firm with an average daily trading volume of just 200,000 shares. The decision to sell was based on research procured from a specialized independent provider, paid for via a separate, hard-dollar research budget.

In the pre-MiFID II environment, the execution process would have been opaque. The manager likely would have called a single large broker, one that also provided general tech sector research, and instructed them to “work the order.” The execution cost would be buried within a bundled commission, and it would be nearly impossible to determine if a better outcome could have been achieved elsewhere. The broker’s incentive would be split between achieving a good price and maintaining a profitable relationship built on bundled services.

In the post-unbundling execution framework, the process is transformed into a systematic, data-driven operation. The order lands on the trading desk, and the firm’s EMS immediately flags it as highly illiquid, noting the order size is 250% of the average daily volume. The pre-trade analytics module projects a potential market impact of 40-60 basis points if executed carelessly. The system recommends a blended strategy ▴ using a specialized algorithm to trade 20% of the position over the course of the day to establish a price presence, while simultaneously using a high-touch desk to discreetly source block liquidity for the remainder.

The system’s broker scorecard shows that “Execution Specialist Broker A” has the lowest historical market impact for small-cap tech stocks, while “Block Liquidity Partners B” has the highest success rate in sourcing natural counterparties. The head trader approves this dual-broker strategy. The algorithmic portion is routed to Broker A via FIX protocol, with strict limits on market participation. Concurrently, the high-touch trader engages Broker B, providing clear instructions to find a natural buyer for a block of at least 300,000 shares, without exposing the order to the public market.

By the end of the day, Broker A has executed 100,000 shares at an average price with only 15 bps of slippage against the arrival VWAP. Broker B successfully negotiates a block trade for the remaining 400,000 shares with another institution, executing at a price superior to the on-screen closing price. The post-trade TCA report confirms the blended strategy resulted in a total slippage of only 22 bps, far better than the initial projection. This entire process, from the system’s initial recommendation to the final TCA report, is logged and auditable, providing concrete evidence that the firm acted to achieve the best possible result for its client, free from any conflict of interest related to research payments.

System Integration and Technological Architecture

Executing this strategy requires a tightly integrated technology stack. The OMS and EMS must function as a single, coherent system. The EMS needs sophisticated pre-trade analytics tools and must be able to support a wide range of execution algorithms and protocols, including RFQ platforms. Crucially, the system must have robust APIs to ingest data from TCA providers and to connect seamlessly with the firm’s compliance and data warehousing systems.

FIX (Financial Information eXchange) protocol messaging is the backbone of this communication, carrying order instructions, execution reports, and post-trade allocations with precision. The architecture must be designed for data capture, ensuring every decision point, every quote received, and every execution report is stored in a structured format that can be easily queried for compliance checks, broker reviews, and strategic refinement. This technological framework is the tangible manifestation of the firm’s commitment to its best execution obligations.

Reflection

The separation of research and execution is more than a regulatory hurdle; it is a design choice that forces a higher level of operational discipline. It compels a firm to examine the very architecture of its investment process. Viewing the acquisition of research and the execution of trades as distinct, specialized modules within a larger system allows for independent optimization of each function. What is the true cost of your firm’s information advantage?

What is the measurable quality of your market access? Answering these questions with data, rather than by assumption, moves a firm from a reactive, compliance-focused stance to a proactive, performance-oriented one. The ultimate objective is to construct an operational framework so robust and transparent that the fulfillment of best execution obligations becomes an organic output of a superior system, not an administrative task. The strategic potential lies not in simply complying with the rule, but in leveraging the clarity it imposes to build a more efficient and effective investment machine.