The following guest article was submitted by Neil Scarth, Principal at Frost Research Systems (FRS). FRS helps asset managers meet all new research regulatory transparency requirements while simultaneously maximizing Research ROI.
Two adjacent vectors are coalescing to drive a culmination in the post-MiFID II research funding journey for UK, EU and US managers using P&L for research.
The first is an increasing number of CSA RFPs, particularly in the UK and EU, which indicates that many P&L managers are at least considering adopting research payment optionality.
The second, and more important, is the growing integration of AI into asset manager investment processes, which highlights – and threatens to explode – the existing MiFID II related information gap between P&L and client money managers.
Executive Committees at asset managers are increasingly realizing that the imperative of leveraging AI in their investment process has two major implications:
- It raises the stakes of the manager research funding decision. This is no longer about short-term client retention – but about long-term franchise survival.
- AI adoption invalidates the widespread view that “doing nothing is a risk-free option”. It’s not.
We’ll examine both of these linked trends.
CSA RFPs
Most asset manager CSA RFPs (and CSA systems themselves) fail to address the issues that actually determine whether a manager’s return to client-funded research will succeed – as defined by asset owner acceptance of manager research charges.
In isolation, CSAs do not directly assuage key client research concerns.
What Asset Owners Actually Care About
Asset owners don’t care which CSA administration system a manager uses as long as it works. — these systems are now largely commoditized.
What asset owners do care about is whether mandate-level research budgets are:
- Transparent
- Fair
- Benchmarked
- Directly supportive of the agreed investment process
- Not cross-subsidizing other products
What Asset Manager Executive Committees Care About
Senior Management and Executive Committees are also largely indifferent to CSA vendor system details. Their critical questions are:
- How much client risk are we taking by moving back to a client-funded research model?
- How can we mitigate those risks?
- How can we construct and quantify a firm-wide risk/reward model for the research transition?
- What factors maximize the probability that clients will accept research charges?
- What can we learn from managers who have already made this transition successfully?
- What are the risks of doing nothing?
A manager’s choice of CSA administration system will have essentially zero impact on clients’ willingness to accept research charges.
CSA RFPs are built to evaluate CSA system details— rather than the questions that actually shape the Executive Committee research funding decision. Asset owner communication and risk strategies are almost never addressed.
A CSA is a payment mechanism, not a management solution. In isolation, an RFP for CSA administration does little to answer the more important questions facing executive committees, distribution leadership, and client-facing teams as they assess whether and how to move back toward research payment optionality.
This is why senior management engagement is indispensable. Unless the research funding transition is treated as a manager-wide initiative rather than a narrow operational vendor-selection exercise, the firms issuing CSA RFPs may find that they have solved the easiest part of the problem first – without addressing the most central issues.
Wider Considerations and Solutions
Two things need to happen in addition to CSAs:
Analytical frameworks that allow manager Executive Committees to quantify the firm-wide risk/reward outcomes of the research funding decision.
Battle-tested Client Communication and Risk Mitigation strategies – customized for
each manager’s product/client set – alongside benchmarks and quant models that give manager Client Service teams a decided data advantage in the critical asset owner conversation.
Almost all research valuation systems are designed to help asset managers figure out how much to pay research providers. Very few systems are designed to help managers manage their relationship with their clients, which is critically important if those asset owners are going to be asked to pay for research.
The Impact of AI
AI has suddenly turned research funding into a core strategic issue for executives & Boards.
The increasing information asymmetry in favour of US managers with client-funded research budgets versus UK/EU managers funding ever diminishing and market sensitive research budgets via P&L, was the catalyst for the revision of MiFID II research rules.
The growing integration of AI into manager investment processes risks dramatically widening this information gap, with potentially existential implications for managers funding research via P&L. This fundamentally alters the risk/return equation for P&L managers considering a return to a client-funded model.
Managers funding research via P&L who decide not to take advantage of research payment optionality for fear of short-term client reactions may find that this important management decision will be judged far more harshly by history – and by shareholders – than the original P&L funding decision itself.
In an AI-enabled market structure, inaction may itself become the high-risk strategic choice.
Risk/Reward Frameworks for Executive Committees
The executive leadership teams of asset managers have to take a broad approach to the research funding decision to maximize long-term value for clients and shareholders – not just to optimize for the next quarter. While some important strategic decisions (i.e. research funding) may not be universally welcomed by all internal constituencies at asset managers, FRS has strategies to help firms to manage any internal (and external) concerns.
The Major New Factor in the Risk/Reward Calculus is AI.
The sudden integration of AI into the investment process is rapidly upending most managers’ preferred “second mover” strategy on research funding: the competitive risk of falling behind AI-forward rivals may now outweigh the client risk of adopting joint payments early — a shift that is also widening, and threatens to blow open, the existing MiFID II-related information gap between P&L and client money managers.
FRS has firm-customizable, proprietary multi-factor risk reward models that help managers quantify the potential impacts of the research transition – from a firm wide perspective, including AI outcomes. This super-imposes a dispassionate quantitative framework that can be applied to what are frequently emotive positions taken by various departments of asset managers.
Critical Enterprise Questions
Asset Owners
Given that performance spreads between 1st and 4th quartile funds already average several thousand basis points per category, is it in asset owners’ interest to contest minor research charges from managers in an AI environment that is likely to widen return dispersion further?
Asset Managers
Which of the two research funding methods, client money or manager P&L, maximizes the probability that the manager will be able to generate competitive returns for clients and shareholders, over the long-term, and regardless of market conditions?
Which of the two models will allow managers to take maximum advantage of AI optionality?
Asset Manager Executive Committees must weigh the relative importance of maximizing their firm’s global competitiveness and franchise value over the long-term, versus minimizing client risk over the very short-term.
In our experience, managers who approach the research funding transition with a thoughtful and robust Client Communication/Risk Framework may be able to do both.
About Frost Research Systems
Frost Research Systems (FRS) helps asset managers meet all new research regulatory transparency requirements while simultaneously maximizing Research ROI.
FRS research valuation and allocation processes are specifically designed to help managers illustrate to clients how their research spending directly supports their return objectives. FRS also accommodates and integrates, ESG inputs, AI Tools, multi-asset/fixed income strategies as well as active ETFs – allocated fairly down to the fund/client level.