Fabio Lopes Abreu (Rothschild & Co) on his 25 years in the sector and evolving role of fund selector
15 SEPT, 2021
Fabio Lopes Abreu, Head of Long Only Fund Research at Rothschild & Co, has spent 25 years navigating the fund selection landscape – from the euro transition and Y2K to the rise of ESG, ETFs and now artificial intelligence. In this interview, the Rothschild & Co fund selector reflects on the lessons diversity has taught him, the quantitative discipline behind his manager screening process, and the cognitive biases that most often derail fund selection. He also shares his view on how AI will reshape the profession - and why he believes it will never replace experience and intuition.
Can you tell us about your experience in fund selection? When and how did you begin your career in the financial sector?
The initial spark behind my career in finance was my first position at Crédit Lyonnais in Brussels. The French bank was going through a very difficult period at the time, and many employees were leaving, leaving the company struggling to fill vacant positions. As a fresh graduate, this gave me an exceptional opportunity: I was hired to test and validate risk management and pricing systems ahead of the transition to the euro, and then the Y2K transition (the changeover to the year 2000).
I ended up in charge of these two major projects, which allowed me to learn in two years what would normally have taken ten. In 2001, at the end of this intense period, former colleagues invited me to join them to set up a fund-of-funds management team in Geneva – initially to put the necessary systems in place, but I was very quickly given responsibility for selecting funds and managing portfolios.
Looking back briefly at your career, what elements would you highlight the most?
Looking back, I would say that diversity has been extremely enriching. Over 25 years, I've had the opportunity to work for very varied organizations: asset managers developing products for both institutional and retail clients, private banks of different sizes serving clients from different countries, and an independent manager as well.
Each of these employers was looking for specific products to meet the expectations of its clientele, while maintaining its own house investment philosophy. All of this took place against market backdrops that have taken on very different shapes over the past 25 years.
Ultimately, it's this diversity that means you never get bored, but also that you keep learning continuously – all the more so since fund managers are constantly launching new strategies.
In your view, what are the most important aspects to consider when selecting a fund for a portfolio?
It's important to fully understand how the fund will be used within the portfolio, because a fund is never selected to be used in isolation, but as a component that needs to fit harmoniously with the other parts of the portfolio.
Among the aspects to consider is whether or not the fund needs to fit within an allocation grid. Take the example of an investment-grade bond fund: it needs to be pure – with little or no high-yield exposure, and a duration in line with the benchmark – in order to meet the needs of the person building the portfolio, who must be able to control their exposure to the various risks.
Conversely, if the portfolio, or part of it, is managed on the basis of a risk budget, this can open the door to funds that invest in a more opportunistic way, which would not be suitable under a different framework.
Which ratios and indicators do you favor the most?
I have a strong appetite for quantitative analysis. In fact, I have never stopped developing a set of indicators that I use in every analysis I carry out. They make it possible to assess the consistency of managers' performance across different market phases. This is essential, because while we all know that past performance is no guarantee of future performance, we can reasonably hope that past consistency – when properly measured – is a good indicator of future behavior.
This approach has several advantages. First, the model is agnostic with respect to the manager's name, which helps avoid certain favoritism biases. The only constraints we apply when feeding the model relate to the size of the fund and having a sufficiently long track record.
Second, it allows for a process of elimination. It is sometimes very difficult to identify the "best" managers within an asset class, whereas it is much easier to eliminate the less convincing ones based on clear comparative criteria, in order to arrive at a shortlist of candidates. The key to the method lies in choosing indicators appropriate to the asset class and the type of exposure being sought.
This approach sometimes also leads us to conclude that no active manager meets our needs, in which case the best candidate is a passive index-replication strategy.
What are the most common mistakes made when selecting funds?
Preconceptions of all kinds – in other words, cognitive biases – are often the source of selection errors. The selector can be influenced by a manager's or a management company's reputation, good or bad, but also by the quality of the contact they've had with the manager, the product specialist, or the salesperson.
Recent performance also distorts perception, especially when it affects the fund's relative ranking over longer periods and creates the illusion that it has been outperforming or underperforming its competitors "for years." Performance therefore needs to be placed back in its macroeconomic and time context, without forgetting to factor style rotations into the analysis.
How has the fund selector profession evolved in recent years?
Over 25 years, major changes have taken place – for example the MiFID regulation, the growing importance of ESG considerations, and the development of ETFs, including active or thematic ETFs. That said, market structure has also evolved: high-yield credit is no longer of such poor quality, emerging-market debt has developed, and previously listed companies have been delisted, among other things. All of this has shaped the range of products available, and we've had to adapt our analyses to each new context, with its own constraints.
Another major development, in my view, is the growing prevalence of highly skilled product specialists. Fifteen or twenty years ago, it was easy to talk to managers directly and get transparent information from some of them about their investment philosophy. Today, we most often find ourselves dealing with communication specialists who have perfectly mastered the line between what they can say and what they cannot say. This makes analysis more complex.
How do you think it will evolve in the years ahead?
Part of this evolution will continue to be shaped by market conditions. Creative financial engineering is responding to growing appetite for complex strategies. This will require major adjustments to our analytical framework.
Another expected change is linked to the spread of AI. Today, we are only at the very early stages of using this technology, but it clearly has the potential to bring about significant changes, both for fund managers and for selectors. It is therefore essential to stay informed and to experiment firsthand, which makes it easier to understand.
In your view, what qualities set an excellent fund selector apart?
Curiosity and experience. It's essential to build as broad a knowledge base as possible, and to know and understand the different economic and geopolitical contexts in which various investment strategies work – or don't.
Being able to connect situations that are years apart, having already lived through similar configurations, drawing parallels… this matters, because it helps sharpen one's analysis.
AI can obviously already help — and will increasingly help — draw these parallels, but I hope it will replace neither experience nor intuition.
