What everyone gets wrong about active management
Hugo: hello and welcome back to Mapping the Markets, the podcast where we talk about the big ideas in investing, and how they impact our portfolios. Today the main event is a discussion about active management. I think its fair to say that people in our industry are increasingly throwing in the towel when it comes to active funds, but I thought this could be an opportunity for us to go toe-to-toe with the sceptics. I have brought in some of the heavy hitters from our investment team. In the red corner I have, Chris Ellis Thomas, portfolio manager for MyMap.
Chris: hello Hugo
Hugo: and in the blue corner is Claire Gallagher, PhD, our lead quantitative researcher. Clarie thanks so much for being here.
Claire: it is my pleasure, thanks for having me!
Hugo: you both employ active asset allocation across the money that you run. So why do you have conviction that its worthwhile? Chris?
Chris: wow, not pulling any punches I see Hugo! Firstly, when we say ‘active asset allocation’ we are talking about making modest adjustments to the mix of investments within a portfolio over time, based on what’s happening in the world.
Hugo: okay, so you need to be able to ‘bob and weave’ depending on what’s happening the in market. That might be leaning more heavily into growth orientated assets when the economy is strong, and when things are more uncertain amping up the defensive holdings, like bonds.
Chris: yes, and also making decisions about the makeup of those growth and defensive buckets. For example, if we anticipated that China was going on a spending spree we might tilt towards infrastructure investments within the growth bucket, or if we were concerned about inflation in the UK we could replace our regular bonds with inflation linked bonds.
Hugo: okay and why do you continue to champion active asset allocation?
Chris: we think that active asset allocation punches above its weight compared to other forms of active management such as stock picking. This conviction is based on two facts that we observe time and again. First, the split between the different investments in a portfolio – its asset allocation - is the most consequential determinant of that portfolio’s returns.
Claire: There have been multiple research papers exploring the impact of asset allocation and evidence suggests that asset allocation accounts for up to 90% of portfolio performance1. Hence why as multi asset investors, we spend the most time thinking about the asset allocation decision.
Chris: and second we know that during different market environments the same investments can perform very differently. When you put those two facts together, it is pretty inescapable that there is a huge potential benefit in being able to adjust your asset allocation over time.
Hugo: I suppose regional stock market returns would be a good example of that principle in action. Over the past decade tilting your portfolio towards the US companies would have enhanced returns, however in the 90’s it would have been better to tilt towards Emerging Market companies2,3.
Clarie: of course when you put it like that it sounds really easy: in hindsight it’s crystal clear which part of the market outperformed. The difficult part is predicting what will happen in the future.
Hugo: a very fair point. So the academic case for active asset allocation is clear, getting it right in practice is another thing entirely. What do we think gives us a fighting chance when it comes to getting it right in our portfolios?
Chris: well when making investment decisions, we consider two key inputs – machine driven investment signals with human insights. In our view, blending those two approaches gives us an edge over the market.
Hugo: I think that investment signals are probably one of the things that really sets our approach apart from others in the market. Claire what is a signal, and why do we use them?
Claire: an investment signal is systematic rule-based clue based on data that helps us as investors decide when to buy or sell assets. These signals come from studying patterns in market history and a fundamental understanding of what sort of data impacts market returns.
Hugo: in fact your team Claire is entirely focused on conducting deep research4 to identify these datapoints, test their predictive power, and monitor them to ensure they are behaving as expecting.
Claire: exactly, I would caveat that these signals are not infallible, they just provide a small edge. It’s a little like finding a great pair of trainers, they won’t guarantee you win every round, as each round will be different, but over time on average they can act as a performance enhancer.
Hugo: I have a very simple question - what makes a ‘good’ signal?
Chris: well one that successfully predicts returns!
Hugo: of course, but what kinds of signals are most likely to do that? How canavoid a swing and a miss?
Claire: Our guiding principle is simple: it must make intuitive sense. In other words, there should always be a clear and logical economic rationale for why a particular data point influences asset market returns. Markets are inherently noisy—price movements can often seem random and disconnected. This is why a strong foundation in fundamentals is essential: it can allow us to separate meaningful signals from the surrounding noise and interpret data in a way that aligns with economic reality.
Hugo: okay, so if you found that the price of Swiss cheese in one month seemed to impact the performance of the Swiss franc in the next, you wouldn’t create a signal based on that?
Claire: No. If we can’t create a rational explanation for why the relationship might exist, then it is likely a statistical coincidence, rather than a genuine insight.
Hugo: so what sorts of things can actually impact market returns? I know you can’t go into too much detail, but the team has been conducting this research for more than 20 years now, what are the general principles that you have arrived at?
Claire: well, fundamentally there are only so many things that can impact the return of an investment. So we have found that good signals generally fall into at least one of four categories: momentum, sentiment, macroeconomic, and valuation and fundamentals.
Hugo: okay so if we break that down, momentum, that’s markets having a tendance to ‘trend’ over the short term. Sentiment, investment returns are influenced by the degree of optimism versus pessimism in the market. Macroeconomic, asset class performance changes based on where you are in the economic cycle. And value versus quality, in other words how expensive a given assets is versus how profitable it is.
Claire: exactly, and they provide us a nice mix between insights into the long-term intrinsic value of an asset class, for example our macro, value, and fundamental signals. Whereas sentiment signals tend to be more short term.
Hugo: One of the most common questions that I have started to get from clients is do we ever use artificial intelligence to inform the investment decision that we make? Now I know that we don’t let AI loose to make investment decisions directly in our portfolios, but do you use it at all?
Chris: everyone in the team has access to AI, we use it as an efficiency tool in our research and also to help us extract investment insights. On the efficiency side, its particularly helping in writing code,
Claire: on the investment side, we have also embedded it into some of our investment signals. For example, we built a tool which creates stock portfolios based on investment themes. It levers a large language model to consume and interpret large volumes of data. That said, human oversight is still important and there are instances where we see hallucinations. For example, I was recently using that tool to build a portfolio themed around weight loss drugs. The AI suggested mostly sensible trades: buying health food companies and so on, but it also suggested tilting away from furniture manufacturers. When asked why, it said that as people lost weight, they would be less likely to break their furniture which could reduce sales of chairs and sofas.
Hugo: that is very funny, clearly a powerful tool, but it does have human portfolio managers on the ropes just yet! Okay, so we build these investment signals, and we empower our investors with AI, but why? What is the benefit of using all of these quantitative tools rather than just convening an investment committee full of really smart people?
Claire: I think of there as being three main benefits. The first is scale. Signals can consume a larger quantity of information than a single portfolio manager.
Hugo: and I suppose that scalability moves our portfolios into a different weight-class, especially when it comes to the breadth of asset classes we invest in. We often talk to our clients about how many different sources of returns there are in our portfolios, this range is facilitated by our signals which can do a lot of the heavy lifting when it comes to identifying potential investment opportunities.
Claire: precisely. The second benefit of the signals is their repeatability. From an investment process perspective, signals are not tied to a single investor, which limits the impact of team changes. But perhaps even more importantly, signals are highly robust, adopting a consistent approach month after month, creating a clear-sighted view of how a given market dynamic has changed over time.
Chris: and to that point, I can’t tell you how many times we get to the end of an investment committee where you think you have come to a decisions when suddenly someone pipes up saying 'I don’t want to buy… Japanese equities – its too expensive'. In the past that could have triggered another 12 rounds of debate, and at worst indecision. But for us, in those situations, we can point to the signals and say – 'the price has already been accounted for, but we still like the market for XYZ reasons'. Its that framework for hanging investment conversations on that I think is really useful.
Claire: the third benefit is objectivity. Signals are not swayed by emotion and are much less likely to be subject to variability in behavioural biases. For example, they don’t get attached to certain trade ideas. They aren’t impacted by the way data is framed. They can’t be swayed by confirmation bias.
Hugo: you often hear that one of the reasons markets are inefficient is due to market participants exhibiting behavioural biases. If we can find a way to avoid them, or at least be less impacted by them, that can be powerful.
Claire: and if we can overcome them, not only can we make better decisions ourselves, but we can take advantage of those biases in others to make money for our clients.
Hugo: okay so there are clearly lots of benefits of using these investment signals. But we don’t rely on them alone: we overlay them with human insights. Chris, when and why do we do that?
Chris: there are two situations where human insight is irreplaceable. The first is to account for information or events that are difficult to quantify or are unique. Geopolitics is probably the best example of this, signals are totally oblivious to upcoming political events, but a human investor can account for them in their decision making.
Hugo: the way we managed our currency exposure around Brexit illustrates this quite nicely. We knew that Brexit was going to create a lot of volatility for the pound and we positioned accordingly. As sterling investors, if you were on the wrong side of those currency moves, it would have really hurt returns.
Chris: indeed and those moves were not motivated by our signals, because they are just machines – unaware of something that was clear to us. The human overlay and input is important.
Claire: Because signals are trained on historical data, they really struggle with regime changes. Humans are more creative. This creativity and flexibility are indispensable when trying to predict future moves in the market.
Hugo: excellent, I think that just about covers off all the questions I had, its time the two of you hang up your gloves. Chris, Claire, thank you both so much for talking to me today, it has been a really useful discussion.
If you enjoyed this discussion then please do look out for our next podcast which should be released in a couple of months time and click that subscribe button to be notified.
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1 Brinson, G.P., Hood, L.R. and Beebower, G.L., 1986. Determinants of portfolio performance. Financial Analysts Journal, 42(4), pp.39–44
2 Source: Bloomberg, US Equity (S&P 500 Index), Emerging Market Equity (MSCI Emerging Market Equity Index)
3 The figures shown relate to past performance. Past performance is not a reliable indicator of current or future results. Index performance returns do not reflect any management fees, transaction costs or expenses. Indices are unmanaged and one cannot invest directly in an index.
4 There is no guarantee that research capabilities will contribute to a positive investment outcome.
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