How do macro themes drive equity markets?

Paul Diggle: Hello and welcome to Macro Bytes, the economics and politics podcast from abrdn. My name is Paul Diggle, Chief Economist at abrdn. We’ve spoken a lot on this podcast about major themes which are shaping the global economy and markets, things like globalization, political volatility, geopolitical competition and technological change. Well, today I'm really excited that we are joined by two equity fund managers here at abrdn who are investing specifically in some of these themes, which are shaping the global economy and markets over coming decades. The approach is called ‘thematic investing’. Blair Couper, Jamie Mills O’Brien, are two great practitioners of it. So, I'm really excited that we're going to learn from them how they are translating the sort of macro themes that this podcast is really concerned with into equity investment ideas. Welcome, Blair. Welcome, Jamie.

Blair Couper: Thank you very much.

Jamie Mills O’Brien: Good to be here.

Paul: So let's start by setting out the framework. What is equity thematic investing? Why is that a good way to think about markets?

Jamie: Yeah. So, I think there's a few ways of tackling that. I think that the primary purpose of thematic investing is to invest in assets whose returns are linked to forces that are broadly independent of the economic cycle. And there's a few advantages to it. One is these are deep asset pools. So, the universes are really large. There are almost as many thematic stocks as there are companies represented in mainstream indices. Pure plays make up a significant part of the investable market versus conglomerates making up a big part of the mainstream indices. They're rarely accessible via mainstream indices and crucially they’re forward-looking in nature versus mainstream indices which are backward-looking. But I think, and Blair will touch on this as well, but one of the main things we're witnessing at the moment is markets that are driven far more by these mega themes than in previous market cycles. And we see this not just in technology, so AI is the obvious one, but really across the consumer, financial, industrial spaces. And in a world where economic growth is slowing, we’re late cycle-ish in nature, we think the value of these structural growth themes is probably going to go up rather than down.

Paul: Is thematic investing a new thing? Are there lots of practitioners out there?

Blair: I wouldn't say it's necessarily a new thing, but it's definitely getting a lot of traction. I think it's traditionally been seen as a kind of retaily, kind of a steady way of investing and I think you're seeing a lot more institutional practitioners understand the value that it can have - adding into portfolios. I think a good stat is the Morningstar, the way that they define thematic investment equities, the assets within that category, had grown to 800 billion by the end of 2021, which was a three-fold increase on 2018. So you're seeing quite dramatic growth in people who are actually allocating to the asset class, and I think that's because it's got quite a different profile to the traditional asset allocation lens through geography or through sector. And to Jamie's point within sector, we're seeing dispersion because of technological change and a lot of these themes that are driving individual performance within sectors. And geographically, it's no longer the case that you're getting pure macro plays on countries by playing the main index. So, I think it's increasingly being adopted by a wider range of investors.

Paul: So let's get into the meat of it then and start with you guys outlining what the key themes you've identified that you're investing in are.

Jamie: So we have kind of what we think is a very unique approach. We kind of identify three unique meta themes and those are energy evolution, health and the generational shift and transformational technologies. And we think of these as the structural enduring trends that are going to drive growth over the next ten years plus. And beneath that we identify more dynamic sets of themes, so 13 sub-themes that are all tied to those meta themes. But crucially, we see these themes as overlapping. So we don't think you can look at energy evolution without looking at transformational tech. Lots of green technologies that are going to enable net zero, for example, are driven by these very important technology shifts. So we view these themes as overlapping. But crucially with those 13 sub-themes it’s very dynamic, and those are all driven by what we're seeing in the regions and talking to our regional analysts, and then we contextualize those at a global level. So we really pick the themes that we think are most important, and we look to build universes from abrdn’s coverage base to populate those universes.

Blair: And I'd maybe just add to that, that we don't think that all themes are created equal. And abrdn has, you know, a strong ‘quality’ heritage as investors, and so we look for, you know, as Jamie said, themes that are very enduring in nature, that we think we have strong visibility on the structural growth dynamics that are happening. So I think, maybe an example or a good way to illustrate that is some of the themes that we don't invest in. So the likes of food delivery, you know, has clearly seen high growth over the years, it's been a successful marketplace, but it's not allowed the returns on capital to the companies who have been participating in that. So you've not seen shareholder returns from most of the businesses that are involved in that space. Another one would be, for example, 3D printing. We've got a very niche market, quite capital intensive. Again, we need to avoid that. Space exploration might be another one where, you know, we don't even know how we're going to get there yet alone the business models or how we make money from these. So we tend to try and focus on much more durable themes like, you know, aging populations, like robotics and automation. We think that these are ones where you've got much more confidence and visibility, not only on the structural growth of the theme, but the ability for companies within that theme to have strong business models, strong moats, and an ability to capture a lot of value from the growth of that theme.

Paul: So let's get into, one of those mega themes, then, technological change. Obviously, AI is a topic ‘du jour’. We can't talk about markets without talking about AI. Let me ask a big question to start with, is the AI theme for real? Is it hype? Is it a bubble? 

Jamie: So I think there's no doubt that the AI theme is profound and it's going to change markets, and will probably come to define my and Blair's investing career. I think there is a difference in what we are talking about sometimes. So I think it's important to define terms. AI has been around for 20 years. What we're really talking about is large language models. You hear some very serious investors talk about large language models, and I'm not joking when I say people have said things like you can leave your computer on and it will start to find a cure for cancer. That is not the case, and that won't be the case for a very long time. These are largely text-based tools. They’re very advanced, but there are limitations around text and obviously limitations around things like hallucinations. So I think we need to define our terms. I think there's a risk that we overestimate the monetization potential of AI in the short-term, and the risk that we kind of underestimate it in the long-term. I think that's what previous technology transitions have taught us, that at the beginning of a technology transition, you see point solutions develop. So the classic example is the steam engine, where you have the electric engine replacing the steam engine. It took a long time for that to happen. Initially it was you just replaced it, but actually the system solution was kind of distributed energy and similarly with the with the internet, initially it was kind of just putting the internet into existing models and it took a while for the Ubers of the world to develop. But I think we're still at that stage where we're at point solutions. So enterprises in particular are kind of trialing and testing how AI - or generative AI - can be used internally but we are yet to see that paradigm shift, those new business models created. And I think it might be a while until we see that. Certainly, I think AI’s at the edge. So AI and the consumer for which the main transmission mechanism is mobile phone, I think is going to take a while. And actually, the way we are looking to, or where we are seeing the majority of value creation at the moment is in the semiconductor space. And unlike previous technology cycles, we think more value than, for example, in the internet, will be created and retained within semiconductors and perhaps in the application software space.

Paul: Yeah, and translating some of what you're saying about use cases into kind of an economist’s language we’ve spoken before in the podcast about AI being a general purpose technology. So it has applications we don't even know now but will explore over decades to come, and it will suffuse our economy in the way that, previous industrial revolutions’ generated technologies that became ubiquitous and use cases where everywhere. But you were talking about benefits accruing to semiconductor manufacturers. And one feature of how AI is playing out in markets at the moment seems to be this profound concentration in a very small number of companies. When you're investing thematically, is that a problem that there's narrow leadership in this thing?

Jamie: So there's an interesting dynamic at play here. So just going back a bit. Up until the 2000 if you looked at the return on invested capital, so really profitability, between large caps and small caps, small caps were far more, much higher return on invested capital than large caps. And in 2000 that kind of flipped. And if you read the literature one of the reasons for that flip was because large caps started to invest in proprietary software, software which allowed them to benefit from the economies of scale and drive things like customization. And what we're seeing, in many ways, is that driving more scale to the larger players. And the real question around something like AI is, do the benefits of AI and so in Clayton Christensen’s language, does it benefit the incumbents or is it a disruptive innovation? And at the moment it seems that because data is so important to AI, the benefits are flowing to the largest players. But I'm not sure whether that stays the case. And certainly in terms of data, when I recently visited Taiwan, it seems like people are spending a lot of money to lower the barriers to entry. But just going back to kind of the heart of your question, which is, ‘is this a problem?’ - it's not really a problem because it's fundamentals driven. So going back to return on invested capital, which is a vital metric for us, to Blair’s point around looking for serial value creators within thematics. If you look the last year the ‘Magnificent Seven’s’ returns, so their economic value creation was about 45% of the total market. So just taking ROC, taking out their cost of capital, multiplied by their invested capital - so they’re creating huge amounts of value. It may not be that persists, but if you look at their growth and return of invested capital metrics, it's far superior to the market. Now going forward an interesting dynamic is that that difference narrows. So if these businesses were growing or compounding earnings and cashflow at 25 -30% over the last decades, over the next decade, it's going to be more like, well it’s going to be lower. So in the next five its more like 10 -15%. And so the difference between them and the rest of the market’s narrowing. So two things can remain true, that these businesses at scale remain fabulous creators of value. At the same time as the difference between them and the rest of the market narrows and you see a more dispersion of value creation. AI could be a driver of that, I think.

Blair: I think we're also going to see a bit more scrutiny now on the returns on invested capital from that massive CapEx spend that we've seen, especially from those large seven. You know, there's over $200 billion being spent on AI CapEx. And as of yet, it's yet to be seen ways they're going to monetize that and the return profile of that. So to Jamie’s point, it was companies that have been fundamentals-driven demand the amount of CapEx they’ve spent, now they need to realize the return on that investment for that to be maintained, and at the same time, they’re going to come up against tough comparables from your real growth and the rest of the market where there is an ability for them to start to show more growth to that, so that difference in the returns profile we think broadens out from here.

Paul: Tell us about ways to play the theme then that isn't just buying some of the Magnificent Seven. How does it spread out across the economy? 

Blair: Well, I think one of the interesting dynamics here, and this comes to Jamie's point around how we think about these themes as being intertwined is the huge energy demand that is being created by these AI models. I think it's commonly known that a ChatGPT search is about ten times as energy intensive as a Google search …

Paul: … which people worried about in the past even with Google searches ...

Blair: Yeah, exactly, so that yet to be seen. But I think what we have here now is a real step change. And you've got that at the same time as these other macro trends around adoption of electric vehicles, around electrification of buildings and air sourced heat pumps. So there's a confluence of factors that are driving electricity demand, and there's key bottlenecks in the semiconductor supply chain that Jamie can talk to, which is one way that we need to play. But another way that you can play that theme is through those electrical equipment manufacturer companies, those that are involved in the grid, the utility grid build out. There are high quality businesses within that value chain that we feel are well positioned to, you know, show pricing power and show strong growth over the next few years as they look to supply the electricity demand growth that's needed. I think another stat, that’s not so widely known as the IEA have forecasted that by 2026, the electricity demand from AI will be the same as the electricity demand of Japan today. Now, that's the fourth largest country in the world. So it's going to be real, and it will be a large driver within the next few years. And how the grid and the systems also cope with new renewable power making up a bigger portion that grid and a lot of the mega caps that have made this cap spend also have, you know, very stringent sustainability goals that they want to try and keep in play, which is going to be an interesting dynamic to see how they play off the need and the race for AI supremacy and their ability to continue with their carbon target trajectories.

Paul: Right. So you can invest in the grid, in grid upgrades, as a way to invest in this theme and also the energy transition theme. Jamie, you were in Taiwan recently you were just saying. One of the ways that abrdn does its equity research is by being present on the ground, visiting companies, learning, directly from the companies we might invest in. What did you take away from that trip? 

Jamie: So I think they're probably a few things. One is the persistence of demand. So we're seeing this from Nvidia, but talking to the supply chain, so anyone from the basic producers of the substrates that make semiconductors, to TSMC all bar none are talking about persistence of demand. And I think the ‘wow’ moment from Nvidia is kind of being replayed a few times, and that's largely been driven by, I think, which is the other takeaway, which is this is a slightly different semiconductor cycle from previous cycles. So in previous cycles the leading edge nodes are the most advanced semiconductors are being driven by mobile. And this time around it’s kind of flipped. So it's really data centres that's driving it. So that's really an AI phenomenon or at least a data center upgrade cycle and mobile is going to come after that. So that speaks to the potential to see more elongated upside at the leading edge logic in semiconductors. And that's really what a lot of the supply chain is talking. I think, and the other thing is, and this goes to my previous point, when speaking to some of these players, it does seem that some of the enthusiasm around things like AI in PC and even AI in a smartphone is going to take a bit longer than expected. So that kind of transmission mechanism from AI to consumer AI at the edg.

Paul: Apple’s putting AI on its phones. So how does that interact with what you're saying? 

Jamie: I guess it's whether the utility of that is powerful enough to drive a really meaningful semiconductor upgrade cycle. It's to my point around point solutions or system solutions.
A really sort of game changing innovation that Apple could do, and it’s something that the people spoke about, we're not there yet, is to have large language models for all of us. So there’s a kind of model that knows exactly what I've done. I go to meetings and there's something I've said in the last five meetings of those types. At the moment, large language models take it as a sort of advanced Wikipedia. It’s taking 20 years of basically Reddit data and then making very advanced predictions in the back of that. And I think we're not quite there yet. So it's similar with something like Microsoft Copilot, where Microsoft is seeing a revenue benefit from AI that's largely for its hyperscale business. So it's application software that Microsoft Copilot is being released and it's in various stages across its suite of products but it's not releasing huge traction or monetization potential. 

Paul: Yeah. So talking about Taiwan gets us into another key driver theme here, which is the changing nature of globalization. We've talked about it a lot on the podcast. How do you think about geophysical risk, reshoring, shifting supply chains as a theme that you can invest in?

Blair: Yeah, well, I think it's an interesting one because there's a broad acceptance of the idea that, you know, in the face of a post-pandemic era, supply chain disruptions that we had and the political rhetoric around bringing jobs back home, that there's going to be this manufacturing renaissance and reshoring of all these jobs from the outsourcing of production that we saw in the late1990s and the 2000. And I think what we can actually see is that that's not really the case. It tends to be what we're seeing in very few specific sectors that are the result of interventionist trade policies. So clean tech and semiconductors are two of the best examples of that. But in terms of what we are seeing from any kind of broad-based manufacturing reshoring to the likes of the Western world, we aren't we aren't seeing that. And you don't see that come through in terms of the data. If you look at manufacturing capacity CapEx spend, it's not back to where it was in 2019 for the US, but it is in China. What we think is far more likely is it moves to these China +1 countries, the Vietnams, you know, Indias, Mexicos of the world and that's where we're very fortunate to have, you know, a great emerging markets team that are on the ground and we've got strong presence of being able to look at ideas and coming out of those regions that could play into this theme.

Paul: How do you think about political shifts then? So yeah, you were in Taiwan, Jamie. Taiwan's obviously a nexus of the US / China competition. Perhaps under a Trump presidency, the security guarantee might come into question. Some of these reshoring trends, whether Mexico is a beneficiary or not will be partly down to kind of how Trump is going to be thinking about it. How do you factor in these political shifts into the framework? 

Blair: Well, we tend to do this at a company level. Again, like, you know, this approach is very much a mixture of top down and bottom up. And for us, the bottom up is where we think we have our edge. So the likes of CATL, you know, I listened to the podcast a few weeks ago that you did on Chinese EV manufacturing disrupting the global economy, and CATL is the largest battery manufacturer in the world, and it's one that we have an investment in. We recently visited the company in China. Now, we have to then take into account the policy announcements that we've seen in terms of 100% tariffs on EVs from Biden, 200% talk that's coming from Trump. Now, we think that's a very low risk given that CATL’s share of the US market anyway, is very very low. But in the European Union, we're starting to see a lot more rhetoric. And you're seeing that the tariffs are being announced there. For us it's a case of looking at what the impact of the tariffs would be on the business, understanding the risk for the earnings for that company. But then more importantly, it's understanding the key drivers of that business, what the return profile looks like, why they have built such a competitive edge and their scale, their brand that makes them a leader. So for us, it's very much understanding that political rhetoric and seeing how it applies to our individual companies and seeing what the impact is on revenues and earnings.

Paul: So this is still very much a bottom-up process, which is how abrdn’s always done equity stock picking. But you're overlaying this unique thematic framework on top of it to kind of think about long term structural support for the sort of companies you’re buying. 

Blair: It might be worth just elaborating on the way that we do the process. So we have that thematic structure that we have, but we also have, you know over 100 analysts that are doing bottom up research and it’s them that have a fundamental view on the company. They have, you know, buy, hold, sell recommendations on the stocks that they cover. And we are asking them to help us populate these universes of themes. And then the fund we are looking to manage is trying to pick the best of the best from those companies.

Paul: Let's talk about the healthcare and demographics theme. I mean, what is it and is it just weight loss drugs?

Jamie: It would have seemed so over the last 18 months - but we don't think so. So just going back to our point around thematics increasingly driving markets, there's an interesting dynamic at play. In 2023 the healthcare sector didn’t perform particularly well but the dispersion in that sector was the highest in 30 years. And that difference between winners and losers, that was driven by what you mentioned, which is this dynamic around weight loss drugs, in particular GLP-1s. And that as a theme is broadening out, and it's something that we're very focused on. It's broadening out from a sort of weight loss dynamic to a broader health care dynamic. And we think that these drugs are building a wall of evidence to support more insurance backing for new therapeutic areas. At the same time, we think we are living through one of the more exciting periods in healthcare and I think that's also driving that dispersion. So one of the other factors beyond weight loss drugs that we're very dialed into is gene therapy. So the shift away from small molecule therapies as a way of treating hard to treat areas to gene therapies and cell-based therapies. And we're seeing an explosion of these kind of areas.

Paul: So what are the diseases – what are the use cases?

Jamie: Alzheimer's, Parkinson's, very rare muscular diseases. There’s a company called Alnylam which is really at the forefront of developing gene silencing techniques. So CRISPR and things like that are gene editing. These guys are doing gene silencing so they can silence the problematic mutations or dynamics for themselves. And the beauty of that from a value creation point of view is it's a persistent therapy. So you don’t do it once you do it over the course of the therapy or the patient's life. And so it's quite profitable. There’s huge value to the consumer and to insurers. And we think that’s a trend is going to become more and more important, particularly in this age that we're living through, where sectors are really starting to be driven more and more by these really big thematic drivers.

Paul: How does how does the healthcare and technology theme overlap? How are breakthroughs in tech being used by?

Blair: There's a really good example there that we can show in terms of the overlap between that demographics theme and that robotics and automation theme. And so there's a company called Procept BioRobotics and what they have done is developed robotic surgical equipment that is treating benign prostatic hyperplasia - so enlarged prostates. So we know that this is a problem for 50% of men age 50 to 60 who will have an enlarged prostate or symptoms of that. And that raises to more than 80% of men who are 80. So we know that there is an aging population. As men get older, they get more affected by this condition and the symptoms that it has. The current treatment for that is currently pharmaceutical first, There's a high rate of failure and then it goes to alternative receptive or non-receptive surgery surgical options such as TURP. But what Procept have done is they've developed a robotic aquablation technology which is using essentially high-pressure water to rescect the prostate and make it smaller. Now this is a really interesting example because there's massive benefits to the patient in terms of improved efficacy, improved safety. There's benefits to the medical practitioner in terms of the time and the number of procedures that they're able to do. But there's also a very strong business model because you have a razor/ razor blade model here where they are putting in a robotic system, and they then need to put new handheld tools onto the system for every surgery. So, these come at very high margins and that's an interesting way of getting exposure to an important treatment that affects a large percentage of the male population that is going to continue to grow – and where you have a unique technology that is using robotics and AI to be able to solve that problem.

Jamie: I mean it’s really in drug discovery. If we think about, as we mentioned earlier, some use cases around AI and those being difficult to see currently. At the moment AI is tackling things like customer service agents, which is not an immaterial market. That's a 600 billion market. There’s huge savings to be made there. But if we think about markets that are kind of ripe for that lower hanging fruit, it's, and we've spoken to a number of potential and portfolio companies around this, it's on the discovery side. You're sitting on a mountain of data about molecules, those that have worked, those that haven't, and being able to use a very powerful predictive tool to sift through that data and allow researchers to focus on the ones that matter quicker, is very powerful. It's a bit similar to why AI is so powerful in the coding world, because it allows very, very expensive coders to focus on the particular line of code they need to develop a tool much, much quicker. And so the savings or the productivity drivers of AI in healthcare are particularly powerful. So if you talk to someone like Microsoft about where the monetization is going to come from, it's always healthcare as one of the key areas to focus on. 

Paul: Jamie, Blair, fascinating conversation. I think it's really interesting to see how you translate in equity markets a lot of the macro themes we often talk about on the podcast. It's often clearer how these play out in fixed income markets or in multi-asset portfolios, but seeing it actually embedded into an equity process, I think it's been a unique insight. That's about all we have time for today. Macro Bytes is on its summer break after this episode. We’ll be back in your feed in late August. Until then, goodbye and good luck out there.