The Four Faces of Trend Following ft. Richard Brennan

[00:00:01] Intro

You’re about to join Niels Kaastrup-Larsen on a raw and honest journey into the world of systematic investing and learn about the most dependable and consistent yet often overlooked investment strategy. Welcome to the Systematic Investor Series.

[00:00:23] Niels

Welcome and welcome back to this week’s edition of the Systematic Investor series with Richard Brennan and I, Niels Kaastrup-Lasen, where each week we take the pulse of the global market through the lens of a rules-based investor.

And I also want to say a warm welcome if today is the first time you’re joining us. And if someone who cares about you and your portfolio recommended that you tune into the podcast, I would like to say a big thank you for sharing this episode with your friends and/or colleagues. It really does mean a lot to us.

Rich, fantastic to be with you again. How are you doing down under?

[00:00:57] Rich

Not bad, Niels. It’s pretty chilly down here, so I’ve got my jumper on. I know it’s pretty warm up there in the northern hemisphere, but I think next week I’m going to be joining all of you up there. So, I’ll get my bathers out ,and my togs, and my sun umbrella, and come up and visit you.

[00:01:16] Niels

Yes, I’m so much looking forward to meeting you in person. It’s strange, after all these years, that I’ve never met you in person, so I really look forward to that. But you know what? So, actually, it’s very interesting in Europe, just to stay on that topic for a second. So, today I’m actually in Scandinavia and it’s very pleasant up here. I mean, it’s not very warm, but it’s warm enough. Switzerland is very hot at the moment, not to say Southern Europe is experiencing massive wildfires and 40 plus degrees Celsius at the moment. But I think you’re coming to kind of the sweet spot when you come to Europe, so, it’ll be super nice.

Now we’ve got a really wonderful outline thanks to you today, which I’ll discuss in a second. But as we always do, I’d love to hear what’s been on your radar, even though I have a feeling I know what it is, so to speak. And you’ll be surprised when you hear my topics. But what’s been on your radar recently?

[00:02:19] Rich

Well, Niels, I’ve been playing around with the latest ChatGPT version. It’s version 5, which has been released over the last few weeks. And I’ve got to say, wow, the future’s coming at us fast.

So, I’ve found that ChatGPT is a noticeable leap from its earlier versions. Faster responses, deeper reasoning, better memory, and it has got a much stronger grip of nuance. And so, but this is where it’s getting interesting, and I asked it a few questions.

So, if you look at how fast we’ve gone from ChatGPT3 to ChatGPT5, the curve is getting far steeper, it’s exponentially rising. Each jump is bigger and the time between jumps is shorter. So, it got me thinking where this trend leads us.

So, at the moment, AI still heavily relies on us. We feed it data, set the goals, and pull the plug if necessary. But at some point, maybe 5 to 10 years on, it’s entirely possible we’ll see AI systems evolve to sustain themselves without human intervention.

They’ll be able to source their own data, refine their own models, and decide which problems to solve next. So, I asked ChatGPT about this, and this is what it said. I asked it to estimate or paint three scenarios: an optimistic scenario for humans, a middle scenario for humans, and a pessimistic scenario for humans.

And it came back, Optimistic Human Control Scenario, 15 to 20 years away. And this is provided that we have strong global regulation, tight computer governance and cultural norms slowly or autonomous handover.

The middle scenario is between seven to 10 years. And this is with gradual erosion of oversight as autonomous research agents become mainstream and open models keep improving. And this is its fast-track scenario. It told me three to five years. And this is where a combination of decentralized compute, permissive open source releases, and commercial competition removes human bottlenecks much faster than safety frameworks mature.

So, under these scenarios, when humans lose their control over AI, the selection pressures no longer will come from humans anymore. They’ll come from whatever the AI itself values: efficiency, self-preservation, problem solving, speed. And that’s where governance becomes tricky, because the old off-switch may not work the same way.

So, the uncomfortable truth is that in the fast-track path, the inflection point could arrive before most governments have any effective monitoring or control systems in place. And that’s why some in the AI governance space argue for treating compute allocation and model release like export control technologies; the same way we treat, say, nuclear material or advanced biotech.

So, I’m not saying to you, Niels, Skynet’s around the corner, but it does raise a question, are we moving fast enough with alignment and safety, given how fast the capability curve is rising? And I’ll tell you this, Niels, if ChatGPT 6 arrives as quickly as ChatGPT 5 over its predecessor. 4, this conversation we’re having now might feel very different a year from now.

[00:06:04] Niels

Well, it’s so weird, and a little bit scary, Rich, that you put down AI as your sort of what’s been on your radar. It’s exactly the same for me, actually. But before I get to that, there was one other story I thought was quite funny.

So over here, obviously, footballers (in the US they would call it soccer) is a really big thing. And the club that won the Champions League, which is the biggest tournament in Europe, the club PSG from Paris, won it for the first time. And then I saw on the Internet this week that they are now…

So, PSG is hiring a quant to trade crypto. And you think, what the hell, it’s a football club. It’s true! So, it says, “PSG is looking for a relatively junior trader. They’re expected to have a PhD and/or quantitative masters and expected to have three to five years of trading experience in crypto, hedge funds, and prop trading firms. The trader will also assist PSG Labs broader efforts to create agentic AI tools to be used across the club.”

I mean, I thought that was quite funny that even a football club is moving into not just AI, but kind of crypto trading. But anyways, back to the AI thing, because it also hit me really hard this week through a couple of interviews that I was listening to.

One interview was with the ex Google head of AI, I think, and I forget his name right now. He’s Egyptian and he’s written a book a couple of years ago. And this is a relatively recent conversation, only a couple of weeks old, and he paints the picture, but he actually thinks that even three to five years, well, it’s going to be even sooner.

And he actually sees a world that most likely could be some kind of dystopian world where a lot of people will be unemployed but they’ll get universal basic income. And so, in a sense he’s saying that it might be really rough initially, so to speak, because people won’t know what to do with their time.

And, and, and of course, universal basic income doesn’t mean you’re going to live well in any shape or form.

But, at some point, he thinks, well, it could actually turn out to be a utopia where we don’t really have to do a lot, but we still get paid and we can actually do what we really want instead. So anyways, It’s such a long, deep conversation.

But all I will say is that, after listening to that, I listened to another conversation from someone who (and again, I should have written the name down), he’s labeled as the godfather of AI. So he’s a 77 year old English guy who had been working on these models for 50 years or so, but way before it turned out to be “AI”.

And he was also quite worrisome, but actually one of his students used to be, or is the guy that was mostly responsible for ChatGPT2 and who left OpenAI because he felt worried about the safety, as you say, that not enough was being done to make AI safe. So, I think he’s called Ilya or something like that, and he’s now working on how to make AI safe.

But in any event, I think your point about safety, I think the point about us controlling this, of course you can imagine worst case scenarios where, as you rightly say, is the self-evolving AI where we have no control.

And if these things are put into, say, if they’re weaponized, and suddenly you may have armies of AI soldiers that decide who to point the gun at. I mean this is not something that is unrealistic anymore. And the other thing is, of course, when you have a world where we really don’t know where it’s heading.

And I personally think, now, I think I’m convinced now, and I’ve mentioned this a few times over the years when we talked about the markets, we talked about that we had to imagine the unimaginable. That some of these market moves like cocoa could happen. And this is exactly why our conversation today is going to be about some of these things.

But, I think we should now expand that. We live in a world where we have to imagine the unimaginable, that in three years, maybe two years, maybe five years, our world and, unfortunately, our children’s world, and their children will be so much different than any of us can imagine. I have convinced myself now that that’s probably where we’re heading and it makes me frankly a little bit nervous.

But from an investment point of view, all I would say is that if you live in a world that can change so dramatically and nobody, even if they claim they have a clue of where that’s going. By the way, what this guy was also saying, the podcast host had conversations with people who know the top three CEOs of the three biggest AI firms. And he could disclose the names, of course, there are only three people here we can think of.

And what he was saying is that the conversations they have in private and what they say publicly about where AI is heading are two completely different things. We’re getting the sugar-coated version of oh, it’s not too bad, it’s going to be great. Yeah, it’s going to improve the quality of life and it’s going to help you do things much better, blah, blah, blah. But that’s not the dark version.

And you know what, when I was listening to that episode, I was kind of thinking (and this is pure speculation), I wonder whether that’s exactly why Elon Musk is so committed to go to Mars? That actually, he deep down believes that the world is “F U C K E D” and you need to go somewhere else because AI is going to ruin our world. I mean, I know it sounds crazy, but maybe not that crazy anymore. Anyways, that’s pure speculation.

But what I was going to say is that in a world like that, what better way to invest your money in something that does not have a clue or tries to predict where the markets are going? And I truly mean that. I mean, I’ve been optimistic about trend following, in particular, in the last year or two because we could see that the world was de globalizing, we could see that economies were becoming more protectionist, we could see that central banks were diverging in their policies. All of that we could see.

Yet it hasn’t played out great yet because of the noise from the US that is, at the moment, overpowering. And our systems need to adapt a little bit to the noise being, maybe, the signal. Not that they will interpret it that way, but it’s just how the data will pan out.

So, I’m still very excited about that. Not necessarily because I think we’re heading into a better world, but it might be much better in terms of these non-predictive strategies. So I know we’ll, we’ll talk about that.

The final point I want to make is there was maybe at all a little bit of an upside to AI because the FT had an article today or maybe yesterday where it talked about how the art of persuasion, that the research now shows, that the top AI chatbots can make people change their political views after less than 10 minutes of conversation. So, I’m thinking maybe all we need is each CTA to install a chatbot. So, when people call them, they start these conversations and within 10 minutes they’re convinced that trend following is the right strategy for them.

00:14:16 Rich

You’re on to something, Niels.

00:14:17 Niels

I don’t know, maybe. So anyway, that was a little bit of a rant but it is super interesting, so we’ll see.

Anyways, let’s go back to something we know a little bit more about, that we have a little bit more certainty about that’s trend following. We were just chatting, before we pressed record, that it feels a little bit more constructive this month for trend followers and there will definitely be some people who’ve caught on to some great trends and see strong performance. But actually, my trend barometer is a little bit… It’s not really confirming that just now. It’s actually pretty weak. Of course, this is just 44 markets that I’m measuring and so, if you are on a one or two outlier markets that that I’m not including in that portfolio you could still have a fantastic month.

But overall, the industries is up a little bit this month and yesterday, which is not included in the numbers I’m going to mention, yesterday was a positive day, I would say, pretty much all round, as far as I can see from the early numbers.

So, BTOP50 up about 48 basis points as of Tuesday, down 3.50% only now for the year. SocGen CTA up about 0.50%, down 7% for the year. Trend index up 1.4% for the month, down 8.77% for the year. The Short-Term Traders Index is down 0.25% and down 5.33. So that’s probably the laggard. It started out great and did well or did better during the month of April, but it certainly lost some of that glory in the last few months.

What hasn’t lost its glory is the traditional markets. MSCI World is up another 2.50% this month, up 14% now, which is pretty strong this year. S&P US Aggregate Bond Index up about 1%, just shy of that, up 4.69% for the year. And the S&P 500 Total Return up 2.04% and up 10.81% as of last night.

So, tell me a little bit about how you see the trend environment similar/differently to what I’m seeing and if there are any markets that particular is standing out to you.

[00:16:31] Rich

Well, the way I’ve seen it, we of course had this very tough first six months of the year. I just had this opinion, and I see it sort of with what happened last month. There was this emergence of trends starting. Directionality started happening last month and things were going great for us last month. We were having a bumper a month compared to prior months. And then the last two days, of course, we had that massive copper reversal and all of that went sideways.

But then again, this month once again, it’s another powerful start for this month. You know, I always get a bit worried when I’m very bullish about a month because, you know, the time I say it, the very next day or the following week, I get an absolute thumping. But things are going well so far this month. I just don’t want to see it like last month, where in the last few days we had this huge reversal.

But you know, the trends for us, you know, of course the equities are booming for us, the metals. So it’s definitely, you know, all of the things we’re seeing, bitcoin, all of these things. It’s a very difficult environment at the moment. And what I refer to as the stress assets tend to be sort of doing quite well.

So, I’m keeping my fingers crossed that this holds for the month and I’m hoping that the last few months of this year bring us back to at least maybe a breakeven for the year. But it’s been a tough year this year, Niels.

[00:18:08] Niels

Yeah, you know, I mean, anything can happen. It could still be a pretty good year at the end. But what’s interesting to me about this month so far, at least from my vantage point, and that is that it’s kind of a barbell attribution in terms of sectors.

So, you have, as you rightly mentioned, equities continue to really take the lead, not so much for the year as a whole, but certainly for the month and maybe last month as well. And then you also have the metals, as you point out, you have the meats doing really well.

[00:18:38] Rich

Oh, meats, yes.

[00:18:39] Niels

Yeah, yeah.

But then you have, at least to some extent, some of the soy complex, which has been doing well, really sort of taking a little bit of a reversal this month. And then you have all the fixed income markets, and softs, for that matter, somewhat more tricky at the moment. So yes, very much depending on… And of course, I don’t have any insights to the crypto world. We don’t trade that on our side. But I understand, of course, from looking at the price that people must be doing well in that space right now. So that is good to hear.

Let’s move across to your topics. Let me try and set the stage a little bit, because I think it’s going to be kind of a fascinating conversation both for the traders, investors (meaning allocators), because we’re going to try and take a deep dive into some of the different archetypes of trend following.

We’re going to deal with some of the big debates that divide our industry and also why you feel that diversification plays a completely different role depending on the type of trend follower you are, or you choose as an investor. Of course, I’m going to let you go through the main topics, but before I hand it over, let me provide the listeners with a brief overview of what we’re going to be talking about.

So, first we’re going to try and break down four broad camps of trend followers, from replicators who hawk an index, to core diversifiers, to crisis alpha risk offset strategies, and finally the outlier hunters who cast possibly the widest net, so to speak.

And along the way we’re going to try and explore some of the key philosophical debates like diversification versus concentration. We have volatility, targeting versus static small bets. We have symmetry versus asymmetry in rules and the speed at which you execute, of course.

And then we’ll go deep into maybe the second part, the second topic – diversification itself, and specifically why you feel that, for outlier hunters at least, maximum breadth isn’t just a nice to have, it’s the edge. And we’re going to look at a portfolio test, the real-world distribution of returns, and how objectives dictate design choices.

So, what I’m going to do, the best I can, is jump in and out where I can keep up. And maybe I have some of my own observations on that. But it’s really going to be, as usual, over to you and let’s see where we go on this.

[00:21:23] Rich

Well, thanks very much, Niels, and yes, you will definitely be joining in this conversation because we often have debates on this podcast together. And I’m bringing them up again, but under a different light to say that there is no right answer. But anyway, we’ll get into that.

So, the thing about trend following is that, from the outside, it looks like one unified philosophy. People think (this is people not in the know) that it’s all just ride trends and cut losses short and let profits run. But once you step inside, you quickly realize even that famous mantra of cutting losses short and letting profits run isn’t universal amongst trend followers.

Some managers build rules that will cut losses short without ever truly letting profits run in the classic sense. Others optimize for smoothness or symmetry that naturally caps the upside. So, while many of us share certain fundamentals, and probably the things we do share is, one, systematic rules, non predictive logic, and participation in sustained price moves. Our objectives can be completely different and it’s those differences that shape the systems we build.

So, if you spend any time under the hood (which you and I do all the time), you see a very different reality. Behind the shared principles we all agree with lies a spectrum of philosophies, objectives, and tradeoffs that can make two manager’s portfolios look like they belong to entirely different worlds.

So, one manager might run 25 highly liquid markets, adjusting position sizes daily to keep volatility constant. Another might run 100 plus markets, never touching position sizes after entry, absorbing the noise in pursuit of a few life changing outliers. Both wear the trend follower label. Both might even post similar long-term returns. But their design DNA and the experience they deliver to investors could not be more different. So, this is where the problem begins.

Most industries conversations skip over these differences where we’re thrown into the same league tables and judged by the same scoreboard. Sharpe ratios, MAR ratios, drawdowns, year to date returns, these numbers are useful, but they homogenize everything, compressing radically different objectives into a single measure.

Say, for instance, a high Sharpe might be gold for a manager whose mission is to smooth a multi-asset portfolio. But for an outlier hunter, that same number might signal that the design is leaving money on the table. So, without understanding the objective, you’re not reading the number correctly.

So, when this misalignment happens, it costs everyone. Allocators end up with strategies that don’t behave as expected in stress events. Managers drift away from their edge, quietly optimizing for metrics that please investors in the short term but erode survivability over the long term term.

So, I’ve seen managers that might start with an outlier mindset and slowly morph into volatility targeters simply because that’s what the scoreboard rewards. And by the time they notice their design is no longer fit for the purpose they began with.

So, that’s why today I want to pull back the curtain and talk about the decisions that really matter. The philosophical divides shape portfolios: diversification versus concentration, volatility targeting versus static small bets, symmetry versus asymmetry in rules, and short-term versus long-term horizon execution. Because there’s no universal right answer to these debates. A replicator tracking an index will land in one place. An outlier hunter chasing fat tails will land in another. And if you don’t know your purpose, it’s easy to borrow rules from a different camp that doesn’t actually serve you and can quietly sabotage your long-term results.

So, we’ll start by breaking down what I believe are four broad archetypes of trend followers and walk through these debates one by one. And after that, we’ll zoom in on one debate that defines my particular approach as an outlier hunter more than any other. And that’s about diversification and just how costly it can be when you cut your universe too narrow. But that is from the philosophical perspective of an outlier hunter. We’ve got to remember that. And through it all, one theme will stay front and center – survivability.

So, no matter what the trend following archetype, the ultimate proof of a design is whether it can remain intact and effective for decades. Through every type of market stress, it can survive.

[00:26:36] Niels

This is a really important point that you’re bringing up because I do think that it’s true that we have been kind of put on the one label.

And I think, also, it’s fair to say that a lot of allocators who want trend probably think that one trend follow is enough because when you look at the correlation, they often look very similar. And so, I’m really glad you brought this up and I’m excited to go through these things and I’m excited to debate some of them, I’m sure. So let’s see where we go.

[00:27:10] Rich

Carry on. All right, so here’s how I see it. Once you step inside the tent of trend following, you quickly realize that ‘trend follower’ is a label that covers very different species based on, for example, listening to the many great interviews you’ve had on TTU, particularly last year, your interviews last year with Alan with the systematic managers, this is how I tend to group trend followers. I group them into four broad archetypes.

So, the first. Let’s deal with the first. The Andrew De Beers of the world. The replicators. Okay, their mission is simple. Track a benchmark like the SGCTA index as closely as possible. They focus on load tracking, error, operational efficiency and delivering the return profile allocators expect from that benchmark.

So that usually means trading between 10 to 30 of the most liquid markets. I think Andrew trades 14 if I remember correctly. They run daily volatility targeting. They steer clear of anything that might create large deviations from the index, and many blending cross sectional momentum and rebalance frequently to stay tightly aligned. That’s the first category.

The second category I call the core diversifiers. So, trend following here is not a stand-alone product. It’s a satellite allocation within a larger multi-asset portfolio. And it’s deliberately built to lift Sharpe or MAR ratios by adding a diversifying return stream that zigs when the rest of the portfolio zags.

So, they might trade similar liquid markets to the replicators. And they often use cross sectional momentum over absolute moment. And they tend to be even more focused on smoothness and drawdown control because their role is to complement, not dominate the broader portfolio.

So, the third I call the crisis risk offset. So, these are the convex hedges designed to shine in equity drawdowns. So, they’re often called long vol variants or tail risk protectors. So, portfolios here might include shorter term trend systems or option overlays to be the first responders when stress hits.

They often keep exposure to assets that deliver convex payoffs in crisis, such as bonds in risk-off markets or certain commodities during inflation spikes. So, a real-world example would be a program that lost money for three straight years, but returned 80% plus in 2008 while equities collapsed. That’s the exact profile allocators want from this archetype – crisis payoff first, smoothness second.

And then there’s this fourth category (which I include myself in), the outlier hunters. So, this is where I sit. Outlier hunters cast the widest possible net above 100 plus markets. Because we know that, in any given year, just a few positions will deliver the outsized gains that define the long-term equity curve.

We’re happy to trade smaller markets, things like cattle that we’ve talked about, small, less liquid markets than the major players. And we can afford more operational complexity because the cost of missing a fat tail is greater than the cost of running a lean, tidy book like some of the other archetypes. So, for us it’s pure absolute momentum. No cross-sectional overlays, no dilution.

So, of course there are the hybrids and the niche players. You know, things like macro trend blends, multi-strat fusions, crypto specialists, commodity-only funds, factor integrated quants. But most still trace back to one of these four philosophical archetypes.

So, here’s the thing. If you don’t know which archetype you are, you’re essentially flying blind. It’s like designing a vehicle without deciding whether it’s meant to be a sports car, a four-wheel drive, a minibus, or a long haul truck. All can get you from A to B, but they excel in different environments. So, a sports car and a four-wheel drive can both do 100 kilometres an hour on the highway, but take them off road and one gets stranded. And the same in trend. Following a rule set that’s perfect for one archetype can quietly cripple another.

That’s why I believe the very first step, in designing a system, is being brutally clear about which camp you’re in. So, without that clarity, you could end up debating tradeoffs that don’t even apply to your mission because there’s no single right answer across all four. So, before I move on, do you want to comment here?

[00:32:12] Niels

Well, I feel there’s one more category.

[00:32:15] Rich

Yep.

[00:32:17] Niels

And that’s one that, oddly enough, I would call pure trend. And what I mean by that is… And I talk about, say for example, someone like Dunn, what our objective is, is to produce the best long-term compound returns. That’s our objective. We’re not designing it to provide crisis alpha during certain periods. We’re not doing that. That’s happening inherently in terms of building for long-term best compound using only trend models.

We’re not building it to be a core diversifier. Again, we’re building it to be the best trend following strategy that we can do. We’re certainly not building it to be a replicator. Maybe you would say, well, you’re a little bit of an outlier hunter. Yeah, but then we have some of the quirks that you don’t like as an outlier hunter. So, I kind of feel that there may be room for a fifth category.

But I do think you would have to be kind of strict about your definition there as well and say, even though people say, oh yeah, we’re pure trend, well then you have to kind of be able to prove that transparently that you are pure trend. But anyway, that would be my thought. But I do like your framework of trying to define the different types of… I don’t know if we should say it’s different kinds of CTAs different kind of trend followers. I’m not entirely sure. So anyways…

[00:33:47] Rich

So, at least it’s a start. I put these four together. Just thinking broadly from my perspective, you’re right. And I think there probably are more archetypes, as you say, but at least it’s setting the principle that there is no correct style. It has got to be objective driven. Anyway, I’ll move on.

So, about these debates that exist and why a lot of them are at cross purposes. So, one thing I’ve noticed over the years is that most of the heated arguments in trend following aren’t actually about facts. They’re about objectives.

So, two managers can be looking at exactly the same rules, the same data, even the same markets, and yet walk away with completely different conclusions about what’s best. Why? Because they’re optimizing for different outcomes.

So, a replicator might want the smoothest possible ride so they can track their benchmark closely. An outlier hunter, like me, might want the most open ended convict’s payoff from a rare runaway trend, even if that means years of bumpier returns in between. These aren’t just preferences, they are fundamentally different design targets. And that’s why so many of these debates are at cross purposes.

Someone says, oh, you have to volatility target or you’ll be too risky. Another says, if you volatility target, you’ll cut your outliers short. Both are right for their own objectives. Both are wrong if you apply their advice to a strategy with the opposite purpose.

So, it’s also why I think our industry has a metrics problem. We’ve ended up with a handful of common measures, Sharpe ratio, MAR ratio, average drawdown, that flatten us into one size fits all comparisons. They make it look like we’re all playing the same game when we’re not.

The truth is almost every metric is only meaningful in the context of the purpose of the strategy. The one exception, the only universal metric that applies to all archetypes, is a long-term validated real track record. Why? Because survivability applies to all trend following styles. And this principle only can be gleaned from a long-term validated track record of survival across numerous different regimes.

That’s the one thing that cuts across styles because it’s the proof that a manager has survived and delivered their particular objectives, over time, in real market conditions. Everything else, however, needs to be matched to purpose.

So, if I think about it, let’s think of replicators. Tracking error versus SG CTA index might be more relevant for instance to them than the MAR ratio. If I look at core diversifiers, portfolio level Sharpe and correlation to the rest of the holdings might matter most to them. A crisis risk offset archetypes, crisis period CAGR, or convexity ratios could tell the real story for them. And for us outlier hound hunters, payoff, asymmetry, skewness, and contribution from top trades will always be more revealing than a point in time Sharpe ratio.

So, without making these distinctions, we risk running comparisons and making investment decisions based on measures that don’t actually reflect the actual mission of the strategy. And that’s why I think, before we even get into specifics like diversification, volatility, targeting symmetry and rules, we have to accept that there’s no universal right answer. There’s only right for your archetype and objective.

So, what do you think so far?

[00:37:35] Niels

Well, I mean, I think you’re obviously bringing up some very important points. A lot of these metrics, of course, will be weaponized in the marketing slide deck. Of course, whatever fits best to your strategy is the one you’re going to say is the most important.

However, what I feel, here, is also a challenge, is that, yes, we can define different kind of trend followers having different characteristics, so to speak, but I also think that, frankly, a lot of investors want more than just one characteristic from a manager, ideally. This is why, initially, in our conversation today, I said it is the wrong view to say that trend follows are all the same, and I just need one.

And I think you show that through this. And, in a sense, if an investor were to be fully satisfied with saying, well, I actually need some crisis alpha, but I also need something that really can compound for me over time or whatever it may be. Well, that’s exactly why.

They probably should come up with a framework along these lines and say, okay, let’s identify all the managers we’re looking at in our peer group, it being one of these four or five different types of managers. Let me decide which of these groups I do need in my portfolio to fulfill my overall objective of the allocation to CTAs. And then drill down on your short list and pick the best one or two in each of these categories. So, you may end up with four or five, I don’t know.

But too, I think it’s a little bit unfair to ask us, as managers, to be able to deliver all the things you would want in one package. Because I think, as you and I will get to, that’s not possible.

[00:39:28] Rich

It’s not possible. Exactly.

And when you design for these different archetypes, you must take different roads, which means that you cannot satisfy all investor requirements. So, this is exactly right.

So, you know, my preferred approach would be firstly to define these trend followers into their different buckets, use different ratios that are valid for their objectives to look at their performance, and then do your selection across multiple trend followers to seek a particular broader objective that marries a range of different flavors, if you like. So, the first thing I’m going to get at… Let’s get into these debates.

So, let’s get into the first debate, diversification versus concentration. Let’s say you’re a replicator. 20 to 30 highly liquid markets is often enough for a replicator. That’s all you need.

[00:40:21] Niels

Or even 10, I think, by the way.

[00:40:25] Rich

That’s right. So, that’s all you need to track the SG CTA index closely, keeping operational, complexity.

[00:40:30] Niels

Let’s define closely as being a little bit loose here, because I think that’s the challenge. Right?

[00:40:34] Rich

Yeah.

[00:40:36] Niels

Even they might…

[00:40:38] Rich

He’s been outperforming. He hasn’t actually been benchmarking them, he’s been outperforming them.

[00:40:42] Niels

Well, that’s the thing. Right? So again, going back to replication, you could almost say the same about replication, where we define it as someone who can just hug the index. But can they really hug the index? Probably not. Right? Ideally they would, because then they’re giving the benchmark to investors. But you know, sometimes they’re going to outperform, sometimes they’re going to underperform, and all of that is fine. But I think you’re right in saying maybe the best metric for them is really tracking error.

[00:41:11] Rich

Yeah.

[00:41:12] Niels

Because that’s what they should be held accountable for if they represent themselves as someone who can give you the index return, ideally plus or something.

[00:41:25] Rich

Yeah, exactly. So back to this diversification. So, for them, 10, 20, 30 markets are sufficient for them to try and minimize this tracking error. But for an outlier hunter like me, that’s far too narrow for me. My whole edge comes from giving myself as many opportunities as possible to catch the big asymmetric moves. That means running the widest feasible portfolio I can, including markets that might be less liquid, might cost a bit more in slippage, or might be more quirky in behavior. But these are objectives that the replicator doesn’t want, because this increases their tracking error.

Here’s the trade off. If you run fewer markets, you might be tidier and more efficient operationally, but you’re also increasing the role of luck in your results. Miss the few markets that deliver the big wins in a given period and you’re stuck with mediocrity.

If you run maximum breadth, like the outlier hunter, you’re reducing that luck factor. You’ll inevitably have more unprofitable markets in the mix. But the one or two that go parabolic hopefully will more than compensate for it.

So, this is where philosophy comes in. For me, diversification isn’t optional. It’s not just a portfolio construction choice. To me, it is my edge. Every additional market is another lottery ticket in the fat tail draw. If I only buy a handful of tickets, my odds of hitting the jackpot collapse.

So, of course, this is not a universal law, but it applies to my outlier hunting. A core diversifier, for example, might not want 70 markets, 100 markets. The extra breadth could dilute their desired correlation profile.

A crisis risk offset manager might be more selective, focusing on markets most likely to respond in equity drawdowns. But for an outlier hunter, cutting the universe down is like telling a fisherman they can only cast their net in one small bay instead of across the whole ocean. Sure, it’s easier to manage, but the chance of landing a giant catch drops dramatically. So, this is where the debates get muddied.

When someone says you don’t need more than 30 markets, they might be perfectly right for their style. But if I applied that same advice, it would fundamentally compromise my ability to achieve my objectives. So before we get into the deep dive on diversification later, cause I’ve actually got an example, from an outlier’s perspective, which I think is going to be very interesting. It’s worth remembering this isn’t just a matter of personal taste.

It’s a structural decision that ties directly to your archetype and what you want your strategy to achieve. So, before I move on into the second debate, do you want to make any comments here, Niels?

[00:44:13] Niels

No, I mean, I think that this is obviously one of the debates that we’ve talked about a lot over the years. I think, again, what often happens is that you get people from each camp talking in absolute, saying, oh, this is better or and the other ones are worse. Of course, the truth is, that’s not what the data shows.

As you rightly said, oddly enough, to some extent at least, many of the people who’ve been around for a long time, if you look at their returns, say, over a rolling 10 year period, they’re not vastly different overall, but they’ll be vastly different in terms of when they occurred, and so on, and so forth. So, I’ve obviously been part of the debate, when people a few years ago said, well, you need to trade 300, or 400, or 500 markets. I never bought quite into that because I couldn’t see it in the performance data. I couldn’t see the evidence.

You can certainly agree that performance is different, but I’m just very cautious whenever I hear someone saying it’s better, and being very absolute about. And this is also, I think, where some of the friction comes from, frankly, in terms of this debate about are you a classic trend follower or are you not a classic trend follower? I think it’s unnecessary.

And I know sometimes it’s being said a little bit to be in gist, and in being a little bit provocative, to get the debate going. And that’s perfectly fine. But I think it’s important, for people who may not be as much into the details as we are, that we’re open about it, that it’s, to some extent, it’s the preference from the design of our systems. But it doesn’t mean that we can objectively say, oh, if you look…

Because, as you well know, Rich, some of the best performing strategies, even in the outlier hunter camp, so to speak, has been portfolios with only 20, 30 markets, 40 markets in recent years. So, of course there is no such thing as it’s always going to be this way or that way.

But you open a very important debate about it in terms of how you see the best way of achieving your design goal. Meaning how many markets do I feel I should be trading if this is what I want to achieve? Because even within, I guess, your group of category, as I said, you’ll find people who trade hundreds of markets. I think you yourself trade less than a hundred, but you still get a lot of diversification. And you’re going to find people who trade even fewer than you do and they feel they get a lot of diversification. So, there is some individual taste as well.

[00:46:56] Rich

But it’s interesting, Niels, that if you trade fewer markets, you do different things with your strategies. So, for instance, with my, say, 100 markets or just a bit less than 100 markets, I have very simple strategies. But if I deployed them with less markets, they’d be far less functional. So, these decisions, they’re not just, you know, a single objective decision. They influence everything in your strategy design.

So, what I think, if you remember, I think it was Harold de Beer suggested that we really should be proud to call ourselves different in a trend following space. We shouldn’t be trying to be all homogenous. And I tend to agree with that. Maybe it’s time that we started looking, seriously, at defining ourselves better into these different archetypes. It might help us better rather than being classified as trend followers.

So, you know, these debates are continually going to surface unless they take the deep dive we’re doing here today to understand this. They’re always going to be debated on the social media, etc., or you’re doing the wrong thing, he’s doing the right thing, all of this stuff. But if they understand the broad objectives, really, that all goes away.

[00:48:13] Niels

And even to a point, Rich, even if they just understand the four or five categories you started out by laying out, I think that, in itself, will be extremely useful. It’s very powerful, actually, from an allocator’s perspective, because then they also know what to, one, expect. But also they would understand better how to combine these different designs, let’s call it that, in order to achieve the highest probability of getting the outcome they want so they don’t get disappointed. That’s the whole point.

[00:48:52] Rich

All right, so let’s move on to the second debate – absolute momentum versus cross sectional momentum. So, this tends to split the trend following world in debates, heated debates. So, absolute momentum measures each market on its own terms. If a market is trending, you take the trade. If it isn’t, you don’t. Positions are allowed to run without being cut back just because something else is trending more. So, this keeps your winners intact for as long as they want to go.

But cross-sectional momentum, on the other hand, ranks all your markets against each other and tilts capital towards the strongest ones, trimming or dropping the weaker ones. The approach smooths returns, controls risk, and keeps the portfolio concentrated in current leaders.

So, for replicators or core diversifiers, two of those archetypes, cross sectional momentum can be ideal. It keeps correlations in check, avoids dead weight. But for an outlier hunter, it can be poison. You may end up selling into strength and cutting the very trades that would have delivered your biggest lifetime wins.

So, both camps have good reasons for their different styles. The key is knowing which one matches your purpose. Are you trying to smooth the ride or are you prepared to hold the choppy road if it means catching the rare monster trend? So that’s a second broad debate, once again, tied to objectives – no right and wrong.

So, the third debate, this is where we talk about volatility targeting versus static small bets. So, this is the debate that gets people fired up. We’ve had many of them, Neils, throughout the series because it touches the core of how you think about risk returns and philosophy as a trader.

So, on one side we have volatility targeters. So, this is the camp where many replicators and core diversifiers live. They dynamically adjust position sizes as volatility changes, aiming to keep portfolio volatility constant. If a market’s volatility spikes, they cut the position down. If volatility drops, they increase exposure.

Now I’m not talking about Dunn here, Niels, because that’s a different form. That’s dynamic position sizing, which classifies itself differently to that. But this is the broad general volatility target as I’m talking about here.

So, from their perspective, this makes perfect sense. It smooths the equity curve, keeps risk metrics, like annualized standard deviation, in check, and produces a more consistent ride for investors. It also helps align a strategy with a specific volatility budget, which is often a mandate requirement.

But for an outlier hunter, this approach can be counterproductive, even dangerous for us. So, my objective is to maximize a payoff from rare extreme moves. Those moves often come with surging volatility in the middle of a trend. So, if I start cutting back my position size just because volatility has spiked, I’m potentially clipping the wings of my biggest winners.

So, if I think about it, if I catch a major uptrend in crude oil and volatility doubles halfway through, a volatility targeter will cut their position by half at the very moment the trend is accelerating, from my perspective. They’ve just reduced their potential payoff, from my perspective, because their metric told them to smooth the ride. I don’t want to smooth the ride.

I want to ride the wave in full. That’s why I run equal small bets, ATR normalized at entry. And then I leave them alone.

I don’t size up if volatility drops, I don’t size down if it rises. Each trade is a small, fixed piece of the portfolio, designed that no single loss can hurt me too badly. But every winner can reach its full potential.

And once again, that goes into diversification. That’s one reason why I diversify so widely, my bets stay so small for any particular adverse volatility move. So, this is where philosophy splits. Are you optimizing for smoothness, consistency and investor comfort? Or are you optimizing for convexity, the biggest possible payoff from the smallest possible risk on any single trade?

Neither approach is right, in a universal sense. Volatility targeting works brilliantly for strategies designed to deliver stable, risk-adjusted returns, especially when investors have a low tolerance for drawdowns. But static small bets work brilliantly when your mission is to catch the home runs and accept that your equity curve will have more noise and bigger swings.

And here is where it loops back to my earlier point. If you judge both these styles on the same performance metrics, you might think one is better than the other. But you’re not comparing like with like. One is optimizing for smoothness, the other for asymmetry.

So, I think this is why debates on social media get so heated. People talk past each other because they’re implicitly defending the approach that fits their objectives, not necessarily your objectives.

So, before I move on to the fourth, is there anything you’d like to say here?

[00:53:58] Niels

There might be one or two things, Rich. Well, again, it’s really about, for me, it’s about the nuances. Because I pay attention to the language you use, and you describe it really well, so, great.

But here are a couple of things. When you talk about the static, you always talk about small bets, right? Well, actually, I think in fairness, I think there are small bets on both sides. I don’t think that the vol targeting managers are taking big bets either. I think they’re taking small bets. I think that’s fine.

And very importantly, the volatility targeting… And of course, we know where that term kind of stems from. Right? I don’t know that there are that many of them left because I think that some of the big firms that we all know, European based mainly, that’s how they started. But I don’t think they do vol targeting today. I think they do the same as Dunn, which is risk targeting.

So, we don’t worry about whether the vol will go from 20% rolling 12 months vol to 30%. That’s just the way it works. But we may have a cap on the overall value and risk we can take on any given day, and so on, and so forth. So, there’s this little hybrid in between, which is very important because I actually think, predominantly, that’s what people do today.

[00:55:26] Rich

Fair enough.

[00:55:27] Niels

Now, that being said, there is one thing that I also think needs to be mentioned and that is, when in your camp, the static position size, even though it may be small to begin with, we have seen examples in the last few years where that little small bet became a monster in the portfolio and drove daily vol drawdowns performance to an extreme. So much so that I’ve seen one fund go from an annualized vol of around 35 to at some point have an annualized vol of like 95.

So, this is what worries me, to some extent, is that it can be a little bit seductive when you say, oh, we just take small bets, so, I’m not worried about it. No, no, yeah, yeah, but things can change. And so, as long as people know that it’s not an issue, it’s not a problem. It just needs to be made clear from up front that these are the differences.

And investors, as you say, they may have a preference for someone who can really, you know, knock the ball out of the park because of one market or two markets moving, or someone where they say, yeah, if it gets too crazy, we’re going to slow it down a little bit. But (and this is the important part, which rarely gets mentioned), for people who do use volatility in the position sizing, whether they’re risk managers or vol managers, so to speak, the risk can also increase.

It’s not always about lowering the position size just because we have a big trend and we’re limiting ourselves to have a great performance from that trend. No, no, we could in fact be actually lowering our position size at a time when the trend has risen, but the vol increases, then the market corrects, then the market goes quiet, then we increase the position size. We’re still in the same position, it’s just now being increased again and off goes the market.

So, there are all these small nuances which, of course in social media, will never be mentioned. So, we end up being, you know, as if we are massively disagreeing. I don’t think we are, because I think we understand what the real differences are. And, as you say, there’s no wrong or right, it’s just a matter of preference.

[00:57:48] Rich

So, internally, within trend land, we do, we have these vigorous discussions. They’re not… In social media it might sort of turn into a bit of conflict here and there, but certainly when we talk civilly, explain our position, I think everyone in trend following land understands where we’re coming from when we’re talking about this. So, yeah, I agree with you.

But let’s get onto this next debate. So, this is something where I might be different to some other people in my space. This is symmetry versus asymmetry and rules. So, this one is about whether you treat long and short trades exactly the same or whether you design rules differently for each side.

So, replicators tend to keep symmetry. So, if the long entry is a breakout above the 100 day high, the short entry will be a breakdown below the 100 day low. It’s the same stop, same trailing logic, etc., same risk parameters. This keeps the strategy clean and the benchmark aligned and ensures no systematic bias towards one side.

But not all camps take that approach. So, crisis risk offset strategies, for example, may intentionally favor the long side in certain markets, especially in government bonds or safe haven currencies because their primary mission is to deliver convexity during equity drawdowns, they might allow for slower, looser exits on those longs, but running tighter stops on shorts in risk assets.

So, outlier hunters like me, sometimes I’m a bit different to other outlier hunters. So, I’ll consider asymmetry if it improves tail capture. So, for example, in commodity markets, the most explosive moves are often on the long during supply shocks. So, like wheat in 2022, natural gas, 2021, in those situations, I might allow more room for longs to breathe than for shorts.

So conversely, in equities, the most violent moves tend to be on the downside during crises, so I might run wider trailing stops on shorts to fully participate in those collapses. So, this is where I might differ from some other of my colleagues. So, the point is, asymmetry is not about prediction. It’s about structural reality.

So different markets and different directions have different historical profiles for speed, magnitude, and persistence. If your mission is to maximize convexity from those moves, it can make sense to reflect that in your rules. So, of course, symmetry does have its strengths. Simplicity, elegance, fewer moving parts to explain to investors.

But if you’re willing to accept a bit more complexity, asymmetry can give you, I believe, an extra edge in those moments that matter most. So, again, this comes back to objectives. Are you designed for elegance and operational simplicity, or are you designing for opportunistic capture of rare directional extremes? So that’s on symmetry.

[01:00:39] Niels

Yeah, you know, this is actually a point that I think is very rarely debated, and I don’t think we’ve talked a lot about it, actually, in the podcast, that being something of a design choice. I think for me, asymmetry has a little bit of a taste of optimization. Let’s be frank. That’s kind of what we’re trying to do.

I think for me, if you just use the same rules across all markets, etc., I think you could argue that maybe it’s a more robust approach to that. But again, why shouldn’t you put your own taste into your design? It’s your system. So, of course… And I think it’s a really important point.

I don’t know that many allocators ask us that question, frankly, which they should, some do, some do. But I think it’s a super important one. And, yeah, I mean, it’s worked really well for you, so why not?

[01:01:34] Rich

Well, it has, and, you know, there might be instances as well where we’re at a historic low on a particular commodity, etc. We know that it hasn’t got that far to go before zero. And you know, that, therefore, constrains how much of an outlier we can get from where it is now to where it is if it gets to those levels. So, that’s why I do need to take that into account when I’m designing strategies.

But let’s get onto this next debate. Speed of execution. So how fast you want your systems to react? So, short-term trend followers and traders running higher frequency systems, they want faster turnover, tighter stops, quicker responses to price reversals. Their argument is that the earlier you cut a losing trade, the smaller the damage. And the earlier you enter a new move, the more of it you capture.

This style often appeals to replicators who need tighter tracking of benchmarks, and to some core diversifiers who value keeping portfolio risk tightly contained. So, on the other side, however, the long horizon trend followers want to breathe through the noise. They’re prepared to sit through more volatility in order to ride the multi month, sometimes multi year moves that deliver the real payoff. This is particularly true, for example, for us outlier hunters, where the mission is not to catch every move, but to stay on the big ones for as long as possible.

So, the danger with too much speed is that you can get chopped out of a long-term trend multiple times, missing the bulk of the payoff because you couldn’t absorb the interim volatility. So, speed is not just a technical parameter, it’s a statement of philosophy. It says something about your tolerance for drawdowns, your patience for building positions, and your willingness to endure short-term pain in pursuit of long-term gain.

Ultimately, the right speed is determined by your true north: which of the four archetypes you belong to, and what your portfolio is designed to achieve. That’s why for me, this debate, like all the others, circles back to clarity of purpose. You can’t choose the right reaction speed unless you’re crystal clear on what your strategy is meant to deliver and over what horizon.

[01:03:44] Niels

Well, I mean, so speed is very interesting because it’s something we often debate and it’s something people will use as a classifier of what kind of manager they’re looking for. Are you short-term, medium-term, long-term? Now, of course, I think the best answer is that you probably should be designing your system to be a little bit of everything, but not necessarily in a static way, which we did in the old days.

I think it was natural in the old days that a lot of managers would sit once a year and have a committee saying 25% of our models should be short-term, 50% medium-term, and maybe 25% long-term. That’s kind of how it was done. Today you can do it in a much more scientific way. You can do dynamic optimization and recalibration of your parameters, and so on, and so forth. The thing that makes a lot of sense.

Now, when I look objectively at a trend model and I just simply apply different time frames, there is no doubt that long-term parameters work best. There’s just no doubt.

[01:04:43] Rich

Yeah. For your objectives. But, for instance, in the crisis offset camp or in those different objectives, if their objectives are different, it might not be for maximum CAGR. It might be to provide downside protection and hence the short-term trend followers – I can see it.

[01:05:02] Niels

Yes, and that’s the narrative that was sold to people a few years ago. A few years ago, I remember that the words ‘risk mitigation’ became a thing in the narrative. And people were saying, well, markets are moving so quickly so you know, you should go with short-term managers.

And some of the managers grew like massively, multi-billion dollar short-term managers. I was kind of thinking, frankly, that’s never going to work because you’re going to have slippage and all these things now.

So what’s happened, in reality? This is a good time to look back on it. Well, it’s kind of the same thing as when people said, well, I’m just going to buy the VIX because when equity markets go down, I’m going to make money, the VIX is going to go up. Well, surprise, surprise, the VIX doesn’t always go up when markets go down. And sometimes the VIX goes up when markets go up because it’s much more nuanced today when you decompose what’s causing, say, the VIX to move or not.

We just did an episode that came out a couple of days ago on the whole VIX composition. I think it’s actually quite interesting to learn from.

However, back to our little sandbox. So, what I’ve seen, at least, or noticed, is that some of these short term managers… Let’s take the April liberation, yeah, they may actually produce a decent or better return for two days, when markets were tanking for two days in a row. But they lose it all the next three days, or the next five days. So, even within that space I think they provide less of a portfolio benefit today than they used to do.

I think they used to… The moves in the markets used to be a little bit longer so that they could actually capture the P&L from, say, a vol breakout, capture that for three to five days and actually add benefit to the portfolio without detracting the next week, for example.

I see that it’s a little bit more challenged today in doing so. And as we talked about in the beginning, if you look at the SocGen Short-Term Traders Index, it did really well, relatively speaking, the beginning of the year. Today, on a vol adjusted basis, it’s doing worse than the trend following.

And you wouldn’t say that the last six months has been, for trend following, a great environment. It should, if anything, be a short-term traders environment, but it’s not. So, I don’t know, it must be something to do maybe with the market structure or something like that. But I think people have to be really careful in terms of saying, oh, we’re short-term, we’ll definitely give you crisis alpha. Hmmm, that’s a question mark today.

[01:07:48] Rich

Validate first.

[01:07:50] Niels

Yes, true. I mean the numbers are a good place to start, right?

[01:07:54] Rich

Yeah, exactly.

[01:08:01] Niels

Okay, where are we going now?

[01:08:03] Rich

So, where are we going to go now? So, now that we’re living this happy, friendly place between trend followers, everyone knows what we’re doing, we understand the archetypes. Now I’m going to start looking at diversification because this, to me, defines my outlier edge. And I just want to explain it to you.

So, if you’re in the replicator camp, you can live with 20 to 30 very liquid markets and still meet your objectives. But if you’re a core diversifier, you might see trend as a satellite allocation and keep it even tighter. But if your objective is to hunt these outliers, maximum breadth is not optional. It’s the oxygen our process breathes. With the fewer markets you trade, the higher the odds you’ll miss the next wheat in 2022, or the JPY 2008 moment.

That’s where I want to go deeper next. Not just why diversification matters, but how the wrong approach to it quickly erodes the very edge you’re trying to build. So, I want to start with a real test I ran because it says more than any theoretical debate could.

So, I built a 68 market portfolio designed for an outlier hunter. The test period was January 1, 2020 through to July 31, 2025. In that time, the strategy generated 3,264 trades.

So, here’s the first surprising thing. Of those 68 markets, 27 were unprofitable over the entire test period. And yet when we ran the portfolio equal weighted across all 68 with no hindsight application, it still delivered a MAR ratio of 0.8. So, why? Because the big winners swamped the losers. The tail events, when they happened, more than paid for the markets that went nowhere or bled.

So, let’s flip this thought experiment. What if you could trade only 30 markets from this 68 market universe, no hindsight, no peeking at the winners? You just have to pick your set of 30 within the 68 and live with it.

So how many, Niel’s, 30 market portfolios can you make from a universe of 68? And the answer is staggering. I don’t expect you to get it.

[01:10:36] Niels

I was just going to say. I hope that’s not a question.

[01:10:38] Rich

No, it’s 4.8 by 10 38 different combinations. It’s almost an incomprehensible number. And to test what that means in practice, I randomly generated 300 different 30 market portfolios from this 68 market universe. Each was built blind by me, no foreknowledge of which markets would perform the best. And I’ll tell you what the results were that I got. Out of those 300 samples, only 35 of them achieved a MAR greater than 0.8, which is just 12% of that sample. Which means there’s an 88% chance from that sample you’d underperform the full 68 market benchmark simply because of market selection. Not because your system was bad, not because of execution errors, but because of the luck (or lack of it) in what you happen to include in your universe.

And here’s the thing.

If you accidentally overweight those unprofitable markets in your smaller universe, you can spend years underwater. This is why, for an outlier hunter like me, maximum diversification is nonnegotiable. And it’s not about operational neatness. It’s not about a clean marketing story. It’s about reducing the role of luck in catching the next fat tail. So, if I think about it, small universes magnify luck. Large universes dilute luck. A great outlier in a market you don’t trade is a missed opportunity. Pure luck if it’s in your book. Pure bad luck if it’s not. Managing that tradeoff is part of defining your archetype.

So, what the portfolio test really exposes is the shape of the return distribution for outlier hunting strategies. And it’s not the tidy symmetrical bell curve that many investors imagine. Instead, it’s lopsided, highly skewed, and brutally unforgiving to small sample sizes. So, if we look at those 300 random 30 market portfolios I tested, the median MAR ratio was nowhere near the 0.8 we got from the 468 markets.

In fact, it was well under 0.5. That’s the median, meaning half the portfolios did worse than that. And if I looked at the 25th percentile, they were the unlucky quarter of portfolios that ended up in the bottom range. And many of them had MAR ratios that would be survival threatening if you’re running real investor capital as an outlier hunter. These are the managers who’d be showing three years of red ink not because their process was broken, but because they simply didn’t have enough breadth to let probability do its work.

And here’s the kicker. At the other end of the distribution, a small handful of portfolios delivered absolutely extraordinary results. But you had to be lucky enough to land on them when choosing your 30 markets. That’s the trap. So, when you’re hunting outliers, most portfolio outcomes will underwhelm, a few will be exceptional. And if you reduce your universe, you dramatically increase the odds of drifting towards the noisy middle of the distribution where the edge erodes and your performance blends in with everyone else’s. So, this is why I say, the real enemy of the outlier is not volatility or drawdowns or even bad trades, it’s the quiet invisible erosion of the edge through under diversification for the outlier hunter.

So, if your process is designed to capture rare events, you need enough hooks in the water to make sure you’re there when they happen. And that’s something the standard industry performance metrics, they don’t reveal. MAR, Sharpe CAGR, they hide the fact the portfolio may be sitting on a knife edge of survivability risk simply because of the small number of markets being traded.

So, this is where the conversation comes full circle, Niels, because diversification isn’t a virtue in itself, it’s a design choice that only makes sense when aligned with your objective. So, if you’re a replicator, 20 to 30 markets is often enough. Your job is to mirror the SG CTA index or a similar benchmark, keep costs tight, deliver a familiar performance profile. Breadth beyond that isn’t necessary. In fact, it might just add operational complexity without adding much to your tracking accuracy.

If you’re a core diversifier, trend following is probably just one sleeve of a larger multi asset portfolio. In that context, you’re optimizing for incremental Sharpe or MAR improvement at the whole portfolio level, not for maximum standalone performance from trend. Again, a smaller highly liquid market will often serve you just fine in that context.

If you’re a crisis risk-offset manager, the third archetype, you might have a longer term, more convex profile. And your bucket list could be biased towards assets that historically provide equity drawdown protection. That’s your north star. And diversification choices will reflect that, even if it means trading fewer markets.

But if you’re an outlier hunter, like me, the game changes. Here, breadth is not an accessory, it’s a core operating principle. My edge comes from maximizing the number of independent opportunities to catch something truly explosive. And without a wide enough market set, the law of small numbers works against you. The cost of missing the big next move is far greater than the benefit of running a streamlined operation, for me.

So, that’s why, in my own process, I’m prepared to trade some markets that aren’t perfectly efficient, some that carry extra operational friction: live cattle, all of these small markets, rubber, all of these small markets, milk. Some that might spend long periods contributing nothing like cocoa did for how many years?

Because the payoff from just one of them hitting a fat tail event can outweigh years of mediocrity. So, diversification, the approach you take should be dictated by your purpose. And that’s why I think some of the industry debates get lost. They argue about the right number of markets or the right market selection criteria without first asking the most important question, right for what objective?

So, I’m going to close off now. I suppose we’re getting to the end. But look, if there’s one theme running through everything we’ve talked about today, it’s that your objective dictates your design. Whether you’re aiming for smoothness index, replication, crisis convexity, or outlier capture, each of these objectives demands different answers to the debates we’ve covered today.

Volatility targeting versus small bets, breadth versus concentration, symmetry versus asymmetry in rules, short-term turnover versus long-term patience, none of these debates have a single right answer. They have a ‘right for your purpose’ answer. And the trouble is, the industry often flattens all these strategies into one homogenized peer group and then ranks them on the same metrics. It’s like lining up a bus, a sports car, a four-wheel drive and a motorbike, then scoring them on their lap time around a racetrack. It’s a misleading comparison and it hides the strengths of each design.

So, my encouragement to traders is this, get crystal clear on what your strategy does and then build it to do exactly that. And for investors, look beyond the surface metrics and ask whether the strategy you’re buying into is actually designed to meet the role you need it to play. So, in trend following, alignment between purpose and design isn’t just important to me, it’s everything. So, there you go, Niels.

[01:18:35] Niels

Great, let me add a few thoughts to the point that you mentioned here. I have two questions for you. One is, is there a number (and I know it’s going to be an approximate number) where you would say, well, anything above this is not going to give me more..? Because you mentioned the word independent markets or bets. So that’s one thing. Because I do think that people also misuse that a little bit saying well, 500 markets surely will be better. I don’t agree with that. So, I wanted to ask you if you in your research have roughly an idea of what that number will be where you feel I’ve got maximum diversification, that’s one point.

And then the other point, and I do think this is relevant because it does introduce some other factors, and that is, in order to have more markets, would you go off-exchange introducing counterparty risks? Would you go to countries where maybe the regulation, the regime is less as we’re used to in the Western world and introduces other risks? So what are your thoughts on that in terms of your preferences?

[01:19:57] Rich

So, my preferences, first, market diversification, to me, that is limited by your capital. I would continue to diversify capital. So, let’s say I had a diversification of 100 markets and my capital significantly increased.

So, typically there’s the decision, how do I scale up my results? Do I increase position sizing or do I invest that extra capital in new markets? Or do I invest that extra capital in new systems?

So, my priority would be always invest in new markets (and this will flow into the second question), always invest in new markets. I do find system diversification, to me, maxes out at about 10 different trend following systems. So, system diversification is wonderful to deliberately inject uncorrelated properties into your market portfolios.

So that’s why I can trade Brent and Crude with trend following ensembles and they don’t produce a correlated result. They deliberately break that down. But there is a limit to the benefit of diversification. So, I’d go 10 systems. As far as market diversification, I would always prefer to invest in more markets and increase my position sizing, for instance.

[01:21:18] Niels

But isn’t there a limit, Rich, where you say, actually at market number 251 I can’t find any independent markets or I don’t want to go to a certain region of the world where…

[01:21:32] Rich

There’s definitely those limits. Liquidity is another key requirement. I do require liquidity. And also, the additional risks you mentioned going off exchange and things, I don’t think it’s worth the risk. So, within that it really does cap you out as we know. But if for instance it could increase, I probably would go with it because my underlying mantra is, I just don’t know where the next outlier is going to come from. I do know that any liquid market, over the long term, has these fat tail properties.

So yeah, I would tend… That’s why to me, you know that Pierce Brosnan, ‘the world is not enough’, James Bond… I say diversification is never enough.

[01:22:20] Niels

That’s a good way to end our conversation, but I don’t want to end it completely. I do want to say a big thank you for providing this wonderful framework of discussing trend in a new light. That’s really wonderful.

I’m sure people will enjoy that and find a lot of benefit from this and hopefully implement some of these things. And if you do, let us know, by the way, if you’ve gone out and quantified or categorized managers in these buckets. That would be fantastic.

I do want to go back to AI though. Before we finish, I completely forgot to mention two things to you. So, in terms of AI, this recording platform we’re recording on, which is new to me. It has this feature where I noticed that in the post production it says, oh, I can do text to speech and it will be used in AI.

So, I thought that could be fun. So, I just posted in a snippet of what I had said in an episode and let it generate an AI voice format me and you know what came out? It gave me an Australian accent.

[01:23:32] Rich

Oh, there, look, you can’t object to that, Niels.

[01:23:35] Niels

Well no, I just thought that was funny. Why would you choose an Australian accent? Maybe I sound like an Australian. I don’t know. I think I sound like a Dane who’d been living abroad too long. Anyways, I thought that was a little bit funny, since you’re on. The second thing, actually, I completely forgot to say is you praised the ChatGPT5.

I read a very different post critique of ChatGPT5 this morning, actually. It was someone who was very concerned about what was happening because, in this post it sounded like what they’re doing is they’re limiting your choices, that you cannot go back and choose number four or number three or whatever. Now they’re giving you just five and they’re saying, oh, we’re going to choose the one for you that’s the best for your purpose.

But what they’re really doing is saying, well, we’re going to give you the one where we don’t constrain our system too much because it’s bloody expensive to do these queries. And then we’re going to say to people, oh, but if you want choice, it costs you $200 a month or whatever.

So, actually this guy was very critical about where it was heading and not seeing this as any improvement. On the contrary, for us as users, this was actually a much, much worse outcome. Even though, of course, it’s going to be sold as the best AI that’s ever been published by Sam and his friends. But not just OpenAI also all the other ones.

So anyways, just to be mindful about these things, I just wanted to throw that in. As I said, Rich, this was fantastic. I can’t wait to see you in person soon here in your Europe.

If you want to say a big thanks to Rich, the best way to do that is just to go on your favorite podcast platform, leave a five star rating and review. And that’s going to be the best way for other people to see the show and listen to this conversation.

Next week I’m joined by Yoav Git. He’s back. So that would be another way for us to tackle some of your questions. So, if you have any topics that’s related to what we like to talk about with Yoav, do send me an email at info@toptradersunplugged.com.

From Rich and me, thanks ever so much for listening. We look forward to being back with you next week. And until next time, as usual, take care of yourself and take care of each other.

[01:25:53] Ending

Thanks for listening to the Systematic Investor podcast series. If you enjoy this series, go on over to iTunes and leave an honest rating and review. And be sure to listen to all the other episodes from Top Traders Unplugged.

If you have questions about systematic investing, send us an email with the word question in the subject line to info@toptradersunplugged.com and we’ll try to get it on the show.

And remember, all the discussion that we have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also, understand that there’s a significant risk of financial loss with all investment strategies, and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions. Thanks for spending some of your valuable time with us and we’ll see you on the next episode of the Systematic Investor.