Trends Don’t Form Randomly. They Form Reflexively 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-Larsen, where each week we take the pulse of the global markets through the lens of a rules-based investor. And I also want to say a warm welcome if today is your first time you’re joining us. And if someone who cares about you and your portfolio recommended that you tune into the podcast, I want to say a big thank you, as well, for sharing this episode with your friends and colleagues. It really means a lot to us.

Rich, it is wonderful, really wonderful to be back with you this week. How are you doing? What, how things down under?

[00:00:57] Rich

It’s great to be here, Niels. And it’s hot down under. So, the Australian interior is boiling and it’s blowing across the coast. So, I’m up in Queensland, of course, and I’m sitting in weather that’s about 35 degrees Celsius at about 7:30 at night. So, it’s boiling.

[00:01:15] Niels

You know what, I actually did notice that I saw a headline that Melbourne, because I know, I think you went to Melbourne earlier this week, that they were up to 44 degrees this week, which is just out of this world really and probably not usual, I imagine.

[00:01:33] Rich

No, it’s certainly not usual. I think we’re all suffering a bit over here in Aussie land.

[00:01:40] Niels

Yes, well, I wish I could give you a little bit of the European cold weather to share. Anyways, it’s great to be back with you. We’ve got a, as usual, wonderful lineup of topics and a little bit of a debate between you and I, I think, might come up toward the end.

And before we get into all of that, of course I would love to hear what’s been on your radar the last few weeks since we last spoke.

[00:02:10] Rich

Okay, Niels, so look, over the past few weeks I’ve been thinking a lot about adaption feedback, and how the human mind fits into modern markets. And I’ve been doing quite a few publications, in my blog posts, dealing with fractals, and the non-stationarity of markets, and all of these aspects.

But I’ve sort of been delving into the likes of people like Ian McGilchrist and Anil Seth, and others, who they’ve explored what’s called the divided brain and the nature of consciousness. And they often describe how the right hemisphere of the brain (you’ve got to think of this), so, the right hemisphere of the brain is taken from (in my perspective) my right side of the brain, that’s the right-hand side of the brain that perceives the world as relational, embodied and full of meaning. Whereas the left-hand side of the brain dissects things into rules, categories and models.

So, if you can think of it, the left side of the brain is your data center. It’s where the crunching occurs, the abstract models are created. It’s using all of the sensory inputs from inside your body to basically create a model of what it thinks is out there in abstract terms. And then the right-hand hemisphere is what contextualizes it. It’s where you get this sense of self, through feedback, which says, I am me and there’s this thing outside me called an external environment. And what it’s doing is it’s mapping those models created by the left hemisphere and saying, how good are those models?

So, the brain is this prediction engine. And this is how both hemispheres of the brain, with feedback between each other, model and they adapt, they allow for adaption.

This is different to AI models. AI models, when we look at it, are very left hemispheric. They’re all about crunching data, simulating, abstracting, creating models. But where they lack this ability to define self is they don’t contextualize that within the greater environment. And this is where both hemispheres of our brain seem to be, from what these writers are telling us, these neuroscientists and philosophers, etc., is that the sense of self is created through the feedback between mapping the internal model that’s being generated by your brain versus what is out there and how well that model works with what is out there. And that’s where you get this sense of self.

And for a brain to work, Anil Seth and Ian McGilchrist argue, it’s not just needing a brain, it’s needing embodiment, a body with senses, measurements, etc. All of these things is necessary to create conscious human beings.

They argue that AI has got a long way to go before they get to that level of self identification, self recognition. They’re marvelous at mimicking, simulating left hemisphere work alone, but very bad at…They don’t have a sense of self.

And the thing is, when you get this sense of self with the right hemisphere and left hemisphere, it tells you how aligned you are with the external environment. And if you are not aligned with the external environment, it creates symptoms such as pain, fear, all of these symptoms which your models then recalibrate to remove that and your model adjusts. So, it’s this continual feedback between right and left hemisphere.

And what I was thinking was that, well, that’s how our brains have evolved in these environments. And it’s been very successful in these environments. But the markets, financial markets, are not something we’ve evolved in. And this is where I think, actually, we want the left hemisphere only. We want to remove the right hemisphere’s involvement in emotion, feelings, all of these things. And we want our rules based systematic processes to be purely left hemispheric.

And this is where I think AI probably does work very well in the financial markets. In fact, AI, if you think about it, has evolved in these financial markets. Humans haven’t evolved in these financial markets. And so, a discretionary trader comes across all of these emotions which comes from a natural environment it evolved in. What worked in that environment was necessary for survival.

But within this context, they’re the things that are really constraining its ability to follow rules, to not feel fear when you get increased volatility. It’s all of these things which are necessary in these financial markets because, as we’ll get to in the later topics, we’ll talk about things such as feedback, reflexivity, all of these issues that show that the markets are not these stationary entities. They’re these alive markets that compress and expand, etc.

And within that context, you don’t want the right hemisphere to interfere with that context. You want rules, systematic processes, and you want data to be your guide, not your emotions to be your guide.

So, I just thought it was interesting and seeing, well, what’s the difference between AI in this context and the human brain in this context. And that’s the sort of conclusions I was reaching.

[00:07:50] Niels

Now, without going into too much detail right now, because as you say, we’re going to talk about a lot of stuff today in a few minutes, but when you say that AI has evolved in the financial markets, how do you see that? Because I saw some comments from some of the big, well known hedge fund managers, I don’t know if it was Ken Griffin or Paul Tudor Jones.

It seemed like some of those guys who came out saying, well, you know, AI is far away from finding anything that can, you know, produce alpha, whatever. There might be some efficiencies in programming, etc. But it’s not really something that’s going to revolutionize our production of alpha, so to speak. So, what did you mean by it when you said…?

[00:08:34] Rich

So, what I mean is that it has evolved in the context of pattern recognition. So, early forms of processes that were looking at pattern recognition have now sort of got to the state of AI, artificial intelligence, where it’s always backward looking, it’s always assessing the data, it’s always looking at patterns. And what it’s attempting to do is predicting forward. That’s what AI is trying to do.

But as we’ll see, in the discussions later, prediction in these markets is like prediction in the weather. It’s ephemeral, it’s got a very short range. And this is because of the reflexivity in the markets. And we’ll talk about how when a trader interacts with this market, they change the nature of the market.

Now because of this reflexivity, what I think’s going to happen with AI is that the prediction horizon, that is currently there with things like convergence strategies, is actually going to shorten. It’s going to compress to be much shorter. Which is going to mean that the influence of AI is going to create much shorter prediction horizons.

And I think this is wonderful for trend following because what it’s going to do is it’s going to reduce the impact of prediction and create much more structural alignments with things such as consolidated concerted directional behavior. All of these things which I think is very good for trends, which as we’ll discuss later on in the episode, are more from patterns to structure. It’s more structural features of the fractal nature of these markets. It’s not about prediction.

Prediction is something now that I think is even going to compress further, the window of prediction. And that’s because of this influx of AI. I think that’s where it’s going. So, I think it’s going to defeat its own purpose in these financial markets, if you know what I mean.

[00:10:35] Niels

Yes, absolutely. And as people can already hear, it’s going to be a super educational conversation once again with you, Rich, and can’t wait to dig into it.

Before we do, so, on my radar, I mean I couldn’t help noticing the sudden explosion of volatility in the precious metals sector that we’ve seen this week. Gold, if people don’t follow this, has had its biggest price drop on Tuesday, I think it was, in over a decade. It lost more than 6% in a day. Silver and platinum losing even more on the day and palladium has given up 16% of its price in just two days.

So, lots of volatility showing up in this sector which will lead into a conversation we’re going to also have later on about position sizing, which will be fun, no doubt. So, that’s interesting.

The other thing I just noticed was a note that I think the former chief strategist at Saxo Bank, Steen Jakobsen, sent out. And you know, it was kind of interesting when you think about the role of oil and how a lot of people have sort of been focusing on oil in terms of its role, its importance.

And, of course, we’re recording on Thursday, and we saw yesterday that the US introduced further sanctions against a couple of oil companies in Russia, and the oil price is up quite sharply today, 5% right now – crude oil up as we speak. But he writes the following. He says, “The world is short of electricity. Data centers need juice, cooling systems need water. Grids are maxed out. Baseload is insufficient. Where economists once watched oil prices to gauge cyclical swings, electricity is now the core input to growth. How to get it, at what price, and with what utility to society? These are new macro questions. Oil was the lifeblood of the post II World War economy. Electricity is the successor. Let that sink in. A society short of infrastructure, energy and delivery mechanisms cannot grow. Inflation will rise. Productivity will stall.”

I mean, kind of interesting observations, and I’m not so sure that the world is really looking at electricity the way Steen Jakobsen is describing it, in relation to oil. I think, still, people are more biased to looking at the oil price. But it hasn’t been doing anything for a long time despite all the challenges we’ve had in the Middle East. And I think he also describes the current, or at least this is my interpretation of it, he also describes the current debacle in private credit as something that maybe it’s a new acronym that we have to get used to. D A D T for markets – Don’t Ask, Don’t Think. So, we’ll see, we’ll see.

Anyways, in terms of trend following update, the environment, I would say, continues to be pretty constructive, albeit there is more volatility in the returns from managers, at least the ones I can see on a daily basis, including our own, for that matter. Precious metals, although they’ve come a little bit under pressure, it’s still pretty constructive this month. But you know, big swings which may illustrate some short-term top correction, who knows?

Equities continue to be fairly well behaved from a trend following perspective. But the new kid on the block, not really new, but certainly this month seems to be also helping out, is the soft sectors.

We have markets like sugar supporting coffee, cotton, in terms of being a source of returns for trend followers, as far as I can tell. And then we have, as I mentioned, energies and currencies pretty much stuck in a range, not doing much for the moment.

What is your take on sort of the state of the trend following environment at the moment, Rich? What are you seeing?

[00:14:51] Rich

I’m pretty bullish about it at the moment, Niels. So, of course we had the metals retracement, to be expected considering their rises they’ve been having, all of them: gold, silver, palladium, platinum, you name it. But I think it’s a very robust environment for trend following and I’m keeping my fingers crossed.

I think we’re going to probably come out of this year (I’m keeping my fingers crossed) strong, which is a welcome relief from the first six months of this. That was when, you know, of course we never know what’s going to happen in the future, and I had all of these bad thoughts in the first six months. I was thinking, Christ, our models aren’t working anymore.

No, of course they do, but you have just got to stick to the process. But it’s a robust environment now. I think we’re in for some good things. In my sort of explanation of the fundamental reasons why these things happen, the more I’m getting into it, the more I’m very comfortable with our methodology and process to the extent that I think that the events we saw, post GFC up to 2018, I think were the anomaly. I think now we’re in an environment where it’s trend heaven, basically, for a period of time.

Of course, what stops that is these, you know, every day a change in policy sort of that creates this whipsaw environment. And that’s what we experienced in those first six months. Which made me think that, if that continues and we continue to get those whipsaws, it’ll be death by a thousand cuts.

But I think we’re out of that now and I think that the markets are reasserting themselves. We’re getting some solid trends, and it’s great to see the soft stuff picking up. They were quiet for a bit, but you know, the energies, they’re turning, they’re going bullish. The metals, I think it might be a bit of a breather for them, but I don’t think they’re necessarily over yet according to our models. There’s certainly, we’re still sort of active in those things.

So, it’s a pretty good environment to be a trend follower, Niels, at the moment.

[00:17:05] Niels

Yeah, you mentioned briefly, and it wasn’t really part of our plan, but I’m always curious, you know, as time passes and we get wiser, hopefully, or at least we see things differently. You mentioned this thing about post GFC, that period, that difficult period, although I will say it was only a few years that really were difficult. There were also plenty of good years as well, but there were a couple of years that were difficult. Do you see those years now any different or do you have a better understanding as to why they were more challenging?

I have my own views, but I don’t necessarily need to share those. But, have you become any more certain about why, specifically, a couple of those years, and I think mostly of ‘20, frankly, 2018 through 2020 or thereabouts, I think both early in that decade there were some good years, like 2014 was a pretty strong year, but how do you see…?

[00:18:12] Rich

I see that it was a period of what I call compression. In other words, it wasn’t a period of equilibrium at all. And this is what I’ll get into a bit later in my topics where I talk about these markets are way away from equilibrium. So, what was perceived as calm was actually compression. And you know that old saying, you know, the conservation of energy. It might not have a visual appearance that it’s there in a compressed state, but it is there and it’s how these markets compress and expand. And I think through quantitative easing and central bank coordination, we had this period of trend suppression and compression.

But I think the surplus of that is being felt from 2020 onward where the markets had exploded out of that compressionary state. The risk never went away, even though it appeared calm over that period, but it was like an avalanche. It was slowly building slow, slow, slow, slow. And then in 2020, it flipped out of this compression and BOOM! We had this amazing explosion into trend.

And now I see this environment, with the decoupling that I’m seeing now and the lack of central coordinated actions, I see this decoupling as an environment where trends are just very favorable. Things are less correlated than they were. When things are in compression, in convergence, we get buying the dip syndrome, which I think, was something of the past. I don’t think buying the dip is going to continue going forward.

But that was this sort of, this phenomenon, this behavioral phenomenon associated with this compression.

[00:20:01] Niels

Yeah, I agree with that. I think I completely agree with that. I think it had lots to do with the success of the central banks, actually. And I think now that they are doing their own thing and they’re not really coordinating because they have different challenges, different problems…

[00:20:18] Rich

I can’t see the success. I think what they’ve done is they’ve kicked the can down the road with compression.

[00:20:23] Niels

No, no, what I meant by it was they succeeded in keeping inflation low and stable.

[00:20:29] Rich

Yeah.

[00:20:30] Niels

And I think that whether they succeeded, whether they kind of completely controlled it or not. But that was the result. And I think people, and even myself included, I think we’ve underestimated, historically, the importance of not necessarily just the level of inflation (because that dictates the level of interest rates, etc. etc.), but it’s actually the stability of inflation, how much that really impacts the markets we trade and therefore the opportunities we see. So, that’s what I meant. But anyways, we’ll get into all of that good stuff.

Let me just mention that the trend barometer yesterday finished at 52. So, that is a strong reading, and it’s coming up, and supports what’s going on. Because the data as of Tuesday evening, and I think, by the way, yesterday was a slight negative, maybe a mixed day for the space. But anyways, as of Tuesday evening, the BTOP 50 index is up 2.12% for the month, up now 2.58% for the year. SocGen CTA index up 2.35% for the month, down still 40 basis points for the year. Trend index up 2.85% in October, up now 53%. Sorry, not 53%. Up 0.53% for the year. And the SocGen Short-Term Traders index up about 1% but still down 4% so far this year.

MSCI World up 40 basis points in October, as of last night, up 18.3% for the year, very strong. S&P US Aggregate Bond index down 44 basis points in October, but still up very strong, 9.8% for the year. And the S&P 500 Total Return up 22 basis points as of last night, of 15.08% so far this year.

All right, enough said about all of these things. We definitely need to get into your agenda today. Rich, you put it together as a true professor and I’m going to basically pass it over to you. I’m going to try, as a good student, to keep up and maybe have a question or two along the way.

[00:22:43] Rich

All right, Niels, we’ve got four topics today, but I thought I’d take today’s conversation in a slightly different direction where, rather than focusing about a determined universe and things like that that we got into in past conversations, we’ll stick to the markets this time. But I’d like to dig into the foundations of how markets actually build themselves.

So, we’ll start at the smallest possible scale, with a single trade, and trace how structure then grows upward, through feedback and interaction, from impact to fractal geometry, which I’d like to get into. So, we’ll then move into how that scaling creates trends and how patterns differ from structure, and finally what all of this means for risk survival and how we position size using closed equity. And then that’ll give you the opportunity to jump on board. I know you want to say something about that.

So, we’ll begin at the beginning with this concept called impact. So, every price move starts with a decision. So, a trader can buy, they can sell, or they can do nothing. That single action, no matter how small, exerts a force on the market. And that force is impact. It’s the fundamental impulse of price movement.

So, most people think price moves because information is revealed, but markets move because impact is applied. So that’s the difference in this interpretation. So, each trade order changes the balance of supply and demand and therefore reshapes the conditions for every trade that follows. Can you see that slight reflexivity there? So, at the micro level, individual impacts interact through the order book and liquidity network. Some cancel, some impacts cancel, and some impacts reinforce. When reinforcement occurs, feedback begins. Now, prices, therefore, no longer just reflect information, they, therefore, create it through this feedback.

So, traders, they observe price, and once they observe the price, they then update their models and then act again. So, they’ll make an initial trade, they’ll see what the impact on the market is, then they’ll update their models, then they’ll act again, and this loop continues.

And that’s reflexivity in action. It’s where a self-modifying system learns from its own output. So, here we’ve got an input – a trader making a decision. We’ve then got an output response – what the market responds to. And then an adjustment with the next input that comes in from that trader. There’s this reflexivity involved.

So, that’s reflexivity in action. Now markets don’t require rational or informed participants, they simply require actors whose behavior feeds back. Each irrational trade changes the landscape and becomes part of the evolving structure. And that’s why markets are seen to be self-organizing. They evolve through interaction, not through equilibrium.

But here’s where it gets really interesting. Market impact is not linear. A trade 10 times larger doesn’t move the market 10 times as much. It’s not a linear relationship. So, Jean-Philippe Richard’s research shows impact grows with a square root of trade size. And what that means is that if you double your order, you only increase expected impact by around 40%, not the same amount, it’s a reduced amount.

And that’s because liquidity is adaptive. It’s not a static pool. It’s seen to be like an elastic surface that moves and reforms with order flow.

So, every trade that we do consumes liquidity and signals potential direction. And other traders react to that, quotes adjust, and the whole system bends. This is where we turn away from linear to nonlinear.

This is what gives markets their nonlinear fractal character. Small trades disappear into the noise. That’s because it’s a sublinear relationship, not a superlinear relationship, a sublinear relationship. Small trades disappear into the noise. But when many small trades align, feedback compounds, volatility clusters, and directional bias emerges.

And that’s how trends, crashes, and fat tails are born. Not from randomness, which the efficient market hypothesis will tell you, but from interaction.

So, if these events were independent, like a Gaussian model would say they are independent random events, returns would form a bell curve. But in markets, events are all conditional. Each one changes the probability of the next because of this reflexive nature. And this is what destroys ergodicity in these markets and replaces it with path dependence.

So, I know you don’t like that term ergodicity…

[00:28:20] Niels

Well, I want you to just remind people what it means.

[00:28:23] Rich

So, an ergodic system is one where an ensemble of results are the same as an individual across time. In other words, it’s saying the ensemble of statistical results at a particular point in time is equivalent to a single outcome, statistically, over the course of time. The both are equivalent. But in a non-ergodic system there’s an asymmetry there.

And we find that wealth paths in financial markets, because of compounding this geometry, you can’t apply ergodic statistics. In other words, the statistics break down. A good example; the expectancy equation is what we call an ergodic statistic. If we remember the expectancy equation, it says your percentage win multiplied by the dollar win, less the percentage loss, multiplied by the dollar loss, gives you an expectancy equation.

Now this therefore says to the trader, ah, if I have positive expectancy, I will be profitable. However, when you look at that equation, what you don’t see is the sequence of events. And this is critical. And this is what Olay Peters found when he found that markets are non-ergodic. And this is because there’s a conditional reaction that occurs with a finite sum of money which has a lower bound of zero.

So it’s an asymmetric system, it’s not an open-ended system like ergotic systems are. The reason why we get the equivalence of ensemble averages and time averages in an ergodic system is because they are open ended on either side. No lower bound, no upper bound, and there’s basically stochastic movement available across all dimensions.

But in a non-ergodic wealth path, there’s an asymmetry in the system, there’s a lower finite bound and there is an open-ended boundary. And this is what we find in fractal systems. And we also find that is why they are non-ergodic.

Which means that in those systems no longer is the statistical account of an individual, over the course of time, the same as the statistical ensemble at a point in time. Which means that the path of compounding is different to what the statistics are going to represent.

Now this is the problem with the Gaussian theory. The Gaussian theory, these independent random events produced a bell curve, and it assumed that there was no asymmetry in that. It was open ended, upper bound, lower bound. And we got all of the associated toolkit with that, which were the statistics that made that model work, which was things you see in modern portfolio theory. Markowitz’s efficient frontier, Sharpe ratio, standard deviation, expectancy equation, risk of ruin equation.

All of these assume an ergodic system. But as reality tells us, it’s actually a path dependent system and all of those toolkits break down.

And that’s because we get in these asymmetric systems that are fractal in nature, we get power laws, we get nonstationary environments, we get a decrease in the predictive power as we extend into the near future, we get no equilibrium away from equilibrium. All of these things are saying that Gaussian model is incorrect.

And then we got this instance where I think in the 1960s Benoit Mandelbrot came out and said, look, we can demonstrate that these financial markets are non-Gaussian because all of these rules are not being obeyed by the financial markets. And he came to the conclusion that it was a fractal system. That’s the conclusion also, that I’ve sort of come to realize, over the course of time. If these events were independent, returns would form a bell curve.

But markets’ events are conditional. Each one changes the probability of the next with this reflexivity. This is what destroys ergodicity and replaces it with path dependence. Because the path matters with what happens previously – there’s memory in the markets. Each of these events, because of this reflexive nature of the market, are not independent. What happens before shapes what happens next through this reflexivity – trader’s impact.

This, therefore, means a sequence that over time there’s a memory in the market and it’s captured by things like the Hurst exponent.

We’ve got these additional new tools we can use that says if there is market memory, using Bouchaud’s analysis, we’ve got a different set of tools in our toolkit which aren’t Gaussian, but Hurst exponent, tail properties, tail decay, all of these things in a power, law-driven system. These are better tools to use that are more representative of our market.

It says that the old tools could never account for all of the calamities that occurred over the course of time by relying on a model that had these assumptions that did not reflect reality. And hence, anyone who relied on those tools (portfolio managers, industry, all of those things) would always get blindsided, regularly, much more than what a Gaussian distribution would imply. And hence a Gaussian distribution says that a five standard sigma event should occur every 170,000 years.

But when I looked at the S&P 500, or the ES futures market, for instance, and in the last 30 years there’s been 32 five sigma events. And this isn’t just associated with a single market. Every single liquid market I examine has these tail properties – fat tails.

This is saying they’re non-Gaussian, they’re fractal. Look, the verdict’s out whether they are exactly fractal. But certainly, a fractal model has a better account and can understand why these situations occur. So, I’m not saying it’s a definitive answer, I’m just saying, at the moment, fractal models and the fractal market hypothesis probably is the closest we get to reality because it explains so much more.

[00:34:57] Niels

So let me ask you one thing while you get a sip of water for your throat, and I don’t know if this is correctly understood, but maybe there’s a question in there as well. And by the way, all this fractal stuff, I think you and I touched up on this before we pressed record, it does remind me of my conversation with Bill Deiss. I think he actually, back in the ‘70s when he started, built his pattern recognition model based on fractal. So anyways.

But if volatility is an output of reflexivity, not just exogenous news, so to speak, are we underestimating the role of our own participation in creating the risk we seek to avoid?

[00:35:48] Rich

The risks we seek to avoid, okay, the bottom line is we can’t predict these things. So, we might have tools that say that volatility equals risk. And this is exactly the problem. The dilemma, I find, is that I don’t associate volatility with risk. I view markets as always having this potential risk event around the corner. What I view is low volatility regimes I actually view as high latent risk events – warehouse risk.

In other words, the risk hasn’t gone away, it’s there, but it’s now compressed. It’s like a sponge that you compress and it’s going to explode at some point in time, waiting for a tipping point when we see this massive expansion, this transition. So, I’m seeing the market breathe, contract and expand, contract and expand. I don’t think we can measure volatility using the standard statistical tools we’ve done. I can understand what volatility is.

[00:36:50] Niels

You mean we can’t measure risk or volatility? Because we can measure volatility, right?

[00:36:56] Rich

We can measure volatility, but then there’s directionless volatility, there’s directional volatility. One might come with correlation, one might come without correlation. It’s a signature of a fractal system that’s alive, compressing and expanding. But because of the undefined nature of predictability in fractal systems, in other words, what I’m saying is that whilst there, you know, when you look at the weather, which is…

When you look at the weather, which is a fractal system, there is a limited prediction horizon within weather. You might get a fairly accurate assessment within 10 days or 14 days. That doesn’t preclude the ability for these large events to interrupt that prediction. But it does, in certain regimes, give you this prediction horizon. But over the long term they are unpredictable. They are deterministic systems that are undefined. And this is where they follow these strange attractors.

And in the financial markets, which I view very much similar to weather systems, where instead of molecules of water in the clouds, etc., I’m viewing agents, traders, all as collectives. There’s no central governor there. They’re all working according to their own mandate, but they are interacting with each other. And these impacts and feedback loops, that occur between each other, make it impossible to calculate or impossible to predict. So, I refer to them as deterministic unpredictable systems, which a lot of people say, well, that’s chaos.

And I say, well, no, we get periods of very rational order. It’s this reflexive nature of the market. They adapt, they respond to what the participants are doing in it.

So, I prefer what I call engineered outcomes, rather than statistical tools, to define how to protect myself in this market. So, when we talk about volatility, I’m not using volatility measures to protect me, or statistical measures, because I think they’re unsound. The assumptions of these statistical measures and the toolkits of statistics have come from a Gaussian world.

I’m saying you’ve got to think more like an engineer. So, in fractals, unfortunately, the thing with fractals is these Gaussian models are just too simple to describe this very complex thing we called the markets. You’ve got to be an engineer, a bit like an engineer designing a bridge.

In a weather system, you don’t blame the weather for blowing down the bridge, you blame the bridge being too brittle. So, it’s the engineer’s fault if they break down. Because the weather, as we know, can be very unpredictable. You can get typhoons.

If the bridge falls down, that’s because it was over-optimised, brittle, could not handle the environment. That’s the same way as I think our systems need to be engineered to not be brittle, not be over-optimised, not be over fit. They’ve got to be robust, resilient, able to stand up against anything that thrown against them. That’s how I view robustness.

[00:40:08] Niels

Yeah, that’s a great way of looking at it.

All right, was that topic one?

[00:40:14] Rich

So, let’s go on to topic two. So, we’ve talked about impact, and now what I want to talk about is how fractals create structure.

So, let’s move up the scale and see how these micro impulses of impact assemble into the structures we call trends. So, when you zoom right in on a market chart and you zoom right down into the detail, it does look chaotic, jagged, noisy, directionless. But as you zoom out, hourly, daily, weekly, the chaos starts to organize visually. What looks like randomness when you zoomed in actually becomes rhythm as you zoom out. And that transformation is a signature of fractality.

This is different to a Gaussian world where, as you drill in, the structure dissipates, it disappears until you’re left with linear results. That is this linear, independent, Gaussian world. But in fractal systems, you can never get rid of the structure. It’s always that there’s structure in there, but it looks chaotic. You’re going down into zooms. The structure never disappears, it’s always there.

But see, at this scale, the different views of scale, perceptions of this at different levels of perception, what looks random at one might have structure in the other, patterns in the other. It’s a bit like looking at a TV screen, when you’re looking at the pixels, and you get it moved away and you’re looking at a different resolution, you start seeing the images that are linked together from the pixels. This is how we’ve got to understand fractality.

So, fractality isn’t about prediction, it’s about relationships across scales. Each timeframe represents a layer of feedback. And we’ll find that people are responding depending on the timeframe that they’re interacting on the market with the patterns that are observable at their scale.

And so, you get this scale difference occurring. High frequency traders, at the lowest end of the scale, see markets in a different way. They’re seeing this sort of almost chaotic frenzy of movement.

As you step out, you start seeing more and more trend followers start participating in the market. Why is that? And it’s just because as you scale out, you start seeing these impacts we’re talking about, with the fractal nature of markets. Some are canceling, some are reinforcing. The reinforcing elements of these structures is what’s creating the trend as we’re zooming out, rather than a cancellation.

So mean reversion is a cancellation environment. It’s where we get opposing forces. So, we get a force of reverting back to an equilibrium, and then we get a force of going away from an equilibrium, this alternating zigzag, up, down, up, down, up, down. They’re almost linear in nature. They cancel each other out as we go out in resolution. But directional impulses, that aren’t canceled out, start aggregating together and they’re compounding structure into the market.

So, this is why convergence systems are linear results. And this is why, when you look at the P&L of a convergence system, you get a linear profit, a linear profit, a linear profit, a linear profit, a linear profit, until they come head on with a negative skew event where you get a at nonlinear loss.

But when we look at trend following, it’s the reverse. We get a small linear loss, a small linear loss, a small linear loss, a small linear loss. When we come up against an outlier, a structured directional trend, we get a nonlinear gain. This is the fundamental difference between them.

And this is because of this cancellation and reinforcement that occurs in the market at the fractal level. So, when short term reinforcement persists and aligns through time, through scale, you get coherence. And the coherence is what we call trend. It’s also called serial correlational bias.

So, positive feedback drives reinforcement. Buying therefore attracts more buying. That’s how it works. It’s reflexivity. When people see a trend, people start jumping onto the ride, jumping onto the ride. Buying begets more buying. Negative feedback, however, drives regulation selling that restores balance. One is moving back to equilibrium. One is moving away from equilibrium. Trend, directional positive reinforcement buying begets more buying or selling begets more selling, moves away from this equilibrium zone. And negative feedback is the reverse, a restoration. Trying to get back to, this is what we call, regulation selling that restores balance.

Markets oscillate between these two drivers. And when positive feedback dominates, energy compounds, and structure emerges fractally. This dance between expansion and contraction creates a fractal rhythm of markets. You know, you get quiet periods punctuated by bursts of volatility, compression followed by release. And feedback is the pulse of adaption.

So, what do I mean? People are reacting, they are impacting. As they see a trend and they’re buying, they are accelerating that trend. When people see a trend, and they want to revert against that trend, they’re applying negative feedback and trying to restore balance. But you see how this works.

So, when feedback cascades through scales, small interactions become large outcomes. So, cancellation gets rid of structure. Positive reinforcement creates structure. And when it cascades across the scales, small interactions create the large outcomes. A local burst of buying becomes a cluster. Clusters form rhythms. Rhythm becomes flow. That’s how feedback builds structure through time.

It also explains, Niels, why you and I are medium to long-term trend followers. Because the frenetic activity that occurs in the shorter-time scales are more mean reverting in nature. There’s more cancellation going on relative.

But as we get out to the higher views, we’ll find that the feedback tends to operate on the structure, not on the mean reversion. When I say feedback, most participant interaction, institutions, etc., out to the medium, to the long end, are positively reinforcing trends. They’re not going against the trends. Mean reversion is something that occurs in the finer timeframes.

So, it’s the same in nature. Ripples becoming currents, currents forming rivers. This is positive feedback in natural systems. In markets, local feedback becomes directional flow. So, take crude oil, for example. At the micro level, traders buy ahead of a report, for instance. Algorithms detect it, and they join. But on the hourly chart, those bursts at the micro level appear as clusters. Zoom out to the daily, and the narrative forms. Oil is recovering, come the headlines, oil is recovering. It starts turning into narrative.

At the monthly level, that narrative becomes systemic. Producers, investors, and policymakers all reinforce that movement. Feedback is scaled from the tick to the macro. The market has literally organized itself around its own success. And that’s what it means from frames to becoming trends.

So, eventually though, every feedback loop reaches its limit. The same alignment that built the trend becomes its constraint. Positive feedback will flip to negative feedback at some point in time. Rising prices ultimately exhaust buyers. Valuations stretch, risk becomes concentrated. One event, or simply fatigue breaks, that symmetry. Then selling accelerates, stops trigger, the system unwinds, maybe even margin calls come into play. The system unwinds.

Reversals aren’t random. They are feedback inverting. And because energy is stored through the long positive reinforcement, you’ll find that during these reversals, it’s released quickly. Reversals are fast and violent. They’re not like the typical buildup that occurs on the long side.

These reversals are fast and violent, exactly what we saw with gold and the metals earlier this week. So, this feedback flip isn’t failure, it’s renewal. System renewal. It resets the system so the process can begin again.

And it’s this continuous process, this continual cycle; reinforcement, alignment reversal. It’s the living rhythm of a fractal market. So that’s topic two.

[00:49:29] Niels

Yes, I’m going to save any questions because, again, we have quite a bit to get through. And so, I’m going to give you as much time as possible to move on in your own narrative.

[00:49:41] Rich

Yes. So, now I want to talk about the difference between what I’m referring to as patterns, structure, and the fractal nature of outliers. So, now that we’re seeing how structure builds through feedback, we’ll explore the difference between patterns and structure, and why the outliers matter most. So, of course, I say that because I’m an outlier hunter, as you know.

[00:50:05] Niels

You are indeed.

[00:50:06] Rich

Okay, markets are full of patterns. We all know that. We see flags, we see triangles, we see breakouts, we see moving average crosses. These are all surface forms; transient, visible, easy to name. When I’m referring to structure, I’m referring to what lies beneath. It’s the geometry that governs how markets behave, not how they look.

Now, I remember, in a previous podcast with you, I’ve talked about that an outlier hunter doesn’t have a prescriptive definition of what it refers to as trend. It’s looking for the structure, the things that create the bias. And that can come in many different visual forms. But those people that are treating trends as patterns are probably too prescriptive. Because with these trends, we’re looking for the structure, what drives these trends. Because as you know, Niels, trends can actually be a random result, from no bias in the price series. We can get a random trend very easily.

We can get a trend that actually is a segment of a mean reverting cycle. Or we can get these structural trends with serial correlation in them that gives persistence into the future. That’s what I’m calling outliers. These are structural things. They’re not patterns.

It’s created by causative drivers that actually create this vast array of different directional patterns which I’m calling outliers. So, it’s the geometry that governs how markets behave, not how they look.

It’s built from the relationships, the incentives, the feedback loops that determine how energy flows through that system. So, patterns describe what price does. Structure explains why it does it. So, patterns are what we observe. Structure is what connects those observations through time.

Notice that structure is what connects those observations through time. It’s talking about a memory. What happens before happens later.

This is a serial correlation. It’s not independent that this feedback occurs over time in these fractal systems. So, trends of patterns. Outliers I regarded as structure revealed.

An outlier is not an accident. It’s a phase transition. It’s a moment when feedback alignment pushes a system far from equilibrium and forces it to reorganize. It’s not just a mere pattern. There’s something structural that’s really changing the system.

Compression, now, compression, you know, I talk about compression and expansion. Compression hides structure. Expansion reveals structure. Outliers are those expansions – the moment when the market changes its own geometry.

So, in fractal systems, small fluctuations typically cancel, but large ones dominate. The tails contain the power. That’s where this adaption happens and where returns are made.

That’s why I call myself an outlier hunter. I’m not chasing a price pattern. I’m aligning with structural change. That’s how I view it.

So, I design system ensembles, which are families of reactive systems that listen for different ways structure might express itself. I don’t trade single trend following systems. I put in these ensembles. They are what I call… I’m not looking for correlations here. I’m looking for behavioral orthogonality (that’s a big word).

[00:53:46] Niels

So, in other words… Yes, I’m going to let you pronounce that word for sure. So, you’re essentially saying diversifying by behavior.

[00:53:56] Rich

Yes, a system’s behavior. That’s why I will consider a breakout. I will consider a mean reverting into a trend. I’ll consider these. These are what I call behaviorally orthogonal. They will never all act in concert together. They are structurally looking at the different manifestations of how trends can form. And it’s not saying there is one prescriptive form. It’s saying I’ve got to diversify across as many because this structural outlier can come in an array of different varieties.

So, I’m not looking for correlations here because these are very fickle things. I’m looking for structural behavioral differences between things. That’s my choice of ensemble.

[00:54:40] Niels

You know, the risk of this, Rich, is that you are maybe less classic than what other trend followers might perceive as being classic. But I like the idea.

[00:54:52] Rich

Yeah. Okay, look, I prefer to call myself an outlier hunter.

[00:54:57] Niels

Yes, yes, I know, I know.

[00:55:00] Rich

So, these different systems, okay, one for instance, might detect volatility expansion. Another might detect smooth persistence. That’s looking for a breakout from a congestion Darvas box breakout, that’s the thing. Another might focus on breakouts. Each here’s a different voice of the same feedback process, but it’s relating to structure.

Together they form a coherent adaptive framework. Not predictive, just ready to strike and activate when the signals erupt. That readiness is everything to me.

And because we can’t know which move will reveal structure, but we can ensure, through this system ensemble, we’re alive to capture it when it does. So, what I’m now getting to (there’s a shift in the topic here), what I’m getting to is the need to survive until the outliers arrive.

So, to summarize, topic three, I’ll say patterns describe form, structure defines cause. Outliers are structure made visible through feedback. That’s why, in a fractal world, we don’t diversify by correlation. We diversify by behavior, by how our systems respond to changing structure.

Okay, so now I’ll get into topic four, the last topic. And this is where we come to our possible debate coming up, Niels. So, this I call the fractal reckoning, path dependence, survival and closed equity.

Okay, so I’m going to connect all of this to the most practical question of all. How do we size positions, manage risk, and stay alive in a world that’s path dependent and fractal; nonstationary, unpredictable, uncertain? How do we survive in that world?

In an ergodic world, a Gaussian based world, like a casino… A casino is a good example of an ergodic system. The average outcome, across many plays, equals the average outcome through time.

You know, I talked about the average outcome across an ensemble is the same as the average outcome over time. In a casino, we’ve got that situation. However, markets are non-ergodic. We live only one path. We can’t live in these parallel universes that statistics say we can. We have one path. And once ruin occurs, the game ends for us. Once ruin occurs, game over.

That’s why survival, not expectancy, is the true measure of success. This is why I harp on what’s the best method of determining the best managers track record? Survival. Survival. This comes down to this conclusion.

You can’t measure this statistically because statistics is the wrong toolkit, because the statistics come from the Gaussian model. So, Kelly criterion, bad statistic; expectancy, bad statistic.

I don’t want to say this Niels, but I’ll say variance at risk is a bad statistic, however, I know your opinion on that. But all of these are coming from this Gaussian model, which isn’t the reality as we have observed. So, in a non-ergodic world, the path is everything.

And the order of wins and losses determines whether you compound or whether you collapse. So, think of two traders with identical expectancy. One experiences losses early and they run out of capital, and they never reach the recovery phase. The other survives long enough for the outlier to arrive. Same expectancy, but completely different destinies. They’re path dependent. Wealth doesn’t grow additively, it grows multiplicatively.

Every trade we make changes the base from which the next trade grows. That’s this reflexivity in action. Okay, it’s not independent reflexivity. Every trade we make changes how the next trade grows. Losses shrink the base and that drag compounds. That’s why minus 50% followed by plus 50% doesn’t equal zero for your wealth. It equals a permanent hole in your wealth.

So, if we have $100 and we lose 50%, we’re down to $50. But if we gain 50%, we’re only up to $75, or whatever. We’ve got a permanent hole in place. This is this sequence risk, which isn’t addressed by the Gaussian world in ergotic systems. Path dependence is everything.

So, this is where closed balance equity, I believe, becomes crucial. We use closed balance, not floating balance or equity to size new positions because to us it represents realized capital, not illusory equity. What I mean there is. Closed balance is the only equity you actually possess after the path of returns has spoken.

So, what I mean is it is already incorporated a non-ergodic journey is every drawdown, every recovery, that is what it represents. By sizing from closed balance, we ensure that our risk-per-trade remains constant relative to survivable capital. It automatically adjusts exposure downward during drawdowns when the system is under stress and scales up only when the process has rebuilt through trades that have been confirmed. This is a structural response, as far as I’m concerned, to a non ergotic environment.

Now, I can, for instance, demonstrate to you how a system with 5% positive expectancy, but have large size positions, and even under that arrangement we get this massive compounding drag and we never get a wealth return out of it, it goes to zero. Reduce the size of those bets – significantly reduce the size of the bets.

I can show you a system where we’ve got a 30% win rate, a 70% loss rate, a three times win amount to a one times loss amount, but with very small positions. The expectancy is only 0.2%, but compounded geometrically, you get a very, very good return.

Now this is this path dependence sequence. So, this is a structural response, I believe, to a non-ergodic environment. It keeps us in the game long enough for the next outlier to appear.

The closed balance principle is a way, I believe, of embedding sort of humility into the system. It acknowledges that we don’t know what the next path will bring. So, we size from what is real; what has survived. We’re not trying to forecast variance or optimize volatility. We’re simply aligning our capital to the geometry of survival in a world that compounds through feedback.

The key is not maximizing returns, it’s avoiding ruin. And that comes from this fractal mindset that I have. Prediction fails because it assumes a world stands still. Process succeeds because it accepts that it will never stand still. So, that’s how I view this issue with closed balance.

So, if we can imagine, because I’ve got positive skew in my trend following models, my open equity or my realized equity always sits above, except at the beginning when I start, it always sits above my closed balance equity, which means I am much more conservative in applying position sizing because I’m using a lower limit to compound. I’m not using the higher limit that equity provides, which is a large component of that is unrealized at this point in time. And I don’t know the way that’s going to go.

If I, for instance, assume that it can support this higher position sizing, I’m starting to leverage up and it’s starting to get towards that model I was talking about; 5% expectancy but very large position sizes. It’s starting to get me an unfavorable compounded path. That’s how I see it.

But over to you.

[01:03:05] Niels

Over to me. Okay, so a lot of the stuff that you said I completely agree with. I think my comments are maybe more in terms of how the difference between how most managers size positions and what you and a handful of others refer to as the classic trend followers. I think it’s more than the way it’s being described that concerns me a little bit.

The way I hear it, when you and some of our friends, and maybe not so much you, talk about this difference is that one is right and one is wrong, and that using closed-end equity is right. And I’m thinking, well, there must be a reason why the majority, at least, of managers are not using that methodology. So, maybe you can’t really say one is right and one is wrong. You can say they’re different.

Now I hear the criticism of the camp that I find myself in that dynamic position sizing is only being done to reduce volatility. It’s also sometimes referred to as managing volatility or volatility targeting, which it’s not necessarily the same, that you’re trying to manage volatility and you’re trying to make the ride a bit smoother, as if there’s something wrong with that.

But then when I hear this closed-end equity position sizing being described, I’m thinking, well, that is also reducing volatility because you’re simply sizing your trades on a smaller equity. So that’s reducing the volatility as well, of that strategy, and thereby creating a smoother ride. So, what is the difference? Why is one wrong and the other one is right? So, that’s kind of the first thing that, where I just think that the way it’s being described is not necessarily how I would describe it.

And the other thing is, you said something very interesting. You said that the total equity always sits the closed-end equity…

Well, that is very important.

[01:05:42] Rich

Except with inception.

[01:05:43] Niels

Yes, that is very important because in my view that kind of means that method works only (and I know I’m going to get some feedback on this), only works if you’re trading your own capital because you were there at the inception. But if you’re trading a fund with external clients that come in every month, then they are not necessarily at inception of that open equity calculation.

And that to me opens up a risk that is kind of undefined because there you’re taking on a risk management methodology that is not specifically geared towards them, but is geared towards “the original investor”. So just putting it out there, just putting it out there.

The other thing that I’ve heard, you describe the open equity as some kind of illusory equity. And I’m thinking, for me at least, because we trade the most liquid markets in the world, we can all (even the biggest managers in the world), we can all liquidate our portfolios in 24 hours. So, it doesn’t seem that illusory to me. It’s pretty real. So again, that takes me towards this idea of actually you should consider your full equity as the basis of your trade sizing.

And the final point, I just want to throw out there, it’s just that having these rules are fine, but I hear from your camp (not you specifically, but from your camp) that, on top of this, you throw in something called the cutback rule, which is a completely discretionary rule about when you want to cut down your position size when you feel the pain more than you are willing to feel the pain, and you keep the positions cut until you hit a new all-time high.

Well, at the same time, I hear that people who do dynamic position sizing, they’re somehow handicapping their own lifting power in coming out of a drawdown. And I’m thinking, well, hang on, if you’re cutting your position size at a certain level of a drawdown and you don’t touch that until you get to a new high, that is cutting your lifting power as well.

And, if you’re doing dynamic position sizing, even if you’re in a drawdown (and I do admit that that is not necessarily something that reduces your risk), if certain things occur, you could actually see dynamic positions being increased whilst you’re in a drawdown. And that could be good, in terms of lifting your recovery. It could also be bad if you’re not doing it in a proper way.

So anyways, my point is that when I look at these track records, the long-term track records, people who’ve been around for 20, 30, 40 years plus, it’s not that I see a massive difference in the returns, I hear a difference in the narrative and one being right, one being wrong. All I just want to open up to… and I don’t want to say one is right, and one is wrong. I just want to open the debate about saying, yeah, there are different ways you can skin a cat. And at the end of the day, we need to do what we believe in and what we think is right.

So, there’s nothing wrong in what you describe. I don’t think there’s anything wrong in what other people do. It’s just a matter of preference. But I am a little bit, “allergic” to this, oh, you’re doing this wrong and I’m the only one doing it right. I’m a little bit allergic to that argument. And I’m not suggesting necessarily…

[01:09:54] Rich

We need a dust up with boxing gloves. And what we need to do is get Jerry on with me, and you get on with your compatriots, and we have a brutal debate about it. It would be fun. It would be fun.

[01:10:09] Niels

I think it would be fun. But more importantly, I think this shows (and let’s spin it positively), I think what it shows is that as long as you follow the golden rules, I think there is a little bit of leeway in terms of how you become a trend follower.

And I will admit, as well, that the way you describe it, where you are focused on a trade by trade basis is of course a lot easier, or at least I think of it (I’m not a quad, so it’s easy for me to say), I think of it as an easier way to build systems, to understand systems maybe, maybe with less moving parts, but also a few discretionary parts, which I don’t personally think helps. But, be that as it may, I think the dynamic approach is more complex. It’s not that I’m a fan of more complexity per se, but I also think that after 25, 30 years, where that’s probably been around by now, it’s proven itself.

[01:11:15] Rich

We’ve all survived. Niels.

[01:11:17] Niels

Exactly. That’s what I mean. We’re all here. So, again, I don’t want this to turn out to be, oh, I don’t like what this guy is saying or what this girl is saying. All I’m just saying is I think we should just think about the narrative we put out there. Of course, some of the narrative will get better headlines for sure, and it’ll be fun and prerogative. So, I understand there’s a little bit of that as well.

[01:11:45] Rich

Well, see, Niels, this is why I’m classifying myself as an outlier. I’m distinguishing myself from a trend follower. So, the way I’m doing that is because I do have a particular set way in my mind of how to deal with these markets. So, therefore, say if I’m an outlier hunter, I’ve got specific objectives to achieve that outcome. I don’t necessarily think we all need the same objectives. There are lots of different forms of trend follower. But I’m trying to set myself apart to say, all right, I’ve got this distinctive philosophy. It is different to a lot of others. These are the reasons I like it. And that’s up to them…

[01:12:20] Niels

Shades of gray, I would call it. Because remember, a few months ago, you did a very good descriptions of a trend follower…

[01:12:28] Rich

Four types.

[01:12:32] Niels

…Four and you said there were five descriptions of a trend follower.

And I actually think that’s a much more important topic than a lot of people realize because it can be difficult to see the difference from the outside. But even more so, investors really need to think about why would you want one over the other?

Because it comes down to what is the objective of making an investment with a trend follower in the first place. I think that’s…

[01:12:57] Rich

They view us as a nebulous lot, but we’re experts in the field and we’ve obviously got our idiosyncrasies.

[01:13:04] Niels

Sure. And I think, actually, that conversation is winning a little bit of traction among investors that we’re not just one and the same. And one should think about the type of trend follower you really want and need in your portfolio.

Now, the funny thing is, of course, when you say I consider myself as an outlier hunter, in a sense, even though we use dynamic position sizing, I do the same because we both want the longer trend than anyone could even imagine. And we’re all long gold or we’re all on stocks. And so, this is the fun part. It’s just the path and the journey. And do we keep the same, you know, exposure at all times during this?

And of course, we often think about, well, isn’t it better just to have the same position size if you’re into a big gold trade or into the cocoa trade a few years ago? Of course, sure, but we do forget that there’s all the other positions that can be troublesome.

So, if you have the same position in those, they can detract from the benefits you get from being in the same position in cocoa, and so on, and so forth. So, there’s all these pros and cons and I just want to invite a little bit of not so much black and white, if you know what I mean.

But it doesn’t take anything away from all your observations, of course, and the fact that it has worked very well for people, using this approach, for many years. At the same time, using dynamic position sizing has also worked very well for those people doing that. Which is the beautiful part of this conversation.

Anything you want to push back on, Rich? And feel free to be brutally honest.

[01:14:57] Rich

You get the last word. It’s your podcast. That’s the way it should be. That’s great.

[01:15:01] Niels

It’s a joint effort, as you well know, Rich. Anyways, this was wonderful. I’m sure people have enjoyed it, as they always do when you are on.

And to those listening, I would encourage you to go and leave a raving review and rating of Rich’s conversation on your favorite podcast platform. It really does help more people to discover the show and help us spread the words of the weird and wonderful world of trend following.

Anyways, we’re going to wrap up. So, next week, a few changes. Next week Moritz will be sitting in for me while I visit my daughter in Montreal and he’ll be joined by Nick Baltas. So, that’s going to be a fun conversation.

And the following week Alan will sit in for me and he’ll be joined by Yoav. That’s also going to be a super fun conversation while I scoot over to our headquarters in Florida and visit some of my colleagues, or all of them for that matter.

But if you do have any questions to any of these esteemed trend followers, by all means send them to me. As usual, info@toptradersunplugged.com and I will forward them to Moritz and Alan.

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

[01:16:30] 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.