Why Markets Can’t Stop Trending ft. Richard Brennan

[00:00:01] Intro

Welcome to Top Traders Unplugged. In markets success doesn’t come from predicting what happens next, it comes from being prepared for what you can’t predict.

In each episode we go deep with some of the world’s most thoughtful minds in investing, economics, and beyond to understand how they think, how they prepare, and how they decide, and the experiences that shaped how they see the world. No noise, no short-cuts, just real conversations to help you think better and invest with confidence.

[00:00:35] 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 let me just also 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, let me just say a big thank you for sharing this episode with your friends and colleagues. It really does mean a lot to us.

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

[00:01:08] Rich

Very good, Niels.

Always good to be back on this podcast, but the weather over here’s been a bit inclement lately. It’s trying to work out whether it should still be the tail end of summer or the beginning of winter. It doesn’t know what to do. So, we’re getting these very heavy storms at the moment in a fairly cold climate. So, it’s very unusual, very interesting.

[00:01:32] Niels

Yes, well I see you have dressed up in the winter TTU merch. I had to change to the summer edition for this week because it’s actually getting pretty hot in Europe, at least in parts of Europe. So, there we are. I know this is an audio podcast for the most part, so people have to really catch us on social media for a quick sneak of what I’m referring to here.

Anyways, we have a really important but also a wonderful topic to discuss today, many facets of it, and so I’m excited about that. But as you know, I always would like to start by just catching up on what sort of spin there is on your radar lately before we get into the main topics of today.

[00:02:22] Rich

Okay Neil, so what’s been on my radar? So, I’ve been thinking about something for the past few weeks and it started when I listened to your conversation with Hari Krishnan and Cem Karsan on the Rise of Passive Investing. Great podcast, so I’d recommend listeners go and find that.

So, Hari and Cem, they’re working at a level of macro and quantitative detail that I do not work at, and what they say is really worth listening to in their own words. But it got me thinking about the same territory from a different angle. Not from the macro side and not from the mathematical side, but from the lens I’ve been working on for the past two years – complex adaptive systems. Because once I started thinking about passive through that lens, something became very clear to me that I’d not seen quite so sharply before. And that’s the thing I want us to walk through into the rest of the conversation today.

So, I just want to set up the picture for anyone who’s been tracking the numbers of passive. So, for the past two decades, the share of US equity capital held by passive vehicles has grown from a small minority to a dominant force in the ownership structure. So, the most commonly cited figure puts passive at around about 60% of US equity fund assets.

And when you include institutional capital, that benchmarks to passive indices, you know, pension funds, sovereign wealth funds, endowments that effectively hug their benchmarks, the share of price insensitive ownership in US equities is meaningfully higher than that 60%.

So, I want to flag something important before we go further. The figures I’ve just cited are US equity figures, but the story is much broader. The rise of passive is not simply an equity-only phenomenon. The growth of ETFs has spread passive vehicles into fixed income, commodities, currency exposure, multi-asset products. So, capital that allocates by index weight rather than price judgement is now a structural presence in markets well beyond US equities.

I’ll use the US equity figures throughout this episode because the data is cleanest, but the architectural argument extends across the whole landscape; all of the assets. So, the story is usually told as, you know, a victory of common sense. You know, passive is cheaper. Active managers have a lousy track record of beating their benchmarks. Investors make the rational choice.

And to be clear, there is a degree of truth in that. The case for passive, at the individual investor level, is quite well made and largely correct. So, I’m not going to argue against that today, but what I want to do today is something different. I want to ask, what has passive’s growth done to the agent ecology of markets? Because that’s the question my framework points at.

And the answer changes how I think about almost everything else that’s happening in the markets right now. So, here’s the move that I want to make. Markets on the view I work from, Niels, are complex adaptive systems.

[00:05:58] Niels

All right, okay, so you’ve used that phrase twice now in your intro: complex adaptive systems. And I think, for those who may not… I think we did a deep dive a few times on this that people should also go and listen to. But I think it’s important to actually maybe to spend a little bit of time explaining it in kind of words and languages that the broad audience can follow, if you don’t mind.

And by the way, I like when you use the word ecology because it reminded me that when I left high school, and I had biology as my main major, we wrote a paper on ecology and we have to stand in Copenhagen Zoo the whole winter observing a big black monkey. So, I was reminded of that. But I’m sure that’s not where we’re going today.

[00:06:51] Rich

No, but, you know, biology’s a good start, Niels. In this quant world, we’re about as far from biology as we can get. But I think we need to move closer to biology to see what we’re talking about here.

When I say a market is a complex adaptive system, I mean something simple. It’s a system made up of many participants, each reacting to partial information, each adapting to what others are doing, and each changing the environment that everyone else must respond to. So that’s the picture.

No participant has the full view. Everyone is responding to a situation that everyone else is also changing. And the behavior of the whole system; the prices, the trends, the cascades, they emerge from all of these interactions, not from any one decision or any one participant.

So, the other phrase I’m going to be using today is agent ecology. So that’s just shorthand for the mix of participants in the market: value investors, passive index funds, momentum traders, trend followers, market makers, pension funds, volatility targeting funds (we love that word), options dealers, retail buyers, hedge funds, everyone whose actions in that market help shape overall price.

So, the agent ecology is just the cast of participants who are at the table in the markets. Different ecologies produce different market behavior, and that’s the whole reason it matters about who is at that table.

So let me develop that a little more carefully. A market is not an object that exists independently of its participants. It is not a reflection of an underlying economy. It is not a representation of fundamental value, however that is measured. A market is constituted by the interactions of the population of agents, each holding a partial model of the situation, each acting on that model, and each affecting the conditions the others observe.

So, the behavior of the market; the prices, the trends, the cascades, the periods of calm and the periods of violence in the market, they emerge from those interactions. Nothing about market behavior is given in advance. It’s produced in real time by the agents in the system and their behavior.

So, there is a corollary I want to make explicit, because it cuts against an assumption that almost everybody I speak to carries about this market. They tend to think the market sits downstream of other systems; the firm, the economy, the banking system, and that its job, the market’s job, is to reflect what these other systems are doing. But I believe that assumption is incorrect, or at least incomplete. Each of those is actually a system in their own right.

So, the assumption is that they correlate. So, these systems, they correlate with the market, they constrain it in some ways, but they are not what I call antecedent to it. They are loose autonomous systems that interact with the market. The market is not their direct reflection, it’s its own thing: a loose semi-contained system of participatory agents using partial models.

So, to say the market should reflect fundamental value is to assume a binding correlation between two autonomous systems that, on evidence, don’t always agree. They run on their own dynamics. They influence each other without being directly reducible to each other.

So, if that is right, the question you can therefore ask about what a market is, is not what its fair value is, it’s not what its macroeconomic backdrop is, it is not what the central bank is going to do. The most important question you can ask is what kind of agents populate that system and what are the properties of the interactions between them? Because that’s what actually determines what a market actually does.

And the property of an agent that matters most, in a complex adaptive market, is whether the agent conditions its behavior on price.

So, some agents do. For instance, a value investor sees a stretched valuation and they push back, a counter trend trader fades the move, a market maker fades the extreme orders, a pension fund rebalances out of an asset that has run too far. These are what I call price sensitive agents. Their behavior responds to where prices are.

But other agents, they push in the other direction. A momentum algorithm buys what is rising, not what is cheap. A volatility targeting fund reduces exposure when volatility rises, not when prices fall. A stop-loss executes at a threshold, not a value. These agents are price sensitive too, but they’re in the opposite direction. They amplify rather than absorb.

But then there is this third category which has become the dominant force in markets today, and which I think is a structural story of the past 20 years that the macro conversation is not quite focused on properly, or at least in complex adaptive systems terms. They are the passive investors.

So, a passive vehicle buys what the index tells it to buy in the proportion the index dictates at whatever price the market happens to offer. Price is not an input into its decision function. It does not buy more when something is cheap. It does not buy less when something is expensive. It does not push back when a valuation gets stretched. It does not step in when a valuation gets crushed. It simply executes whatever inflow it receives in proportion to the index’s current composition.

So passive is not price sensitive. And because it’s not price sensitive, it does not participate in price discovery. So, the active work of figuring out what something is worth is price discovery. And that is not what passive is doing. Passive is doing something else entirely. It’s allocating by rule.

[00:14:03] Niels

Okay, so just first of all, very interesting when you list all these different things and you can tell that it’s all behavioral. We’ve always said that trend following is rooted in human behavior. This takes it to another level when you kind of break it down and explain like that. So, I really like that. But let me just ask you something here.

When it comes to passive investors, either they might say (and I don’t think we could argue that they are necessarily wrong) that the whole point of passive investing is precisely to avoid making bets on price. Right? So, it’s the discipline, it’s what works. Are you going to argue that owning index funds, to some degree, might be the wrong thing to do?

[00:14:53] Rich

Not really, Niels. So, I need to be clear about this, because the argument I’m making is not at the level of the individual investor. So, at the individual level, owning a low-cost index fund is, for most people, a perfectly rational decision. I’m not arguing against that.

What I am making is a structural argument about what happens to the agent ecology when a growing share of capital becomes price insensitive by design. So, that is a separate question from whether any individual investor should hold index funds. Both can be true at once.

So, index funds can be a sensible choice for the individual. And the aggregate consequence of millions of those sensible choices can have changed the structural behavior in markets in ways the individual choice never anticipated. So, that’s the move I want to talk about today.

So let me develop what actually follows. In a complex adaptive system, or CAS for short, I want to talk about passive not being price sensitive. So, because passive is not price sensitive, we get a few consequences.

The first is the most obvious one, the population of price sensitive agents in the market, the ones who are buying and selling responds to where prices are, has therefore shrunk as a share of the total. Not in absolute terms, there’s still, you know, trillions of dollars of active capital. But as a share on the table, the price sensitive participants have displaced by price insensitive ones.

The second consequence follows from the first. And this is where the CAS lens does its real work. In any complex adaptive system, the structure of the behavior that emerges is determined by the interactions between the agents. And there are two kinds of feedback which run constantly through those interactions. There’s what I call reinforcing feedback, and this is where the feedback amplifies movements. And there’s balancing feedback, that’s where the feedback absorbs and slows down price movement.

So, the interplay between those two determines whether the system trends, ranges, or breaks. So reinforcing feedback comes mostly from agents whose behavior is conditioned on price in motion. Momentum buyers chase a rising move stop, losses triggering into a falling one, volatility targeting reducing exposure as volatility rises, index inclusion forcing buying as a stock joins a benchmark. Algorithmic flow, in general, follows this process. All of that pushes prices further in the direction that they’re already going.

The balancing feedback, however, comes mostly from agents whose behavior is conditioned on price relative to what they can perceive as value. Value Investors stepping in when something looks cheap. Sellers of overpriced assets, countertrend funds. The market maker who fades extremes. That balancing feedback is what stops reinforcing loops from running unchecked. It’s what produces the equilibrium seeking behavior that classical finance assumes has always been present.

So, what passive has done, by design, not by accident, is to reduce the share of the market doing the balancing work. The reinforcing agents are still there, the algorithms are still there. The forced flows around index inclusion and rebalancing have actually grown as passive has grown. But what has shrunk is the population that pushes back.

So, let me put that in the language I use in my own practice. So, in systematic trading we talk about divergent and convergent traders. Divergent traders ride moves in the direction price is already going. Trend followers, momentum traders, they are divergent.

So, convergent traders position for prices to come back towards fair value: value investors, mean reversion traders, counter trend funds. They’re convergent. So, map that onto the feedback architecture.

Reinforcing feedback comes from divergent traders and from passive long-only investors. Passive joins that divergent side because its flows buys more of what is already large. Its rule of allocation amplifies the existing distribution.

Balancing feedback comes from the convergent population. And the convergent population is the part of the ecology that is actually being diluted by passive. The residue doing the balancing work is therefore shrinking. The reinforcing side, which is divergent plus passive, that has grown. And that is a consequence for the structure of market behavior itself. So, we see trends now run further before they meet resistance, because the resistance is structurally smaller. Reinforcing feedback operates in an ecology where the balancing feedback that used to slow it has now thinned out.

The system is more prone to extended directional moves, more prone to overshoot, more prone to cascade, not because the reinforcing forces have grown stronger, but because the balancing forces have grown weaker. So, there is a third consequence which I’m going to plant here but develop properly later in this episode when we get into the topics.

That’s what I call the float that does respond to price.

So, the active price sensitive capital, that is still doing the work, is operating in a structurally thinner pool because the price sensitive buyers have now dominated. So, most of the outstanding shares sit in entities that will not sell into a rising price, they will not buy into a falling price, they will not adjust on valuation. So, when the price sensitive capital does move, its impact on price is larger than it would have been 20 years ago. (There’s a bit of a counterintuitive statement here, which I need to explain later.) So, the market is structurally inelastic in a way that the old framework didn’t anticipate. And the size of that inelasticity is more profound than almost anyone realizes, which we’ll get to.

So, that’s what’s on my radar. One of the biggest structural changes in the architecture of global markets over the past two decades has not been algorithmic trading, has not been high frequency trading, has not been the dominance or the mega caps. They’re all downstream effects.

The structural change is the transfer of a growing share of market ownership from price sensitive agents to price insensitive ones. And the consequence is that the feedback architecture of markets, the part of the system that produces trends, cascades.

The fat-tailed behavior, that any honest practitioner has observed for decades, is operating in a structurally different ecology from the one market theory was built for.

So let me put it one more way, because I want to make sure the listener can’t mishear it. The issue is not whether passive is good or bad for the person buying the index fund. The issue is what happens when passive becomes so large that fewer participants are left asking whether the price itself makes sense. Both of those things can be true at the same time.

Index funds can be a perfectly reasonable choice for any individual and the aggregate consequence of millions of those reasonable choices can change the structural behavior of the system. The individual level and the structural level are different questions and they’ve got different answers.

So, the question is not whether passive is good or bad for the individual investor. At that level it might be entirely sensible. The structural question is different. What happens when a growing share of capital no longer asks whether price makes sense? What happens when fewer participants are left to push back against that movement?

That is a question that leads directly into the topics that we’ll be discussing later today. Why markets trend, why they have always trended, and why the modern agent ecology may make those trends actually structurally present going forward, more so rather than less so. So that’s where I’ll leave it over to you.

[00:24:06] Niels

I mean, your radar must have been over flooded. That was probably the most comprehensive answer to that question, which is great. It’s an important one and it’s a great framing, of course.

There’s one thing I was thinking of when you were kind of talking about convergent, divergent, and the changes in the structure. And I don’t know if you’ve thought about this and that is we do hear a lot about it, and I know Cem talks a lot about it as well, a lot about kind of structured products, issuance of structured products, and how they suppress volatility, and so on, and so forth.

And I wonder, to some extent when, if they have the kind of build up, a little bit, of the role that other sort of convergence strategies may have filled. I agree with you that passive is winning, and is taking over, and that is changing the structure. But I wonder if the blow is being softened a little bit with this enormous amount of issuance of structured products, where a lot of it is trying to keep certain price spikes from happening. Don’t know if you thought about that.

[00:25:18] Rich

Look, I suppose we shouldn’t perhaps wish for too much because what I’m actually going to be saying, later in these topics, is this rise in passive is great for trend, so it’s fantastic for us. So, this rise of structured products, however, might not…

So, the more different ecologies you have, the broader the ecology, the more the different forces at play create structural diversity, that creates a foundation of strength. However, the less you get, when things start dominating, you get a concentration effect. And a good example, let’s say a calm market. A calm market starts off where volatility is very low, and we start seeing strategies adapting to that calm market. They start deliberately operating within that calm regime. That’s the only way that they can make their edge from that calm regime.

And what you typically find, because it’s a calm regime, they start leveraging up or adopting structured products that allow them to leverage up in their calm market. So, if you could imagine the market is actually building up risk in that calm environment because it’s getting a concentration of strategy in it. We’re getting a loss of diversity. The loss of diversity made the entire market fabric much more structurally robust.

Now we’ve got this calm market where this concentration occurs, and this buildup of risk occurs. And because of this leverage that’s introduced into this calm environment, because investors have got to make the return somehow, and there’s only small volatility in this calm market, they naturally leverage up. But that makes the system more sensitive, more sensitive, more sensitive. It’s actually making the system more fragile. So, when you get a small deviation out of that, that structural calm, a small deviation, you suddenly get this nonlinear amplification and this massive breakout that occurs out of that because the risk was building up, it wasn’t dormant, it was there. That’s how I’m seeing it.

[00:27:22] Niels

Yeah, and to some extent, the way I… And again, I have no evidence of this, it’s just an observation, is that there was certainly a period of time, maybe a few years, where perhaps these pod shops, multi strat funds were dominating and they were growing like crazy and they were essentially probably often trading against the trend followers and keeping markets from breaking out. But then, as you say, when the markets then do break out, they find themselves on the wrong side of that trade. And then that actually amplifies the outcome in the end, even though you have to be a little bit more patient. So yeah, I think we’re saying the same thing.

[00:28:08] Rich

If we think of, say, passive, this rise of passive, what it’s leading to is theoretically an extinction level event of passive. Everyone’s going passive, a concentration into passive. What happens when suddenly they’re met with this explosion out of a market regime or a behavior that satisfies? You suddenly get this massive uncoiling of this system that has been building up risk. And this is what I call extinction level events.

So, the way these complex adaptive markets work, you know, you get these growth, growth, growth, growth, extinction; another one, growth, growth, growth, extinction. It’s continually adapting, going through these different…

[00:28:54] Niels

And actually, what works against passive, to some degree, is demography. And at some point there will be a change. The challenge with some of these concepts that we talk about often is that you and I may only experience one side of that, and then the other side of that will be for the next generation. And so, it’s kind of frustrating that you only see the headwind, not the tailwind. But there we are.

[00:29:23] Rich

Our kids will get it.

[00:29:24] Niels

Exactly, as long as our kids stay trend followers, we’ll be fine.

Anyways. All right. So, I mean, as I said, wow. What a radar. And I feel my observations on my radar are absolutely not very important, having heard.

Nevertheless, I couldn’t help noticing a story that was reported. I didn’t see it on CNBC, but it was reported with the source of being CNBC, which, I guess, we still think of as being credible.

Anyways, it seems like, and I don’t mean to make fun of this, but it is just so unusual that I couldn’t help mentioning it. And that is that there is now a new mandate if you want to be a Russian central banker. Because according to CNBC, it says, Russia has passed a law authorizing its central bank and other financial institutions to repel drone attacks with their own defense systems as the country struggles to defend against Ukraine strikes.

The law passed by Russia’s lower house of parliament on Tuesday will allow staff at Russia’s central bank to be armed and to operate systems used to down unmanned aerial vehicles, UAVs, or drone attacks without the involvement of special forces. And it goes on. I’m not going to read it anymore. But I mean, the point I’m trying to make here is just what a crazy world we live in. I mean, you know, it’s not good for anyone and it’s just completely crazy. So, I’m going to leave that. I’m not going to even ask for a comment on it.

However, on a more related note, I noticed in the latest Spring 2026 Investor Sentiment Survey, from our friends over at SocGen, that CTAs, and specifically CTA trend, has moved up two places from the autumn of 2025, although not quite back to the spring of ‘25, not quite back to the fall or spring of ‘24 in terms of popularity, but a little bit better than last year where I guess people were a little bit stung by the drawdowns by mid-year. So, they probably asked people just after June of last year, what do you think of trend? And the response wasn’t great.

But anyways, oddly enough, I think that this survey shows how many investors, as a percent, are interested in a strategy and the winner is not trend, unfortunately. But with 59% respondents, it’s discretionary global macro. I have to say it must be difficult to be a discretionary trader in this environment. So, good luck to them.

Number two, maybe not surprising, commodities. Okay, that makes sense, 43%. 42% equity market neutral stat arb. Okay, there’s a convergent guy for you. The same with the next one. Equity long/short. Then comes quantitative global macro. Okay, I guess we can, we understand that, 37%. Multi strategy fundamental, not sure exactly what that is but that’s up one place, but with 31% interested. Then comes event driven, and then I don’t even know what place it is, probably around number 8, anyways, CTA trend with 29% interested, and 28% interested in CTA non-trend. So at least we beat the non-trend guys by a place and by a percent. Anyways, that’s what it looks like.

And further down than the last couple of surveys, actually, the lowest of the last five surveys, in terms of ranking, is actually (as we just talked about) multi strategy systematic. So, I guess that means the pod shops are not as popular as they were only about a year ago.

Anyways we’ve got a long program today with this speed definitely going turtle speed today, pardon the pun. The pun was probably intended.

Anyways, a trend barometer update, it doesn’t look good coming to the end of the month. It’s down to 39% which it hasn’t touched for quite a while. So, we are seeing a little bit of weakness in the trend space. It doesn’t mean performance, necessarily, will reflect this. This is the shorter-term time frames but it is coming down a little bit. And, I guess, looking at the markets this month, mid-month everything looked okay, but in the last week, 10 days, we’ve seen a lot of it, isn’t it?

Yeah, I mean I see the oil markets the last week is down more than 5%. Anyway, we’ve given up some of the performance but I think the month, when I look at the numbers, which I will quote now even though they do not include yesterday which was a down day I think for most managers. Yeah, just slightly up. So, I’d love to hear your thoughts but let me just run through the numbers.

BTOP 50 up 79 basis points as of Tuesday night, up 10.43 for the year. SocGen CTA index up 72 basis points in May, up 11% for the year. SocGen Trend up 1.07, up 11.31% as far as I remember. SocGen Short Term Traders Index up 0.25%, up 5.5% so far this year.

MSCI World is, however, having a good month still up 3.8%, up almost 10% for the year. The S&P US Aggregate Bond Index completely flat for the month, and pretty flat for the year up 28 basis points. And the S&P 500 up 4.43% in May, up now 10.38% so far this year. It’s probably at a new all-time high, I would imagine.

Any thoughts, any takeaways, from your side, in terms of the trend environment or what you’ve seen?

[00:35:37] Rich

Your TTU barometer I think, I’m actually finding, because I’m reporting on it with you each week or whatever, I’m finding it’s a very good leading indicator. I’m finding that you know the moves we’re experiencing in our fund, your indicator tends to sort of almost be the lead indicator for that. So, you know, we’re seeing that now.

[00:36:04] Niels

The lead indicator…

[00:36:04] Rich

It’s faster, it’s shorter term, it picks up these things I think a bit quicker than our lagging funds do but I’m finding it’s a good directional indicator.

[00:36:15] Niels

Yeah, I was going to say, maybe I could license it to Andrew, he can replicate the index.

[00:36:20] Rich

Oh yeah, that’s not a bad idea.

[00:36:22] Niels

I’ll ask him in a couple of weeks when I talk to him.

[00:36:25] Rich

A few errors in it.

[00:36:26] Niels

Exactly.

Anyways, actually my observations over the many, many years, I think it came about to life in 2007, so it’s almost 20 years old now and we have not changed the formula here but I will say it’s not very good as a trading tool, in my opinion. But I think what it does do well at month end, if you just look at it at month end, it actually has an extraordinary way of giving you an indication of what was the month like, partly because of its lookback period. So, that’s really what I think it’s useful for is just to, say, yeah, was it a good environment, was it a bad environment?

And the reason it came about, in 2007, was there was a period of time where trend followers didn’t do so well and people couldn’t really explain it. That’s when it was born. And even some of our very large competitors, who have all been guests on Top Traders Unplugged, came and asked if they could use it in their marketing material, which we of course back then were very honored to have that request.

Anyways, enough about that. We do have a question. We have a question all the way, if I’m not mistaken, from Costa Rica, from Jose, whom I get many emails from in support of what we do. So, I really appreciate that. And Jose is asking you, Rich, he’s asking whether systematic trend following pays too much attention to correlation and not enough to variance at the instrument level.

And the point he’s making is that correlation tells you how stocks spread between markets, but variance might be an earlier and more direct signal of where stress is becoming acute within a single instrument. A position can look diversified on correlation matrix but still become a disproportionate source of portfolio stress when its own volatility rises sharply.

So that’s his concern essentially. So, I’m sure you have a professorial answer to share.

[00:38:49] Rich

Okay, so it is a sharp question from Jose. So, he is pointing at a real distinction. So, correlation is a between instrument property, it tells me how shocks travel across the portfolio. But variances are within instrument property, it tells me where shock is becoming locally intense. Both matter. They both matter to me.

But this is where I’d slightly push back on the assumptions in the question. The premise of the question is that trend following overweight correlation across instruments and underweights variance within instruments. But it often looks that way in the commentary. We’re always talking about correlations, etc., across instruments because, you know, that’s what we talk about. But the engine itself, the trend following engine, is absolute momentum style that runs on variance. So, every position, for example, in my program is sized by average true range (ATR), which is a direct measure of the instrument’s own variance.

So, when a market’s variance rises, my position size shrinks automatically, no discretion, no judgment call. So, the program is already taking less of it before the next bar prints. So, the same applies to stops, to portfolio level volatility targeting when it’s applied properly (Niels, you know how I feel about whether this is done well), and to draw down management at the position level.

This is all what I call variance driven. So, the framing I’d offer to Jose is this. Correlation is the diagnostic, it tells me what the system is doing. Variance is the control, it tells me how to size, when to cut, where to back off. A trend follower needs both of these things at different levels of the architecture.

The system, I feel, tells you the weather, and variance tells you whether to put on another coat right now, effectively. So, thanks for the question, Jose. It was a good one.

[00:41:04] Niels

Now we are already 40 minutes in and we’ve only served the appetizer, Rich. How about that? So, I know you are ready now for delivering the main course, I feel. But it is, as you say, it’s a continuation of the framing you just said. And I actually think, I mean, these are really important topics and, as you pointed out to me before we press record, you’ve noticed that more people talk about complex adaptive systems and I would not disagree with that. So, let’s dive in, let’s see where it takes us.

I’m excited about it and I think this will be incredibly useful for those who just sit down, relax, take a few notes because there’s a lot of information in that three word concept.

[00:42:04] Rich

All right, so here we go. I’m going to start with an image. So, I want you to picture a murmuration of starlings over a winter field, late afternoon. The light is dimming and there are thousands of birds in the sky moving as one. Not a single bird is leading. There is no choreographer, there’s no plan. There’s no bird that has the full picture of where the flock is going.

The flock turns, expands, contracts, ripples, splits, recombines and produces this coherent moving shape of extraordinary complexity. And it does all of that with no central control whatsoever. So how does it work? Each bird is following three simple rules – heuristics.

You might be familiar with the trend followers heuristics: cut losses short, let profits run. However, in the starling’s world, stay close to your nearest neighbors, match their speed, avoid collision. That’s their simple local heuristics. That’s it. Three local rules applied by each bird to the few birds nearest them. Nothing in the rules specifies the shape of the flock. Nothing in the rules specifies the turn. Nothing in individual birds behavior predicts the structure of the whole.

But from those three local rules, applied in parallel by thousands of birds, the collective produces a coherent structure that flows, turns, reorganizes itself in real time. So that structure is emergent structure. It does not exist in any individual bird. It’s not built, it is not planned. It’s not the sum of the birds intentions. It emerges from the interactions between the birds. The structure lives in the interactions, not in the components.

So that’s a picture I want to place to the listener to hold on to for the rest of this episode because markets work the same way. So, every participant in the market, every pension fund rebalancing its allocation, every momentum algorithm adjusting its position, every retail investor responding to a headline, every options dealer hedging an exposure, they’re following local rules based on local information.

No participant has the full picture. No participant intends the collective outcome. But the interactions between them produces structure: trends that persist far beyond any single catalyst, crashes that accelerate beyond any single piece of bad news, volatility that clusters in ways no random process ever could, concentration in a handful of names that no rational allocation framework can explain. That structure is all emergent. It doesn’t exist in any single participant. It emerges from the interactions between them. The market is the flock.

[00:45:13] Niels

Yes. Okay. I mean, it’s interesting when you paint that picture because I remember a paper that had that picture on it, written by one of our good Dutch friends in this industry. So, markets are really just a murmuration or a flock? Is that how it is?

[00:45:32] Rich

Exactly. So, here’s what I want the listener to hold from that image. Because it changes everything about how they should be thinking about what markets actually are. So, most of the last 50 years, finance has taught markets as if they were a different kind of object entirely.

The dominant intellectual framework, the one taught in every sort of MBA program, embedded in every institutional allocation model, written into every textbook, they’ve treated markets as if they were governed by the same kinds of laws as physical systems: particles in motion, forces pushing prices towards a stable equilibrium, deviations from fair value being treated as noise around that equilibrium, random short-lived and unprofitable to exploit.

That’s the equilibrium model. It’s elegant, it’s mathematically tractable. And as a description of how real markets actually behave, the wrong picture entirely. So, here’s why.

So, the equilibrium model treats markets as though they are clocks, not flocks. A clock is governed by physical laws. The parts don’t observe each other. The pendulum does not change its behavior based on what the gears are doing. The springs do not panic when the hands move too fast. A clock is a kind of system where you can describe the whole by adding up the parts. But a market is not that kind of system.

Market participants observe each other constantly. They imitate, they panic when others panic, they anchor to reference points other participants have just set, they build models of what other participants will do and act on those models, which changes what other participants do, which changes the models. So, the agents in a market, they’re not particles, they are responsive. And their responsiveness is the entire story.

So, when classical finance treats markets as equilibrium-based seeking systems whose behavior can be derived from the rational decisions of independent agents, it’s making a category error. It’s treating a flock as if it were a clock. And the consequences of that category error show up everywhere in how markets are described, modelled, and explained in the mainstream conversations.

[00:48:01] Niels

Okay, so let’s just take a quick pause here because, I mean, this is quite interesting because my understanding is that that’s pretty much how we’ve taught everyone in terms of finance, that there is this equilibrium model. And if we are now proposing that that’s wrong, I’m surprised. And maybe you can explain that. How has that survived for so long if we think actually…?

[00:48:31] Rich

It’s been a useful model, Niels, even though it’s been wrong. So, the equilibrium model gives you a way to talk about prices, value securities, construct portfolios, convince investors. It produces numbers for a spreadsheet. The alternative, however, is admitting markets are not equilibrium seeking, that there is no fair value that prices are converging on, that the system is constituted by reflexive interactions. That makes it incredibly difficult. So, that doesn’t give you a spreadsheet, Niels, that gives you a research program. And no one wants to hear that.

So, the equilibrium model has survived because it’s convenient, not because it’s right. And here’s the deeper problem with it. The equilibrium model has a particular assumption built into it, that once you see it, you can’t stop seeing it. The assumption is that price movement is driven by news. So, the mental model is (this is how they view it), information enters a system, rational agents process the information, price adjusts to reflect the new reality: cause, then effect, clean and linear. Every move has a story.

And that’s the story CNBC tells us every day. The market moved because of inflation data, because of Fed minutes, because of an earnings surprise. External event causes price change. The world delivers information, the market reflects it. That’s how they view that model. And it is, in most cases, much less of the story than it sounds.

So, news can matter as a trigger, but the size and duration of the move are often determined less by the news than by the internal feedback that the news activates in the system. So, the match or the trigger starts the fire. The fire’s shape and extent depends on the dryness of the wood, not the size of the match.

[00:50:36] Niels

Okay, fair enough. But people listening to that explanation might want you to dig a little bit deeper and explain that a bit more. Even though I think we have talked about these concepts before. And of course, this is also what our friend Dave Dredge likes to talk about. And I’m hoping to bring you and Dave together on a conversation very, very soon.

So, if you’re listening, Dave, you are warned. But let’s just back it up a little bit and go into the microstructure of this.

[00:51:17] Rich

Okay, so we’re going to talk about microstructure here. So, there is a substantial body of this microstructural research, 20 years of it. So, let’s look precisely at this question.

The likes of Jean-Philippe Bouchaud, Gabay and Cohen, they’ve looked at this microstructural research, and the answer keeps converging on the same thing. The vast majority of price movement can’t be attributed to identifiable news. Prices move often on days with no news. They move in ways that bear no proportional relationship to the news that does arrive. So, some of the largest moves in market history have occurred without any corresponding catalyst of equivalent magnitude.

So, if news were the primary driver, the relationship between information and price change would be consistent. But it’s not. So, if news is not the primary driver of price, then what is it? And the answer is what I call the endogenous engine.

Most price movement is not driven by anything coming from outside the system. It’s driven by the system processing itself. Price changes behavior, behavior changes price, and around the loop it goes. A small breakout lifts price. That lift draws in momentum systems that buy what is rising. Those systems alter volatility. The volatility change triggers volatility targeting and rebalancing. The rebalancing shifts positioning across portfolios. The position shifts force further, each price adjustment.

Each step in that sequence happens because of the price action, not because of any external information. The original spark, whatever it was, perhaps news, perhaps just an order imbalance, becomes increasingly irrelevant as the cascade develops. And the cascade is what matters. And the cascade is internal.

So, this is what I mean by the endogenous engine. Markets don’t simply reflect the world, they process themselves. Each price sensitive participant is watching price, acting on it and changing it for the next price sensitive participant. And the picture matters here. So, the engine runs on the agents who respond to price.

And as we said earlier in the introduction to this podcast, passive participants, they’re not in that loop. They are present in the market, but they’re not the ones whose actions drive the recursive cycle.

The whole system of price sensitive participation is running a continuous recursive loop where the output of the system becomes the input to the system’s next state. So, external events may trigger cascades, but the engine that sustains them, amplifies them, and determines their ultimate magnitude. And that’s internal. The system moves itself, so to speak.

[00:54:32] Niels

And which is probably what Soros wrote about many, many, many years ago. Right? Reflexivity. I think Cem talks about that as well, in the conversations that I have with him. Would that be…?

[00:54:48] Rich

Yes, that’s exactly… Soros, I think, had it right. So, price sensitive participants, they don’t simply observe markets and react to them. Their reactions reshape the markets, which reshape the next observation, which reshapes the next reaction. So, the loop runs continuously among the agents who respond to price.

It’s the operational consequence of this endogenous engine, the mechanism by which the system is processing itself. And once you have that picture in your mind, a lot of things that seem mysterious about markets stop being mysterious.

So why do trends persist far beyond what any single catalyst can explain? Because the endogenous engine keeps running long after the trigger has faded. Why do markets overshoot fundamental value? Because there is no force in the system pulling them toward it.

And as we established in that ‘what’s on the radar’ segment, fundamental value belongs to a separate system that does not have to agree with the markets. So why do the largest moves often occur without corresponding catalysts? Because the engine produces arbitrarily large moves when the architecture is sensitized. The trigger is the match, the system is the fuel. And here’s the move that changes how I see my own craft.

So, the picture I’ve just drawn: markets as a flock, the system processing itself through reflexive feedback, prices emerging from interactions rather than from rational pricing, it’s not a strange theoretical claim. It’s the description any honest practitioner has been operating inside of, intuitively, for their entire career.

So, the trader who has watched a stop-run knows price is moving because price is moving, not because anyone new information. The trader who has felt a market squeeze, they know the cascade is internal. We’ve known this for decades. The CAS framework is not adding new information. It’s giving us a language to say, effectively, what we already knew, but it’s articulating it in a better way.

[00:57:08] Niels

So based on sort of what you’re sharing with us today, you know, let’s use your terms. So, markets are “flux”, so to speak. And the model that we’ve been all taught about equilibrium, that’s not right anymore. And prices are not necessarily relating to fundamentals, which is something I spoke about the last couple of times. You just look at the month of April, you wouldn’t expect markets to behave the way they did, so I’m completely on-board with that. But of course it leaves the question for all investors, and that is, so how do we deal with that?

[00:57:47] Rich

Well, that’s a great question. That’s exactly where we’re going to go in section two. Because once you accept that markets are a complex adaptive system, that the endogenous engine is what drives most price movement, that reflexivity is the operational mechanism, once you have all of that in your hand, the next question is what is the architecture of the engine? What are the feedback loops actually doing? How do reinforcing and balancing forces interact to produce the patterns we observe? And what does that mean for a practitioner like us trying to operate within that system? That’s the architecture of feedback. And that’s where we’re going to go next in the next sectional topic.

But before we get there, I just want to leave the listener with one final image because everything in the next section is going to come back to it. So, the flock is not chaos, it is highly structured. The starlings produce coherent, complex, beautiful patterns in the sky. And they do it without anyone in charge. The same is true of markets. The patterns are real, the structure is real, trends are not anomalies, cascades are not aberrations,

concentration is not coincidence. These are emergent properties of a complex adaptive system populated by agents whose interactions produce structure.

So, the fact that news is more often a trigger than a driver does not make the structure less real. It makes a structure something different from what classical finance said it was. And once you see that structure clearly, you can begin to align your craft with it, which is what trend following has been doing for 40 years now, even when its practitioners might not have had the language to say so. So, let’s get into that next second topic.

[00:59:46] Niels

Absolutely. And by the way, I love the talk about architecture. I was sharing with you, before we press record, I listened to a wonderful conversation with Norman Foster, the very well-known famous architect, and actually he was talking in language that I thought, wow, there could be some analogies useful in trend following. And here we are. Well, I don’t know, but here we are talking about architecture.

Anyways, obviously, you painted a picture. So far, we’ve talked about complex adaptive systems, kind of this endogenous engine, and we’ve now introduced reflexivity as well. And now you’re adding architecture. So, what is the engine actually made of? And how do we know, empirically, that this is what’s going on?

[01:00:45] Rich

Okay, so let’s be concrete about what feedback actually means, because the word gets thrown around loosely in financial commentary. It’s almost lost its meaning. So, in any complex adaptive system, feedback is a mechanism by which local actions become global structure. It’s how the flock turns. It’s how the cascade builds. It’s the operational machinery that lets the system process itself. And in markets, feedback operates in two distinct modes. The interplay between them determines everything about what the market actually does.

So, the first mode, the reinforcing feedback, it amplifies movement. A small buy order nudges price upward. That nudge activates trading systems by what is rising. Models recalibrate, institutions rebalance, performance chasers arrive late but with size, each action reinforces the previous one. The local behavior forms a directional pattern, and this is the fuel of trends.

But the second mode is balancing feedback, it absorbs movement. Market makers quote both sides of the book, value investors step in when prices fall below perceived worth, risk managers reduce exposure when volatility rises beyond tolerance. These responses counter extreme movement and create temporary zones of calm. Without them, every reinforcing loop would grow unchecked until the system collapsed.

So, the two run simultaneously against each other. When reinforcing loops dominate, the system becomes directional and transforms. When balancing loops dominate, the system becomes range bound. When the influence shifts rapidly from one to the other, phase transitions occur and volatility surges.

The market breathes through the opposing loops, never still, always adapting to the consequences of its own actions. And that’s the architecture: two kinds of feedback continuously interacting, determining whether the market trends, ranges, or breaks.

[01:02:57] Niels

And so, the architecture actually shifts because, what you said earlier, that passive now contributes to reinforcing the feedback and diluting the balancing agent, so to speak, if that’s correctly understood.

[01:03:12] Rich

Spot on, spot on. So, that’s why I want to land that connection properly here, in this segment, because it’s a structural fact that drives almost everything we’re going to talk about. So, the reinforcing agents amplify price movement. The momentum chaser chasing the move. The stop loss selling into the fall. The volatility targeting fund reducing exposures as volatility rises. The passive fund, allocating by market cap weight, buying more of what is already large. The options dealer who must delta hedge into the direction of the move. The forced index inclusion buying as a stock joins a benchmark. All of these things amplify direction.

Some are price sensitive: the momentum trader. Some are price insensitive: the passive fund responding to index weight rather than price. Either way, they take what the market is already doing and add more force to it.

So, the balancing agents, however, absorb price movement: the value investor stepping in when something looks cheap, the selling of an overpriced asset, the market maker, fading extreme orders, the counter trend trader, all push back against price in motion. So, what passive does (and this is a point I want every listener to hold), it doesn’t eliminate balancing agents. The value investor still exists, the counter trend trader still exists, the market maker still exists. But what passive does is dilute their share of the ecology. They reduce their weight relative to the capital that no longer pushes back on price.

So, the reinforcing agents, they haven’t been diluted. The forced flows around index inclusion and rebalancing have actually grown. But what has been diluted is the proportional weight of the agents conditioned on price relative to value. The balancing feedback has been thinned.

So, the structural consequence, the architecture, reinforcing loops now run further before they meet resistance. The system is therefore more prone to extended directional moves, more prone to overshoot, more prone to cascade.

Look what’s happening for example, with the rise and rise of the S&P 500, when the world about it is falling down. This is this passive injection which is reinforcing feedback. But as it progresses, any relationship it might have to fundamental value is being progressively widened.

So, the structural consequence, how reinforcing loops now run further before they meet resistance. The system is more prone to these extended directional moves, more prone to overshoot, more prone to cascades. Not because the reinforcing forces have grown stronger, but because the balancing forces have grown weaker.

And there is a further amplifier I need to plant here. There’s been research over the past few years on what is called market inelasticity, and the findings are striking. You had a podcast, I believe, with Al Alan, talking about this effect from Gabay and Cohen. So, let me set this up carefully because the picture’s a little counterintuitive.

So, when the majority of shares are held by passive vehicles; index funds and long term holders who do not respond to price (price insensitive), the effective pool available to absorb active buying or selling (the price sensitive agents) is structurally thin.

So, think of it as a price discovery pool. The price insensitive holders, they own the shares, the bulk of the shares, they’re not transacting on the basis of price. So, when an active buyer or seller comes into the market, they have to find the other side of the transaction with a much smaller fraction of the available float. And the fraction, that’s the fraction still responding to price, the price sensitive fraction, it’s operating on a thinner float. So, the impact of the price movement, from the interaction, is greater, not smaller.

Here’s the consequence that surprises people. Each unit of active flow has to move price further to find a willing counterparty because the pool absorbing the flow is smaller.

So, US$1 of net buying doesn’t push the price up by US$1 of value, it pushes the price up by multiple of that. And the best work in this space from Gabay, Cohen and, I think, Bouchaud’s worked on this as well, suggests the multiplier is on the order of 5. US$1 of net buying can increase aggregate market capitalization by approximately US$5. US$1 of selling can destroy approximately US$5. So, that’s what economists call inelasticity.

An inelastic market is one where price moves a lot per unit of flow. What the rise of passive has done is make markets structurally less elastic.

You know, I talked about in car markets we get price sensitivity. We get markets being more sensitive. This is this effect. It’s becoming more inelastic. Fewer price sensitive participants in the active pool means less capacity to absorb flow without price moving. So, the same dollar of buying produces more price movement than it would have 20 years ago.

Now, counterintuitively, fewer price sensitive players in the active mix means more violent price action per unit of flow, not less. So, the agents who are not in the active pool, they’re simply present, holding their share, not absorbing the trade.

So, the feedback architecture does not just sustain trends, Niels, the inelasticity ensures that the trends it produces are larger than the flows that created them.

[01:09:23] Niels

Yeah, I mean, I had heard about the multiplier before. Not sure I had heard how big it was. But five is a pretty striking number, though it does ring a bell. How can that be proved? Can it be proved?

[01:09:36] Rich

Well, according to these studies, they seem to have demonstrated it, but yeah, yeah, good piece of research, but it definitely warrants further investigation. So, I’ll stand behind this claim. The exact multiplier itself, I suppose, depends on which study you look at and how the methodology is set up. So, the range in the literature that I’ve seen runs from roughly 3 to 1 at the conservative end to closer to 7 to 1 in the more aggressive estimates.

So, the point that I want the listener to hold is not the specific number, but the structural fact. The multiplier is large, whichever way you view it. It’s much larger than classical finance assumed. And it’s a direct consequence of the agent ecology we’ve just been describing.

So, the price insensitive holders are now so large such a share of the float that the marginal price sensitive dollar is doing the price discovery work in a structurally thinner pool. Each unit of active flow moves price more than it would have 20 years ago. Much more. So that’s the architecture as it stands today.

So, I want to walk you through the evidence. And this isn’t a theory, this can be empirically demonstrated.

[01:10:55] Niels

Okay, let’s do that. It’s exciting. Just be mindful of the time. But, but it’s very exciting. So, let’s see where we’re going with this.

[01:11:03] Rich

Okay, so you remember in our last podcast I discussed this research series, the Fractals of Finance?

[01:11:08] Niels

Yeah, absolutely.

[01:11:09] Rich

About a year ago I worked through a second phase of this research, Niels, and that was where we’re talking about the statistical fingerprint of feedback driven markets across 40 years and 68 markets. That was the test I conducted this research on.

So, in this second phase, a colleague and I extended the work in a particular direction. We built what we call a Minimal Agent Based Model of a financial market. So, the model had only two kinds of agents in it.

The first kind was what we call chartists. They’re like us, divergent traders, agents whose behavior is conditioned on recent price action. Rising prices attracted them as buyers. Falling prices attracted them as sellers. They were the reinforcing or the divergent agents in our simple model.

The second kind were fundamentalists. They were convergent agents who traded on an estimate of intrinsic value. And when price strayed too far from value, they pushed back. They were the balancing agents. So, the model we generated had one knob. You can turn the proportion of chartists in the population up or down, from 0% to 100%.

And we asked a simple question. What happens to the statistical behavior of this simulated market as the proportion of price sensitive chartists change? So, the result was so striking. So, I want to walk through this with you.

So, with zero chartists in the population, meaning all agents were fundamentalists, no reinforcing feedback in the system, the simulated market behaved exactly the way classical finance said real markets should behave: independent returns, Gaussian tails, no memory from one day to the next, no volatility clustering.

[01:13:13] Niels

All right, Rich, let’s just take a quick pause here because it seems like you are about to spread some statistical terms here. I know we spoke about one of them, the hurst exponent, I think you and I talked about not that long ago, kurtosis. And let’s just maybe, again, to make sure we have everyone listening on board here. What is kind of the non-quant version of that?

[01:13:43] Rich

Okay, so the hurst exponent is just a measure of memory in a time series. So, a reading near 0.5 tells you that today’s price move tells you almost nothing about tomorrow’s. The series therefore has no memory.

The further the reading drifts above 0.5, the more memory the series has, the more the past is leaving a trace in what comes next. That’s all it is. It’s a measure of memory.

But kurtosis, that’s a measure of fat tails. So, in plain English, it asks whether extreme moves happen more often than the textbook bell curve says they should.

So excess kurtosis near zero says the world is behaving like the textbook. High excess kurtosis says the world is producing extreme events that the textbook said were almost impossible. That’s all it is, a measure of how fat the tails are.

So, here’s the result we got with our research, with zero chartists in the simulation, the Hurst exponent sat at about 0.54. That was statistically indistinguishable from a random walk – no memory. Excess kurtosis was negative 0.2. Tails were thinner than the textbook, not fatter. Five sigma events, there were zero. Every assumption of efficient markets held in this world of zero chartists.

But when we turn the knob and raise the proportion of chartists or divergent participants in that market, the behavior didn’t drift gradually. It changed through what I call a phase transition.

So, a phase transition is where behavior changes suddenly rather than gradually, like water turning to steam. You can heat water from 20 degrees to 90 degrees, it stays watered the whole way, it just gets warmer. But somewhere between 99 and 100, the same ingredients suddenly produce a different kind of behavior. The water becomes steam. That’s what a phase transition is.

And we got that in our research when we turned the knob. But we got that at a figure where we had around about 25% of the model being chartists or divergent practitioners. The balance being convergent. It wasn’t a 50/50 transition. This was we only needed to put 25% chartists, who are price sensitive, working in the reinforcing direction to create this phase transition in the market structure.

So, above this 25% chartists, everything changed at once. Kurtosis erupted, the hurst exponent leapt above 0.85. So, memory appeared, fat tails appeared, volatility clustering appeared. Every statistical signature of real minds.

Those signatures, they’ve been documented across 40 years of data, and across every asset class we looked at, and it appeared at once at the same threshold for each market. That’s interesting. So, we ran the comparison, the simulated market.

Once feedback was introduced above this critical threshold, it became statistically indistinguishable from the 68 real futures markets spanning eight asset classes, six continents, 41 years of trading history. The simulated market and the real markets had exactly that same fingerprint.

[01:17:05] Niels

Yeah, that’s kind of completely crazy, right? So, you take a simulation where there’s no news, no fundamentals, no central banks, no geopolitics either, and you get the same statistical behavior as a real market, across four decades of time span, just by having, let’s call it, a critical proportion of agents whose behavior is conditioned on price?

[01:17:43] Rich

They’re reinforcing the feedback. We started with the fundamental investors, they’re convergent traders. And when we had 100% of them, it exhibited convergent properties, which is very similar to the Gaussian properties.

But then when we got to 25% participation of chartists or divergent traders (effectively the reinforcing agents, not the balancing agents), we suddenly got this phase transition. And the market signature; the signature across every market we examined was identical to real market structure.

So, the implication is this is just feedback alone that’s doing this, without any of the conventional narrative which is said to drives markets. We reproduce the statistical signatures of real markets without any of that conventional narrative.

The absence of the chartists produced a world that has never existed. So, the controlled experiment shows that feedback is sufficient to generate the statistical signatures we observe. That’s much stronger than a loose correlation. It’s evidence that feedback is not a minor mechanism sitting at the edge of market behavior. It’s the evidence that feedback IS the central mechanism.

So, what I want to get the listener to hold, about this phase transition, is 20% to 25% of chartists, that’s not a high bar. A modest minority of price sensitive participants in the reinforcing direction is a enough to push the entire system across that critical boundary.

So, let’s now set passive to one side since we’ve established that passive participants are price insensitive by design. So, among the rest of the market, virtually every active participant is price sensitive to some degree: stop loss orders of feedback, margin calls are feedback, volatility targeting is feedback, options hedging is feedback, performance chasing is feedback.

Whatever fraction of the market is doing active price discovery, the proportion of that fraction that responds to price is not at 25%. It’s far above that level. We’ve just demonstrated with passive was represented as 60% of the equity market.

So, the real markets comprise these price sensitive reinforcing agents that far exceed that 25% threshold. In fact, they’ve exceeded it over the last 40 years. There has been no decade where we’ve observed this statistical fingerprint is lost. This is a really important conclusion.

So, we did these rolling five-year windows, across the 40 years of data, and we saw that the hurst exponent never once fell to the random walk baseline of 0.5. It fluctuated between 0.62 and 0.82, responding to both crises and regime changes. It never approached the level that would indicate independence.

The fingerprint, for instance, survived Black Monday, the transition to electronic trading, the global financial crisis, the rise of high frequency trading, the COVID crash. It’s been present continuously since the first day of available data.

[01:21:03] Niels

In other words, Rich (and I know we have to kind of wrap it up maybe and continue on our next conversation), but essentially what you’re saying is that markets can’t stop trending.

[01:21:18] Rich

Yes, and the evidence points to it’s going to increase and get better. So, I’ll just finish off this topic and then we’ll conclude. I won’t go into the final topic and I’ll leave this for an exciting installment at one of our next meetings together.

So, the data that we looked at started in 1985. The fingerprint is there from the first observation, from 1985 going forward, across every asset that we looked at: 68 futures markets, 8 asset classes. There is no era of available market data in which markets behave like the equilibrium model said they should. That world doesn’t exist in that historical record.

So, here’s a move that I think how a practitioner like us should see their craft. If feedback driven behavior has been present for at least 40 years, never weakened, appeared in every market on every continent, across every asset class, then what trend following has been doing for those 40 years is not exploiting a temporary inefficiency, it’s not catching a window that might close, it is harvesting a structural feature of how markets actually work. So, the directional moves that trend followers capture are the natural output of the endogenous engine running through the feedback’s architecture. They are the murmuration in motion.

And given what we said about passive diluting balancing agents while leaving the reinforcing ones intact. The architecture is not weakening, it’s hardening: the float is thinner, the multiplier on flow has increased, the system is producing the same fingerprint with more violence per unit of flow than it did 20 years ago.

There’s one more empirical finding before I go that I want to share with you. So, we ran one further test, and instead of looking at markets one at a time, we asked whether 68 markets across the world were behaving as 68 independent systems or as one permanently coupled system? So, the methodology assembled every market’s rolling feedback state into a single cross-sectional view and asked if, at any given date, what fraction of markets were sharing the majority feedback sign? So, if markets were truly independent, that fraction should have averaged around 0.55.

But what we found was that the fraction averaged 0.6 persistently across the entire 40 year sample. So, in no year since 1987 did the cross-market coupling fail to the level that independence would predict. During the most coordinated regimes, the fraction sharing a majority sign ran as high as 0.82.

So, the implication is that the world’s futures markets are not 68 independent feedback engines. They are one permanently coupled feedback engine with different surface manifestations in different asset classes, all running on the same underlying architecture.

And the reason for the coupling? When you look at it, it’s not mysterious. Many of the same investors apply the same models across many of the same markets. That’s what’s creating the coupling – the portfolio diversification that we all run as practitioners. Because we’re never investing in just a single market. We’re investing our models across many markets.

So, this means we’re deploying correlated models across asset classes. And when the models produce a buy signal in one market, it often produces a similar signal in another. The coupling is a structural consequence of how systematic capital is being globally allocated. And the feedback signature is occurring across the entire coupled network.

The coupling shifts position depending on the macro backdrop. So, during crisis episodes, the system collectively tilts posture towards positive feedback back and trends emerge. But during quantitative easing, suppressed regimes, the system collectively tilts towards negative feedback and oscillation emerges.

But the coupling itself doesn’t disappear because the underlying reason for it doesn’t disappear. So, what that means (and this is where I’ll conclude) is that trend following is not a strategy that captures opportunities in some markets some of the time, it is a process that aligns with a global feedback architecture that is present simultaneously across every market that humans trade. The architecture is not local, it’s structural.

[01:26:06] Niels

You know, it’s funny, I mean, I wish I knew all of this, like, 37 years ago when I came into this industry, because it would make me so much more confident that I’d made the right career choice, but I didn’t. But what I did observe, starting out as a government bond trader, is that market moves up and down. And I thought that was the most common sense to actually embrace a strategy that would be agnostic to direction and just basically, you know, chase direction rather than something that you hope for will stay still but is clearly not when you look at the charts.

So, my very basic non quant brain came to a conclusion, but would have certainly, how should I say, probably made the journey a little bit more convincing, initially, had I known all of this research that you’ve shared today.

So, impressive work and I know we’re not even done, but I have a feeling people know where we’re going with this in terms of how to deal with this complex adaptive system world. And so, I think the story only gets better. And if we can get Dave to join you next time, or the time after, we’ll probably explore it in many directions.

[01:27:30] Rich

Good discussion.

[01:27:32] Niels

So, that would be cool.

I mean Rich, we could have gone on for a little while longer. I know you had prepared another section, but I think we’ve done a pretty good job today and I really do appreciate all the time and the preparation in all of this, especially a week where I still feel a bit jet lagged from trip to HQ last week. I don’t know why but I wake up at the weirdest times at the moment.

So, appreciate all the prep work for this and if people are listening and feel that they, as I do every time I speak with Rich, learned something from this, please go to your favorite podcast platform and leave a rating and review so that more people can learn from the professor as well.

Next week I actually have another very smart person, as I have every week, I should say, but Katy Kaminski will be joining me next week and we can have a very special guest, someone who has not been on the podcast. So, I’ll keep the suspense for a week, but I think we will be tackling some of his latest work.

But also, if you want to have both of them tackle some of your questions, you can, as always, email them to info@toptradersunplugged.com and I will do my best to bring them up in the conversation.

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

[01:29:11] Ending

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