251 Systematic Investor Series ft. Richard Brennan – July 8th, 2023

[00:00:00] 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.

Rich, it is so great to have you back this week. How are you doing down under? What’s going on?

[00:00:39] Richard

Very good Niels. It’s quite cold down here but I’m getting ready after this podcast to head off down to the farm to do some controlled burning because in winter down here, this is the opportunity to do some controlled burning to stop those big fires coming in the summer season. So I’m going to be Smokey Joe for the next week.

[0:01:02] Niels

That sounds interesting. You know, it’s funny, obviously we had the US unemployment numbers yesterday, and we hear a lot about the US economy in general. But I don’t have much of a feel for the Australian economy. How does it feel from where you are?

[00:01:20] Richard

We tend to be a mini US, a mini me, a mini US. But what’s happened over here, it’s quite interesting. Property markets are booming. We’ve got a rental crisis over here where rents are skyrocketing on our property prices. The Reserve Bank yesterday decided to just put interest rates on hold for a moment. They believe that they’ve had a sequence of very small uplifts over the course of time, and quite a large number of them.

Obviously, people in Australia can’t remember back to the 1980s where we had 18% interest rates and things. So they start squealing when interest rates get up to about 4% or 5%, but anyway. What’s happened is the Reserve Bank of Australia has had a number of lifts in a very incremental way, a small way, not in a big heavy way, but they put it on pause for the moment just waiting to see what happens.

But it’s a funny state of affairs over here. We feel in our bones that a recession is coming but we’re not actually seeing it yet. So who knows what happens, Niels?

[00:02:32] Niels

Exactly. I think actually recession, not just in Australia, but certainly in the US and Europe has been so anticipated and I think a lot of people are scratching their heads as to when will it arrive. Because if we wait long enough, it will arrive. It’s just a question of when. Now, normally before we dive into the topics, Rich, I also ask what’s on your radar. I kind of jumped in and asked you a little bit about the Australian economy. That may or may not be on your radar. What are you paying attention to in the world of finance at the moment?

[00:03:06] Richard

Well, with my trend following models, the last two months, I’ve had small losses over the last couple of months. That’s basically because there’s been a shift in my portfolio going on where some of the big outliers that I had such as London sugar, and sugar, and orange juice, etc., they’ve gone through significant retracements now. So they’ve been significantly de-risked. They’ve hit those trailing stops. But fortunately, the yen, it’s all about the Japanese yen at the moment, and the Nikkei, the yen, the yen pairs, they tend to be giving a lot of support to the portfolio, providing a bit of opportunity to pay for some of those givebacks of my sugar trades and orange juice.

So it’s interesting that there are certainly trends about but I’ve yet to see it significantly evolve into the performance of my portfolio because there seems to be some offsets going on between the givebacks of sugar and orange juice and the growth of the yen. But fortunately now, I’ve closed my positions down in London sugar, and in sugar, and orange juice I think is all over. It might not be all over, when I last looked I think it’s still going. The yen pairs might take over but it’s de-risked out of the sugar trades and it’s now well-positioned if those yen pairs continue trending going forward. So it’ll be interesting to see what happens.

[00:04:46] Niels

Yeah, absolutely. Are you involved in any of the grains? I did notice as part of my little trend following update that there’s been some massive swings in corn and stuff in the last few weeks.

[00:04:59] Richard

I trade quite a few of the softs, but there’s no real significant impact this month on the grains in my portfolio. I trade oats, trade soybeans, soy meal, soy oil, wheat, etc. But cocoa is actually doing a bit of assistance at the moment for my portfolio, which is very unusual because cocoa traditionally for me has been an awful market.

[00:05:25] Niels

Not just for you, I think. I think most trend followers would be nodding right now saying that’s one horrible market to do a trend following.

[00:05:31] Richard

I know. It made me go and get a nice warm cup of cocoa after this podcast, Niels.

[00:05:37] Niels

Yeah. Okay, good. Yeah. I was just from transitioning into our little trend following update before we dive into the topics. Certainly, from the numbers that I track, it was a pretty quiet start to the third quarter really. Obviously, first half, now that we’re a week into and we start seeing the first numbers, very respectable. I think after a strong 2022, a lot of people think, oh, you’re going to give back all of it in the next six months, but actually, yeah, there was a little bit of a giveback in March, but most people are back and have recovered all of their March pain, which is great to see.

But as I was alluding to, yes, commodities, there were a few things going on this week. But the big battle this week really was, as far as I can tell, in the traditional financial markets, not so much currencies, frankly, although a bit of a retracement in yen, in terms of strength in the yen all of a sudden, but really fixed income and stocks. What’s interesting to me is that, first of all, we’ve kind of gotten down now to the same level of interest rates, I should say, up to the same level of interest rates as we have prior to the SVB crisis.

So we’re hitting those lows in price now and some market seems to be breaking through those lows, making new quote-unquote “all-time lows”, which when I mean all-time, I mean this interest rate cycle that started a couple of years ago, which is quite interesting. But then at the same time, a lot of equities are only a few percents away from an all-time high. So I think a lot of people who are not price followers will be left with somewhat of a conundrum in terms of what is going on with these markets. So of course it speaks to our own beliefs that following prices is a pretty commonsense way of approaching these markets.

[00:07:32] Richard

It makes it easy, doesn’t it?

[00:07:33] Niels

Yes. Sometimes at least, Rich. Anyways, my own trend barometer, yeah, 48, finished it last night. I will say that it’s moving towards the higher end of neutral, but it would be nice if we could make a breakout and actually give some real solid indication that performance was coming from the trend follower side. But anyways, BTOP 50 Index up 70 basis points in July, up 0.5% for the year. SocGen CTA Index up 60 basis points for the month of July and for the year. Trend Index up 0.25%, up about 40 bips for the year, and now SocGen Short-Term Traders Index is the only one that’s down. That’s down a quarter, down 3.6% so far this year. MSCI World down 1.4% for the month of July. The bonds, World Government bonds down 1.1%, and the S&P down about 1.2% so far in July. But of course, the equities are still up strong for the year.

All right, Rich. I have alluded to the fact that we have, as usual, some great topics, topics that you brought along, where we’re going to try and peel back the curtain a little bit in terms of how we see trend following and trend following models and some of the key things that people should think about, let’s put it that way, when they want to try and understand some of the things we do, some of the choices we make when designing trend following models. So as usual, I’m going to have a sip of coffee while I pass the mic on to you. I’ll do my best to keep up.

[00:09:17] Richard

All right, Niels. That’s great.

So look, this first topic, what I wanted to talk about is this. You and I often talk about this ourselves and this preference we have for trading medium to long-term models and why we keep away from the shorter-term models. A lot of traders and investors, they believe that opportunities exist in all timeframes. So they’re sort of viewing the market as a big open system with an infinite upper bound and an infinite lower bound, and they believe that trends are just as valid in the micro scale as they are in the macro scale.

But I’d like suggest that perhaps there’s a different way of viewing that perspective to suggest that there actually is a fundamental reason why our backtest processes and all of the research that we’ve undertaken seems to fall on the medium to long-term as a preferable stance for hunting these outliers than at the small scale. It’s not to say that outliers don’t exist at those small timeframes. They do. But noise is far more prevalent in those lower timeframes or those small timeframes.

As you stretch out to the medium, to the long-term, the prevalence of noise decreases. I just want to explain how a complex adaptive system works, and in particular, applying this to the financial markets. So the core principle of our approach, Niels, as we’ve discussed before is that we believe that trader impact is the dominant reason why prices move.

Now, of course, there are a lot of different investors and traders who apply derivative reasons that are things like fundamental factors, what’s inflation doing at the moment, what are interest rates doing at the moment, what are the global macro factors, but they’re more derivative reasons for what moves price. Because we’re price followers, all of our information has been derived from price itself.

Our philosophy lies in the fact that it is actually the trader impact of the buy and the sell decision and the quantity that’s being bought and sold at that particular instance that is responsible for moving price. So we see that as trader impact. The way we explain that trader impact is we say that everybody trades using their own models. Those particular models and the rules that are contained in those models determine the level of impact that a particular trade outcome has.

Now, it’s to be viewed like a big auction place where the brokers match the buyers and the sellers. They’re looking at matching quantities of the buy and the quantities of the sell to match them, and then that determines the price that is struck for that particular transaction. So it’s the trader behavior and their models which are basically implementing an applied force to the markets.

So if we get a dominance of one particular behavior associated with a dominance of a participant or a group of participants that are applying a particular model that all gives a buy decision at the same time, they get matched to sellers, but the bias of that impetus of that behavior starts exerting these pressures which creates a bias in the price series. So understanding that context, we can now come down to what is a financial market.

Now, we view a financial market as what we call an open, asymmetrical system. In other words, it has a lower bound. We view the lower bound as being the fundamental unit of trade activity, which is the tick. So if we look at market activity comprising all of these participants in a market who are all interacting in that market through their buy and sell decisions, the most discreet level we can get to is the tick. That basically is the starting point for the financial market.

As we accumulate more and more ticks of more and more trader impact, that starts consolidating into a price series over the course of time. So in the very short-term space, there is this lower bound. So it’s an asymmetric open-ended system because there’s no upper bound, but there is this lower bound. We also have other complex adaptive systems that exhibit that asymmetrical property, our universe being one. It’s an open, asymmetrical system which has a lower bound but an undefined upper bound.

In other words, the universe is still in the process of creating. We’ve got the stretching of space-time, we’ve got the creation of new things. It’s open-ended, but it has this asymmetrical lower bound. Now, when we come back to the point of the Big Bang where it all started, we see this asymmetry in place the same way as financial markets exhibit this asymmetry. So at this micro scale, when we start getting a few participants all with a fairly common behavior, so in the very shortest timeframes, the most participants that interact with those very short-term timescales are things like the high-frequency traders. They occupy that short-term space.

The people that are trading actively with precision with their models, a lot of convergent models are operating in that space. There’s a lot of ups and down pressures going on but there’s no defined bias at this point of time. It’s a very noisy environment, competing pressures going on all the time. But as we slowly, in this asymmetric open system, start increasing the space of possible states, in other words, the number of participants increases in their behavioral activity and the way they impact with the market, as we start going up to longer and longer timeframes because we’re getting more and more participant interactions and behavior, we notice this in sound acoustics.

This is where we find that in our sound acoustic studio, we’ll find that there is a lot of noise in our particular acoustics. The way to remove that noise is to start overlaying random series of sound onto that noisy environment. Now, we understand randomness as being indeterminate. In other words, we’re not saying that this is randomness in the true sense of the physics word, but we’re saying that it’s a noisy environment. There is no clear bias in the series. There’s a lot of linear up moves, linear down moves, etc.

So when you overlay another random series onto that, you get destructive interference working where the noise gets canceled out as we get more and more participants starting to interact in that behavioral mix. So the process of more and more participants and their different models being applied in that environment, we start to see the noise get dampened and we start seeing the bias in that frenetic activity, the bias starts exerting itself and amplifying because we’re getting a reduction in the noise and we’re getting an amplification of the signal.

That’s why when we look at monthly trends versus minute trends, if we’re looking at the minute timescale where we see this massive of different behavioral activity, a lot of noise in there, there is no order emerging from that noise, it’s chaos down in that region. But as we start going out to the daily timeframe, the weekly timeframe, the monthly timeframe, we start seeing these biases start amplifying and the noise reduces. So at the monthly timeframe when we observe a particular market, we can say clearly, ah, there have been fundamental factors that have been driving price from the low in 1980s up to the high in the 2020s.

We can start assigning key fundamental reasons for that overall movement in price. But the minute of that price movement has had a hell of a lot of noise in it over that interval. But what has led to that monthly time series is the bias in a series that has progressively amplified and it’s distilled the signal from that noise.

So when it comes to trends and these outliers that arise, when we look at a monthly timeframe and we see the big moves say from 1980 up to 2020, it hasn’t been through the change events associated with the noise, the up and down frenetic activity that has caused that directional change over the last 40 years. What it has actually been has been the major price moves, the major anomalies, the major directional shifts in the market, the major transitions in the market, that has created that overall bias in the series. It’s not the minutia that’s contributing to that, it’s the emergent effects of all of this behavior and the bias that has been amplified from that behavior that has therefore risen in that longer-term timeframe.

So, if we view it this way, we can understand then there is a preferential reason why we choose to adopt medium to long-term lookbacks because in a physics context, as far as a physical system, we can see that there are certain destructive interference and amplification effects which is actually creating these beneficial opportunities as we step out with more participation out to the medium to the longer term.

So I just want to suggest that that is a way to view it, which therefore gives a definitive reason why we don’t want to be trading in the short-term timeframe because the impact of the short-term price movement is inconsequential to the overall price movement that you’ll get of a series from this phenomenon of bias and from this phenomenon of amplification and positive feedback and all of these things we’ve talked about before in our series. So I just thought that that was an interesting way to start looking at that question.

[00:20:29] Niels

Yeah, I completely agree. I think for me at least, I think this is also why when we talk about short-term, and of course, there is an index that we quote every week, the SocGen Short-Term Traders Index. But from my point of view, I actually don’t think that that’s short-term trend following. I think some people might get confused with short-term means short-term trend following. For me, it just means short-term, because as you say, there is too much noise in these data series for a trend following system using the golden rules, which is always important, to essentially make money from that, at least that’s not my experience.

I have not really come across anyone who could demonstrate that they were doing trend following but on shorter-term timeframes. Of course, there’s exceptions, I’m sure of that. But generally speaking, I think “those of us who call ourselves trend followers,” we will gravitate based on the evidence towards medium to long-term timeframes because of exactly what you say, that’s kind of how we can find the true signal, so to speak, in the data series.

[00:21:42] Richard

Just on that thing as well, Niels, there’s a term that’s often used in complex adaptive systems called emergent artifacts, things that are created by the system itself. I think when we’re looking at these massive outliers that we all like to get involved with, they are actually emergent features arising from this complex adaptive system. They’re an emergent structural feature, not from an individual participant’s activities, but from the collective actions of all of these participants to create these emergent structures.

We see these emergent structures in the physics lab when we start trying to understand how we get a universe arising from quantum fluctuations. So if we go back to the Big Bang where they start basically concentrating the universe back to a primeval point, and they say, what created the large scale structure of our universe, and back at that resolution at that very fine scale or resolution, it sees minor perturbations, these minor variations between what’s random and what’s non-random, these minor variations that create this necessary bias, that ultimately, as the universe unfolds, gets amplified into these emergent structures like the galaxies, like the large scale structure of the universe, in exactly the way that in the financial markets, we get large scale structures unfolding, these emergent structures from these biases that arrive at the very small scale.

Because when you’re in the world of nonlinearity, you might have heard of that term, the butterfly effect, where a very small flap of a wings of a butterfly over in Brazil causes a hurricane over here in Australia, or a cyclone. That’s a term we use over here for hurricanes. But it’s this amplification property. It’s a small bias and it’s this nested architecture of these complex adaptive systems that serves to amplify that effect, that actually, small variations can create massive structural differences as an outcome in a nonlinear universe.

So that’s the way, I think, it’s useful to think in terms of these trends that we like to hunt. We know that they’re outliers. We know they’re anomalies. They’re massive in extent, they’re these emergent structures. It’s a bit like when you examine any complex adaptive system with a microscope at the fine scale, it looks like chaos. It’s a bit like looking at the computer screen or the TV from a very close perspective. You see all of these pixels, and they have no form. But as you step back medium to long term, you go back to back to back, you start seeing these emergent structures from all of these pixels consolidating into a picture. That’s an emergent structure arising from the interaction of these pixels.

It’s the same way in the financial markets from the interaction of all of these ticks, the behaviors of the participants in the market, how they’re interacting, that as you scale back out, you start seeing these emergent structures unfold such as these massive trends that ultimately get manifested at the monthly timescale.

[00:25:10] Niels

Yes, absolutely. Just to sidetrack completely here, Rich, but since you mentioned this thing about the butterfly, I don’t know if you’ve come across this. In the last month or so, there was this, I guess he was a contractor on the South Pole, a US contractor on the South Pole, that came out and said, well, he was very surprised to learn when he was there, that actually, there is some kind of nuclear reactor on the South Pole similar to what they have at the CERN here in Switzerland, where you can make these incredible fusions happening.

So what he said was, and this is the scary part, and talk about a butterfly here, he said, twice when they started it up, there was an earthquake in Christchurch. So we have to be aware of these butterflies. Anyways, now, another thing we have to be wary about is the next topic. So let me frame that a little bit because we are going to talk about correlations. There are, of course, some trend followers out there that believe the correlations are only important up to a certain point and there are others who believe that they’re actually important maybe beyond that certain point. But that’s not what we’re going to talk about today.

We’re going to talk about how, for example, when you know you have markets that for most times are highly correlated, how you might actually get a little bit of diversification into those by trading them slightly differently, so to speak. Is that fair?

[00:26:46] Richard

That’s fair, Niels. That’s fair. So to start off on this, in trading these outliers, we have a conundrum because diversification brings us two benefits. One is the benefits of correlation. In other words, the benefits that are brought to a portfolio by trading an uncorrelated mix of return streams, that is one benefit that’s brought by diversification. But there’s another benefit in our hunt for outliers in diversification increasing our ability to target more outliers or have more representation of outliers in our trade outcomes, this balance operating.

So most traders and investors recognize that if you have genuinely uncorrelated markets, there comes a point in time where the marginal benefits of diversification start getting less and less and less when you get up to a certain level. You might get up to 30 or 40 markets and you start seeing a significant reduction in the benefits delivered in terms of adding more uncorrelated markets into the mix, whether that assists the overall portfolio. So there is this limit reached or this marginal threshold that gets less and less. But what an outlier hunter will maintain is I say, yes, we recognize that and we accept that principle.

However, recognize also that with the vast majority of markets that we trade in our universe, or the available markets, there is a degree of correlation almost in every single one of the markets we’re trading. So whilst theoretically it might come to a level of 30 uncorrelated markets, you could probably get away with trading 60, 80 different markets that are less uncorrelated than the optimal 30 to 40 and we’ll start getting this marginal benefit decrease at say 60 or 70 markets. But then beyond that, why go further beyond that?

Now, the outlier hunter is saying, well, the reason we go beyond that is because diversification offers these additional benefits in increasing the frequency of representation of outliers in our distribution of returns at the portfolio level. So we’ll say we want to go further than the 60 or 80 where the marginal benefit decreases. But there is a little trick we can apply by using system ensembles. We talked, Niels, before about how I like to use what I call ensembles of trend following systems with my program.

So in other words, if I’m trading a single market, I might use say up to 10 different trend following systems to trade that single market. But each of those systems that have been developed using a trend following philosophy, they’ve got a different entry, they’ve got a different initial stop, they’ve got a different trailing stop, and a different exit mechanism. So they’re all individually unique. The reason why we make them unique is that when we bring them together as an ensemble, the system design itself serves to break the level of correlation that exists in the trade outcomes of trading that ensemble as opposed to trading a single system on a single market.

So let’s say then that we had two very correlated markets. Let’s say we had sugar and London sugar, two very closely highly correlated markets, or let’s say we’re trading Brent oil and we’re trading crude oil. Historically, they’ve had very close correlation. They might have a 0.7 degree correlation, highly positively correlated markets. Now, when I apply this ensemble approach with these trend following systems, what I find is that A, I get a much greater trade frequency per market that I trade because I’m using multiple systems as opposed to one system.

What I’m finding is that the ensemble effect because it’s using principles of what I call co-integration and correlation, co-integration is a long-term relationship basically through structural design that enables you to ensure that one system operates independently from another. So when we have different entries, different initial stops, different trailing stops, it’s forcing an outcome where they’re not going to be trading in exactly the same context as the other system. It’s breaking the relationship of correlation that exists because you’re trading these uniquely configured structural systems.

This is bringing this feature called co-integration into your portfolio, which is therefore allowing you to trade fairly highly correlated markets with an ensemble of systems and you get totally different results when you’re applying those ensembles on those two markets even though those two markets are highly correlated. So an example is when I’m applying an ensemble of systems to say Brent and crude, if I visually look at the price series say for 30, 40 years, they are incredibly highly correlated. But remember before, Niels, we were talking about the small variations that can create massive outcomes in our nonlinear world.

We see that the very small differences that occur, in those highly correlated series, are enough when trading an ensemble of systems that we get very different trade histograms produced from each of those markets, even though we are deploying the same ensemble of systems to one market as another. That’s because the interaction of our systems with those markets, there’s two things that’s going on.

There’s the relationship of the market itself and if that market itself like Brent or crude have these material price moves or outliers, that’s one aspect of the outlier. The other aspect is our ability of our systems to capitalize on that outlier. So that’s the design of our systems. Do they have the ability to deliver outlier trades through their design? There will be certain events, just based on the randomness of the market, where one system will catch a large component of an outlier and another system just because of one very small thing like too tight a stop or too tight a trail is going to be snagged out of that outlier of that price series. But when we’re trading an ensemble of trend following systems, we find that we’ve got a better ability to capture the meat of that outlier as opposed to being dependent on a single system extracting all the juice from that outlier.

So a single system is problematic because it’s got a single design being applied to that outlier, we might find that that system gets kicked out and it might take a long time to get active again back into that outlier. But when we’re trading an ensemble, we’re finding that we’re getting the benefits of some systems get snagged out, but because I’m applying 10 systems, maybe 8 systems are still alive, 2 have gone. Or I might find one system coming in and out, in and out, in and out because it keeps on getting snagged out of that particular outlier, but the other systems on balance stay for a long period of that actual outlier event. So the trade histograms produced are considerably different.

When we’re looking at the problems associated with correlation in a portfolio, it’s associated (I think we’ve talked about this before, Niels), we say it’s the problems of correlation that exist in our trade positions in the portfolio. For instance, we might find, just by happenstance, that when we’re trading a single system on these two markets, it might take fairly similar trades at similar times over the course of the event. When we plot those trade outcomes, we’ll get this similar histogram produced for both Brent and both crude.

But when we’re trading this ensemble of systems, we get this significant dispersion in results between the outliers produced in Brent and the outliers produced in crude. So what we’re finding is that the nature of outliers themselves are very disruptive for correlation. Correlation is a linear property that exists between the relationship of the moving parts of one system against another system. It’s looking at the linear relationship between those moving parts. It’s not looking at the magnitude of those moves.

We might, for instance, get one market that is highly volatile but moves in a very similar way to a much more subdued market. The movements, overall movements are almost identical, but the degree of magnitude of the move or the volatility of the movies is significantly different, and correlations won’t pick that up. They’re going to say that they’re very correlated because there’s this linear relationship between the two. But in our world of outliers where nonlinearity is so important, we find that the actual magnitude of the move and the volatility of the move are very important. It’s not just correlations that are important.

When we’re applying our ensemble of systems, we’re finding we’re getting the best of all worlds here because it’s allowing us therefore, if we can produce a fairly uncorrelated relationship between two markets that are highly correlated like Brent and crude, if our trade outcomes can be fairly uncorrelated when we’re trading our ensemble on one versus the other yet the markets themselves are highly correlate, what that means is that at the portfolio level, actual return streams in our portfolio are less correlated and less dependent on the market correlations that exist in that portfolio.

So what that allows us to do, when we use an ensemble of trend following systems, it actually allows us to trade further more diversified across markets because we’re less worried about the natural correlations that exist between the market data, we’re more concerned with the correlations that exist between the trade data. Does that make sense?

[00:37:37] Niels

No, it makes complete sense. Of course, it will go against what some people believe where you say, well, we just apply the same system with different timeframes. Maybe they’re not actually different enough by doing that. I think what you argue is very compelling. Of course, I don’t have any research to back it up, but it is compelling. I know on our side, at least within our firm, we do have two completely different approaches to trend following.

So not as diversified in terms of systems as you do, but we have it certainly not just trading one approach on top of the fact that you need tens or hundreds of different combinations of parameters to further diversify, of course, if you use fewer systems. But I do think it makes sense, Rich. I think it’s a very compelling way of looking at things even.

But again, this comes back to something that I always feel is great even though sometimes there are some heated debates about how one group of trend followers might be doing it. I think the beauty of all of this is that even if we do things differently, first of all, the important thing is that we believe in what we do, so we don’t have to be the same, we should be different. But even when we do things differently, as long as we stick to a few golden rules, we’re all pretty successful. I think that actually is a testament to the underlying philosophy of trend following, which is super important and should give many more investors confidence in the strategy than what we see today. But we’ll keep reminding them, I guess every week.

All right. With that out of the way, there’s a couple of topics I think we have left that we want to touch on. So in the interest of time, I’ll let you decide. We have about another 20 minutes or so, how you want to split it. So feel free to dive in, Rich, where you want to go. I know one of the topics for sure is why I would say most trend followers, and I don’t know if this is true or not, but I think most trend followers probably treat all markets equal, meaning the same. I think there will be some who have specific systems for specific markets, but that’s certainly one which is quite an interesting topic. So I don’t know if you want to dive into that. But I know you also had something else on your mind.

[00:40:15] Richard

Yeah, look, the reason I’m bringing up this topic which is this general appreciation by trend followers that every liquid market has a propensity to deliver these outlier properties. Now, the reason I’m bringing that to the attention of people is that you’ve got to be very careful when we make statements regarding we only trade uncorrelated markets and all of these things. Because we know that when we undertake our research, when we undertake our backtests, we know that historically, we can see correlations considerably change over time. We also notice this non-stationary nature of markets.

Things that have occurred in a backtest does not necessarily mean we can project that forward into the future. We understand those principles. One of the things I’d just like to state here is that the reason why we believe that these features called outliers are these universal phenomena found in all liquid markets is that these financial markets, they’re not physical systems, they’re human systems. They’re systems that have participants that are humans or they’re systems that have algorithms that have been designed by humans.

So it’s very different to the physical systems you see in physics such as Newton’s world of the universe, which are physical systems. These systems, called our financial markets, have humans as participants, intelligent participants, they are all fairly intelligent participants in this market, though on Twitter, you might not agree with it all the time. But there is inevitably these biases and this limited information available to each participant that creates these universal properties that we can find in any liquid market.

So when we examine any liquid market, we see that there is this leptokurtic tendency of every liquid market over the long term. Now, when I’m saying the long term, I’m saying over 30, 40, 50 years of market data, you can see that the properties of that distribution have this leptokurtic tendency. That’s why we’re saying that if we treat this game of trend following that we do as a long-term venture, all of these liquid markets offer these tails, offer this leptokurtic tendency, offer this universal feature that can be capitalized on with our models.

So our game is a game of patience. It’s a game of applying our principles to any liquid market. So when we start talking about correlations of markets, what we’re saying is, I’ve got to be a bit careful because historically, I see that these particular markets are correlated. But does that mean I exclude them from trading them? Not necessarily, because even trading highly correlated markets, we can see that the correlations change when we’re actually trading these things.

History has told us what was done before, but the present is telling us how they’re actually relating to each other today. That’s why I brought up that topic before about why I liked these trading ensembles. Because what it’s doing is it’s actually taking a risk management approach to breaking down the dependency that we might have over correlations. What I’m saying is there is a process that we can apply that naturally will reduce the level of correlation in your trade distribution or your portfolio by applying this ensemble of trend following systems.

We’re applying this static rules-based, systematic design-based approach to deliberately break down the correlations that exist in those markets. So one of the key things is that when we look at what are these outliers, why are they universal, we find that any complex adaptive system, whether it’s any liquid market or any of the systems that are applicable, a city, or planet, a universe etc., we find that these universal properties appear in many different forms but they are regarded as what we call these transition events, Niels.

So when we talk about change in a system, there are two forms of change going on. So if you could imagine, imagine we’ve got a system that is operating and over the course of its life evolves, over the course, of time to an equilibrium state. Over the course of time, it unwinds until ultimately, it’s in a level of equilibrium and no change is possible because everything is perfectly distributed.

That’s the way physical systems tend to evolve over time. That’s the way we believe our universe is unfolding over time, from what we call a point of low entropy to a point of high entropy. The order in the universe is slowly dissolving or dissipating, and ultimately, we’re getting to this cold point in the future called equilibrium. At that point, we’ve got to be worried because at equilibrium, when we’ve got thermal equilibrium, there is no activity, everything is a cold state form distribution of heat throughout it. Everything is basically frozen in time, there is no movement.

So when we see these complex adaptive systems evolve, they do change over time, but they don’t change in a linear predictable way. We find that they change in what we call fits and starts. They have a period of stability, then they have a massive transition to bring a new regime of a new form of stability, then we have a massive transition, then we get another period of stability, a new regime. So we find that as the complex adaptive system is unwinding, it’s going through these stasis periods interspersed by these transition events.

What these transition events are signaling and why they are so significant, these transition events, in any system we find that these transition events are usually accompanied by horrible things, Niels, like mass extinction events. We find that the participants in the prior regime who are adopting these particular behaviors, they were focusing on the predictable attributes of their prior regime. They find suddenly, when this transition takes place, that no longer do their predictable methods work. There is no predictability in the transition because the transition is actually a state between two regimes.

What we find with these transitions is that the transition itself disrupts all of the stability, interrelationships and dependencies of the prior system, to destroy that predictability and then a new state is created, a new regime is created, which is almost significantly different to the prior regime. We get these transition events being a period of separation, a period of massive change between these fairly predictable, stable regimes. So most traders and most investors, we get caught in this predictable regime for a period of time, 10 years, 15 years, 20 years. We start thinking that things are very easy.

We’ve seen the stock market rise for the last 10 years. From 2008 GFC up to 2020, there was this continual growth in the stock market. We had this particular regime, we had central bank intervention suppressing volatility. There were all of these characteristics, these predictable characteristics, and most of the participants that were alive during that regime, they started to see the predictability in that regime and they started jumping on those opportunities and exploiting those predictable regimes.

But lo and behold, we suddenly get a transition event. The transition event is this period of massive change where there is huge disruption to the prior stable state. So previously where the central bank was buying the dips and selling the tips or buying the tips and selling the dips and applying this volatility compression control, we suddenly find that now we’re in quantitative tightening. We’re not in quantitative easing back in those days. The regime has flipped. There’s a totally different environment at play now.

We’re finding that it takes time, after these transition events, for this new stability to emerge. So this separation of the stable regimes is interspersed with this transition event that takes a fair while to sort itself out. But during all of that period that these transition events are occurring, this is when we get these major trending periods. This is when we get these outliers. This is when we find that all of the changes that we saw in that predictable regime were associated with oscillations about predictability. We saw it as a bit like a swinging pendulum.

We got this predictable pendulum in that prior regime where everything was converging about the mean, oscillating up and down in a predictable way, people were exploiting it. But suddenly, we see that this level of change in that prior regime, during a transition event, becomes a totally different form of change. It’s much more chaotic form of change. There is no predictability in this change because what it’s trying to do is it’s trying to find the next stable state to settle on. So it could be undefined in as far as time, it can be undefined where it’s going to settle. We don’t know.

There is no quantitative statistical techniques during the transition that we can use to define how long it’s going to go, how big this transition is going to be. In fact, what is the nature of that transition? It’s almost chaotic. But this is where these outliers emerge. So in this complex adaptive system, we’ve got a bifurcation of system states. There’s a state of stability and then there’s a state almost of chaos or transition.

There are these two states at play generally. We get these periods of transition on either side of them, we’ve got these different regimes. One might have moved to a lower order, one might have moved to a higher order, totally different regimes. Different participants will benefit from those different regimes. It’s a bit like being presented with a new environment. We’ll find that a different form of species is successful in that environment. New participant behavior is successful in that environment. In the prior environment, a different form of species was successful in that environment. Same way as we should view traders.

A particular type of trade activity, a very successful investment method might be totally inappropriate for the next regime. But separating those regimes is this transition, this chaotic frenzy where the entire arrangements of the prior regime are reshuffled and reconfigured to create this totally new regime. These transition events are these universal features we find in every single complex adaptive system we see. So why shouldn’t we expect them in financial markets?

We must expect them in financial markets, and we do see them. But I think that the general economic mindset is to not consider it this way. I think the general economic mindset is to treat the form of trends in our transition environment the same as the form of trends we find in a stable regime. In that stable regime, yes, there were trends in that stable regime, but they are not more of a mean reverting character, they are much more predictable. But in this zone of transition, these trends are much more chaotic, Machiavellian in nature, totally unpredictable.

So that’s the way I like to view our world of finances as having this bifurcation of states. Where we focus on is this very chaotic world in the tail regions, which you can’t rely on things like backtests to give you guidance, you can’t rely on statistical measures to give you significant guidance in this chaotic zone. You’ve got to rely on age-old principles like cutting losses, short, let him profits run, structural design features applied at the portfolio level to mitigate your adverse risk.

These other complex methods using a heavy leaning on statistics, etc., are certainly valid for more predictable, more stable regimes, but perhaps are less applicable in these more chaotic regimes. I think I’ll just finish it off there, Niels. What do you think?

[00:54:07] Niels

I think that’s great. Another way of highlighting this, we talk about golden rules, but I think also what we’re saying is that there are some universal truths that we can pinpoint and that we can stick to. When I was listening to you talking, I was reminded of a few things. One is, of course, regarding this thing about whether markets are efficient or not. I was reminded about my conversation a few years back with Andrew Lo, Professor Andrew Lo, who basically said yeah, markets are efficient most of the time, but it is when they are not efficient that these transition periods that you talk about happen. Of course, we can see this very clearly.

Look at a chart or just turn on the news. I think this is the thing. Sometimes I feel that we talk about financial markets and how to invest in them as if we’re doing something that is a little bit outside the norm. But if you just turn on the news, probably 90% of a news show is about things that are disruptive, things that are changing. It’s never about some kind of rosy picture of stability and tranquility. It’s something that is going wrong somewhere, whether it’s almost civil war in France at the moment or whatever it might be. It’s never stable. It’s never static. So why wouldn’t, as you say, the financial markets display that?

But I think what I really liked about your explanation, and I think this is something that we’ll continue to try and educate people about, and that is, it’s also seen in the book that I’ve mentioned a few times on the show probably a while back called The Fourth Turning, which, as you rightly say, when we design our models and why we believe that these are complex adaptive systems is that they are based on humans deep down. When you read the book The Fourth Turning, of course, what you’ll learn is that every fourth turning is disruptive, is something where things simply break and new things emerge.

What is even more great is that Neil Howe, the author, will be on the podcast in a few weeks explaining his new book called The Fourth Turning is Here, and I simply can’t wait to hear that conversation and for us to record that conversation. But these are deeply ingrained human behaviors that we, as trend followers, at the end of the day exploit through system design, through discipline, and by following a very few number but very important rules, and then of course, we then apply this with 100% discipline through a systematic or mechanical process. That’s what it’s all about.

Of course, we’re still the minority in this world, but it’s so important and it’s fascinating to think about this. Now, one of the things that I quickly touched upon, before the last topic, was this thing about most trend followers use the same rules across all markets. But I think it’s fair to say that there are probably some and maybe there are good reasons why you may want to treat certain markets differently.

But my question to you is, and I don’t know if you thought about that, if you wanted to say well, hang on, part of my risk budget doesn’t have to apply all the same parameters to all markets, would you then (and I guess it’s kind of an open-ended question), have to say, well, okay, I would then at least have to cluster certain markets in groups that exhibit more of the same profile (obviously, probably based on correlation), and say, okay, so if I was to have another set of parameters for this part of the portfolio, it should be applied to markets that are very highly correlated, like fixed income markets, for example, some commodities, like you mentioned, Brent and crude, etc.

I guess you would have to have some kind of correlation clustering if you wanted to apply different rules to different markets, if it wasn’t just by applying different systems across all markets.

[00:58:45] Richard

It’s easy to imagine. For instance, let’s say a trend follower wants to target a different form of edge to what others are targeting, they might say, all right, this particular group of markets have these characteristics which are very similar, and therefore, I apply a model that trades them in a similar way, which might be different to another group of markets that is trading in a different way, I’ll apply a different model. So you’d be bifurcating your portfolio to allocate to one form that’s basically using the characteristics of the market and the other that’s more a universal application.

It’s complicated, because then you’d have to say, well, how do I balance that entire portfolio of those two outcomes in a way that I can get the benefit of both of them basically shining through? That to me is very complicated. I don’t know how you would do it. So, one, you might find that a reweighting or a rebalancing is putting a bias or a favorable bias to one form of system versus another form of system. I’m not sure how you do it, Niels. I assume it’s a bit like an exercise where we’re allocating a trend following portfolio into a traditional portfolio.

I suppose you would use the backtest as a basis to what is the appropriate weighting to do, and then you’d apply a particular weighting to those different types of strategies. Hopefully, then that weighting holds some tenure into the future. But yeah, I’m just so fortunate, Niels, that I don’t have those decisions to make, and that I can invest all of my focus or my very focused mindset on a particular form of trend following. That’s all I do. That’s all I’m good at.

[01:00:31] Niels

Absolutely, and so you are. All right, Rich, this was great. Again, some different views, hopefully, things for people to think about when they consider investing in trend following, why they should do it, but also for those who want to dabble with it themselves, hopefully, there’s also been some stuff for them to think about and maybe do some further research on. So I highly appreciate that.

If of course people listening to us today do appreciate these more in-depth conversations as well, then why not go and show it by leaving a rating and review in the preferred podcast app that you use? Because actually, these reviews do help quite a lot.

Next week, I’m joined by Rob. I think that’s probably his last appearance before his always prolonged holiday. But there we are. We’ll live with that another year. But it will be your chance to get Rob to talk about some of the stuff that is important to you, some questions that you might have. Obviously, he’s just published his latest book. So if you have any questions, feel free to email them to info@toptradersunplugged.com and I will do my best to get Rob to answer them.

That’s it for now. From Rich and me, thanks so much for listening. We look forward to being back with you next week. Until next time, take care of yourself and take care of each other.

[01:02:01] Ending

Thanks for listening to the Systematic Investor podcast series. If you enjoyed 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.

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.