267 Systematic Investor Series ft. Richard Brennan – October 28th, 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 market through the lens of a rules-based investor. Rich, it is great to be back on the podcast after my three weeks being off air.

A big thank you, of course, to Alan for allowing me to not having to record from various hotels during my travels. And I thoroughly enjoyed the conversations that Alan had with Andrew, Nick, and Rob in the last three weeks, so you and I have a tough act to follow today. But it’s been also a while since you and I have spoken, so I’m always curious to what’s happening. What’s going on down under?

[00:01:05] Richard

Well, Niels, firstly, congratulations to Alan. Great job he did. It’s in good hands when you decide to have a bit of a break and a holiday. It’s been a great few episodes he’s been looking after for you, so he’s done a great job.

But look, I suppose what’s happening over here in Australia, well, all eyes in Australia are pretty well focused on the escalating tensions in the Middle East. So that’s something that we’re pretty well concerned about over here. Fortunately, we live in a very safe country, but we are concerned about that.

Basically, we’re seeing these rising geopolitical tensions across the world in certain hotspots, Ukraine, Middle East, etc. We are sort of entering a more unstable time. But over here in Australia, we’re also particularly focused on the interest rates, because here in Australia, we’re very much dependent on our export trade, particularly to China, etc. So the interest rates effectively significantly affect the exchange rates and our ability for our export economies to do well over here.

But also, in Australia, for at least the last few decades, Niels, we’ve had a significant boom in residential property prices. COVID of course sorted out commercial property prices over here, so they’ve been struggling a bit since COVID. But the residential property market over here, which is a mainstay of most Australian investment over here, has been in a bubble for the last two decades. So we’ve been watching this rise in interest rates with a bit of concern thinking that perhaps this is the time where we face a difficult time in residential property.

The US and Ireland, etc., they experienced these problems in 2008 I remember following the GFC. But we were protected from that because of our dependence on China and the boom in China at that point in time which sort of helped buffer us from the impact of devaluation of global property prices. But here in Australia, they just continued to rise. But sooner or later, this bubble is going to pop. So in Australia, we’re particularly looking at that.

In my other job, I do a bit of work with a fixed income broker, we’ve been keeping our eyes very firmly placed on the US yield curve. We’ve just noticed that I’m actually quite happy with how this yield curve is slowly morphing. So we’re seeing the rise in the short-term interest rates of course with inflation, which is putting short-term interest rates back to a level that I saw back in the late 1980s when I was doing a lot of business valuations back then and there was a significant premium incorporated into speculative investments. So we see the short-term curve has lifted.

But now we’re also seeing the long-term end of the curve also rise. Obviously, in the US, the expectations of a hard landing are sort of coming off a bit and their expectations are starting to build their premium in the long-term rates which is lifting up their curve as well. So what I’m seeing is a return to almost the 1980s normal yield curve, which I’m hoping is the case because to me, that was a much more sensible time for valuing investments.

You and I always talk about this decade since the GFC perhaps being the anomaly in as much as the quantitative easing that went through the system post-GFC created a large number of problems for the global economy. One was this increasing correlation between all asset classes and the difficulty in finding value in any investment class because of this rising correlation amongst things that were typically uncorrelated previously to them.

Also, the reduced premium for risk or speculative investments. We had things like the growth in Bitcoin currencies, DeFinance, this mad speculative rush for NFTs That to me was symptomatic of an economy gone mad; QE, loose economy, massive inputs of money into the system. The only place to invest was in speculation, speculative investments, hence the massive growth in the equity markets post-GFC.

So what I’m seeing now, with this regime shift back to a more quantitative tightening posture, is the resurgence of the risk premium, which I think is essential, and perhaps the resurgence of better valuation methods, the resurgence of fundamental investment. All of these things that have been struggling during this very speculative mad economy might see a bit more blue sky going forward.

Of course, trend followers, they’re going to be the major beneficiaries of this decoupling of this fairly correlated global economy. That’s how I’m seeing things anyway. But yeah, what do you think there?

[00:06:42] Niels

No, no, I love the fact that you describe Australia as a safe country because when I think of Australia, I think of all the deadly reptiles that you have in Australia. But that is of course a slightly different risk than what you were referring to.

But you know, I don’t disagree with what you’ve said. I think people who have listened to the podcast for a number of years know that I’ve been very concerned about interest rates for a long time now. I’m not even sure that we are quite coming to the end of the initial wave of where interest rates are heading. I think long term, they can go a lot higher.

I’ll reiterate the fact that we need to probably imagine the unimaginable. That may still take a few years and I think at some point, probably not in the too distant future, I think we will get a period of time where interest rates will come down and things will calm down a little bit. But I think that’s going against the much longer interest rate cycle, which I firmly believe in exists. So yeah, the investment landscape I think has changed perhaps for the remainder of our lifetime, actually, and for a lot of investors’ lifetimes and we need to prepare our portfolios accordingly.

[00:08:07] Richard

It is not so unimaginable over here because back in the day of the 1980s, about 1987 I think it was, I think we had interest rates over here at 18%. So I think there’s a lot of potential for more upside in interest rates. But I don’t necessarily think that’s a bad thing. I think it’s bringing back a decent level of risk premia into things. Aside from that, Niels, the battleship is starting to fire.

So over the last 10 months, my battleship has been experiencing tepid waters, doldrums, a breakeven. But I think probably with the Middle East uncertainty, etc., I’m starting to see some of the trends start resurging. So my hope is that we see a strong end to 2023. The things of course in my very small portfolio, my retail-driven portfolio, we’ve had OJ, London sugar and sugar being things that over the last few years have really been supporting my small little battleship.

But I’m starting to get a good contribution now from Bitcoin, the long trade in Bitcoin starting to pop up and also some of the FX markets that I trade. I’m starting to see some good opportunities in the Swiss yen for instance, and the Canadian dollar and the Swiss dollar. I’m seeing good trends emerging from that side. But as you know, I’m pretty limited in my bond exposure. So I don’t have the benefit of the short ride we’ve been having in the bonds, but yeah.

[00:09:46] Niels

It hasn’t been much of a benefit this month, I think. It’s been a little bit tricky. Or maybe even this year.

[00:09:52] Richard

It would probably be fortunate for me.

[00:09:55] Niels

With the March event as well. But you’re right.

[00:09:58] Richard

Before we move on, Niels, I saw something and I’ve got to commend and congratulate the DUNN team because I noticed that DUNN won the HFM European Performance Awards in the UCITS managed futures category. So very well done. A big clap to the DUNN team. How do you feel about that?

[00:10:21] Niels

Well, of course, I’m happy with that. It’s true. We did actually a couple of weeks ago, I think it was, maybe, it was announced that we had won that. What was interesting about it was, first of all, that I actually was in DUNN HQ during the time when it was announced because we were celebrating. As we do all always at this time of year, we were celebrating another anniversary, this time for the 49th year in business. So next year is the big 50, which I’m sure will be quite spectacular.

[00:10:56] Richard

That’s going to be a big year. Is that the biggest year for any trend following fund that’s currently active today?

[00:11:03] Niels

Well, I think it’s hard to define. But I think maybe you could say that we are the longest running pure trend follower because I think Campbell is older by a couple of years, but I’m not sure that they, well, they have other strategies today. They still have trend, but anyway, it doesn’t really matter. It’s a long time to do one thing and I actually don’t know of many hedge funds in general, I think this is the strength of being systematic and trend follower, is that we’re starting to see these incredibly long track records from us and from some of our peers which far outstrips the general hedge fund world, and I think that’s great.

But you know what? What also makes it special, if I can say two words about it, is actually because I know how competitive this space is and how great are our friends and peers and how talented they are. So naturally, we have to be very grateful when we receive an award like that in recognition, in a sense, for our continued effort to find better ways of doing trend following.

Interestingly enough, the fund itself, our UCITS fund, was actually launched all the way back in 2011. So it does have one of the longest-running track records as well in the UCITS space. Back then, when I started working with it nine years ago, it was such a small vehicle and not a lot of people even knew what the word UCITS stood for.

Maybe the final thing I’ll say about it, Rich, since you brought it up is just to say that we also, obviously as a firm, we have to recognize our great clients who are invested in the fund and have stayed true not only to trend following through the good and the bad times, but also to DUNN for a very long time. So we’re quite privileged in that sense to be partnering with these investors on this trend following journey. But I appreciate that that also had made it to your radar.

But before we get too much focused on us, let’s move on. A little trend following update perhaps for the week. Certainly, I think there was maybe a little bit of giveback this week. We had energy prices that came under pressure. I think crude was down about 3%, maybe some of the other products as well. So that wouldn’t have helped trend followers I think at this stage.

Also, fixed income, you mentioned it yourself. Yes, it’s been a great downtrend, but we have seen a little bit more support coming in with this Middle Eastern thing for sure. So that certainly gave a little bit of headway this week in trend following land. And then equities. Of course, speed of trend is very important here and some managers may already be short and therefore would have enjoyed last week. For longer-term managers, it’s probably still costing a bit of money when equities go down as they did this week, especially the tech stocks had a rough week.

Funnily enough, a sector that we don’t talk much about, but I did notice that meats had a big reversal this week to the upside for people who may not follow them. So I think that, since they were in a bigger downtrend, would have cost some money. And then the bright spots, you mentioned it yourself, some of the softs, I’m sure OJ (but maybe not for that many trend followers), but some of the other softs did quite well this week, and grains, and maybe even some of the metals.

So yeah, anyways, what’s really funny though is that the trend barometer finished that at 39. People might say, well, what’s funny about that? Well, the funny is it finished at 39 last time I spoke with you six weeks ago. So nothing essentially has happened since we last spoke, which I actually think is kind of true if you look at performance, a little like of last month, a little bit down maybe this month, or flattish. So maybe it’s not a bad indicator of what’s been going on or lack of it, despite all the turmoil we see around the world.

That brings me of course to a little update on performance. Down 41 basis points this month for BTOP 50, up 2% for the year. Down 84 basis points for the CTA Index, up 93 basis points for the year. The Trend Index up 0.25%, up 1.19% for the year, and the Short-Term Traders Index is up 0.5% but still down 1.25% for the year. Of course, that contrasts equities which are down more than 4% for the World Index, MSCI World, still up 5% for the year. World Government Bonds another month of downward pressure, down 60 basis points so far this month, and the S&P 500 is down about 4% month to date but still up 7.25% for the year.

Now, Rich, we need to jump into some great topics courtesy of you. But before we do that, you kind of mentioned one thing when talking about the battleship, and that was OJ. I started thinking on my trip recently because I hear a lot of talk about OJ. We don’t trade OJ, but a lot of people do. I say a lot of people do probably where they are still able to. But then when I ask people, so what’s going on with OJ, I don’t necessarily get a great answer in terms of why is that trend just continuing to go up. As trend followers, of course, we don’t really need to know that. I still think it’s interesting.

So what do you do if you don’t know? You google it. So the Google response was the drinks price has shot up due to hurricanes and bad weather that slammed Florida, the main producer of orange juice for the US, last year, which reduced the crop to its lowest level in nearly 80 years. A late freeze in the end of last year also devastated the crops. Okay, so maybe that is a reason.

But then I thought of something else. Because it’s a lot, or at least to me, if we have had a problem last year with some bad weather, it’s still a long time following that to see the trend just keep going up like there’s no tomorrow really. So I was kind of thinking, and I don’t want to make it sound the wrong way, but I could imagine that maybe at some point, an uninformed journalist might pick this story up. So I thought maybe we should get ahead of it.

So I was thinking, given the low volume in OJ, could OJ, inadvertently perhaps, be kind of cornered? I don’t necessarily think like the way the Hunt brothers did it with silver. But I’m kind of thinking, if you have a market with low volume but you have someone with an interest in the market moves up either from a philosophical point of view, systematic point of view or not, then essentially, it just keeps pushing the price up like we’ve seen. So a little bit of a cheeky point.

[00:18:44] Richard

If you consider it, Niels, I’ve been on the trend of OJ since 2020. These reports you were getting from Florida were based on last year. So to me, something else is going on and it has been going on for numerous years. So I wouldn’t be ruling out the cornering of the market. I can imagine someone’s been watching a bit too much Trading Places and have put the squeeze on frozen orange juice.

[00:19:15] Niels

I think it’s still OJ, wasn’t it? Yeah.

[00:19:16] Richard

I think it was. It was. So yeah, something’s happening. I don’t know what it is. But I can’t account for it in terms of Florida’s orange juice production being a single factor.

[00:19:27] Niels

No. Anyways, when I was in Florida last week, the weather was very nice. No hurricanes in sight. So maybe we’ll see. Maybe we’ll see some kind of normalization. When we do, it will be interesting to see how “the exit will be handled” by various systems. Only time will tell. All right.

[00:19:48] Richard

I’m going to get a big slap in the face and be quite depressed when I speak with you that day.

[00:19:51] Niels

Okay, fair enough. All right. It’s time. It is really time to dive into your topic, Rich, and it’s a good one, because we’re going to talk about different investment philosophies demand different interpretations and that there is no one-size-fits-all approach. Now, when we last spoke, which I think, and of course, when I say like that, I know it was episode 258 back in August. We dug into the nuances of complex adaptive systems.

Now, these concepts, while often overlooked by the investment world, they do offer a unique perspective on the behavior of financial markets, and fully understanding them might reshape the way we decide to interact with these financial markets to extract opportunities and manage the risk. That is really how important this topic is in my view and I know in your view as well.

Now, these concepts highlight the perspective of a systematic trend follower on the market and guide how we harness our advantage from these intrinsic systems. Our previous discussion was very technical, quite dense in that department, to say the least. So I think we promised back then that in the next conversation, we would distill some of the key ideas, and through this new lens, maybe address some common queries on topics like system design, portfolio management, risk management, and the topic of diversification.

So as mentioned before, I really strongly recommend that for those of you who haven’t listened to that conversation, you really should do because it provides a foundational understanding of Rich’s perspective on the intricacies present in the financial markets. Now that we have you back, Rich, this week, we’re going to get into certain topics that are typically addressed independently.

However, for a diversified systematic trend follower, these themes are more, can I say, intertwined than they might initially appear, and recognizing these connections is crucial for investment managers, especially those centered around absolute momentum like our friends, including yourself, who are classified as outlier hunters. So with this brief introduction to today’s masterclass, Rich, the floor is yours.

[00:22:30] Richard

Thanks very much, Niels. So yes, last time, we did enter into some fairly complex territory that might have lost some listeners. So I’m going to give it a second cut today, looking at some of the important principles we discussed then, and focusing on some key areas such as risk management and diversification which do have direct linkages and they certainly influence the way I approach trading these markets.

So at the heart of our conversation in that last episode, we looked at different interpretations of trend arising from a complex adaptive system which we’re saying, are these financial markets which represent this complex adaptive system. So it’s crucial to understand that this is a vast domain, a huge domain. Even within the umbrella of trend following, there are diverse interpretations of what a trend is and how we can capitalize on directional price movement.

So in one instance, we could analyze, for instance, the consistent momentum pace and quality of a price trend within a single market, or one might assess trends relationally comparing momentum qualities across correlated markets, choosing optimal signals or adjusting risk dynamically within correlated groupings. So these myriad of different interpretations lead to varying philosophies on understanding trends. Consequently, Niels, we have different strategies to benefit from these nuances.

Our previous discussion aimed to underscore the point that trend following isn’t a monolithic exercise. There are lots of diverse groups within trend following land and they all have different approaches to extracting different facets of this edge from their concept of what is trend within these markets. There isn’t a singular definitive method. There’s lots of different styles and approaches and subsequent philosophies.

So these variances sometimes spark intense debates. You and I have heard them, numerous debates. How much diversification is enough? Is use of stops critically important where others might not use stops? All of these debates stem from the different models tailored to capture different facets of momentum within trending markets. If we’re not all chasing the same edge, our trading models have to naturally diverge. They will not be the same.

So under this sort of broad interpretation, this broad church of trend following, we can envisage trend edges as a spectrum with two predominant types of momentum highlighted in the academic circles. On one side of the spectrum, we have cross-sectional momentum, and on the other side of the spectrum of trend following, we have absolute momentum. So these sit at these extreme opposite poles of that spectrum.

While some trend followers specialize in one type, others may harness both or have different aspects of each within their trend following programs. Consequently, there’s a very rich tapestry of opinions on how to best leverage trends depending on one’s position along this spectrum. In this previous chat we had, Niels, we discussed the impact of both cross-sectional and absolute momentum, noting their influence on various trend following manager strategies. So how do you feel about this so far? Recognizing as a broad church, we’ve got these different interpretations.

[00:26:23] Niels

Yeah, I think that’s great, Rich. It kind of sets the framework, and then maybe we could dive into each of them and then pull together some of the risk management in the end. I will do my best to maybe voice my thoughts on where I might have a different interpretation or aspect, and then we’ll just see how it goes.

[00:26:45] Richard

All right, Niels. That’s great. So we’ll start with cross-sectional momentum just to give a bit of a summary and an overview. Cross-sectional momentum focuses on trending patterns observed across various assets. So it examines how a pronounced trend in one market can influence the trending behavior of other correlated markets. This momentum, this form of momentum, is concerned with the interconnectedness or the spatial relationships between returns of different assets at a particular point in time.

We can picture cross-sectional momentum as the tide that lifts all boats; how a trend in one market can ripple through and affect another. The origins of such trends can be both endogenous and exogenous. So let’s look at the exogenous factors. Exogenous factors might include things such as global macro fundamental elements such as geopolitical tensions, climate shifts, significant economic events, natural disasters like the Fukushima impact in Japan, technological advancements.

These macro events can influence multiple markets in different ways, leading to coordinated price movements relationally across these markets, which can be positively or negatively correlated reactions.

[00:28:13] Niels

So external events, just to be clear.

[00:28:17] Richard

External events coming into the financial markets that are very interconnected. They can have broad consequences across markets. So that’s looking at things cross-sectionally. But also, when we look at things from endogenous events, we recognize that all trading activity and investment activity in the market exerts these behavioral pressures through the buy and sell instruction of their trading impacts in the markets. So we can view things also endogenously from a cross-sectional perspective where participant behaviors alter over time.

There are lots of different reasons for why these participant behaviors alter over time. But you can see that cross sectionally, because these participants are not involved in just a single market, they’re often involved in diversified portfolios across markets. so when their behavioral tendencies change, they will not necessarily be restricted to a single market.

The behavioral influences of the trading or investors activities, which span lots of different markets, are going to have a cross-sectional impact in degrees of correlation from these endogenous sources within the market itself. So there are both these exogenous and endogenous sources within a cross-sectional approach.

[00:29:43] Niels

So let’s make a very concrete example. Let’s just say that for whatever reasons, we have a portfolio, a diversified portfolio. This is then a control that we’ll get to by some kind of correlation matrix and other things. But let’s just say, for argument’s sak, that markets in the equity sector start to sell off and long positions are being exited.

What you’re saying is that because the manager in question would be reducing its exposure to equities, that might have an impact on positions in say fixed income because the correlation effect will force the portfolio to make some adjustments for the fact that there is now a lower exposure to equities. That’s really what you’re saying.

[00:30:33] Richard

That’s one aspect of it. Another thing is that an investor’s bank balance might be spread across different markets, and they might be trading leverage markets. So they might be experiencing a crippling blow in equities, for instance. That therefore might be influencing margin requirements, all of these things which is forcing them to do things in other investments that they’re invested in across asset classes. So their influence is never just restricted to a single market. Because of these connected markets, it ripples across all of them, lots of these different behavioral impacts.

[00:31:11] Niels

Great. Super.

[00:31:13] Richard

So for trend followers interested in leveraging these spatially correlated features of cross-sectional momentum, it’s essentially the interdependencies that exist between assets or asset classes at any given moment. They will pay very close attention to correlations and the importance of correlations in their trading methodology. So they’ll analyze how price shifts in one market might sway another market.

Given that these relationships stem from participant behaviors across various assets in financial markets and across asset classes, these correlations can persist for long enough to offer valuable insights to extract edges from these opportunities. So trend followers can use this information to pick the most robust trending signals from a market grouping or adaptively manage risk across different markets with correlation serving as a crucial risk management tool in their process.

Now, the thing is with correlations though, correlation does not always equal causation. So what we’re talking about is correlations observed in market price actions don’t always equate to causation. It’s possible that correlated behaviors between markets simply result from random occurrences. Or if there’s a causal link between markets, such relationships might evolve or change as market participant behaviors shift with changing market regimes. Or two causally collected markets may show minor price differences, which dependent on the method of correlation analysis deployed, may make them seem uncorrelated.

Moreover, there might be inverse relationships between two markets, one rising in price, the other falling in price. There is still a causal connection between the two which is causing this inverse relationship. But when looked in tandem together, the rising price versus the falling price, when looking together, this might relate in an uncorrelated relationship when in fact a causal connection exists between those markets. So these are all inherent difficulties of correlation. But I think correlation is an important measure.

[00:33:38] Niels

Agree. I think there’s one more twist, and I don’t know maybe you’re going to get to it. But I think there’s one more twist, and that is, it’s not necessarily the correlation between markets that’s relevant, it’s actually the correlation between the positions of the investor that’s relevant.

[00:33:55] Richard

Trade outcomes of the cross-sectional approach produces return streams, trade outcomes in the form of return streams, and this correlation is a transformation from the market correlation to the trade return streams produced which might have these correlated relationships. So this comes into the field of portfolio management; how to control risk through altering position sizes, all of these things.

So for trend followers who pay a lot of attention to correlations through the lens of relationships or interrelationships between markets, it presents a distinct set of challenges that influence their trading models and underlying philosophy. So with the pursuit of advantages through cross sectional momentum, trend and portfolio models might exhibit some of these aspects.

One is what preferential markets to trade? This underpins a slightly different philosophy between those that practice cross sectional momentum and those that practice absolute momentum. So those that focus on these correlated dependencies, naturally in their philosophy and in their approach, are going to focus on the following key points. One is preferential markets to trade.

So it introduces possibly a selection bias towards the strongest trending markets from a pool of options as opposed to the assumption that all markets have the same trending opportunity. It introduces dynamically changing markets. In other words, some particular markets or assets have stronger opportunities than other assets, which therefore can introduce things such as dynamic changes to how you manage risk, how you capture trends within this different perspective, this cross-sectional perspective.

It’ll introduce things such as rotational strategies or the rebalancing of allocations to capture prime opportunities or manage portfolio level risks. It will also introduce more statistical tools to manage risks. So a shift away from say that more traditional risk management tools of hard stops in favor of more perhaps dynamic models that adjust risk based on real time correlations as opposed to the old classic way of handling risk through stop treatment.

It’ll introduce things such as a focus on correlation and risk management, in other words, extensive use of correlation statistics to minimize portfolio risks, the dynamic redistribution of risk, a propensity for dynamically redistributing risk in a portfolio rather than distributing it equally across all return streams. It’ll introduce things such as risk management overlays, so overlays on traditional models, which naturally increase the complexity of these models with these risk overlays, demanding more precise risk and return management processes.

It’ll introduce things such as filters or market external information sources to evaluate signal strength, trade confirmations. It might also have less emphasis on diversification, because if the process, the selection process, is focusing towards those opportunities of best trending signal, best trending strength from a pool of options, it naturally reduces the degree of diversification in the portfolio as opposed to a carte blanche approach of investing in all of these various return streams, some of which may be very highly correlated. So it might therefore reduce the size of the overall diversified portfolio for those that approach it this way.

It might introduce things such as the use of traditional statistical models found in economics. I know we don’t really want to go back into this, Niels, because we hate this term of ergodicity, but it introduces spatial ergodic statistics, examining ensemble distributions over discrete timeframes using traditional measures like standard deviation as having primacy in risk management. This therefore leads on to things such as why there might be a preference to using standard deviation, Sharpe, etc., for this class of trend follower as opposed to the other class which we’ll be talking about shortly.

It possibly introduces the concept that not only are they focusing on a discrete class of trends that we might refer to as outliers, it’s broader in its definition of trend, sampling trends from different areas or different regions of the distribution of market returns. So it’s not just focused on trends that exist in the tail regions of the distribution. It’s looking at a broader class of trends that might exist throughout a broader area of the overall distribution of market returns. It just goes on, Niels.

Emphasis on risk management, at the portfolio level, is an important thing from a cross-sectional perspective. So rather than an emphasis on trade risk at the trade level, we’re now looking at trade risk management at the global portfolio level, a conviction that risk can be managed at the portfolio level relying on these more complex models to gauge and adjust risk.

It also perhaps introduces a degree of skepticism for my love for the term outlier, because these strategies prioritize frequent portfolio adjustments over long-term holdings. What I’m saying there is that with dynamic position sizing methods, volatility adjusting methods, that might be prevalent in this school of thought. An outlier hunter such as me would be saying, hey, don’t adjust your position sizes, you’re on an outlier. And they might be saying no, there’s a very good reason for why I’m adjusting my position sizes on this anomalous trend because I want to balance my risk across the portfolio, I don’t necessarily want to carry all of the unrealized equity risk you’re carrying with your orange juice trade, that sort of stuff. It’ll perhaps also introduce a focus on, you go, Niels. You wanted to say something there?

[00:40:28] Niels

Yeah, I wanted to dig into this just a tiny bit, just so I understand it. Since you’re also going to talk about absolute momentum, maybe it will be covered there, maybe it won’t be. But I just want to maybe highlight a few things. You mentioned initially at some point something about that one of the differences would be that the people focusing purely on absolute momentum treat markets more equal, if I can phrase it like that. But then I’m thinking, well, is that really true?

Because let’s imagine last year, or maybe in Q1, 2021, you were positioned well to get an entry signal on a short trade in short-term interest rates. Volatility at that time was incredibly low, so you would get a big notional position. But during that life of that trade, unless you adjust for volatility when you roll your position, but then you are adjusting for volatility, right? So if you are pure and you don’t do that, you would have ended up with a position in those two or three markets around the world that has massive impact on the overall portfolio performance.

So I don’t think you could say, at that time, you are treating all markets equal because those three markets would have had a massive impact on a day-by-day basis. So I think there are nuances in how we talk about these things. I think some things can be said to actually be true for both. Do you know what I mean? That it’s kind of dependent on me where in the life of the trade we are talking. Are we talking about initial point or are we talking about during the life of the trade? I just want to clarify that.

Because in my world, it’s not so black and white. I mean, yes, there are two different ways of doing these things, which we’ll come to maybe towards the end, but it’s still trend following. So I’ll leave it at that. That’s where I’m coming from in that sense. So anyways, I don’t want to interrupt your flow.

[00:42:48] Richard

It won’t disrupt it, but I’ll just respond to that. When an outlier hunter is talking about treating all markets equally, we’re talking about treating all markets equally at the point of entry. So we’re applying this normalization using ATR, etc. We’re allocating an equal amount of risk based on our realized capital or our realized equity at that point in time, and equally allocating risk to all markets with our view that each market has the opportunity to offer outliers, we don’t know where they’re going to exist.

However, and this is where your point is very valid, when we actually do catch an outlier, say orange juice for me, I’ve caught this outlier for three years. When I look at the level of impact it has on my portfolio, it’s massive. So we have a diversified proposition at entry, but then our risk becomes incredibly concentrated when we are riding an outlier, which builds up this very volatile position, potentially volatile position. But that is one of the symptoms of our approach which we’ve got to come to grips with. So there’s swings and roundabouts, lots of different swings and roundabouts here.

So these different nuances, what I’m describing here is just different interpretations, different nuances, but they all come with their different set of challenges each one of them. But just going on with the finalization of the cross-sectional momentum, when you’re dealing with allocating risk across the portfolio, looking at correlation, spatial correlations between the two, it produces this focus on optimal allocation of equity at all times, which is different to the outlier hunter who treats realized balance or realized equity different to how he treats total equity.

So I can totally understand how someone invested in cross-sectional looks at optimal allocation of equity at all times. They’re dynamically adjusting risk at all times. They’re continuously managing risk in a dynamic and an adaptive way over the course of time from trade entry to trade exit across the portfolio. But this is due to this focus on how best to deploy equity at all times through their correlation measures, their analytics which they’re doing in real time looking at the risk that resides in a portfolio at any point in time.

For us the outlier hunter, we tend to let it all go, swing our baseball bats at entry, which is different. You guys arem when I say you guys, Niels, I’m not pointing at you, by the way. I don’t know.

[00:45:32] Niels

Well, DUNN does, we do use vol adjustment.

[00:45:37] Richard

Yes. There is a much more centralized focus on managing equity at all times. Now, this therefore produce smoother equity curves. This focus on how best to allocate that equity, how to reallocate it across the portfolio inevitably results in much more consistent returns, less lumpy returns that we get, because you are continuously assessing risk in your portfolio, you’re adjusting position sizes, you are reallocating risk to different areas of the portfolio, and as a consequence, you are producing more stable, consistent, straighter, less volatile portfolios.

[00:46:20] Niels

You know what? Sorry to interrupt here. You know what? I think we should look into that because I’m not so sure that’s true. DUNN has never been accused of being smooth, let’s put it that way. So I’m not so sure.

[00:46:34] Richard

You certainly weren’t smooth in your early days. You’re a lot smoother these days.

[00:46:37] Niels

Anyways, it’s an interesting point. But maybe we do need to back it up with some data. Because I think there are also some people who started out their career as a certain green animal, still is a green animal, but is smoothing out their returns a lot by trading through most markets.

[00:47:02] Richard

I’m looking at things like Transtrend, I’m looking at things like Man AHL. Their consistent return streams are staggering to believe. They’re impressive results. I do know that they are applying these advanced techniques. Transtrend, they’re looking at what they call trends at the edge. They’re looking for uncorrelated trends. Not everything related to one single trend, but where different trends manifest. They’re looking at the interrelationships between markets to see where new trends that arise from different factors occur.

Man AHL is using very advanced principles to manage risk through very dynamic adjustment of portfolios, etc. So the consequence of this is a smoother equity curve compared to some of the more volatile jumping bunnies like me, and some of the other approaches. Another feature of those that might focus on cross-sectional is the use of continuous signals, dynamic adjustments.

You can do continuous position sizing methods as opposed to the more binary methods that we might deploy, which therefore means that their signal is based on signal strength. They’re making an assessment of signal strength looking at how well is this trend doing. In comparison to other trends, what is the strongest trend, how much to allocate, do we increase the allocation as the trend signal gets bigger? Stuff that Rob Carver loves.

Also, preference for the smooth equity curve in as much as they do rely more heavily, I believe, this is my opinion, on things such as Sharpe, standard deviation of returns, given the more consistent returns generated by the approach and the ability to assert with a greater degree of confidence the expectation regarding future returns. Now, that’s something.

Future expectation is something I really struggle to come to grips with in my lumpy world of not knowing where the next outlier occurs. But those trend followers who have these consistent returns, much smoother returns, there is a greater degree to be able to project that with a degree of confidence into the future because they’re less volatile. So with those measures, all of these things also make it much more popular for investors.

So these people that profess to this style of treatment because of the consistent returns, the less lumpy returns, the smoother returns, is much more appealing to the investment community as they do not like the pain arbitrage of the volatility of trend following like some of us absolute guys do. So they’ll be attracted necessarily to that. Go for it, Niels.

[00:49:55] Niels

So just two things just to comment on that. One, I want to make sure that it is understood that even in the world that say DUNN lives in where we do have an overall adjustment of positions based on portfolio risk, correlations, volatility etc., there is a separate component called trend strength that is purely based on signal. So that is kind of similar to the absolute momentum world. Because you also have trend strengths. You may not call it that.

But if you have four signals in a row going long, that is just a stronger trend strength in our world. That’s how we would define it as well. It’s exactly the same. We may just use 100 different entries to get to a full strength and some people may only use 20 or 5 or whatever. So I think in our approach (and I say our just simply because as I said, we do make use of that at DUNN), we certainly look at both trends strength, as driven by signal alone, and then the impact, as you rightly say, the risk management impact, essentially, of the portfolio.

[00:51:17] Richard

Yeah. When I think about DUNN- now, I don’t work there, but I certainly look at their return; their spectacular performance. So when I look at that, I’m plotting them more towards the absolute end of the spectrum as opposed to someone like Man AHL, as opposed to some of the more quanty-type firms. Here’s another aspect. There will be a preference shown by quants towards this form of trend following because it requires advanced analytical techniques, computer power, all of the benefits of the modern society and analysis to manage risk through these approaches.

So there is a strong leaning towards more quant methods in following this style of approach. But that gives you, in essence, the list goes on, of course. It’s not all encapsulating of cross-sectional. But that’s sort of the philosophical approach where things are going. That’s where I see things aligning, those people that are either extreme cross-sectional or blended trending towards cross-sectional, these are the sorts of things that pay more and more attention as they get towards that extreme area.

So I certainly believe that there’s no one size fits all here, Niels. There is no criticism here from me. This is just my understanding of it. I’m just sharing my viewpoint of it. I suppose it helps when people debate with me about this and that or whatever, at least if they understand how I am considering this alternative approach. I’m not totally ignoring it, refusing to accept it. I’m trying to appreciate it and understand where it’s coming from.

So let’s get on to the absolute momentum side. Now, this is a side of the spectrum that I exist on. I love this side. Absolute momentum differs fundamentally from cross-sectional momentum. Cross-sectional momentum, as we’ve discussed, examines correlations between different assets at a given moment in time, whereas absolute momentum delves into the momentum of a single asset over time.

In other words, this type of correlation is known as serial correlation or autocorrelation. This analyzes how momentum, at one point in an asset’s return stream, affects its momentum at subsequent times along that return stream. So absolute momentum traders often focus less on these external factors influencing trends and more on the outliers that arise from internal market dynamics or endogenous sources. They’re the bread and butter of what we focus on within absolute momentum that gives us serial correlation in the return streams.

So these endogenous factors is why we perceive market trends as highly unpredictable because we don’t know where these serially correlated clusters are going to occur across the time evolution of a single return stream. They could occur anywhere. When they occur, we just need a process that jumps onto them as opposed to trying to have a rationale for why they occur. So we inherently are price followers who have very little attention to the media, global macro, all of those sorts of things.

We just apply this rules-based process with the understanding that at certain points in time, over the history of the financial assets, we see these serially correlated clusters across all of the asset classes that we trade and the assets that we trade. So to better grasp the time-dependent momentum properties in price movements, one can turn to concepts such as convergent and divergent models. We won’t go into that now.

We have done that to death in prior podcasts, Niels. But both classes, the cross-sectional and absolute momentum is bound within the divergent class of trading model in that they’re capitalizing on opportunities arising from when price diverges away from an equilibrium as opposed to mean-reverting models and pattern recognition, all of these things that are focused, and value investing that’s revolving around the concept of something reverting to either an intrinsic value or an equilibrium condition. These are both classes of divergent models.

But absolute momentum, the difference is we’re really focusing on these endogenous properties, something that Jean-Philippe Bouchard brought out in his great episode with you Niels where we believe the fundamental drivers of the majority of these major price moves that we want to capitalize on lie in these endogenous sources within the market itself, relating to participant behavior in the market as opposed to external sources from outside coming in and influencing those markets.

So those that are at these extreme ends of the absolute momentum end of the spectrum have distinct characteristics. When we talk, you’ll hear that in how we talk in our philosophy and underpinnings. So some of the things that we apply is this notion of equality across markets. The unpredictable nature of identifying the next standout asset or where the next outlier is going to be means we need to treat markets all equally.

As a result, we use ATR as a basis to normalize markets first so that we can apply a trend following strategy in equal risk dollar terms to all of our markets. So we normalize cryptocurrencies against bonds so that, despite the fact that they inherently have far different volatilities, cryptocurrencies has extreme volatility, bonds are much more stable and volatile. By using ATR, we’re normalizing this so we can treat them all equally in terms of how much risk we allocate to each of those markets that we trade.

[00:57:29] Niels

I don’t want to interrupt your flow too much, because also, we’re well into the recording time. But just this one point, and that is, I hear what you’re saying. But when you talk about that you treat all markets equally, I think that that can be said about both approaches. You treat all markets equally on the day of entry. So on one single day, you treat all markets equal. You could argue that,by the way, I think here’s another point.

I think maybe there’s a third category here, Rich, because when I think about cross-sectional momentum, I also think about that one market can influence the signal of another market. But the way, for example, we do it at DUNN, that doesn’t occur. So we actually treat all markets individually. It’s just the risk management where there is an impact.

So I think there’s a third one. There’s actually where you’re right in saying that price action in one market can actually influence the signal in another market according to that approach. Then you have one where it’s the risk management that gets changed. And then there’s the one you fall into where there is no link on the signal or the risk management. So I actually think there’s three.

[00:58:56] Richard

Well, what we need to do, Niels, is we need to update this. In our search for better information, we will progressively update this. So this is a start. It’s not the old be all and end all, this is my interpretation. So we’ll update as we improve it. Look, another thing you often hear, Niels, is this preference for what we call large sample size. You’ll often hear, oh, what’s the sample size of the dynamic adjusted models versus the models that might be applied in our absolute camp.

The way we view this is a consistent application of a simple static yet normalized model. In our opinion, at entry when we’re treating all markets the same way and we are applying a normalized treatment, we also apply our rules-based process across every single market. So we are trading the same strategies in a normalized way across all of our portfolio which leads to this concept of large sample size in relation to that particular model we apply.

[01:00:09] Niels

Again, Rich, and I think that’s absolutely true, but I don’t think you can say that other people don’t do that. I think they do exactly the same. I know this will not sit well with some of our friends, but it’s just simply not true. We get just as many signals as the camp that you sit in. We may get more signals that you could say these are risk management signals and maybe you exclude them from your sample size when you do the evolution. But to suggest that “classic trend followers” get more sample size than people who-

[01:00:44] Richard

I’m not saying which one is better, Niels.

[01:00:46] Niels

No, no. Exactly. So no, I’m just saying that that’s important to understand. I don’t think that-

[01:00:54] Richard

I know, but you know the problem? If we look at your models, from our perspective as outlier hunters, when we look at your models, we don’t know how to count your sample size. We don’t know how to do it. Is it dynamically adjusting? You possibly do. But from our perspective, the way we treat it, we can’t see how you allocate things into buckets and how you count things. From our perspective, we don’t think you’re doing it, but clearly, from your perspective, you are doing it, so this is this different philosophy.

[01:01:25] Niels

I know. Sorry to interrupt. I will be quiet now.

[01:01:28] Richard

That’s okay. I’ll keep going. We also strongly believe in this principle of no selection bias where a cross-sectional approach will necessarily have a selection bias. They will be choosing the best signal from a particular market at the expense of perhaps other less better signals in other markets. So we tend to treat all markets equally. This sort of comes into our frame as applying no selection bias.

This therefore avoids us making choices based on inference, which negates the need to shift risk between assets, which you guys do, because you need to with your particular approach, you do. The fact that your signal strength lies stronger over here or your risk lies stronger over here, you need to shift because this is how you’re managing risk, this is how you’re capitalizing on your edge.

But in our world, where we’re capitalizing on this different edge in the tail regions of the distribution, chaotic regions, we think that we need no selection bias in that particular area because things are so unpredictable, chaotic in those areas. Also, we have this philosophy that we define a market universe and stick with it for very, very long periods of time.

The reason for that, because we’re looking at absolute momentum, which is the momentum that resides within a single return stream, we need to invest in that asset for as long as we can, 30 years, 40 years, 50 years. As long as that asset lives, we invest in it because we don’t know where these outliers occur. When we are actually riding an outlier in that particular market, we never entertain the prospect of shifting to another market that is not offering an outlier.

The reason we’re doing that is because these outliers are such infrequent anomalies. If we lose our emphasis or concentration on these outlier periods, which could occur anyway, and focus on shift risk, shift dollars to another asset that’s not offering that, that’s breaking the whole notion of the fact we’re hunting outliers. It’s saying, why would we do that? If we’ve experienced an outlier in orange juice, I’m never going to change my commitment to that, which is different to a cross-sectional guy.

So it revolves around this principle of once you set this consistent market universe, you stick with it. You might add new markets to it continuously to build your diversification, but you never drop an asset simply because it’s been underperforming. You keep cocoa in your portfolio even despite its poor history. We always do it because we never know where it might arise.

Another thing is instead of these dynamic risk adjustments way to manage risk, we like using traditional risk measures such as hard stops and trailing stops. The reason we’re doing that is because we’re focusing on the return opportunities within a single return stream, we’re looking at minimizing our risk event in a serial way over time. The way we do that is through these applications of hard stops and trailing stops. This is minimizing adverse risk per return stream.

We’re not looking at minimizing risk through across the portfolio. We’re looking at, if we do this on per return stream level, and we bundle lots of different return streams together, yes, we might get a correlated ensemble of return streams there, but at least we’ve mitigated adverse risk at a per return stream level. This leads to measures that we deploy such as the use of ensemble methods for trend following.

We might deploy a suite of different trend following methods for one single market. Now, the reason we’re doing that is twofold. One is we don’t know which system is going to be the one that’s able to ride the outlier for the greatest period of time. These outliers are unpredictable, volatile. Some of our systems get snagged out with small perturbations, others hold in for the long term.

By trading on an ensemble, we increase our chances of capitalizing on the fruits of an outlier because it’s more loose pants. There are more opportunities to capture an outlier than a single system that might be thrown out of that trend consistently if it’s quite volatile. But also, the primary reason we use an ensemble of trend following systems is to break down the level of correlation that resides in a single market.

So what that means is yes, we can therefore bundle more correlated markets together or pay less emphasis to the degree of market correlation between markets because we know with this ensemble system approach applied at the return stream level, I could apply that to crude oil and I could apply it to Brent, which are two highly correlated markets in terms of market correlations, but because of the ensembles of systems operating differently with the small variations between the two, some are producing outliers, others aren’t producing outliers, etc.

This breaks down the correlation, enabling us to have less reliance on overall correlation risk measures to manage risk at our portfolio level through using these things such as hard stops, trailing stops, ensemble systems, etc. Another big point in our models is we let profits run to their conclusion. So the outlier hunter is not in the game of prediction and in assessing how best to redeploy capital when riding these unpredictable anomalies. So they therefore view any method that adjusts position size between entry and exit and potentially compromising the principle of letting profits run. So this an anathema to us.

What happens from entry, stays on entry, and then we ride the trend. Even though we become particularly concentrated when we’re riding those outliers and we swing the big baseball bat, we just love it that way. So the basis of our approach is on the principle that no one knows how long a trend can persist.

Any method that preempts or attempts to make an assessment of how far the trend has to go through adjusting things along the way we see as potentially losing future profit potential just through the fact that they’re not committed to concentrating on that outlier because we don’t know when it’s going to end. Another thing is we tend to view things in terms of single asset-level risk as opposed to portfolio risk. So we’ve talked about this, the hard stops.

We tend to ignore correlation statistics at the portfolio level in terms of the market correlations and the trade correlations. But instead, we use these ensemble system methods to decrease the level of trade correlations in the portfolio. But inevitably, we’re much more volatile than you, Niels, because correlations do impact us. We might do very well during 2008, 2023 with these spectacular trending blasts, etc., but we are trading highly correlated markets and that’s why we’re doing so well. But we get stellar returns.
Of course, we pay the price with trend reversals in these periods where we are trading highly correlated markets. That’s the nature of our game. We accept that.

Another thing is we use risk release valves. So when we talk about cross-sectional methods using correlations to dynamically adjust risk across the portfolio, we prefer to use more traditional mechanisms such as hard stops to actually remove risk. So we think that it’s preferable to us to not try and attempt to transfer risk about the portfolio and just release risk, have these risk release valves in the portfolio that are continually releasing risk steam.

So what this means is that this allows us to trade highly chaotic regimes if we’ve got these risk release valves in there, because when things such as correlations break down in chaos, at least we’ve got these measures to small bits, hard stops, trailing stops. Risk comes out of the portfolio as we’re utilizing it. That’s given us the principle that the portfolio is able to absorb more future risk and it’s not warehousing risk in any way shape or form.

We view transference of risk about the portfolio as possibly a problem when it comes to a regime shift, a new regime, a chaotic market. It’s holding on to this risk and it could expose it. So this is where we see possible negative skew events arising in alternative trend following treatments. We sit there on our pulpits giving the sermon. We are into positive skew. We embrace positive skew. We take risk off the table with our stops. We don’t hold on to it. We don’t warehouse it.

We also have this preference for very simple models as opposed to complex models. The use of overlays to us, risk management overlays, is anathema to us, because remember, we are focusing on the opportunities in the tails, chaotic regions. So it’s no value to us to apply these models, we believe, in these tower regions. This also then gets to the point of how much diversification does the outlier hunter prefer? Okay, this is a debate heard numerous times, and we’d say there is no such thing as maximum. We go for maximum diversification. There is no threshold.

Now, I just need to explain what I mean there. In a predictable market regime where things are moving very sedately in a very predictable manner, we pay a price for that with our highly diversified models because you don’t need much diversification in these stable predictable regimes. Andrew Beers basically demonstrated that with his replicating model of 14 markets.

The fewer markets you have in these stable regimes, those markets you’re focusing on with the greatest strength, etc., such as cross-sectional approaches, will avoid this massive diversification and focus on the markets that matter in terms of correlations, less focus. But for us, we have a massive diversification focus because that’s during stable regimes. When things start getting chaotic, correlations break down. When things start moving independently, no longer is there this correlated sort of regime that was fairly predictable.

In that regime, massive diversification to us is what we want because the more diversification we have in uncertainty, the better it has. So to explain what I mean there, we talk about a lot of people come on the show and they say, well, you can achieve diversification benefits up to about 60 markets and then there’s marginal benefits that reach a threshold, and then there’s very little improvement beyond that.

Now, what we’d say is, yes, that’s over a regime, a particular standard regime. However, we say, let’s make that regime much more chaotic. Let’s say the future is much more chaotic than the past. We think in a much more chaotic regime, the threshold goes up to say, 150, 200, etc. It can support that level of diversification. But because we know that in markets up to 60, there is this threshold, we do accept that we fail in these areas with massive diversification.

But there is no significant drag on adding additional markets. It’s just that you don’t achieve much greater benefits there. However, where the benefits arise is if markets become much more unstable, uncertain. We think that can support much more diversification in terms of both market and system. That’s where we say max diversification is always what we’re striving for.

[01:13:50] Niels

I promised not to interrupt so I’m kind of breaking that promise because I just want to push back a little bit on that. The concept of massive diversification did not come from the outlier hunters. It actually came from the opposite group, because people like Transtrend and Systematica, etc., that you don’t classify as outlier hunters, they introduced hundreds of markets many, many years ago before it even became a part of our conversation on the podcast. So I think we just need to make note of that, at least.

I don’t know what the other point I wanted to say from what you said, it’s just that when I look at managers that have been around for a long time, we started the conversation today talking about that there are a few of us who have been around for almost 50 years, I think during those 50 years, markets have probably been pretty chaotic from time to time. You have managers with long track records that kind of embrace both sides of the spectrum. Some managers have stuck to their 60 markets, some managers have expanded it dramatically. The one outlier hunter that you love to refer to…

[01:15:10] Richard

We do love to, and he’s got low levels of diversification by the way.

[01:15:14] Niels

He has almost no level of diversification in your world because they only trade 30 markets. So I just think this is why I’m saying that for me to be part of this discussion, which I love to be, of course, I just try not to make it so black and white, two camps. It’s almost like two different ways that one is a real trend follower, the other one is not a real trend follower. I just don’t buy into that. I think there are lots of nuances.

But as I often come back to, what I love about our industry and our methodology is the fact that actually, it works for both sides of the spectrum. If I look at returns, long-term returns, you and I write the report, we just published it a few days ago, there’s really not a big difference in long-term returns of some of these great managers. Really, there isn’t. I think that actually comes back to the strength of trend following, the strength of being systematic. So that is much more important to me than putting a label on one or the other.

I think if you could adhere to the golden rules of trend following, and maybe for marketing purposes, some people want to be in one camp and be seen as the only one who does that, I just don’t buy into it. I just don’t see the evidence. I think one of the strengths of our industry is that we always look for the evidence, we always look at the data. That’s what drives us. So I fully appreciate you describing the differences.

I just encourage people, when they listen to this, not to sit back and feel, oh, these are two completely different ways of doing trend following. I think they’re very close cousins.

[01:17:04] Richard

They are. It’s interesting. I think that we recognize as trend followers, we all love trend following and we all are specialists in our field. This specialty, this leads me to a certain approach, this specialty leads you to a certain approach, etc. So this is preference. The way we understand it, when we’re looking at our particular approach and we’re comparing it to other people’s approach, to me, I don’t know if you do this as well, I’m assuming you do, you’re looking at all of these different facets, all of these different debates we have and you’re connecting everything. You’re connecting this to that to that, to that, to that to see which is the most consistent narrative in your own head in how you understand trends and how you understand these markets to behave. So the way we interpret these models, in our heads, influences the outcomes in the way we trade and invest in these markets.

Now, my model that I’ve got in my head is based on my science passion, my physics passion, the way I see complex adaptive systems work. Other investors, traders who have had different influences in their life are going to see those influences really pervading their models. So we’ve all got these different models in our head trying to work out which is the best model for me to apply in trading these markets? We ultimately have to come to this point where our models are different. This is divergence in models.

[01:18:36] Niels

You know, when I think about it, and maybe this comes from the fact that as managers, we are often asked by investors, so what makes you different? Of course it’s a relevant question, but the reality is we don’t know what other people doing. We can talk about what we do. It’s really up to the investor to find out how managers are different. It’s not our job. But I just don’t like this divisiveness that I sense or I have sensed in the last couple of years where you try and label one as classic or true or pure trend followers and others as mismanaged futures and all of that stuff.

I just don’t like it. I don’t think it helps. I don’t think it helps our industry. Because we’re all part of the same industry for a very long time and those who have been around for a long time have done very well from being part of that one industry. So I don’t think we should try and change that. I think we should try and lift everyone, lift the industry as a whole. That’s what we try and do on the podcast. It doesn’t mean we can’t have different opinions.

[01:19:50] Richard

You raise a good point there. Harold from Transtrend, that CTA mini-series of yours, he announced with fervor that it is important that all of us have our different unique approaches to trend following. Now, I personally think that is a very positive thing because I do think we need to spell out our differences to the investment community for them to be able to make a decision. They don’t all want to think that we’re all this monolithic group.

They want to think, well, what do you do? And then their minds will say, well, that’s how I see markets, I want to go that way. Or that’s how I see markets and risk, I want to go that way. So having these different opinions I think is fantastic. And yes, look, people accuse me of being dogmatic religious fervor sermoning from the pulpit, all of this sort of stuff. The reason I’m like that is that I’ve come from the school of hard knocks, Niels.

[01:20:45] Niels

I think all trend followers have, actually.

[01:20:48] Richard

I think so. This pain arbitrage, all of this sort of thing, this results in a fervor or a passion to be dogmatic, to say this is the way to do it. I can’t help but do that because I’ve gone through these experiences, these incredibly chaotic markets. I’ve got this appreciation that markets can do anything they want to do and I think that there are a lot of investors and traders out there who haven’t experienced those conditions who are a bit complacent and think that it’s the same old, same old, but I know what these markets can deliver.

Therefore, I am a zealot when it comes to my way, my model takes that into account. Heed ye, all you people out there, for risk lie upon you if you do not adopt from the pulpit the bible that I speak of. But look, slightly off track, I’ve got to get to the end of this because I have a feeling we’re not going to get…

[01:21:47] Niels

Well, I wonder, Rich, whether we should save this part to our next conversation because it kind of is nicely put. Yeah, kind of nicely, we’ve described both sides of the coin and then we can try and bring things a little bit together but under the guides and the lens of risk management, which I think would be a wonderful theme for the next conversation plus whatever we come up with in the meantime. If people are still listening to us after 1 hour and 23 minutes, then they want more of this stuff.

[01:22:28] Richard

I’m a lonely guy over here, Niels. I love these conversations. That’s why I talk so long because there’s no one else to talk to over here in Australia.

[01:22:35] Niels

So do I. So do I. So do you think that it’s a good idea we say, okay, let’s focus on the next iteration?

[01:22:43] Richard

Part two, next time.

[01:22:45] Niels

Yeah. Okay, cool. All right. With that, we really want to say that we do appreciate you hanging in there for this conversation, but they are important. Even though Rich and I don’t agree on everything, we do agree on most things. But we also agree on the fact that it is important to talk about the nuances of trend following, so we hope you appreciate these conversations.

Send in your comments, your questions for next time Rich is on in a few weeks, and make sure you share these conversations with your friends and colleagues and family members because we do live in a world where I think these kinds of concepts, and maybe that’s one thing to leave on, Rich, where I think we do agree, the world is not getting more stable or predictable in any way, shape or form. So having the ability to participate in a broad range of markets but with an incredible laser focus on discipline actually I think will be incredibly important in the next 5 or 10 years in the markets. Would you agree with that?

[01:23:58] Richard

Totally. Yes, we leave the session on a neutral note.

[01:24:03] Niels

We leave the session on a high neutral note and of course on a note of high appreciation for these different nuances of trend following.

Anyways, next week, talking about things that are unpredictable and volatile, I am joined by Cem Karsan, who will, I’m sure, tackle some of these questions that falls more into the volatility camp and uncertainty and what is going on between the relationship between the VIX and the S&P 500 which have left these long vol strategies bleeding in a year like this year, in the last maybe 18 months, despite equities having struggled.

So if you have some questions for Cem, which I hope you do, send them to info@toptradersunplugged.com and I’ll do my best to bring them forward. From Rich and me, still friends here, thanks so much for listening and we look forward to being back next week. Until next time, take care of yourself and take care of each other.

[01:25:10] 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.