Today, we are joined by Nick Baltas to examine how narratives, signals, and structural design are reshaping trend following at the start of 2026. The conversation moves from investor storytelling and information digestion to a sober review of what truly drove dispersion in 2025. We explore why speed and universe choice mattered more than expected, why recent outcomes may be misleading, and why reacting to performance is often a mistake. The discussion then turns technical, unpacking new academic research on nonlinear momentum, signal construction, and the deeper mechanics behind trend following’s defensive behavior during stress. The episode closes with a reminder that discipline, not prediction, remains the strategy’s core advantage.

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Episode TimeStamps:

00:00 - Introduction and welcome

01:05 - A disrupted start to 2026

03:10 - Narratives, information, and price formation

07:06 - Why stories often fail to move markets

09:31 - Recurring themes and market attention

10:59 - Strong early conditions for trend following

12:01 - Dispersion across strategies in 2025

15:06 - Familiar patterns in an unfamiliar year

18:42 - Speed versus universe in trend design

23:17 - Why recent outperformance can mislead

31:08 - Institutional views on trend following

40:21 - Nonlinear time series momentum research

50:30 - Autonomy of trend and crisis behavior

56:35 - What theory explains about trend robustness

59:00 - Closing thoughts and next episode

Copyright © 2025 – CMC AG – All Rights Reserved

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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.

Welcome and welcome back to this week's edition of the Systematic Investor series with Nick Baltas and I, Niels Kaastrup-Larsen, where e each week we take the pulse of the global market through the lens of a rules based investor. And let me also say a really warm welcome if today is your first time you're joining us. And if someone who cares about you and your portfolio recommended that you tune into the podcast, I would like to say a big thank you for sharing this episode with your friends and colleagues. It really means a lot to us. Nick, it is wonderful to be back with you this week.

So far so good. I would say good morning, Nils. I would even say still Happy New Year. In Greece. We tend to kind of extend the wishes until month end.

It's been a hectic start of the year, but not necessarily just for business reasons. We're in Greece with family and I'm not sure if you saw this kind of aerospace in issue that came about in Athens.

Long story short, there was no possibility that flights could land or take off from the Athens airport. It was a Sunday on the 4th and we're supposed to be flying back and then flight got canceled. The entire family.

Took us four days to come back at the end of the day because you had the big fat Greek return. So all the Greeks are coming back to London post Christmas. So there's no direct flight at some point or at least with availability.

So that was number one. And then a bit of like stomach bugs and stuff, you know, have, have, have, have let us down specifically like the kids.

So it's been quite of a of a rough start, I would say. But you know, we keep our smiles and the year has started well otherwise and you know, business goes well and we're busy. But yeah, glad to be back.

Glad to be back.

It's good to have you back.

When you, when I know you have young kids and I you, when you, when you mentioned that, it kind of reminds me of of one of my own travels back from from Christmas and New Year's many years ago when my and where the snow essentially while we're sitting in the plane just made it impossible to get out of Copenhagen and. And we end up also spending a couple of days extra to before we could get tickets. But there we are. There are things we don't control.

Nevertheless, as we always do, it's always interesting for me to besides all the topics that we are going to talk about and they're going to be they're super great. But I'm always curious as been sort of what's been top of mind with you since we last spoke or in the last couple of weeks.

So many things are going on of course, but is there anything that kind of stood out for you?

I'm a bit interested if you like and I'm spending a bit more time these days looking into the way that if you like, investor narrative and thematics are impacting asset allocation or even more so are determining investors views. And we discuss in this podcast series for years momentum and trend following. What is momentum and trend following?

At the end of the day it's just perhaps an expression of slow digestion of information and we see that in the price path which allows us to then document a positive or a negative move that in itself, supposing that we have this kind of continued digestion of information at a slow pace continues and that's for us a signal. So what happens before we kind of digest this information at a slow pace?

There is clearly information that is produced and news that is produced in the market and information that gets disseminated via the media and the Internet.

So that cloud of information if we end up achieving to get hold of prior to the price formation happening, it's almost as if we'll get the momentum before the momentum itself plays out in the prices.

So long story short, I'm actually quite engaged and focused on a partnership we're doing with kind of a vendor, if you like, or an aggregator of this type of data, of narrative data.

And I'm almost trying to think how this information or this type of data set can be utilized for signaling for asset allocation purposes, for strategy design, you name it.

But if you ask me then what keeps me excited or maybe what keeps me thinking in a productive manner, that's certainly one not a concrete answer, but certainly a concrete framework or maybe idea that I'm kind of spending my days when I'm kind of running these days or I'm doing things outside of just replying to emails or pitching to clients. There you are. That's the one thing.

Yeah, no, it's kind of interesting. It is slightly related to something that I had written down, not like something that's top of mind, but still interesting. So, for example, if I had.

If let's just say that the narrative had been, oh, Japan has increased its interest rates by a lot, clearly that must mean that the yen goes up, right? Let's just say that for an example.

And I just saw a chart this morning that showed how much actually of an increase the Japanese have been seeing in the interest rates. At the same time, the yen has just done nothing in terms of strengthening. In fact, it's probably as low as it's been, more or less. So.

So I understand the interest in this thing about what if we could get hold of the momentum before it happened, so to speak.

But I'm also reminded from last year's price action in a world that is getting crazier by the day, how important it is not to have any kind of prediction in your, in your model.

So I'm kind of doubling down on this trend following where a lot of people don't like the fact that we have no story and we have no view as such, but just the fact that we're super disciplined at following what has happened rather than trying to get ahead of ourselves.

I probably reverse the argument a bit in the sense that I hear you on the example about Japan. And I guess after the fact, maybe the explanation is there is no exposure or maybe there is no sensitivity to the narrative itself.

And I think that's maybe the direction I'm thinking of going in terms of there could be some story around, whatever, let's not name it, but let's say an event. The question is whether there is, at the minimum, any price reaction that we should pay attention to or not.

So the fact that there is more intensity on a specific topic does not necessarily translate to the fact that your price response will be significant. So I don't have an answer. I'm thinking out loud.

Let's say, for instance, equity prices are not necessarily impacted too much about inflation unless inflation rising beyond a specific threshold. So it's a bit more of a conditional exposure.

And that exposure, to the extent that we can harvest it and monitor it and quantified, then we might have a chance of making use of it.

I'm not necessarily sure that there is an end to it, but I'm actually kind of experimenting the other way that I would think about it to your point, and I think we've discussed it quite a few times, because at the minimum, I'm quite of a proponent of this idea that signal to noise quantification is important specifically for the V shapes. So how do we get hold of signal to noise ratio in the market? It's not too easy, right?

I mean beyond the returns scaled by volatilities, which is a return over volatility or like signal to noise if you think about it in a more kind of heuristic manner, we have to get hold of I guess non price information and maybe recycling of themes and narratives could allow us to get there.

I don't have an answer by all means, but that's a place I'm quite excited about just because I think we've done as much as we could with prices and volatilities and maybe there is more to be done outside of that spectrum, maybe there isn't. But unless we experiment, we will not be able to bring news and innovate. So that's the topic.

But by all means, I think challenges are there for us to at the minimum, if you like, entertain. So everything is well received.

Speaking of narratives, I'm sure you won't believe this, but it is true.

So I was looking at my notes from last time we've recorded one of these episodes and I don't remember exactly the date when this was, but it's been a few months ago because October maybe. Yeah, something like that. And so I always see what was on my radar back then. I had written down two things.

One was Greenland, the other one was Trump's attack on the Fed.

And I'm thinking what I can just reuse these topics and say oh, this is very much on my agenda and I know you can't talk about politics of course, but.

Maybe give your audience the signals then now like the mag is yours.

This is the thing. I have no idea what the narrative or what the markets would even do if we had, you know, a massive outcome in, in either of two of those two.

But anyway.

And what, what would actually what is interesting relating to, to these two, of course is that the, the person at the center of attention in, in both of these cases he is coming to Switzerland and to Davos as far as I understand. So there'll be lots of narratives coming out of the mountainous country, I'm sure in the next week or two, I'm.

Sure, I'm sure anyway people will pay attention for sure.

Anyways, let's move on to something where we can talk freely, namely trend following.

We're also seeing a continuation, I think it's fair to say, of what really drove performance in the second half, namely metals and equities. Yes, certainly setting the pace.

It's possibly one of the largest dispersion years we've seen without necessarily that meaning that we have seen correlation breakdown between trend programs. Specifically, if we look into more of an outlier type of a scaled correlation analysis.

In other words, to make it very, very simple, if a drop came in April, I'm sure it came across the board, maybe a different scale.

But if we assume that the scale is somehow diluted in a correlation analysis, and there are ways that we can do so throughout the year, whether you're following a three month or a six month or a 12 month trend, like short term to medium to a longer term, the correlations were relatively elevated anyway because to your point, there were a few markets that exhibited strong trends and this is probably equities and then precious metals, that's it. And then there were pretty much the same markets that actually failed and maybe it's now quite continued failure for some time.

Interest rates, obviously that's the one I'm talking about, that's the elephant in the room.

So even if the moves were captured relatively successfully, if you like, by a variety of trend speeds and programs, the dispersion of outcomes was quite significant, I guess from that perspective, do we learn anything?

Frankly, nothing more than just yet another year that we know we do the diagnostics and we realized that yes, if you were too fast and possibly that V shape did not play at your benefit, or if you were too diversified, you actually missed those few markets that did the job and the rest were just now adding noise to your program and diversification did not play out for this year. And maybe that's what we have seen the last couple of years. Are we entering a phase of should we think differently now from this point onwards?

The fact that we discussed that at the year end, conversations.

Yeah.

Conversation we had with the rest of the crew. I think the universe and the speed are probably the two most important pillars of dispersion, at least for the last year.

And it's fair to say that had you been more on the faster front and had you been more diversified or if you like a broader universe, probably you wouldn't be at the top ranks. But the fact that we now have a universe of very positive and very negative returns, that in itself is an interesting observation.

As to how we react as product developers, how do we react as consultants, how do we react as investors, how do we react as, you know, asset owners? Let me make a pose here.

Yeah, I can, I can move on more to what we're seeing and, and how we're discussing with specific investors, but maybe question. I just put your views. I can, I can, I can bust it on.

Let's do that in a second because let me. It gives me a chance to do two things.

One, just to give a quick trend update as I normally do on that, but also just to reiterate something I said to Rich last week. And by the way, I think Rich brought a really, really interesting set of topics last week.

So for those who missed that conversation, you know, you should really go back and listen to that. But anyways, in so many ways you talked about the dispersion, all of that, but in so many ways you could say that. Trent, Sorry.

The last year was, you know, unusual, extraordinary, unpredictable, all of those acronyms that we hear. But in another way, the year was also very, very familiar from a trend following perspective.

Now I know the outcome, there were differences, but if you think about the fact that most managers probably made all the money in only a handful of markets and other than that, we had many small losses, maybe one or two bigger losses in markets. I mean that's very classical trend following. So that is to me is very comforting.

Despite whether people ended up plus 5, plus 10 or minus 5 doesn't really matter. But the structure of how the year ended and the return distribution, all of that stuff actually looked pretty familiar to trend followers.

Anyways, we talked about trend. Sorry, CTA is coming off to a good start so far this year. So let me quickly run through the numbers.

We have The B top 50 index as of Wednesday evening. And by the way, I think yesterday was a pretty quiet day.

But as of yesterday, Wednesday evening, up 3.74% for the B Top 50 Soc Gen CTA Index up 4 1/4%, very strong. Soc Gen Trend Index even more so, up 4.67% and the short term traders index up 1.75%. So all coming off very strong now.

I actually completely forgot to look at the traditional markets, equity markets and, and so on and so forth. But so far just eyeballing it a little bit.

Obviously we've had some positive developments in many of the equity markets already this year, while fixed income is probably only slightly up, if anything. So we'll, we'll leave it at that.

Now, before we get into the topics that we're going to be talking about, Nick, just allow me to mention to our listeners that I just published the eighth edition of my Ultimate Guide to the Best Investment Books of All times and that essentially now has more than 600 book title titles that people can dive into. So it's more of a reference guide if they want some inspiration.

Hopefully now that we put it in in one PDF, it makes it easier for people to grab hold of. If you don't, if you're not on any of my email lists, you can go to toptraders unplugged.com ultimate and you can get your copy.

A little bit of a continuation of the conversation we had with the the rest of the crew, as you said back in December.

Let's do that. And by the way, as you were speaking, I was kind of checking some of my stats for, for the month to date or like year to date. Yeah.

Strong performance broadly speaking, like on the 4 to 5 to 6% depending on the speed. And that's more mark for a 15 volt type of a measurement, primarily driven by equities and then commode.

So kind of similar things to what we saw in the kind of second half of the year. Maybe pull out some details for the discussion. Right. So you asked about, I guess, speed and universe.

Speed is probably the most important differentiator and then universe is the second. If we kind of stick to the core design principles of trend following.

Yes.

Whether it's dynamic sizing or static is important. Yes. Whether we use correlations or not is important.

But long story short, assuming that there is some form of stability of volatility and correlations, I think the speed will create more of a dispersion than the sizing. I haven't tested it but that's my hands as it stands.

So we did this nice exercise which I very softly discussed in the group review that looks into fast programs, let's say looking into like a two month type of a signal on a half life basis that goes up to 12. And that was kind of similar to what Quantica did, which I think also we kind of briefly touched upon back in December.

And then we did a similar exercise whereby we fixed the speed, but then we changed the universe and we went from something very concentrated and I believe we used something similar to DBMF's universe just to have an anchor which is kind of independent to our choices up to something that is very broad, more than 800 markets or so. And then we tried to have some sort of medium scaled universes, maybe a bit more liquid, but actually well thought through.

Like in the sense of if you use for instance non US equity indices, you might as well just use the eafe, or you can use uk, Germany, I don't know, you name it, like four or five major markets ex us. So you can broaden up a small universe by just having maybe more tenors in specific interest rate markets, or maybe you have more in that sense.

So if you like, the medium sized universes would be reasonable rather than saying oh let me get 20 currencies and three equities.

And we tried to see year on year for the last 25 years, if you were to rank purely by annual performance, however big or small the differences are, which was the universe that outperformed and which was the speed that outperformed, and then do some sort of a ranking, which was the best speed, the second best speed, the third best speed, and so on and so forth.

There's something quite striking coming out frankly, and this is that for the last three years, and that's exactly what I said in the December discussion, if you were slow, you would have outperformed. There is no other time in those 25 years that three years in a row being so slow would have allowed you to outperform.

And by the way, the second best speed is just 9 months instead of like 12 months.

So slow has been the thing for SVB, for $YEN, for Liberation Day, the 3V shapes you and I have been discussing for the last, I don't know, however many months now, universe wise, having been small, is possibly quite dominantly the outperformer for the last four years. If it's not the first, the second best, which is also the first time we're actually seeing it historically.

But there have been years in between whereby being very small and concentrated would have delivered the worst performance. It's only the last three, four years that a concentrated universe is top ranked so stably vis a vis history. Right.

Can I be even more explicit because you shared the data with me, what really is striking to me when you talk about the portfolio size, because I think.

And again, I'm not trying to pick a fight here with my good friends in the replicator space, but clearly replicators tend to use smaller universes, even though that we have seen some new entrances using larger universes.

But then what was really striking is that if you go back to the environment post the tech bubble.

Yeah, exactly.

Exactly.

Being small or trading a small universe was the worst univers every single year for something like seven years in a row.

So what I'm trying to say here is that I think we need to be very cautious of making too many conclusions based on people saying, oh, but look, we can.

And here I may be maybe picking a little bit of a fight with replicators by Replicators coming out saying, oh, look at this, we've now demonstrated for the last three or four or five years that we can outperform the CTA index. Sure. That is true and it is in line with what you're showing in terms of your analysis. Right.

But if we were to expand this over a 25 year period, I'm not so sure that that would have been the same conclusion based on your completely independent type of data here.

Yeah, yeah. I think that translates into a question which is, do we learn anything on the back end of this analysis?

In all fairness, my mindset has always been try to be reactive, try to be diversified. Did we get it wrong?

And of course you can't, you know, you know, you can't see whether it's just a, you know, how much of the difference is. You can just, you know, you can't.

To my point on this version, but.

So, so that, that also strike me as being very interesting as well.

Yeah, you know, so that was my point. Right, that was my point.

But then from the plus 40 to the plus 35, fine, you can still rank them. But in reality in terms of absolute returns, that's actually quite substantial.

So what this analysis is missing at this point in time is also an indication of what's maybe the spread between the top and the bottom ranked that I might end up kind of adding. Right. So to give an indication of that, what is the spread in that ranking? Because it might actually be like very, very small for that to matter.

But you know, setting that aside, I think it's a useful kind of grid for us to get a better sense of the impact of those decisions.

But as I keep on saying, and I've discussed that with you, and I've discussed that back in the days with Morris when we talk about how we think of strategic design in the QI space, I think reacting to out or underperformance is the wrong recipe. Right. Either way, I think discipline is important. I think trust to the process is important.

And if there is anything to be learned, frankly, it's not that a specific universe is better than another.

It is more about that the V shapes are the ones to have caused this Dispersion rather than, oh, actually you should have been longer term because there are longer term trends. No, that's not what's going on here. What is actually going on is something else much more subtle and it's a reversion.

It's not that the signals and the directional moves that we have seen had like a longer duration or a shorter duration and therefore a certain speed is better than another.

And in reality you just make the wrong decision at the wrong time. So it's not about reading back the past, but I think some of those fact patterns are actually quite interesting at the minimum to be aware of.

That was the whole point of that analysis. Right.

And I would add to that because I did my own little different type of analysis as a. We're going to talk a little bit about that.

But it is interesting to see if I go back about 20 years that all sectors have actually been profitable. If you look back then we've been able to find opportunities in all sectors but it's clearly changing over time.

And so the true diversification, sort of the truly diversified portfolios, I still think over time is a better, is maybe a strong word to use but to me it's a more intuitive way of doing trend following is to have the full breadth of markets in the portfolio and not trying to be too concentrated.

Well, I'm in favor of, of dynamic allocation and if the opportunity set allows you to enter into a space that at Least seems to be delivering some return. You might as well just go so. So I see it more as an opportunity.

Said that not to the extent it's available to us, we tilt in favor or against depending on the inter temporal trend signals. So I'm with you on this one, perhaps with this conditioning information.

Sure. Cool. All right. Well, we have three papers. We'll see if we get through all of them, but I think we will certainly touch on all three.

They're very different. And the first paper, actually they all came out I think in December of last year, so they're relatively new.

The first one is probably the most accessible for most people to read. It's from Mikita, the consultants. People might remember a paper we discussed a while back, certainly on the podcast.

Not exactly sure who I spoke to about where they talked about trend following in the light of sort of crisis alpha and they introduced this term, I think they were the ones who introduced the term about first responders, second responders and so on and so forth.

The RMS framework.

Yes, exactly. And actually I think that was very helpful, frankly.

I think it helped people to understand when they should expect positive performance during crisis periods and so on and so forth. So I'll let you go through this paper.

We don't want to repeat things we've talked about too much, but still it's always useful, especially when it comes from people who probably have a lot of their or have the ear of a lot of large institutions to. To hear about how they talk about trend and what they're focusing on.

So I'd love for you to maybe pull out some of the highlights in your perspective.

Yes, and as you said, that's more of an introductory paper in this regard.

So that is, at least for the connoisseurs of the space, it's more of a reiteration of what the following is, you know, what's the reason of its existence and how we can get hold of it and so on and so forth. And speaking obviously of the RMS framework, of course, very well known, it's pretty much kind of in line with our own principles.

So what we had done independently at the time kind of aligns quite well with their framework. So Effectively what they came up about. I think to me it's more interesting the fact that themselves put out a paper on trend.

They generally speak about themes and bigger topics and aggregation of exposures and doing one piece on trend it's probably an indication of I guess an increased interest in the space which I think was interesting for for us to at the minimum kind of bring up. They talk about what trend following is. I'm not going to go into the details.

I think there's a nice section that talks about the differences in the programs which by all means I'm just going to read them out, list them effectively because we kind of know and we have discussed them. So universe we just talk about it speed, we just talk about it diversifying non signal, non trend signals.

I'm going to come back to that because that's quite interesting.

Stop loss and profit taking triggers static risk allocation or dynamic risk allocation or maybe equal risk or risk budgeting between assets or asset classes. That's an important one.

We have also discussed it and I think I'll just mention it earlier on on this dynamic allocation of the opportunity set and then the impact of volatility, targeting leverage.

That's another topic I think you and I discussed a few months back as to how we feel and we see the impact to leverage coming from portfolio design and V shaped dynamics and correlation shifts. So they kind of mentioned a few of those topics as being important for I guess for the, for the design of a strategy.

They all performed in line with at least the premise of diversifying trend following. So that did make I think a significant or had a significant impact in this dispersion specifically for those that implicitly use them in the design.

And I think they can have some value not to make trend following better but allow it to be maintained in a location at times that it's actually not performing well. And even if it's understandable underperformance to your point, like yes, you're holding equities.

What do you think is going to happen when equities fall by 10%? One plus one equals two.

I'm with you on this one, it's more the point that ultimately investors follow an increasing concave utility function to put the economic term to it, which basically means that we enjoy gains less than the pain that we incur when a similar magnitude of a loss is coming and withholding a loss is tougher. So to my earlier point, should we be able to allow allocations in trend following be maintained by those non trend components?

I think we eventually make the investment process more complete in the longer term and I think that's important.

I think the last point that I would mention about the Makita paper is that they're actually quite open by the fact that now institutions have a variety of possibilities in terms of accessing the style. Of course they talk about CTAs, but then they talk about mutual funds, they talk about usage, they talk about ETFs, they even talk about QIs.

So I think to me it's a recognition that the possibilities of accessing it have widened and broadened, but also the need for it has become more mainstream than before.

Now, whether that is a policy portfolio discussion or whether that is still an overlay discussion, it's not of secondary importance, but maybe of a secondary topic that is not necessarily covered here. But at the minimum being there, it's an indication of interest. And I think I just wanted to bring it up in our discussion. That's what it talks about.

So I think people, as you said, so I'm sure they'll find it very easy to go through as a, as a quick overview.

Yeah, no, absolutely. And I agree. I think that, I mean you put it in the sense of diversifying signals in order to maintain an allocation to trend.

And I think also from memory, at least it's been a little while since I read the paper in detail.

But I think it's this idea also that a lot of trend following allocation kind of fails mostly because of the behavior or mostly because of the kind of reaction pattern of, of investors. And, and therefore it is definitely a, an important, an important point for sure.

Now I think we are going to move on to the little bit more hardcore stuff where you need to be the translator of these papers. It's actually two papers we're going to talk about.

We'll probably come back to them given the fact that we, we feel that they are very important, they deserve respect and, and maybe we haven't been able to devote enough time to really get into the nitty gritty of that. I'll, I'll let you give your own disclaimer since you're going to be the. The narrator or the translator of these very important papers.

But the first paper is by a group of distinguished people, I'm sure Tobias Moskovich, who among other things, it seems like he's linked to our friends at aqr.

I'm going to turn it over to the expert and since I'm not a quant, I'll be allowed to ask some stupid questions along the way, I'm sure.

No, thanks. Thanks, Nils. I'm sure this paper has appeared on people's kind of inboxes. You know, it came out in a seren just before Christmas.

Right.

With his colleagues at AQR at the time or in Peterson, Lasse Peterson. So it's called kind of time, you know, non linear time series momentum. It's quite catchy to talk about nonlinearities in the trend following space.

I think my honest opinion before we go into the details is that they spend time discussing a topic that I don't think it's necessarily completely new and this is how past information predicts future information and whether that is a linear or a nonlinear transformation. And I'm going to go into details.

I think the novel thing that they bring about is that if there are those nonlinearities, instead of us forcing them by some sort of a parametric approach, let's say we utilize something that is called a sigmoid. So it's a signal that goes from negative to positive, but it flattens out in the tails.

Their point is that we know, maybe we let the data speak and maybe we use a neural network that allows us to uncover those relationships and then we can determine the positioning on the basis of those models. So let's kind of break it down, I guess into pieces, into components, I guess. Let's think of trading signals in the trend space.

What are the possibilities? I would probably say that the simplest of them all is the binary signal. The market goes up, you buy, the market goes down, you sell.

And maybe you size your exposure statically or dynamically by some Level of oil. Fine, let's assume that all volatilities are the same.

So you just buy a unit of oil when oil goes up and you sell a unit of S and P. If S and P falls, that's it.

Assuming that they have the same wall right now, maybe one step of, I guess, of departure from that is to say, well, maybe if my signal is too close to zero, then yes, the sign is positive, but in fact I don't have so much confidence on it, so I might as well just reduce my bet as a function of how big that signal is. In other words, if it's a basis point positive, then maybe I should do a basis point allocation rather than a 1% allocation.

And if it's 1% of a positive return, then maybe I should do a 1% allocation. So I should scale linearly my exposure to the signal estimation. And that moves us from a binary mindset into a linear mindset.

The bigger the magnitude of the signal, the bigger the risk or the sizing that I should do.

But there comes now kind of the third iteration of that signaling, which again has been studied in some form or fashion, maybe not as much in academia, even though there are some nice papers that do some empirical analysis on that topic.

But certainly I would want to believe in the industry and certainly if I were to speak about the work that we do, is to acknowledge the fact that, yes, fine, you might actually use the magnitude to scale. But how about an extremely positive or an extremely negative signal? Let's say now you witness S and p up by 3 units of Sharpe ratio.

How confident are you to size your exposure so much that this three is going to kind of repeat? So there comes a point whereby confidence and maybe estimation noise and maybe reversion dynamics come into play.

And then you kind of say, like, you know what, there is a point whereby I just want to flatten out my exposure. So I get it, the higher the better.

But, you know, probably there should be some sort of a concave form on the positivity side and more of a kind of a convex profile on the negative side that flattens out the exposure. So I still maintain my linearity around zero.

So positives are positive, negatives and negatives, but then in the extremes I should just flatten it out.

And I call it the sigmoid, they call it in the vapor, S shaped, I'm using the kind of the Greek, if you like, translation of what an S is called, like the sigma. So that's if you like the third iteration.

So again to repeat Binary, long or short linear, just use the magnitude sigmoid, maintain the magnitude but then bring some non linearity. And if we stick to those three, before we go to what the paper really really focuses on, there is some form of a benefit, performance wise.

If you look into empirical analysis, going into that more like of a sigmoid type of mindset or going into this kind of linear space, maybe the only places that you'll see some loss of performance is when a tiny bit of a trend is enough for you to get full into the position with a binary signal and then benefit from an early start.

But imagine a signal that goes bit positive, bit negative, a bit positive, bit negative, with 2 full positive and 2 full negative exposure, you'll end up accruing a lot of costs. So net of costs. It can be a debate how the binary signal can actually help. So this transition is always a bit helpful.

So there is empirical evidence and they also showcase it that this non linearity, even if we force it to be like a sigmoid, and there are a variety of ways that we can produce that mathematically and parametrically you get good performance or better performance.

Their point is why would you have to force the shape of that sigmoid, as in everything I've discussed so far, is not necessarily driven by the data, it's almost as if we're forcing it.

So the sign of past return is our determination, the linear is our determination, the smoothening of the tails is our determination and the signal we put in place is our design. And yes, maybe after the fact we look into the empirical stats and say hey, you're actually doing better.

That in itself almost says that yes, probably the data generating process is such whereby you have this continuation of return, but in the tails it ends up reverting. So they say, why don't we let the data speak?

So instead of us scaling up and then smoothing the tails, or scaling down on the negatives and then smoothing it out in the tails, let different markets, be it equities or rates or commodities and so on and so forth, force this non linearity while still preserving. And that's important, the fact that the signal is positive, I buy, the signal is negative, I sell. And that's important. Why?

Because the minute we depart from it, it is no longer just a trend strategy. Imagine me being here and telling you hey, your signal is positive, you're going to short.

Well, this is no longer a trend strategy, it's something of a mixture. So they use a neural network, don't want to bother you.

Too much with the details, but they use a neural network to effectively, if you like, extract the way that past return translates into future return. What's the transfer function? How do I document a signal and how do I determine my position? And should my position be linearly related to my signal?

Should it be fading out? If it's too extreme, should it maybe just fall? Let's say to my earlier point, if my sharp ratio is three possible just go to zero.

I think crossing zero and going to negative. I see the value of that. Because if the data tells you you need to actually go short, then it basically tells you that there are reversion dynamics.

Now whether we bring the reversion dynamics into a trend system or we use them as a separate engine, to my earlier point, that's maybe cleaner from an ASTA location and from a performance attribution standpoint.

But they do show, I guess to go probably closer to the end of the, of my overview, that empirically the data spits out transfer functions that have the non linearity that we're discussing, which is the smoothening in the tails. But even you can end up having reversion dynamics as well in the very extremes.

Now I said that those pictures of fitting data, at the end of the day, it's just like if you put past returns, future returns and you do like a non parametric analysis, you'll see it.

So there are some papers a couple of years back, I believe some of them we have discussed here, but maybe my memory is kind of failing me now that show this relationship. But what they say is that they are now actively monitoring it and fitting it to get to that point.

So to conclude, basically the paper says, look, there are non linearities, those nonlinearities exist in the tails. This is something that we should at the minimum be aware of. Whether we design that parametrically or non parametrically.

It's a good question whether the neural network is bringing value. Maybe it brings value.

That's why one of the findings that the neural network agrees with the theory and the fact that in details, possibly the estimation error is greater and so on and so forth, and some reversion dynamics are there.

I would probably say it is an important discussion to be had as to whether this incremental complexity will bring significant value versus having asygmoid that in itself kind of in itself behaves in line with expectations. So I leave it in this regard. But by all means, you know, very good work and I'm sure people will spend time looking at it and maybe trying it.

Okay, well, the last paper is from a fellow dean actually.

So we're going to be talking about a paper from a gentleman called Christian Kerr who works at the one of the largest pension funds in Denmark called ATP. And he also posted a new paper in December called On the autonomy of trend.

So I know we're going to have a hard stop in our conversation today, so I'm going to let you talk as much or as little about it.

I think it is important because it touches on some of the key things that people actually consider when they incorporate trend following into their portfolio. And that is kind of the, again, the defensive nature and mechanisms. So I'll let you talk about it, Nick, and we'll see how we go.

Yeah, so that's a nice one by Christian. So I also happen to know Christian and it's a nice piece of work. It's actually quite mathematical, it's more theoretical.

But I think what is important at times with those theory papers is to kind of look through how beautiful sometimes math can be and kind of identifying what we experience in reality and kind of giving a bit of a fundamental base, if you like.

So his point is more about, okay, what is actually driving trend following and how do they behave across different market environments and what is the underlying process of the data that gives rise to trend following. And somebody could argue that you have autocorrelation in returns. Positive is leading to positive, negative, negative.

There are some good papers from back in the days that showcase that even if returns are independent, if volatility in itself is serially dependent, you can still benefit from return following strategy. I'm not going to go into the details of this one, but that was the first that came to mind when I saw the paper. He builds a model.

He says that no log returns are following our ocean process. We design a trend signal which is just the summation of past returns. I'm not going to bother you too much.

They're using a binary signal to go back to our previous conversation.

But then what they try, or what he tries to come about is a mathematical explanation of what is the expected payoff of a trend following strategy unconditionally and then importantly conditionally on what has happened in the, in the recent past, which is more of a, of a trend following behavior in itself. Specifically condition upon some cumulative return of a specific horizon.

And I think the direction of travel here is to say, well, if I'm observing a significant drawdown, what is my expected payoff from being a trend follower?

And there are two kind of components that come out of this analysis, which is one being the directional component, by all means we know what that means. You know, if you get on average the direction right, you will do well.

So if you're holding equities more often than not with an equity risk premium that is positive over the longer term, you'll probably do well. And that was, I think, the challenge we had with equities and bonds. You know, three, four, five years ago.

I'm sure you remember the days that everyone said, hey, you're buying equities, you're buying bonds, you're just benefiting from carry and equities premium no longer the case. I think that, that, that doubt is out of the window for now. But then the second element which is also more important for us is the timing element.

So how quickly can you turn into a situation whereby your price process is aligned in terms of direction with your signaling?

And I think one of the more interesting things that his analysis showcases is that beyond the dependence that we have, and we know that getting the signal direction is the recipe for success and so on and so forth, there's also this part of the analysis that says even if returns of the process you're trying to allocate to, on the base of trend following is what we call iid. So in other words, independent, identically distributed.

So every single day the equity returns are drawn from the same distribution, but today's value has no dependence to yesterday's value and so on and so forth. So whatever happens every single day is independent, conditional upon observing a drawdown which can be a consequence of independent draws.

Like, you know, I can do heads or tails 10 times and I can get heads 10 times, they are still independent. Right. But it can happen.

Sure.

They show that, you know, beyond specific thresholds, mathematically defined, a trend follower can still deliver positive return. And specifically in the drawdown, specifically as a crisis alpha, which goes kind of hand in hand with some of the empirical observations.

So I know I went a bit quick in the interest of time, but two things to flag. It is a mathematically heavy paper, which is actually quite nice.

But I think what's more important is to associate now the findings that if you like formulae and variables, if you like contain to the experience we're observing and I think going past the fact that serial correlation is important and the fact that even an IID process can allow you still to be profitable with the trend following strategy is an important finding.

Again, it reminds me of that work that says conditional dependence in the volatility space is enough for you to benefit from a trend following strategy or in other words, to predict the directionality. So I don't know if you have any reflections upon that. I think from my end that's the soft summary that I wanted to bring.

As I said, whoever is keen to, I guess to open the paper, you'll see much more to that. You'll see some nice charts, the convex profile of trend followers, the smile shaped and all that lot. It's all there. It's all there.

No, I mean, you mentioned it's math heavy, so it definitely rules me out.

But I will say from the work that we did today, I will say that it's kind of for me a little bit also of a mathematical explanation of how Katie talked about Crisis Alpha a while back with me on the podcast and, and what actually was the original definition of Crisis Alpha, not what we made it into in terms of narratives, but what she actually meant back then as to why maybe this strategy does do well when there are crises so people can go back to revisit that. Maybe, I don't know, maybe Katie will bring up this paper next time I speak to her to put it into perspective.

You might be bringing this up again once you've had a chance to, to digest and so on and so forth.

But I really do appreciate all the hard work, given the stressful period that you've been in, in order to come and talk to us about this and give and give people a taste of what these papers are about so they can go and find them and read them themselves. So I really appreciate that Nick, for sure, of course.

And of course I think an even better idea would be for you, the audience, to go and leave a really nice rating and review and thanking Nick for the hard work that he put into this and to all of the co hosts that put in every single week, a lot of time in preparing for these conversations so that we can hopefully bring you some new and meaningful angles and nuances into a topic that we've now been talking about for many, many years and that we are are very passionate about. Anyways, as I mentioned also earlier, if you want the new version of the Ultimate Guide, you can find that on the Top Traders on Block website.

Next week I'm going to be joined by Yoav, so we'll see what he brings along. I'm sure it'll be fun and insightful and it's also your chance to ask him some questions.

If you have some, you can email them as usual to infooptoptraders on block.com from Nick and me. Thanks ever so much for listening. We look forward to being back with you next week and until next time.

As always, take care of yourself and take care of each other.

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