The Hidden Cracks in Systematic Strategies No One Talks About ft. Nick Baltas

[00:00:01] Intro

You’re about to join Niels Kaastrup-Larsen on a raw and honest journey into the world of systematic investing and learn about the most dependable and consistent yet often overlooked investment strategy. Welcome to the Systematic Investor Series.

[00:00:23] Niels

Welcome and welcome back to another edition of the Systematic Investor series with Nick Baltas and I, Niels Kaastrup-Larsen, where each week we take the pulse of the global market through the lens of a rules-based investor.

And let me just say, a very warm welcome if today’s the 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 also say thank you to the person for sharing the episode with friends and colleagues. It really does mean a lot to us.

Nick, it’s wonderful to be back with you this week. It’s been a little while. That’s how it feels. But how have you been? How’s 2026 treating you so far?

[00:01:03] Nick

Yeah, it’s been some time. Good to see you, Niels. 2026 has been quite a year so far. I think we all started with this kind of booming expectation and here we stand debating how much of a contagion effect we have ahead, or maybe not. But other than that, it’s been quite busy at work, busy at home, some travels here and there. So, good so far.

[00:01:28] Niels

Yeah, I would say.

[00:01:29] Nick

How about you?

[00:01:30] Niels

Yeah, absolutely, I can’t complain. It’s been busy. I feel that the conversations are highly interesting. I feel investors are, yeah leaning a little bit more into our space. So, so far, as you say, so far, so good.

But you know what, by the way, before we even dive into some of the things we’re going to talk about, I couldn’t help noticing (maybe I should have put it on my radar in the next section), I couldn’t help noticing that there was an article out only a couple of days ago. I think it was a Bloomberg article where they were already saying, you know, CTAs have their worst drawdown since Liberation Day.

And they were quoting like the trend index being down 4%. And at the time, when I looked it said, first of all, the number was wrong. The index was down like 2% for March at the time. And I was just wondering, oh wow, is that, you know, is that all it takes? You know, a couple of weeks of correction after eight months of positive returns, that’s all it takes for these headlines to come out? Maybe I’m not surprised, but maybe I am actually, that that’s really what they find interesting.

But you and I are going to talk about things that we do find interesting, at the moment, and let’s jump into kind of what’s been on our radar recently. I know you have an interesting observation about the QIS space, I think.

[00:03:04] Nick

Yes, what’s been on my radar. So, I would say I can start professionally, right, and speak about the days in the office and obviously discussing QIS and systematic strategies and, obviously, I can easily give like a personal perspective as well. But I would say, obviously something that is top of mind is what is happening in the Middle East and how that is impacting performance, and how that is impacting specific discussions we have with clients, and how we respond to the current environment in terms of commentary, in terms of performance analysis, so on, and so forth. And, you know, one of the most popular trades across the QIS space, and maybe it’s probably the most long standing QIS strategy across the industry, is the classic short commodity spread; the curve carry.

So a few days ago, I think it was last week, Bloomberg came out with an article specifically quoting the back correlation dynamics in the oil market, obviously, as a consequence of the supply concerns with the current geopolitical tensions, and how that has impacted quite aggressively and maybe kind of wrote off a few years of performance in a commodity curve trade.

Obviously, implementation matters and we can have this conversation at any level of depth. But this came pretty much weeks after another drawdown in that particular strategy was caused by a completely different event, and that was obviously the cold weather in the States and in North Europe that led gas prices to kind of skyrocket, temporarily still.

But you know, if you look into kind of a back to back events of substantial backwardation in the gas market and eventually in the oil complex, you know, for a strategy that, effectively going along the back end of the curve and short the front end of the curve, historically has been well rewarded because that is the risk premium that is shared between consumers and producers. It leads to some challenging performance that you know, we spend time kind of understanding.

And, you know, we spend time discussing with clients positioning, we spend time discussing the recovery dynamics. By all means it’s a trade that historically had very strong mean reverting properties because backwardation, unless the supply shock is permanent, mean reverts very quickly. So, normalization of the curves would bring back if you like, on a mark to market basis the lost return.

So yeah, that is a topic that, you know, we spend a lot of time. That is a topic that, you know, it’s on our radar for sure. And it’s a consequence of the macro dynamics that literally came on the back end of us kind of defending the nat. gas exposure and how that would kind of turn around should things normalize, I guess, on the weather side.

So yeah, there was this coverage on Bloomberg. Obviously, it was circulated between the team. Obviously, it has been discussed with clients, and I think it’s a topic that you and I discussed as an interesting one to kind of bring about. The article itself obviously quotes a number of players in the space, from the kind of a buy side, the sell side. It quotes, obviously, how big the QI space has become, which is something that we have discussed. I think I discussed that with Moritz back in November. It is probably now on the US$600 to US$700 billion mark.

Again, it’s quite substantial. It’s quite substantial. But this is now a multi asset space, delta one vol, retail, institutional, so, it kind of spans across a number of return profiles. And yeah, I mean, I mentioned that this is probably one of the oldest because it is perhaps one of the reasons why QIS products were built 15 years ago or 20 years ago.

You just want to have commodity exposure. You don’t want to hold physical commodities. You’re going to go to the futures market, you’re going to roll systematically. That’s a systematic strategy. Then you realize that doing it on the front has negative roll yield. You do it on the back end of the curve, you realize you have some alpha, you short the front. Here’s your curve.

[00:07:02] Niels

Talk to me about one thing. I’m definitely not an expert in commodity spreads and certainly not in the details of some of these energy markets that are being affected. But I did hear a conversation very, very recently that talked about something that I had never really thought about too much. And I’ve noticed obviously the price differences and the, and the volatility differences between the gases and the oils. And what’s quite fascinating about this conversation was really that the person explained it’s very different dynamics that drives those two because whilst the oil transportation cost is really not a big part of the setting of the price, but in the gases it plays a huge role, is my understanding in terms of the pricing of gas. And with this week’s, I think, bombing of perhaps the largest gas facility in the world, in Qatar, I think it was. And I think that’s, that’s why it prompted this guest to be invited on a podcast to talk about it.

But it’s quite interesting, something I’ve never really thought about, that what makes up the price is quite different between those two “energies” that we just take for granted that, you know, they’re either in the pump, or they’re being used for heating, or whatever we use it for.

[00:08:34] Nick

Yeah, it’s quite interesting. And also, from a portfolio construction risk management perspective, you think of them as like energy markets but in reality you start looking into even statistical correlations, you know, there’s pretty much zero. There’s very little correlation between the gases and the oil complex. The oil complex, in itself, is much more homogeneous. That obviously has significant implications when you build a portfolio.

So, then you have to start thinking, obviously, nat. gas is the most volatile probably of them all, specifically I guess on the right tail. So, it spikes quite aggressively. For oil, we can have a conversation on both sides of it because you can argue that there is a supply shock as it currently is the case, but also there is a macro shock that can drive the oil price further down because of reduced productivity. So, it can be more bidirectional with fat tails. I think that gas, and maybe I’m just front running myself here without having looked at the data recently, but I think it’s more of a right skewed.

But that level of volatility, specifically, because it’s very obviously tough to foresee, and it’s also driven by, obviously, the winter more than anything, it has implications of portfolio design. So, you build a trend following strategy. How do you think of those markets? How do you think of clustering them? How do you think of volatility scaling them?

You look into the bitcoin complex that has specific weights that are driven primarily by, if you like, production and liquidity. It doesn’t account for volatility. So, then you end up being exposed, in risk terms, more than what your notional exposure suggests. So, all these are important questions and all these are important considerations when we build a portfolio.

And I should say, that the oil complex and moves, that have happened recently, may be bringing that kind of closer to home for us. They were very well captured by trend following which, in itself, given the rise in the volatility, it is reducing risk exposure precisely because, yes, it might be spiking, but it’s spiking so aggressively so that the volatility itself suggests, look, there is a trend, but maybe you should be more conscious of the amount of risk you deploy.

So, I guess in the broader context the benefits of diversification and multi strat portfolio, when it comes to commodity systematic strategies, keeps on playing out quite well. Like now, the skewer value dynamics, we have discussed here, have been benefiting recently because they are benefiting from the petroleum sector.

So, in a way strategy level details and nuances are always open to question. The benefits of the diversification, they have played out quite nicely, year to date, at least from a QIS or systematic standpoint. But you’re very right on the volatility dynamics. You’re very right on this one.

[00:11:17] Niels

Sure, yeah, super interesting. I mean, I think Bloomberg will be mentioned quite a few times, on my radar was not something I spent a lot of time looking at. It was just an interesting headline and that was that they had an article out, today or yesterday, about… I mean, we talk a lot, at least we refer a lot to these pod shops and the success they’ve had and all the assets they’ve gathered. But they actually had an article out talking about how traders are now ditching these pod shops and much prefer to go their own way instead.

And they have numbers for ‘23, ‘24 and ‘25. And in ‘23, according to their numbers, or the numbers quoted in the article, 9% of people would leave and set up their own. In ‘24 it’s 12%, and in ‘25 it was 17%. So, kind of an interesting little take on that.

And also, I think they have some numbers, actually, from Goldman Sachs. I can see the source is Goldman Sachs. That must be really, really good reliable data, Nick, I’m sure you would agree, that fewer investors are actually looking to invest in the space compared to before, you know, and a few more expecting to actually decrease the allocation to some of these types of strategies. So, just a little fun observation.

The other thing that hit my sort of inbox was just, obviously, the latest FOMC meeting and the comments. It was interesting that Fed Chair Powell kind of does not really think we’re in a stagflationary environment just yet. I think he made the distinction that when the term was invented, or coined, back in the 1970s, the difference was really that we also had very high unemployment. And I think, for him, maybe that plays a role, that we’re not quite in the same scenario. But at least they’re talking about the word stagflation even from central bank’s point of view.

So, on that note, I mean, interest rates have been moving higher in the last few days, the last few weeks. And maybe some of the things we’ve talked about on the podcast, not least people like Cem, and others, in terms of perhaps the wishes of the White House, in terms of low interest rate, is not what we’re going to see. We’re going to end up seeing the opposite. That may well play out that way.

[00:14:03] Nick

I mean, I would not disagree. Some of the discussions we’re having at the moment, and I personally have had with our colleagues in the research, as well as with investors. The year, as we said 10 minutes ago when we started, was very, I guess, bullish in the sentiment and it’s now kind of turning around. I don’t think it’s yet in a place whereby we feel that kind of a down market or an aggressive down market is coming.

But to your point, I think memories of 2022 are coming back. I think the discussion around trend following is here, the discussion, you just said it about volatility in the interest rate market, inflation, volatility, is back. The question as to how central banks will act or react to the current situation is here. Obviously, some of that is quite mechanical with the oil moves that fit into, I guess, a forecast of inflation.

I guess the bigger question, certainly, I don’t have an answer for, is how much of an escalation we see and how much of that oil shock is permanent or about to be subdued. Historically, those events take some time before they die out. Eventually they do, but I think that’s the biggest kind of question mark at the moment.

But to conclude, I think the stagflation, maybe the stagflation risk is probably elevated vis a vis kind of two months ago.

[00:15:34] Niels

You know what? There was also, and obviously I’m not trying to make any forecast or political statement or any other kind of statement, but just an observation (we’re both becoming very neutral here), but, at least as a Swiss person, I am generally quite neutral, as people would know. But anyways, joking aside, you know, there’s one thing I thought about, we talk about, oh yeah, you know, the war can stop any moment, and things will go back to normal.

But I was thinking about this, people who, if you think about the Gulf states, yes, for a lot of them, you know, oil is so important. But in the last two or three decades, what has also become super important to them is tourism. And I just wonder, I mean, are you likely to say, to suggest another holiday in some of those countries even if the war stops? I don’t know.

So, I just think that these aftereffects in terms of activity, growth, whatever, could be much more impactful. Because we know memories are long. If you’d just been in an area where there’s actually real conflict, and bombs are falling, and whatever, you’re not likely to suggest that as your next destination for your family holiday, I would have thought. So, it’ll be very interesting to see what the real fallout is, in my opinion, other than just higher oil prices at the moment.

Anyways, let’s jump over to the trend following update that we always do. I mean, so far March, not surprising, has been a month of some corrections. Nothing too dramatic, I think, but obviously things like equities, where markets have been selling off bonds, markets have been selling off precious metals quite severely actually, in some of the markets where maybe it’s an easy way for people to raise cash and therefore they’re selling those markets in particular, I don’t know. But yeah, so we have some corrections.

This is of course completely normal, especially after a long run of eight consecutive months for the industry to deliver positive returns. So, this may come to an end this month, but it may not. I mean, who knows, the month is still pretty long. But is there anything that has stood out to you, in the first few months, from changes in positioning or performance wise that sort of stands out to you?

[00:18:08] Nick

So, I think what is very interesting, from my perspective, is that was more of a March effect rather than necessarily a year-to-date. But I guess you make the extrapolation, it can be like a year-to-date realization, you know, what we’re discussing back in December and pretty much I would say over the last couple of years that kind of slower speeds would typically allow to navigate through V shapes.

What we’re seeing at the moment is almost like the flip side. And perhaps, because we’re in the middle of the situation, but faster speeds at the moment are substantially outperforming slower speeds. Like, for instance, if I look into some of our performance in March, it’s close to being flat, frankly.

So, it’s not that it’s been a negative performance, and obviously commodities have been the big driver of performance here. And to your point, it’s the oil complex. So literally from heating oil, gas, oil, branding oil, this is really the spectrum of performance.

But you also get some positive return from some of the rates complex, specifically, you know, some sort short positions that have delivered good performance month-to-date. Obviously, equities have been the ones that have been hit, but net/net, so far, the month is not just within statistical ranges. It’s almost like, I guess, a non-event from a trend following standpoint. Right?

So, I mean to your earlier point, the 4% fine. I mean, we’ve seen 4% up and 4% down. I mean, I would not get any concerns in this regard. So, that is maybe the one point I would kind of flag here, that I’ve seen a dispersion in the speed complex that is almost mirroring, but antithetically, what we saw last year.

[00:19:58] Niels

So, let me ask you just to put some context on that. When you say it’s been the commodities. In a typical QIS portfolio, what would you say the split is between commodities and financial markets? What would, typically, be what you would expect? Because, obviously, if you have, like we do at Dunn, we have a higher weight to commodities, which we like, but not everybody likes to have that, of course. So, what would you say is your sweet spot in terms of allocation between those two types of markets?

[00:20:33] Nick

If I ask for one clarification, you say you have a higher allocation, is that…?

[00:20:36] Niels

No, I mean just the number of markets in the portfolio because the risk allocation is obviously dynamic, right? So, we obviously don’t know what that’s going to be. I’m just thinking, if you put together a portfolio of, say, 60 markets, or however, I don’t even know how many markets would be typical in a QIS, how many of those market would be commodities as a percentage?

[00:20:58] Nick

I would say, from implementations with a core universe, that can have maybe 10 commodities out of a universe of like 20, 25 to a much broader. You can have up to maybe even 40 markets in commodities.

But I mean, if I were to make a guess, and that’s just a guess about the industry, I would imagine something like a BCOM universe maybe tilted more towards the liquid market. So, I don’t know, 15 to 20 is the reasonable number. But risk wise, and again that’s more my hunch, would be that people allocate a quarter of the risk vis a vis equities, and rates, and currencies.

There is an argument to be had that commodities are much more diversifying as an ecosystem. Specifically, if you have a broader universe, and therefore there is a reason to have maybe even a high-risk budget to reflect that the tagging of that universe being commodities is not as similar as the tagging of equities being equities, because in equities the commonality and the homogeneity is significantly higher. So, the clustering we do in commodities is more of a tagging exercise.

And I know that some of the CTAs would think of commodities as kind of energy, and metals, and ags, with each one of them being an asset class in isolation. So, in other words, instead of having four asset classes you have seven, but three of them span the universe of commodities whereas the correlation between those classes remains as low as it is between, let’s say, equities and currencies or bonds and ags, and so on, and so forth.

So, I think it’s a significant philosophical divergence between models that is not heavily typically discussed. I think we speak about the speed, and the risk management, and kind of correlation structure. I don’t think we speak too often as to how many or in what context commodities are part of a trend following portfolio and it can have a significant effect. And specifically, over the last eight months, to your point, it was a very strong precious metals rally and now oil rally. So, it’s been big and actually…

[00:23:06] Niels

I would say probably ever since 2023 when cocoa started sort of making noises in the portfolio, and then came coffee, and live cattle, and all of that stuff. So, it’s been an interesting period for sure. We may touch on this a little bit because we will review a paper that talks about sort of the importance of market universe for sure. So, we’ll probably touch on that.

So, yeah, from my perspective, my trend barometer finished at 45 last night, so that’s kind of neutral. So obviously not really showing any signs of stress even though performance is probably down a little bit so far this month.

Nothing major in terms of changes, as I can tell, exposure wise, just yet. So, no real changes of sector exposure perhaps, maybe fixed income being one where we may see some changes soon. I have seen some markets, you know, going from long to short, or some more markets for sure.

But anyways, performance wise, as of Tuesday this week, yesterday, I should say, was probably a negative day for sure for the industry. I’m pretty sure of that. But before that on Tuesday the BTOP50 index… Sorry the SGCTA index was down 1.38% for the month, up 6.82% so far this year. SocGen Trend down 1.77% for the month, up 6.87% for the year and the Short-Term Traders Index, as you suggested, down just a little bit, a quarter of a percent, not quite keeping up for the year, up 3.55% but still doing better in March.

MSCI World, on the other hand, having a tougher time, down 4.64% so far this month, down now for the year 1.34%. The US Aggregate Bond index also no diversification benefit there, down 1 1/2% in March, up 17 basis points though for the year. And the S&P 500 Total Return down 3.61% as of last night, up almost 3%… sorry, down almost 3% I should say, so far this year.

Before we jump into the topics, we have a couple of questions from Tim that we’re going to deal with.

But before I do that, I just want to mention to everyone that if they haven’t been on the website recently, there is a new version of what we call the Top Traders Ultimate Guide and that’s essentially a guide to about 600 books now and hopefully there will be some of those that will be inspirational and we put it all together in one resource. So, you can go to toptradersunplugged.com/ultimate and then you can get the guide for free.

Anyways, Tim, who has been following the podcast for a long time and is very kind to share and like our content on social media, sent a couple of questions in and I think it’s quite an interesting question.

So, he writes, “The options markets have experienced incredible growth in recent years, especially post COVID and the widespread of use of zero day options. Trend followers mostly only use futures price data as their inputs to their models. Have we reached a point or will we reach a point in the near future where trend followers should be looking more closely at the option markets and the information within those?”

And then he says, as a follow up question, Do you think that by ignoring options data the performance of existing trend following models could deteriorate or that incorporating options information would produce improvements on existing models?”

So, who better to ask than you, Nick?

[00:27:06] Nick

Okay, I would need Cem on this one but I’ll give it a go. I’ll give it a go. I’ll give it a go. I mean the observation that options markets have become, to a good extent, dominated by the zero DTE is not just something that we read in the news. You look into the hard data.

In anticipation of this discussion, I kind of pulled up some numbers. So, currently the volume in S&P options, 60% of it is zero DTEs and 40% is anything from daily up to longer term tenures. And in that zero DTE complex more than half is digital participation.

So, we can make the argument that it’s a place that almost democratized access to intraday leverage and equally access to those, I guess, cheap bets that you can put on the day; out of the money put or out of the money call, depending on the view, for a significant premium to be had, or significant payout to be had at a very small premium.

I guess the question on how this, I guess, market evolution in the option space can, to a certain extent, impact trend followers. I will look at it in two ways. The first one is how we calculate signals, how we think about establishing market trends, that we kind of capture them, and then we end up deciding to go long or short. But secondly, I would also look at it in that the use of those options and derivatives can have in risk managing trend following portfolios.

So, if I go to the first one, right? If I go to the first one, it’s all about the marginal or not the impact that dealers would have when hedging their gamma, which is a consequence of investors selling or buying options.

So, if investors are selling out of the money options not to generate some carry for instance, then dealers would typically be like not long, for example, and therefore in this activity they would be buying when the market drops and they would be selling when the market goes up. So, they have a tendency to mean revert, to force a mean reversion behavior.

Conversely, if you have the opposite activity, you can be in a negative gamma situation. And levered ETFs is perhaps one reason why this dynamic can play out, that you end up buying as the price goes up and sell as the price falls. And that is exacerbating trends.

So, you being a trend follower, you might be in a situation whereby either volatility is suppressed and mean reversion is, I guess, an artifact of this auxiliary activity or that prices accelerate at a higher volatility dynamic.

So, then the question becomes, in the absence of those dynamics, how would trend in itself perform and you know, would be established for us to be able to capture it? And I think there is a question here to be had, you know, should we somehow take that into account?

Not too clear to me how, but there is certainly a good question to be had specifically for shorter-term managers. I think the longer-term managers, you know, if your signal is very close to zero anyway, your risk exposure would be small, to be honest with you. So, does it change, if you like, the measuring of the signal? Probably not.

The other point, which perhaps is equally interesting, is that on specific days that those options specifically, not just the zero DTEs, but more broadly, have an expiration, you have this kind of so called pin risk. So, there’s a bit of gravitational power towards the strike price that the open interest is large. Again, it can have an impact as to how the signals themselves are documented for a trend follower. For a medium-term trend follower is that impactful? Not very clear to me, just to be open. At the same time, should we ignore it? Certainly not.

But frankly, I think more value, at least in the present market environment of utilizing those tools to maybe take into the exposures when the market goes against you. So, suppose you’re holding equities, and your equity starts selling off. So, your long position in your long-term model would either take some time to revert or during the day it’s going to suffer.

Maybe there is a premium to be spent on a zero DTE option to just cover that exposure and that is more how we can use those, more shorter-term, to risk manage a portfolio without cutting the exposure but rather adding to it at a cost a short-term protection. So, it remains to be seen what the outcome can be. But these are some of the quick thoughts.

Maybe the last one I would have. I think there was a paper recently but you know, I hope I don’t misrepresent it now. It just came up to my mind actually, I think it’s by Greg Vilkoff and some colleagues. I think it’s a project perhaps sponsored by the CME, if I’m not mistaken, or Cboe, it doesn’t really matter.

The point I think they’re making, they’re looking into zero DTE volumes and they find that dealers are typically more long gamma than short gamma. And therefore, there is an argument to be made that short-term reversions have become more prevalent than intraday trends. Which again, in itself can have an impact as to how markets trade intraday. I think we’ve seen a good amount of reversals recently, and over the last few years Intraday. Is that caused by zero DTEs? It is very certainly a force into it.

So, that’s how I look at it. Point number one, how the models can become aware if that is required. Number two, can we actually make use of them for risk management? I think the latter is more of a direct use case. The former, maybe.

[00:33:06] Niels

Okay, maybe so. So, I would say I tend to agree with you on this one, but I think about things in much simpler non quantity terms. So, in one way I would I just make it sort of a simple observation saying yeah, the last period of time it’s been definitely more challenging for, say, short-term strategies.

And maybe that is exactly because there is a force out there that is much more convergent, and has this sort of mean reverting interest, and that could well be from all the people around, you know, surrounding the zero DTE options markets and how they manage risk, and so on, and so forth. That would be one observation, could also be the pod shops, but they’re probably also part of maybe the zero DTE options market. I have no idea.

But the other thing, spotted specifically to Tim’s question about, should we use that data? And I’m thinking what would we use it for? We’re trying to find trends that last for 3, to 6, to 12, to 24 months. A zero DTE option volume (and again, I’m not a quant here), I’m thinking, okay, that gives you a sense of people making a bet for a single day. That’s not really going to inform me what the price is going to do over the next 3, 6, 9, 12, 24 months. But even for risk management purposes, why would we start trading options when we have plenty of liquidity in the futures markets?

And I think one of the trend followings, I mean one should never say never, but one of the trend following mantras has always been, you test what you trade and you trade what you test. Right? And we are using futures data to build all our models and so we should trade futures. And by the way, we adjust positions on a daily basis. It’s not like we’re making massive bets any one day unless something really crazy is taking place. So, I’m less certain about the use case of option data for classical trend following models. But as I said, I’m not a quant so I could be completely wrong here.

[00:35:15] Nick

I mean, I can see the hypothesis that periods of mean reverting behavior can create excess turnover without there necessarily being any particular reason for it. I think most of our models do some sort of short-term smoothing anyway. So maybe we can look at the symptoms of the whip sawing dynamics and ultimately, if averaging helps a bit, moderate that, as it has historically done with let’s say asynchronicity, possibly it’s captured indirectly. But I can see the hypothesis there.

I can see the hypothesis of maybe some sort of option implied information like no skew providing some sort of sentiment indicator that, I don’t know, that in itself might be like a penalty to a specific direction of the market or maybe a multiple. So, there could be nuances.

[00:36:07] Niels

Yeah.

[00:36:07] Nick

Full stop, full stop, anyway, there’s no comment in my sentence. I think these are like some of the hypothesis that will kind of push forward as a consequence of Tim’s questions.

[00:36:16] Niels

We appreciate the questions. It allows us to think about stuff that we may not think about on a day-by-day basis.

[00:36:24] Nick

Always do. Always do.

[00:36:25] Niels

All right, well let’s jump to the papers now. We had kind of several choices. I think we’ve narrowed it down to two or three that we liked in particular.

Now the first one, and, by the way, courtesy mostly by our friends over at Man Group because have been very busy recently, and they have put out some really interesting ones although the first one is a probably a few weeks old, and I think Alan and I quickly touched on it. That’s why I was delighted when I saw you wanted to say a few words about it and even more delighted to say that I think I might get one of the authors of the paper on the podcast along with Katy, in a couple of weeks, so we could also maybe leave some questions for him. You never know.

So, as I said, I may have touched on it already a little bit. It’s the paper called A Trend Following Deep Dive: The optimal Market Mix for a Trend Follower. Now, of course the topic itself is relevant because we talk a lot about it over the years; which market should we trade, do we trade too many, too few, and all of that. And we all have our different views and we favor different solutions but it’s not always we’ve been very good at eloquently describing the benefits of doing one choice versus another choice. And this is what I love about the paper.

It’s a very visual, easy walkthrough in terms of what people should be likely to expect from choosing managers trading different market universes. I think they made that in a very eloquent way. Of course, if you want to read the paper you should go to the man.com and the insights. That’s where you will find the paper.

So anyways, I very much look forward to hearing your thoughts about it, Nick, and then we’ll take it from there.

[00:38:28] Nick

It was a very good read, I think, so, why did that resonate quite well with me? Because we always discuss how defensive your trend follower is; what’s a universe, a core market, a broad market, alternative market, whatever market. I think the reason why I like this particular one is because it connects a use case to the universe that you use.

So, we typically say you want to be defensive, be faster; you want to be longer-term performing, be slower; but bear in mind, you’re going to get a lot of beta risk in your portfolio. We always have this conversation, but I don’t think we have ever touched upon the point of, you want to be defensive in this universe, do it quickly.

I think that’s why the paper is interesting because it says… Or maybe taking a step back, when we speak about managed futures, there is this kind of duality of objectives. I think Cliff Asness wrote about it a couple of years back and I keep on using now this kind of duality term. It’s one of the very few systematic strategies that deliver long-term positive returns and kind of downside protection, some sort of a crisis alpha (to use Katy’s term), some sort of a reactivity when the markets are falling, but not obviously sharply but in a kind of a medium-term.

So, this duality of objectives, frankly I don’t think anything bad trend following commodity curve to basically say the one that we started from. So, it’s typically defensive as well because it’s kind of shorting in front of commodities. So, in a non-inflationary recession it actually performs well.

And then, I guess, interest rate volatility, there’s nothing else, at least in my mind, that doesn’t showcase a tradeoff between kind of reactivity or defensiveness and some sort of a kind of cost associated with it.

So, if we look into this duality, which is performance and defensiveness, we can stretch it out and say, well, how can I maximize my defensiveness and how can I maximize my kind of long-term Sharpe ratio? And surely there’s going to be like a tradeoff, now, between, I guess, the choice I have available while still obviously maintaining the duality. So, it’s not about making, I don’t know, negative long-term return or making non-reactive kind of a profile. So, I think there is a commonality here in the solution, that being that the duality is preserved, but it’s more about kind of the major and the minor in the objective.

And they make the point that, look, if you really want to be defensive, and defensive is more about generating positive return when the market goes through a stress situation, then you should have a market universe that is more kind of core, and more kind of standard, and more mainstream, and perhaps more liquid, because, no surprise, when you have a massive correction, it is not that assets fall, it is principal components that are falling.

So, equities, as a risk, materialize. Perhaps, I don’t know, in the duration space you have central bank intervention, and you just want to be long bonds or maybe some of the cyclical commodities, except maybe precious metals, are suffering from a drop. So, ultimately, in a period whereby asset prices get squeezed and maybe correlations are shifting to the extremes, you just want to trade the principal component.

So, if you stick to a core market universe, even for the same specification of speed and correlation, so on, and so forth, you’ll end up maximizing your kind of crisis alpha or defensiveness. And I think Andrew would actually be quite opinionated on this one because I think that’s part of his speech, that the universe actually quite short but very much representative of the macro moves.

Conversely, all the alternative markets, maybe the alternative commodities, or we can go into kind of EM equities, or even equity factors that they also utilize in the paper, this expansion of the universe, by design, brings idiosyncratic risk that, with the assumption of trendiness, would provide longer-term diversification and therefore return, and therefore maximum long-term Sharpe ratio is achieved with a broader universe with more independent bets.

Now, this is not as defensive as the tight universe would be. It is still defensive to my earlier point, but longer term has a better Sharpe. So, now this poses the question, you being an asset allocator, what do you want to solve for? If you want to solve for defensiveness, how should you look into it? Is it like a speed discussion? Is it a universe discussion? Is it the risk management discussion? But there has to be a discussion.

If instead you’re looking into it more as an alpha seeking overlay, then maybe a broader universe can be more associated with your objective, and you can meet that objective more successfully. So, with the recognition that you might not have the best defensiveness in downright. So, I think I’m pausing here for you to reflect, but this is how I think about it.

[00:43:20] Niels

Well, I mean, I just wanted to ask your opinion as well, it’s a leading question here, would you agree that, actually, that’s exactly what I think this discussion brings because previously I’ve always felt that the crisis element, protection element, was really always a discussion that related back to speed.

[00:43:45] Nick

Yeah, 100% and I plead guilty that I was always going back to the speed discussion. Or maybe the amount of controlling of your equity risk, you can do it with data, or maybe some force constraint exposures, or all that. Somehow, I don’t think it was very natural to think of the markets as directly as this one says. I mean, yeah, we could control equity risk, maybe we kind of thought about it, but not to that extent. So, I think it’s interesting, to your point. I think it kind of shifts a bit the discussion, completely.

[00:44:16] Niels

Keep going.

[00:44:18] Nick

Look, I mean, that’s pretty much it.

I think they have another interesting kind of, I guess, path that they follow, which is in addition to the maximum Sharpe ratio and the maximum crisis alpha portfolio, looking into also cash efficiency and obviously the fact that we trade futures.

You know, futures are instruments that trade on margin. So, you don’t have to spend, you know, US$100 for US$100 notional exposure, you just pay a fraction of it, which is determined by the exchange. So, the question then becomes, obviously, how is that margin determined? And that margin should be financed from actual dollars. So, the lower that margin is, the more capital efficient the portfolio can be.

So, they make a supposition here that a portfolio that is more cost efficient, and therefore you can get higher level of volatility for the same dollar value of notional, sorry, of margin, is a portfolio that most likely would have liquid markets because that’s a component of the margin determination. Now that, in itself, brings you to a universe that is more of a core universe. So, it’s not a surprise that the cash efficient portfolio, from that perspective, resembles the maximum crisis alpha portfolio.

Why? Because then too, for different reasons, one to capture the macro moves, one to capture the more liquid assets, ends up allocating to the same ecosystem of, if you like, assets. Anyway, for me that’s more of a byproduct of the discussion. Very nice to see.

It’s almost, if I were to kind of reverse the argument, if you were to have the maximum crisis Sharpe portfolio, it is more likely that your margin requirement will be lower. But yeah, to me the biggest point is really the association of the objective to the universe.

[00:46:17] Niels

Another stat from the paper, that I thought was kind of interesting, was they managed to say that there are approximately 900 different markets we could trade. That surprised me that they could find so many.

[00:46:32] Nick

This is very true. The one thing I would point out (you remind me that I had forgotten about this one), if you look into equity styles, so they use equity factors and they have 60 markets or 60 factors. I mean, I’m sure they would attest to it (and I guess you can ask one of the co-authors when he’s here), there are no 60 factors that can be seen as, to a good extent, diversifying in the equity space.

You know, we can talk about earnings to price and book to price as valuation ratios. We can think of those as two factors. But technically the amount of cross-sectional correlation is very high, and they are value descriptors. So, in a way, 60, it’s almost like (at least that’s my understanding and maybe I’m wrong), 60 here is almost like a signal by signal characterization of how many degrees we have to rank single stocks by, and build single stock equity factors.

But that is slightly, at least in my mind, not dissimilar to saying I’m going to have 60 commodities because I think in the equities world, maybe five, maybe six factors probably explain the cross section of returns.

So, I think 60 commodities versus 60 descriptors of equity returns would give you much less of a diversified universe in the equity space rather than in the commodity space. But setting that aside, 900 is 900. So outside of that, I cannot really pose significant questioning on the count.

[00:48:02] Niels

That’s fine. We’ll look forward to having one of the authors on the show in a couple of weeks. And we’ll probably touch on this again, no doubt.

But we’re going to stay with the Man Group. As I said, they’ve been really busy writing. And you identified another paper which is a little bit different, I think, in terms of the topic. It’s called Alpha Trend and Agentic Research Workflows. So, not necessarily specifically about building models, but maybe processes that can help do it easier or perceived to help to do it easier. What were your takeaways from the questions they raised?

[00:48:46] Nick

So, I picked this up to discuss, not so much because it talks about trend following. Obviously, it came to my inbox as the output of the research at Man.

I think to me this poses a bigger question as to how we see those new technologies and AI, Gen AI, and LLMs becoming not just tools that we use, but eventually become core components of the research process. And for the sake of, I guess, those that are listening, the paper talks about kind of designing an agent that can build a trend system.

So, to a certain extent, running a trend following strategy or any systematic strategy is a very deterministic process. You have your data, you clean your data, you build a signal, you estimate risk, you throw that into an optimizer, maybe a ranking methodology, whatever that is. You get target weights, you have some liquidity control, you have some volatility target. And here you are at the quantities to trade on a daily basis or whatever basis.

So, that process is very deterministic. And certainly you can think of a world whereby single agents do that job for you and there’s a kind of a supervisor that allows you to command this whole ecosystem.

And they make the point that the current kind of chatbots that we have are probably kind of more shallow in the amount of depth, but very broad in the topics we can discuss. This model of conducting research is much more targeted. So very narrow in terms of scope, but very deep in terms of analysis.

So, in that context, again to their paper, they kind of give it out, they give it a breakout signal, and they deliberately remove some good feature out of it, and then they obviously ask the model to find what this feature could be by, I guess, describing it with words.

And yes, the model does pick it up and does outperform, but then there is another feature that should not be value accredited. And then the model indeed finds that it’s not as useful as you’d expect it to be. And broadly speaking, they make the point that the model can actually go and operate as a human being, good. But equally, I think what’s even more important, that human judgment remains extremely important not just from how you frame the questions, and how you guide the process, and how you interpret the results, but also kind of safeguarding against multiple testing, overfitting, things that we have been discussing for years and how the culture of a research team is more important than the research team itself. I think they make the same point, but now the research team happens to be like kind of an agentic model rather than an individual or like a team.

So, I’m kind of bringing that up, maybe the last point, they kind of use different models like Claude, and GPT, and so on, and so forth, with the same prompts, and they found that the outcome was actually quite different, but not necessarily worse or better. So one, for example, was more of a kind of a single direction minded with very correlated outcomes. The other one was a bit more dispersed in the way that it kind of treated the data. But broadly speaking, the one thing that at least remains, in my personal view, is that we still require this kind of critical thinking.

I think the synthesis that those models can do is insane. I’m personally surprised every single day by using the tools, but I think having some sort of a disciplined evaluation of the outcome and having critical thinking of the outcome is extremely, extremely important.

I’ll give you this example, unrelated to the paper. I went to one of those engines recently, and on purpose I asked the following question. How has the first two months of the year been for trend following of all things? Because I know the answer. And I know that January and February were probably one of the best two-month periods to start a year for trend followers. Right?

And the first sentence that comes out, and I don’t even care about what followed, was, like, “the start of the year has been mixed”. And I’m like, you’re so wrong. So, to that point I think some level of supervision is more than important here. It’s actually essential, specifically when you end up kind of managing money on behalf of policyholders, and pensioners, and insurance.

[00:53:33] Niels

And on top of that, actually, I think that the value of what our esteemed researchers do nowadays is actually trying to keep things simpler. Meaning, I have a feeling, without being an AI expert, that they love to expand on things. That’s kind of what they do. They find sentences and words and all of that stuff.

But actually, when you build a trend following system, yeah you have a lot of options but actually the skill and the experience goes towards actually stripping things down so you get the cleanest signal, less noise and I’m not so sure that the DNA of AI may not actually be very compatible with that.

[00:54:23] Nick

We shall see. I think the amount of evolution we’ve seen, in the last few months, is extraordinary. If I were to speak about my personal experience and how I use it. It’s just, for the same task, I used to get rubbish. I’m getting high quality outcome now, so it’s quite impressive. We shall see where time goes.

But I found it very interesting that they actually ended up kind of writing actually the second report on how they use AI for research purposes. And obviously kudos to them, I guess, opening up and making that a topic of discussion rather than saying oh we use this model, here’s the line, go trade with it.

[00:55:06] Niels

Let’s make it three for three, Nick, because you identified a third Man paper; The Quant Renaissance. Talk to us about this paper and why it caught your attention.

[00:55:23] Nick

Exactly three for three.

Now this is now shifting gears away from trend following. So, this is about quant equity. So that’s from the numeric crowd at Man. So, I’m spending a good amount of my time with our equities, kind of single equities strategies. It’s a very interesting space because it’s maybe one of the places that systematic invested started from.

Obviously, trend following is probably the longest living from the 70s. But if I were to pick what the second one is, probably equity factors is the second one to come, and it could have equally been an ‘80s or a ‘90s gig, you know when Palmer and friends came about, kind of the whole factor and then obviously, who was it, Steve Ross with APT.

So, this space of equity factors, driving returns and therefore being rewarded for a specific risk exposure, could have been an investment mantra back in the ‘90s. But it only became much more popularized when we had high performance computers to churn all this data, and the cross section of stocks, and trade, corporate actions, and so on, and so forth.

So, that space obviously became very, very popular. And then coming 2018 to 2020 it suffered from this kind of quant winter that had significant consequences also for the buy side, managing those strategies. Now, lo and behold, post COVID, significant resurrection.

So, the multi strat, the quant equity, the long/short space, has had very strong performance. And what this report tries to bring into, I guess, the discussion is how much of a risk we have of a repeat of a winter, but also maybe how more resilient the space, from a research, product, and investment management, has evolved over the years.

So why did we have the winter back in the days? Frankly, there are two reasons they bring about. I would possibly just agree without having seen them. One was that the macro regime was not very accommodative for some of the factors. Obviously, with the interest rate markets operating as they did back in the late part of 2010s, value was certainly impacted quite significantly. Then you had some reversions and the momentum did not play out well. And the overall complex was just not accommodative of the macro environment.

The second one is possibly some factor crowding. So, the theme became quickly too popular, having not just perhaps alpha decay consequences, but also having forced unwinding events when some sort of a macro shock or funding liquidity driving some of the performance. And then you have this circus of unwinding and obviously one is causing the other, and so on, and so forth. So, some of the ‘fire sale’ dynamic.

And if I were to quote some of the stats in the paper, they say that during stress periods classical macro factors can explain more than 50% of factor return variation, which is quite substantial if you were to think that normal times it’s more like 20%. So, that was what happened back then and now the question is, obviously, if we bring this world to today, how things have changed.

They use an internal model they have for regime identification. So, no surprise, you typically have kind of recession, early expansion, late expansion, and overheating. So, the four typical regimes that the equity market, or, broadly, the macro market goes through, they utilize that to understand better the quant winter and associate different environments.

But then they make the argument that, with us understanding better how the macro cycle works (at least through the lens of factor investing), their argument is that now we, as investment practitioners, as managers, are utilizing more dynamic allocation in the factor space and less static. And I think that’s something we also see, and we have seen the QIS space doing.

And I think it is now much more of an expectation that you have a dynamic allocation mindset rather than a static one. So, there is some value that dynamic allocation can bring with, I guess, the caveats of concentration.

There is obviously new data. So what historically used to be your quality score, and your momentum score, and your value score, can now be either enhanced or expanded in the factor space by geolocation data, and credit card data (and you name it), and patents, and kind of sentiment from the buy side, from the sell side, using ML models. So, there is new data to be utilized which inherently provides some diversification.

So, they put some quotes that, for example, alpha models, back in the days, would have correlations of 70% to 80%. These days, more like 40% to 50%. So, there is some diversification at the signal level.

And lastly, they make the argument that going through the more dynamic allocation, going through new data, going through more modern techniques, designing portfolios, or crafting alpha scores, eventually provide some sort of a macro regime resilience. So, what was, back in the day, a macro regime dependence can become a macro regime resilience with the use of all those technologies and data sources in the quant equity space.

So, I think equities, because of their dimensionality, they have been the most researched and they will continue being a space that the return can be harvested. So, I found this one quite an interesting one, both for myself as well as, I guess, for this discussion to be brought forward.

So that’s the whole story, right? Is the quant winter something we can see again? And if that is the case, what drove it back then? How the changes that we’ve seen in the space have maybe reduced this probability if the macro regime is not very accommodative. So, that’s the whole story.

[01:01:33] Niels

In a sense, we could link that to trend following right there. People talk about a trend following winter a few years ago as well, right? I mean, have we…

[01:01:42] Nick

I thought you said in early March.

[01:01:45] Niels

No, I was thinking about the 2010s where people were complaining…

[01:01:49] Nick

I know, I know.

[01:01:49] Niels

And where people were sort of disappointed with returns. But when you think about the macroeconomic environment, low inflation, stable inflation, very little movement on GDP, and so on, and so forth, of course, that’s not necessarily the best environment. The question is, of course, with all the research we do, should that happen again, would we cope much better with it? That’s kind of the same argument or question that only time will tell, I guess.

[01:02:19] Nick

I mean, I think we’ve discussed that maybe that was the reason why we first met in the very, very first place. Right? You know, this whole discussion, I remember, back in the day, having talked about fundamental certainty and uncertainty, and how the Fed put brings a lot of fundamental certainty. I would not claim that currently we’re sitting in a fundamentally certain kind of environment.

[01:02:36] Niels

It doesn’t look like it.

[01:02:37] Nick

Certainty brings reversions, uncertainty allows price trends to continue because a price trend, in a certain environment, provides information about where the fundamental value should sit. Whereas, certainty brings you back to your prior, which is very informative of valuation. So, I think this is the dynamic, at least in my mind, that the Fed was bringing to the trend space and therefore the challenges that came about.

[01:03:02] Niels

Absolutely. This was great, Nick. Thank you so much for spending time preparing for all of this, finding these papers and discussing them, of course. And for all of those of you listening, you know, feel free and please do head over to your favorite podcast platform or YouTube and leave a rating and review to support the channel. But also, as a thank you to Nick and all the other co-hosts who do a tremendous job every week in preparing for these conversations.

Before I wrap up, let me just say that if you have questions for next week, which is where I will be joined by Yoav, then send them to info@toptradersunplugged.com, just like Tim did this week, and I’ll do my best to bring it up in our conversation.

With that said, from Nick and me, thank you ever so much for listening. We look forward to being back with you next week. And until next time, as usual, take care of yourself and take care of each other.

[01:04:00] Ending

Thanks for listening to the Systematic Investor podcast series. If you enjoy this series, go on over to iTunes and leave an honest rating and review and be sure to listen to all the other episodes from Top Traders Unplugged. If you have questions about systematic investing, send us an email with the word question in the subject line to info@ptoptradersunplugged.com and we’ll try to get it on the show.

And remember, all the discussion that we have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also understand that there is 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.