TTU129: Svante Bergström, CEO & Founding Partner of LYNX Asset Management

[00:00:06] Svante

We are in an environment where uncertainty is extremely high, and that’s normally a good spot for trend following. At the end of the day, we all know that the best environment for us is when something totally unexpected happens. I can’t forecast what that would be but let’s keep our fingers crossed that things happen and we’ll try to make money out of that.

[00:00:38] Intro

Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more. Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

Before we begin today’s conversation, remember to keep two things in mind. All the discussion we will have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also understand that there’s a significant risk of financial loss with all investment strategies, and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions.

Here’s your host, veteran hedge fund manager, Niels Kaastrup-Larsen.

[00:01:33] Niels

Hey everyone and welcome to another edition of Top Traders Unplugged, where today, Alan Dunne and I are joined by Svante Bergström, Co-Founder and CEO at Lynx, as part of our mini-series focusing on the one investment strategy that beat everything else in 2022, namely, trend following and managed futures more broadly.

First off, Svante, it’s great to have you on the podcast. Thanks so much for joining us today. We really have been looking forward to our conversation, and I hope you’re doing well and had some time off over the holidays after what I can imagine was a busy year in Lynx.

[00:02:05] Svante

It was indeed, yeah. Busy markets and a busy year and a very positive year, of course, so it was great, but also great to have some time off after that.

[00:02:16] Niels

Yeah, absolutely. Now, before we dive into all the specific topics that we’re going to discuss, I would like maybe to set the stage for our conversation so that the audience knows a little bit about your firm’s background. So perhaps you could share a few highlights about what type of strategy or strategies you focus on and where the business stands today as we head into 2023.

[00:02:39] Svante

Sure. I guess we’re a bit different to some other CTAs in the space since we’re based in Stockholm, Sweden. The team, the three founders, we started working in the mid ’90s together to build models. We didn’t even know that there was a CTA industry back then. So, we built things a bit differently and did it our own way. Therefore, I think the models that we have in the portfolio are probably a bit different from those of our peers. But since then, since we started the firm, we have continued to grow.

We are now slightly over 80 employees. Out of that, roughly half of it is research, so we are fairly heavily leaning on research. We manage $7.5 billion now. So it has been a great journey over these 20-plus years. We are a trend follower in the bottom, but then we have diversifying models on top of that. We are a kind of multi-strat CTA, but we try to keep the trend following characteristics of the return so that’s an important part.

[00:04:06] Niels

Sure. Okay. That’s great. We’re certainly going dive into lots of topics around that. Now, for our conversation today, Alan and I have created a list of topics that we find interesting when talking to our colleagues in the industry. We may not discuss all of them, but we certainly plan to pick a few good ones. So Alan, why don’t you, as usual, kick it off with the first topic?

[00:04:29] Alan

Great, thanks. Good to speak to you, Svante. It’s interesting. You mentioned how you went through that research process back in the mid ’90s and then I guess you stumbled on trend following. Can you talk a little bit about that process and how you settled on trend following being at the core of your investment approach? Was that just purely based on the research or did you have an inkling that there was momentum in markets or was it driven by academic studies? What was that process?

[00:05:02] Svante

I would say, if we go back to the mid ’90s, there was a totally different environment where we just got to Excel back then and we didn’t have price data that you could use. You had to actually buy data from the US and they sent you a floppy disk with some data. So we started playing around with those data series. Of course, at the beginning, you will find the momentum.

Trend following models is really something that you want to have in your portfolio, especially back then when they worked even better, maybe over a period. But then also, we found other patterns that we could make money from, and it was more pattern recognition type of models that we used. We don’t have them in the portfolio anymore. We had diversifying models in the portfolio since the start, and we have had 25% to 30% diversifying models since then, basically.

[00:06:11] Alan

It sounds like the philosophy is, try to find models that work and blend them together. In that process, do you set out with a hypothesis about what might work and what may not work and test that or is it more of a process of just rigorously assessing the data?

[00:06:34] Svante

We always want to have a concept that we believe may work and some logic behind the model. So we don’t do pure number crunching. Even though we use machine learning quite a lot in the firm, we have done that for over 10 years, but we are kind of cautious when we do that and we try to feed the machine learning models with things that we know work, so we don’t let them run by themselves and just find strange patterns. So, the starting point is always to understand the concept and try to systemize something that we think makes sense.

[00:07:21] Alan

You mentioned having the multi-strat approach but maintaining the trend following characteristics. Is that driven by an investment philosophy or is that a business decision? Why is that?

[00:07:38] Svante

If we take a step back, I think that trend following is great. It’s something that has worked for many, many years and we can see it coming time after time and coming back when people doubt it. It takes a couple of years and then suddenly you have a home run again, like last year. So trend following is something that I firmly believe makes sense and makes money.

Having said that, it’s so painful. The ride with trend following is so painful. It’s painful for me as a manager. I can imagine for an investor, it may be even more painful because I know much more about how our models behave, and why we lose money in a drawdown, and so on, than the client that sits and just looks at our daily or monthly numbers and starts to question his investment.

Therefore, I think we need to try to improve the Sharpe ratio, try to make some money when trend following doesn’t work. Therefore, we started out with having diversifying models in the portfolio, still trying to deliver crisis alpha, trying to deliver good returns when we have a lot of stress in the markets and so on, but also smooth that out a bit with other strategies.

[00:09:09] Alan

You touched on, I suppose, the difficulty, that the ride is so painful as you said. What’s your perspective on the nature of the return from trend following? Why is it like that? If I just ask you, why do markets trend or if you ask trend followers why markets trend, people talk about the different speeds of reaction or the speed of dissemination of information in markets or the fact that economies go through cycles as well, all of that makes sense, and the behavioral element, I guess. But when we have these protracted drawdowns, none of those factors change but we still have these possibly two to three-year periods where it’s not working. So what do you think is happening in those periods?

[00:09:51] Svante

Many times, it’s a question of timeframe. I think the human mind, we like to see the daily returns, we like to see weekly returns and maybe monthly. But to have an investment with a 10-year horizon, that is nothing that we can live with, basically. We can do it when we construct a portfolio and look at the backtested numbers and we put together a portfolio with different equities and bonds and hedge funds and CTAs and stuff.

But then after four months, people start to look at that again. If one of the components doesn’t deliver, then they start to mess around with the portfolio. I think that’s good for us that [when it comes to] the human mind, we’re not comfortable with being so long-term that you have to be invested in some components in the portfolio. Therefore, I think trend following will continue to work for the coming hundred as well because it’s not really suitable for humans to ride this and experience the pain over the years when we don’t make money.

[00:11:09] Niels

I want to expand on that a little bit, but also stay on that topic because I’m curious about your thoughts. Whether you read the article or not, it doesn’t really matter. But, for example, Cliff Asness came out last year talking about how maybe we, as an industry, have become too focused on Sharpe, and that we were putting too many “diversifying strategies” into our trend following philosophy. How do you think about that? How do you balance that? How do you ensure that you still, as you said in the beginning, you want to keep that trend following profile to some extent? How do you balance all of these decisions?

[00:11:50] Svante

Yeah, that’s a really good question. I think we have been a bit lucky at Lynx because if you look at our client base, it’s very heavily relying on big institutions, it’s pension funds and some sovereign wealth funds and extremely professional investors. They expect us to stay where we are. They don’t want us to style drift and suddenly have a very different holding period or add things that make the return stream different from what they have invested in. So therefore, we have a very solid process where we look at a lot of key ratios when we construct our portfolio of all these models that we trade.

We have around 45 different models in the portfolio. But when we put them together and we allocate risk to the models and to the markets, we want the expected return to be in line with what we have delivered historically in terms of how it behaves in different market environments and how it correlates with different assets and so on.
I also understand that the industry as a whole had a very difficult period, if you go back a few years, and it was tempting. If you looked at the returns, we could see that in our portfolio as well, that it was tempting to increase the holding periods because that would improve the performance somewhat. It was tempting to put in some carry components to add returns and so on. And clients, they like the managers that normally deliver the best returns over a couple of years so the industry started to tilt toward that. But we were not a part of that process and we have tried to keep away from diluting our portfolio.

[00:14:02] Niels

Sure. Now, you and I exchanged a couple of emails before our recording and one of the things you mentioned was the year 2022 as an interesting year, maybe for you and certainly for the industry as a whole, but maybe for different reasons. For you, what stood out in 2022 and what do you take with you into the future, so to speak, from those lessons learned?

[00:14:34] Svante

Sure. It was an interesting year for all of us in the industry and we are all happy that we finally got to prove ourselves again, not only with good absolute return numbers but also being able to deliver especially when interest rates went up as I guess you have talked about several times before. So that was fantastic. If I look at Lynx, there are a couple of things that we were especially proud of. One was actually that our diversifying bucket, that is not trend following, actually had an even better Sharpe ratio last year than the trend following bucket. For me, it was very impressive since it was such a homerun in trend following.

Also, the other thing that I am happy with is that if we look at the risk utilization that we have had over the year, and I guess that goes for the industry as a whole, we didn’t have that much risk on. We didn’t hit the risk limits very often, if you compare it to 2008, for example, where I think we ran the portfolio at that point at roughly half of what the models wanted to have in the portfolio because the volatility in the markets was too extreme. But 2022, we were able to run the portfolio with more limited risk utilization and still, I think it was the second-best year we have had since we started.

[00:16:18] Niels

Yeah. You mentioned also earlier the term crisis alpha. I think Cliff, in his paper, also talked about, do we have this dual mandate or not in terms of creating absolute returns but also creating crisis alpha. I don’t know specific managers who put into their model something that is designed to make money when there is an equity crisis. I know that that’s what tends to happen though, that we do end up making money during those periods. How do you think about this dual mandate? Do you feel you have it and are you actively trying to make money when equities go through crises?

[00:17:00] Svante

Yes. In our case, we do try to deliver both actually. We try to deliver the maximum Sharpe ratio given that we also deliver portfolio protection. That has been the case since we started basically, that we want to focus on both things. As I mentioned before, we have 45 different models. Of course, to, in a discretionary way, allocating risk to all these models, you can’t do that. You need an optimization of some sort and we have worked, over the years, on optimization software that we have internally where we also can track a lot of key ratios in different aspects and crisis alpha and how well the portfolio delivers returns in the different market scenarios. For example, when stock markets sell off, that is something that we track and look at those numbers.

[00:18:11] Niels

Okay, very interesting. I might have some follow-up questions but I don’t want to step too much on one of Alan’s favorite topics, which is research. So Alan, why don’t you dig into that?

[00:18:21] Alan

We might tackle research, but also curious to delve in a bit more on the diversifying strategies. That’s quite a remarkable comment that the diversifying strategies had an even better Sharpe than the trend, because we all can say well, it was very easy to see why last year was such a good year for trend following. Can you give us a flavor of the diversifying strategies and why they were so well-suited to the market environment last year?

[00:18:50] Svante

Absolutely. I think the reason why is that diversifying models can be so different. Some have carry in that bucket, some may have systematic macro models (like we have for example), or you can have contrarian models and that should of course have been painful if you had too heavy a weight on that last year. But if we look at our portfolio then, some of our machine learning models, they are in the diversifying bucket and they behaved very well last year and found a lot of the moves in the markets that they could profit from.

Also, systematic macro models were very profitable, where we have quite a few models working with other input data than price basically, like GDP numbers or inventory data in the commodity markets and so on, or seasonal data. So of these diversifying models, most of them performed quite well last year.

[00:20:10] Alan

You’ve referenced machine learning a bit. It’s interesting that they did so well last year. You get different perspectives on machine learning. Obviously, they’re very adaptive by nature. But at the same time, when you get regime shifts in markets, if they’ve trained on a certain type of environment, then they can underperform. Philosophically, why do you like them as part of the portfolio and what do you think are the pitfalls of employing machine learning strategies?

[00:20:42] Svante

I agree that they can be nasty in some scenarios, especially in turning points when markets change dynamics like we had in the COVID crisis, for example. They lost a lot of money during that period, our models in machine learning bucket. I wouldn’t say that they are better than traditional models in all market environments. But in an environment where the models have seen similar patterns historically, then they tend to make good money. If we look at how we use these models and how they are constructed, I think we tend to pre-process data a bit more before we feed the machine learning model with data.

So we don’t take a lot of price data and just put it into the machine and let the machine find patterns. We would pre-process and maybe, for example, put in seasonal patterns, for example, that we know have some positive return expectation, and then we let the machine learning model use that type of data.

[00:22:06] Alan

For people who are not statisticians or are not familiar with these techniques, if you were to try to describe what a machine learning model is doing relative to a more traditional model, how would you describe that?

[00:22:19] Svante

I would say that they are more flexible and change over time; that we don’t have any predefined parameters that you normally have in traditional trend following models, for example, that you predefine what kind of parameters should be used. In the machine learning model, you have a much more open mindset when it comes to the parameters and that can shift over time. Talking about timeframes, a machine learning model may change the timeframe holding period of the positions much more compared to traditional models.

[00:23:09] Alan

Obviously, underpinning that is the belief that once the model change becomes faster or slower that new market environments will persist at least for a while. So the model can sometimes trade faster, sometimes trade slower. Any intuition as to why markets operate that way, that those shifts occur and then when they do occur, that they tend to be persistent for long enough to be able to capture that change.

[00:23:35] Svante

I think if we look at machine learning models versus traditional models, I wouldn’t say that they are better. I would say that they add diversification, that they have a different return stream. If you look at our history and what we have delivered, I would say our machine learning models, they have a slightly higher Sharpe ratio over the 10 years that we have traded them compared to other models. That is not the reason why we have them in the portfolio. It’s more that we want the diversification and we want a different return stream. We know that they will lose money in some environments, and we’re fine with that. They will make much better returns in other environments, so it’s pure diversification.

[00:24:30] Niels

Alan, can I interject a question? I’m curious because I understand the argument for having these diversified models. You said earlier, Svante, that you know that one of the reasons why you wanted them is because it’s so hard to hold trend following for many investors. But I was thinking of the other side of the story, and I’d love to hear your thoughts about it. And that is, well, what if these diversifying models prevent you from really making a lot of money in one year when trend followers are really making a lot of money or even making a lot of money in a year where equities are down a lot and where classic trend is doing well.

So it’s the flip side of yeah, maybe we don’t lose so much perhaps, but also, maybe also not making the same amount of money as other trend followers during the time when investors really want trend following to kick in. I don’t know if you’ve experienced it, but that’s the other side of the coin when you decide to go into these more diversifying models. So I’m curious just how your thought process is around that.

[00:25:40] Svante

I think you’re absolutely right that it’s not an easy decision to say that it’s better to have them in the portfolio. Because there is a flip side, of course, that you may drag on returns when you don’t want to drag. But going back to what we started talking about, I think that you need a return stream in the total fund or the total program that is good enough for investors to stick with you in good and bad and for many, many years.

Therefore, I think diversifying models make sense, for us at least. You would get some diversification if you put in long-bias in bonds, for example. That adds diversification, but we don’t want to have that in the portfolio. You don’t want pure carry to a great extent, because that will drag on performance when we have good trending environments,

[00:26:57] Alan

To take the point a little bit further, if you were running a portfolio of say 50% equities and 50% multi-strat, would the composition of the multi-strat be different than if it was run on a standalone basis?

[00:27:10] Svante

Let’s put it like this. Some investors I’ve talked with over the years, they may want to be extremely smart and say that why don’t we trade your trend following models but we don’t want to take long positions in stocks because then I’ll get much better diversification to my traditional portfolio that I have. It makes sense. The problem is that such an investor would stick with us for a year or two but then realize that this is so painful because his Sharpe ratio in this investment will be much lower and he will not keep that investment over a cycle. That’s my guess anyway.

I think sometimes, you need to put together a portfolio of models that you have a Sharpe ratio of at least 0.5 or something that you can live with over time. But even that portfolio is painful over periods of three or five years now and then. But I think you need something that you can live with. That’s the most important thing.

[00:28:31] Alan

Maybe just coming back to the research process, I suppose, the process of adding and removing models. You mentioned that back in the ’90s, you had obviously trend but also you found other patterns that worked in the market, so kind of a pattern recognition approach. When you find something like that that’s working for a while, what’s the threshold then for removing a model when it starts to underperform and replacing it with something else? I guess it’s primarily based on performance. How much of a drawdown do you allow for individual models?

[00:29:08] Svante

That’s a very tricky thing. It’s easier to add models, to find new models and put them in the portfolio and you feel good about it and think you have added value. But then after a couple of years when a model doesn’t perform as expected, there is a big question, when should you pull the trigger and remove it from the portfolio. Performance is one thing but you normally need to give them quite a few years before you really can tell that this model is not behaving as expected. So, if you look at when we have stopped trading models, I would say the most common reason is that we find a new model that tries to do roughly the same thing but is very different in some way, and then we terminate the old model.

I would say that’s a very common reason. But also, of course, for performance reasons. What we have done, I talked about the software that we use for allocation to these models. That software actually automatically nowadays reduces allocations to models that don’t perform as expected. That is a process over several years, but it feels fairly good to know that if it doesn’t perform, then it will automatically reduce the weight so it doesn’t really destroy the total portfolio performance.

[00:30:59] Niels

I was just going to say on that topic, I actually remember, and maybe you still do it, Svante, but I remember many, many years ago, reading your annual letter where you were talking about how you had changed from shorter term models to medium term to longer term, etc., etc. I don’t know if that was done by committee back then or whether that was done by this algorithm you mentioned, but it’s always been very fascinating to see that kind of transparency and see, well, this is exactly what we’re doing.

Now, I want to shift gears a little bit, because one thing that also stood out to me, at least in 2022, was this fact that although it’s not a new area really, but it did get a lot of attention and that’s CTA replication. There were certain ETFs that came out that grew a lot, certainly compared to many of the managers. They raised a lot more money than the managers that were underneath the index. You are in the SocGen CTA Index, and therefore, you are being replicated, Svante, sorry to tell you. I wanted to ask you what you think about replication in this particular case.

We have talked to the people doing it and they will be on the podcast in a few weeks as well. They’re not building their own trend model, which we saw maybe 5, 10 years ago. People were coming out saying trend following, it’s so easy. We can do it and we’re going to charge you 50 basis points or 1% or whatever. Then they also realized that maybe it’s not that easy and so these products didn’t do so well.

Now, there’s a new wave, not just CTA, but also hedge fund replication, where they essentially just study the daily returns of these indices and then they make some kind of regression analysis to figure out roughly what exposures we as an industry have on our books. They narrow it to the very liquid markets.

So far, they’ve done a pretty good job in replicating the performance. Again, what are your thoughts about replication? Do you think it’s a good thing or do you have some concerns? Do you think this approach (because you know much more about this than I do), do you see any weaknesses in using this type of analysis to try and replicate our industry?

[00:33:24] Svante

I think, yes, you can probably replicate CTA returns and they have proved that, of course. What I’m more skeptical about is really, will they have the same returns as we have, as the flagship programs that they try to replicate? I’m very skeptical that they, at the end of the day, will have the same returns. Again, we need many years of data before we can say if it worked or not. But if we look at what we do in terms of putting a lot of resources in how we allocate diversification between markets and models, a lot of execution, how we execute the trades, we have a full team of people working with those algorithms that we use for execution. There is a lot of things that you don’t get when you try to replicate this.

So I don’t think they will have the same Sharpe. I think they will certainly deliver performance that is good enough, but I don’t think they will outperform the industry. Also, again, coming back to what we talked about in terms of confidence, this is a confidence game. The clients, they need to be confident that their investment will deliver over time. When we are in a drawdown, we can talk to our clients, we can try to explain why markets have behaved in a certain way, why our models haven’t performed and so on.

If we look at the replication strategy, because that strategy will outperform in some periods, it will underperform in other periods. During a period when they underperform, how should they explain what is going on? And how should the investor feel that the drawdown or the underperformance will stop at some point and he will come back? I can’t see that happen. Actually, I think they will, at that point, redeem and invest the money in the real managers instead.

[00:35:52] Niels

Yeah, I think that’s a valid point, Svante, for sure. I want to go back to something you also just brought up yourself when you talked about execution. We have already had one short-term manager in our series. Obviously, they brought up the point about execution. For trend following strategies as such, I guess I’m curious to know whether for medium or long-term trend followers, and let’s just say that was what you did only. I don’t know if you have some shorter term model, but let’s just say we stay in the trend following realm.

Do you think execution plays a big role for those strategies up until a certain size meaning does execution for trend following strategies really only become that important once you hit a certain AUM? Because if I look at a lot of the longer term managers, for example, their annual brokerage cost is 25 to 50 basis points. So there’s also a limit to how much you can squeeze out of that because they don’t trade so often. So how do you view execution within Lynx?

[00:37:02] Svante

I think when you talk about execution, timeframe is one thing. If you’re a long-term trend following manager only, then of course, execution is not that important. You can execute the following day and you will get roughly the same returns. Since we are diversified also in terms of timeframes, we have long-term models but we also have short-term models that have a holding period of a few days. In that space, it’s really important to limit the slippage and the trading costs that you have. Because it’s not the execution cost with the brokers that is interesting really, it’s the slippage.

The better execution you can get and the smarter our execution algorithms get, then we can trade even shorter term, and shorter term will add both diversification and returns and crisis alpha. So for us, execution has been a very important part of the business for the past 15 years basically.

[00:38:14] Niels

You talk about diversification which, of course, is one of the founding principles of trend following and it has always been that way. There are obviously different types of diversification. You’ve talked a lot about different strategies and models. But there is the other classical way of diversifying and that is just the number of markets you trade.

I’ve been on record saying I don’t really see the performance difference between people trading the 60 classical liquid markets and people who trade 300 different markets, except I understand that from a capacity issue, of course, 300 markets will give you more capacity. I understand that. How do you see this point about market diversification? Are there any things that are like a no-no for certain markets where you just don’t want to go in that direction? I don’t even know how many markets you trade today, to be honest.

[00:39:05] Svante

We trade, today, 100 markets and we have roughly 20 more markets on the way into the portfolio. I agree with you actually. I think you don’t add a lot of value when you increase the number of markets too much. Of course, it’s much better to trade 50 markets compared to 10 markets or maybe 100 compared to 50. But after that, between market number 200 and 300, I can’t really see that you add much value. I think also, you can look at what you try to achieve.

When you look at the major markets, the most liquid markets out there, maybe the 50 biggest markets, it’s there that you get the crisis alpha. You get the trend following characteristics very nicely there. When you go to the markets that are not liquid like market number 200, you may have them in the portfolio for different reasons, I would say, and that is that you want to add return. Their liquidity is so bad so you can’t be short-term, you need to be long-term. And when you’re long-term, you don’t get the crisis alpha to the same extent.

So for us, I would say the 100 markets that we have, that’s good enough to deliver the crisis alpha and the trend following characteristics. We have now 20 markets, and that will help us to pick up a couple of basis points in return. But in a portfolio context, it’s not really something to be very proud about and talk about too much.

[00:41:06] Niels

I like that. Thanks very much, Svante. What about you, Alan, where do you want to go next?

[00:41:12] Alan

Just moving on to think about risk management. You talked already about that at the model level in terms of managing drawdown. How do you think about risk management of the overall portfolio? Obviously, it’s a multi-strat, not just pure trend. Are you targeting a given level of volatility? Also, a topic that comes up all the time on this podcast is dynamic versus static position sizing and vol sizing and targeting the level of vol on an ongoing basis. Is that part of your philosophy? Why do you think it’s a good idea or not?

[00:41:50] Svante

Dynamic positioning in terms of adjusting to volatility, for me, that’s a no-brainer. To have it static, that was 20 years ago, we could discuss that, have a predefined number of contracts for a certain signal in a certain market.

[00:42:11] Alan

I guess there is still a view that may still be a valid approach in terms of delivering convexity and skew and that when you get a market starting to move, you really want to participate in that market. But as you say, it is the old-school perspective. Obviously, you don’t agree.

[00:42:27] Svante

Exactly. I think it’s old-school. I think if you look at a year like last year, for example, or 2008, the less sophisticated you are, the better actually. When you keep big positions, you don’t care about volatility increasing. You just ride the trend and make a lot of money. But it’s in market environments where you struggle that you need to adjust to make sure that you don’t take too much risk and use that in a risk management context. That’s how we do it.

In terms of volatility targets, for the program, we have an 18% yearly target in the standard leveraged version, but then how we may be different from some other managers is that we are very flexible. Even though that is the long-term target, we can have much more or less risk in the portfolio at a certain point in time depending on market behavior. If we have lots of markets trending and strong signals, we will normally carry more risk than average, and the other way around. When we come into a drawdown, the models themselves have integrated risk management components, so they will reduce their risk.

That goes for the total portfolio as well, that when the models don’t want to take that large a position, then we will have a very limited risk in the total portfolio. So for us, it’s integrated. We don’t have any kind of overlay on the top to take care of the risk level. That is something that is done by the models.

[00:44:26] Alan

Eighteen percent is still quite a punchy level of volatility you would say. When you’re talking to investors, potential clients, how do you guide them in terms of drawdown expectations? Is there a mechanical ratio between expected drawdown and volatility in your opinion?

[00:44:46] Svante

Of course, it is. Yes. Actually, since we use managed accounts for a lot of investors, they can choose their volatility themselves. If you’re very sophisticated, then of course, you should have maximum volatility and then just adjust your investment. So invest less money but to high volatility and put your money elsewhere to make them work instead of having them as margins in an investment in a CTA. So that’s really a good selling point for CTAs, that we have this kind of flexibility in terms of volatility. But then, of course, investors should also be coming back to stay in power and confidence. They shouldn’t invest more than what they can live with the drawdowns.

It’s better to start with a lower amount and then after a few years when you get the confidence to increase instead of investing too much day one and then be too nervous and redeem after a couple of years.

[00:46:02] Alan

Just on this topic, you mentioned how you really need a lot of data to assess whether something has stopped working statistically. From the same perspective, if you’re on the allocation side, most people in the industry look at manager performance, three years is probably the default that you get a sense just speaking to people maybe. As you said, 10 years is a very long time for people to endure. Five years is probably pretty long. Three years is probably fair enough. From a statistical perspective, what is the correct answer if you were to evaluate whether something is no longer statistically valid for the model or even a manager?

[00:46:43] Svante

I think on the manager level, to evaluate the manager if I was on the allocation side, I wouldn’t look too much on the numbers. I wouldn’t pick the manager with the highest performance. I would dig into the program to see what they do and the people behind it, the research process, and get confidence in the team because I think that’s a much better prediction of the confidence that you will have in a manager in difficult times. The numbers are good to some extent. Of course, if you need decent numbers. You don’t want to invest in someone that hasn’t delivered performance over the years even though they seem to be a good team. But if you have delivered 10% a year return or 8%, I’m not sure that that’s the best prediction of future returns actually.

[00:47:49] Alan

Going back to that period, the trough-to-trough drawdown period for trend following back in say, the period up to 2019, I suppose allocators were starting to question whether something had structurally changed. Obviously, in hindsight, clearly, that wasn’t the case. But how did you answer that question at the time in terms of the underperformance in that period?

[00:48:21] Svante

Getting back to what Niels said that you have read our letters and that we are transparent. That’s really how we try to get the confidence from our clients. We are very transparent with what we do. We discuss the models and the concepts and we try to get them confident in our investment management process. That helped us a lot, actually, during the difficult period, because they understood. We could explain to them what types of models underperformed and why we thought that was and what we tried to achieve when we did changes and so on. We were lucky enough to keep most of our clients over the difficult period. I think that transparency was really the key there.

[00:49:14] Niels

I want to dig in a little bit. Well, maybe it’s more of a comment actually, because I do think it’s a fascinating topic. I think there’s this narrative being built up right now that, as you said, Svante, 2008, 2022, the static position-sized managers did better. Actually, I don’t think that is true. I think there’s one or two that did really well. But if I look, if I just go back and quickly look at 2008 and 2022, I think a lot of the larger managers who definitely used dynamic positions did really well. Also, in 2008, I think the whole industry did really well where I wouldn’t say yeah, no, that’s because of the fact that we kept the same positions in catching these outliers. I don’t think that was the case at all.

So I really do question this idea or narrative that dynamic position sizing doesn’t allow you to capture these big trends. I actually think it does. But maybe it’s because we don’t lose so much, potentially, during the corrections that it shows up as a very competitive performance. That was just something that I thought about when I heard you speak.

[00:50:31] Svante

I agree. I maybe was a bit hard on the wording there. But also, if I look at last year, for example, and also 2008, if I look at our model performance, our really good performance came from very basic models. Actually, the model that made a lot of money last year was a model that we have had since we started and that’s not a very sophisticated model at all. We use it nowadays with dynamic position sizing, but we didn’t back in the day.

[00:51:11] Niels

Now, another thing I’d love to hear your thoughts about is, I call it capacity fees and flows. Speaking of 2008, we know that the industry did well. I guess, also, this is around the time when Katy Kaminski coined the term “crisis alpha” so a lot of institutional investors kind of thought, “Yeah, this is great, we definitely need a lot of that or more of that.” So AUM increased significantly following 2008.

Now, I don’t know if we can say that maybe some managers overstretched their capacity back then and that was the reason why performance didn’t continue as strongly. I’m not so sure that really is the case. But I’m curious about the issue about capacity from your point of view, both from what you see your own capacity might be but also capacity in general. How do you even measure what your capacity truly is? Let’s kick off with that.

[00:52:10] Svante

I think capacity, it’s never a firm number, actually. It’s more a question of how much slippage do you want to pay before your return suffers too much. And that is not a black and white answer to that question. What I can say is we have, now $7.5 billion under management. I think we could probably have $10 or $12 billion without a lot of increase in our slippage. So what would happen if we got another billion in assets, I would probably hire 10 more researchers, and those researchers could hopefully help us to increase the expected returns and compensate for the increased slippage that we need to pay because we have another billion under management. So it’s kind of a trade-off.

I don’t want to be too greedy. I would rather close the program instead of having too many assets and suffer in terms of slippage and performance. From the industry point, I don’t think the performance of CTAs is driven by capacity or how much assets we have under management. I know that question comes up from time to time and it normally comes up after a couple of bad years.

But our performance is driven by how markets behave and totally other things than how much money we have under management in CTAs. We are just a very small part of the financial markets and markets are deep. What we do in the fixed income markets or equity markets, that doesn’t really affect the market moves at all over time I would say.

[00:54:30] Niels

I don’t know if you mind, Alan, me jumping into a couple of other things that I know Svante might enjoy talking about, but maybe areas that we haven’t talked about so much with the other managers. Two things actually, and then you can decide how much time you want to spend on each. ESG, I find interesting because a lot of people struggle with putting it into context of a CTA or a systematic manager like we are, so I think that’s an interesting area. Feel free to talk about that.

But also, you said, “Well, then we’ll just hire another 10 researchers.” There, actually, I think location is interesting because, as you said right at the beginning of our conversation, you are based a little bit off the beaten path, because you’re in Stockholm, you’re not on Wall Street. So how do you find all these researchers? And does it change the way you think about research from the fact that you may be hiring more from the “same sources” than maybe people based in New York or London?

[00:55:30] Svante

So let’s start with that one and we’ll dig into ESG after that. I agree with you. Being in Stockholm, we have a much smaller pond to fish in here when it comes to talent and you have to be honest. We could hire people from London and ask them to come here but the problem is that after two years, they would move back because it’s dark and cold and high taxes. They just want to get out of here. So we will have Swedes, mostly Swedes. But the good thing for us is we are the only large CTA in this part of the world and we can offer them a fantastic work environment and they will have really fun things to work with at Lynx.

So when you look at the team that we have, we have very low turnover in the team. A lot of our senior researchers or I would say all of them have been here for 10 to 15 years. All the senior researchers are partners. So what you get is a very open environment where you can discuss everything because you know that they will not jump to another CTA after we pay the bonuses in February but they will be here for the long term and enjoy the work. But of course, the pond is smaller to fish in.

[00:57:03] Niels

Sure, but it seems to work so that’s good. What about ESG? How do you approach ESG?

[00:57:12] Svante

I think it’s an important part of not just what we do, but for me as a person and a part of the society, I think we need to be responsible. Our investors and our employees also expect us to be a good company with good values and ethics. So therefore, ESG is important. Now, having said that, since we trade futures, we don’t have any voting rights for stocks, for example. So ESG will be more difficult. You can also discuss since we are long and short in different markets, how should you calculate your ESG impact. But what we have done so far is really that we encourage exchanges to start ESG futures.

So we do trade some ESG stock indices for a couple of years. They don’t have that much liquidity yet in those markets so it’s more hassle for us than something that helps us to drive performance. But we do that to be a part of the industry and try to encourage liquidity and try to get those markets going. The other thing we do is that we have a sustainability forum and we engage with the exchanges to try to get them to change contract specifications when it comes to sourcing and production methods and so on, so getting contracts to be more ESG friendly, if you say.

But I think ESG is a big field that will evolve over time. It’s in the early stages so far and we don’t have a really clear view on what we mean with it and how you should behave. It’s also different. If we look at the Nordics here, for example, we look at the environment and climate and we have fossil fuels that we see as a problem. If you look at the US for example, they are more focused on diversity and inclusion. So you need to look at it in different ways.

[00:59:41] Alan

Yeah. I just had one more really, something you touched on earlier around trading more markets and liquidity and obviously, you have a multi-strat approach. You are trading short-term in some markets and you mentioned the slippage and how the algos have to be pretty smart to navigate that. But from a pure liquidity perspective, it is a topic that comes up periodically in markets that, is liquidity not as good as it was. You’ve got more high-frequency traders.

We’ve had a couple of episodes in the last while with treasuries in 2020 where the market seemed to be on the point of breaking. This year, we’ve had major dislocations in the gilt market around the UK pensions. What’s your perspective on liquidity in general and trading conditions? Are you seeing any shifts or is it something that you’re concerned about or these isolated issues in the individual markets?

[01:00:48] Svante

In a long-term perspective, liquidity has changed over time, and how markets behave has changed over time from floor brokers to electronic trading to algorithmic trading and so on. So you need to adjust and adapt in that sense. What we have done is that as an integral part of our execution algos, we take into account the liquidity and we have done that for quite a few years. So we will not trade aggressively if there is no liquidity in the market, for example, and we will adjust the positions also, not just to volatility, but also to liquidity, also in a shorter timeframe to make sure that we don’t leave a footprint in the markets when we execute and instead try to be very passive.

[01:01:51] Niels

I’m just curious. For many years, for about a decade or so, cash management was not really that important. Not that it wasn’t important, but there was not much you could do because we lived in a zero-interest-rate world. We don’t do that anymore. How do you approach cash management for the funds you manage where you’re not using a large part of the cash itself for margin purposes?

[01:02:14] Svante

We have a very conservative approach, so we use just government T bills. And then, of course, we try to get as much out of that as possible. So we try to do that in a very efficient way. But we don’t do any credits. That goes back to our client base. They are very professional institutional investors. They don’t want us to be credit managers but instead, have the margins in a safe place.

[01:02:47] Niels

Yeah, that makes sense. Now, I don’t know how to phrase the question, but I’m curious in terms of if there’s one thing you hear people talk about trend following that you really disagree with when you hear people talk about trend following.

[01:03:03] Svante

Well, not to a great extent, but one thing is that some people say that since all CTAs are so correlated, you just need one or two managers and then you get your trend following exposure. I think that that is probably true in a year like last year when everybody made a lot of money and markets are trending. That’s fine. You can pick one or two managers and you will be in a pretty good spot.

The problem is when you have more difficult markets and markets going sideways, not trending, then you need diversification and to have several CTAs because we are not correlated over those periods. You can get the monthly returns all over the place depending on if you have shifted your positions or not or how you have allocated the risk and so on.

[01:04:01] Niels

Yeah. I think that’s a great point. Final question, and that will be something like what gets you most excited as you look into 2023? Are there any concerns that you see? It’s kind of a broad question. You can take it however you want.

[01:04:19] Svante

I’m pretty bullish on the coming year actually. I think we are in an environment where uncertainty is extremely high and that’s normally a good spot for trend following. We also have central banks that have pulled back a bit compared to where they were a few years back or even they may be at the same side as we are and that will help. You will see a lot of divergences between different regions and different inflation cycles in different parts of the world.

So there should be plenty of opportunities in the coming years. At the end of the day, we all know that the best environment for us is when something totally unexpected happens, and I can’t forecast what that would be, but let’s keep our fingers crossed that things happen and we’ll try to make money out of that.

[01:05:24] Niels

I think that the one thing we can be sure of is that we’re going to be surprised on an ongoing basis about things that we didn’t think about that actually did turn out. This was excellent. We’re going to wrap up our fascinating conversations. Svante, thank you so much for being on the podcast, for sharing your thoughts and insights with us, and we hope to do this again sometime in the future.

To all of you listening today, I hope you’re able to take something from today’s conversation onto your own investment journey. If you did, please share these episodes with your friends and colleagues. From Alan and me, thanks so much for listening. We look forward to being back with you on the next episode of Top Traders Unplugged as we continue our deep dive into the CTA industry.

In the meantime, go check out the show notes for this episode and all the other resources that you can find on the website. Of course, not least, take care of yourself and take care of each other.

[01:06:19] Ending

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