Thanksgiving is just a week away in the US, marking the end of an excellent year for The Derivative and we’re inviting you to join us at the podcast table for a bountiful episode delving into the unique flavors of managed futures and fundamental expertise. As we carve into the Thanksgiving spirit, The Derivative is serving up a feast of insights with Patrik Safvenblad, the mastermind behind VOLT Capital Management.
In this compelling conversation, Patrik unveils his approach as a fundamental specialist in managed futures programs, offering a unique perspective that distinguishes his strategies from the norm. We delve into the differences between pod shops and in-house multi-strats, explore the penchant for shorter-term models, and uncover the meticulous process of managing over 9000 signals.
Join us as we navigate the complexities of commodity quant and gain invaluable insights into quantitative strategies just in time for the Holiday season — SEND IT!
From the episode:
Check out the complete Transcript from this week’s podcast below:
Marrying Fundamental Factors into Commodity Quant with Patrik Safvenblad of VOLT CM
Jeff Malec 00:07
Welcome to The Derivative by our RCM Alternatives, where we dive into what makes alternative investments go analyze the strategies of unique hedge fund managers and chat with interesting guests from across the investment world. Hello, they have one week to go until Thanksgiving here in the US, which means we’ve made it to the end of another year on the podcast. Alright, hope you enjoyed the guests and the talks and all the rest. We’ll be back in January with a new slate of guests plus some old favorites. Plus, I may change things up a little bit, add some new segments, I’m not entirely sure I’ll noodle on that over the holidays. So enjoy this episode and enjoy your holiday season. Okay, on to this one where we zoomed over to Sweden to talk with Patrik Safvenblad of VOLT Capital Management, Patrik describes as a fundamental specialist, and we dive into just what that means and how it makes them different than most managed futures program. We talked through the difference between pod shops and in house multi strats why their models tend to be more short term how long it takes to run 9,000+ signals, and more. Send it! This episode is brought to you by RCM’s managed futures group, which helps investors find unique managers like the one we’re talking about today VOLT, which is managed futures but not correlated managed futures. Riddle me that. Learn more on how our sales team can help you find the right non correlated piece for your portfolio at RCMalts.com. Now back to the show. All right. Thanks, everyone. We’re here with Patrik Safvenblad. Did I get the last name? Close?
Patrik Safvenblad 01:47
You will turn it last time perfectly close. Yes, I’m happy, very happy with your pronunciation.
Jeff Malec 01:53
How do you pronounce it in Swedish?
Patrik Safvenblad 01:56
I would say politics and laws. So it’s kind of similar. But you have a vowel. Vowel Sounds are quite different in Swedish.
Jeff Malec 02:05
Got it. And you are in Sweden currently? What part Stockholm?
Patrik Safvenblad 02:10
Yes, I’m in Stockholm. I’m in the city centre of Stockholm. So on one of the main main streets here in downtown.
Jeff Malec 02:17
Great, and you’re born and raised and spent your whole life in Sweden for the most part.
Patrik Safvenblad 02:23
Yes, I grew up in bit south of Stockholm. And in the days when winters were still cold. Now it’s much warmer than than it used to be. But it’s still dark during the winter. And then moved to Stockholm when I went to study.
Jeff Malec 02:42
And give me what what do people not know that they should know about Sweden or about Stockholm?
Patrik Safvenblad 02:47
I think they, they should know that it’s a nice place. And they should also know that they should only visit in the summer.
Jeff Malec 02:55
But just too cold, too dark.
Patrik Safvenblad 02:57
It’s too dark in particular. So it’s the summers now are very, very pleasant. And it’s a bit warmer than London, for instance, and not as hot as some other places. So it’s very nice to this in the winters. You know, you wake up and it’s dark and you go to go to the office, it’s still dark, you go home and it’s still dark. It’s all over. You don’t get these bright winter days, but you have in Chicago say that it’s sort of all a little bit, you know, dark and dark. So, no, I don’t recommend it. It’s nice for for a day perhaps but, you know, living through, you know, 50 of these winters. I think I’ve had enough.
Jeff Malec 03:37
Yeah, where do you head anywhere for the winter? Get a little sunshine? Yes, we
Patrik Safvenblad 03:42
we tend to go to Italy during the during the winter. So you know, over Christmas, you get some some sunlight. So that’s quite nice.
Jeff Malec 03:50
And always blows my mind. How many people what’s the population of Sweden in total?
Patrik Safvenblad 03:56
Sweden is around 9 million these days. So it’s sort of the biggest of the Nordic countries. But of course, the Nordic countries are quite similar in size all of them. So yeah, blows
Jeff Malec 04:07
my mind of your Olympic teams are always very good, especially in the Winter Olympics, right on a population of 9 million versus our 360 million, whatever us is.
Patrik Safvenblad 04:18
Yeah, but I think that we all feel that, you know, the Norwegians are out come out competing us in all the winter sports. So you know that, you know that? Yes, we have some good athletes, but the Norwegians are or enormous ly impressive. They
Jeff Malec 04:34
are as well. Alright, well, it’s on my list. Next time I visit. I want to come see you.
Patrik Safvenblad 04:39
Yes, you showed calm during the Somerville
Jeff Malec 04:43
although I want I’m a skier so I might want to come in the winter and get some skiing.
Patrik Safvenblad 04:46
Yes, but now we don’t have snow any longer. So that was your 10 years too late for snow skiing around here. You’d have to go quite far north to get snow these days.
Jeff Malec 05:00
Well, thanks for coming, I don’t want to get into your program went up. But let’s first talk, you’ve got a bit of a interesting past professor, few different hedge funds before starting your own gig here. So take us through a little bit of the personal background and how you got to where you are.
Patrik Safvenblad 05:17
Yeah, so I’m, you know, I obviously came came to Stockholm to study and I enjoyed studying and I stayed overstayed, became a PhD in with the market microstructure as the the focus. So market microstructure, of course, that’s trying to understand what happens when investors meet in the marketplace, how does sort of the composition of traders or investors affect when exchanges work? And, you know, I really enjoyed that it’s sort of a great learning experience, and I then became the finance professors for a number of years. And, you know, that was, you know, I enjoyed teaching and joy learning things, but, you know, the producing research was a bit on the slow side, I think it’s a very frustrating thing to do.
Jeff Malec 06:07
And how are you when you’re a professor, you must have been a young professor.
Patrik Safvenblad 06:11
Yeah. Reasonably, I think I got my PhD at 40s. I was a professor like between ages 1414 43, four or something like that. But so that was a sort of a question then is, you know, I’ve been very interested in Mark as being very interested in market structure, what you do with this, you know, these skills and I became a hedge fund allocate. So I started out in Stockholm with a firm called rpm and I was allocating in the CTA space. So I was allocating to, you know, our trend followers, and you know, all these people that that you also talk to, and I met Manuel, all the people that have been on this podcast in that capacity years ago now. And we’ve
Jeff Malec 06:57
had, what’s his name? I’m forgetting his name at rpm. We’ve had him on the pod, we’ll put a link to it in the show notes,
Patrik Safvenblad 07:03
Aleksandr Mandor, perhaps? Yes, yeah, yes, yeah. But you know, I’ve met a lot, a lot, a lot of CTAs as well. And of course, Alexander is a person I hired to rpm many years ago. And that was definitely one of my best hires, in the of people I’ve hired in my career. So, you know, very happy that, within is strong, has a strong lot of experience. And you know, good, good insight. So very happy about that.
Jeff Malec 07:34
Definitely. So then professor, then rpm, and then on to where, yeah,
Patrik Safvenblad 07:39
so then I became an allocator, with the Norwegian state owned bank of the cold DNB. Nowhere in those days now only called D and D. And that was running a hedge fund allocator team, but allocated across all strategies, I was looking after macros eta myself, but I was allocating puzzle strategies. And, you know, that was, you know, you learn a lot from that. And I think the key learning experience in that job is that, you know, you can never, there is no, nothing that can compensate for, like, experience, you know, dedication to what you’re doing, having a strong team having integrity, and, you know, the, the other parts, you know, trying to understand the exact details or strategies turned out to be less important than figuring out you know, who, who is good at what they’re doing, really dedicated to what I do. And that was sort of good, the biggest, the biggest sort of takeaway from that period, but you know, at the end of the day, I’m a quant, and I, you know, the, the PhD going in, damages you for life, I guess. And I needed to get closer to markets. And that’s when I joined Richard Conyers and David Pendlebury at harmonic capital in London. And eventually replaced taking on the role as CIO there. And that’s what were they what what they were doing a while. Harmonic, you know, closed down a couple of years ago, but harmonic is our winner was a quant, macro shop, and the quant macro shop based on quite careful modeling, and also using an approach that was borrowed from the fixed income space, namely being long, short, neutral. So having equal risk long and short in everything that’s Australian is. And, you know, that was working quite well and we had up to, I think 1.9 billion in assets. So at peak, so, you know, that was a very quite quite useful experience. Unfortunately, the partners at some point, you know, we started to have different interests and people wanted to perhaps, you know, spend more time that their family, someone wanted to retire, etc. And at some point,
Jeff Malec 10:23
the problems only someone with 1.9 billion under management can have right? Well, you don’t have those problems when you have 100 million under management.
Patrik Safvenblad 10:32
No, you have other problems with management, I think you’re, I think was one of the a lot of lessons to be had from from that experience. And I think that, as you point out here, you’re almost having more problems, when you’re doing well, compared to when you are pressed against a wall kind of financially, because you are using your excess cash to set up costs and set up structures that then become very difficult to undo in a leaner times. Yeah. And, you know, looking back, I think the only would have been still around, had we, as a group, not, you know, over invested in hiring more people, you know, or have you been taking on costs, I’ve made it tricky to handle leaner times.
Jeff Malec 11:29
And then coming back to your allocator days, you just piqued my interest there a little bit of sounds like you’re kind of saying that quant your quant mind didn’t quite fit. With the allocator, you could do all this work, you can identify which were the best performers perhaps are the best risk adjusted, but then that might not have been the key, the key might have been just that they’re dedicated and good people. What
Patrik Safvenblad 11:49
you know, once you have identify them, then you know, there isn’t that much else you can do, you’ve identified that this is a strong, strong team, and they’ll keep working 10 years, and you can’t really improve on that by you know, trying to be timing or something like that, that’s, you know, that’s a fool’s errand to try to do that. So, in that sense, you know, running, even if, you know, running big multi manager portfolios is relatively slow paced work. And it’s, you know, come when you are instead trading markets, you know, it’s, you know, every day you get a little bit of feedback on what you’re doing, and you know, how well you were doing it? And that’s, you know, suits my personality, that?
Jeff Malec 12:37
And did you find it was hard? Like, what was your experience on persistence of returns, and some of the things in the allocator seat of some of the difficulties there, find you found a great team they had performed? Well, maybe they don’t, and you have to have faith in that team to know that they’ll come through the other side?
Patrik Safvenblad 12:55
You Yes, I think that you have to, to separate two things. One is in the Do I have faith in the team, and that’s going to be evaluated on things like Norway dedicated to, to the business, perhaps they start having other interests. And, you know, our molecule is a good example of a type of a setup where at some point, people started looking outside and having other interest. So that’s, yeah, and, you know, like, personal integrity, and, you know, are is a team stable, those types of things, you know, that’s where you where you decide where you’d like to him on the, on the performance, you know, clearly there is a big picture, which is do I believe in this overall strategy. In when I will, in my allocated days, I stopped believing in equity market neutral factor based, which was a big industry at the time, was, you know, some stumbled in 2008, I believe it or perhaps it was seven, in this quantity and quantity crash, and at some point is sort of seen that that alpha had had been, there was too much competition for it. So that’s a, that’s a situation where you, you don’t really care if a manager is good or bad, you just say that I don’t believe in the strategy. And the longer that could be other there are other strategies like that where, you know, on the fixed income websites, for instance, there’s certain sub sub fields that we walked away from in the bank, direct lending, for instance. And then when it comes to, you know, the strategy being implemented, I think the key there is, when the management doesn’t perform, it shouldn’t hurt your portfolio. And you achieve that by allocating intelligently not too much to each manager. And also you’re saying that, well, I don’t care if you’re good or bad that if you’re losing money, I’m going to be reducing your allocation, you know, possibly to zero, but you know, in a drawdown or prolonged flat period, you can’t be overly smart about Not all interpreting too much what’s what’s going on. But what you can do is that you can protect your investor capital, you know, much like we will do in trading where we have stop losses that stops us out of trades that we inherently believe in. But, you know, we also know we need to preserve investor capital. Right.
Jeff Malec 15:18
So somewhere along the way, there, you became one of those people looking elsewhere at harmonic and said, Hey, let’s start our start my own thing.
Patrik Safvenblad 15:28
Later, well, actually, I was probably more one of the people that were more into keeping going. But regardless of that, you know, I had, you know, strong contacts in Stockholm. And the, we may, you know, some, you know, I knew that there was a team being formed back home. And when, you know, harmonic Well, when we, when we sold our Monique, I could then join that you’re in that team and the friends from before. So I’m quite happy about that. Today, you know, it’s a group, and I think that sort of group of people with experience all wanting to do things, like the right way people exactly. And I think that’s been a very stem stimulating journey so far.
Jeff Malec 16:19
And have you all known each other beforehand. So there are four partners? Correct.
Patrik Safvenblad 16:24
There are four partners, we have our five employees now in total in the firm, so five people total. And we’d kind of everyone knows someone new someone, so it’s a little more like a chain, rather than everyone knowing everyone. So you know, of course, David here, who is our CEO, he has also worked at RPM, for instance, and told me who is our sort of the business development person, he used to be sitting in the same office as me at the point at some point, and you know,
Jeff Malec 16:59
and then you everyone got to come back home back to Stockholm? Or were those guys already there?
Patrik Safvenblad 17:04
Yeah, so I was the only one move, moving, moving back. But that’s nothing. It’s, you know, people with experience in the industry. And we wanted to do something that, you know, maybe made a difference in the CTA space, you know, identifying that, you know, there aren’t that many fundamental CTAs out there, and the ones that are there are quite, you know, much into the TA space. So we felt that this was a good business, opportunity for us, based on our previous experience, we managed to get a local family office to support us with seed capital and working capital. So that was, you know, the, the, at that point, we just, you know, let’s go for it.
Jeff Malec 17:57
Let’s go for it. So yeah, let’s dive into the, what year was that? When did you guys start? 2017?
Patrik Safvenblad 18:04
Yeah, so the I think the process was sort of going on from 2016. The first trades were done in 2017.
Jeff Malec 18:18
So let’s get into the model, the strategy. So take us, you just mentioned a little teaser there, but take us what you guys identified as, as your potential edge of what you thought you could do.
Patrik Safvenblad 18:30
Yeah, so if I’m, I’ll just make sure I’m no, no, what I’m saying. So I mean, the, I mean, the starting point here is we want to be fundamental specialists, right. So this is fine. We, you know, we, we know that the world is full of trend followers. And that’s not really a business to build. Unless you have, you know, you’re already inside a big, big firm, we wanted to set up an independent, you know, classic CTAs live training, like the ones that were starting 20 years ago, as opposed to, you know, being a team inside of a bigger organization, you know, good reasons for doing that. So we want to be a fundamental specialist. And, of course, you Eukarya who’s my co pm here, he had experience of building fundamental models in his previous job, which was inside Lynx Asset Management. Now, they are wisdom, mostly known for trend following, but he was on his separate team that was building fundamental models. His focus was more on the, the commodity side, my harmonic where it came from was more on the financial side. So in fixed income and effects equities, and, you know, that’s the sort of the, sort of the skill sets that were bringing in the thinking here was, you know, you look, let’s look back and say Uh, see, what are the things that haven’t worked, you know, before, you know, what hasn’t haven’t worked in, where we, where we worked before. And, you know, you know, I take full responsibility for for, you know, harmonics performance and what we did in the research process and all of these things, but, you know, clearly a number of things that I can identify that I wanted to do different awards. I’m sure we’ll get to some some of those right and same thing goes for for for UConn are a whole bunch of things that we felt that he felt that we needed to do different.
Jeff Malec 20:33
And that was from like a model standpoint, as well as we should trade these markets and trade. So it was like a universe Plus model standpoint.
Patrik Safvenblad 20:43
I think it’s on the universe side, of course, we were constrained to regular future space. So that’s not much to do there. On the signal side, we know that there is a lot of fundamental information out there that’s not being used frequently in the CTA space, we wanted to use that and bring that to, to the market to investors. But I think the biggest, one of the biggest things that we both had seen in our earlier lives is that, you know, the world is changing. world is always changing. You know, that’s one of the constants in macro trading. You know, right now we have inflation, we didn’t have inflation a couple of years ago. Now, that’s a different state of wars. And the question is, you know, how can we build a program in fundamental space and add can handle the fact that the world is moving forward? When I was starting out in the CTA space, you still had natural gas spikes in the summer? No, that’s just going away, right, because, you know, natural gas, maybe not gas markets, it’s just changed how its operating, because of all the shale gas and other other sources, and also, of course, better betrayal in the availability and all kinds of places. So, you know, the world is changing, and fundamental traders, you know, often have the problem that, you know, now the world has changed my previous, my preconception about our world works, especially if your discretionary is not valid any longer, how do I move forward? One thing that doesn’t work is to to do this in an investment committee setting, and that’s something I expect, experienced, firsthand, you know, investment committees, you sit down, you have intelligent people, well informed and well intentioned, but they, you the more you talk, the less likely you are to change your mind. It’s for, and I guess we know this in all kinds of other, you know, from politics, and from our own life situations as well. And, you know, as you were talking, you are convincing, perhaps not the person across the table, you’re convincing yourself, you’re convincing yourself to the point where you your thought processes become antagonistic, you know, it’s my model is your model, you know, which one should we be cutting, if you feel it’s your model, then you say, well, as the draw down below, it will be recover. If you think it’s somebody else model, you sort of use it as opportunities to stab someone in the in the back. Very, very unhealthy. And, you know, I, you know, as CIO, I, obviously was part of this, we were trying to find solutions. And the solution we were moving towards was to make more of a decisions formula based now, so that you had stockpiles for models, and you might have stopped beans, and you had certain conditions that needed needed to be met. And that’s that fit very well with Yuka thinking because he had sort of taken, you know, clean sheet design and said, Well, how about if we asked a bullish Investment Committee, we have our models, the models have observable, observable characteristics, and we’ll have a machine that handles all these allocation decisions. That’s what we call machine learning in when we talk about vault. And what we do is we do not sit in the committee setting and try to decide decide which model should be trading now we’ll have we are discussing a model or sort of an over overarching machine learning framework that will make that evaluation for us. And in practice, of course, it’s easy or in theory, it’s easy or other that you know, how much returns to expect from this model, you know, how much does the model correlate with with other models and then you create some sort of all the optimal allocation based on that.
Jeff Malec 24:46
But my experience the difficulty there is you get you crystallize the losses way more frequently than you get the gains right. If you are always stopping models out, you kind of are crystallizing losses and it can be The difficult path? Yeah,
Patrik Safvenblad 25:01
you’re, you’re definitely right on the knot with. And I think that’s where one of the the sort of the other innovations, I would say that we have is, we have a large number of models. And that means that when you’re stopping out a model, you actually have something else that is similar with that you can allocate that risk to. So it’s a bit different from a situation like, so we had at harmonic, we had the two models are traded emerging market effects, if one of us was stopped out, you’d sort of have half allocation to emerging market effects, and you would miss out on your own recovery. But in our setup, we are not saying we’re new, this is definitely a concern. But by having a larger number of models, you have something else to allocate risk to, you can do that. And one of the nice things, by the way, is that in a in an investment committee set setting, you would tend to allocate more risk to recent winners. And that’s sort of the other side of loss of the, of losing money in in a stockpile setting, right, because you’re swapping out something that recovers is the same kind of loss as entering something that just made money and then loses. It’s just, it’s equally costly, just the flip side of the same problem. But the the framework is one that actually does not look at recent, you know, very recent performance is something that we’ll be allocating more to. So let’s have those are things where you can control in a systematic setting, but that is very hard to handle in person to person. world, right.
Jeff Malec 26:56
I want to back up for a sec. What do you mean exactly by fundamental? Because that this is where it’s always confusing me. I’m a fundamental specialist, but you’re also a quant, right? So you’re kind of bridging those two pieces. So maybe a couple of examples of fundamental models or fundamental data that you’re using to inform the models.
Patrik Safvenblad 27:16
Yeah. So, you know, the the trading what we use, it will look quite similar to like a checklist of a discretionary trader, you know, it’s going to be quite similar to that in sort of in logic. So, if we are looking at something like crude oil, we would know you know, what are the things that matter for crude oil? First of all, of course, growth is good for crude oil. So, you then go and say, Well, how can we identify growth? Well, we can look at GDP growth, we can look at inflation, we can look at inflation expectations, things like that, we can then we can look at things like supply and demand. So we can look at you know, how much your energy is being used by say, European refineries by us refineries by Chinese refineries, we can look at how much is B, we can look at supply demand in terms of storage, we can look at demand in terms of higher costs of all the oil freight, if the price of orienting and oil tanker goes up, that would indicate more demand for oil. If the if there is sort of bigger, a bigger spread between inputs and outputs from refinery so crack spreads type type indicators, you know, that would be something that indicates more demand for oil. We can look at what investors are believing are investors optimistic or post pessimistic? So, you know, what are what can we learn from the equity market? What can we learn from the from the prices of energy companies or stock prices of energy companies? Well, I guess in energy, we have the heating days and cooling days process more of shorter term, and sort of it just it continues, right? You know,
Jeff Malec 29:25
for crude oil, there might be how many? Yeah, for crude oil, and we have roughly 3030 and for something like cocoa, maybe five or something. Yeah,
Patrik Safvenblad 29:33
I think, well, I can, I can actually check, I think is more like 10 or
Jeff Malec 29:38
- But yeah, okay. But yeah, but there’s just not as many global.
Patrik Safvenblad 29:42
We were happy we split the difference. We had eight models in cocoa. There we go. Alright.
Jeff Malec 29:49
And so a normal factor model, right would say, Hey, we’ve got these 30 factors, fundamental factors, but we’ve got these 30 factors. We’re going to create a model that informs where prices are going right where we think it should be priced based off these factors. So you’re doing something a little different than that. Right? Like, it’s not just one factor model based on those on the data. Yeah, you
Patrik Safvenblad 30:09
could, you could say that it started start the factor models, you know, 30, I think that’s, that’s fair enough. Each model has its has its own prediction of wherever the world is going. And then we’re using this sort of dynamic machine learning set up to allocate across these different predictions. So, but
Jeff Malec 30:28
right, it’s not one model saying, Here’s the 30. And I’m going to wait, I guess it’s somewhat the same thing, if I’m going to weight factor 370 5%, factor eight 10%?
Patrik Safvenblad 30:40
Well, in essence, it is going to be something along those lines, for for each, for each factor you’re looking at, there are two, two things that Matt, you know, the one is, you know, how much or how profitable do we expect this factor to be? And that’s going to be based on things like, you know, back test performance, consistency, or back test performance, you know, other other things that we sort of put into that side of the calculation. And the other thing would, would be to say, is, you know, how much does this correlate to other things in our portfolio, both inside the market? We’re trading, I mentioned here, for crude oil, we use inflation. And we use inflation expectations are obviously both are very related factors. So, you know, as whole, they would have to be, you know, we need to adjust for the fact that some factories are, you know, distinct, but related. And that would be sort of a correlation type argument where you want to allocate more to signals that fewer substitutes, if I put it that way.
Jeff Malec 31:53
And then it’s fully dynamic with the machine learning. So what the weightings of these factors today might be totally different than they are six months from now or a year from now, or even a month from now.
Patrik Safvenblad 32:06
Well, it’s, you know, the the alpha is generated using the signals, the signals run with a holding period of 12 days, so we’re quite quite a bit quicker than then most systematic fundamental traders, and the machine learning runs, you know, more on the sort of, say, quarterly horizon, something like that, that signal can go from fully allocated to zero in a month in some cases, but you know, generally speaking, it goes, it’s a bit more, it’s a bit slower. And the idea here on machine learning is not that we want to override the signals, instead, we want to wait effective effectively to signals so that when the world moves forward, you know, one year from now, two years from now, five years from now, we make the portfolios or gradually move to the factors that are relevant at that point in time.
Jeff Malec 32:58
Got it so? And what why the shorter timeframe? That’s just how it worked out? Did you target that, or as you were doing your modeling, it became shorter and shorter, because you have more data points.
Patrik Safvenblad 33:13
was, first of all, we like that it’s sort of dried in a shorter term is helpful for risk management, if you want to have stop losses. And this sort of comes back to your question, isn’t it costly to stop out models, one way of reducing that is that you have other models to allocate to. Now the same thing goes on your trades. And if you have a stop loss, and you were stopped out of your your crude oil position or something like that, then it is useful that you have new fresh signals that you can allocate to so the shorter term, shorter term, so having shorter term signals means that you’re less likely to stop yourself out and buying back exactly that same position. You know, like an FX carrier trader say, your trading FX carry, you’re guaranteed to buy back exactly the same position you were stopped out of, which means that if you’re trading FX Terry, you basically never use stop losses.
Jeff Malec 34:06
That’s part of our due diligence process of like, okay, you use stops, when can you get back in the same position right away? Okay, well, you really have risks, which I’ve seen in real time, like you kept buying it six months in a row and so it’ll control that automatically by going into the different models and so all these together I think somewhere in your deck, there’s like 9000 Plus drivers you can
Patrik Safvenblad 34:34
see Yeah, so if you take all the models times all the markets, you get a very large number of signals and that’s also one of the reasons we need machine machine learning to you know, make the allocation for us. We wouldn’t it wouldn’t be efficient or, or you know, we wouldn’t be able to do this in a in a manual way in a bid all the things that are moving. You know, that said, you know, a lot of these You know, of course 9000 signals sounds like a lot, but of course, they are sort of variations on the theme so that you’d have, you know, multiple timeframes, obviously, obviously, motor signals and things like that, that sort of makes the number look very large.
Jeff Malec 35:14
Yeah. And then the end result is it’s how many markets 17 or so. So
Patrik Safvenblad 35:20
seven two markets. Yes. So and with footwell day holding period, we trade roughly half of those every day. For people that know futures, that’s we have a 900 downturns per million, so on the, on the portfolio as well.
Jeff Malec 35:35
Got it. So the machine learning, it’s giving you one sort of portfolio every day, and you’re adjusting? Or is it considering each market separately? Or a little bit of both?
Patrik Safvenblad 35:51
Why is it now solving machine learning is running? Little bit of both, I think is the right? Correct answer, meaning that meaning that you take in both sort of the big portfolio and the sector into account, if you’re trading energy, that will make a difference? What is, you know what, you know, if you have a signal, and it works in crude oil, if it works also in, in heating oil, then it will be treated in a slightly different way than if it doesn’t, you know, things like that. So there is a, you know, we’re trying to extract information about whether a signal will be the profitable in the future, looking at all kinds of things, right, and like the consistency of the back tested performance, but also, for instance, whether it works in other markets.
Jeff Malec 36:41
And then, so each signal is you have tested and it’s profitable and its own right. And no,
Patrik Safvenblad 36:48
it’s actually that’s a very good point there. Because each signal is tested. And we believe in it, we believe this makes sense. We believe that, you know, higher growth, higher growth is good for energy prices, say we believe that’s something that that is true, we believe we find ways of measuring this effect. And then if it makes sense to us, we will add it to the program. If it doesn’t backtest Well, the machine learning will basically say, well, item will not allocate to that. If it looks too bad, perhaps we’ll take it out just to save on computing time. But roughly 10% of all the signals, perhaps even a little bit more than that are zero allocated, and in some sense are waiting for, you know, different world, different state of the world where we’re seeing where these can come in and the allocated.
Jeff Malec 37:44
And then how did you go about selecting all these signals? So do you have someone in house who had that fundamental mind, right, like, you need a different skill set to know, the factors for crude versus the factors for Cocoa versus the factors for Japanese yen?
Patrik Safvenblad 37:58
Yeah, no, I think that’s, that’s a, that’s a very good question. And, you know, it’s really comes down to having done being in the space for 20 years. So, you know, we we look at each market, you know, repeatedly, you know, is there something we’re missing out here in trading, the Swiss franc are trading the trading the cocoa or whatever is going to be? And can we apply some concept that works in another market? Yes, or no, and, and this is something that, you know, in some sense, is some of the, you know, 20 years of work from it all on my side and 20 years of work for for yoka.
Jeff Malec 38:41
And you might not care, right? If it doesn’t match up exactly, with what the guy show trading is doing of his 50 factors or something, right, if they work for you, they work for you.
Patrik Safvenblad 38:55
In some sense, you, I would even say, perhaps flip it around and say that, you know, the person that shared it will tend to be quite being discretionary, I will tell you, most, most of the time, I will be quite open about the factors used. Right. So if you’re listening to, you know, trade or podcasts or things like that, with discretionary traders, you know, they don’t mind sharing things that work for them. And, you know, that’s one of the way you’re, you can sort of get, you know, one extra you can learn something that is that adds value in our, in our, our space without stealing their edge because they have a different different approach. And that’s also why I said here, that is some of what it kind of looks like the checklist of a discretionary trader over the years who identify things that matter. And that’s, you know, definitely on the commodity side, you know, we, you know, that’s, you know, following what other people do and talk about is, you know, very helpful And
Jeff Malec 40:00
then have you delved into like alternative data, you’re not getting satellite images of parking lots and things of that nature? And
Patrik Safvenblad 40:09
not? Well, we’ve tried tried a few times, but, you know, on the in the macro world, right, given that we are trading, you know, a handful of markets, perhaps, you know, attend energy contracts or something like that, you know, the a lot of alternative data is so, so granular, that it’s hard, you know, you have to kind of go through a very long exercise of sort of getting out to refining a single signal. And we haven’t sort of found anything that worked. We tried a couple of a couple of times, we do use some indicators that are where someone else has done this work right away who themselves have taken, you know, I don’t know, Twitter feeds or weather or something like that created an indicator that gives, you know, has predictive power. And I think, in some sense, that’s preferable to us. Because if you’re building your own composite signal, you’re going to curve it also in your signal in your data construction. Right? And if it comes from the outside, at least you are not overfitting the, your indicator construction.
Jeff Malec 41:19
Right, but it seems right outside looking in, like you must spend a fortune on gathering all this data. But is it all just relatively accessible via Bloomberg or something?
Patrik Safvenblad 41:29
Yeah, no, most I mean, it’s not, it’s not cheap. But Bloomberg is our main sort of the provider for you know, like run run, or the run of the mill kind of data collection, it works well, it’s a solid system. And then, you know, depending on on what it is, we might have, you know, either either, you know, sort of FTPS, and feeds and things like that to other providers. But in by and large, Bloomberg works really well for the type of data that we need, you know, stable, stable platform, and, you know, very happy with that.
Jeff Malec 42:11
You mentioned computing time, just how long does it take to run these models, and then compare that with what it would have been 10 years ago, also? Yeah,
Patrik Safvenblad 42:19
- So the running is relatively straightforward. I said, we run with a 12 day holding period, we update the sort of core signals three times a day, and the actual running might be an hour or two or something like that, 90 minutes, something like that. But you know, and I’m sure we want to make it faster, if you haven’t, but what is taking a lot of time is testing. So we have more powerful machines that are basically run, not 24/7, but you know, easily 12 hours a day testing stuff. And especially if you’re testing portfolio construction type ideas, you know, that is very time consuming, because you want to test for, you know, all the models that are live or the models that have a word live or you know, some other models that you might not consider, but are useful for testing, making sure that you know, all the features of your portfolio construction makes sense. portfolio construction, of course has its it has all this waterbed feature, and you know, whatever, if you have a target risk, whenever you’re putting something, you’re increasing something else to maintain your your target race. And that’s, that’s very time consuming. So the generally, my day often starts as part of the research process by seeing, you know, what are the results that came out overnight from the machines, you know, you have read through the report, and you think about that, and then you spend a day and perhaps towards the end of the day, you set up some new set of tests, and then that can run overnight, and then you come back next days to see how that works. And the same thing goes for signals. But yeah,
Jeff Malec 44:04
is that frustrating? You want to be like, run it right again, right? I want to increase this parameter. Change that and run it again. You gotta wait.
Patrik Safvenblad 44:10
Yeah, yeah. Yeah, I know, it can be frustrating. But but the thing is that you also can’t just get get a single reading and say whether it works or not, you need to look at a large number of dimensions. And that’s really what what takes time. Because if you’re, if we’re now talking portfolio construction, you know, clearly you have to think about slippage. You have to think about, you know, how the AR and VR constraints, you might you need to think about margin margin usage. And you need to make sure that things work in all these, these dimensions. So it’s not that you can just sort of run it again and you know, get the new result. That’s pairs more when you’re testing the code that you can, you know, run it and it breaks and then you can fix it. It’s Once again.
Jeff Malec 45:02
And then how does that work? Do you still kill the Investment Committee? So if you have some new testing that looks promising, how does that work to turn it live?
Patrik Safvenblad 45:11
Well, we’re, you know, there are three, three people on the investment side here. And I, you know, I’m the CIO, so I can, I can decide the agenda will be discussed. And then if it makes sense, it goes live. I think it rarely. At this point where the program is mature, we’ve been running with the same, you know, the same ideas in different iterations for you know, seven years is rarely something that is a source of conflict, if I put it that way, you know, is, you know, if. And I think the last last thing that came in, that somebody was straightforward was where we introduced a separate valued risk limit for grains, while we had one for each market, and we had one for this of the sector that we didn’t have for grains separate in so that’s an extra restriction. Now, that type of thing, you know, what it’s going to do, it’s going to cap your portfolio, certain situations, something else, something else is going to get more risk. And if it’s, you know, having having these tests on several that that around, you can quickly make a judgment call and say that’s, you know, it makes sense to do, let’s do it. And
Jeff Malec 46:28
talk a little bit about those portfolio level and sector level constraints. I don’t think we passed over that before. Yeah, so
Patrik Safvenblad 46:35
here. So risk management, of course, as you know, it’s key is, if you want to stay trading for another for 10 more years, when risk management is what you need. So you’re, you know, we I mentioned, stop losses here and stop losses, you know, stop you from being in a position that loses money over and over, you know, day after day after day, right, that’s the purpose of a stoploss search, you know, you can have a one day shot that continues and we are in 1987, or, you know, march 2020, or something like that, you know, you you need to be able to get out. Now, the pre Trade Risk management is really about balancing risks and a portfolio to make sure that you don’t have too much risk on any single factor, if I put it that way. Right. So and the, you know, we have risk limits, we use value at risk as are key to layer risk limits on the market level, we have risk limits on the six sectors, we trade, we have a risk limit on the portfolio. And roughly speaking, the risk limit is such that the max risk is not tighter than 1.5 times the average. And if you think about, like trend following trends, ultimately will tend to be much more opportunistic over time, right. So your, your max risk might be doubled, or perhaps in two and a half or three times the average risk as your as empty work for a while, and then you sort of load positions and some trend folders, of course, even, you know, gear up on profits, and you can get even higher spikes, we tend we tend to, and we target a stable, stable risk taking. We have, you know, we want to make sure that your your biggest our biggest one day loss is, you know, not too big. If I put it that way. We’re looking at our own track record, we I think we’ve had two or three, three sigma losses over the 1400 trading days we have. And that’s compared to something like nine or so for CTA for CTA index, and you know, perhaps even more for equities. And that’s sort of, you know, one of those things that I’m looking after you looking for the portfolio is on a really bad day, I think our biggest loss is something around 2% You shouldn’t be losing more than, you know, 1.8 or so.
Jeff Malec 49:09
And then on the so that’s market based, you said yeah, and then also sector based and then we’re like come adjust those on a portfolio level or it’s just gets there by adjusting the market and sector based and no,
Patrik Safvenblad 49:24
and then we are capping risk also on the overall fund level.
Jeff Malec 49:29
Patrik Safvenblad 49:32
So it’s, you know, but in some sense, there is a universal lower level, everything needs to be roughly, you know, what needs to be balanced, one level higher, where it needs to be balanced and the top level where it also needs to be balanced and balanced over time. So
Jeff Malec 49:49
yeah, so question popped into my mind, like, how do you view this versus like a pod shop right or when you are an allocator of like, I’m gonna allocate to these 15 or let’s call it 20, fundamental managers, I’m gonna get that diversification, I can control it at the portfolio level by position, sizing the risk. So like compare what you’re doing with that approach. In some ways, they’re very similar, but in a lot of ways, very different.
Patrik Safvenblad 50:16
Yeah, and I think my biggest, the biggest differences is the human side of things, right? You know, that. On when I worked at DMV, we did have a multi strat farm that I was involved in that had individual traders, whereas you know, of course, someone trading energy, because it’s Norwegian bank, and so on trading shipping, because it’s Norwegian bank, and there will be some other things going on. And, you know, the first thing you notice is that, you know, you can’t turn off a trader and then turning back home, you know, when you go to zero, you go to zero, and it’s sort of you never come back. And I think that’s a big makes a big sort of has a big impact on decisions. And the same thing goes actually, when it comes to the, to sort of this research team approach where I mentioned that, you know, you have someone who’s wants to push their individual model into the book, you know, that if, if they don’t get to trade what they want to trade, they’ll be very upset. And that’s not helpful for the organization. And you know, they might leave or something, you know, they’ve spent all this time developing an option model, and then they don’t get to trade it and then they feel frustrated, and they leave. So I think that’s the biggest difference. On the right,
Jeff Malec 51:33
you’re none of your models are gonna get frustrated and want to
Patrik Safvenblad 51:36
leave. No, exactly.
Jeff Malec 51:39
For now, until AI really gets fancy, right? When when the models have emotions, then we’re in trouble.
Patrik Safvenblad 51:44
He could be that. So so far, I think they are happy to live here in our in our private clouds.
Jeff Malec 51:51
But then just from a pure do you think you’d get the same return profile, like assuming those fundamental traders could stay focused and everything’s good? Do you think you get somewhat similar return profile? Or I’ll ask a different way. I think allocators if I’m saying I’m going to allocate to vote to get that profile, my worry would be like, oh, there’s too much similarity between the models. Right, I’m not getting enough diversification, because you guys created all of them.
Patrik Safvenblad 52:18
Okay, that’s, that’s a, that’s an interesting take. I think that, you know, of course, we know that our models aren’t that similar one to the other, like a, you know, weather model for trading wheat is not the same thing as Yeah, you know, trading crude oil based on what the energy company energy share prices are doing. So, you know, these are very, very different models. So we do have a lot of diversification. However, you know, if thinking of diversification from an investor perspective, I think it makes perfectly good sense to say, here’s wolves, did they do one thing, and, you know, they died, like, like the team, and they do it well. And here’s, like, a trend follower that I trust, and it does things with, you know, diligently and, you know, know what they’re doing. And here’s someone else’s discretionary macro guy that does something useful for a portfolio and, you know, create diversification that way. On the future space, I think the the examples of all sort of just futures on multi team discretionary, I think that I don’t have any recollection of someone doing that really well, actually. A lot of failures are but yeah,
Jeff Malec 53:39
myself included. What else? What else? Do we miss here?
Patrik Safvenblad 53:51
No, I don’t think we’re missing anything I think we are in. This is a snapshot snapshot or what what vault is about, right, you want to deliver returns to institutional investors, we want to do that in a way that is unique, and it has as risk control. We specifically have built a book so that we have good opportunities in crisis, you know, and that’s, you know, that’s why an extra reason to have strong risk management. But if you want to make money in you know, highly volatile environments, you definitely need your stop losses.
Jeff Malec 54:27
Talk through that for a second. So each signal basically has a positive skew on purpose.
Patrik Safvenblad 54:33
And the, or
Jeff Malec 54:35
just by risking a little in the opportunity to make more. Yeah,
Patrik Safvenblad 54:39
- So actually, that’s the, what’s really happening is that when you have shocks to the system, the world becomes a bit more predictable. So if you’re walking into March 2000, or some similar events, in on average, the world becomes a bit more and more predictable. It’s not Clear were in March 2000, we lost money in equities because we were long equities coming into the shop because we made money being short energy and the money in long fixed income, for instance, next job that comes around idle will have another pattern. So it’s important to be diversified. But, you know, what we have observed in our trading history is that we have had better than average, chances are making money in, you know, bad months for equities or crisis in general, you know, that the world is when when people are, you know, running for the exits. I mean, they are more predictable than then your recall on the normal day, if I put it
Jeff Malec 55:45
in your video, it’s also to me just a function of math, right of like, your steps are going to generally be about the same, but the opportunity is much larger. Right, if the range is greatly increased?
Patrik Safvenblad 55:55
Yes, yes, I think it’s, I mean, in terms of skewness, you know, our best day is twice our worst day or No, is our best week is twice our worst week. And, you know, so we know we have skewed and I think our best month is twice our worst month. So we know there is Skewness in returns, and part of it comes from, you know, the stop loss type logic that, you know, if things go go wrong, you reduce your position size. And the other part comes from, you know, when every once in a while the opportunities cluster together. And that’s, you know, true for that’s true for us. And it’s true for trend following and it’s true for discretionary macro radio, you are waiting for the setup where, you know, a lot of things moving in the direction of your hypothesis, right.
Jeff Malec 56:40
And that’s a good point that trend followers and macro they’re not necessarily designing the model to do that outside of the small stop, profits run setup, right, but they’re not saying we were betting against this, or we’re trying to get a crisis, it just sort of happens.
Patrik Safvenblad 56:58
I think that’s something that is true. Also for trend following is that you think or something being say, an equity crisis, right. And, and, but the opportunities, they come in random places in the book, it’s each crisis is different. And it’s just part of part of trading. So being diversified. You know, at our money, we have, for instance, a separate FX program writers in the FX program, universe, perfectly good FX program, but you every once in a while you’ve had this situation where the FX were just sort of miss out on some opportunities that appeared in the bigger book that were quite visible, because, you know, that was a shock that didn’t make it into FX.
Jeff Malec 57:42
And how do you think with the shorter timeframe? Right, it seems counterintuitive that you could capture like to larger gains in 2008 type extended move down crisis, right, the crisis period tends to think of months long, two years long down moving equities. When you’re just 12 days, you have to be getting in and out successfully throughout that period. Yeah, no.
Patrik Safvenblad 58:07
And then, of course, the and again, and again and again. So that’s, you know, it is, it’s really so you know, more thinking about in the big space of instruments that we’re trading, you know, well, what’s the best place to be in our winning trades tend to be more like 20 days holding period and our losing trades for it’s more like eight. So you do have an asymmetry that comes there as well. We are not forcing the models to have it while they’re holding very, this is more of a statistical thing. That, that means that if you have something that is persistent in the world, you know, the world is persistently, you know, going south or north, whatever we want to call it, then our signals will will naturally become more persistent. So, for instance, in, you know, our best year was 2000. And we actually had a slower trading in that year than we had in average years. Because the the world was persistent. You know, the world was persistent in a crisis in the early part of the year, and it was persistent in recovery in the later part.
Jeff Malec 59:10
And then given all your experience, allocating to trend followers and other CTAs would be fair to say you wanted to lack of a better term have less negative carry, right, have less bleed between those crisis periods in designing this model,
Patrik Safvenblad 59:27
I mean, that’s definitely right. Do you want to lose as little as possible, you know, as you’re waiting for the next big move? Now, clearly, there are localized crisis in these individual markets where we are trading but you know, the target here is to make money in every market every day. But you know, it’s, you know, the big opportunities lie they lie lineup every once in a while. And but I think it’s safe to say that there is no sort of automated bleed if I believe that right Whether that trend following as an option, replicating type strategy will have bleed, you know, like the sort of long choppy whipsaw losses of those periods, they will be part of the strategy. And that’s a feature. And we definitely try to, to minimize that.
Jeff Malec 1:00:26
Yeah. Why did you say, Yeah, I was sitting in my office at these other firms screaming at the wall saying, You’re down again, down again.
Patrik Safvenblad 1:00:34
Right, yeah, I think that’s a trend following is an interesting product, because you obviously have to have investors that can handle these long flat periods, or long negative periods. And, and, you know, minimizing your, your, your bad years or bad months is key to having keeping your investors on board, you know, and in our, you know, sort of a way we’re setting up rules, we’re setting this up to trade for 20 years or so. And that means or longer, but that means that, you know, there will always be, you know, a new CIO at the firm that’s investing that comes in and wants to kind of, you know, clean house a little bit. You know, if you’re, if you’re at the end of your, of your whatever, for your your drawdown you know, you’re always at risk of being, you know, kicked out from the portfolio just before a big move. And, you know, this was in many of those things happening. And in over over history, of
Jeff Malec 1:01:37
course, many your goals don’t stand out. Right. You have to know I either dive in there.
Patrik Safvenblad 1:01:43
Yeah. And I think also, you know, and I think this is a sort of a general comment for anyone who’s managing money, right? Is that, you, you, you don’t want to stand out on the downside ever, right? You know, you because you’re, you’re really, really at, at risk, even if you made money before, right? Even if this is, you know, you’re you may if you’re if you’re down 20% a year, even if you made up for the year before, you know, people are going to feel this is too much for to handle. And I think that’s just, you know, keeping, keeping, keeping an eye on that, or sort of having a strategy for how to handle the downside. I mean, that’s very important.
Jeff Malec 1:02:33
I think we’ll leave it there. Thanks so much for your time. And all the good info on vote, we’ll put your the website and some other goodies in the show notes. Anything else you want to share? Where can they find you?
Patrik Safvenblad 1:02:46
Well, they can. Well, you can Google for me and send me an email. That’s easy. As easy as that.
Jeff Malec 1:02:52
I fly to Stockholm. Yeah, no, I
Patrik Safvenblad 1:02:55
think that’s actually one of the it’s a it’s a good point, actually. But we are in Stockholm. And it turns out, we probably have, you know, more people calling and more people visiting than Harmonix gives out in London. Really. And, you know, being a little bit off the beaten track is means that people are out there, they are looking, we’re looking for things that are different, you know, nowadays, of course, you do a zoom call or something. But, you know, it’s it’s definitely not a disadvantage to be the in stock calm for that. So
Jeff Malec 1:03:31
yeah, I would have to get few and far between visitors. But that’s good to hear. Mostly Europeans, I would guess.
Patrik Safvenblad 1:03:37
Well, I think probably was more reflecting like, being in London is not giving you any more, right. I mean, we are in a newspaper in a small industry. So it’s not that we are being disturbed every every week here. But, you know, we do get a good number of people calling us and then a good number of, you know, Zoom calls and what have you. And I can actually reveal that our I think our Nordic business is when the 5% or so that our total business. So we are in Sweden, but you know, our client base is not Swedish. You know, it’s broad European, we have clients from Australia, we have, you know, us clients, so it’s a
Jeff Malec 1:04:13
how do they handle that? Do they do it in the local currency?
Patrik Safvenblad 1:04:18
The northern, yeah. Well, the future in Australia is straightforward in the US. So we have funds and the funds, you know, we’ll know the Euro dollar share tosses. If you are running managed accounts, of course, you’re running you, you handle the funding in your preferred currency. So that’s quite straightforward. We have eight managed accounts. So you know, they’re those clients, they know what they’re doing and set things happen by so that worked for them.
Jeff Malec 1:04:50
Awesome. Love it. Well say hi to the team and look forward to visiting you in person one day. Yes,
Patrik Safvenblad 1:04:56
you’re not you’re most welcome. We have never been planned. Do nice for you. I know there are plenty of things we can we can take take you to and let you see an experience for sure if you come by so, and otherwise I trust I’ll be seeing you here in here in the industry. Yes.
Jeff Malec 1:05:12
All right. Thank you, Patrick. Right. Okay, that’s it for the pod and as mentioned at the top that’s it for the season. Thanks to Patrick and vote for today. Thanks to all our guests for the year thanks to Jeff Burger for producing and making this look good. Have a happy Thanksgiving holiday season and new year and we’ll see you back here mid January.
This transcript was compiled automatically via Otter.AI and as such may include typos and errors the artificial intelligence did not pick up correctly.