In Conversation with Martin Lueck – Aspect Capital

The Aspect and AHL co-founder tells his story

Originally published in the September 2014 issue

Niels Kaastrup-Larsen in his podcast, Top Traders Unplugged, interviews some of the most successful hedge fund managers in the world. In episode 37, Niels speaks with Martin Lueck, co-founder and Research Director at Aspect Capital. Lueck is well known in the alternative investment industry as one of the co-founders of AHL in the mid-1980s.

Niels Kaastrup-Larsen: Now Marty, I think it’s fair to say that many people who are involved in the hedge fund industry, and certainly people who are involved in the managed futures industry, are very familiar with your name and that of your partners from AHL. So instead of me doing my usual summary of some of the key points in your journey so far, I’d like to start a little bit differently today, and I hope that’s OK with you? So here goes, let me start out by asking you this question: imagine that you are invited to a dinner party with people who don’t know you, and a few minutes into the dinner the lady sitting next to you looks in your eyes, she smiles politely and asks, “So Marty, tell me what you do?” How do you respond to that? How do you explain what you do?

Marty Lueck: Typically that’s the question I, usually, dread [he laughs], so I first of all have to get a sense of whether that lady is genuinely interested or if she knows the finance space. I start with something fairly neutral like I run a quantitative investment business, and I see whether her eyes glaze over instantly, and then change the subject. Assuming that she says, “Oh do tell. What’s that for? Why do you do it?” I can sort of walk her through a little bit of the story: that there’s a happy accident that I got into quantitative investment research and applied my original physics background to developing these models. Then in terms of the question of what’s it for? Well I think that the sweep of my career has sort of, unwittingly of course – I don’t think I set out in the early 1980s with this vision in mind, but I think with the benefit of hindsight, what we and others in the industry have managed to do in some sense is democratize a new form of investment, that actually I genuinely believe it has a contribution to make to pension funds, and to individuals’ investment portfolios. And Niels, if that hasn’t put her to sleep, or to talking to the person on the other side of her, then nothing will.

NKL: It’s funny Marty, because actually it’s something that I struggle with myself. When people ask “What do you do?” I think it’s so hard to the uninitiated to come up with an answer which both makes sense and is somewhat interesting.

ML: And keeps the conversation going.

NKL: Absolutely. Now, of course we’re here to learn about the great things that you do at Aspect, but we can’t ignore that your first company, AHL, had such a profound effect on the whole managed futuresindustry. So I’d like to spend a good deal of time going all the way back to where your journey begins and find out what led you to where you are today. To my knowledge, the AHL story has never really been told to the same degree as the story of the Turtles. But since they’re taking place more or less at the same time, and both had a huge impact for decades after the event, so to speak, I want to be sure that we cover a lot of that background as well today.

ML: Gosh, Niels, be careful what you wish for. Well, as I said, this is a happy accident. I studied physics at Oxford and my school friend and then also chum at Oxford was a fellow called Michael Adam, and he and I competed at math and physics. When Mike – he was a year ahead of me at Oxford, he took a year off and then started working for his father and the family business was originally… they were of Mauritian decent, they were involved in physical commodities on the London commodity markets. Michael went to work for his father and Cyril Adam said, why don’t you see if there’s anything in these technical trading models? So here you are, you’re about 1983 and personal computers were a bit of a rarity and Michael bought a Hewlett Packard 9816 and taught himself to programme in Pascal. I left and got my first job at Nomura, in January of 1984, and spent all of my lunches hanging out with Michael and just really thought what he was doing was great, and my trying to sell Japanese equities was not so great.

So I left Nomura after a whole nine months there, so that was the last real job I had, and started working for Brockham Securities, which was Michael’s father’s small broking house. Together we investigated a huge range of trading strategies and sort of distilled it down to some fundamental rules. I’m glossing over a lot of work. Our first portfolio was six commodity markets. I think we traded cocoa, coffee, sugar, aluminum, copper, and zinc, and we had £25,000 of Adam family money and we built that first systematic portfolio. Along the way we met this chap, David Harding, who was a Cambridge graduate and I met him when he was working for Sabre Fund Management, and that story, I’m sure, David would tell it far better than I. David was the understudy to a man called Robin Edwards who was a chartist – a fund manager, but it was entirely based on chart patterns. So every day David, the sorcerer’s apprentice, would take the books off of the shelf and open up the charts and add the latest tick data point. In would come the grand vizier and determine whether that was indeed a rising pennant or a double bottom or a head and shoulders, and determine what the day’s trading would be.

So David and Michael and I, when we met, we said, if we could encode Robin’s rule set, that would be something, and it was just sort of a meeting of minds. Then there was a bit of a tussle. The Sabre folks wanted Michael and I to join them, but in the end blood was thicker than water, so David joined us in the Adam family business. In early 1987 Michael and his father had a difference of opinion on the direction of the business, and we left and AHL was founded. We managed to… we had one client that had been with us over the transition, so we had a whole $100,000 of investor money that bridged that interlude. Then we set up in February of 1987. We sort of floated around town. We didn’t really have office space. We were in my father’s office for a few weeks, then we had a cupboard at the back of the Sabre office for a while, and if I recall, Sabre were generous enough that they took a stake – they backed us for a while. Later on we bought those shares back. Eventually serviced offices and then on to Jermyn Street.

The relevant thing here is really, genuinely, Michael, David, and I didn’t know that there was an industry doing this in the States. So you talk about the Turtle experiment going on. We woke up, and we found thatwe were doing something where there was precedent, where there was an industry and in fact it was quite a burgeoning industry. I think that that’s relevant because we weren’t looking over our shoulders at how other people had done it or were doing it. Literally this was three nerds, and we approached the process of model development like a scientific experiment. We had the historical data; we had some models that we distilled down to some essential characteristics and then we said can we do this better? Can we add additional markets? Can we improve the models? Can we go faster? Can we go slower? Can we add more components to it? It was a scientific exercise. I think that that DNA, that approach really took off in the industry. I would contrast that, and I know it’s a gross generalization and therefore not true, but I think that a lot of the roots of the US early CTA industry were in floor traders that encoded their rule sets. So you look at the Richard Dennis story which you referred to, or John Henry, these were very smart folks, but they had a rule set that they were comfortable with, and in fact, we came really close to that with MINT.

So MINT was a pioneering systematic CTA in the early 1980s, and they did a 50/50 joint venture with the Man group. So through the course of the 1980s the MINT funds were marketed by Man in their global offices. So Man the commodity brokering firm had offices in Ulan Bator, outer Mongolia, and all of these places thanks to their two centuries-old commodities business, and they started distributing this managed futures fund to a broadly retail audience. They also embedded the MINT programme inside a guaranteed structure. You remember those with the zero-coupon bond and you use the remaining cash to fund margins on the trading account. That was just genius, absolute genius. So by the end of the 1980s I think that MINT was probably the first alternative investment shop to be managing more than a billion dollars.

NKL: Sure, I’m curious about something from that time and that is that we know that the Turtles were really taught break-out methodology, meaning you have a 20-day high or low, or 50-day high or low, and then you are on top of that, you add some exit rules and you add some risk management. All this taking place in the mid-1980s. I’m curious to know if you remember, I’m sure you do, but if you could share what did the initial AHL model look like? I’ve always, personally, been under the impression that it was based more on the moving average kind of methodology?

ML: The really early days, Niels, it was more of a break-out model. It was a break-out model, and it would scale into a position. So if you saw the repeat, it sort of patterned an end day high being supported multiple times. You would scale into a substantial position. Unwittingly, despite us all having scientific or physics backgrounds, this was uncharted territory, so all of the schoolboy errors that we all know and everyone that works for us has learned back in finance 101 – all those things about over-optimization and degrees of freedom – we did it all. We optimized the living daylights out of these models in their back history. Actually, by the happy accident of this scaling in feature of the models, that sort of disempowered our ability to over-optimize, so those were reasonably robust models. When we started dabbling in the moving average that you allude to, those are far more prone to over-optimization because if you do the schoolboy error of trying to optimize what would have worked in every single market over time, you come up with a fantastic simulation, but reality doesn’t turn out to be nearly so nice.

NKL: When was that, actually? Just before we go back to your story, when did you first move into the moving average type area?

ML: I wouldn’t say we moved into it. We explored it, and it became part of the programme, but not the wholeprogramme. I’m not clear on the dates now, but I’m going to say that that was probably in the late 1980s or early 1990s.

NKL: I interrupted your fantastic journey, so by all means go back and tell us what happened when you got involved with Man relatively early on.

ML: Yeah, I was going on to make a point because I was just drawing the contrast between the US managed futures industry, which, in my gross generalization or unfair generalization, was based on a set of traders’ rules, versus the AHL happy accident of some scientists who just began distilling and evolving and in the broad sweep of history, improving what we did, but it wasn’t always a linear improvement. What that turned into in the fullness of time was, I think, the difference between the approaches. People talk about the AHL DNA or diaspora, I won’t say dominating, but being a major feature of how the industry has evolved. I think that was the sort of introduction of the scientific method. Going back a little bit, I think that a couple of pieces of the story… as AHL still in… it was really a couple of years before we got very close to Man and before they took a stake in us. The three of us were over-enthusiastic and distractible kids. Through none of this story did we have the laser-like focus that we would all love to claim.

As I say, Michael was fascinated with the software language that he was developing and was making his best efforts to commercialize it and sell it. David was very driven by the asset management business, and he would be out on the road and spreading the good word about these programmes that we were running money in. I was somewhere between researching the programme and also doing consultancy, so we had a number of consulting clients that we would encode their trading businesses on our software and give them commercial advice. We did that for a Gilt market-making house in London. Then, interestingly, we did that for one of the commodity divisions at Man. They said, “We see a lot of this fund activity increasingly and wonder if you could model what the different funds are doing in these various commodity markets.” Of course! This turned out to be a useful intelligence for later on in our business when we started to be undermined by other market participants. So I think through that we got to know the Man folks, and in 1989, they took a stake in AHL. As I say, MINT had been gloriously successful and was probably over a billion dollars, and was essentially at capacity. So they had a very successful business. They really couldn’t trade any more. Their agents in Ulan Bator and far-off corners of the earth were just hungry for more of this terrific product. It sold like hot cakes, generated enormous fees (we can come back to that) and performed uniquely well. So Man took a stake in us, put suits on us, dressed us up, said no to the commercialization of the software, said no to the consultancy business and we all buckled down and started working on further development of the AHL programme for the development of a number of different products.

NKL: How much assets did you have under management in 1989?

ML: I think when we first sold a stake to Man, it was a huge $30 million under management [he laughs]. Then, scrolling forward very quickly, in 1994, when they bought out the remaining minorities and IPOed the Man group, I think we were at the heady figure of $300 million… I think that’s right. That was an interesting journey in and of itself because what Man had was the MINT business, which was over a billion dollars; it was spilling off enormous revenues and I think that the business was so successful so early that at the time they were not committed to building a research effort, so from where we sat, or rather, from where the Man’s suits, as we called them, from where they sat, they had this billion-dollar business with 24 people, somewhere over in New Jersey, and these three kids and their $300 million of business, and we were over 70 people with a big trading team, with a big research team, with a big technology team and the Man guys said why do we need all of this stuff? Why do you need all of these researchers and technologists? We sort of looked at them incredulously and said, of course you do because its research; you have to keep improving it. That turned out to be the case. In the mid-1990s, I think some of the MINT funds struggled and AHL started to deliver on the research promise and, in a little bit, the rest is history.

That history was that at the end of 1994, once the IPO had happened, Michael, David, and I had pretty much gone our own ways. Michael left the firm and started a software venture capital firm and had a glorious set of products that they developed and sold on, and then rejoined me at Aspect later on. David had a separate research team within the Man group, Man Quantitative Research, and I held the baby of AHL through the course of 1995. It was a little bit of a depressing year, because on the back of 1994 was challenging performance for managed futures, and against this backdrop… so on the one hand they’ve got 24 folks spilling off money in the States and 70-plus folks in London – mouths to be fed, so I spent a lot of 1995 dismantling much of the research and technology team that we had built up over the years. At the end of that year I just said, I’m not going to do this. I left and took a year and a half out and then started Aspect with Anthony Todd, Eugene Lambert, both of whom had worked at AHL, and Anthony was also an Oxford friend of Michael and mine. We put in place the vision. Aspect’s vision was to take the managed futures business that AHL and Man had developed and bring it to an institutional audience.

NKL: You’re not really of a financial background, but what was it that convinced you to apply models to financial markets. What was it that made you believe that this is really the best way to make an investment strategy that’s going to last for years or decades to come?

ML: Happy accident, Niels. I don’t think… there was not a realization or a determination… I don’t know how David would answer that question, but certainly on Michael and my parts that said, “Gosh, if we systematize this we’re going to be rich.” So first of all I have to credit Michael’s father with the “Why don’t you see if there’s anything in this,” and then just backing us with some money. You know what, Niels? There was something in it [he laughs]. Number two, this is a fun one: I recall that what Mike and I did to amuse ourselves was … so we had this database of market data, much of which – this tells you how old I am – we actually typed in the back history by hand. The brokers would send us over those big green fan-fold print-outs of historic prices, and we literally had to type them in. We came up with this trading game, and it would randomly select a market and randomly multiply it by a random multiplier which could be a negative number, so it might invert the market and it would present you with a number of days of data, and then you had to trade. You had to make a decision: am I going to buy this or sell it, or hold, or various trading rules, and then you’d click the space bar and move forward a day and it was easy. So without the emotion and the information flow that makes markets so challenging for all of us humans – so challenging and so interesting. I think that there was this sort of realization that clarity… if you could just get rid of all of the noise, then there was a lot of information available in what the price had done that could inform what the right thing to do was.

There wasn’t a eureka moment, Niels, just sort of gradually accumulating comfort with this approach that we suddenly found, “Oh, this is a real business.” Allof our friends at Oxford and Cambridge – the talented folks had all gone off to become investment bankers and I remember that for the three of us, it felt a little bit like revenge of the nerds because it wasn’t something that physicists or scientists thought about doing in those days.

NKL: Absolutely. Just a question that pops up in my mind, when you left Man, and of course as you say Anthony Todd was very much part of this equation at this time, but just out of curiosity, why did you not just start like AHL 2.0 and keeping the team together do you think? What was the reason that you parted ways with David at the time, other than maybe he was a Cambridger rather than an Oxforder?

ML: [laughs] David had different priorities for the business. I think he was focused on the research that he and his team were doing on the other side of London in that quant research, and I think that formed the genesis of Winton Capital Management, because I think he probably found the Man machinery a little bit smothering and he left and set up Winton. As I say, Michael was doing something else. We’ve remained good friends, and we see each other from time to time, but it actually was never a debate where the three of us were going to do AHL 2.0. I think we all felt that that was one chapter that had closed, and it was time to move on. So sitting down with Anthony and Eugene, Michael was a shareholder and a backer, but he wasn’t an executive in the business in the early years just because he had his software company. It really was, “How can we do this and get it to a broader audience?” The MINT and Man and AHL within Man was predominantly a retail business, Niels. It was retail; it was a structured product – if you can call a guaranteed fund a structured product. It was high fee, and it was extremely opaque. I think that the sales pitch of the early MINT funds in all those far-flung offices was, “Look at that, bottom left to top right, very clever, trust me.” You can imagine that doesn’t go down well with institutional investors today. So the whole premise of Aspect was this has to be right for a broader audience. So we set up a business that would be institutional in outlook, in set-up, in fees, and built from there.

NKL: You certainly did. Now give me a short, sort of Aspect summary, just to bring us up from the beginning to now and then I want to learn a little bit more about how you’ve ended up organizing these things and so on and so forth. Of course, Aspect in itself is a journey, and I would love to learn more and share more of this with the audience.

ML: Thank you Niels. So Aspect, as I say, was predicated in the belief that managed futures was too well-kept a secret. We felt that statistical models had a broad applicability, and that was the irony here. Having developed these models from… I’ve talked about the arcane roots and chartism. Gradually you put more scientific rigour on them and you find they are signal-processing techniques with certain statistical characteristics, and it all became, in a sense, more respectable. In the early days, as I say, the legacy of the managed futures industry, driven by the US, but it was also Man and around the world, it was the high-fee, opaque, sold product, and most self-respecting institutional investors turned up their noses at it.

Meanwhile, you saw the advent of statistical arbitrage models and anything that had equities in the title that was fair game. That was a decent alternative hedge fund, wasn’t it? We were a separate, slightly scruffier industry. That was Aspect’s first and foremost mission: to bring this thing… I don’t need to tell you and I hope I don’t need to tell our audience, but just the intrinsic characteristics of managed futures – that liquidity, directional agnosticism, ability to move risk around in various places, and the diversification that itaffords a portfolio – to me it is (if I say it’s a no-brainer) it just has an integral part of a balanced portfolio. So with that kind of passion and belief we set out to do that. In the early days of Aspect, we had even more ambitious plans to be a quantitative multi-strat shop. So along the way, along the journey we have developed and been modestly successful in the quant equity space. But with the pall of the trauma of 2008, we actually closed down that part of the business and have, as we speak now, we are a one product… there are various flavours of Aspect Diversified, but one product managed futures, predominantly trend-following business. It has been, as you say, it has been a journey.

NKL: So you have more than 100 people working for you today. How have you set up the infrastructure, so to speak?

ML: From day one, Niels, we felt that to attract the kind of institutions that we wanted to attract we needed to do it thoroughly, so we didn’t manage a dollar of client money until we had enough people that we could man a 24-hour-a-day trading operation until we had a disaster recovery facility. So that speaks much more to an approach of build it yourself, which doesn’t mean we’re Luddites. Back in the early days of AHL we’d even write our own back-office software. That would be nuts these days. We have organized the research team, and again the evolution of the business and the evolution of the research team and the research process is as much a part of the journey as the evolution of the models themselves. That development of the team from, once upon a time when everyone did everything [laughs].

We’ve been through phases where we divided the team up and people were siloed by asset classes, and we thought that created conflicts and incoherence, and then you sort of alight on where we are today where we’re divided into, in fact, it’s about 120 people in the organization, 70 of us are focused on research, trading, and technology to support those activities, and there’s focus. There’s division of labour so you’ve got from the technical production team, to the software development team that supports both the production systems and the research systems: you’ve got a core research team; you’ve got a dedicated risk management and risk review team, and I want to come back and talk about that for a minute; and then we have a fantastic product management group who basically are sort of an interface between our clients and the research team, that sort of protect the research team to stay focused on the job at hand; and then our technology team also supports data and gives us the tools to keep looking in new areas.

NKL: What are the personal traits that a good researcher should have in order to have a chance to come and work for a firm like yours?

ML: Yeah. It’s a great question. I think that one of the lessons along the way is that the cultural piece is more important than you might think. I think that that has … along the way we’ve hired a lot of people, and a lot of people have left and the core team that we have, and now they’re really experienced. I think that the average tenure with Aspect of my core research team is over seven years, some of them well over 10 years. I’d say two things in answer to your question. First of all we don’t… there isn’t a graduate intake. We aren’t just hoovering up PhDs willy-nilly. I think we have certain targeted hires, where we may have determined that we need a skill-set. So, for example, we needed some statistical muscle, and we went out and we hired someone who is just the most fantastic statistician I’ve certainly ever come across. In terms of bringing in junior folks, I think it’s… obviously these days the qualifications are certainly more than Michael, David, and I had in terms of PhDs and finance and programming skills. It’s an attitude, and it’s a cultural fit that are the most important features.

NKL: I guess, it’s the three Cs: the Character, the Competence and the Chemistry, when you want to put together a strong team. On that theme, how do you build a strong culture in an organization?

ML: I think that’s a great question, and I can say it, I can talk to it because I’ve made mistakes along the way. I think that the nature of what we all do in the asset management space lends itself to, how can I put this delicately… to individuals. There’s the cult of the individual. Whether people start to believe their own hype or investors want to believe the hype. Couple that characteristic with the fact that once upon a time… every business has to start somewhere, and if you’re successful, you might believe your own success. Where I’m going is that it’s hard for a business to make the transition from being very much top-down, character-led, so I’m going to tell you lot what we’re going to research today. Making the transition from that to a genuinely academic multi-disciplinary collegiate research effort – that’s actually a harder transition than it may sound, and people may overlook that.

So your ability to build a team and to trust and to delegate, trust and verify, to build that robust team is essential to creating longevity in the programme. You can come up with a fantastic system, but I think the real tribute to AHL, to Winton, to Aspect, is these businesses have continued to evolve. It’s more the research process that we’ve all subscribed to rather than the genius of the individual model or any component piece along the way.

NKL: Speaking on longevity, you’ve also had a very long partnership with key individuals, and I wonder what is the recipe of keeping a partnership going for such a long time? Like in a marriage it takes an effort to keep things alive and well.

ML: Well, so Michael, David, and I all ran into one another and we made it work. We were the three musketeers taking on the world, or taking on the Man group. Man were absolutely great partners, but just at that stage we needed someone to focus our ire on. The point I’m making is that actually through the transition when we went our separate ways, and then David did his thing at Winton and we formed Aspect, I remember Anthony and Eugene and I and Michael just said, look, we have a chance to build something great and let’s do it with the people that we want to work with. So we haven’t gotten it right every time. You said the three Cs. You sometimes take your eye off the ball and the chemistry isn’t right, but by-and-large, Niels, I think it’s about the people that you choose to work with. Then it’s about commitment. I’m sure Anthony could have killed me at various times along the way and vice versa. You keep going, and it has been a tremendous experience.

NKL: Absolutely. I want to shift gears a little bit now. We’ve talked about the organization and what sort of underlies your success on that side, but I want to talk also now about the track record. My contention is that investors often look at a track record of a manager and they think, “Oh, so this is what I am going to get in the future.” Of course we know that, as you rightly said before, programmes evolve and therefore a track record isn’t necessarily meaningful in the current form, and I would argue maybe that, to get a better feel, you should ask for a back-test of the current configuration of any system, but usually that is not easily available.

ML: Yeah. Great question – I think if I’m sitting with a potential investor or an investor who may be struggling with recent performance compared to what they might have expected. I am, first of all, at pains to point out that the lengthof the track record, of Aspect and of AHL before that and of Brockham Securities before that, is more a testament to two things: one to the persistence of predominantly trend-following models, the persistence and the ability to capture positive returns in multiple economic scenarios. That’s sort of big take-away point number one. The big take-away point two is that it is what you say, it’s an evolving process. It’s a business. The analogy I use for our research team and the research process is (so maybe this would have been a better answer to the lady at that dinner table) is it’s much akin to pharmaceutical research. Not that I’ve ever worked in pharmaceuticals, but the point there is that at one level you look at Sandoz or Novartis or something like that and you get a sense of how good that business is at developing new drugs and how good are they both medically, and how good are they at exploiting them commercially. But meanwhile in the bowels of the business, it’s not just one drug. You don’t just say how good… tell me about that one anti-cholesterol drug. It’s a pipeline, a pipeline of products, so the drugs in and of themselves at any time are important and are generating the revenue when you take a snapshot of the business, but they continue to evolve.

NKL: What have been the biggest changes over time and is it really small incremental changes, or is there something where you look back and you say in the last 15 years, 2008, or 2009, or whatever it might be, we did actually discover something that we would say that was a big upgrade or that was a big find – key finding?

ML: By and large it is very much an incremental process and we make a virtue of that because you don’t want – the last thing we want to do, especially with our focus on institutional investors and a high level of transparency, the last thing you want to do is surprise an investor. With the benefit of hindsight, I highlight two particular features about the evolution of the approach. First, in an odd way… the first, Niels, is the importance of risk management and portfolio construction. I think this is something that investors and maybe managers that haven’t been doing it for that long may underestimate the importance of in the process, and again I’m saying this because I did [laughs]. After all of the shenanigans of looking at chartism and distilling it down into fundamental tech models, you come up with a pretty robust diversified set of medium-term trend following models, or we did anyway.

The neat thing in the 1980s was the range of markets that have sprung up around us, Niels, afforded us a level of diversification that essentially… the combination of trend-following across that range of markets it risk-managed itself. You didn’t have correlated risk shocks. You didn’t have … there was enough intrinsic diversification that if one sector was melting down you’d have opportunities in another sector. Risk management… I couldn’t spell risk at the time. Then a couple of things happened. First of all (I’m going to foreshorten this) you got to an era where I think some of those trend-following models became less efficient. You got to an era where markets did become more homogenous, so there’s both a sort of macro effect as your pension fund manager in Japan begins to hold a similar-looking portfolio to your pension fund manager in Sacramento. Whereas, once upon a time they didn’t; it was much more parochial. You begin to get a greater coherence of both investor holdings and then also with the advent of VaR metrics and that approach to risk management, you also got a more correlated response to events, so that everyone around the world who thought they were doing independent things would react in the same way to an event.

In response to those kinds of increasing correlations in the markets and increasing propensity for shock effects we – bothAspect and as an industry – began to look for sources of diversification. Once you start to diversify, obviously markets is one axis and time scale is another, but once you start putting in other models, then how you bind them together, and that admixture becomes super, super, super-important. I’m sure this is kind of obvious. It’s been an area that we’ve focused a lot on. How do you put them together carefully? How do you make sure that you constrain, because just the simple thought experiment: if I take two models which have zero correlation between them and I leverage them up to achieve the same standard deviation of returns as one model on its own, well hurrah! I’ve just improved my returns, but I’ve also let the kurtosis creep out. It becomes increasingly important as you make the portfolio more complex that you deal explicitly with all of the edge cases – the risk management edge cases. That’s actually stood us in good stead as the markets have gone really into strange places since 2009.

NKL: Since you mention that, what have you learned in these last few years in terms of trading and systematic models and how the environment plays such a key role in all of this?

ML: I’m making note to come back to a question you asked earlier, but, oh gosh, this environment has had a… we could spend another session talking about this. Clearly in a period of underperformance for the strategy, we can… it’s human nature, why is it underperforming? What’s going wrong here? Ah, I invested in you, Marty, because I saw your 2008 performance, what are you doing? Have you all saturated the markets? Have market dynamics changed? Has trend following stopped working? It’s all of those questions, again, and you just hit the nail on the head. I don’t want to appear glib, so of course we investigate all of those things. We look at both our market footprint and what we think is the footprint of our entire industry to satisfy ourselves that we’re not… this isn’t shooting ourselves in the foot that’s happened here. We look at the low-volatility environment and what that is likely to do to both the opportunity set and to the risk management challenge.

It’s been a trying time, but then I guess in one sense I’m fortunate or cursed with having lived through periods like this before. After 1987 – a lot of parallels. There was a great… after the October crash of 1987 there was a huge run-up. Managed futures delivered its crisis alpha. We delivered our crisis alpha, and it went roaring profitable into 1988 and then basically hit the doldrums. The analogies between then and now in terms of recession, savings and loan crisis… remember that one? Government intervention, managing the yield curve, suppression of risk appetite – a lot of similarities. You’ve got to dig and scratch a little bit at the AHL track record, but it took from the high in the middle of 1988. I don’t think AHL was back in new highs until some time in about 1993 or 1994.

It was a similar length to the doldrums that we’ve been in here. It actually didn’t end neatly. It wasn’t just a sort of pleasant recovery of favourable returns. Just when you thought things were getting better we got kicked in the teeth by a surprise rate hike in 1994. But that sort of presaged… it almost felt like the tubes had been cleared, and the starting gun went off and the markets returned if you will, to some sense of normality, whatever your impression of normality is, and there was a great round of performance. I’m not predicting that, but what I’m saying is that I draw comfort from having been through it before that actually, provided the models are able to adapt to the fact that no two days are going to be the same, and you know that’s the beauty of what we do, because it’s not scenario-specific, so the models can adapt to whatever the markets present, number one. It does speak to the persistence and having confidence in the approach.

NKL: I think the other thing that people should do if they want to satisfy themselves about why these strategies shouldn’t actually have made money in the environment we’ve just been through is just look at the price range compression that we’ve seen. Just looking at what’s the high and low been for the last rolling three, or six-month basis, it is so clear what happens to the prices a few years back and when you do trade momentum and the price ranges compress as they have done, it’s very easy to visually see that we shouldn’t be making money. So it’s very interesting, and I completely agree with everything that you’ve said.

ML: Niels, the thread that I forgot there was just it reconfirmed my belief that a systematic approach is… I mean horses for courses. There are some great macro traders but I can tell you I’m glad I do what I do rather than be a macro trader, because how many times do you think folks have said, “Well, yields can’t go any lower than this.”

NKL: Tell me about the Aspect Diversified Programme today. How does it look, and sort of visually what does it look like?

ML: Well, I’m just going to tie in one other piece to an earlier question. By and large it has been very much a gradual evolution. If I look back, I think that the focus on risk management both as an activity in the business and as an implicit component to how we build it and put it together and run it. That’s been a major feature of how the programme has evolved. The other thing is, actually, in the period of 2004, 2005, where the trend capture models transitioned from a binary implementation if you will, to an analogue implementation, and that’s interesting.

NKL: Can you explain that a bit?

ML: I think about break-out models: typically either your model is a long, short or out. The way we predominantly implemented, again there were varieties on this, but the early AHL and early Aspect models were a range of binary models across a range of different time scales. So that meant that as you’d scale in and out of a trend, you would trade in discrete chunks. So a model would essentially flip its sign, which meant that when that trade was delivered it sort of came out as a belch of a considerable amount of trading. We thought little of it, because that’s the way we had always done it. By 2004, in particular, a couple of trends converged, or effects converged. Number one, actually Aspect was doing reasonably well. We had had a run of good performance. We had managed to raise assets, so I think we were a fairly sizeable account for many of our brokers and market makers and when we’d hit one of those discrete trading points, I’m sure it was a very attractive piece of flow. So first of all we were noticeable. Secondly, there was an era of… I want to highlight in the FX markets that this was the era of disintermediation of the interbank market, do you remember that? As it became more democratized and everyone had access to the same price feeds, well the bank trading desks had to make a living and spent time understanding our models. So back to the early point about being picked off. What that told us was that we just had too much of a market impact. We were too visible to the markets, so around that period these were the same effects we were capturing, Niels, but in a different way that meant that our entry and exit to the markets was much, much, smoother and effectively invisible.

Part 2 will be published in the next issue. Listen to the full interview at