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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.” All of 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 it affords 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.

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