The following article contains extracts from a conversation between Anders Lindell, chairman and co-founder of Informed Portfolio Management and Niels Kaastrup-Larsen from the Top Traders Unplugged podcasts.
Niels Kaastrup-Larsen: Before we go into all of these details about your company, where it is today, I’d really like if you could take us back to the beginning, and tell us your story and what let you to take this path. Feel free to go back as far as you want – what were you like as a teenager, or whatever you feel like sharing with us today.
Anders Lindell: Fantastic Niels. Well, we shouldn’t go so far back as my teens; that would be far too much detail for this particular conversation. I think it’s material for my future development and also the ordinance of this firm that I actually started out as an engineer. I went to Technical University in Sweden and focused on control theory and mathematics. Indeed, my first work or job immediately after university was actually selling and designing, building, and implementing control systems for paper and pulp mills. Obviously, this is a very structured process. One has to account for various inputs and data given by a large number of various sensors. One has to respond or programme the system to respond in a repeatable and controllable fashion. So I think this is actually quite relevant.
Following that, I further educated myself in the field of finance, and then I started up a career at a fixed income trading house called JP Bank, a domestic Swedish bank at the time, and now we’re in 1993. Here I started out as an analyst and basically spent my first year there analyzing commercial paper programmes for various corporate (interests) that the bank represented on the market. Moving on, I moved to the position of economist. This is basically very much focused on macroeconomics, and indeed, as this was a domestic house, 99% of the focus was actually Swedish government finances and political development. As I’m sure you’ll recall, those were quite interesting days following first in the early 1990s, with a famous attack by a certain George Soros on the Bank of England, and probably, globally a half-coordinated attack on Swedish currency into bank rates, moving as high as 500% in the late fall of 1992. Then we had our own crisis.
So these were very interesting times, obviously trading fixed income and Swedish government debt in the early 1990s. As the Swedish economy started improving in the mid-1990s, and basically throughout the 1990s, spreads to international markets – most notably the German market – started compressing quite a bit, which is obviously what our clientele had focused on, and hoped for. But at the same time as things started stabilizing, actually the fixed income market domestically in Sweden got a lot less interesting than it had been. So by 1997 or so, I actually started thinking, together with my co-founding partner who also worked at JP Bank at the time, we actually started thinking about a next move – what could we do together that would make sense going forward? Neither of us felt that this particular market was the place to stay around for another few years.
So early in 1998, and with a background of many of our clients being not only international hedge funds, but also a large number of more traditional asset managers – pension funds, life insurance companies, etc., most of them in Europe – we started thinking a little bit about our experiences in dealing with them, and one topic that came up over and over again was,from a pension fund perspective and a life insurance company perspective, what’s the most important decision that you have to make, as a long-term investor, to gain relevant returns on your funds? The asset allocation decision seemed to us to be central.
Obviously there are two parts to this. Most of these funds then and now have a strategic long-term asset allocation, where they set targets for any number of years – three to five years typically – how are we going to be invested and what asset classes are we going to be invested in? But there’s also a shorter-term quite interesting decision that is being made frequently by these funds, and that is how do we deal with tactical deviations from these long-term strategic targets? You can end up in a situation where you deviate intentionally, or you can end up there because you have let markets basically push you in that direction. So irrespective of what has actually taken you to that point, how do you deal with the situation? Do you want to continue to deviate from your benchmark and allow that tactical debt, whether intentional or unintentional, to continue to play out; or do you want to reduce or indeed even increase that positioning relative to the benchmark? So this tactical asset allocation decision is immensely important.
Now, at the time most pensions and lifers, I dare say at least in Europe, made these decisions in a very traditional way that obviously had made a lot of sense up until then. Obviously they were also exposed to the analytical community and economist community in changing their views and revising based on data points, so there’s a lot of noise going into this process. Another thing one has to bear in mind is that most of the decisions made by these committees, or other organs making those decisions were typically relatively modest in size. People, based on this information, knowing full well that it’s noisy, and it’s hard to analyze, rarely dared to make the bigger bolder bets that would actually make the needle turn at the bottom line. So perhaps I’m exaggerating a bit, but the typical outcome of this would be to change your 60/40 stock bond exposure to 58/42. This may or may not have been right. You may not have had the solid background to do it, but irrespective of which, it’s a long and cumbersome exercise that rarely contributed a whole lot to the bottom line.
What we had also observed, was that over in the US a few firms had already started helping pension fund and long-term institutional investors with these decisions by way of providing basically an investment service to help them make more informed decisions based on a solid set of information, solid methods, and to really do this in a repeatable fashion. So this is really what triggered us to start up this firm. In early 1998 we sat down and said, let’s try and do this to help, mostly European pensions and lifers with their tactical asset allocation. So this is really the starting point of the firm.
NKL: Now I think for today’s talk we’re going to be focusing on the Systematic Global Macro programme, but perhaps you could just mention the programmes that you run today, and when they started, and what kind of assets you run in each of them?
AL: Sure. Basically we only have two programmes here at IPM; one is the systematic macro programme, which is a systematic global macro hedge fund, and the other one is an equally systematic but long-only equity programme. In the macro box, we are currently managing about $3 billion, and in equities about $4 billion. However, the macro programme can also be run in slightly different forms, so you can carve out, for example, the currency programme and trade that alone; you can carve out, and people do carve out currencies and fixed income and trade that as a separate strategy. They’re all part of the broader programme. All we do when we carve something out is we simply turn off the other processes. The macro programme has been trading in account format basically since 2002. Our pool vehicle started in 2005; that was a currency vehicle, then in 2006 the full macro programme. Both are domiciled on the Caymans. The equity programme has been running since early 2006.
NKL: Now before we jump into the first topic more specifically to IPM, I want to ask you… you mentioned the traditional 60/40 bond stock, or stock bond, depending on whether you were European or whether you were a US institution – they seem to have a little bit of a different asset allocation, as far as I remember. The world has changed in the last 10, 20, 30 years of course, and I just wonder, from a really big picture point of view, how do you see them dealing with this asset allocation, not from what you do, but from what they do and how they may interact with firms like you. Are they becoming more open, so it’s not just 48/52 that they changed to or whatever it might be, are they starting to take bolder decisions in their own asset allocation?
AL: There’s a whole range of answers to that. I think, generally, the answer will be yes, on sort of a global basis. I think if you look at US institutional investors, they probably come further than their European counterparts in allocating very significant parts of the risk budgets to folks like ourselves, and generally hedge funds. Whereas, in Europe, finding a long-term institution having a hedge fund allocation exceeding 3%, 4%, 5% would be unusual. Finding the same in the US would be the norm, being north even up to 10%, 15% that would be the norm. But then you have, obviously, various examples. If you look at your own native country and the big pension fund there, ATP, they instituted, I believe about 10 years ago, a radically different asset allocation structure from the traditional, where they basically, and you can correct me if I’m wrong here, but basically what they do is they sort of equal weight their risk budget across 10, 15 different asset classes ranging from infrastructure to traditional markets.
So people are doing a large number of different things, but I think the general observation holds true that North Americans, generally speaking, are much more into seeking alternative sources and allocating significant parts of the risk budget to those alternative sources of returns, than Europeans are. Obviously this also has something to do with the state, generally, of the pension systems.
NKL: You mentioned roughly $7 billion under management today. I want to talk just a little bit about the organizational structure of IPM. Running a big company like that, how have you decided to organize it, and what things, if anything, are you able to outsource today in terms of taking advantage of technology, or specialized firms helping out, and just tell me a little bit about your organization as it stands today.
AL: I think the first point to note here, by way of background is to note that we are 100% systematic, and by that we mean that everything that we do inside these two programmes is actually coded into software code. That’s sort of a starting point, but it’s also very important, because with that structure we can do things, and we can scale the organization, or the output from the organization completely differently from people that act more exclusively discretionary.
So what we have at IPM, at the heart of IPM, is a research team comprising about 10 people, supported by another four systems developers. Next to them, out here on the trading floor we have three traders and a couple of guys on risk, and this is at the heart. So their daily task is really running and maintaining the research team I’m talking about. Obviously they are also the people hitting the button on a daily basis to generate the trades and generally supervising that, but that’s in relative terms, a small part of the daily work, typically. So that’s the main part of the machine.
Supporting them we have our own back, or rather more middle office, that does the traditional things – they check for risk and they check positions and they reconcile with counterparties and clients, but that’s actually only four people. The remainder of the firm, then is traditional functions, legal, compliance, IT (as opposed to systems development IT would be infrastructure), business development, and key account management, or as we refer to them as investment strategists. But it’s really… the organization is really designed with us being 100% systematic in mind. Really, to compare with some, we are probably organized pretty much like your average CTA.
NKL: In terms of the strategy of the years, before we dive into the strategy itself, what would you say have been the main upgrades, or the evolution of the strategy over the years? I understand the point about research and finding new ways of improving existing models, but is there actually something that you would say model decayed, to a point where you would say a model doesn’t work anymore?
AL: I think, generally speaking, this is again a long-term evolutionary process, rather than sort of big changes, short-term. Typically we would introduce two, maybe three changes to the model on an annual basis. Those could be additions of new risk factors or sub-models in a more general lingo. They could be new risk analytics, methods being introduced into the system. They could indeed be entire new models or asset classes. We just started trading last year emerging market currencies. We haven’t done that previously.
Historically though, I would say probably one of the biggest changes was when, four year ago, almost to the day, we relaxed the traditional tactical asset allocation restriction, namely for every dollar you’re long something, you have to be a similar amount short. Which really comes from the classic CTA world where if you overweight something, you have to underweight something else. We still retain this in all of our relative models where we trade equity markets against each other or bond markets against each other and currency markets, but what was previously referred to as the global asset class decision, where you went long $100 of global equities based on some composite, then you had to go short $100-worth of bonds – that one we relaxed four years ago. So today, we can actually be, in that dimension, long both global stocks and bonds, or short both.
NKL: So how would you describe the strategy, the objective and the environment it has been designed to work well in?
AL: This is a strategy that we can run at pretty much any risk level, and we can always discuss what we mean by risk here, but let’s say that we can run this at any level of expected volatility in the programme. Most investors, and as we cater only to institutions they would start and we would start feeling uncomfortable north of 20%, 25% expected. Your typical institution, really what they need to get out of something like this is 10% net or there about. So what we’ve done is that we’ve said let’s find the sweet spot for the strategy, at sort of 10% net per annum to the client. We need to run this at a particular risk level, measured as acceptable volatility, to make sure that we deliver in the end. And indeed, that’s pretty much where we are over the course of all of the published track record, which is based on one of our investment funds. We’re at 9.7%, 9.8%.
With a strategy like this, this is not accomplished by running a Sharpe of 1.5 or 2. We don’t think this is realistic for this particular type of strategy, given that we’re always in the market in all of the instruments that we trade on a large number of themes. There is a significant risk of drawdowns on any different dimension, anything north of a 1 over the longer period doesn’t really sound very plausible. A reasonable target for a strategy like this and this goes for most systematic managers, would be just south of 1 in terms of Sharpe.
NKL: What kind of themes are you focusing on, and how have you really structured taking so much information that you can obviously get when you are talking about the globe as your playing field, but structuring that into a systematic trading programme?
AL: One way of looking at this is to say that any global macro trader – and let’s forget about systematic or discretionary for the time being – any global macro trader would be interested, based on sound analysis, sound principles, identifying price discrepancies, based on either something that is supposed to mean revert over time but is now moving south and you are taking a position based on it’s going to be moving north at some point. Or you are taking a position based on you shorting a risk premium that seems to be excessively compressed at the current time, or you are going long in an opportunity. So you try to use sound analysis to identify these situations, and what really differentiates us from a discretionary trader would be to say that we have identified a large number of such opportunities and they’re coded in the form of risk factors. We’re always in the market for all of those, but to varying degrees. So we’re actually going to take position directly proportional to identified opportunity.
Whereas your discretionary manager, he would probably distill this and try and find one to three, maybe four of the strongest themes and then bet everything there, and then he’d capture his profits, and then move to the next area. Our model is for each of the markets that we trade, for each of the themes that we trade, pretty much in the market at all times. This is at the heart of the model. We believe in mean reversion for certain phenomena, we believe in trading obvious risk premia. You can go long or short depending on where you find yourself, etc.
On the one axis you have five dimensions, and those would be the sub-models that we trade, so again we trade relative models of emerging market currencies, developed currencies, equities, and fixed income. Then we have this, previously known as asset class, but directional components, so that’s five dimensions on one axis. The other axis you would find the types of phenomena that we are trying to pick up on, and this is common for all of the five sub-models. We are trying to identify valuation drivers. So, for example, you could probably think that something like purchasing power parity is a sound valuation methodology for currencies, so that would go in the value box and similar type factors for other asset classes. The next dimension would be risk premia. Well, obviously risk premia long-term or bonds, in currencies you would probably be talking, you may say that the carry is a risk premium, and indeed I think it is. So things like that will go into risk premia. We’re trading both of those.
Thirdly we find macroeconomic factors. As aim to apply monies expands or contracts, as government debt issuance expands or contracts, as trade balances shift between countries, you could take a position on that. Finally, we have a fourth box that we refer to as market dynamics, which is really our way of saying, here is the box where we put stuff that is really idiosyncratic to each of the asset classes that we trade. So, we would look at the government bond curve, the deal curve or term structure if you like; that would be something that we focus on in our efforts in bond markets. It doesn’t really apply the same way when you trade currencies. We are trying to forecast things like investment flows, cross-board investment flows for currencies, which is probably much less relevant for our equity trading. So, five sub-models, sharing the common four themes, and all of them are equipped with factors identifying value, risk premia, macro developments, and then the fourth box, the market dynamics.
NKL: I’m trying to visualize it just how you do these things? So is it something along the lines where you would say, OK let’s look at inflation. Say inflation is now going up in the US. So if that happens, we have a model that looks at this, and it gives a certain score into a bigger pot, and then you have other things that give other models that look at different things, and they all give a little bit of a score. How do you put all these things together?
AL: That’s not a bad way to phrase it. Actually, let me try to rephrase it back and paraphrase it a little bit. So the first thing to know is that when we model, we model everything in relation to global composites. This is really to get rid of otherwise the need to model absolute developments. So when we are looking at government bonds, or government bond futures to be precise, we are actually looking at how the prices and the factors driving those bonds behave in relation to the global basket of such government bond futures – the same for equity index futures; the same for currencies.
We’re not looking, and this is probably enlightening, we’re not looking at the traded currency pairs, which is what most people would be looking at, the dollar/yen rate or the euro/dollar or whatever, but we are actually looking at the yen in isolation against a global basket of currencies. We are forming synthetic instruments when we model. Additionally, everything is modeled in relation to its own history, and risk-adjusted. The fancier way of saying it would be we’re looking at normalized risk-adjusted and really what comes out of this is ZED scores – number of standard deviations away from equilibrium – long-term equilibrium. So when we are looking at, for example, something as simple as 10-year government bond rates as an indication for value in bond markets, we’re taking, for example, the JGB rate and we’re comparing that to this global composite. But we’re not taking it outright. We’re taking its risk-adjusted deviation from where JGBs normally trade, and that’s what we are comparing. So it wouldn’t be meaningful to take the JGBs and compare them to T-Notes because they would always be expensive.
NKL: How many markets do you trade today?
AL: Currently about 40 markets.
NKL: Am I right in saying that you actually don’t trade anything over the counter?
AL: Well, currencies would be over the counter for the most part, but otherwise exchange rate, government bond futures, exchange rate equity index futures, and then currency forwards.
NKL: And it’s all financials, so no commodities at this stage?
AL: That’s correct, which is not to say that we’re not going to start trading commodities at some point in the future. At the current time, we don’t.
NKL: Are there any of the types of strategies that you use within each theme that you could try and visualize for us. I know you’ve talked a little bit about it. For example, I’d like to talk a little bit about carry. I’m no expert, but obviously carry has been quite a big source of return for many people for a while. My last guest was quite concerned about some of these trades that are being put on (in his opinion by large asset managers) to compensate for not making so much money in the directional arena. If we look at currencies, at least developed currencies, volatility has gone down dramatically in recent time. So, maybe talk a little bit about how you see it. If you are going to drill down in your carry models, how do they work, what do they look for in your world?
AL: Well I think that, as a starting point, I don’t know who said that first, but carry trading is really about picking pennies in front of a steam roller. The greedier you get, the closer to the steam roller you aregoing to be. It more often than not ends in tears. One typical example of that would be the carry game that started in 2006 and continued through 2007 and into 2008 involving, obviously, the Japanese yen, and that ended (I suppose for many of the people that continued pushing that trade all the way into first quarter of 2008), that certainly ended in tears for them. What our model did was go against this to a very large extent. So we paid a little bit on our positioning in yen in 2007, late 2006, all the way through 2007 up until January, where the first bank came in March of 2008, when the second instance happened, when we profited quite a bit on the collapse of that carry model if you like. That’s a general observation.
More specifically to our models, there’s nothing fancy in what we do on carry. Obviously we have our own models trying to identify both carry in the traditional sense of the word, and changes in carry and a few other things related to that. The most notable point with our model, again, is that it’s relative. We’re not interested in the absolute level of carry per market. We’re interested in the deviation from the longer-term average carry by a particular market and risk-adjusted. So, just because we have a situation where statically over time Kiwi in Aussie has probably delivered better than most other markets over the past 10 years or 15 years, whatever, that doesn’t mean in our books that we, necessarily from a carry perspective always have to be long in those markets, because we are interested in how much does carry in Australia, today, differ from what it has been on average over time. That makes a big difference when you trade like that, or then try to evaluate these sorts of absolute opportunities.
NKL: How many of these “models” would you say that there are combined in the whole strategy, because it sounds like there are a lot of moving parts that can influence each of the markets and the themes?
AL: There are a little over 40 different risk factors or sub-models that go into this.
NKL: And given all of your experience, just out of curiosity, is there any indicator when you look at the global economy that you like better than others that you think is more reliable than others?
AL: It’s a matter of horizon. Personally, I do like traditional valuation metrics. I think ultimately that they are reliable. They make a whole lot of sense from a fundamental and theoretical perspective, but it can take significant time for them to play out.
NKL: How frequently do you run the model? Because obviously some of these factors don’t change every day, maybe let alone every week. How does it actually work when running the model on an overall basis?
AL: It is actually run on a daily basis, and the model is re-estimated on a daily basis because what goes in… there might be an odd price here, there might be an inflation number from there. So, if you look at the world today and all of those markets that we trade, it’s likely there’s going to be a couple of new inputs at least, per week, even on a daily basis quite often. Obviously some periods are going to be more intense than others, but also as market prices move around, the model will observe this and try and adjust positions accordingly. What the model comes out with is the side exposures per market on a daily basis, and if the market has pushed us in a direction where exposure has grown or contracted, relative to what the model wants it to be, then some level of corrections will have to be done. So there’s a little bit of trading actually going on, on a daily basis, but compared to most other managers in the traditional systematic space, I dare say our trading intensity and portfolio turnover is very, very low.
NKL: What’s the most difficult sector to trade based on your method do you find? We all know equities were tough recently in particular because of the uncertainty of the US default risk. Is that a particularly difficult sector to trade when these things happen because they react quickly?
AL: Yes. Equities in general, in particular relative equities, with our way of modeling, is quite difficult. Currency markets; I wouldn’t say they’re easy, but a little bit more straight-forward when you get the responses that you are striving to get.
NKL: What do you think drives these relationships that you profit from? Is it more economic sorts of changes, or is it investor psychology, or is it just human behaviour? I mean, you know, as I mentioned before, things go in cycles, and we tend to come back to the mean. What have you observed over the years?
AL: I would be inclined to say all of the above. Actually I think it’s a combination of different things. One way to exemplify it would be to step back to the fall of 2012 when they started talking about the introduction of the quantitative easing programme down in Japan. Obviously a lot of people were pretty quick to respond to that. The likelier it became to them, based on their analysis of the political and central bank situation, people started running ahead of themselves and taking positions on this. The running ahead of themselves probably is the wrong expression there because a lot of people do that with significant profit. As that continued, some of the people that were earlier in probably started taking their profits and saying that this has run its course, whereas others once it has trickled down to the more general investing community, they continued in that same direction.
Whereas our models, they wouldn’t hear the open mouth operations by Bank of Japan. They would only note this when it starts materializing in our data. Which means, here’s a trend in markets established by people trying to anticipate what the Bank of Japan is going to do, and how the currency market is going to react, how the fixed income market will react, and certainly how the equity market is going to react to this, and what corporate profit is going to be like in Japan going forward. So based on that (and this is something that typically happens) people extend what initially seems to be a reasonable trend, they extend them way too far. And then when the final realization comes through that, hey, the corporate sector in Japan isn’t going to be as profitable as they could have been based on these, and the yen isn’t going to 180 against the dollar, or whatever number they had targeted. Then you have a reversal of that, and this is typically what a model like ours would try and profit from. So in general investing behaviour, people trying to not unfollow the trend, but people generally overextend it far beyond the point it has contact with underlying fundamentals. That is one major point.
Another major point that I want to mention is a time horizon or investment horizon. People may trade in or out, or create trends and prices may deviate. You know that’s noise of the shorter time spans. We’re trying to step back and avoid that by explicitly trading and holding positions a longer period of time.
NKL: A couple of things that come to mind when you say that. In one sense, it actually is a great injustice, not just to your strategy, but to many strategies. (The injustice) you could say, is for investors to look at these things on a monthly basis and kind of judge you based on what you did last month. Because what you’re really saying is that part of the success is having the ability to take a much longer view and not be concerned about an adverse move, over the next few weeks, because you know it’s going to work out, maybe over the next few months. But I also wanted to ask you about position sizing. Is that the secret sauce, do you think? Meaning that a lot of the themes that you end up in, and I imagine a lot of the strategies and positions you end up in wouldbe somewhat similar to other global macro managers. But I do note that you’ve done quite well compared to your peers, and do you think position sizing is part of the success, that you simply manage the risk differently maybe?
AL: Yeah, I think there are several parts to it and several moving parts here. But what I tend to say to clients that want to evaluate what it is that we are doing, nine out of 10 clients, for good measure I’ll throw in the consulting community here too, they are spending way too much time focusing on what we refer to as those risk factors. They want to know them all, and they’re looking at purchasing power parity and they want to understand how exactly we built that particular factor while noting that, academically speaking, PPP has a mean reversion time of 50 years – clearly not investable. And they’re spending all of their time trying to understand, do you have the right alpha sources? Are they advanced enough? Are they sophisticated? Do they differ from your competition? And, you know, that guy over there, they also have PPP factors. Well yeah, those alpha sources, risk factors, yes they’re important, obviously they’re very important, but risk and portfolio construction respectively contribute to the same degree to defining the results.
I would say spend one-third of your time trying to understand what we do on the alpha side. The other third you should spend on risk management, and the other third you should spend on understanding our portfolio construction. Because it is a combination that yields the final result, and in each of those areas I would say you will find differences between what we do and what a lot of other people do. Some people may have almost the identical set-up when it comes to alpha factors, but in all likelihood, and to my knowledge, they would differ in either or in both the portfolio construction and risk management, etc., etc. As you mentioned, position sizing, that’s one area where we do things differently from most of the investing community, when we say that we want positions that are principally proportional to the perceived opportunity.
Another area where we’re different, clearly, is the way we construct the final portfolio by way of transitioning signals into positions. Most people would use some form of more or less traditional optimization scheme where they are in the middle, and they would, for the most part, make use of the correlation structure when they construct their portfolio. And here’s where we differ. We don’t use the correlation structure, and to exemplify: if you have positive views on both the Australian and New Zealand dollar – and we know they’re strongly correlated – it is relatively likely that any traditional or almost traditional optimization scheme would like you to short one and to go long on the other due to the correlation structure.
This is from the method wrong, we believe, for a couple of reasons: A) it will have a positive view on two assets, two currencies. We want to be long both, because we’re not forecasting the correlation structure, and it would be fundamentally against our principles to short something we have a positive view on. B) That optimizer will, relatively often, push you into what is referred to as corner solutions, right? That means as something then changes over the next week or month; it is not unlikely to want to push you in the direction of another corner, and that will – if you take position based on that then – that will cause a lot of trading costs for you. You will change your positions quite a bit. Finally, if you do this shorting one going long on the other, your base concentration is going to increase quite a bit, and neither of these three are the sort of trades that we are very comfortable with. So indeed here, we don’t use correlation structure. We transition signals into positions, then when it comes to the risk overlay, then we’re using the correlation structure.
NKL: Have you ever had to override the models?
AL: Not the actual models. We wouldn’t override the models in the sense that we would alter the positioning or such. What we can do, and we have done on occasion, is that we can de-lever the entire programme, and this is a measure that we would take after obviously careful consideration. It would basically be based on a view on the functionality of markets. So the best example here would be September, October 2008, when we de-levered quite a lot.
NKL: Now the next topic that I have is research, and now generally investors, they want managers to innovate and come up with new things and evolve, but they don’t really want them to change at the same time, which is sort of quite difficult. How do you balance these two things and what does research look like inside of IPM?
AL: Well, I touched upon our frequency of changes just briefly earlier. Obviously we have to evolve the model. It has to improve. We have to add new components, but there’s nothing built into it saying that we absolutely have to change just for the sake of it. What we are trying to do is to add where we find weaknesses; we have to add where we find interesting opportunities all within the same set-up. So what we wouldn’t alter is the fundamental way of doing things. We wouldn’t take – to take something ridiculously extreme – we wouldn’t take a trend-following component and just add it, because it’s outside of our self-imposed limitations on what we do philosophically.
So there’s going to be a few changes over time, but it’s a gradual development and this really reflects the research process also, because the research process here is very, very thorough. It would be rare to find something newly introduced that hasn’t been in the works for six to 18 months – at least six. Just finding the right data sets, scrubbing all of the data. By the way, it’s starting even earlier, identifying what it is out there we think can fix whatever problem we have identified, coming up with a couple of ideas. Identifying the prior assumptions that we want to lock away and measure the ultimate performance of this new thing against, to verify that it is actually doing what it was supposed to be doing, rather than just being a sort of general source of alpha. Then finding data that is reliable enough long-term. We can’t build something based on three or four years’ worth of data. It’s got to be a long-term history, and it’s got to be clean data. Then building the model, testing it against all the other factors, all of this is a very time-consuming process.
NKL: Now you have obviously been in many, many meetings and due diligence and phone calls, and had thousands of questions over the years, but what do you think is the question that investors should be asking you, but they never really do? Because you alluded to, earlier, that they all seem to be very focused on something, trying to understand things that actually you didn’t feel were so important, but what is really important for investors that they should be focusing and understanding do you think?
AL: That’s a tricky one. Again, over the past 15, 16 years I’ve had a very large number of questions. I honestly think you’re touching really upon the most important part. I think people should really focus on trying to understand the components. You always get the questions, so how do you differ from your competitors? Well the competitors don’t tell me what they do, so you’re probably in a better position to tell me, for starters. I can’t single out any one area, but you should take a more holistic view and look at the whole. How do all of these components that we’re talking about contribute? How are they put together as a whole? Because what we’re talking about is a system thatis sort of purpose-built to do just this, and all components have been tailored to fit this particular structure. We could principally have built these same alpha factors but traded instruments down the long. Why don’t we do that? So how do you put all the pieces together? It’s like a puzzle: you may have excellent, good-looking pieces, shiny and all of that, but if they don’t fit together, if they’re not designed to fit together, than irrespective of how good the alpha sources are they’re not going to deliver. So I think that’s really critical. How do you put all of the pieces together?
NKL: What’s the most difficult thing that you have to deal with as a hedge fund manager today, do you think?
AL: There’s any number of things you can mention, ranging from the ever-changing regulatory environment, which is that sort of technical, that’s principally a problem that you can solve by way of throwing money at it. What you cannot solve though, that is related to regulatory, but that is also related to perception and other things that are on the investor side, where – if you are looking at the Dutch market as an example – where investors have, I wouldn’t necessarily say been squeezed out of the hedge fund market by the regulator, but close to it, very close to it. That’s very hard; how can we be without? We simply have to go to another market, and we have to start afresh in building trust and confidence with investors in other markets.
NKL: I ask my guests whether there is a fun fact that they can share about themselves. Something that even people who might know you don’t really know about you, is there anything that springs to mind?
AL: Well, I don’t know how fun it is but, I tend to think about, probably about in excess of probably 75% of my valuable insights and ideas; they’re actually generated between the time I wake up each morning and the time I step out of the shower. I actually read a book on a related topic just a couple of years ago, and it turns out the human brain actually works a bit much that way, but some people prefer evening times, I strongly prefer morning times. So I even have materials in the bathroom to take notes.
AL: How fun it is I don’t know.
NKL: Anders, thank you so much for all of your time. It’s been fantastic conversation. I really appreciate your transparency and your willingness to share these insights and views on your strategy and your firm, and some personal stuff as well.
AL: Thank you very much Niels. It’s been a pleasure. It’s rare to have such a conversation. An interview with such insights into the business in general, so I highly appreciate it. Thank you.
Listen to the full conversation here.
Top Traders Unplugged is a podcast created for the investor, trader or research analyst. As in the Market Wizard books, each week in Top Traders Unplugged Niels Kaastrup-Larsen talks to a current successful hedge fund manager or commodity trading adviser who shares his or her experiences, successes, and failures. Hear their views on investing, portfolio construction, risk management, research, how to handle the emotional roller coaster and what it takes to become successful and a market wizard. For more information, visit www.toptradersunplugged.com.