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TTU31: Why Investors Should Not be Worried ft. Marc Malek of Conquest Capital Group – 1of2
15th September 2014 • Top Traders Unplugged • Niels Kaastrup-Larsen
00:00:00 01:13:10

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This guest had a different path that eventually led to owning a hedgefund in New York. Marc Malek got a grant from NASA to study how different armored tank positions would lead to winning results on the battlefield. Traveling to Wisconsin to begin his research, his advisor steered him to do a similar project on stocks, bonds, and equities instead. He went on to work for UBS and finally founded his own firm, Conquest Capital Group. His story will fascinate and inspire you.

Thanks for listening and please welcome our next guest, Marc Malek.

In This Episode, You’ll Learn:

  • The story of how Marc became interested in the financial markets after a university project, a bit unexpectedly.
  • About Marc’s upbringing in Beirut, Lebanon.
  • How his studies at Caltech in neural networks and decision support systems eventually led him to the stock market.
  • About his grant from NASA to research the position of tanks.
  • His job offer from Oracle that he turned down.
  • About his first job out of university at Salomon Brothers and why he left after one year.
  • How Marc got hired at UBS and moved to Europe and then Asia during his time with the company.
  • Marc’s departure from UBS and how he started Conquest Capital Group.
  • How trader’s thought processes are turned into trading models.
  • Why models are not black boxes and why investors should not be worried.
  • The history of trend following and the old systematic approach.
  • How markets move for alpha and beta reasons.
  • About “turtle strategies” vs “trend following 2.0”.
  • How Marc’s strategies and models have evolved over time.
  • About his product Conquest Macro and the two mandates that the product has.
  • How his product makes the bulk of its return during periods of risk aversion and high volatility.
  • How his firm developed a risk index in a time before anyone was doing them.

Resources & Links Mentioned in this Episode:

  • See Episodes 13 and 14 for more discussion on “Turtle Strategies”.

Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.

Follow Marc Malek on Linkedin.

IT’s TRUE 👀 – most CIO’s read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.

And you can get a free copy of my latest book “The Many Flavors of Trend Following” here.

Learn more about the Trend Barometer here.

Send your questions to info@toptradersunplugged.com

And please share this episode with a like-minded friend and leave an honest rating & review on iTunes so more people can discover the podcast.

Transcripts

Niels

Marc, thank you so much for being with us today. I really appreciate you taking the time.

Marc

Thank you, Niels.

Niels

Now as I was preparing for our conversation today, I noticed a couple of interesting things that I'm sure we'll have a chance to discuss but just to give you some of my initial observations, it strikes me that you have quite a large number of different strategies that you offer both in the alpha strategy space, but also some alternative beta strategies, which to me suggests that you have diversified your business into more of a solution oriented firm rather than being more of a standard type of alternative investment company. The other thing that I noticed was that you began your career working for some of the very large institutions in the world, yet you chose the entrepreneurial path. I can imagine that that's quite a change in itself, and I'd love to hear more about that. Then I also noticed that you developed this risk aversion index, which I find very interesting and would certainly like to spend a bit of time discussing these things. I'm excited about all of these topics that we can talk about, but before we go into that, and before we find out where your company is today, I would really love for you to take us all the way back to the beginning of your story and tell us about what led you to take this path in life, and really feel free to go back as far as you want, Marc.

Marc

I graduated from Cal Tech in:

So, I traveled over a summer from Cal Tech to Wisconsin, to Madison, to do the research for them. We started getting to know each other, and he finds out I'm from Lebanon and says something like, you know you just came from a battlefield; do you really want to spend the whole term looking at tanks in a battlefield? Why don't you apply the grant to the optimization problem of something like stocks, bonds, and currencies. Mathematically it's the same project. Whether you are using green tanks and blue tanks and yellow tanks or stocks, bonds, and commodities or FX, it's really the same sort of mathematical work that needs to be done. So I said fine.

I think at the time (this is:

hedge fund back in the early:

d that until about the end of:

Niels

Did you know what you were going to do at that stage? Did you know from day one this is exactly how I'm going to do it, or was it more like let me...

Marc

my days at the hedge fund in:

Throughout this whole process I had developed and used systematic models to trade in the market. I've always had the philosophy that if you look at basically any successful discretionary trader, they don't wake up randomly one day and decide to put on a position because they had a dream...or...maybe some do, but usually not the successful ones. Usually it's a thought process that leads you to put on a trade. You look for certain things to happen, either technically or fundamentally, when these conditions are met you put a trade on. When you put that trade on, you know ahead of time how much you are willing to risk on that trade, what position size you need to have for that risk, and where the stop on it should be, and where you take profit on it. When you list all these things one after the other, it's no different than the algorithms that go into creating a trading model. So I think with a lot of the very successful traders, if they had the mathematical and computer background they can very easily turn a lot of that thought process into an algorithm, and once you have it as an algorithm, then it's very easy to program it. At that point a computer is a much more efficient tool to execute your own view for a variety of reasons, stretching from taking the emotions out of it, to efficiency of execution, to giving you the flexibility of trading many more markets that you cannot follow yourself, but a computer can do it in a much easier way and so on. So I always had a very systematic approach from my days at UBS, so on day one, when I left and went on my own. I had a suite of ideas that I knew I wanted to start with and work on. Also when I started I had a partner at the time and he also came with his own ideas and basically our product was a combination of a lot of brainstorming through the background that I brought and the background that he brought.

Niels

Let me just ask you a spur of the moment question here. When you explain why people, if they had the knowledge of programming or putting together systems rather than trying to just do it as a discretionary trader, even though they might use internal rules in their own mind, which obviously is a very logical way of doing things. Why do you think it is so difficult for systematic traders to explain to investors that they shouldn't be worried about the fact that we use computers to do this and in fact that isn't a black box?

Marc

Well, that's a very good question. I don't think it's difficult for a systematic trader to explain their strategy. I think there are a lot of systematic traders that deliberately try to be very opaque about what they do for a variety of reasons: one of them is they don't want to give away the "secret sauce" of what they are doing, and I think to a large extend in the old school trend following world, primarily it's to keep how simple it is from getting out and making people realize why on earth am I paying $2 and $20 for that?

Niels

That's from the manager's side, Marc. On the other hand, I would argue that investors, they have a difficulty with accepting these systems, and they try to make them, in some ways, more complicated by calling them a black box. So I think that you are right about the manager side. That's probably one reason, but I think also the investor side in a bit to blame here, that they don't embrace the fact that technology... if you board a plane, you know that the pilots not going to sit there for 12 hours flying the plane manually. He's going to use an autopilot, and I think most people would prefer he uses an autopilot.

Marc

I think that is changing over time. I think that there is a wider acceptance of quantitative strategies now than there were before. To use your example of the plane, passengers know that the pilot is relying on a lot of analytics and tools to fly that plane, but the most important thing is that they know there is a pilot; that if something goes wrong the pilot can turn off whatever machines are there and just fly the plane. I think with systematic strategies and the way that a majority of them are explained and portrayed, that people are fond of saying we're 100% systematic, and when you say 100% of anything it tends to make people nervous.

fund. Historically, from the:

When you take a trend follower... it's a very simple tool that I just gave you. A lot of people might want to get into the space, but they don't really want to question why trends happen, they just know that this exists and that if you do this you are going to make money. They start a fund, and they go out and start trying to get investors. Investors, also at the same time, they can sit there and say well, this black box, they don't even know how it works, but the explanation that the manager gives at that point is look, we know that trends happen in the world and our models take advantage of trends, and they make money when there are trends, and historically we've made a lot of money, and so on. In that kind of process from the manager side as well as from the client side, there is a lot of uncertainty. So the client will say, why do trends happen? How do I know if they happen before they are going to happen again?

When you're having that conversation with a manager that is very orthodox in their approach, look, I don't know why trends happen, I don't care why they happen, they happen, and that's how we take advantage of them, which is really the way a lot of people answered these questions. It creates lack of understanding and people... if you're not comfortable with an investment, it's going to be very difficult for you to get to invest. What ended up happening for a lot of CTA investors is that at the initial pitch they passed, then they saw CTAs making a lot of money. They'd go through a period where they would make 40%, 50% for a year or two. So they get dragged into investing, kicking and screaming, at which point their investing at the top in these strategies, which, by definition, are very cyclical, only to see that investment plummet in value because they pretty much bought at the top of the cycle. Given that they didn't understand exactly what they were buying, they don't have a conviction to hold as it's coming down. They basically get out at the bottom, and we saw this cycle happen so many times with investors who would always buy the trend followers at the top and sell it at the bottom. My cynical side says that, over time, investors as a group have lost money investing in CTAs just because of the timing.

Using that same approach, the way I would explain it, is that first of all looking at CTAs, even though we trade 50, 60 different markets, for any decent sized CTA our bread and butter comes from the financials. You're talking global fixed income. You're talking currencies. You're talking equities. Commodities are a nice diversifier, but that's pretty much what it is, a diversifier. If you look at any economic region: US, Europe, Japan, Asia, every one of those regions has its own business cycle. It has a certain either growth or declining economy and the business cycle around, let's say it's a growing economy, so you have an upwards sloping line, and the business cycle oscillates around that line.

In each one of those economic regions, the job of the central bank is to minimize the amplitude of that business cycle relative to the slope of the line. If a central bank is hugely successful, then they keep that economy on that slope of the growth - there is no volatility. Obviously, no one is that successful. What happens is as the business cycle starts going quite a bit above that slope of the line, central banks come in and try to increase interest rates to slow things down. When you are at the bottom, below your growth trend, they come and lower interest rates to essentially bring you back up. When you look at the way a central bank changes interest rate policy, it's not plus 50, minus 25, plus 75, minus 10, plus 5, minus 10, plus 5, that's not how they do it. They go +, +, +, +, stop. They stop for a while. Then they go -, -, -, stop. Then they stop for a while.

Now think of what happens whenever a central bank embarks on these cycles. Every time that they start with a plus, there is a wave that starts and propagates through all the financial markets. Changes in interest rate policies have effects on currency prices; they have effects on stock prices; they effect on bond prices. Now you start at the beginning of a trend. When they come again and they do another 25 or 50 and so on, now your trend is getting momentum, then it goes again and now the trend is getting more momentum, and then they stop and that's when your trend levels off and, in a perfect world, that's when your position reverses and you take it on the other side. Unfortunately, that would have been too easy if that's exactly how things work. What happens is for every market, every market moves for two reasons. It moves for alpha reasons, and it moves for beta reason. Alpha reason being conditions that are only relating to that market, and beta reason are the overall macro condition or interest rate policy and so on. What I described in the action of the central bank, that's really macro; that's beta. If in the absence of any alpha reason, then we'll get perfect trends and everything will work fine, except that things are not that easy. Every market again has its own alpha reason.

You get the best trends when both the beta and the alpha are pointing in the right direction. Let's think of what would make a perfect trend. Let's say you're looking at crude oil. We know that in a very hot economy an expanding economy there is a natural pressure on crude oil prices to go up as there is more business activity, more manufacturing, so that's an upwards pressure on crude oil prices. Let's say that in the middle of that happening you get a giant explosion in Saudi Arabia that causes crude oil to spike for a local reason - for supply reasons...then you get a real turbo boost to that move that was happening in crude oil and you get the perfect trend at that point, because you have both alpha and beta kicking in the same direction. When you get really bad trends, is when they are completely opposite, where the alpha and the beta are fighting with each other and you get these choppy markets where you are constantly getting stopped out, and back into position, and stopped out.

. What happened, in the early:

Niels

That's a very interesting observation, and I think, as I mentioned in the early part, it's one of the things that I'd love to discuss with you because you were certainly one of the early adopters on this. But here's my question about it, to some extent I feel that managers will have this alternative beta, or this CTA replicator to give investors very similar returns to these CTA indices, but at a lower price, and that's fine. There's nothing wrong with that. However, I feel that the CTA indices themselves, because the underlying CTAs have changed, and therefore I'm not so sure that these replicators today are very... I say it a little bit, but I'm not so sure that they track the CTA indices as well as they used to, simply for the fact that I think CTAs have changed. I think that's another thing that you have made observations about, and that is that managers today have migrated from being pure trend followers maybe to doing other types of strategies, in particular in the last few years, to compensate for maybe lack of trends in the usual sense. I don't know whether you think that's an issue, and maybe do these alternative beta strategies or CTA replicators, do they themselves actually need to be evolving and maybe you do them differently than you 10 years ago when you started?

Marc

nd following, until the early:

The old school is the ones that stuck to the Turtle approach. The new age guys changed things in a couple of ways. One of them is fairly technical, I'm not sure how much we want to go into it, but essentially it has something to do with the fact that basically, historically CTAs sized their position in any given market in a relationship that is inversely proportional to the volatility of the market that you traded, meaning that you want to take the same risk per trade, you want to risk the same amount in your crude oil trade as you did in your Euro dollar trade, but these two markets have vastly different volatilities, so you can't take the same dollar position size in them. What you do is you adjust it for volatility and then you take a vol adjusted position. What happened is the old school way of doing this is you had your position, you stuck to your position until that trend reversed and then you exited and you reversed and do the opposite side with the new position size at that point. One of the biggest criticisms for CTAs, historically, has been that CTAs have high volatility, large profit giveback, big potential drawdowns.

What eventually became the new school guys started thinking about the problem, they correctly realized that the volatility at the beginning of a trend is fairly low. As that trend matures and starts showing signs of weakness and reversion, volatility goes up significantly. Essentially they said, look, it doesn't make sense that we are taking a position at the beginning of a trend when volatility is low and therefore we would have relatively large position size, and holding this same position as the volatility in this trend itself is changing, which is causing us to have the higher vol on the portfolio. So they said, what if we do more frequent sampling of the volatility and adjust our positions accordingly? The thinking there is that you'll be taking profit on your position as the trend is developing and then by the time volatility spikes, and the trend reverses, you go through that reversal with a very small position and therefore minimize the effect on your portfolio, which is great except that it leaves one gap in the thinking. It automatically assumes that an increase in volatility is a precursor to a trend reversal and not a trend continuation.

e of that is what happened in:

w in CTAs starting in the mid:

The question is, are investors investing in CTAs as just an absolute return strategy, in which case they would go with the new age guys, but in which case I would argue that if you are investing in CTAs as an absolute return strategy then really you should go look elsewhere because there are other hedge fund strategies that do better on an absolute return basis. Or, are investors investing in CTAs as a portfolio tool that works very well against the rest of what they have in the portfolio? In which case CTAs really have to stick to what investors expectation is of their performance in various market conditions. To give you a practical example, when we noticed (it's a long answer to tie back to your question) whether CTAs are tracking or not or whether trackers should change or not. When we noticed this a few years ago, we went back to the ivy league endowment and presented the numbers and said look, this is what we think is happening, we think that you can have better risk-adjusted numbers from us, overall, if we add a long risk strategy to what we do, which we already had because in our other fund Conquest Macro, we have a very successful long risk strategy. It's a very easy thing for us to just turn it on for you in this one, and then the fund will switch from being a Turtle approach to being a new age approach. It's really not rocket science. It wasn't rocket science to do Turtles, and it's still not rocket science to do the new age approach. The answer from the endowment was a resounding no. They wanted us to stick to the Turtle approach precisely because they used us as a portfolio tool, and they said they have plenty of long risk in their portfolio. They think they are a better judge of what long risk strategy to be in than us with a genetic long risk strategy, so they wanted to retain that flexibility in what they do, but they wanted us to continue to deliver the characteristics of what their expectation is from traditional trend following, which is fine.

You mentioned that we have a lot of products. The reason we have a lot of products is... look, the way I think of portfolio construction is it's a two-step process. A portfolio is made of individual models. I think that we are very good at creating different models that do different things in various market conditions. Maybe it's from having three young children and spending a lot of time with Legos, but I think of these models as Lego pieces. You can take those Lego pieces and put them together to make a plane, then you can take it apart and put them together in a different way to make a submarine, or a car, or a house, as long as you have good solid Lego pieces you can build them to create any profile that you want, which is exactly what we do on a portfolio basis. I think we are very good at making these Lego pieces. They're very solid. They're good quality pieces, but the construction of them is really in how we want the portfolio to look: what characteristics, all that other stuff. If I think of our generic Turtle approach as one Lego piece - a big Lego piece, then I think of our long risk as another Lego piece. Each one individually can give you a very different risk profile. By putting them together now, we can go from a Turtle based trend following strategy to a new age absolute return trend following strategy.

Niels

Let me just say to the listeners who are listening, and may not know what we refer to when we say a Turtle strategy, if you go to episode 13 and 14, that's actually with Jerry Parker and he's probably the most successful Turtle, so if you want to hear the whole Turtle story go to episode 13 and 14.

I want to ask you, Marc, to go back and then take us from where you started realizing this and then how Conquest evolved as you were adding these strategies, what was your thinking behind adding this particular one as a separate offering, and so on and so forth, and before we leave your story as a whole, I think we need to go from the inception of Conquest to where we are today, before we dive into more of the specifics. I'd like for you to tell us that story, how the product range evolved in the last 10 years.

Marc

Absolutely. Look, all that stuff that we spoke about so far that was just almost like a side project for us. That has never been our bread and butter. That's something that started out of an intellectual exercise, which we had the luxury and privilege of being the first one to test it and bring it to market, and now you can see how many different people are adopting that approach.

ol environments. Periods like:

While those strategies, whether you do it in fixed income or whether you do it in equities and so on, while they have low correlation, most of the time when the environment is not very risk averse, their correlation goes to 1 on the downside, whenever risk aversion rises. The reason for that is that each one of those strategies that is benefiting from buying the risky asset and selling the less risky asset needs one very crucial thing for it to work, which is liquidity. Liquidity is like the oxygen for these strategies. When there is oxygen each one is doing its different thing, low correlation, everyone is happy. However, when risk aversion rises in the markets one of the first casualties of rising risk aversion is liquidity. Liquidity dries up significantly, so what happens is that suddenly all these strategies that look to be uncorrelated, where in reality they did have one common risk factor which is liquidity, but they start all losing money at the same time. That's when investors start scratching their head saying, what happened? We thought we were diversified, why are they all losing money at the same time? The reason is because they pretty much all long a lot of liquidity, and when that disappears they lose at the same time. We've seen that happen time, and time again.

My thinking about Conquest Macro, from the beginning, was that I wanted a product that would do well in a risk averse period, because it's needed. Like I said, over 90% of strategies out there don't have that profile. Historically, the way people hedged some of that rising risk aversion is they could have allocated to short sellers. The problem with short sellers is that stocks go up over time, and it's pretty much a negative expectancy strategy. Very few short sellers survive for decent parts of time. Markets can go up, as we saw in the last few years, for a significant period of time. I mean Keynes has never been more right than now. Markets can go much longer and be much more irrational than it can be solvent. The problem with short sellers is when you have that type of strategy, given that markets can go for long stretches of time, let's say going up in this case. Investors find it very difficult to hold onto a short seller over that period of time. Just every month, losing, and losing, and losing. So they end up redeeming out of their hedge, probably just at the time where they needed it the most. So short selling is not sort of an ideal hedge to portfolios.

s and early:

m - think of what happened in:

ok by Risk books in London in:

Our expectation for the fund is to return somewhere between 5% and 10% in risk seeking periods - in low vol environment periods, and return over 30% in risk averse periods. In our actual trading, since we started, we have annualized over 30% in those risk averse periods - we have checked that box. It's on the risk seeking side where we've had to do a lot of work to improve our strategies. Depending on when you look at our track record, and so we've had many improvements that have happened over the years, I would say that our actual track record is probably flat to slightly positive in risk seeking periods, which by itself is still a very significant improvement over short sellers and long vol strategies, but looking at our track record since we've made a lot of the improvement in our risk seeking performance, that's tracking closer to 5% to 10% annualized. If I think of our return, based on our risk index and our analysis of the risk environment, we model the world to be roughly about 70% of the time risk seeking - low vol, and about 30% of the time risk averse. Just a quick back of the envelope calculation, if the 70% of the time that you are risk seeking you are going to make 5% to 10%, call it 7% and change average. You are going to make roughly 5% in that period. Then in the 30% of the time that you are going to make 30% from your risk averse performance you are going to make another 9%ish, so I think we're at 14%, 15% type strategy over time, but with a very, very important portfolio benefit in the way that we deliver those returns.

Niels

True. Here's a question. That, obviously, is very, very interesting that trying to design a program that not only gives investors the bulk of the return when they most need it but actually also can make returns when they don't really need it, but obviously it's an absolute return strategy and therefore it's nice to have an absolute return through those periods. In a sense, you could say it's the best of both worlds. With traditional trend following my observation is that it can't deliver both. There's simply going to be a period where it will lose money. Of course, the question is then, how much will you lose, and so on, and so forth. It's kind of universally accepted that trend following can't be the best of both worlds. It sounds to me like a very tall order to try and do both. How do you achieve that?

Marc

It's a lot of very hard work and a lot of trial and error. In a way, 15 years on, I think that we have the best product that we've ever had and I think it's both an ability to design individual models that over time will deliver exactly what... not just the return but the risk profile that you expect. I don't understand how anyone can promise a certain return profile because really returns are a function of the market it gives you and no one really know ahead of time what the market will give you. Our biggest effect comes on risk control. If you build up a certain risk profile, that risk profile is going to be associated with different return and different market conditions. We start with that concept and what we do is we have, I think our risk index - we have one of the biggest benefits that we've reaped in the portfolio was when we came up with the idea of the risk index. Again, at that time very few people thought of risk indices or even had a risk index.

Niels

Why did you want to develop that?

Marc

It was very simple. In trying to have the best product that responded very well in periods of higher volatility, we wanted to have a quantifiable way to go and measure what we defined as high volatility versus low volatility.

Niels

Was it more to visualize to people, or was it something that you use in the program?

Marc

No, no, no, we use it on actual programs. The premise before we started building the risk index was that different strategies behave differently in different risk environments. If we had a proper way to measure the risk environment through a risk index, we can use that to affect our risk allocation methodology across the different strategies within our portfolio and therefore have a better portfolio - a simple premise. The first step was to go out... because historically when people looked at risk, they looked at it in individual pockets, meaning either through the prism of the VIX, but really VIX is only equities, or through effects on volatility for people who traded, effects on options, or swap spreads for people who did more fixed income, but people were not looking at a much more comprehensive view of risk. What we basically observed is that one of the benefits of having a risk index is it really pushes your tentacles across all the different parts of the capital markets. On a daily basis, you're able to measure the temperature of pretty much all the different areas from liquidity risks to credit risk... the way we define it is we said what are the different risks that people look at? You have liquidity risk. You have credit risk. You have emerging market risk. You have equity risk. You have foreign exchange risk. When you have a much more comprehensive view of these risks, it gives you a much better way of assessing market vulnerabilities let's say, because a lot of the time, depending on where the risk aversion ends up coming from, you start seeing signs of that in that particular dark corner of the market way before anybody else starts feeling it or seeing it. So it gives you some preparation time to go and think about what you want to do and how you want to do it.

Once we built our risk index, it turned out that our intuition was spot on, which is that there is very strong statistical evidence that different strategies consistently do very different things in very different risk environments and that there was a certain level of autocorrelation in the risk index that allowed you to use some of that information. In periods of switching from one risk environment to the other, there is the expected noise in the data around those points, which is not something insurmountable. You can very easily filter out that noise, but once you got into the body of the risk environment, your probability of staying in it was much higher than the probability of reversing, until you had an event that caused it to reverse, and again you go back to that noise and so on. What we found is that using an asset allocation strategy greatly benefits our return because it allowed us to put our resources where they had the highest expectancy given the risk environment.

in our portfolio in March of:

As I said, we made the changes to go from static risk allocation to dynamic risk allocation based on the risk environment. We improved our returns by about 30%. However, the way I viewed that improvement was more like the sort of improvement you get from cost cutting. It was just a better rearrangement of the deck chairs. I still wanted a component that actually added positive returns in risk seeking periods. That's where we went out and built our force of strategy which is what we call long risk, which is a strategy that essentially has about 80% correlation to hedge funds; that uses only futures and FX; that as a standalone could be a very good product by itself, because, gain it gives you a sharp of about 1, 80% correlation to hedge funds using only futures and FX, which as a standalone strategy would qualify it as a CTA, so you can be technically invested like a hedge fund, but getting a tax treatment of the CTA, which is, by the way, in our product offering we also offer it separately as a standalone.

It was hugely helpful to the Conquest Macro portfolio, because now we had one more risk bucket that we can allocate to in the risk seeking period that really would give us pure, simple, absolute return on the positive side. The rest of the evolution of the models within Conquest Macro is historically we started off with only price based models. After about 7, 8, 9 years we progressed into an area that we call Quant Macro, which is models that take a combination of two things: fundamental data, as well as technical data. That, again, allowed us to have more leeway on how to allocate within the different risk environments and so on.

Niels

So you have these four strategies within the Macro program, are you able to visualize and talk just briefly about how each of them implement what they do, just to make it simplified a little bit...

Ending

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