TTU132: Dr. Edward Tricker, CIO of Quantitative Strategies at Graham Capital Management

[00:00:06] Ed

We are masters of our own destiny. We don’t need to cross our fingers and hope for the best. Systematic strategies can, to an extent, make their own reality, and I think that means that we can create things that people will find increasingly helpful as a lot of their traditional outlets for offsetting diversifying risk are starting to diminish. So I think the outlook looks very, very positive, from a research perspective, and also from an ability to raise assets and to play an important role in people’s portfolios.

[00:00:38] Intro

Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more. Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

Before we begin today’s conversation, remember to keep two things in mind. All the discussion we will have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance. Also understand that there’s a significant risk of financial loss with all investment strategies, and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions.

Here’s your host, veteran hedge fund manager, Niels Kaastrup-Larsen.

[00:01:28] Niels

Hey everyone and welcome to another edition of Top Traders Unplugged, where today, Alan Dunne and I are joined by Ed Tricker, Chief Investment Officer of Quantitative Strategies at Graham Capital Management as part of our mini-series focusing on the one investment strategy that beat everything else in 2022, namely, trend following and managed futures more broadly.

First off, Ed, it is great to have you on the podcast. Thank you so much for joining us today. We really have been looking forward to our conversation. I hope you’re doing well on your side of the pond.

[00:02:04] Ed

We are, yes. I will say it’s a terrible, terrible day of weather today, blowing a gale and minus 30 degrees outside at the moment. So I’m nice and cozy in here talking to you guys, which is much appreciated.

[00:02:17] Niels

Absolutely. Before we dive into the more detailed topics of our conversation, Ed, maybe just to set the stage and frame the conversation a little bit so that the audience knows a little bit about Graham, perhaps you could share a few highlights and also maybe mention some of the strategies that you focus on and where the business stands as we have entered into 2023.

[00:02:41] Ed

Absolutely. Graham is an alternative investment manager. We have approximately $18 billion under management as of the start of this year. For the last three decades, nearly three decades, Graham was founded in 1994 by Kenneth Graham Tropin. We specialize in providing compelling returns in both the quantitative and the discretionary macro space. We attempt to provide alpha opportunities across a wide range of market environments specifically with low correlation to traditional investments. In my world, on the systematic side of Graham’s business, we started out in the beginning as a very traditional CTA with a focus on trend following.

As time has gone on, we have evolved and diversified the suite of strategies that we have at our disposal. Most notably and successfully over the last couple of years, we’ve had our broader quantitative macro strategy, which attempts to generate returns across the widest possible range of market environments, and more recently, we’ve also focused on diversifying into other asset classes, most notably systematic, long-short, market neutral equities. So there’s a lot going on. It’s an exciting place to work. Never a dull moment.

[00:03:54] Niels

Yeah, and exciting for us to be able to dive into some of all of this. As I mentioned, we really appreciate that. Now, Alan and I, we do have some different topics that we want to bring up and we tend to alternate a little bit about who’s taking the lead on each. So perhaps, Alan, as I always do actually, maybe I could ask you to kick it off.

[00:04:16] Alan

Sure. Ed, Graham is probably quite interesting in the sense that, as you’ve described, they have both this big discretionary macro and systematic side to the business. Whereas, a lot of the people we’ve spoken to on this series tend to be very systematic. So, it would be helpful to give a sense of the overall philosophy about markets that drives the belief that both the discretionary macro and the quant approach are valid approaches to extracting alpha markets. Where does that come from? What’s the belief that underpins that belief in running the two types of approaches?

[00:04:57] Ed

Got it. From a pure business perspective, there is a valuable diversification to be had. If you look back historically, to put it bluntly, if one has been struggling, typically, the other part of the business has been doing pretty well. It’s given a stability to Graham, and all of the benefits that stability confers. It means that we can invest in the future. We’re not quite still living day-to-day, month-to-month as a lot of funds may be, and that’s been certainly very, very helpful. I think culturally, it has a lot of very positive benefits as well. If I was to make a very, very broad generalization (this is unapologetically simplistic), a lot of systematic strategies tend to take a relatively simple idea, an idea that has a relatively small edge, and then to compensate for that small edge, they deploy the idea extensively across a wide range of assets and then they sweat the details like risk management, execution, and so forth, because you need to squeeze every last drop out of the alpha that you have. In contrast, the discretionary trader, they’re only human as good as they may be. They necessarily need to focus on a smaller number of opportunities. Now, to compensate for the smaller number of opportunities, they need to bring to bear a far more diverse range of predictive information sources. These two approaches are by no means exclusive to one another.

If you can combine the breadth of information and maybe the sophistication of the alpha that a discretionary trader looks at with that rigor and that structure and the efficiency of a systematic process, you can produce something that is surprisingly effective. We’ve also benefited in other respects, risk management, for example. I think the way you necessarily have to manage risk with human traders is decidedly different than how you have to manage risk with a computer that doesn’t get out of bed feeling upset in the morning or something like that. It has given us a focus and a rigor there that perhaps would not be the case if Graham were just a systematic business, I think.

Finally, having experts; domain experts in markets under your roof is very, very helpful. If I think of my quantitative team (and I think the world of them), they’re a fantastic group of men and women. They have more degrees than a thermometer. They can build you models on nearly everything. But what are the best ingredients for those models? By having access to a pool of people who have maybe been trading markets for 20 or 30 years, in some cases, they’re not going to tell me exactly what they do.

They don’t want me to replicate their behavior. But guiding us as to, hey, this is the type of information I look at all my peers are looking at, that gives us a tremendous head start when it comes to constructing systematic models that are trying to access, broadly, the same theme. So it’s been very, very advantageous.

[00:07:51] Alan

I suppose blending different ideas and blending different backgrounds, has that been part of that evolution in the systematic side away from pure trend into diversified trend into, as you say, quant macro being the program that you’re maybe best known for in some senses now. Is that reflecting that evolution or is that more of a conscious decision that this might be a better way to run quantitative strategies in markets?

[00:08:20] Ed

We definitely believe in diversification. If I look at core investment philosophies that we have, diversification is definitely at the heart of many of them. Now, that can take many different forms. It can be the people that I hire. In some ways, my life would be easier if I could go up the road to Yale University, it’s a great place, and every year hire their five best mathematicians. They’d be very clever, very hardworking, but they’d have been taught the same stuff by the same people and the way they approach problems is likely to be quite similar.

We need a diversification of thought process and approach. That’s definitely something that we focus on. We need to trade a diverse range of markets and we also need to trade those markets in a diverse way. So we need a variety of different strategies as well. Look, I’m a pretty pragmatic person. No single thing works well all of the time. I think if you want to produce a strategy that can work well over a variety of market conditions, necessarily, you do need to combine things together. Now, that is not always people’s objective. But if the objective is a pure absolute return one, then combination is almost required.

[00:09:37] Alan

I guess we’ve heard the case for why trend following can work in the sense of behavioral biases and speeds of reactions, etc. Is it a similar case or what is the case for why it’s possible to model, from an economic perspective, the relationships between economic variables and markets and find predictive relationships that you can exploit in a systematic program?

[00:10:04] Ed

It’s not trivial, but certainly, many of the arguments that are made for trend following, they apply to the use of macro data as well. There are some that are more straightforward. You can fairly accurately model supply and demand, for example. You can do that for commodities. The range of data you can get these days is remarkable. It can range from weather forecasts to people that will drive around the Midwest in a truck and they will check the planting progress of every single field and give you an incredibly detailed markup. That gives you a good predictor of what the future supply and demand balance or imbalance is going to look like.

There’s also a behavioral aspect to it as well. We see that with sentiment, and that’s very clear at the moment. You take inflation data, currently. Two years ago, nobody cared about inflation data. Two years from now, maybe nobody cares about inflation data. But today, inflation data is all important. Now, curiously, it’s not all important in and of itself, but it’s being used as a prism through which other macroeconomic data points and relationships are viewed. So good news is sort of bad news today for markets because people are seeing inflation data and they’re extrapolating what likely future monetary policy is going to look like.

So, it’s not straightforward, but the markets, the majority of the time, do respond in a relatively predictable way to certain pieces of data because people have expectations around what that is going to lead to in the future. If we can preempt those and if we can model it and if we can predict them, then it can be a valuable source of return. Now, I’ll be blunt, it doesn’t always work. 2020 was a good example. 2020, the relationship between traditional macroeconomic data and markets completely dislocated. You would have a situation where unemployment was at 10 million and stocks actually looked pretty good. That doesn’t make a great deal of sense. An economist would struggle to explain that one. But that’s a good example of why you need a diverse portfolio.

2020, trend following didn’t really care. It had a blissful naivety about the world. It didn’t need to understand why stocks were going up. It just said stocks are going up, my expectation is they will continue to go up and it will establish a position accordingly. So by having a range of models that look at a range of different things, they’re able to capture the widest possible array of different market conditions. No single thing, unfortunately, works well universally.

[00:12:33] Niels

Ed, you mentioned trend following a couple of times there and you also mentioned in the beginning that that’s the root of Graham. So I want to maybe focus on that a little bit. Within trend following, we obviously have also seen a large evolution from the early days. I think, in particular, maybe after the global financial crisis, that was the next chapter where some managers started to, you could say, diversify within the trend following but they did it in different ways. Cliff Asness wrote a paper last year saying, hang on, if we’re doing too much diversification in trend, we may not behave like a trend follower to the same extent when trends come back with a vengeance, which they did last year.

I think a lot of people did it probably motivated by we’re all getting compared based on a Sharpe ratio so we have to kind of do something with this trend to fit into the world of maybe institutional investors and how they look at that, or consultants, whatever it is. I’m just curious, from your perspective, if you were just looking at your trend following side, what you may or may not have done in that area, and also whether we’re becoming too concerned about Sharpe so that we kind of lose the profile that people really should want from us, so to speak. I know from a business perspective, it’s a hard thing to do, but from a pure investment perspective, what are your thoughts on this?

[00:14:12] Ed

Good. I think your last point there is a crucial one. I think we, as an industry, often lose sight of how our strategies are used and I think it can lead to an unfortunate misalignment of incentives. Many managers want to optimize their portfolio. They want to have, as you said, the highest Sharpe ratio so they can generate the most fees. Is a strategy that is itself optimal going to make the end user’s portfolio itself optimal? Almost certainly it is not. In the case of trend following for example, over the last 10 years, a surefire way of making it better would have been to give it a long bias to equities. Equities tended to go up. By giving a trend follower a long bias to equities it would have incrementally improved its Sharpe ratio most of the time.

Most investors would probably say that’s a pretty undesirable property for the strategy to have given the reasons they have for including a strategy like trend following in their portfolio. So I think it’s very important to view your strategies through the lens of how an investor is going to use them. A lot of the time, that means producing not just a Sharpe ratio, a Sharpe ratio is a very convenient average, we need to produce a return distribution. Now, for some people, they just want it to make the most money possible over time, for whatever over time may mean. For others, they’re more concerned about a conditional return. They want it to make money in certain market conditions.

Now, the type of strategy you’d construct for one is probably quite different from the type of strategy that you would construct for another. My preference, rather than trying to second guess what investors want, is to have a suite of strategies, building blocks, if you will, at my disposal, so that you can have, hopefully, a good conversation with an investor and you can start to understand what it is that they want, what is their objective. Now, they need to specify that objective. They can’t say, “I want something that makes money when markets go down.” That is an almost meaningless objective to have.

What does go down mean? Does it mean go down 5%? Does it mean go down 25%? Do you mean go down in a week or go down in a year? Does it have to make money every time or make money most of the time? If people can pin down some of these objectives, then we can build a strategy for them that helps meet them. I think that’s so important because the best relationships are partnerships.

If you can work with an investor to build a strategy that achieves what they actually want, then everybody is going to be happy in the long run. This notion of one size fits all is problematic and I think it leads to managers maximizing their own portfolio rather than ultimately maximizing the portfolios of their investors. I haven’t really answered the question, I’ve realized, but look, does that mean diversification of trend is appropriate? Sometimes, yes, if done through the lens of what the appropriate objective is.

[00:17:16] Niels

Yeah. I think that’s a fair point for sure. What I’m taking away from your answer is that you have a suite of products, including pure trend, but also trend plus something. We’ve heard from other managers we’ve spoken to this idea of having more of a solution approach to clients and working with them, etc. I also understand that fully. But if I was going to, I wouldn’t say push back on the idea, but if I was going to raise one question about it, and that is: how do we know if the investors are best at telling us what they really should have in their portfolio, if you know what I mean?

Because the way I look at it is, let’s just look at pure trend. You have maybe some managers with, say, 50 years of actual data. So you can see how it works and as long as you can confirm with the managers that they’re still doing pure trend and not certainly doing something crazy, then you have some evidence you can work with and it should give you a certain idea of, and I completely agree doesn’t work all the time, but how it should work.

Sometimes I would think of that as being pretty good evidence that you can then use as your building block. But once you start asking people for specific product designs, we have to start then relying on certain backtests of ideas, etc., etc. to give them that. So, how do you balance these two, a little bit opposing forces, so to speak?

[00:18:50] Ed

It’s a very good point. Undoubtedly, with great power comes great responsibility. I think sometimes if you give people the opportunity to customize something, they feel obligated to do so and that may not always lead to the best outcomes. But just because you have the ability to be flexible, doesn’t mean you necessarily have to be. I think for many investors, a standard fund account is sufficient. We have a $6, $7 billion trend following business with very, very modest degrees of customization. There are a large number of people that look at that and think, “You know what? That is right for me.”

Now, for more sophisticated investors, that could be your large public pensions, your sovereign wealth funds, where they have teams that look like my team. They have very, very talented analytical teams. Those, you can engage. They’re typically looking for longer term partnerships and relationships. They have a very clear understanding of what their investment objectives are, then I think you can engage in that. What you can’t do is just give people a bunch of Lego bricks and tell them to, “Just put them together how you like and we’ll trade it, don’t worry about it.” There’s a spectrum, and I think somewhere in the middle is probably the right place to be.

[00:20:08] Niels

Yeah, I agree with that. Even though I’m Danish and obviously have a big passion for Lego, I have to say there are certain times where Lego may not be the right thing to give to people. One other thing, before I turn it back to you, Alan, and that is this thing about the other point that Cliff Asness raised in his paper was that we have a dual mandate, absolute return, but also this crisis alpha where we should make money when equities go down. What are your thoughts about what our real “objective” should be or mandate should be?

[00:20:43] Ed

Well, I think that’s where the investor’s objectives come to the fore. Different investors will have a different expectation for what trend following or an associated CTA strategy should do. Some investors, and all of this depends on their investment horizon, will have a different tolerance for crisis type of returns. Now, I mean both in terms of magnitude, but also in terms of consistency. I think consistency is often overlooked. Trend following is pretty good at producing returns when equities go down, but let’s be clear, it is not a put option. It is likely to do well, but we can contrive scenarios. 2020 was a pretty good one where if something changes in a very specific fashion, trend can be a little slow to turn around.

So if you want something that’s going to make money 10 times out of 10, not clear that trend following necessarily offers that. What does? You’re probably looking at some type of tail protection in your portfolio. Are you willing to pay the premium associated with that? This is the compromise that one has to enter into. The way I sort of think about it and sometimes explain it to investors this way is you can think of all strategies as being imbued with two properties. One is their long-term return, and then the other is their ability to generate returns in a crisis. You start plotting these various strategies on a chart and you’d see your put option does great when markets go down, but its long-term return is pretty miserable.

In contrast, your investment in the S&P 500 does great over time and it does pretty badly when stocks go down as one may expect. Other strategies exist at various points in this space. There is no single strategy that is bulletproof. Interestingly, you can start to combine strategies together. You almost generate an efficient frontier, where some combination of the strategies is actually better than any of the individual strategies themselves. But people need to recognize that there are necessary compromises that they have to make. I think different investors will find themselves at different points along that curve.

Some people will be willing to have something that works less well but more reliably. That would be true if they were an underfunded pension and they have obligations or they have a very short-term investment horizon. If, however, you’re a sovereign wealth fund, in theory, they don’t need to care about anything for the next 50 years. They’ll be like, “You know what? I just want to make as much money as I can. If every now and again, it doesn’t work as I expect, I’m willing to tolerate that given the rest of my portfolio.” So I think that’s a good example of where at least a discussion should be hard with an investor about what exactly it is that they mean by a crisis.

[00:23:23] Niels

I think that’s a very, very important point. I’m glad you raised that.

The other thing I just wanted to ask because I was thinking about it while you were talking- so sorry, Alan, I have one more little thing I want to squeeze in here when it comes to crises alpha. We’ve talked about it over the years on the podcast, in a crisis, how trend following works. But then once you dig into trend following, what we and other managers have found is that there’s one particular part (and I don’t know if you agree, of course), but there’s usually one particular part of the portfolio that works better in a crisis, and that’s the commodities.

If you agree or if you disagree, do you want to venture into why you think that might be (from your perspective)? As to why other people.. Certainly also what I’ve seen so far is that commodities actually play a very important part during a crisis period in order for us to generate those returns.

[00:24:24] Ed

So, this is going to be my statistician’s answer here. I don’t agree, but I don’t disagree either. I don’t see sufficient evidence to argue one way or the other. I think one of the challenges of crises is that we have a very limited data set from which to infer. I’ve been constantly lectured, over the course of my education, that the plural of anecdote is not data, and the analysis of crises necessarily relies on a handful of observations. One could look at last year as an interesting point and counterpoint to the commodities argument.

A lot of trend followers did very well trading commodities last year. Is it conventional, however, in a crisis for risk assets for agricultural and energy commodities to spike in the way that they did? It’s somewhat unusual, in an environment where equities are going down, for energies to go up as much as they did. Now, that was because the specific cause of this crisis led to a huge appreciation in energy prices.

Had that energy move not occurred because rather than Russia invading Ukraine, we’d seen a war in some non-commodity-producing parts of the world, could we have seen similar declines in risk assets without the corresponding boosts that we saw in wheat, in energies, and so forth? Maybe, and then returns may have looked decidedly different.

So commodities 2008, 2022 have been friendly to trend followers. I’m not sure one can rely on them always being so. I will say that commodities have historically been a bigger and more important part of portfolios than people realize. They are admittedly very, very episodic. A number of times, I’ve had conversations with investors where they’ve been pretty candid and they’ve said, “Why do you trade commodities again? You’ve not made commodities for two or three years.” Then we get a year like last year and I try and be very polite, and I don’t say, “I told you so.” But I know, in a couple of years, people are probably going to start asking about commodities again.

It is cyclical and this is why diversification, risk management, and so forth, are all important. I do believe they are an important feature of portfolios, but I wouldn’t go as far as to say that they provide more crisis protection than other assets that we trade. At the end of the day, we have a huge advantage in this space that we can go both long and short. We can stick a minus sign in front of a declining market, and I think that’s what ultimately gives trend following the ability to produce positive returns. It’s that convexity that we have, that negative correlation in declining markets.

[00:26:57] Niels

I think that’s a great point and I do take on your point about sample size, because of course, we don’t have that many crises even though some of us, and I’m probably looking at myself, have a few gray hairs by now. But it is true. Of course, I didn’t mean to infer that commodities would always go up in a crisis. As you rightly put it, at the end, we can also make money like we did in 2020 from oil collapsing and all of that stuff.

Alan, I’m going to be quiet for a few minutes now and turn it over to you.

[00:27:33] Alan

Well, I just wanted to follow up on this topic because I think it’s really interesting, the whole idea of building blocks. I guess how you use them maybe depends on what type of investor you are. But maybe just to delve into a little bit more, start from the premise of an investor holding a 60/40 portfolio. In the industry, we’re always telling people to look at managed futures, look at global macro for diversification.

Take, for example, a typical private investor, say, with a 60/40 portfolio, or maybe any type of investor, and you were going to add something, would it be trend following or would it be trend with capped equity beta or would it be trend plus quant macro or trend plus quant macro plus discretionary macro, diversified managed futures? How do you think about, if you have one slot to fill in your diversifying bucket, what’s the best thing to go into that slot?

[00:28:32] Ed

If I could ask them one question, it would be, what matters more? Would it be your long-term return or that crisis return? If it’s that, you really, really, really want to make big chunks of money when markets go down, as we saw last year, trend following is nearly unbeatable in that respect. When there are trends to follow, and trend following works well, you guys said it in your introduction, it’s the best strategy that’s out there. There aren’t many things that were making 30% to 40% last year, but trend following was one of them.

Now, if they want to make some money when markets go down and they perhaps don’t want some of the cost of carry that trend following has, then something like a quantitative macro strategy does pretty good. The way I think about quant macro strategy versus trend following is you have that CTA smile, that nice convexity that trend following has. The problem is, with that CTA smile, everyone focuses on the end. They’re very exciting when you make lots of money, but they don’t happen that often. What you live with day-to-day, month-to-month, year-to-year is the bit in the middle. For trend following, the bit in the middle isn’t quite as exciting.

So for some people, what they want to do is they want to take that smile, they flatten it out a little bit. It’s not a frown, but it flattens out a bit, but it lifts up. So on average, it gets a little bit better. Now, conditionally, it’s not quite as good. Different people have different wants, needs, objectives and so forth. They’re not radically different, frankly, but it’s always a worthwhile conversation to have.

[00:30:04] Alan

If you think about it in terms of the types of things you can do to generate a bit more crisis alpha, if that’s the bent you want to bring in, some of the suggestions are capping the equity beta could be one, or trading faster is another, so you’re more responsive. What do you think in terms of the mix of things you can do so the portfolio can be more crisis alpha orientated? Obviously, buying puts is the ultimate, but that comes at a big cost. So what do you think is the most effective way of achieving that?

[00:30:39] Ed

I think you described the spectrum there nicely. At one extreme, you’ve got those puts. They’re going to work. Unless you really screw them up, they’re going to work and you’re going to be very glad that you have them. You’re going to hate them 9 years out of 10. At the other extreme, you can tweak your models a little bit. You can speed them up. Now, that has historically come at a cost. 2020 was an instructive example. We took a good look at our portfolios to say, what would we have to do to make our trend portfolios consistently make money in a March of 2020 type equity decline?

We can do it. We can speed them up to a point where we would very consistently generate positive returns. It would have been terrible for the 20 years up till that point. Interestingly, it would’ve been pretty terrible ever since. So you have to be careful that by over-optimizing to a crisis you’re not resulting in a portfolio that is unlivable the rest of the time. Then there is that middle ground, something like reducing equity exposure is interesting because, look, if you don’t have it… What’s the problem with trend following? The problem with trend following is inflection points. There’s a path dependency. So, if markets have gone up a lot and then all of a sudden, they go down a lot, it takes a while to turn around.

By not having that long exposure, you turn around more quickly. It’s sort of analogous to speeding up the system. The problem is you do incur an opportunity cost along the way. Now, historically, that opportunity cost has been about equivalent to the diversification benefit that you get, so it’s really been a wash. One could argue that fixed income is no longer the reliable friend it once was. I think, probably, that calculus has improved somewhat. There’s probably a little more utility to a capped beta version of trend following, for example, that looks a little bit more like fixed income used to.

But again, I keep going back to saying I think there is no one size fits all solution and these are the types of conversations that you need to have. Now, for a lot of people, they are going to say, “Look, I don’t care, just tell me what I should do.” I think for the majority, the regular version of trend following is sufficient. However, there are more customizations that one can make.

[00:32:56] Alan

Very good. Maybe moving into the whole research process a bit. You’ve talked about the evolution of the trading strategies run at Graham. I guess two things to get a sense on. One, obviously, you’re unearthing new sources of alpha all the time, I guess. So what’s the way to do that? How do you set up a research team? Do you just say, “Hey, go and look for ideas.” Do you come together and brainstorm? What does all of that look like? Secondly, it would be good to get a sense of the ongoing evaluation of strategies and the decision-making around whether to keep strategies or retire them, etc.

[00:33:34] Ed

Got it. If I think about philosophies that we have, really when it comes down to it, there are two. The first is that we do believe that the rigorous application of scientific principles to trading markets is key to success. Now, by that, I don’t just mean finding some complex and convoluted statistical model. I mean more the entire logical, systematic process of inquiry and decision-making. I think that’s singularly important. The other, I’m sure we’ll talk about in a bit, is I very much think that risk should never take a backseat to return, and that does enter into our research process to some degree, but we try and take the best parts of the scientific method.

You start, ideally, with a clearly articulated hypothesis like, “This is why we’re doing this, this is why we expect it to work.” It is informed by some expectation, some econometric theory, or whatever it may be. That hypothesis should be testable so that you can then take that idea, you can flesh it out, you can conduct research on it, you can run tests, simulations, you can accept, you can reject, and so forth. Peer review and transparency are very, very important. Very rarely has a wonderful strategy been developed by the lone person locking themselves in their office and emerging victorious six months later clutching a piece of paper with a fantastic result on. You need your peers to help you in this process.

I think one of the privileges, frankly, of working somewhere like Graham is I’m surrounded by a bunch of people who can do things that I can’t do and I learn things from them. It makes me better at my job and I hope it makes them better at their job. I think that’s important. We employ all of these hyper-educated people, and you don’t want their education to stop the moment they walk through the door. What a damning thing that would be. So we very much believe in collaboration. I’d include innovation in this actually as well. They’re not things that you can mandate, unfortunately. I can’t stick a motivational poster on the wall to encourage people to collaborate. They’re just not going to do it.

You have to create an environment where you foster that, you encourage it, you reward people that do it, you punish people that don’t, because having that collaboration (I’d include the innovation in there as well), that’s very important to this process. So I rely on the entire team to be responsible for idea generation. I don’t have a monopoly on good ideas. I’m sure my guys would agree very much with that. Everybody’s responsible for coming up with new ideas, and then we will start the rigorous process of testing them. That’s done very, very publicly.

[00:36:25] Alan

Where would the ideas typically come from? Would it be from academic journals or just observing the markets or you heard somebody else looking at something? Is it that type of thing?

[00:36:36] Ed

It’s any and all. The reality is, and that worries me, this job is very difficult. It’s not getting any easier. The easy stuff has been done. It’s been pretty picked over. So you can’t be dogmatic and overly selective about where good ideas come from. Yes, academia is a reasonable source. You can have conversations like this, conversations with investors. It can come from observing markets. You have to cast a pretty wide net. Now, in doing so, efficiency becomes very important. It means you have to try and churn through the bad ideas pretty quickly.

So if you can have a framework where, a rapid prototyping is possible, it means that you can start to sift through these things more quickly. You have to be mindful of multiple testing and all the other overfitting problems that come with that. You can’t just throw stuff at a wall and see what sticks. But having an efficient framework is certainly very helpful.

[00:37:31] Alan

The second part, in terms of strategies that have worked for a while and then start to perform less well or perform poorly, from being on the other side speaking to investors, they often evaluate managers with a three-year time horizon. If you’re underperforming, there seems to be this view, maybe that’s the timeframe to give a manager. I’m thinking that your timeframe is probably a bit longer than that in terms of evaluating strategies, but give us a sense on how you make those calls, which effectively, ultimately are value judgment calls.

[00:38:10] Ed

They are. I think the process for inclusion and exclusion, they’re sort of equivalent backwards but the same. I think systematic strategies are imbued with a certain advantage here, which is their behavior is ultimately codified. Now, what does that mean? That means that we can have a pretty good view of their conditional expectation in a sense that you know with a high degree of certainty how they’re going to perform under a certain set of market conditions. I know that if markets go down a lot, trend following is going to get short and it’s going to make money.

Now, what I don’t necessarily know is are markets going to go down a lot. But this very clear view of conditional performance does give you a lens through which to assess strategies. If there were lots of trends to follow and trend following performed poorly, that is a very different scenario than if markets went sideways and trends performed poorly. So I think using that lens of expectation that you should have, with a systematic strategy, because they’re not a black box. We should be able to explain, to a certain degree, the type of behavior that’s expected. I think that is a very useful tool, I’d say, probably an essential tool for assessing performance.

You don’t want to throw the baby out with the bathwater and find yourself in a situation where you’re getting rid of a strategy that’s done poorly in an environment when it’s always done poorly and always will do poorly in the future; but you’ve got it in your portfolio not for that time period, but for other ones. So I do encourage people to think of it that way. You gave the three-year example. You haven’t necessarily seen a realization of all possible market outcomes over three years. You’re not going to in 10 and 20 and 50 either, but you get my point. It’s also why that objective is important.

Again, it’s like if investors have something in a portfolio to just make money, it’s very easy for them to throw down. If, however, they have it in the portfolio to make money when markets do x, y and z, well, then they can start to use that as an assessment tool as well. So the objectives, I think, are very important when making decisions about inclusion or exclusion of a strategy in a portfolio.

[00:40:21] Alan

Maybe just one final one on this. The whole idea of shifts, regime shifts in markets and markets starting to trade differently and maybe a sense of the way the markets have traded in the last few years is maybe different than the previous 10 years. Obviously, we’ve had much more volatility in macro variables anyway, and growth and GDP. But you touched on March 2020 or Q1 2020 and how those breakout systems, fast breakout systems, did really well and they had done badly for 20 years or whatever, 10, 20 years.

If those systems did well, say, for two years, how long would it take for you to start to say, “Actually, maybe there’s something changing here in how markets are behaving, maybe these things should be in our repertoire on an ongoing basis.”? Hard to say specifically, but would it be two, three years of recurring performance would you say?

[00:41:14] Ed

I think you’ve touched upon one of the biggest challenges that systematic managers face at the moment, which is that everything we do is predicated on an extensive analysis of historical data. I don’t know what the next five years are going to look like, but I’m pretty sure they’re not going to look like the last 10. What does that mean for the way we construct models and the way we build portfolios? The best trend following strategy that you can simulate today, over the last decades still goes really, really long fixed income most of the time. But we all know that fixed income is not going to play out in the future like it has done in the past. So what do we do about that?

I’m very hesitant to make a judgment call personally. I would always rather try and systematize this decision-making. An area of research that we focused on extensively, really for the last five or six years, and I will admit, somewhat fortuitously, because my thinking around this has evolved somewhat, is to make strategies that are adaptable, strategies that can learn by experience and that can adjust their behavior depending on the prevailing market conditions. We started this originally because we wanted to remove the dependence on a human expert, good as they may be, for parameter selection. So you take something like trend following, there is no single speed that has historically been the best.

Ideally, you build a model that could speed up and slow down as the market conditions change. That is to try and reduce the overfitting problem. Sitting here today at an inflection point for many markets, that actually seems like a pretty good idea in the sense that it allows us to contend with a wider range of potential market outcomes. We don’t know what’s going to happen, but we can build a strategy that will be able to adjust come what may and I think a strategy that can do it incrementally, rather than having to have a team of people sit down and try and decide, “Oh, gosh, I think it’s time to start trading faster.”

I think to do it in a systematic, process-driven, algorithmic way is likely to be more robust and resilient and to be more timely and to do well across a wider range of market conditions. Now, this isn’t just something that’s unique to trend. Take inflation data, we spoke about it earlier, very important today. Two years ago, it wasn’t important. Two years from now, who knows? Maybe it won’t be important again. Having strategies that can pick up pieces of data and then put them down when they’re not relevant. Again, they should be able to do well across a wider range of conditions. They may not be the best in any one of them, but they’ll do well, on average, across a much wider range.

Philosophically, that’s how we started thinking about this challenge of this inflection point, this changing behavior. Rather than trying to predict it, let’s build models that can react to it in a timely way.

[00:44:07] Niels

I would love to dig a little bit deeper than that because that’s actually something we haven’t talked so much about so far in our conversations. Recalibrating parameter selection, doing it systematically, I’m in big favor of that. I think that makes a lot of sense. Can you give us, without giving, obviously, any secret sauce away. I think it’s easier for people to conceptualize just lookback periods as a timeframe. It’s much more difficult if you use other types of parameters – just lookback.

Can you give us an idea of how big of a sandbox do you then allow your models to look at in terms of finding these parameters and also, how often do you want it to go in the sandbox to look for these? How do you think about these things?

[00:45:05] Ed

Got it. Let’s take a prototypical trend following model as an example. Frankly, nearly every trend following model under the sun is a function of recent returns, and it takes a position tomorrow depending on what the recent returns have been. If you have a very large lookback, it means that what happened yesterday is relatively unimportant. It may be one of 100 days, and it gets diluted down. Therefore, the model is slow, because it’s not reactive to yesterday. In contrast, if you look at 10 days of data, then yesterday is actually quite important and the model is relatively reactive to yesterday and it’s fast.

We can control the speed of a trend following model by controlling the amount of data that flows in. You can think of it as having a tap or a valve that you turn one way and lots of information floods in and the model slows down or you tighten it restricting the amount of information that flows, it comes in, and the model speeds up. There are a relatively large number of statistical techniques that will do this. These are very common in the field of signal processing. A Kalman filter essentially does this, and many other bells and whistles.

Ultimately, a lot of these techniques fall under the broad world of reinforcement learning. In essence, they make a decision. They say “Today, I’m going to set my valve here and this is the amount of data that’s coming in.” And then tomorrow, they say, “How did I do?” If they let in the perfect amount of information and no other decision would have been better, they say, “Well, it ain’t broke. I’m not going to touch it. I’m going to walk away and let it do what it’s going to do.” If however, there would have been some alternative course of action that would have been more beneficial, perhaps it would have been better to have been a little faster.

They will incrementally turn that valve to speed up and then tomorrow, they’ll repeat. That is the process by which these models will update their view. It is much better to do things in a continuous fashion than it is to do things in a discrete fashion. The traditional way of doing things like this would have been to say, “Hey, here are half a dozen models, tell me which one is best.” Discrete problems are bad. Discrete problems mean that a very small change in what goes in can lead to a very big change in what comes out, and you never want to see that. So doing it in a continuous way is definitely advantageous.

Now, I’ve made it sound very simple. As with many things that sound very simple, surprise, surprise, it isn’t. So while it feels like I’ve given away all of the secrets, far from it, but that’s the framework under which these things operate. Now, in terms of the range, you do want to be sensible. The way we thought about it is historically, you go back to Graham’s early days, we’d have some trend following models that would look at trends that last for a few weeks and you’d have some trend models that would look for trends that last for many months. That feels like a pretty good range to give this model as a max and a min, and then we’ll let it learn its best way as to what it should be doing.

You don’t want it to flip-flop positions every day. You don’t want it to grind to a halt. One has to be mindful that anything that can learn, can learn bad habits, and you want to try and prevent that. But that process of positive and negative reinforcement is how we found that’s a good technique for these models to use.

[00:48:32] Niels

I think that’s actually a great way of explaining. I really appreciate that. There’s one other thing I wanted to ask you in this area before jumping to another point, and that is, I think most people can think about or have a good idea of this idea in terms of timeframe when it comes to signal generation. But there are other places within our strategies where we use timeframe and lookbacks, and that’s when we look at volatility, when we look at correlations. I was going to ask you if you could talk a little bit about that in a sense, because very often, these things don’t change maybe as much as one would think.

But then we got into a regime, for example, where volatility got pretty compressed by central banks and then you thought, “Well, if I kept using what I used to use, that might give me some huge exposure I’m not that interested in.” So that may not actually be (maybe it is of course on your side), something that happens completely automatically as well. I don’t know. But do you approach this part of the “lookbacks” when it comes to correlations and volatility, do you approach that differently than when you look at just signal generation?

[00:49:50] Ed

Absolutely. It’s a great question. I mentioned how one of our key philosophies is that risk should never take a backseat to return, and volatility is a great example of that. Virtually every CTA strategy that you will ever see will take positions inversely proportional to asset volatility. Their ultimate position will be their signal divided by the volatility. That means the volatility is a first-order driver of positioning that is as important as the signal. I’m going to venture to say that people, on average, have probably not spent as much time looking at volatility and measuring volatility as they have tried making and predicting market returns, but they are of equivalent ultimate importance.

Our philosophy with risk management has been and continues to be, we want to be as contemporary as possible. Let’s get the most up-to-date estimate of market risk that we can. We will then work out what to do with it. We will give it to a portfolio optimization. The portfolio optimization can elect not to react to it, but let’s at least present it with the most contemporary view that we can. We typically measure volatility over the course of a few weeks. Correlation is a corresponding measure, a little more difficult more often. There, we’re looking at a month or so.

I very much agree with you that volatility has become more volatile, and a good response to that is to speed up your estimates. We’ve also seen the opportunity cost of sometimes not adjusting for different volatility regimes. Now, some of us are slightly fortuitous. We spoke earlier about the benefit that commodities had on many trend portfolios in the first quarter of last year. But not everybody did so well. One of the reasons is because of volatility. You have to wind the clock back a little bit, but the day after Thanksgiving of 2021, it was a pretty miserable day for us. Yeah, nodding heads.

We all remember it. I think crude oil fell about 13% in a day, not great. Markets were very illiquid, everybody was on holiday and so forth. Crude oil moves 13%, everybody’s volatility estimates go up, everybody’s positions come down. Now, here we are, February of last year. War breaks out in Ukraine, the price of energies goes up dramatically. Depending on the volatility lookback you had, it was going to radically impact the size of the position that you had. If you’re using a one-month volatility, you’ve completely forgotten what happened, in fact, over Thanksgiving and your positions are probably going to be pretty big and you make a lot of money when energies go up.

If you’re using a six-month volatility, you’ve still got a hangover from that day back in 2021 and you have a small position, and you don’t make as much money. Now, that is an extreme example. But it just goes to show how the selection of volatility lookback can be quite impactful in portfolios. It introduces a path dependency that I think people don’t always appreciate. Volatility was certainly very helpful to our space last year.

I think one of the reasons that trend followers, in particular, were able to do so well is that they made money on relatively big positions early in the year, and they lost money on relatively small positions late in the year. That’s because volatility increased significantly over the course of the summer, exposures were reduced because of that inverse scaling, and in effect, volatility functioned like a take profit. You don’t always see that. That is, in fact, very, very rare. But the way volatility evolved over 2022 was very, very advantageous for trend following positions.

That’s why you can see trend followers that were long energies all year, yet energies were flat on the year. It’s because they made lots of money on big positions and then lost a little less money on smaller positions. So volatility is very, very important and I do think it’s something that people don’t always pay sufficient attention to. We spend as much time thinking about risk as we do about return. One side effect of that is that we do try and measure risk as quickly as possible.

[00:54:01] Niels

I just can’t help not asking you, then, the follow-up question to this, because we have some very good friends of the podcast and people I’ve known for decades. They have a different view on position sizing and believe that you can and should stick to the original sizing of the position. You should have your stop as your way of managing your risk and that this allows you to make more money on those 5% outliers, and that, over time, is a better approach, in their view. That’s perfectly fine. Now, clearly, you have a different philosophy.

But I was wondering if you had a way of maybe voicing why you think that doing it dynamically is better because in some ways, you could say that if you look at people with a long-term track record, that one has done static, the other one has done dynamic, maybe there is not that much of a difference in the long-term returns. Maybe there is, I haven’t done the detailed analysis. But I’m just really curious because you said you spend so much time thinking about volatility, you might have a much better explanation than I’ve ever heard. So I’m just really curious about this.

[00:55:23] Ed

I can’t guarantee that. The way I think about it is if you want to run at a variable level of risk, as a precursor, don’t you necessarily need to run at a constant level of risk and then modify it in some way? I feel that people often come at it backwards, that they say, “Oh, look, our portfolio runs with a different level of risk and we found that that’s in general a pretty good thing.” To me, that’s slightly unsatisfactory. Ideally, you’d say, “Here is our baseline. Here is our market timing signal that we have which we think we have a way of saying that our models know that this is going to be a good opportunity for our strategy. Therefore, we’re going to take more.” That is the implicit assumption that is being made here.

I will say that the timing of systematic strategies is notoriously difficult. In fact, trend following of trend following is almost mathematically impossible. It’s interesting. Trend following, I think, is interesting. Trend following relies on markets having autocorrelated returns. Necessarily, if the markets been going up, it assumes it’s going to continue going up in the future. Once you apply trend following to an autocorrelated set of returns, it actually destroys the autocorrelation so you can’t then trend follow the trend following. Similarly, you can’t mean revert it. So timing is difficult. There’s also a big asymmetry here.

As we’ve seen in a year like last year, trend following when it works, it works very, very well. It tends to have a very heavy right tail, and then when it loses money, it kind of loses money slowly. That’s a bit of a problem from a timing perspective. If you miss a good quarter, imagine that you’d missed Q1 of last year, you gave up your entire year. If you missed Q2 or Q3 or Q4, it didn’t matter that much. So the asymmetry in returns induces an asymmetry into the timing risk and the timing decision. It’s the opposite to equities where equities tend to go up slowly and go down quickly.

So your odds of missing a good period are not as bad as your odds of having a bad period. It’s the opposite asymmetry. So people get careless about it. For trend, it’s very, very difficult. We’ve elected to run our portfolios at a constant level of risk for two reasons. One is, we can’t time them. I know I should. I know if I could take more risk when these things were working and less risk when they weren’t. It’s not like I don’t know that that’s a good idea. We’ve just not found an ability to do it. I will say that the raw signals themselves tend to be a pretty bad indicator.

Trend followers, as a rule, have their biggest positions just before they’re wrong, because markets go up until they go down and vice versa. So maybe people have some other timing signal, I’ve not found one that’s compelling. The other reason is that we found that investors like to have a known quantity. If you’ve got something in your portfolio because you want it to have a certain protective property, you don’t want to wake up one morning and find it running at half the leverage that you thought.

It’s like only insuring the bottom floor of your house. They like knowing that there is a consistent volatility of returns, particularly if they’re using it for some protective properties. So part of this is pragmatic in the sense that we can’t and the other has been a business or investor-led reason.

[00:58:59] Alan

I guess maybe just a couple of final ones. One of the topics that we hear in the media a lot is around liquidity in markets and how from time to time, people say liquidity is more challenging than it was and they look at the older books and say there’s not the same depth there, etc. Periodically, we’ve had stressed conditions in markets like in treasuries in March 2020, etc. But from your perspective, do you agree with that or do you think markets are as liquid and as easy to trade? I mean, easy in the sense of an execution sense as they always have been.

[00:59:45] Ed

It’s a good question. At Graham’s, we’re not small so we definitely have to think about what we’re pushing through the markets. I think of liquidity as can you do the trade you want, at the size you want, at the time you want, at the price you want? You need to consider all of those different aspects. So volumes tell you part of the story, but not all. Bid-ask spreads tell you part of the story, not all. Older book liquidity (you mentioned) tells you part. You need to build up a sort of nuanced picture of these things. When you boil it down, I’d say liquidity is good, not great. It’s not what it once was. It’s not something that is keeping me awake at night.

We have had to modify the way that we trade. Now, we always are. It’s always an evolutionary thing. I will say that last year, markets were a little less liquid. We normally, in that type of environment, would want to slow down trading, because ultimately, there’s always this push and pull of taking liquidity, which is expensive but reduces the opportunity cost and market drift, or you can trade slowly, which is cheaper but you run the risk of the market disappearing away from you. Normally, when liquidity goes down, you’re going to want to trade more slowly to reduce that market impact.

But last year, markets were kind of ripping up or ripping down. Opportunity cost was looming very, very large and actually outweighing the cost of taking more liquidity. So it was advantageous to actually spend a little bit more money on execution by being more aggressive even in less liquid market conditions because you had to spend money to make money last year. It was worth the extra cost. Over the last few years, we’ve seen no material change in our cost of execution. It’s something we do pay attention to. To me, it’s the canary in the coal mine.

If your bid is getting more expensive, something bad out there is happening. I think we are helped by the fact that we only trade the most liquid assets. We’re not one of those funds that has made the decision to trade a wider range of assets, and liquidity is part of that. I think we’ve seen some of those markets get very, very crowded, and I have no intention of venturing into them for a bunch of other reasons as well. I would be nervous if I was trading a market where CTAs were a big proportion of the overall market activity. For many of the markets that we trade, as big as we are, and big as our industry is, it’s still a sort of relative drop in the ocean.

[01:02:19] Alan

Outside of that consideration around footprint, what else would you say drives that decision around market selection? I suppose specifically, we had the issue with the LME last year. Those types of events, when they happen, cause you to reassess those trading venues. But what was your response on that, and more generally, what are the issues that drove that decision?

[01:02:52] Ed

When I think about the logic of expanding the market universe that you trade, when it comes down to it, you need one or both of two things to happen. You either need to see a diversification benefit (diversification improves your risk-adjusted return), or you need to be able to generate higher returns in these new markets than your old markets. One or both of those things necessarily needs to be true. I guess other than it sounds cool, I guess that could be a third reason, but let’s not be cynical and assume people do it for that reason. The diversification argument, I’m sorry, it doesn’t hold up mathematically.

The utility of trading extra markets scales with the square root of the number of assets. That is a direct consequence of the law of large numbers. In the best-case scenario, that assets are all uncorrelated (and they’re not), then the best you’re going to see is that your risk-adjusted return is going to increase with the square root of n. That means that going from 10 to 30 markets is a big deal, but going from 100 to 130 gets you next to nothing. Square roots are horrible in that respect. So, the diversification argument just doesn’t hold up. At that point, you’re saying you can make more money in these markets than you can your existing ones. I find that a little difficult to believe.

You could say these markets are less picked over, they’re less efficient, but I don’t know. You’re not getting in a Toyota Landcruiser and driving across a desert to access these markets. You’re just electronically accessing a difference exchange. The barrier to entry is so low that it’s difficult to see that the efficiency argument is that great. Maybe they do a little better, but are you just being compensated for some illiquidity premium that we were just talking about? So I have not found the argument for expanding the market universe to be compelling from either a diversification or return perspective.

I’ll be honest, over the last five years, I felt a bit like King Canute standing on the beach yelling at the tide not to come in because it felt like everybody else was doing it. I don’t think it’s been detrimental to our returns. I think in fact last year, we can run parallel versions of our strategies to trade loads of different stuff and they wouldn’t have done anywhere as nearly as well as the market selection that we had. So it’s ebbed and flowed, but over time, it’s washed out to be generally a good thing. We’ve not had the operational headaches and so forth that come with it. So we’re not going to expand the list just because. We don’t see any compelling reason to do it.

You mentioned the LME. Yeah, that was fun. Not something that I thought I would see. We only trade copper, aluminum, and zinc on the LME. We don’t trade nickel, so we weren’t impacted. It gave us pause for concern about the LME as a viable trading entity that’s for sure, but we were not directly affected by what we saw. The LME have pledged to change. I will admit that I’m a little surprised that we haven’t seen competing exchanges start to offer similar products because I think people would jump at the opportunity, but we’ll see.

[01:06:01] Niels

Can I just quickly ask, Ed, how many markets do you trade? Because I’m not fully aware of that.

[01:06:05] Ed

In our trend strategy and our quant macro strategy, we trade 55 markets exactly.

[01:06:12] Niels

Very cool. By the way, I did like your explanation here because it’s rare that you hear someone make the argument, which I happen to agree with. But not that many people would say that because it sounds easier to say, “Well, if you trade more markets, you have more opportunities to find outliers so therefore it’s a better thing.” I kind of stick with your point of view as well. Alan, do you have more you wanted to ask?

[01:06:36] Alan

Maybe one final one. I know we’re over on time. It’s kind of around discretionary decisions that you can understand running systematic portfolios. I guess Graham has a discretionary business anyway so there’s always consideration of making discretionary calls. But one thing that I thought about and we haven’t really discussed with anybody else, but if you go back to March 2020, in that period, one of the themes that came up is that managers were starting to think about whether they should stop trading at all because of the risk of exchange closures.

What were the discussions like in Graham at that time? What kind of events would cause you to say we need to actually stop trading the portfolio or significantly cut back on risk for various random risks?

[01:07:33] Ed

It’s a good question. I have a very deliberately broad criteria for discretionary intervention in the systematic strategy, and that is there has to be something going on that the systems can’t comprehend. Now, just because the definition is broad doesn’t mean it’s widely applied. I can count on the fingers of one hand the number of times in my 12-year career at Graham we’ve made a discretionary intervention in our systematic strategy. So it’s very rare. But systems, they can’t comprehend everything, right? If a market moves a lot, they can understand that. They’ve seen markets move a lot, historically. If volatility gets high, they can typically understand that.

But what if there is a counterparty risk, as you described? The counterparty risk is not an input into our daily systematic process. What if there is a very hard asymmetry introduced into a market, a pegged currency or short ban in an equity market for example? Well, systems typically assume asymmetry of returns and you don’t want to see a situation. We saw people come unstuck with the Swiss franc a few years back for that reason. So those types of things that the systems can’t understand, those are reasons that we would maybe have to pull out of markets or even an exchange temporarily.

Then there are ones that are a little bit more sort of nuanced, I guess. What if volatility is increasing even more quickly than our models can comprehend? What if there is a well-telegraphed macro event on the horizon, a referendum or an election? The systems don’t necessarily know about that. One, I think, interesting feature that Graham has is we do have a daily risk committee meeting. I’ll be honest, I don’t love being in meetings, I don’t love being on committees, but this is something that I do actually find is valuable.

At 09:30 every single day, senior people at the firm sit down and we review positions and changes in positions across our whole business, discretionary and systematic and where they have changed, we want to understand why, and a lot of information filters down from the discretionary traders because they have these very short-term market views. So, we can use that to help inform the decision-making on the systematic side. If we identify something that we need to be mindful of, we can then run a series of stress tests, scenario analyses, and so forth.

But I think that the rigor of that daily process is something that is relatively unusual, particularly when coupled with the domain expertise within the building. I think if we were just a systematic business, I’d struggle to say hand on heart that that’s something that we would have the diligence to do. So, I think it’s a very useful function of Graham’s broader trading experience.

[01:10:16] Niels

Fantastic. I’ve just got two questions left that I wanted to ask you about, and these are different types of questions because we ask all people on this series of about the same. This is a little bit specific, again, back to trend following and that is, what’s the one thing you hear about trend following that you disagree with the most?

[01:10:39] Ed

Apart from the fact that it’s dead, because I’ve heard that it’s dead so many times over the last two years. I think it’s that people can time it. I’ve often found that investor flows are quite a good contrarian indicator for trend following performance. The people who use trend following successfully are the people that make a strategic allocation to it and they stick with it. You can take something like the CTA index, and you can do buy and hold, and it’s done just fine, or you can run a strategy where every time it draws down, I don’t know, 10%, you redeem and then you don’t allocate until it’s back to high water. That makes sort of single digits over the last decade.

You have to ride it out to a degree and I think the people that have done that have tended to use the strategy most successfully and have tended to have the most compelling results. So timing it is very, very difficult to do. I can’t do it, my team can’t do it. I’m not saying it can’t be done, but it means it’s very, very challenging.

[01:11:34] Niels

I completely agree with that one. The final one is just what gets you most excited or concerned (depending on which way you want to look), as we are heading into 2023, anything in particular?

[01:11:47] Ed

Look, I think 2022 came at a good time for our industry. I think we did what we said we would do and I think that’s very, very important. We were able to fulfill an important role in people’s portfolios and I think that’s absolutely essential. I think it got people excited about the space once again. I do think it’s important that we don’t get complacent. I think it’s very easy in this business to assume that the success that has got you to this point is this given right that is going to continue into the future. One of the things that makes this job interesting, it also makes it challenging and borderline depressing from time to time is that it is always changing. You always need to be innovative and to be creative and to try new things.

There’s a lot of things that I think we can improve and enhance. We touched upon a few of them. The need to make portfolios as adaptable as possible, I think, is of singular importance right now. The wealth of data we have available to us today is radically different from what I had at the start of my career. It is radically different than what we had five years ago. Contending with both the blessings and the curses that come with that type of data, I think, is very interesting. We’re seeing a pool of people who, for the first time, have the necessary skills to do this job. There was a real drought of talent for many, many years. It was a combination of inadequate supply and too much demand.

To a certain extent, we’ve seen demand lessen. But we’ve also seen that there’s a lag. To go to university to study a postgraduate degree, it takes time. We’re now seeing people come out with the type of skills that we really look for and that’s great. That’s been a bit of a revelation for us as well. So I think the outlook looks good from that perspective and I think investors are becoming acutely aware of the importance of diversification in their portfolios. For a long time, they had bonds and bonds were great, they made you money, and they went up every time stocks go down well.

Now bonds are losing money and they’re losing money when stocks go down and I think people recognize the need not for passive diversification in their portfolios, but active diversification. We can create that. We joked earlier about we can stick a minus sign in front of something. But that becomes very important because it means that we are masters of our own destiny. We don’t need to cross our fingers and hope for the best.

Systematic strategies can to an extent make their own reality and I think that means that we can create things that people will find increasingly helpful as a lot of their traditional outlets for offsetting diversifying risk are starting to diminish. So I think the outlook looks very, very positive from a research perspective and also from an ability to raise assets and to play an important role in people’s portfolios.

[01:14:39] Niels

I think that’s a wonderful way to end our conversation today. It has been fascinating. We really appreciate that. Thank you so much for sharing your thoughts and all of your insights, and of course, we hope we can do this sometime again in the future.

To all of you listening today, I hope you were able to take something from today’s conversation onto your own investment journey. If you did, please share these episodes with your friends and your colleagues.

From Alan and me, thanks so much for listening. We look forward to being back with you on the next episode of Top Traders Unplugged as we continue our deep dive into the CTA industry. In the meantime, go check out all the show notes for this episode and all the other resources you can find on the website. Not least, of course, take care of yourself and take care of each other.

[01:15:24] Ending

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