Are QI Strategies Sustainable? ft. Maia Mathieson & Faheem Osman

[00:00:07] Maia

What’s different about commodities versus say doing it in equities is that you have quite a large basket of commodities to do that over. So that creates a really nice diversification because something that causes a shock or a spike in volatility of one of those commodities may not affect the rest of the basket. We do see that you can get a really stable and steady return profile from capturing vol carry on a basket. It’s not to say that you don’t see drawdowns in commodity vol carry. Obviously, you do, particularly in some of the commodities like Brent, gold, where they’re very macro-driven. But there are ways of diversifying around that.

[00:00:51] 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 products before you make investment decisions.

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

[00:01:46] Niels

Welcome to another episode in the Open Interest series on Top Traders Unplugged hosted by Moritz Seibert. In life, as well as in trading, maintaining a spirit of curiosity and open-mindedness is key, and this is precisely what the Open Interest series is all about. Join Moritz as he engages in candid conversations with seasoned professionals from around the globe to uncover their insights, successes, and failures, offering you a unique perspective on the investment landscape. So with no further ado, please enjoy the conversation.

[00:02:22] Moritz

Hey, this is Moritz Seibert, and I’d like to welcome you to episode number 2 of Open Interest on Top Traders Unplugged. Today, I’m in New York, and I’ll be speaking with Maia Mathieson and Faheem Osman, who work for Macquarie Bank in London. I met them there last week in person, and I thought that we should speak about Macquarie’s quantitative investment strategies business or QIS business in short, especially in the space of commodities.

I thought I should really invite them because I found that there are some really interesting similarities, but also differences between these quantitative investment strategies and what hedge funds and systematic traders do. These similarities and differences, the risk premia, which drives these strategies, and the QIS business in general are going to be the subject of my conversation with Maia and Faheem today. Without any further delay, to both of you, Maia and Faheem, welcome to Open Interest.

[00:03:12] Faheem and Maia

Thanks, Moritz.

[00:03:13] Moritz

You’re both in London today. Before we get going, I’d like to set the scene a bit. Quantitative investment strategies, we will abbreviate it probably most of the time with QIS. This is a big and still growing business not only for Macquarie but for many investment banks around the world. Before we take a deeper dive into that and what you do there, give us a little bit of a background. Who are you? You work obviously for Macquarie, but what brought you there? What’s your background? Just present yourselves to the audience.

[00:03:43] Maia

Yes. So for me, I worked in another bank and then in 2012, I joined Macquarie really to help set up the commodity index business. Macquarie has a very big presence in terms of commodity trading in the physical markets and with corporates and they had decided there was an opportunity to expand that offering to the investor space. So I joined in 2012 and we started setting up a commodity index business and quickly realized that the opportunity and the demand was moving more towards, at that time, what we called alpha strategies, now risk premia or QIS, but really indices and products that were delivering some return that was uncorrelated with market beta.

We grew that business in the commodity space, really hinged and worked together with the commodity fundamental guys at Macquarie to build up some good strategies there. Then about five, six years ago, we decided there was an opportunity to use that platform in the cross-asset space. Now we are across asset QIS business here at Macquarie, but commodities remains very much an area of expertise. It’s in our DNA. So it’s great that we can focus on that today.

[00:05:00] Moritz

Great. How about you Faheem?

[00:05:02] Faheem

I joined Macquarie just under seven years ago. I joined to lead up the product development and structuring team. Before that, I was at another bank doing something very similar for about 10 years initially starting off in the trading side in natural gas and gold and then moving over into the index business to help build indices. So pretty much most of my career has been spent developing systematic strategies and these risk premia indices.

[00:05:34] Moritz

Fantastic. So both of you are true experts in the space. Maia, it was interesting you mentioned that you started with the commodities index business given Macquarie’s background in that space, but now it’s a cross-asset business. So is it fair to assume that you run these strategies on equities, fixed income instruments in the currency markets, maybe credit even, volatility, commodities, all of the above is what you guys focus on in the QIS team?

[00:06:03] Maia

Exactly that. We’re one team with a structuring function that Faheem runs that looks at all asset classes. But we have people within that specializing in each of those different areas. But what I think we’ll see and we’ll talk about today is there’s a lot of cross-pollination of ideas. Something that works in commodities actually might be an interesting idea to explore in equities and vice versa. So it’s a great place to be in terms of covering all of the asset classes.

But still, a lot of what we do is in commodities because it’s such a rich place to extract risk premia. We find that really people like to include commodities where they can in a risk premia portfolio because of the properties that we can speak about today.

[00:06:52] Moritz

Faheem, when we met, you mentioned something about the size of that business which I think we should speak about so that people get a view of how big these businesses or your business in particular really are. I found it surprising, but maybe give us those numbers again. To what extent have these businesses grown and what’s the volume?

[00:07:15] Faheem

Yeah, I think with factor investing, it’s been around for a very long time. But I think what we’ve seen more recently is more and more investors are looking to QIS for the benefits that it provides and the portfolio, diversification being really one of them or the main reasons investors would look to QIS strategies. But in general, you’ve seen a huge growth in QIS. More recently, over the last year or so, I would say we’ve seen about 10% to 15% increase in growth across QIS in general.

These numbers are not that easily available or measurable. But I can give you some estimates on where I think these AUMs lie and of course, AUM is dependent on the embedded leverage within these strategies so it can vary. But I’d say there’s about over a trillion dollars, in general, across QIS, and this would involve long-only and long-short strategies.

But then more specifically in commodities, if you just think about the long-short strategies and you think about factor investing in commodities, I’d say that’s around $60-$70 billion. Again, like I said, a lot of that growth or I’d say 10% to 15% of that growth has occurred over the last I would say 12 to 18 months.

[00:08:41] Moritz

Wow. It seems to be really growing and popular with your investor base and clients. Maia, what would you say are the most popular risk premia or return drivers? What do most investors and maybe also you as a bank, what do you focus on the most?

[00:09:00] Maia

In commodities, you’ll find similar factors to what you find in other asset classes. The most popular, or those that form the biggest parts of portfolios, I would say firstly carry, which in commodities at Macquarie, we mean curve carry, so taking positions on different points of the curve and extracting some value by the relative real yield. Also, vol carry, so systematically selling volatility and capturing the difference between implied and realized.

You also have relative value strategies driven by value, and maybe more specific to commodities, the curve shape, so backwardation-driven strategies. This is where you’re long some commodities, short different commodities, so they’re relative value strategies between commodities. Then we also have trend following which is very popular particularly for commodities and some of the off-benchmark commodities. The returns in that space have been really strong so definitely seeing more interest there.

Then we also have congestion strategies which are driven more by index flow and investor flow and we have strategies that trade around that and generate some return as well. But I would say the interesting thing about these commodity strategies is outside of that congestion factor, they are typically being driven by really fundamental physical features of the commodity markets and that is what makes them quite interesting, quite robust, and quite high capacity even compared to other bigger asset classes like equities and FX.

[00:10:44] Moritz

Let’s speak about that because this I find interesting. You just mentioned something which I think is very important. You said aside from the congestion strategies, there is real fundamental drivers or reasons why this risk premia exists.

Maybe if I can just say that, I think what you mean with these congestion strategies, for people who’ve never heard about this, is there are certain periods during the month or days during the months where there’s a lot of flow that comes into the market that’s essentially one-sided from say large commodity indices, for instance. There is an impact which the role of a process of these commodity indices has at that specific point in time it is known, and by providing liquidity to these flows, you can essentially earn a liquidity premium or a congestion premium. Is that a fair summary?

[00:11:33] Maia

Excellent summary.

[00:11:34] Moritz

Okay. Like you said, this is not really structural. This is the way the market is currently set up in terms of flows and where the investment dollars lie essentially. But when we speak about carry, for example, you say carry is really driven by fundamental factors or other things in the commodity markets which are just a fact, which are there. They don’t depend on flows. Can you go into some detail around that, please?

[00:12:08] Maia

Yeah, of course. Let’s take carry as a really good and fundamental example. This factor is driven by the storage cost of commodities. What we mean by that is when you look at a commodities futures curve, typically, not recently, but typically over the long term, those futures curves trade in contango. So, the front month contracts are cheaper than the further dated contracts and the difference in price, between each month, tends to be biggest at the front of the curve.

The reason for that is that commodities are obviously physical items and it costs money to store them, insure them, finance them. So if you have a commodity that is being delivered in six months time, you need to factor in the cost of all of those things that I just mentioned. Some commodities have pretty high storage costs due to their nature. So, something that is flammable, like natural gas, you need very specialist equipment to store that commodity so we typically see very large contangos at the front.

The longer that you store a commodity, the more efficiently you can do that. The longer-term storage gets cheaper and cheaper. So storing something for one month is typically more expensive than storing it for six months or a year. The same, for example, (another great provider of carry) is lean hogs. Hogs being a live animal, you have to look after them, they need specialist care, they take up quite a lot of space relative to their value. Again, you see quite steep contangos at the front of those curves that then evens out.

What we do in a carry strategy is typically we’re short the front part of the curve and we’re rolling down the curve and we capture that negative carry that you see at the front of the curve and then your long the further dated part of the curve where that carry is smaller. So you’re paying a smaller cost of carry than you’re receiving, and that differential is the carry return.

[00:14:25] Moritz

Got it. Who would you, maybe Faheem, think is usually on the other side of these trades? If you execute them, you’re short the front and long at the back, would you say there is a market participant out there that is very motivated and willing to take the other side of that trade and essentially be long the spread as opposed to short?

[00:14:43] Faheem

Yeah, so a lot of the liquidity is provided by the producers and the consumers who are hedging the production and consumption. If you’re a producer, for example, you might be selling forward a different part of the curve to lock in some potential gain. Similarly, you have different flows along the curve depending on which commodity you’re looking at where producers and consumers are buying or selling different parts of that curve.

[00:15:10] Moritz

It sounds like this is great, the curve is in contango, it rolls down differently, you’re making that money. But obviously, nothing is ever pitch-perfect. So what would be the situations or the scenarios where that strategy stops working or produces a loss?

[00:15:29] Faheem

I guess because, as Maia has explained, these strategies are typically short spread, if you think about carry, your general default implementation of curve carry is to be a short calendar spread, short the front, long the back. So where these strategies may experience some drawdown is if you have a big spike at the front end of the curve. Imagine you’re short a particular contract and the front end rallies for some reason, it might be a supply or demand imbalance.

A good example of that is the polar vortex during November 2018. Very cold weather in the US and limited storage of nat gas in the storage facilities meant that extra cold weather or prolonged cold weather had an impact on the demand that you saw for natural gases for heating. As a result of that, you had a big spike at the front end of the curve.

These carry strategies, by typically being short spreads, experience a drawdown because you were long a contract further out that didn’t necessarily experience the same spike up and you are short to contract right at the front end of the curve that experienced a huge amount of volatility with prices spiking up at the front end of the curve. Now, that is typically when a short spread strategy may undergo some drawdown.

But I think what has become really important to consider is that when you’re establishing these calendar spreads, how do you think about managing or controlling that risk? If I go short a winter contract but I am now long a summer contract, of course, these summer and winter contracts are going to behave very differently. Therefore, in your construction of your strategy and in the design, you may want to account for that and perhaps adjust your spreads so that you’re not crossing these two different seasons.

[00:17:24] Moritz

Right. It sounds like, as probably with any kind of carry strategy that we can think of, it works well, it carries well if the world doesn’t change. If nothing bad happens, you’re expecting to realize the profits that you see on the curve. But if something weird happens in the world, you enter a drawdown and you lose money. So it’s generally I would say probably a left-tailed, negatively skewed strategy.

The question is, do your investors or your clients realize that and therefore say let’s combine a bunch of different strategies (because you obviously have more than one) and create a portfolio so that the overall experience is going to be more diversified and more stable over time?

[00:18:11] Faheem

Yeah. So I think with a lot of these strategies each factor will have a different performance profile and you are right that some factors have negative tails. If you think about vol carry for example where you’re harnessing the difference between your implied volatility and realized volatility, yes, those strategies will have negative tail. But if you look at other strategies like curve carry that is a bit more consistent in the performance profile was a very low vol, you might find that actually, the left tail is quite small, and actually, it ends up carrying very positively.

If you just look over the last 19 years, you’ll see 18 of those 19 years have been positive for curve carry, for example. Many of these factors do have that profile of being consistent through time. But I guess to your point about perhaps combining different factors together, the first thing you’ll notice, these factors are uncorrelated and orthogonal.

So if you think about backwardation versus contango in the commodity space, or a relative value strategy in the commodity space that’s using backwardation or the shape of the curve to go long one commodity, short another, that is going to have a very low correlation versus something like curve carry which is implemented using spreads or congestion that is trying to capture this structural behavior of the market.

All of these factors being largely uncorrelated with one another, often when you put them together into a portfolio, what you’ll find is that when one factor performs negatively, for example in the example I used earlier where the front of the curve in natural gas rises or rallies and you have some negative performance in your curve carry strategy, you might find that your backwardation versus contango strategy is generating some very positive returns because it’s gone long the backwardated commodity, i.e. natural gas in this case.

So you can have situations where these factors balance out over time, and I think we’ve seen a lot of interest from investors to look to diversify their portfolios with QIS, but within QIS itself, diversify the portfolio across the different factors and even across asset classes.

[00:20:26] Moritz

In a minute, I’d like to speak about your investor base and who’s generally interested in getting exposure to these strategies. But before we go there, Maia, you just mentioned a few minutes ago volatility carry or vol carry as another structural factor. Can you enlighten us a little bit about this?

Why does that factor exist in the commodity space? Is it essentially the same risk premium that you would also see in the equities world where you have implied vol trading in general, but not always, higher the realized volatility? Is it the same behavior or is there something else going on?

[00:21:02] Maia

Yeah. High level, it’s the same behavior that we’re trying to capture. So in commodities, again, you have this flow of both consumer and producer hedgings. They’re looking to lock in price so they can make their capital commitments and invest in supply-side build-up, etc. So we do see a reasonably large flow of consumer and producer hedging across commodities. That increases or that creates a premium of implied to realized volatility that you can then capture by systematically selling that implied. We do that.

I’d say what’s different about commodities versus, say, doing it in equities is that you have quite a large basket of commodities to do that over. Typically, we’re including anywhere from 5 to 15 commodities in our vol carry basket, and that creates a really nice diversification because these commodities often have very different fundamentals, some are weather-driven, some are macro-driven. So something that causes a shock or a spike in volatility of one of those commodities may not affect the rest of the basket.

So we do see that you can get a really stable and steady return profile from capturing vol carry on a basket, whereas obviously with equities, you typically have one or two major indices that you might be trading on and when that blows up, you can see a significant drawdown. It’s not to say that you don’t see drawdowns in commodity vol carry. Obviously, you do, particularly in some of the commodities like Brent, gold, where they’re very macro-driven. But there are ways of diversifying around that.

[00:22:52] Moritz

When we met in London, what I found interesting is that, with respect to vol carry, this is actually a strategy that you run on an intraday basis and you’re really taking advantage of the infrastructure that you have as a bank; your trading access, market access and all that. Whereas some of the other strategies, for instance, congestion where you have essentially business days number five to nine, where for example the GSCI Index executes the roll, that is an end-of-day strategy which somebody else could probably run as well, maybe not as efficient as you can.

But this volatility carry intraday delta hedging, that I thought was really interesting because it is just the do-it-yourself type of behavior, or the do-it-yourself option doesn’t really exist for other people. Is that fair to say?

[00:23:41] Maia

Yeah, I think it’s a combination of exactly that, the intraday delta hedging, and also, when you’re selling vol across say 10, 12 commodities and we’re using listed options to do that, we are selling multiple options. For each commodity, you can end up with really quite a large portfolio of assets, thousands of lines of options and features that you’re trying to manage. So both from a practical trading perspective and also a risk calculation and data capture perspective, it’s quite heavy.

That is why I think we’ve seen so much demand for those strategies coming not just from our typical client base, but other clients. So, some macro hedge funds who have very strong trading capability internally, they still want to outsource this sort of strategy because it is so calculation-intensive.

[00:24:41] Moritz

Let’s speak about your investors. You’ve just mentioned the investor base. Who are you in general or usually talking to and who’s interested in getting exposure to these indices and strategies?

[00:24:55] Maia

It’s an amazingly broad audience. As Faheem said, the space has grown both in terms of size but I would say also in terms of breadth of people who are interested in these strategies. But our core client base remains pension funds, asset managers, insurance companies, and they’re often coming from a place where they see risk premia as an asset class in its own right, so they’re allocating to strategies across different asset classes.

Commodities forms a reasonably big part of that. I would say typically in a risk premia portfolio, it can be anywhere from 15% to 30% of the risk will go into a commodity strategy. There has also, more recently (say in the past couple of years) been a big drive towards clients looking for defensive portfolios that protect against the typical 60/40 equity bond portfolio, and they are using systematic strategies to create some kind of defensive or hedge overlay to that portfolio.

They can combine more typical hedges, so buying volatility, buying puts and equities with some defensive risk premia, so strategies that perform particularly well in a downturn, and then some positive carry strategies to pay for those hedges. We’ve seen also increasing demand to include commodities in those portfolios because something like a curve carry, as you said, has an ongoing positive carry, but it does have a bit of a defensive lean in terms of the return if you construct it in that way.

So I’d say that’s our core client base, but we also see increasing demand from hedge funds, from corporates just looking to generate a return that’s complementary to their other activities. Something like vol carry is very complementary to trend strategies, for example. So people running trends strategies are coming for specific premia that are complementary to what they’re doing.

[00:27:05] Moritz

I found that very interesting when you mentioned that last week that your client base includes hedge funds. I would have thought that the hedge funds, they would produce their own risk premia or return stream and not go to a bank for additional sources of alpha or risk premia exposure.

But you said, well, they do and maybe it is exactly because of infrastructure bottlenecks that you have, essentially, a trading business that can execute these delta hedges more efficiently and cheaper during the day than a hedge fund possibly could, and so they become your client. Is that correct?

[00:27:43] Maia

Yeah. Obviously, there’s plenty of hedge funds who have amazing internal trading capability and data processing, but some maybe who are not commodity-focused find some efficiency both in terms of cost and also just in terms of time spent and risk management to develop those strategies, to implement them, to gather all the data. They may find that it is more efficient to outsource that to a bank.

We also trade a lot of bespoke strategies. So for clients like that who are really deeply in the details of the market and have their own views on how things should be implemented, we can support that too. We can take our strategies, change the rules, change some of the ways that they’re working to suit a very specific purpose, and I think that’s also been helpful to some of that particular client base.

[00:28:39] Moritz

Right. One, as we were mentioning some similarities and differences, a difference clearly with QIS strategies is the delivery formats that are available to clients. For instance, when you think about an insurance company, they could get exposure to a risk premia or factor strategy in the commodities or equity space through a swap or you could probably wrap it into a node. It can become tradable. It has daily valuations.

It is, I presume, also fully transparent to the client because what you do is at the end of the day entirely rules-based. We haven’t mentioned that yet, but it’s entirely systematic. It’s written down in an index methodology document, there is a benchmark regulation concept around it. So this, I presume, is a great advantage for the client. Would you agree with that?

[00:29:33] Maia

Absolutely. No matter what the client is trading, at the end of the day, they will get a position file that explains exactly what was in the index, what drove the return down to the individual futures and options level. So for clients that are looking at their risk, they’ll see exactly what positions were in the index.

As you say, all of the rules of the index are fully transparent, so that does also provide a lot of comfort to clients that they know what they’re getting. But it saves them the trouble of dealing with multiple exchanges, contract types, all of the rolling and completely removes any risk of physical delivery which, particularly for institutional clients, is something that they’re very much keen to avoid, or they’re simply not allowed to trade commodity futures directly in some cases.

[00:30:25] Moritz

Right. Certainly, there must be also investors for whom this is not good. They’re reluctant to explore the space or they don’t want to have anything to do with it. What would you say are the main reasons for that if you come across clients like this?

[00:30:32] Maia

I think the two things that come up quite a lot are regulatory concerns, particularly around UCITS. Sometimes, investors running UCITS funds think that you cannot trade commodities within that vehicle. But that’s certainly not the case. Actually, the majority of our business in Europe is with UCITS funds, but you just need to abide by the guidelines, which are very clear and explicit. One of the key conditions there is that you have to trade a diversified exposure, diversified index.

You cannot trade one single commodity. Whatever you’re trading has to trade multiple commodities. But that’s the case anyway for most of our indices, because as we spoke about, that diversification is really a key part of what makes these strategies robust and delivering consistent returns. Typically, that’s not a problem. Then the other thing that comes up, I would say somewhat more recently has been a topic, is around ESG. That’s a concern for a lot of investors whether they want to be seen to be putting money in this space or having some carbon footprint.

But that said, I think the direction of those conversations is going more in the direction of what commodities do I actually need in my portfolio to protect against future inflation or how should I position myself to better be hedged against energy transition and how that might affect my client’s disposable income or some of the other assets that they have in the portfolio? So I think we’re going more in the direction of tweaking the portfolios or tilting them towards an energy transition focus versus excluding commodities altogether.

[00:32:32] Moritz

Faheem, one thing that I’d like to speak about also is the development process for these indices. You’re not the only provider of QIS strategies in the space. Essentially, every bank, I think, offers something that is similar or also risk premia driven, factor-driven across all the asset classes.

How do you make sure that what you come up with, to the extent that you can make it sure, is actually robust and resilient (which is a topic that’s near and dear to my heart)? What I want to say is, it’s not perfect, it’s not just based on a backtest that looks pretty beautiful in the past, but as soon as somebody invests, the disappointment just lurks around the corner. Can you speak about that a bit, please?

[00:33:17] Faheem

Yeah, sure. For me, the way I like to think about product design and strategy design is really to think about the idea of why something should work and then put that together in terms of the strategy, code it up, use the data that you have available as far back as you can, see what that looks like historically.

Of course, this is a simulated backtest and you’ve got a sense of how the parameters that you’ve chosen make the strategy work or not work. But I think thinking about the idea from the bottom up is the approach that we like to take. So really try and justify why something should work to begin with. Once you see what that looks like, then you really engage with the broader teams that you have at your disposal.

For example, we spend a lot of time with our traders who have really in-depth experience and knowledge of the markets that we trade, and then also our research teams to think about the positions that the strategy takes, does it make sense from a research perspective or from a fundamental perspective to take those positions that we’re taking? Then you build upon that so that at the end of all of those discussions you come up with a product that you initially thought would work, you’ve seen that it does work. Then, to convince yourself, what I like to do is to run sensitivity analysis on the parameters that you’ve chosen.

For every single strategy that we launch, we have a set of robustness analysis and I guess parameter analysis that we have run to ensure that what we have selected is not just, by chance, the set of parameters that works, but we really like to look for a region of stability. Once we are convinced that we’ve selected the parameter that it’s not overfitted, then oftentimes, we actually like to run that strategy, I’d say offline, meaning we will run it, we will monitor it, we will check how it evolves through time and then eventually we will launch it.

There’s a few examples of that where we’ve had that in the last couple of years where we’ve thought about new ideas, thought about new products, and just not had a chance to launch it. We’ve just been monitoring it live and then at one point in time, you think, “Okay, let’s launch this.” But it’s very comfortable and comforting to know that you’ve actually been running it for the last two years or three years and you’ve seen the live track record build up, and it gives you the confidence to go and launch that product. That’s the general approach we like to take.

When we think about the strategy itself, one way to do it is to think about it from an academic sense, where you look at a paper, you think about what that paper is looking to achieve and then you build upon that. That’s one way of doing it. But we also like to augment that with the practical view as well, from a trading perspective, from an analyst perspective. Does it really make sense to implement it in the way that perhaps the academics have suggested?

[00:36:23] Moritz

Do you see similar investor behavior in your space, as we sometimes even often see in the hedge funds and alternative investment space, which is to say that as soon as something works, it makes new highs, investors become interested? They’re essentially return chasing and trying to get in. FOMO, whatever it is, plays a role but as soon as it then stops working or it goes into a period of drawdown (which is at some point inevitable), they redeem and they essentially say I’ve had enough of this.

Do you see that same behavior? Would you say you have a relatively stable client base that are more strategically allocated to the space and really want to keep it for years and years and years?

[00:37:03] Faheem

I think it’s a mixture of both. I think if you think about what these strategies are looking to achieve, these are factor strategies and they are strategies that are expected to deliver a positive return over the long run. I think trying to time it is quite difficult, although you can implement a view in terms of, or you may have a view on what factors you think are going to work or not work for the next let’s say three months, six months. I think that is perfectly possible.

But I think realistically, if you want to earn returns from factor investing, then you have to think about it a little bit more in terms of a long-term investment. I think for the majority of our clients, they do see it like that. The way that they would implement these types of strategies is of course if something is working really well and structurally there is a good reason why it works.

Congestion is an example of that where if the index footprint for example on BCOM or GSCI, which are commodity benchmarks, if that grows and that continues to grow, then of course, the index footprint becomes larger and the effect of rolling from one contract to another becomes more relevant. Therefore, these congestion strategies that look to avoid that congested period are a factor that you tend to see work quite well.

So of course investors might look at that and say, okay, I want to maybe tilt my investments from let’s say something like momentum or backwardation into congestion because I’ve seen it work and I believe structurally that that should continue. Yes, you see that over time. But I think also what is really important to consider is that if you are able to maintain a position and hold a position for a long period of time, what you often find is that these temporary drawdowns that these strategies see, from time to time, are often mean reverting in nature.

If you think about a congestion strategy or a carry strategy that is implemented via spreads, spreads mean revert in nature. So when you see a big drawdown, it’s often that these spreads mean revert in the subsequent period. Another example is vol carry as well. When you have elevated realized vol in the market, these strategies undergo a temporary drawdown, and often, these drawdowns recover pretty quickly.

So there are ways in which you can think about constructing your portfolio to buffer some of those losses at times where certain factors become more dominant. One way is diversification across factors, another way is to allocate to more defensive strategies within the portfolio. So when you have, I would say, a risk-off event, these defensive strategies then kick in and subsequently buffer some of the losses or provide some gain during those times.

There’s many ways to solve this problem, but I think for the large part, the majority of our investors understand that and they’re in it for the longer term and they’re able to withstand some of these variations or increased volatility through time when they arise.

[00:40:01] Moritz

Maia, you mentioned you joined Macquarie some 12 years or so ago. In these 12 years, a lot of things have happened. Would you remember any structural changes or specific things that you changed the way you manage risk for instance as it relates to these strategies or how you build them in general, some key lessons learned or takeaways during your 12 years at the firm?

[00:40:27] Maia

Yeah, I would say the way that we manage the drawdown risk has definitely come more and more into focus for the exact reasons that Faheem alluded to earlier. The strategies have had really good consistent year-on-year performance, but within that, there’s been drawdowns and often the issue is clients just not being able to survive those and then capture the mean reversion and continue to capture the good performance. So we focused a lot on that.

We learned from that nat gas experience around the polar vortex. I think there, people sometimes look at that and think that’s my worst-case scenario. But when we talk to our gas analysts, what they tell us is that there has been a big structural shift in that market. So the size of that market, in terms of supply and demand, has grown substantially over the last 5 to 10 years, a lot of that driven by the associated gas coming from shale production and the drive to using cleaner energy sources.

Obviously, nat gas still is a carbon emitter, but it’s cleaner than a lot of other fuels like coal. So the size of that market has grown, but the storage capacity really has not. What it means is that market is much more vulnerable to changes in weather that create a pull on storage and get us closer and dangerously close to running out of storage and supply available particularly in the winter months. I think what we saw in 2018 is actually an example of where we got pretty lucky because while the weather was extremely cold, it then warmed up towards the end of winter and we didn’t actually run out of gas.

But our view is that that market is very vulnerable to spikes in the front around winter and the drawdowns that we saw there could have been two or even three times bigger if we had got really unlucky with the weather. So that’s one of the commodities that is a great return driver both in terms of curve carry and also vol carry due to the nature of the market, but it’s one where we’ve spent a lot of time thinking about how to risk manage it or at least make clients very aware of the risks they’re taking and then they can decide do they want to try and capture that premium over those winter risky months or do they just want to remove it entirely.

I think that lesson and that logic we also have looked to apply in other commodities and other areas, but it’s really very apparent in the gas market.

[00:43:14] Moritz

I’d like to speak about the potential for innovation with these strategies maybe with respect to alternative data or machine learning techniques. Before we go there, allow me to step back on the congestion or GSCI, BCOM indices. Faheem, you have mentioned that probably a lot of investors implement long exposure to commodity indices during inflationary periods of times or in expectation of inflation.

But have you seen the landscape change given that a lot of people are now aware of that GSCI and BCOM roll during certain periods of time? Would you say they’re trying to step away from this like let’s enhance that beta exposure by allocating money to indices that roll differently or avoids that congested period or have a more constant maturity type of exposure to these contracts?

[00:44:16] Faheem

Yeah, so clients have always been looking to enhance their long-only allocation to commodities, and the way in which they do that; you can think about avoiding the congested roll window, you could think about weighting your commodities differently. You can think about having an indicator on top, like a momentum indicator or something like that. That has always been the case.

But what we’ve seen, I think since the middle of 2020, after or during the recovery post-COVID, is you had a lot of investors looking to allocate or increase their allocation to commodities mainly to protect against inflation. When they thought about doing that and when they thought about upsizing their commodity allocation, if you think about what was happening to the curve at the time, the curve was in steep contango as you had just come out of COVID.

From that point, the demand for commodities, as an inflation hedge and also just the global economic recovery, had meant that a lot of the momentum or a lot of the appreciation in the prices of these curves occurred at the front end of the futures curve. In fact, if you were to think about investing in any form of commodity allocation, the most opportune or I’d say most optimal part of the curve was really the front end, and that is precisely the exposure that these very traditional benchmarks were providing.

I think in that period, from middle of 2020 to let’s say the middle of 2022, it was really the front end of the curve that was generating a lot of the positive returns. But outside of those periods, there is a huge amount of emphasis and focus on how do you maintain exposure to commodities but be very mindful of the fact that, okay, after the recovery, now prices might not be as bullish as they were before and if the curves rotate back let’s say into contango, how do you mitigate the negative performance that you might incur as a result of holding commodities?

So that might mean moving the allocation further down the curve, whether let’s say the volatility is subdued, or perhaps overlaying, as I said before, some indicator or some form of differentiation between choosing the commodities or perhaps choosing the exposure to your commodity allocation in light of how the environment is changing. So that focus has always been there, but I think the focus shifts depending on the environment that you’re in.

[00:46:55] Moritz

Are you making more and more use of say alternative data or machine learning techniques to come back to the point that I just mentioned earlier? Is this an area of innovation that you actively follow through?

[00:47:08] Faheem

Yeah. We have spent a lot of time looking at machine learning and alternative ways of creating strategies. We’ve actually launched a few strategies in that space already. But I think what is really useful, when you think about machine learning, is that the machine learning technology gives you a lot more capability to consume and to cater for different types of data, and that can be an enormous amount of data that you put into your model, and the machine learning will dictate or assess, through time, the relative importance of these features or of those data streams.

If you look at how that evolves through time when the machine learning model is able to tell you, let’s say 10 years ago, on a simulated basis, there were let’s say five factors or five features that were relatively more important than the rest. But as the environment changes and as the market evolves, you might find that that five that was initially very important turns out to be only three that is more relevant now and there are two or three other indicators or features that have become more relevant in the recent past. I think that ability of the machine learning model to do that for you has been immensely helpful.

What that ends up resulting in is that you’re able to use more data than you were able to use previously in a way that investors are becoming more and more attuned to being familiar with in terms of how those models operate, and ultimately leads to a return stream that is largely uncorrelated to let’s say a traditional QIS implementation that is let’s say based on one information source, let’s say prices, for example.

[00:49:00] Moritz

Faheem, what do you think? Can a commodity-focused QIS team like yourselves learn or have takeaways from other asset classes?

[00:49:10] Faheem

Yeah. The way I like to think about this is we have cross-asset strategies that we develop products on. The way that we like to think about it is that when you think about an idea, or you come up with an idea, whether that be from an academic paper or it might come up from maybe a research piece that you’re doing, is to really vocalize that and talk about that across the entire team. Within the team, we have people with backgrounds from equities, from FX, from rates, and a lot of these people have spent an enormous amount of time developing strategies themselves in their own asset classes.

Having one team provides the forum where you’re able to discuss the idea and really take your idea and make sure that you’ve tested all the different options and avenues to ensure you get to the end place or the end product that you’re looking to create. An example of that is when you’re thinking about let’s say congestion and you’re thinking about congestion in commodities, of course it occurs because you have some structural reason why investors are doing a particular thing, so you therefore want to trade around that. But that idea is transferable across the different asset classes.

So when you sit and discuss that idea and say this is what I found in commodities and this is what I do to trade around it; or the other way around, you say this is what I found in equities because perhaps there is some flow that I see in the equity space and this is what I do to create around it, then that conversation is very helpful because somebody with a background in rates or somebody with a background in FX is thinking about the same structural flows that they see in their own market.

Therefore, you’re able to combine your ideas together to create a product that is harmonized across the different asset classes. What’s more is that once you design your product, you’ve designed it in a similar way. The index manual and the methodology is written in a very similar way. So it’s very easy for the end client to look at this and really understand across the different asset classes what you’re doing.

That information sharing, it’s not just one way. It’s not what the commodity team can learn from everyone else or it’s not the other way around too. It’s more broadly what do people learn from everybody else’s experience.

[00:51:37] Moritz

Right. Maia, you mentioned when we spoke that there’s an increased interest from clients in what some people call the non-standard commodity markets. It’s not Henry Hub natural gas, but maybe UK gas, or TTF (if you want to call that a non-standard market), or we can speak about the China onshore commodities. Could you give a little bit of background on that? What are the developments that you see in that space?

[00:52:03] Maia

Absolutely. The main area in terms of risk premia where we see people incorporating those commodities is the trend space where obviously the large number of assets that you have to take bets across, the better your return profile can look. So we see a huge demand from clients running trend strategies or buying our own trends strategies to incorporate some of these commodities like particularly, you mentioned TTF, UK gas, where we’ve seen extremely strong trends over the past year. That’s one area.

The other area where I think clients find these commodities useful is where they’re looking to hedge inflation, then taking exposure to the commodities that are driving that specific inflation in that region can also be very helpful. Then finally, from an ESG perspective, there’s definitely demand for things like carbon. So UK emissions, EU emissions contract, even CCAs and some of the New Zealand contracts, people are looking to those as part of an ESG-linked portfolio and then also some of the commodities that are going to be used more and more in the energy transition, so for example, lithium, cobalt, battery-related commodities as well.

It’s quite broad and I would say it’s definitely an area that we’re quite excited about growing because some of the commodities where still liquidity is relatively low but the pace of change and the growth in liquidity is quite high. So the opportunities in the coming two to three years are very interesting.

[00:53:47] Moritz

Do you include the onshore China commodities in some of your indices and are they therefore investable or accessible for investors in an indexed format?

[00:54:00] Faheem

At the moment, we have clients who are trading these strategies but individually as futures, so they would trade them separately outside of an index. But we do have trackers that track the performance of these underlying commodities.

[00:54:14] Moritz

Interesting. Maia, Faheem, before we wind it down for today, is there anything you’d like to mention, anything you think that’s important that we didn’t touch on?

[00:54:24] Faheem

I think we’ve covered most of it from my side, but maybe Maia you might have something?

[00:54:29] Maia

No, I think it’s been very thorough.

[00:54:33] Moritz

Okay. Well, good. Maybe we’ll do a follow-up on it maybe next year or thereafter and we can review some of the performance or even new structural changes that you’ve discovered in the markets. I find it very interesting. I definitely follow it with a keen eye, the growth of that space, just the number of indices that are coming out and that are now accessible and investable for investors. I think that is great, a fascinating area. Again, I have a keen eye on it.

Listeners, I hope that you found it insightful and that you had some interesting takeaways from my conversation with Maia and Faheem. Please make sure to follow Maia and Faheem. If you have any questions, very importantly, we’re here to help. Just send an email to info@toptradersunplugged.com where we will pick it up and we will absolutely respond.

Thank you big time for listening. Really appreciate that. We’ll be back soon with another episode of Open Interest, so please stay tuned.

[00:55:38] Ending

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