Portable Alpha: Asking the Questions That Matter ft. Harry Moore

[00:00:01] Intro

Welcome to Top Traders Unplugged. In markets success doesn’t come from predicting what happens next, it comes from being prepared for what you can’t predict.

In each episode we go deep with some of the world’s most thoughtful minds in investing, economics, and beyond to understand how they think, how they prepare, and how they decide, and the experiences that shaped how they see the world. No noise, no short-cuts, just real conversations to help you think better and invest with confidence.

[00:00:35] Niels

Welcome and welcome back to this week’s edition of the Systematic Investor series with Harry Moore and I, Niels Kaastrup-Larsen, where each week we take the pulse of the global markets through the lens of a rules-based investor. I also want to say a warm welcome if today is the first time you’re joining us, and if someone who cares about you and your portfolio recommended that you tune into the podcast, I would like to say a big thank you for sharing this episode with your friends and your colleagues. It really does help us.

Anyways, Harry, it’s wonderful to see you and have you back on the show this week. How are you doing? What’s going on in your part of the world?

[00:01:09] Harry

Yeah, great to see you, Niels. All good over here in New York. I think we’ve hit 100% humidity today, so a bit uncomfortable, a bit of an uncomfortable journey in. But other than that, all good over here. And yeah, thanks for having me back. I really appreciate it.

[00:01:28] Niels

Yeah, absolutely. We have a great lineup, not least thanks to you. And so, I’m excited about it. Before we dive into all of that, I love to just get a sense of kind of what’s come across your desk that you found interesting. It doesn’t have to be anything related to what we do. But is there anything that you have come across that you have enjoyed recently?

[00:01:55] Harry

Yeah, I think… So, as I mentioned, I moved to New York kind of two and a half years ago and obviously you look around, you see the bridges, the highways, you’re like, oh, where did this kind of all come from? So, the book that is supposed to explain that best is the biography on Robert Moses. So, it’s called The Power Broker.

So, I ordered this book, it’s like 1,300 pages. So, I started kind of lugging that around on the subway. It’s a bit of a brick, to be honest. So, I’ve kind of given up. I don’t know, it’s a failure on my part, but I’ve given up and I’ve gone for the Kindle. So, I’ve been reading about it. But it’s just very interesting just to see like why is the west side a huge road when we could have had some coastline here. But you just get a feel for a bit of a biography of New York, in a way, I feel, for how the city’s been built. So, been making my way through that. Haven’t quite finished it yet, but yeah, did give in to the paperback and had to go to Kindle on that, sadly.

[00:02:50] Niels

Yeah, super interesting, super interesting. Now, for my part, by the way, speaking of bricks, I don’t know if you remember but back in the very early days of mobile phones, actually my brother-in-law still keeps his first-ever mobile phone and literally it was like a brick with an antenna on. I mean it looks so weird now, but it’s, that’s how people were carrying them around back then.

[00:03:19] Harry

Amazing. So, my first was a 3210. I don’t know if you remember the 3210, but it had Snake. I think it did actually have a tiny little antenna, but it was one of the first ones that you could like easily kind of carry around in your pocket. So not quite as far back as those huge brick phones.

[00:03:37] Niels

Yeah, exactly.

[00:03:39] Harry

This book would have put me in good stead for lugging around one of those old phones, I think, Niels.

[00:03:45] Niels

Yeah. Now, so on my radar, nothing really surprising I guess, but it’s just some of the headlines that I followed. Nothing really that is, well, somewhat linked to what we do, of course. But the first one is this story about this hedge fund, young guy, Leopold Aschenbrenner, and the situational awareness.
And of course, you read the headlines and the news stories, and you shouldn’t, probably shouldn’t believe everything that it says. But in any event, I mean some of the things that you do read, the level of experience, the level of assets that he suddenly commanded, the level of leverage he was using that people probably should have been aware of, so to speak. There are just so many red flags, in my opinion, when you see that.

And yet for many firms with decades of experience, and having demonstrated their ability to manage risk, and so on, and so forth, you see how difficult it is to raise money. And here someone with very little experience, very little track record ends up having $45 billion or so under management before losing two thirds of that, it seems, in a matter of weeks. So, I mean, it’s close up probably from location wise to where you are. But, yeah, it’s just a lot of red flags when you see something like that. Is that anything you talk about in New York at the moment? I imagine so.

[00:05:27] Harry

Yeah, I mean I think it’s a good example of where you can be right and still hit issues. So, I mean it called the trade, he obviously had a great story from writing the essay. He was very well known in those circles. And while it was, I guess, known as a hedge fund, the hedge was essentially the other side of the AI trade. So, even if on paper you look somewhat like dollar neutral or market neutral, your theme has kind of caught you out there, I think.

I chatted through this a little bit with like Yoav and Nick a few weeks ago on sort of capturing those themes, and it was a big bet on a theme. And that theme, obviously, reversed in July and that can show if you kind of compound on one idea what can happen if it goes wrong.

[00:06:24] Niels

Yeah, I think that’s a good point, actually, that diversification really does matter, so to speak. The other thing where I guess you could say it’s not necessary that the people who act are always right, but when they do act it does have an effect in the markets, and that’s the central banks of Japan and possibly, I guess, the US as well intervening in the Japanese yen only a couple of days before month end, which it did have an effect. It did show up in, I think, performance of our industry as well.

But, of course, on the back of 30 years of zero interest rates, only a couple of years ago, the BOJ started to raise rates for the first time in ages. And, of course, interest rates have continued to move higher, and the yen has (despite) actually continued to move lower. But then they made a real effort at least to change that. They’ve done it, I think, a couple of times before in the last two, three years. It’s never really lasted very long, but it was interesting that they decided to come out.

[00:07:37] Harry

Yeah, it’s one of those that, from a trend perspective, is tough as well. Right? Because sometimes I think it kind of works because you take that uncomfortable door position, and you hold it, and you write it down, and there’s rumors is there going to be intervention? Humans start to panic. The rules-based systems don’t. But of course, on this occasion, the intervention you did see a bump up. I mean, I did enjoy the photo. (I wasn’t sure if it was a meme.) Apparently, it was real, of Scott Bessent’s To-Do list for the day and it just was a blank sheet of paper with one bullet point that said ‘buy yen’. So, I mean I might need to fact that if that was an AI generated image. But it did make me chuckle. But yeah, it’s the importance of sizing and making sure that one position can’t hit you too much. But yeah, a bit painful when an intervention like that…

[00:08:35] Niels

Yeah.

[00:08:35] Harry

…makes something turn around.

[00:08:38] Niels

Sure. I mean you probably know this much better than I do, but if I’m not mistaken, I think it was like a 10 standard deviation move, which is rare. I know that when the Swiss franc got de-pegged from the euro, I think it was like 20 or 30 standard deviations or something like that. But still, 10 standard deviation is not something we should expect really to see.

[00:09:02] Harry

Not at all. Not at all. Right? That does not appear, and if you’re building market data, it’s very hard to actually simulate something like that. But we know they exist from time to time. I think when you’ve had a move go so far for so long, natural market forces weren’t bringing it back and I think that was where they decided to intervene and that human intervention’s caused the, as you say, very, very large move in the end.

[00:09:32] Niels

Yeah. All right, well, let’s do a little bit of trend following update and then we’ll get into some of these wonderful topics that we’re going to be talking about. So, my trend barometer finished yesterday at 27. That’s a very low reading actually. It’s a pretty weak signal. So, it suggests some softness to the beginning of August, which we’ll see also in the numbers in a second.

We just finished July, which was, I would say, a month that, I mean, it’s not that it was overall that bad, but there was definitely some dispersion among managers, which of course there always is, but still maybe more than I would normally expect in a single calendar month. Any observations, any takeaways from your side so far, whether it’s August, July, whether it’s year to date, anything that you find interesting about the environment for trend following at the moment?

[00:10:27] Harry

Yeah, I mean July was, if you look at July, it was kind of characterized by sort of two big themes. I would say you had a bit of a stopper on the AI trade, as we already briefly discussed, with obviously one hedge fund taking some pretty big losses. You also, of course, had kind of rallying energy markets again.

And so, I think from a trend model perspective, if you’re sort of taking sensitivity around three months, like a continuous sort of moving average, you probably would have been long markets like the oil complex, you would have been long stocks. So, I think this is why you’re seeing such dispersion. So, choices about how much to have within each asset class, how much risk weight to have in that in the long run; choices of whether to exclude certain markets or whole asset classes would have created a lot of dispersion in July.

So, I would expect people long equities would have been… that would have been tough, in general, particularly for some of those trend following kind of themes as well. So, if you were trend following themes and you were long, let’s say, sort of tech hardware companies, you’re long semiconductors, and you were short kind of software, that would have hurt us as well. So, I think that kind of explains a bit of that dispersion. You’re seeing that Niels – more energies would have been good, more stocks generally bad. So yeah, big dispersion in July.

[00:12:08] Niels

I don’t know if you managed to listen to it but last week I had Rich and Dave Dredge on the show and there was one thing that really, it wasn’t surprising in that sense but still when you hear it you think, wow! And that was this example that Dave brought, up early on in the conversation, about, I think it’s South Korean stock, SK Hynix or something like that, where the stock had gone up like 120% and then down to flat for over a 3 month period. But the 2x levered ETF was down like 40+% for that period. Again, obviously highlighting the dangers of leverage and how some of these “retail products” are put together. But anyways, it certainly has been an interesting summer so far.

But as I said I think for a lot of people it was really the yen that kind of defined how you’re going to finish the month of July. So, let’s look at that.

So, the BTOP50… Obviously, we did the July figures last week, but as I said, actually, it’s a little bit soft getting into August. So, we are down 67 basis points for the BTOP 50 (and that would be as of Tuesday night, we are recording on Thursday this week), it’s still up 7.17% so far this year. The SocGen CT index is down 88 basis points, up 7.15% so far this year. The Trend index down about 1% or so – 94 basis points, up 6.89% for the year. And the Short-Term Traders Index actually up for the month 14 basis points, but up just 3.32% so far this year.

If we look at the traditional markets, we have the MSCI World Index (and this is as of yesterday) up 2.77% in August, up 12.45 for the year (stripping out US and Canada based on the proposal from a listener) the E index is up 1.83% in August, up 11.8% for the year. US S&P US Aggregate Bond index up 64 basis points in August, up 31 basis points this year. And the S&P 500 total return of 3.12% so far this month, and up 12.83 this year, a really strong, by the way, start this month to equities for sure.

I mean, from my perspective, before we move on, not a massive amount of change, directional change in kind of a trend following portfolio. There are certainly changes in terms of the levels of commitment but, generally speaking, CTAs are still long equities, probably long metals, probably (as you said) long energies, probably long the dollar I would imagine, and then still, most likely, short fixed income, to some extent. So, not a lot there.

When I look at another thing that you can comment on if you want, you don’t have to. When I look at like a standard trend following model and I compare just different speeds, because you mentioned speed could have been a determinant, and I think that that’s very true. So, I tend to look at a traditional trend following model and looking at say four different speeds, meaning lookback periods, 20 days, 60 days, 130 days, 260 days. And if you had chosen a 20-day lookback, again this is just from this specific model, you would actually have been down this year, and if you had chosen a 60 day you would also have been down but not as much. If you had chosen 130 days you would have been slightly up and if you had chosen an even further out 260 days you would have been up a little bit more. That’s kind of how I see speed impacting, at the moment, the environment or the results. I don’t know if you have anything that you want to say, comment, on here.

[00:16:31] Harry

Yeah, I would say that completely makes intuitive sense to me. So, if you’re running a slower trend system in 2026, you would have been fairly more persistently sort of short bonds which I would have certainly paid in the past few months. Once you built into those long energy positions, you tended to hold them a little bit longer which on a year-to-date basis would have helped. You might have suffered a little bit more volatility there, but would be also adding.

And then in stocks, I mean the longest models have done the best there as well. So, very long lookbacks. You’ve been persistently long stocks. Again, we’ve had some wobbles along the way, but if you look at stock indices, in general, across the board, they’re up.

So, it’s certainly been a year so far. I would say slower has been beneficial. We haven’t had a turning point that’s persisted for long enough for the fastest speeds to kind of shine. So, echoes a little bit of what we’ve seen over the past few years. That slow has tended to outperform a bit. Again, we’ve done research on this. You’ve discussed it on your show. I mean, my view is always to have some dynamism there that you can still capture some of those turning points. But yeah, I completely agree with what you said, in general, you’ve on longer out which eventually means you’ve held onto your long energy positions, you tended to hold on to your short bond positions, and you’ve just been long stocks throughout the year which has worked well.

[00:18:07] Niels

One thing I don’t personally follow so closely, I would imagine you do more so, and that’s because at DUNN we just trade kind of the most liquid futures. But when the other thing that drives performance, as we often talk about before we get into the topics, is of course market universe. You’ve written beautiful paper about this that was published in late January actually. So, I don’t know if you remember this, THIS is not something we kind of plan to talk about. But do you think there’s been a lot of dispersion based on market universe this year as well?

[00:18:42] Harry

Yeah, absolutely. I would say this year you have really seen your kind of old versus traditional markets like diverge to a fairly large extent. And I think there are two places where this is most evident, or maybe three actually. So, the three that we could touch on would be in alternative energies, in fixed income, and then also in stock.

So, in energies, if you go down that alternative market spectrum, you’re starting to look at things like Nordic Power or kind of Italian Power. Those markets have just tended to be a little bit more range bound than we’ve seen in the more traditional energy markets this year. So, again mid frequency models tending to be profitable year-to-date in things like crude and heating oil, etc., and less so in some of the old energy use.

In fixed income, fixed income has been pretty difficult for a few years for trend. Right? As you know, you’ve had these kind of competing forces in fixed income. You’ve got the administration shouting for rate cuts, rate cuts. You have markets follow that occasionally. Then you look at the inflation print, you look at rising deficits, and you’ve got these kind of two forces.

And I think that’s been even more pronounced as you move down the spectrum and away from your big traditional government bond futures, so away from 10-year treasury and your gold, and you go and look at, for example, Polish interest rate swaps or other markets like this. Again, those kind of gyrations have just been more pronounced and they tended to be a little bit more difficult.

The final one to talk on, and we did discuss this to an extent in the paper, and actually my colleagues have recently written a paper on this as well if we get time to talk about it today, great, but otherwise we can of course save it for another time.

[00:20:36] Niels

Save it for next time.

[00:20:37] Harry

Essentially there, typically, when you move away from your stock indices, so move away from just like S&P and NASDAQ, Euro stocks, and you start to look at cash equity sectors, for example. So, you might look at for example like STEMIS versus software, as we mentioned in the intro. You might also look at things like value, momentum, quality. Those are much more cross sectional constructions. Right? So, as we were discussing, a slow model would have been long the indices. The indices have gone up this year and that’s worked well.

These cross-sectional models, I mean, from a high level, if course they have also been profitable just in a very different way. Right? So, if you think, if you construct a quality factor, quality has really struggled this year on a year-to-date basis. That factor you can imagine may have added as well. So, you’ve got three areas, I think, which have contributed like a big portion of the kind of variance between what you might call your traditional futures and forwards versus your more kind of OTC type instruments. So yeah, great note to notice that Niels, and I think that’s absolutely worth a comment there.

[00:21:56] Niels

All right, well, let’s move on to the topics you brought along. And before we get into kind of the papers, so to speak, I think you very nicely laid out something that ties into the initial part of our conversation, both in terms of AI and macro. So why don’t you take us into today’s main feature, so to speak?

[00:22:21] Harry

Yeah, absolutely. Thanks, Niels. And I think this was kind of motivated by a few things. Of course, the events in the past couple of weeks with the sort of unwind and people stepping in to buy this AI trade that hit the headlines, but also really motivated by your conversations you’ve been having on diversification. So, with David Dredge and Richard Brennan last week, and then a couple of weeks back with Nick and with Yoav. From a trend following perspective, looking at the AI trade and AI theme in markets, it’s a very interesting time to kind of be part of it and be living through it.

Because on the one hand you have all of these comparisons back to the railroads, back to laying cable in the dot com bust, and these forecasts that it has to be a bubble. This is one side of the argument, it has to be a bubble. It’s got all the echoes, it’s got all the hallmarks of these prior periods where CapEx went through the roof. There’s mania about a new thing that’s appeared, and it all kind of all ends in tears, so to say. Not to say that there isn’t some very useful infrastructure that’s left behind, but from a market’s perspective, it’s very painful.

That’s kind of one side of the argument. The thing is, I always try and really stay balanced. Right? Like that is a gripping narrative. It’s a great story. It’s a gripping narrative to kind of tell to a client in a meeting. But you’ve also got to consider the other scenario, the kind of upside scenario. And if I look within Man Group, we are integrating AI across all of our teams, across all of our processes. We’re seeing benefits to quant research – just a huge amount of productivity gains.

What do you want your researchers to be doing? You mostly want them to be thinking and being creative rather than writing lots and lots of Python code, where the Python code is now kind of table stakes and you can do it much, much faster with these tools.

Do I see the benefits here? And you hear obviously the Frontier Labs, as they would say, talking about what the future might look like and so, I always try and stay balanced on this. Maybe there is this huge upside scenario where the trade that we saw go wrong last month is the right trade and it works over the next 10 years. There’s this kind of real level of uncertainty and I think it links to your tagline of the show – being prepared for what you just really can’t predict. And that’s how I think kind of trend following comes into it. Because if you think, you’re trying to build a portfolio to be resilient in this environment. Right? So, you’re allocating to many different asset classes. You probably, in an allocated portfolio, have a decent amount of equities, you have a decent amount of bonds, you probably hold potentially some diversifiers like commodities, you might hold some private assets.

Now in that portfolio, AI trade keeps going, economic growth keeps rising, CapEx keeps rising. That portfolio is rising. Now on paper you look diversified. These assets, they seem to have, in normal times, somewhat low correlations with each other. You feel diversified and you feel comfortable.

Now, in a crack to that trade or a full blow-up. And again, I mean here like the S&P isn’t down 5% or 10%, it’s down 30% or 40% over a period. When you look at that book that you own, what in there is going to protect you against that huge drop?

So, you would say, well the equities, we’ve just said, potentially they’re down 30% or 40%, private assets – you can slow your marks, they may mark down more slowly but at some point you’re going to take hits there. Right? And we’ve all seen the slowdown of return of capital. We’ve seen the FT and others running articles on what a client’s going to do when they’re getting a lot less capital back than they expect. You’re certainly not getting any long-term protection there. You might get some paper protection for a period.

Then in bonds and gold, earlier in the year we saw, again, both of those sell off in a fairly correlated manner. The difficulty as well is if you look at the environment today and you look at the Fed in the US and you say well inflation’s now been running above target for many years. It’s not just like it’s a short period, it’s many years. Do they have the tools to really cut rates like we’ve seen in the past to kind of buoy markets? And if you’re worried, like maybe they do, but if you’re slightly worried about that then you might be in an environment where your bonds are also selling off very hard as well. And this is where I think trend kind of comes into the equation because, as we know, trend doesn’t have a long-term view on any of these markets. Right? It trades them all.

Okay, it doesn’t trade private assets, but you’re trading equities, you’re trading bonds, gold, oil, etc. It doesn’t have a long-term view on those. It’s just simply following the prices and saying, well, what I’m seeing in the last two weeks, in the last four weeks, in the last two months, is actually the narrative is changing. And I don’t actually want to be long bonds and long equities. I actually want to be short bonds and short equities. If it’s an inflation shock, you want to be long commodities. And so, trend actually moves into those positions. And now that’s not to say it’s always going to get it right. Right?

It’s going to cause you some pain from time to time. It’s really being prepared for this tail, this left tail – the scenario I described at the start, when there is a crack, there’s a break, maybe AI does deliver, but it’s just not at the level that people have priced in. And having an asset like trend following in your book that can go short bonds, it can go short equities as we described, it might even go short the AI theme. Right? It might start to short SEMIS and short the software, the hardware, the tech hardware builders like you could go short those names.

And really it’s just offering that something completely different in your book that, as an investor, when you look and you say, okay, my assets are uncorrelated, but are they uncorrelated in that left tail shock? Like, what does your covariance matrix look like in that shock? And typically it’s trend following that is zero or kind of negatively correlated. And other asset classes of correlations typically pick up.

[00:29:17] Niels

Yeah. I mean, you’re kind of tempted to say that the beauty of trend following is that it’s aware of the situation, it’s situationally aware. But I think that name is taken, unfortunately, or maybe fortunately in this case.

Now let me ask you something. You mentioned something that because I’m not a quant, I find it interesting. You mentioned this thing about the productivity gains that you see from having AI tools, and so on, and so forth. Just sort of on a very basic level, I understand the gains that someone like yourself who were taught how to program, you can essentially tell it what to do and you can also review the output.

Are you, when you sit in your quiet moment in the underground, on your way home, are you thinking, well, what about the next generation where they will use AI as kind of their first tool, but they actually don’t have the background and experience in having done programming myself, so how are they going to tell if what the output was is any good? Is that something you think about at all?

[00:30:36] Harry

Yeah, absolutely. Niels. And I think to best answer this, if we go back and kind of the story of when I joined Man Group, so I’m originally an actuary and basically in actuarial work 10, 15 years ago, programming is not a big part of the syllabus. Right? So, when I joined Man, the first thing I did was go headfirst into all the developed courses run here and spent hundreds of hours in Python being able to do financial analysis, this sort of simple pandas, data manipulation, etc.

Now today, when I look at the tools available like that was essentially a complete waste of time. To the level that I can program it’s absolutely a complete, useless waste of 200 hours sort of seven years ago now. So, at that level the tools are absolutely fantastic.

Now, if you think of a quant researcher and what you’re really employing them for, what’s the value of them in their seat? Right? So, 5, 6, 7 years ago, how’s your time spent? Well, it might be 50% doing the creative stuff, thinking, and 50% (maybe more than 50%) is on implementation details – being good at Python, knowing how to run and set up a backtest, maybe replicating a test across multiple assets.

Now, if you think where the value is, the value is not really in that second 50%. There are many, many more people, maybe 5, maybe 10 to 1 times as many who you can employ to do the second 50%. It’s kind of table stakes in quantitative research – being good at Python, being able to run a backtest. Where the value really is, is that if you could split that 50/50 and make it more like 90/10 so that your top researchers have plenty more time to think and be creative. I think that’s a big AI enabler. Right?

AI, while it can summarize everything that’s been written before, it can summarize the tax code, it’s very, very difficult to come up with brand new kind of quantitative high alpha ideas. That’s what you want your researchers to do. And it’s then what I would call table stakes jobs, speeding up and picking up.

Now how that links to hiring. I mean, I think this is one we’re all still trying to figure out. Because those very junior level graduate type roles, where you would spend more time on the kind of BAU implementation, etc., it’s kind of being squeezed out by these tools. And we’re automating a lot of processes that used to require more humans.

So, it’s a great question, like, how do you really interview for these skills? And I think it’s, again, on the optimistic side, the people coming in now have grown up and been more involved with the kind of frontier models. Maybe they are more AI literate than kind of all of us. And so, they have that advantage. I think the big disadvantage is that just temptation at every moment. The answer to the problem you want to solve, especially when you’re junior, is just one button click away.

Whereas you and I, we had to go and dig out the papers, we had to read the theory, we maybe did some examples ourselves. I mean the actuarial exams you literally hand write out, like, ruin theory you were talking about last week, you’re writing out all of the maths behind that.

The new generations will never have to do that. And you have to have some serious discipline to go and dig the book out when you know you can get a perfect one-page summary with a click. So, I think you’ve got to test, like, give someone an AI generated piece, get them to go and critically analyze it, like what’s wrong with it? What would have I improved on it? Because it’s just a fact of life now. Locking someone in a Wi-Fi free, computer free box is not a good test of what working life looks like today.

[00:34:58] Niels

Right? Yeah, that’s very true. That’s very true. I don’t know about you, but I actually often get the question during sort of more due diligence type meetings. And people might ask, well, do all your researchers do their own programming? And of course, the answer so far has always been yes, of course they do. That’s part of what we want them to be able to do. And they need to review each other’s code and all of that stuff, right?

And I guess soon we’ll just say no, they don’t know how to program, and we’ll see what face they put up, the investors, if that’s a confidence face or a less confident face, but only time will tell.

Now, before we move to the paper, I want to make sure, I think there might be more things. Do you want to link to more things before we dive into the first paper? Because we’re going to talk about a new paper that you co-wrote with a few colleagues, Jonathan Smith and Chris Pi, I think it was, which is on Portable Alpha. But is there something more you wanted to bring up before we move into that, Harry?

[00:36:07] Harry

Yeah, I think we can jump into the paper. Maybe just to set the scene a little bit, I guess a couple of the things that the paper really dives into you sort of discussed last week, the week before. And if I go back to as well, Rob and Katy, you mentioned some of the topics there as well. So, I think a good way to lead in it to start with kind of the cash efficiency of trend following because that really builds into portable alpha.

And I mean all of your audience already know this, but trend is running on futures, right? You don’t need to put up a hundred dollars of capital to get your hundred dollars of exposure.

Now this unlocks quite a few interesting features as an investor, right? If I only need to use US$20 or US$25 to get my US$100 of trend following, it gives me a few new levers and things that I can do with the freed up cash. One common thing I hear is, I like my portfolio. I’ve been happy with it. Like I’m going to have to really kind of sell something down to free up some space for trend. And because of that capital efficiency, well, you don’t have to sell very much or if anything at all, right? So, you can get an allocation, you can get the potential benefits, the defensiveness, the convexity, the things that all your audience know very well for very, very little capital up front.

Now, the next thing I want to mention, and, again, it links to the paper that Rob and Katy discussed was on, it’s cash efficient. You’re getting this big benefit from directionally following markets. And in the paper that they discussed, they said, well, what is the additional benefit of the cross-sectional trend models? Could I replace cross-sectional trend models with, for example, carry? And I think the paper goes into what other alpha sources might you want to put alongside trend?

Now, because it’s so cash efficient, right? You’re not having to stump up the full amount of dollars that frees up cash to go and, I would argue, keep the cross-sectional parts, but it had lower correlation, it still had positive Sharpe, it was still additive and add additional alpha models on top. So, in this sense you can sort of have your cake and eat it too, right? You’re getting the full benefit of your trend allocation. You can fund it with, let’s say, your US$20 to US$25. You can use that freed up cash to add some extra alpha in there. So, that’s one way of looking at the problem. Right?

Imagine, again, you’re not getting your cash flows back as fast as you want from your private allocations. Maybe you have less equity, therefore less cash available to invest. Trend following is potentially a nice way to get access because you don’t need all of that cash back to get the full allocation.

Now, the way this then links into the paper is essentially the paper is fully focused on portable alpha. So, we’ve talked about a few ways that you could maybe take the cash and invest in other alphas. You could just simply use less cash to allocate to trend. What the paper is doing is it’s jumping into the portable alpha topic and saying, well, what I’m going to do here is I’m going to sell essentially no equity exposure. I’m going to keep my portfolio exactly as it was. And what I’m going to do is I’m going to take a portion of my equities, let’s say I own some passive S&P and I’m going to hold that via a future or a swap, and I’m going to port my trend following or my other allocation on top.

Now, before we kind of dive in, the reason that we wrote this, and Jonathan and Chris were superb as well in contributing a lot of the analysis and some of the structuring as well to this paper. But the reason we wrote this is that there’s tons of introductory material on portable alpha now. If you go and search, you’ll be able to find how should you do it? Should you use turnkey? Should you do it yourself? What are the kind of risks of combining over 100% exposure? There’s plenty of introductory material.

What I found, sitting in meetings and listening to people asking questions about portable alpha, was the kind of next level down, what I would call this sort of second and third derivative just wasn’t covered very well. So, if you go online and you search, for example, how should I rebalance portable alpha, or how much cash should I hold in a portable alpha structure, you don’t find great answers and you don’t really find much analysis on it. That was really the impetus to go out and write this. Let’s use some simple examples to kind of make those points and bring them home. And yeah, we published it this week and yeah, happy to dive in. But if there are any questions up front, Niels, please go ahead.

[00:41:22] Niels

No, I mean, I think you’re absolutely right. I think it’s an important point that. Because I remember first coming across portable alpha, back in the mid to late ‘90s, talking about it. At the time, the firm I work for, we never launched anything but the concepts were there. And then, for me, I think really it came to the forefront again when Eric Crittenden and his group of people launched the Standpoint product in, I think, 2020, just before COVID, if I’m not mistaken.

That’s really where it came back into light and says, yeah, this is interesting because, to be very frank, there is definitely a slide that has been sitting in my slide deck for many years. And that is just to visualize for people what happens when you combine trend and equities. Because a lot of people think, well, if I put 50% here and 50% there, I’ll probably get an average of the two. But actually, you get almost the sum of the two return streams, which is the crazy thing.

And so, obviously, as you say, it’s become popular again. Many people have launched products. Corey and his friends have really made it very public with the return stacking concept. So, that’s another way of thinking about it. So, they’ve done a great job in that and had big success in raising assets for it. So yeah, it’s an important point.

I also have some pushback that I get in meetings, which I’ll save and ask you a little bit later. But what I really loved about your paper is the fact that it actually does ask questions that even when I saw it, I thought, yeah, we never really talk about these topics. We should, because they are really, really important.

So, for from my point, I would just love for you to go through these questions that you so eloquently ask and answer because I think people will be… And obviously, people should go and download the paper and read it, of course. But they’re truly important in the context of this conversation, without a doubt.

[00:43:52] Harry

Yeah. Thanks so much, Niels. And it really was to answer some of those questions, actually. When I first kicked off doing some of this work, we thought, oh, this will be a week and it’ll be so fast. And as you do it, you realize just how many questions you need to answer to build a resilient structure.

So, yeah, if we dive in firstly on the title. I know last time I was on with Katy, and we were sort of touting writing a portable alpha paper. I think the working title was something like Covariance of Alpha and Beta in Stressed Markets – some horrible mouthful. Fortunately, we have a very good editorial and marketing team here and the title is now Portable Alpha, Ask the Hard Questions, which I think is a much more clickable title than what we had penciled originally.

[00:44:46] Niels

Sometimes it’s good when you just say it actually does exactly what it says on the tin. And that’s kind of what this title illustrates, yeah.

[00:44:53] Harry

Absolutely. And so, if we dive in, what were we really trying to answer here? So, there’s a few things that kind of continuously come up in portable alpha. Now we know that in 2008 many of these structures failed and the lines down which those structures failed, those risks still exist. Right? So, not enough liquidity, correlations between your alpha and your beta, those things can still blow up and hurt you today.

So very much not saying, oh, don’t worry about it, like the world is different to 2008. It’s very much saying, well, how can we build resilient structures that would survive docs like the GFC, like the dot com bubble, and like COVID.

Now, the first thing I would pull out is essentially what we found on cash buffers in these strategies. Now, if you think of a portable alpha structure, you’re buying your beta. Let’s use S&P 500 as an example. You’re then buying an alpha. And now, throughout the paper, we use SG Trend, obviously, as a trend podcast.

[00:46:11] Niels

Slightly biased here.

[00:46:14] Harry

But fits nicely. You could pick multi strategy, you could pick other alphas in there. And we’ve used trend in the analysis, but internally we’ve done this analysis across many different alpha structures. And so, you’re buying 100% of your beta and then you are layering the alpha on top.

So, what you’re aiming to do is replicate the passive index. You’re aiming to deliver the S&P to your investors plus whatever alpha you can get on top.

Now, obviously when you put your S&P onto a derivative, onto a future, or a swap, you’re no longer putting up the full US$100 to get your US$100 exposure. Now you can put as little as US$10 in to get your US$100. Now, that frees up US$90 to go and buy lots and lots of alpha.

The problem is, in a stress scenario where your beta starts to sell, well, your counterparties are going to start calling you and saying, hey, your position is losing, you need to send us more margin. Now, if you’ve only supported your S&P with US$10 of capital, and the S&P falls by 10%, all of your margin is gone. Right? And so, you need some unencumbered cash. Though it’s a given, you have to have some unencumbered cash in the structure.

Now what I think is really interesting is you might then say, well, okay, I’ll have an extra US$10 of unencumbered cash. That should keep me nice and safe and I can weather a pretty big drawdown.

Now, that 20% unencumbered cash, if you hold it and you stimulate this S&P plus trend portfolio through the last 26, 27 years, that gets fully drawn down three times. So, you get truly drawn down in the GFC, in the dot com, and in COVID. So, your structure will function fine most of the time. And then in these big left tail events, well, you’re most likely going to have to close out some of your beta position.

Now, this is a really good question as well because, is that bad? Now, if you are an allocator, and you have benchmarks, and from one day you are 100% long the S&P like your benchmark, and then the next day you are 20% long the S&P because you’ve had to sell a lot of that position. You have now massive tracking error versus your benchmark.

The second point is that, over the long run, equities have tended to go up. And after big crises they’ve tended to rebound. And so, one part that we look at is if you were selling in the early part of 2009 at a low in your equity component and you’re knocked out of that position, you miss kind of 29%, 30% of the, of upside. So, it can be a pretty bad outcome. It’s certainly an outcome, as risk managers, we do not want to happen. Right?

We want it to be a very, very left tail, very far away probability. So, what we look at is we say, well, what is a cash buffer that starts to really make it very unlikely this will happen (now I say unlikely, not impossible)? And in the paper we end up with essentially 40% cash to support your beta. So that’s 10% margin, 30% unencumbered. And our approximate calculations mean that will get shut out, as in you’ll have to re-gear that beta about once in every 1 in 200 years. So, it’s not impossible.

Essentially, what we’re saying is, if you look at market data back through time, look at live data, it survives. But obviously, you’ve only had one run of markets. Again, you talked about this at length last week. You’ve only had one path of the truth that actually happened.

Now, we know you could get bigger drawdowns than that in the full distribution. And so, by running Monte Carlo, we’re able to sieve these sort of severe drawdowns. And we think that kind of 1 in 100, 1 in 200 year sort of time frame is reasonable. It’s very unlikely but manageable. If it happens, you’ll have less equity exposure, you can re-gear it afterwards. But that’s sort of where we looked at that.

Now, what I think is the interesting finding for listeners is if you’re looking at say a 20% buffer or suddenly you’re massively increasing the chance that you get knocked out… And some people might be fine with that. They might have callable capital available, they might have a credit facility, they might be happy to get on the phone with counterparties and post that margin at short notice.

Now, a lot of investors that we work with do not want that. They want us to take on the operational complexity, the cash flow complexity of risk management, etc. They do not want to be getting margin calls on a Monday – please send cash by end of day. So, that was one finding on the cash buffers.

Now the next bit that we essentially were also looking at, I mean, firstly you can look at the alphas that you hold. Like why does trend make a nice alpha and portable alpha? Well, it’s super liquid. Right? If you have something that’s like weekly dealing or monthly dealing, you can recapitalize quickly. So, it makes trend a very, very nice candidate for it. But you can of course use others.

And the next one we really dug into, which again, it started coming up in my meetings, was how should I rebalance my alpha and my beta? Now, at first this sounds like the most simple question. Well, obviously just pick monthly, or pick quarterly. It can’t be that difficult, right? That’s how I rebalance my alts portfolios, how I balance my equities and bonds. So, then I would say, okay, so we’re going to pick monthly rebalancing.

Now, at year end, do you want the return of the S&P and your beta component? And they would say yes. And I would say, well, as soon as you select a monthly rebalance, you no longer replicate the S&P. And this is actually quite interesting new news to a lot of people. And when I first was looking at this, I was like, oh yeah, this makes complete sense, right? If you own the S&P, you don’t buy it and sell it every month, you just own the index. If it goes to 110 and then it makes another 10%, it compounds on itself.

If it goes to 110 and you sell US$10 of it to rebalance, you’re no longer tracking the index. When you get to year end, you’re going to get a surprise that, oh, my portable alpha beta component has lots of tracking error versus the index.

So, we’ve run a number of different rebalancing rules. If you want to track the index as close as possible, you should actually buy and hold that index. So, maybe do an annual rebound or even longer, just hold it and hold your alpha.

Now in the paper we say we wouldn’t do this ourselves, because as you buy and hold, you’re going to slowly get this drift. You might own too much equities or too little. If other investors come in, how do you get them to the nav, do you give them this non one-to-one mix that they’re looking for?

So, we think, essentially, if you can let that beta replicate closely, so rebalance a little bit less but have some sensible tolerance. So, let’s say not going to rebalance, but if my S&P and SG trend goes more than 10% out of whack, I will rebalance.

Now, it’s all very good then talking through this. We put some numbers on it to look at specific episodes. So, something like 2008, it pays to rebalance very slowly because your equities are falling, your trend allocation is rising, every time you rebalance, you rebuy, you increase your equity allocation into a continually falling equity market. You sell trend that’s rising. Essentially all rules, other than a buy and hold, underperform in 2008.

Now, that’s just one example, right? And it’s one that everyone quotes, like everyone who is in this business will always talk about 2008 and rebalancing. But what about over the long run. And over the long run we found that, in performance terms, it kind of doesn’t matter. So, whether you do monthly, quarterly, annually, whether you do 5%, 10% tolerance, it doesn’t matter over the very long-term. But you will get periods where it really matters, like 2008, potentially like COVID.

And so, all we wanted to do was just to highlight, look, but it’s a good question to ask, just have a look at the paths. Are there any paths that you really wouldn’t like in there? And therefore that might push you towards a certain rebalancing rule.

So, we look at that. Again, it comes up in so many meetings, we wanted to put something out. We’ve internally done it with other assets, but here we’re doing trend.

And the final bit of the paper, which again, if you Google, should I use swaps or futures in portable alpha? There’s not a ton amount of information. Right? So, that’s another one that we looked at. So,
essentially kind of no free lunch, no arbitrage assumption, which it obviously holds in a market as big as this. But I just wanted to see it proved. Because you come to this, and you say, hang on, so my counterparty bank’s going to charge me 40 basis points over offer. Why don’t I just go and buy the future where I don’t see the 40 basis points over?

Whereas there’s an explicit cost in the swap, it’s implicit in the future. And if you run this over time, it washes out. Right? You don’t get a structural performance advantage. So, what we described there is essentially it comes down to operational preferences. Internally, we’ve run features, we’ve run swaps. We make the case that it’s probably down to investor preference and there’s some operational considerations. Obviously, futures, you don’t have the counterparty risk, that might be a consideration for some in a structure like this as well.

So, we dive into that and that’s kind of where we end. And really, the conclusion is that there isn’t a completely right or wrong answer for any of these, but you should be talking with your providers about these questions. You should understand kind of what will happen if you draw down 20% in a month. It’s important to know ahead of time.

[00:56:33] Niels

Yeah, I mean, that’s great. But I also think it’s important because, I mean, obviously you do touch on correlation. We, obviously, talked initially about why trend is such a great candidate. But as these strategies become more and more popular, we have seen some other combinations that may look great in a backtest, but where there might be some risks that haven’t been exposed in the last couple of decades, partly because of correlations, partly because of other things. But, I mean, you did a great job in touching on all of that.

Now, in consideration of time, I think we have maybe another sort of 10, 12 minutes left. And I know there’s another paper that we came across that you really liked and it’s about the mathematics of… Turtle Trading is the name of the paper written by Nicholas Polson and Vadim Sokolov. And I would not mind if you could give us a little bit of a flavor of what’s in the paper, why you liked it. So, maybe we don’t have time for a full deep-dive deconstruction of the paper, but maybe kind of focus on some of the highlights and then people can go and read the paper. I’m sure I’m going to link to it in one of my weekly emails. But I know you found it very interesting, so why don’t you talk a little bit about that, Harry?

[00:58:15] Harry

Yeah, absolutely. And thank you for flagging this one, Niels. It was a superb read. I really enjoyed it. It did absolutely take me back 10 plus years now to my actuarial days. A lot of the concepts in there, like you’re tested on in the fairly abstract, and it’s lovely to kind of see them all unified here.

But essentially what the paper is doing is it’s saying the Turtle rules that were applied in the ‘80s and then published in the early 2000s, like these were kind of rules of thumb, hand calculable, pen and paper, easy to do in a few minutes approximations of later decision theory mathematics that was kind of published in academia. And what the paper is doing is it is linking those original rules, from the ‘80s, to academic papers starting from the ‘50s, but all the way up to now.

And so, it’s a beautiful combination of some history, some financial history, behavioral economics. I know you’ve talked about kind of the Turtles and the behavioral side, but here it’s really relating that to what was later published in kind of decision theory, probability theory by authors.

So, when I read this, I also opened the original Turtle rules and read those original Turtle rules as well. And obviously, the story there, a group of people with limited or no trading experience, given a set of rules, follow these rules, and you will be a profitable trader. And we know some of those came out and were very successful. But I mean this isn’t the area to touch on. You’re a much bigger expert on that area than me, Niels.

So, let’s actually jump into the concepts that they bring out in the paper. And the first one, which is a favorite of the audience and all of us practitioners is estimating volatility.

[01:00:14] Niels

Right.

[01:00:14] Harry

So, the Turtles have a property N which is essentially the volatility of a market. And they use a range-based estimator, very simple to update each day. It’s essentially like an exponential decay, half-life of 14. The people calculating this with pen and paper were simply looking at open/close, high/low, and getting some volatility estimate for each market. So, that’s what we would today see as our vol estimate in crude or our vol estimate in treasuries.

Now, what’s interesting is the paper then links that to published research. For example, Parkinson who says look, this is actually an efficient way of estimating vol. Their kind of pen and paper calcs that they were doing on the fly is backed up in academia as like, yep, this is a legitimate way to estimate vol.

Now, where I think it starts to get more interesting is on, well, you now have vol estimates for your markets. How do you position size? Like, how do I take that information and risk weight my positions?

And so, the original Turtles, they had this idea of a unit such that one unit of each commodity or financial asset they were trading would be equivalent in risk terms. You can imagine a unit of crude oil, while you might need a unit of crude oil versus a unit of 10-year US treasury, they’re equal in risk terms even if you have to buy five times the dollar amount of the treasury. And so, really what this is this volatility based position sizing, right? So, it’s risk-based position sizing done in the ‘80s by the Turtles.

Now, where this then starts, and what the paper does, is it then relates this to a lot of the academics around these position sizes, optimal bet sizing. And so, they bring in the Kelly criteria which, again, you’ve discussed at great length. They quote Ed Thorp. And for your listeners, if anyone hasn’t read his A Man for All Markets. I mean that is probably, I would say, the best description of how to implement this in practice. And essentially, it’s saying that look, the optimal bet that you want to take to compound and grow wealth over the time, you need to take some account for the probability of going bust, like the ruin theory that you’ve discussed.

And as you increase your bet size, you increase your expected return, but you’re always adding this tail that you might hit. I mean what as actuaries would call ruin. I think we have ruin theory is very applicable here. And what the Turtles did is they had stops in based on the risk of that position. Through this quantity N which is your estimate of vol at 2N, you take your position away – you stop out of that position.

And what the paper is saying is, well, look, this 2N equates to 2% of your total equity in your book, i.e. that stop is set that you can’t lose more than 2% of your equity. And it links it to the Kelly criterion – optimal Kelly. Now, what the authors describe and what they actually show, and they do attest with their own data later on, is this is quite far on the kind of Kelly hill. You have some optimal Kelly which is under no uncertainty and that maximizes your long-term compound growth.

Now, any prudent manager will be on the left side of that hill. Right? You are taking a little bit less of the upside for a much lower probability of ruin. And what the paper shows is that these Turtle traders, back in the ‘80s, were running a version of that, a pretty prudent version of that where their risk of ruin was much much reduced. So again, for listeners, I think the Ed Thorp book describes it fantastically well. The paper also kind of links all these concepts really nicely.

Now, the next thing they look at, and again very interesting for a modern day trend follower, is in that position sizing obviously it’s not just volatility that impacts that. As we all know we should be considering things like liquidity. We should especially be considering the correlation between these markets. So, any modern risk position sizing will also include, obviously, information on the correlation between markets.

Now, remember, back in the ‘80s these traders were using pen and paper to calculate. Right? They weren’t inverting covariance matrices and they had to have rules of firm. And so, the way they dealt with correlations was not in the actual construction but it was on position size caps. They correlated markets, they would cap, and, again, what the paper does is it essentially just links this to academics, more modern decision and portfolio construction theory, which is just, it’s beautiful that they were kind of doing it in the ‘80s.

And the final point I would touch on which was the (I mean there’s a few more in the interest of time)…

[01:05:42] Niels

Sure.

[01:05:43] Harry

… they also had drawdown rules. Right? So, like many managers will have this certainly multi strategy pods, you have these very strict drawdown rules. Now, for anyone kind of really interested in the maths of kind of Kelly and drawdown control, you know, under your Kelly, if you are left of the top of the hill you shouldn’t go into ruin. Right? You’ve got a decent probability of avoiding ruin.

The issue is, if you’re down 80% in your strategy, you still know it’s going to work. If it’s your strategy, and you’re following the rules, and you are indestructible in your confidence, you don’t suffer from human biases, you implement that, you should be fine. The issue is, and in the Turtles case, obviously they’re being given a notional book of someone else’s equity. In a manager’s case, you are running allocators money, you are running the money of pension scheme participants, teachers, etc., you cannot stomach an 80% drawdown. So, what is an optimum rule for mitigating those drawdowns?

And so, the Turtles back in the ‘80s, I’m just trying to recall, I think they lost 10% in the market, then they would cut their equity by 20%. So, you’re essentially approaching a limit where you essentially can’t lose any more at some point.

Now, what the paper does is it links this to the later published work of Grossman and Zhoe in the ‘90s on optimal kind of drawdown control metrics. And again, it’s like a beautiful link that these hand-count rules, back in the ‘80s, that were given to the kind of regular people, you can actually express that in maths very, very nicely and people do that later.

So yeah, I really enjoyed reading. It is a bit of a history lesson as well as some very nice tie-ins there. Again, Niels, you’re probably the expert on the history more than me, but I did enjoy this one.

[01:07:51] Niels

That’s very kind of you to take us through that and the links to sort of research that’s been published later on. But you’re right, I mean, I think there was a time where people thought maybe that because the Turtles, they were not the first trend followers, there were trend followers in the ‘70s as well, but they became the most known in a sense because of the books that was written about them and the narrative. We know how strong narratives can be.

And I think there was certainly a period of time where maybe people mistook the clarity of rules as being something that was simple. But actually, the genius was not so much in buying 100 day breakout. The genius was, as you point out, in the way they approach risk. And I also think that sometimes with trend following and CTAs in general. I think it’s not understood well enough that we are probably risk managers first and foremost. Actually, the returns that we generate is not something we control, but we do control to large extent, the risk we take. So, I’ve always felt that we, as an industry, we came from a risk management first and foremost.

So, yeah, sort of building a system that takes small losses, and scales intelligently, and remains intact when the outsized opportunity finally arrives, that’s essentially what we’ve been doing for the past five, six decades in our industry. And it’s nice to see these things being continued, talked about, and written about in all of that.

So, really, really grateful for you doing all of this heavy lifting today, Harry. It was really good. And also getting your additional perspective and thoughts. Let me, actually, go back one step, before we round out in the next few minutes, because there’s one thing I forgot to ask you about the portable alpha. I was so excited about hearing your run down of the portable alpha paper.

I do come across people who are interested in the concept, but where they feel a little bit worried about adding leverage, which is essentially what the product does. It combines two return streams and it’s more than US$100 in total. It becomes maybe US$150, maybe US$200. So, in their view, it’s adding leverage and it’s replacing to unlevered investments, so to speak. I asked the same question of Rich and Dave last week, but I’d love to hear your kind of brief perspective.

And that is, and I don’t want to make it too much of a leading question, but I think it’s difficult for me not to. Why should people perhaps be less worried about the fact that you’re stacking these two return streams on top of each other? You’re putting the alpha on top of the beta so it looks like it’s a leveraged product? Why should people be less concerned about that when we’re talking about CTA or trend being the alpha generator?

[01:11:27] Harry

Yeah, Niels, it’s a great question. It’s like the leverage does just push up a bit of a kind of aversion barrier in some investors and I mean rightly so in some areas of the market. I think Dave and Rich, last week, were talking about the kind of three times levered ETF and the asymmetry of the return profile, like leverage there is scary.

The way I see leverage in the way kind of consider it is that it is a tool, it’s a portfolio construction tool, it’s an investment management tool. And before linking it to trend and portable alpha, let’s just take a portfolio of equities and bonds. So, if you start with a portfolio of 50% equities and 50% bonds, what you find is that pretty much all the risk of that book is coming from the equity portion. 85% of the risk is equities, 15% is bonds, even though on paper it says 50% / 50%.

Now, the optimal risk weight or the optimal Sharpe ratio weights of those assets is going to be more bonds than equities to get more risk balance. It might be 30% equities and 70% bonds. Now, nobody likes that portfolio because the return’s gone down.

[01:12:41] Niels

Sure.

[01:12:42] Harry

The bonds are less risky, they have lower return. So, if you allow leverage in that portfolio, maybe you hold your 70% bonds, you’re still able to hold 50%, 60%, 70% of equities, you get a very risk balanced portfolio, you get a higher Sharpe than you would by having the US$100 constraint.

Now, in portable alpha, the alpha that you port is very important to your overall outcomes and your probability of having a bad experience. So, in your alpha component, you’re layering alpha onto 100% beta. So, you already have your 100% S&P, you’ve got your 100% equities, now you’re introducing an additional alpha driver on top.

So, when you look at that alpha driver, what alpha driver do you want? You see, almost certainly, that you don’t want to be introducing more beta. That’s a risk in the same direction that is really pushing out your volatility, it’s pushing out your potential of a big left tail loss. We also need to really investigate not just that it’s uncorrelated, but that it’s uncorrelated in the left tail. And the reason this is important is there’s plenty of alpha strategies that are kind of liquidity provision and they look very good in normal times but have a big left tail. Short vol is the most extreme of those.

You could even think of something like merger arbitrage or, I mean, the treasury basis trade, something where if you have a very, very big shock, that little rule that was making you money, I mean all the deals might break in merger arb or in the basis trade, the liquidity dries up and you get really hit because you no longer have the liquidity you need.

So, a good thing to do is actually look at the returns of your alpha source, look at your returns of your beta source, not just the whole data set but the worst 5% of beta returns, the worst equity periods, and check that you still have no correlation. Now why does trend sit nicely in there? Is because in those worst periods trend actually tends to have a negative correlation to equities.

In your real tough times, for your structure, where your equities are really getting hit over a three, four month period, you’re down decent amount there. Trend historically has had a very load negative correlation. So, it makes a very good complement.

And back to your original question on the leverage. It’s extremely cash efficient. You’re not actually deploying many dollars to get that exposure. And also, when there’s a big turning point in markets, what happens? Vol in markets goes right up. Your trend positions come right down. Like we just discussed in the Turtle paper, you scale with volatility, risk has gone up, your position size has actually come down.

So, where people are worried about margin calls, you’d be especially worried about this in like a liquidity provision strategy like the basis trade or other structures, trend is actually reducing positions. It needs less margin to run. So yeah, that’s a great point on leverage. I see it as a tool and certainly a tool to be well understood. But if it’s increasing diversification then my view is that it can be used to reduce risk.

[01:15:59] Niels

Yeah, fantastic. Good stuff. Harry, wonderful, really wonderful. Thoroughly enjoyed it. I know the audience will as well. And for those of you listening in, if you did, show Harry how much you appreciate all the preparation he did by going to your favorite podcast platform and leave a rating and review because it really does help more people discover these conversations and concepts.

And of course, as mentioned, make sure you go to the insights page on Man’s website and find the paper that we discussed today and there are lots of other great resources on that.

So, that will give you enough to read until you join us next week where I will be joined by Andrew Beer and Tom Robel where we will, I’m sure, continue our conversations about many of these topics. If you do have a question, feel free to suggest your topic and send it over to me and I’ll do my best. The email is info@toptradersunplugged.com which actually happens to be also the email address you can use, if you leave a really nice review, as mentioned in last week’s conversation, where you can actually win a TTU merch in terms of a vest, a really nice vest (I have to say). You might have seen some people wearing it on the show. I need to make sure you get one as well, Harry, of course.

But anyways, there are some instructions in last week’s episode at the very end. So go and check it out because at the end of this month we will find a winner and some of the reviews we’ve had so far are actually very good. So, you need to up the game if you want to be considered for that.

Anyways, from Harry and me, thanks ever so much for listening. We look forward to being back with you next week. And until next time, as usual, take care of yourself and take care of each other.

[01:17:54] Ending

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