B (2:55)

That is quite funny. I mean, since I have to come up with a lot of titles and headlines every week for the podcast episodes and the emails we send out, I'm always interested in a catchy method of getting people to listen or open what we share. I have to say, I didn't expect a German word to do the trick, but I might have to try it out.

C (3:20)

Well, there we go, Niels. There we go. All those long compound German words just waiting to be used in your email subject lines.

B (3:27)

Clearly, Clearly. Okay, well, let me tell you what caught my interest. And I know this first topic definitely caught your interest as well. And instead of putting it in the trend following section, I think we'll discuss it now, maybe spend a little bit more time on it. And it is what happened in the last week or so? Well, actually the last few months, but it kind of really came to a head on the last day of January.

C (3:56)

Freaky Friday. I've decided I'm going to christen it Freaky Friday.

B (4:00)

Freaky Friday. Especially if you're trading precious metals and especially metals, and especially if you're trading silver. And actually, I would say this week has been pretty interesting as well. So why don't you tell us a little bit about your silver experience and maybe we can then talk a little bit about kind of the good old days and why silver is not gold in some way.

C (4:28)

Yeah, I mean, so I obviously trade silver. It's a futures contract, it's liquid. So it would be on the table for me to trade. Now, I've talked about this before, but the way I trade futures is a little bit complicated. And it's mainly to do with the fact I have a relatively small portfolio, so I have a notional exposure to 150 futures contracts. Many of some of those I couldn't trade anyway because of regulatory restrictions. Of course they're not liquid or they're too expensive. Silver does not fall into that category. And then I basically dynamically optimize my portfolio every day to match, to get the best possible match I could do to a sort of abstract portfolio, which I could have if I had a notional value of running into the hundreds of millions of dollars, which sadly I don't have. So that will mean that often that I won't necessarily have a position in a given instrument, even if I have quite a strong forecast on it. Now, Silver, though, I do have a position, and I did have a position in, I should say, and if I look at my history of exposure to that position, it's quite interesting. Now, again, it's slightly complicated by the fact that I have this dynamic optimization. But broadly speaking, you can sort of say that CTAs generally will have positions that are a function of two things, the strength of the trend and the volatility of the instrument you're trading. Now, that's obviously a broad brush thing because of course, some CTAs will take basically binary positions, so they'll, you know, they'll kind of go from fully short to fully long. Others will sort of do what I do, which is sort of continuous adjusting forecasts. So getting, you know, the moment the trend starts to look positive, they'll kind of increase their forecast and that would increase their position, all other things being equal. And of course they're not, which is what we'll get into in a second. And also, of course, some CTAs take volatility into account when they initially buy a position, but then don't make any further adjustments to that position as volatility changes. So we can characterize the silver story over the last few months as a positive, bullish signal getting stronger. This is all up till last Thursday, obviously, because obviously on Freaky Friday, things change very dramatically, but at the same time the volatility also increasing. So my position, broadly speaking, and the position of anyone trading like me would be a function of those kind of two effects. And if I could actually look at my position of my exposure to silver, I can see that although I was trading it on and off last year, the kind of current position, I bought some on 27 November, and I bought some more on 3 December, and that would have been down to the forecast increasing. And then actually on the 31st of December, I sold a contract and that would have been inevitably because the volatility had increased. So the combination of those two effects often leads to these interesting effects. And then subsequently on, well, actually not on Freaky Friday itself because I have a daily rebalance, I didn't do any trading in silver during Friday itself because my position was based on Thursday's close, which was kind of normal, if you like. But so on Monday, so that's a couple of days ago now, I actually then sold that contract and that would have been again, because volatility had increased. My trend signal's still long because, you know, unless you're trading an insanely quick trend following system, one day of negative returns, even a day like Friday where silver dropped by the most in one day since, and you know, you, you'll remember this, Niels, of course, it's about.

B (8:23)

It was decades, at least decades ago.

C (8:26)

Many decades ago. They were 40, 45, 46 years, 47 years ago to be exact. So that's why I then I then sold, sold out of that, that silver position on that day. So that, that's kind of interesting. Now a big question is, did I, you know, useful question to ask is, well, did I actually make money doing this? And the answer is yes, I did. Because although silver dropped precipitously on Friday, it only dropped back to the level where it had been a few weeks beforehand, a couple of weeks beforehand. So I can actually give you hard numbers. So I bought into my silver position at levels of 53 and $59 per ounce, respectively. And then I sold at the end of December at $71 and I sold again at $81. So just looking at those trades, you know, that was nice. That's a profitable trade. Of course, what that misses out is that in between the end of December and the Freaky Friday, the price briefly went up to, you know, $110,020 an ounce, I think it was. So that's kind of my kind of story with silver. Now obviously that would be different for someone who doesn't do the continuous volatility adjustment because what they would have done is seen the value of the silver in their portfolio kind of get bigger and bigger and bigger and then not done anything about it and just hung onto that and as a result would have basically would have seen a massive run up in value. And then Freaky Friday would have resulted in quite substantial drop in value. And we've got the sort of monthly returns for January. Now for many CTAs and certain CTAs and many viewers of this podcast will know who we're talking about, who take very large leveraged positions and have very high risk and don't do this volatility adjustment. I think at one point probably went from being about sort of 50% up for the month to being 25% up for the month. Now 25% up for the month is still amazing, but the actual one day loss on, on Freaky Friday would have been, you know, very substantial indeed.

B (10:25)

I think I can help you with the precision actually think about being up 72% on the Thursday and ending the month up 27%. Yeah, no names here. Of course it is, it was, it was a large move. I will say, by the way, there is I guess one other way that you could. Even though the, you could say largely the trend is still up for Silver in many respect. But of course, if you were using a hard stop as one of your roles, and that stop may be calculated from what has been the most recent high of some sort, maybe with a vol stop linked to it. I guess you could have also been stopped out on Freaky Friday because the move was so big. But it really depends on your specific rule.

C (11:09)

It does, yeah. And because I don't use stop loss rules. Yeah, yeah, you're absolutely right. Any, any stop loss. I mean, I'd say that because it depends again on how what you do with your stop loss rules as your position runs up. So when I, if I was to run a system using stop losses, which I don't, as I said, but I design it so that the, the initial entry, the stock would be set as a, a ratio, a multiple of the current volatility. Now if you, as the price runs up and the volatility increases, if you then also increase your stop at the same time, then the position, you know, having said that, you know, a 25% fall probably still would have breached any reasonable stop. But importantly, if you do do that, if you do increase your stop width, you also should reduce your position at the same time. Otherwise you're basically taking more and more risk effectively on the position. If you don't increase your stop level as volatility rises, then you'll, you risk basically being shaken out the position too early. Now on Friday that would have been fine because it would have been very nice if you'd, I don't know, you know, how realistic would have been to have executed a type stop on Friday. I don't know how, how rapid the move was, but it would have been, you know, you probably would have done better than somebody with a much looser stop. But you know, that also meant you would have been shaken out on a much smaller move in the middle of a bigger trend, which would have been the wrong thing to do. So you've got to be careful if you are using stocks how you adjust them as volatility changes. Just as I adjust positions as volatility changes.

B (12:38)

You mentioned kind of, you know, if you had the ability to trade on Friday, clearly it was a huge volatile day. There's not that many people I've heard from in terms of a logical explanation as to what happened. I've seen some speculations about a large US Investment bank causing all of this, but I don't want to speculate on air on that. But do you have a sense or something you. You monitor in some way how, how liquid? Because I think maybe because silver and gold and platinum, they've all been going higher in the last many months and of course it's been great for trend followers. Maybe people have kind of maybe become a little bit complacent thinking that these are all the same to trade. And they think, oh, they're all going to be as liquid as gold, which is super liquid market. But they're not the same in my view. So I don't know from your perspective in terms of liquidity and those kind of risks, if you have any thoughts on what happened?

C (13:45)

Yeah, I mean, obviously because I'm trading a relatively small amount of money, I kind of have a fairly broad liquidity filter. So I would probably take positions in instruments where a lot of big CTAs would either not bother or have a very small position indeed. So silver, you know, there's no, no question of silver not falling into the liquid bucket for me. So that, that wasn't an issue. But I think that one kind of thing that does happen in, in markets that are not as liquid as, you know, the most liquid markets is that obviously if something runs up in price and you get a slight pullback, well, all the buyers are gone, right? Everyone who wants to buy is bought, so there's no one sitting around waiting to buy off you. So people treat liquidity as a constant number, as a constant number of volume of contracts they can expect to trade. But of course it isn't right. And we know that in market crises, liquidity disappears and that happens everywhere. And obviously the less liquid the market is to begin with, the more likely that is to happen. And I think there is a risk if you, let's say something gets hot, right, and it runs up in price and lots of people start trading it, maybe you hadn't traded it before. And you look at the volume and the open interest numbers and you think, well, I can build up a pretty substantial position here because there's a lot of volume going through, there's a lot of open interest going through. You build up that big position and then, and then the class, you know, if the price starts to move adversely against you, then everyone jams into. It's like the cinema with only One fire door, right? Everyone gets jammed trying to get through the exit at the same time. And the liquidity you thought was there, wasn't there. And part of that's just that can be driven by a few things, right? Part of it could be driven by the fact that, yeah, a lot of these people are silver tourists who don't normally trade silver. And what you really need to do is go back a year or two years and say, well, over a longer period of time, what does liquidity look like? The other thing is the classic thing where if everyone's trading similar kind of strategy, I mean, if everyone is trend following, if everyone is doing volume adjustment, and that does, you know, don't forget that CTAs do volume adjustment, but so do risk parity funds and so do other kind of quant strategies. So anyone who sells on the hint of volatility spiking, the more people there are in a particular market like that, the more it'll magnify the move. I mean, the proximate cause of all of this supposedly was the fact that a relatively sane person got elected to the Fed chair next. And we'll talk about that in a second, I guess. But as to the mechanics of why it affected silver more than gold, and silver went down more in price than gold, I mean, we can speculate all day, but I think the main lesson here is, is for me is risk management. And the two key things about risk management are position sizing and diversification. So on Friday, I lost 3% of my total account value because I have a reasonably diversified portfolio. If I was very highly concentrated in silver, that would have been a much bigger number. And because I adjust my positions according to volatility as it changes, again, I was relatively protected from a large move, whereas as we've mentioned, funds that don't do that and have much more leverage potentially are exposed to even bigger moves. And yeah, month over the month, still profitable. Great work, guys. But you know, it scares the hell out of me the possibility of that happening, I have to say.

B (17:13)

Yeah, well, first of all, I mean, I think just to comment on that number, I think that the number you mentioned was pretty among people with that kind of annual volatility that you run it at. I think 3% is very decent and I think that's kind of what I saw on the day from the people I can follow. So I think the industry as a whole did really, really well. And as you say, of course the largest CTAs will be doing something similar to what you do, meaning Adjust the position along the way. Which means, again, just maybe to reiterate for people, because I remember during the cocoa debacle where prices also had a kind of a similar move, as we saw in silver, where it becomes kind of a parabolic, and gold for that matter, kind of a parabolic move. It's very easy to blame the quant funds to be behind the parabolic move. But I would hesitate to say that most CTAs probably got into silver more than a year ago, for sure. And the majority of the larger CTAs, they're the ones who have been selling silver all the way through January, as you rightly said, maybe even through December, as volatility started to expand. We're just going to have to reduce our notional exposure. So we're actually kind of trying to be the buffer here and limit these parabolic moves, if anything. I think that is important to say because most people will think the opposite. The other thing you touched on is risk management. And I just want to maybe share and I won't do this story justice in any way, shape or form, but of course, a lot of people remember silver specifically from the story of the 1970s where the, I think Texas oil billionaire Nelson Bunker Hunt, he became convinced that the US dollar would fail. I mean, it's kind of almost like deja vu at the moment. Anyways, in his mind, silver was not real money. That was kind of, or I should say silver was not just an investment for him, it was real money. So he started to buy silver and he did it physically and he charted several of these Boeing 707s and he flew that silver, as far as I understand, millions of ounces of silver to Switzerland to be stored in vaults. And so again, this for him was not about making a lot of money necessarily. It was about kind of controlling and making sure that the wealth he had built wouldn't disappear. Well, here comes the problem. I mean, he kept buying, but he kept buying now for borrowed money. So the leverage of his position became, you know, quite high. And very similar story as to what happened last week as the volatility rose. What happened in 1979, price wise, was that silver went from $6 to about $50 an ounce. And that causes a problem for the exchange. So Comex started to change the rules on margins. We saw the same thing happening, I think in January once or twice, where margin got increased. And at some point the people who have made money, but they've made money from borrowed or speculation from borrowed money to keep up with the margin calls, at some point they run out of money and so did he. So I think don't know the details, but he ended up having, you know, a catastrophic experience. Maybe he still ended up making a little bit of money, but. But not nearly as much as he had. And I think there was some agreement about paying back debt. So risk management and not letting positions get completely out of control by pyramiding and so on and so forth are all very important lessons. And even though this happened back in the 70s, as we can clearly see they get repeated over and over again just in different markets at different times. But I think the beauty of all of this is the fact that many of the managers that we talk about, they've all been around for decades. We kind of know this, it's part of our DNA. We don't get emotionally attached to it. We manage our risk in a systematic, dispassionate way. And that's super important. And regarding this idea that people should also remember that gold is not, sorry, silver is not the same as gold. I've heard stories or information about the fact that the reason why this is important is that silver, you don't mine very much silver directly. It's only like 30% of the production actually every year is specifically mined as silver. The rest comes from a byproduct of gold and copper. So it's maybe less controllable or the supply side maybe is a little bit harder to predict and therefore it can maybe also be the reason why silver can be a much more difficult, much more volatile market to trade compared to gold. But these are all very important points in my opinion.

C (22:29)

Absolutely. Thing is, May also briefly mentioning, I think that Friday and actually continuing to this week, we're seeing a lot of dislocations across markets generally. So long short quant equity factors have gone a barmy. Apparently AQR's long short fund had both one of its worst days and also one of its best days in the last few days. So that's interesting. Bitcoin of course is selling off massively. There's been kind of disruptions, particularly in the sort of stocks related to AI. So it's all kind of interesting and at least at the moment, Touchwood fingers crossed. My trend following portfolio seems to be coping pretty well with all of this. So yeah, it's an interesting time.

B (23:10)

Can I just mention, I mean we talk about precious metals as if it was the only thing that moved March natural gas, just to mention from 9 January to 30 January, it was up something like 60% and then on 2 February, in one day it lost 26%. Similar move to more, more or less to Silver. And this is, this is again, this is weather related. This is due to the, to the massive storm.

C (23:40)

So we can't blame that, we can't blame that one on the Fed chair.

B (23:44)

Well, we can try, but. Okay, we'll speak about him in a second. But yeah, what's interesting about it is, and this is why I think we've been advocating for so many years now on the podcast and that is people need to realize how important it is to have the commodities as part of their trend following universe because this is where we sometimes see lots and lots of opportunities and often at times where there might be a risk off event. We didn't see any risk off event here in equities. They didn't really bother too much about what happened. But they're just wonderful in terms of opportunities. Not always for sure, but at times. And this is why we like them. But speaking of what you said could be the reason, I don't know that it really was, frankly. Again, I hear this story in my back of my mind about a certain investment bank that was behind a lot of this. But yes, at the same time we were told that we will have a new, if confirmed, we will have a new Fed chair with the name of Kevin Walsh, which was a surprise to.

C (24:55)

Me because anyone who listened to the Christmas specials will know that my outrageous prediction for this year was that the new Fed chair would be Barron Trump.

B (25:02)

So fortunately you're already out of the. I'm already out of the running predictions. Oh my God. Yeah, tough, tough start to the year actually. Anyways, tell me what you think about this. Not that our opinion is particular insightful, I'm sure, but what are your thoughts?

C (25:25)

I must say so, you know, I think I kind of want to sort of fess up that I'm not a fan of the current President of the United States. And this will come as a surprise to many people and I was of the opinion when he came into office a year ago that it would be very bad for markets. And mostly I admit I've been wrong. There was obviously the tariff tantrum and which did something very silly and non systematic and solve all of my equities. So that's what cost me fair bit of relative performance, sadly. But generally speaking, markets have kind of shrugged off to an extent and maybe that's because of Trump always chickens out the taco trade. Maybe no one really takes them seriously. Who knows? But I think it was an expectation that he was going to appoint someone to the Fed who would be basically in line with his wishes. So now there's sort of a view that if you look at the spectrum of people who he could have appointed, then that this guy is on the kind of more sensible, insane end of that spectrum. I mean, he does have some opinions that are less mainstream around things like QE and the Fed balance sheet and so on and so forth. But in terms of just interest rate setting, actually in, you know, some people have said, well, actually, this guy is quite hawkish if you look at his, you know, look at some of his decision making over the years. Now, a key question is, you know, given that Trump has installed or, you know, let's assume he gets confirmed. And I think he will do, to be honest, because I think the, you know, the Senate and the House will be just so relieved. They'll be like, yeah, okay, this, you know, this guy's probably better than any other option we could get. So I think he will be confirmed. And this will all happen before the midterms anyway. So I think numerically the numbers are there. The question is, once he's actually there, given that he's been appointed by someone who demands loyalty, will he then basically do as he's expected to do, which is just to kind of cut rates and keep the former real estate developer happy, or will he not do that and then have pressure put upon him, like the current Fed chair has had pressure put upon him, and then will he then crumble and say, okay, fine, I'll do what you say? Or will he, like the current Fed chairman has, fight back and say, no, you've appointed me now I'm basically independent. I'm going to do what I think is best. So, yeah, it's an interesting. It's interesting. And I think the true answer is no one really knows. And this is true of most things related to the current president. No one really knows what's going to happen in May. I think it's May when Powell's terms up. No one really knows what's going to happen. So I think the reaction was if that, you know, I think that was quite a big reaction, to be honest. If that really is approximate cause. And, you know, if you actually look at, say, the bond market, where you could, which you could argue is the most direct measure of this, it was a roughly muted mood in the bond market. So I haven't got the figures to hand, but I don't remember anyone's, you Know, I don't remember the, you know, the two year bond kind of moving by sort of 50 basis points on the day, which is what you would have expected if this genuinely was like a novel and interesting piece of information. But. So I think everyone's relieved. Got a bit carried away with their relief perhaps. But as to whether this is good news or bad news for the bond market, for the economy generally, we'll have to wait and see.

B (29:10)

I think you kind of touched on, hit it on the nail here because. Hit the nail on the head here because the reaction in the financial markets were very muted. So this thing about him being the cause of a silver sell off, I think it had much more to do with positioning and how people had been piling into this and they talk about the Chinese retail investor and all that stuff. I really don't think it had anything to do with him really. But of course we always here in the news that there is some kind of cause you can point to. I think you are, I mean for people who don't know, but as far as I can tell his father in law is a massive financial backer of Trump. So obviously it may not be that surprising that he got the job. Having said that, let's not forget it was Trump who appointed Powell. So you never know. As you say, once they are in and they can't be in something which.

C (30:01)

He appears to have forgotten as well.

B (30:03)

Right. And you know, once they're in they really should concern themselves about their own legacy and not so much the legacy of others and then do the best possible job. So we don't know. And then also let's not forget he's one of is it 12 votes in total? I mean it's not like he can decide what interest rates are going to do. He has to compel or convince or force others to go in his direction. Now there might be one or two that is very easy to, to convince Steering Steve Moran and maybe another one.

C (30:36)

But we're still waiting for this Supreme Court decision as to whether we Trump can effectively then pack the rest of the board with whoever he wants. Yeah, fire, fire and higher will.

B (30:44)

Yeah, yeah, yeah. All things up in the air but, but what you could say in general is of course that because you mentioned this thing that yeah it a year ago you weren't too bullish about equities. But I mean at the end of the day, however high this market goes, it could still all end, you know, pretty pear shaped because of all these things that are building up to it. It's Always the timing of these things that are impossible to know. But we'll see. And I think as again as we mentioned many times in what's going on with what's going on in the world right now, being a systematic diversified investor I think is so much better than trying to make any sense of this from a discretionary point of view. Even though those who get it right will probably get it really right but you can really get it wrong as well.

C (31:33)

So yeah, well Discretionary Rob sold all of his equities in April last year and Systematic Rob is up since then, is up well that you know, 20, 25%.

B (31:44)

So I think you should listen more to the Systematic Rob.

C (31:46)

Clearly, clearly.

B (31:48)

Anyways, talking about quote unquote the systematic Rob before we move often to the trend following part and I know maybe we'll have time, I'm not so sure to go back to this point about AI but what did hit my radar just this morning related to AI was just this headline I think in Financial Times that Google said that it's planning to double its capex this year to as much as $185 billion to really bet huge on AI. Now I know they just announced results and they made profits of something like 132 billion. So I mean they have a lot of money to spend but still doubling the AI spend to almost $200 billion. I mean this does sound like a bubble to me but who knows.

C (32:34)

Yeah, we're both old enough to remember 99, 2000, right?

B (32:38)

So yes we are unfortunately. Anyways, let's talk about some trend following updates before we dive into a really really interesting article. People should really look forward to this. Anyways, not surprising, my trend barometer is having a great time. It finished yesterday which was Wednesday at 64. That's a super strong reading. And of course what it really tells you is that there is a lot of breadth because the percentage or 64 means that there are 64 of the market that in the portfolio it tracks that are quote unquote trending. So it's about breadth in the environment right now. So that's the important part and if we look at, I mean we already alluded to the fact that January was a strong month for managers. February is off to a really good start as well. Mainly this time driven by, as far as I can tell, the financial sectors like equities, a bit of currency, some of the fixed income markets even are supporting the P and L this month. And also things like natural gas, a little bit of meats, some of the Grains. So the breadth of the trend environment as far as I can tell is, is pretty good. So before I, I dive into the numbers, Rob, anything you want to add to kind of where we stand and, and, and your from your vantage point overall?

C (34:04)

Yeah, yeah. So I actually don't think I've got a position in natural gas, sadly.

B (34:10)

Well, the ball is huge. So yeah, the position size would be.

C (34:13)

Very too big for my tiny account. So yeah, the one thing I would add, and I know not everyone trades it, but bitcoin, obviously I think the market that's on everyone's lips this morning, at least on X Twitter, is bitcoin. And I think most CTAs would trading it would probably, I trade it just for the futures. Just to say most CTAs who trade it would probably be short. And that's especially my biggest short position at the moment. So that, that will, that might be kind of doing some interesting things for some people as well, I would say. But definitely, yeah, no, I'm also my, my risk is a little bit higher than it has been for a while. So that kind of matches up with your trend barometer. Definitely.

B (34:58)

Yeah. Okay, cool. All right, good stuff. Let's run through the numbers quickly and then we'll dive into the first article. So these are numbers are as of Tuesday because the Wednesday date numbers have not been published at the time of we're recording today. But as I mentioned, a positive start. Beta 50 up 33 basis points, up 5.38% so far this year. So very strong suction. CTA index up 27 basis points, up 5% so far this year. Suction trend up another half a percent in February, up five and a quarter for the year. And the Short Term Traders index also up 13 basis points and up 2.37% so far this year. The MSCI World Index on the other hand is down half a percent so far in February as of last night, up 1.76 for the month. And the US aggregate bond index so far pretty flat in February and was pretty flat in January. So that means we're pretty flat for the year. And the S&P 500 down 81 basis points so far in February and up 63 basis points so far in 2026. Now before we get to the article, we actually got two questions in from Pedro who wrote in and let me just run through those with you before we dive into it. The first question, and this is a long one, so I'll probably, probably butcher it a little bit. I'll do my Best estimating realized book correlation from P and L Rather than estimating pairwise asset correlations, I've been thinking about measuring the daily correlation of my entire book's return using something like a exponentially weighed moving average. The logic is that this already factors in both asset correlations and signal correlations as expressed in realized P and L. I understand this adds noise, but the alternative, assuming a high fixed high correlation like 0.7 doesn't match reality. For my book, my average realized correlation is much lower. So assuming 0.7 means I consistently undershoot my vault target. Is there a sensible middle ground here or a better way to calibrate this interesting point? Because also if you listen to which I'm sure you do, Rob, last week's conversation with Katie we did actually talk about not so much correlations, but certainly also Vol. And how that really can have a huge impact as we just talked about with silver in terms of your overall position size and and so on and so forth.

C (37:35)

Yeah, I'm a little bit unclear about this question, so I'm going to make some assumptions and apologies Pedro, if my assumptions are wrong and I'm answering the wrong question. But what I think you're talking about is imagine you're trading a bunch of instruments, so you've got a bunch of trend following systems, one for silver, one for gold, one for, you know, S&P 500 or whatever. And then what you need to do is essentially scale, look, kind of work out what the sort of total leverage is required on that to get to a given risk target. Now if all assuming that you're doing volatility targeting and everything and it's got the same volatility, that means the only thing that's going to change your risk outcome is the correlation of those things. So if they're perfectly correlated, if you're targeting say a 15% risk target on each of them, you'll end up with a 15% risk target on the entire book because it's perfectly correlated. Now if your correlation is less than 1, which is hopefully the case because we love diversification in a trend following space. We absolutely love it. We like nice low correlations. The lower that correlation is, the more likely it is that you'll undershoot your risk target. So instead of getting to 50% on your whole book, you'll only have 10 or perhaps five. And actually the kind of when I actually do this exercise and work out how much I need to increase my position size to compensate for the diversification effect across instrument, I get numbers of between 4 and 5. So a 15% risk target would turn into just 3% risk if I didn't do this scaling up. So the question Pedro's asking I think is how do we actually kind of calculate this number? Now he's saying use a fixed correlation of 0.7. My gut feeling is that he's got that 0.7 from one of my books and it's a slight misunderstanding on his part because 0.7 is actually roughly speaking the ratio between the correlation between the underlying instrument returns and the correlation between what happens if you trend follow those instruments. Because trend following them, because you're trend following them, you're not going to get, you're going to actually get a lower correlation than if you were just holding them long. Any positions on each. So that's where I think the 0.7 comes from. So in answer to the question, how should you actually estimate this correlation? Well, yes, it's a perfectly reasonable thing to do to look at the, the P L stream that comes from Silver, the P and L stream comes from S&P 500 and then measure the correlation of those. And Pedro's exactly right. That will basically be factoring in both asset correlations and signal correlations. And yeah, you can use, you can get, can just use a rolling estimation window of about six months works quite well. Or you know, if you want a smoother correlation estimate, you can use an exponential weighting of correlation. That, that's also fine. So, so yeah, I think the answer is yes. I'm not sure what he means by adds noise. Maybe he means referring to the fact that if you used a fixed because of the correlation estimates always changing, that will result in some extra trading. But to be honest, that is as long as your correlation estimate's quite slow, like six months is pretty good, then that's going to be effectively irrelevant because your actual underlying trading process would be giving you a much, much bigger effect.

B (40:51)

Yeah, I think that's a beautiful answer. He had one more follow up question that is predictability. He says the research suggests correlation is weakly persistent but that the best forecast is often just the long run average with the main predictable component being volatility regime. Do you find it worth adjusting correlation assumptions based on volume regime or is a constant conservative assumption sufficient?

C (41:19)

So this is a little bit. Again, I think there's a bit of confusion maybe in the way the question's phrased from Pedro's understanding. Now most of the academic research on estimation is around the estimation of covariance matrices, not correlation correlation matrices. Now a Covariance matrix is basically what you get if you combine a volatility and a correlation matrix together. Because in most kind of financial optimization it's the covariance matrix that you use. If you're doing your Markovits, you invert your covariance matrix. Now what I found and what the academic research finds as well is that the predictability of volatility and correlation is different and it's better to estimate them separately and then combine them together if that's what you want to do. So volatility, for example, is much more predictable than correlation. And I think that's kind of what Pedro's saying. And that's true, that's correct. But the other thing is that the correlation is still pretty predictable. So if you want some hard numbers. So off the top of my head, if you do a regression of next month's volatility on last month's volatility, you get an R squared of about 0.35. If you don't know what R squared is, don't worry. But 0.35 is pretty good. Okay, that's pretty good. And for correlation you get something more like 0.2 or 0.25, which isn't quite as good. But if you were to try and do, to try and predict, you know, means which is the other moment of the distribution, you would, you'd get nothing. You can just get noise. So that it's, it's, these things are relatively predictable. Now the other thing about correlation is, as I said, you probably only use something like about a six month rolling estimate versus about a one month rolling estimate for volume. So the kind of correlation structure changes more slowly than the volatility structure changes. So what I personally do and what I recommend is to, yes, is to actually estimate these things separately. If you are using the correlation, the volatilities for this scaling thing that we've talked about already, well then the things you're actually playing with should already be at the same target volume. So you don't actually need to estimate a volatility estimate. Obviously you will still need your volatility estimate for position sizing, as we've already discussed. But for actual sort of risk management kind of portfolio construction part, the nice thing about doing things in a vol targeted way is you don't need to estimate vols. Again, you just assume that your volume targeting works, which does a first order approximation is fine. Then you just focus on your correlations and then you can focus on estimating your correlations in the best possible way. So it's quite a technical answer, but it's quite a technical question. And I think if you understand the question, you'll understand the answer. If you don't, then don't worry too much about it.

B (44:13)

Yeah, no, perfect. Absolutely. Thank you for doing that. Okay, well, as we just talked about predictability, and there's another thing that's very predictable and that is when we do review papers, which we do very often on the podcast, it's very predictable to say that it's most likely from either MAN or aqr. And yet again, here we are. Now, in the interest of time, I don't know if we will have time for both because I think the first one we're going to be doing is the MAN paper. It's a super good paper. I'm going to try and remember to put a link to it. But this is a paper about not really about better models or faster signals. It's really about what markets managers, you know, kind of choose to trade and therefore what kind of trend following they're running. But I'm going to let you take us into the paper and I'm going to follow along and see if I have any, any thoughts along the way. But yeah, I'm gonna turn it over to you.

C (45:14)

No, it's a very good paper and it's actually almost maybe three papers in one, to be honest. So there's an awful lot of content here. Now the, it's been written by three people and I have to say it's now been so long since I've been away from AHL that I don't know any of these people are, I've never met any of them. But so that, that also means I shouldn't be biased in loving this paper because it's not like it's been written by an old friend of mine who's going to buy me a beer in exchange for mentioning it. So be reassured. So the, I guess the main thing that they sort of do in this paper is say, well, actually there are kind of different kinds of markets, right. And we've already discussed, you know, the fact that different markets have different levels of liquidity, for example, and they, what they say is, well, we're going to divide our market universe into three categories, traditional markets, alternative markets, and alternative esoteric markets. And I guess although a lot of that is liquidity. So you know, they've got this really nice graph. It's Figure 5, if you're following along at home, which shows liquidity on one axis and complexity on the other axis. So ESOTERIC markets may be less liquid, but not necessarily so. One of their esoteric markets they've labeled as China. I think they might mean futures markets in China rather than the whole country. Another one is physical cryptocurrencies, many of which are relatively liquid, of course, but those are quite high on the complexity axis. Whereas onto traditional markets they put cotton futures, which are. They put low complexity because here I guess complexity means more like a future. But they have actually on their graph those are less liquid than China and physical cryptocurrencies. So it's quite. There's a bit richer than the normal categorization around liquidity or around. Are these futures not futures? They look at both, but then what they do is say, well, the interesting thing about these different markets is kind of where the money comes from. Now, finance people love doing something called a factor decomposition. And basically what a factor decomposition does is say, well, given that I'm making some money from this portfolio, can I identify sort of where the key sources of those returns are from? And in trend following? One thing you can do is to say, well, it looks like if you do something like a PCA or principle of the bank analysis, it looks like a lot of the return in my trend following portfolio is actually coming from a kind of risk on, risk off factor, which I'm trend following. So if you look at 2008, for example, CTAs made a lot of money. Bonds went up, equities went down. That was a perfect example of that factor in action. And then you basically, then what you can do is say, well, what's left over? What do they get left over that isn't explained by that factor return? And they call that the idiosyncratic return. So you could argue that the returns we saw in January, whereas we've said bonds have gone nowhere, equities slightly up, February's looking a bit different maybe, but financial markets generally didn't. That kind of first principle component didn't contribute much. So maybe it was more something idiosyncratic around gold and silver or something else, or in natural gas. So what they do is say, well, if we look at this idea of decomposing into the main things explaining a portfolio return and the idiosyncratic return. The key thing that's interesting about these alternative and esoteric markets is that a much bigger proportion of their returns are idiosyncratic. They're not coming from these, these big factors and in hard numbers. So roughly Speaking, in traditional CTAs, about half the returns are from factors and about half of idiosyncratic. Actually, it's probably more like 45, 55, 45% idiosyncratic, 55% factors. But in these unusual markets, these alternative and esoteric markets, it's more like one third, two thirds. So only about one third is coming from the main factors and two thirds is idiosyncratic. So the main thing about alternative markets is they give you an exposure to weird stuff, to weird sources of return. Now, if you're doing any kind of portfolio construction, you like weird stuff, okay? You like diversifications, we've discussed you like idiosyncratic returns. If the only returns in your portfolio are coming from trend following, a kind of equity risk on, risk off return, that's going to be not very helpful. You want to be able to be trend following other things as well, getting idiocentric returns from lots of different places. So what that means is in a completely unconstrained portfolio, if you do a standard portfolio optimization, you are going to want to have quite a lot of these alternative and esoteric markets. And of course this is one of the common themes of, I think ever since I've been talking to you Niels. Alternative markets, which obviously has been a big thing for ahl, but also for people like Florincourt for example, is a very successful CTA that focused just on alternative markets. And most big CTAs have gone into alternative markets to a greater or lesser degree. So this is kind of something that's not a surprise that we already know about. And if they do this kind of sort of standard portfolio optimization, they find that only about 30% of their portfolio needs to should be going into these traditional markets, which isn't a lot. Right? And just to emphasize, again, this is unconstrained. So this, this assumes you've got no issues with liquidity and you can put as much into the weird stuff as you as you possibly want, which a big CTA of course can't do, right, because they're just too big. And then they have the rest of that portfolio, the 70% is in a mixture of alternative and alternative esoteric. And there's a bit of a breakdown. And I can see that they put 5% into crypto, for example, so the crypto bulls would love that, I'm sure. Now, one question that again we keep returning to is why should we invest in ctos? Why should we invest in trend following? And it depends, I think I've mentioned this before, it depends on whether you are looking at it as a standalone product or as something that goes alongside an existing 60, 40 or some other traditional kind of assets. Right now if you're doing as a standalone product, you want to the first order approximation, you want a maximum Sharpe ratio of, you'll do this kind of standard portfolio portfolio optimization. And so as a standalone product, as a cta, without constraints around liquidity, what you would do is offer something that was 30% traditional, 70% alternatives, and that would be the best product for the investor to buy. But most people of course don't, aren't doing that. Most people are putting a slice of trend following into their 60, 40 portfolio. We'd like it to be a bigger slice, of course, but you know, that's life. And that means what they're looking for is an element of tail protection. And it's a pity we don't have Katie with us because of course she, she wrote the book and. But they're looking for, for some kind of, you know, crisis alpha tail protection, whatever you want to call it. So what the these guys did was say, well, what if we, instead of looking at a portfolio optimization that basically just treats all, all states of the world the same, let's focus in on periods when equity markets fell. Because what we want to do is find the best portfolio for that scenario because that's when people really want to benefit from what CTA performance is like. So they constructed their correlation matrix differently to account for that. And what they found was that the traditional portfolio, the traditional trend for things you trend for like, you know, so.

B (53:42)

The liquid markets, let's call it the liquid.

C (53:44)

Well remember Niels, it's not just about liquidity, it's about, it's about. So basically it's, it's kind of, it's futures, traditional futures markets that a CTA would have had in it 30 years ago before people started doing all this weird stuff like trading German power and CBS and all this kind of stuff. Right, Exactly. So it's things that are relatively liquid, but also basically futures. And therefore for a cto, relatively simple. But more importantly, it's things that have a, whose returns are mostly being driven by this factor risk rather than this idiosyncratic risk. That's the key point about this article. That's the key innovation in how they categorize markets. I think now it turns out that actually what you want to do in the crisis situation is not have 30% traditional markets, you want to have 50% traditional markets. And then obviously the non traditional stuff gets squeezed down to a smaller fraction. And the intuition behind that, and which they explain is because it's that Total Factor return, that thing that explains most of the risk of traditional markets, that makes traditional markets look bad from a kind of generic optimization perspective because they haven't got much of the syncretic risk. It turns out in a crisis, that's what you want. Okay, that's what you want. And this kind of makes intuitive sense because in a crisis you basically want to just make fairly big bets on going 2008, going short equities, long bonds. You're not really going to need your fancy, you know, other weird markets there to sort of help you. And of course there is a risk that many of those markets might have issues around liquidity, which is another thing that they talk about next or around, for example, whether you can go short because a lot of these things in a crisis you do want to go short because a lot of them trade light risk on assets because things like, well, we talked about cryptocurrency in the spot. Cryptocurrency for example, does that. So that's kind of the interesting thing. Now they then take this a step further and say, well, all this time, you know, we've been kind of assuming that we're completely unconstrained. But actually another fantastic thing about CTAs and or about anything that trades futures is the fact that they're very cash efficient. So if I look at my own futures account, which has a kind of a relatively high risk target by institutional standards, some people are a bit crazy, of course, as we discussed, but I'm probably running at about one and a half times the volume of say AHL is on most of their funds. I'm only using 30% of my cash as margin. So a typical CTA, that number's 20% and that's super cash efficient. And that makes them very attractive from a structuring perspective. So the next thing they did was say, well, let's say we want to go a bit further and say, well actually we want to kind of maintain this cash efficiency. Now the non futures markets obviously aren't as cash efficient. Most of them have margin requirements that are higher than future. Well, all of them have margin requirements that are higher than futures. Some of them even have 100% margin in the sense that you just, you can't margin them. You have to just put, put up cash essentially. And if they do that again, it's not a surprise. But then what happens is we get even more into traditional markets. So we end up with 70% in traditional markets and just 30% in the alternative markets. And one final point to make is that when is cash efficiency most important? It's when we need cash. When do we need cash? When the world's ending. So in 2008 the fact that CTAs had plenty of cash daily liquidity, no gating meant that many people use them as cash machines and just you know, so we had this weird situation where although, although the you know, CDS industry made a lot of money at the same time people were withdrawing it because it was a source of ready cash that wasn't available elsewhere. So that, that, that's. Yeah, so that's the, that's the article. I mean so there's a lot of really interesting things there and like many, like you know, my many articles, it's very well written, not technical, lots of nice graphs, very well presented and yeah for me at least some really interesting innovations around a few different topics.

B (58:38)

Yeah, no, I couldn't agree more. People should definitely go and download it and read it and as I said, I'll try to remember put in the link in the show notes but I just want to kind of summarize all of this just to make sure people really get the importance of this. And that is if you were just kind of trying to maximize and looking at your trend portfolio in isolation and you say oh what could be what. You know, what kind of market portfolio should I go for when I'm looking to maximize my, my long term sharp of. Of that in all periods. Well, you would go for a portfolio that probably trades more markets. That's kind of what they, they conclude. However, as Rob rightly say, a lot of investors and certainly I would say most investors that I come across, they include trend following and CTA specifically because they want to have something that really does well for them. When equity markets are not doing well or when bond markets are not doing well and in their work they find that then you need to find managers who are more focused on the traditional markets, the more liquid markets, the, the classical futures markets we've been trading for the last 30, 40 years and go we want a manager that, that does that and then on top of that and I think I completely agree this is the interesting point they make and that is if you then also are an investor where you might need to get some cash out in terms of a crisis, if your private equity fund is not giving you any money or drawing down or whatever comes during a crisis, then you need to be even more focused on managers that trades the, the classical liquid futures markets because the ca. The, the cash efficiency is important and, and So I thought it was really well done as well. And, and to make that point the way they did so that everybody kind of understand the difference between the choices we all have to make as managers. And so it's not really all about kind of returns. It's also about, you know, what's the best manager fit. For me, in terms of what my objective is with investing with the cta, that should lead you down the path of identifying a smaller number of managers that might be relevant for you. So really good paper. Kudos to the team for writing that. The next paper, which we won't have time to do justice, so we'll, we'll wait with that. But it's also a super interesting paper. We'll see if we wait until Rob comes back or maybe we'll do it beforehand. But it's something that is also super relevant in the current environment and what with what's going on in some of the, in some of the markets. I don't know if you want to spend five minutes. Rob, it's your, it's your call on the Financial Times article you sent me this morning. I have not read it myself, so I don't know what's in it, but, but please do if you want to spend five, seven minutes on that.

C (61:45)

Yeah, so the, the, the articles on the ft, alphaval, Alphaville part, the ft, which is important because that's actually built in front of the paywall so anyone can read it. And it's called the Quant Shop, AI Lab Convergence. And it's kind of interesting because basically what they do in the article is they draw parallels between kind of finance quant and AI quant, for want of a better word. And their sort of thesis is that basically these people are becoming more and more like each other. Now, obviously there's always been a crossover between the sort of worlds of finance, quant and, you know, software engineering more generally and kind of, you know, so, and obviously a lot of places are now using. Well, let's, let's be careful. So in finance, in systematic finance, we've always used something that I suppose you could wave your hands and call machine learning. Now, machine learning to some people has to be very complicated. But for example, if you're running something like a simple regression, which is a very simple, a relatively simple way of working out, for example, as we've just done, how much money you should give to a particular market, you could use a regression to do that or you could use an optimization. Those are forms of machine learning. They're forms of what you might call supervised machine learning because we're sort of telling the computer what to do and then it's giving us the numbers that come out. So that, that's, you know, all the systematic finance is machine learning. Although some, you know, some people say, oh no, that can't be machine learning because it's, it's too simple. But, but you know, then they can never tell me where the boundary is. So I say, well, no, it must be machine learning. Now then you have something which you might call unsupervised machine learning and that kind of then blends into our AI and LLMs and Neural Networks and all this kind of stuff and the sort of. I think there's certainly a lot of people trying to use AI and that goes for every industry, of course, not ours. I think pretty much every fund of a decent size will have some kind of AI thing going on. And how much of that there is is an open question. How much of it is just a marketing thing? Is just, is an open question. How much of it's kind of blue sky research, like a, you know, let's buy a call option on this technology. And you never know, we might find something that no one else can do, is an open question. I think the people who are doing it best are ones who are using it in very tightly defined areas. So things like, for example, execution research, I think that's where it's potentially more interesting because that's where something like, well, first of all, you've got a lot of data. So you know, we're building our kind of normal models with 50 odd years of daily data. And you know, in execution research you've got tick data which is, you know, obviously much, much richer, much faster, there's much more of it. So it's much more suited to something like building any kind of nonlinear analysis, which would include something like training an LLM on that data. All that aside though, the article is quite interesting because rather than just making the obvious point that you know, well, everything's AI now, you know, which is a slightly lazy argument. And I do think they do, and this is probably because they're talking their own book, they do slightly overstate, I think, the amount of AI that sort of is going on. But they do kind of go further and say, well, if you actually look at the kind of workflow in quant finance and the workflow in AI that's similar, you know, if you look at the sorts of people that are being hired, if you look at the technology stack, they even say, well, if you look at the way that kind of people are employed. So you know we're used in finance to sort of non competes and gardening leave and things like that and signing on bonuses. Well they've got that in AI now. And you know you hear stories of people being offered kind of nine figure sums to move from meta to move from OpenAI to meta. So actually, you know, many of the top AI researchers are making far more money than any person in finances, sadly I have to say. So yeah, it's quite an interesting article. I am a skeptic of this stuff. I've said it before, I've said it again, but I still found it an interesting article so it must have been good.

B (66:40)

Fair enough, fair enough. Anyways, we have come up to a little over the hour so we're going to wrap up our conversation today. Rob, this was fantastic. Thanks so much for spending all the time looking into this and for answering the questions for Pedro. I hope everybody found it useful because I think actually our conversation today touches on so many important points when it comes to CTAs and trend following and investing in general and all of that stuff. So if you want to show your appreciation for Rob and all the other co hosts, please go to your podcast platform of choice and leave a rating and review. It really does help and we really appreciate the support we can get. If you have a question for next week where I will have two guests, Andrew Beer will come back and he will be joined this time by Tom Robl from SocGen. So you can send your questions as usual infobtraders on block.com I'll do my best to to get them in front of Andrew and Tom when we speak. But for now, for today from Rob and me, thanks ever so much for listening. We look forward to being back with you next week and in the meantime, as usual, thank you. Take care of yourself and take care of each other.

A (67:55)

Thanks for listening to the Systematic Investor podcast series. If you enjoy this series, go on over to itunes and leave an honest rating and review and be sure to listen to all the other episodes from Top Traders Unplugged. If you have questions about systematic investing, send us an email with the word question in the subject line to infooptoptradersunplugged.com and we'll try to get it on the show. And remember all the discussion that we 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 is 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. Thanks for spending some of your valuable time with us, and we'll see you on the next episode of the Systematic Investor.