Rob Carver returns for a conversation that quietly questions the foundations. Is trend following an edge - or just a reward for holding discomfort others can’t? From the role of skew in shaping outcomes to the blind spots in most robustness frameworks, Rob and Niels takes you through the mechanics with uncommon clarity. Listener questions open up the deeper layers: when volatility targeting helps, when it hurts, and why Sharpe Ratios can mislead. They end with a shift that may matter more than it seems: CalPERS moving to a Total Portfolio Approach. Not just a new framework - potentially a new lane for CTAs.

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Episode TimeStamps:

00:00 - Intro and welcome to the Systematic Investor Series

00:23 - Catching up with Rob and a cold, sunny UK

01:35 - Is trend following an edge or a risk premium?

03:38 - Overcomplicating edges and the Cliff Asness perspective

04:30 - Renaissance’s bad month and how even legends struggle

09:25 - Managed futures ETFs, performance narratives, and media framing

11:22 - AI, Nvidia and what an “AI bubble” might mean for trends

13:10 - Trend barometer, current positioning and where returns come from

18:35 - George’s question: robustness testing, overfitting and multiple testing

25:45 - How often to re-fit models and when to leave parameters alone

27:44 - Frederik’s question: intraday versus end of day for medium term trend

32:10 - Why trend following struggles on single assets and very fast timeframes

34:07 - Abraham’s question: what Rob would do differently after a decade live

41:05 - Carlo’s question: static vs dynamic sizing, skew and volatility targeting

46:07 - Rebalancing frequency, buffering, and asymmetric volatility risk

50:49 - Dario’s question: sentiment indicators, skew and what Rob actually uses

53:10 - Andreas’ question: ATR vs standard deviation and daily vs weekly data

56:29 - Stops, intraday execution and combining slow trend with fast mean reversion

59:27 - CalPERS adopts the total portfolio approach: what changes and why it matters

01:08:12 - Boxes, sleeves and why CTAs never fit neatly anywhere

01:11:55 - Could TPA be a game changer for trend following allocations?

01:14:19 - ChatGPT, consultants and the future of portfolio construction language

01:16:18 - Closing disclaimers and how to send in future questions

Copyright © 2025 – CMC AG – All Rights Reserved

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Intro: 00:00:01

You're about to join Niels Kaastrup-Larsen on a raw and honest journey into the world of systematic investing and learn about the most dependable and consistent yet often overlooked investment strategy. Welcome to the Systematic Investor Series.

Niels: 00:00:23

Welcome and welcome back to this week's edition of the Systematic Investor Series with Rob Carver and I, Niels Kaastrup-Larsen, where each week we take the pulse of the global market through the lens of a rules-based investor.

Rob, it is great to be back with you this week. Hope you're doing well. How are things in the in the UK?

Rob: 00:00:40

It's cold and sunny. I think we're recording video for this episode, so, people can probably see I'm wearing a lot of layers as my garden office is not particularly well insulated. So, I've got a heater running, and I'm wearing lots of layers, and hopefully I won't freeze to the death before the end of the podcast.

Niels: 00:00:57

Now I'll make sure we'll keep you warm with some pointed topics for sure.

Rob: 00:01:01

If I get, if I get nice and angry, that will definitely keep me warm.

Niels: 00:01:04

Absolutely. And speaking of those, we've actually got quite a few questions this week, so that's going to keep you warm, I'm sure. And, as well as a very, very important topic that I personally think could be a game changer, not just for trend following and CTAs, but generally speaking. But we'll come to that a little bit later.

Of course, before we get to any of that, I'm always curious what you've been thinking about the last few weeks, since we spoke. Anything that stood out to you?

Rob: 00:01:35

Well, as always, I've been writing on my blog and people can go and look there, but I'm not here to plug my blog, so.

Niels: 00:01:41

Of course not.

Rob: 00:01:42

There are some very good articles on there, I have to say.

Actually, the thing that kind of came to my head this morning, I was trying to think of stuff to talk about was actually a discussion I had on Twitter (now known as X) yesterday. And it's quite an interesting discussion, it comes up every now and then, and it's basically this discussion about whether trend following is an edge or a risk premium.

And of course, you know, I think the word ‘edge’ is kind of one of these words that people use in different ways. So, I think the most loose sense of it is basically if you can make money in the markets and you have an edge.

My personal belief is, if anyone can do it then you haven't got an edge (If you're making money out of something, but it's something that anyone can do). And, of course, anyone can sort of follow a simple trend following system if they've got the right amount of capital, the right resources and make money, hopefully, in the long run. Then that money must be coming from what I would see as a risk premium. So, basically you're only making money because other people are uncomfortable taking the kinds of risks that you're taking.

So, we can talk about psychological biases, we can talk about delayed reactions to information. Those are the main two explanations for why trend following makes money. In fact, there's a recent Odd Lots podcast, with Cliff Asness, where he talked about that exact subject. So, that's worth looking up as well.

So yeah, it was kind of interesting because this debate comes along every now and then and it's amazing how heated people get about this stuff. So, very insistent that, oh no, there's definitely a trend here, an edge in using, I don't know, a moving average crossover or something to pick up trends. How could it be an edge when literally anyone with a spreadsheet can do it or even just a calculator? So, that was the thing that got me heated up yesterday.

Niels: 00:03:38

Yeah, it's kind of interesting. I mean, I think sometimes we also try and overcomplicate stuff that we have to kind of be able to explain in a very academic way. So, yeah, I'll check it out. And by the way, the conversation with Cliff Asness, it's queued up on my phone because every time he speaks I'm sure I learn something and have a laugh at the same time. So, he's one of my favorites to listen to. So, I'll definitely make sure I get that.

A couple of things that stood out on what's been on my radar… Don't know if you saw this. It kind of helps make CTAs not look so bad this year. And that is what happened over with the brilliant people at Renaissance Technologies, in October. I did notice that, in an article, a couple of their funds were behaving very differently. The Renaissance Institutional Equity Fund was down 14% in October and down 8.3% so far this year. And the diversified Alpha strategy lost more than 15% last month, according to the article, putting it down more than 10.5% last month. Of course, what they're really known for is none of that, it's The Medallion fund, which they didn't quote the performance. I have a sneaky feeling it might have made money last month, but that's purely on my side.

Yeah, interesting.

Rob: 00:05:04

Yeah. I mean there's always been a big disparity between the Medallion fund, which has been closed forever. I think it's only a very few small number of non-outsiders that have money in it. Most of the money in there belongs to the employees or the beneficiaries of the wills of the employees. Because, of course, Jim Simons is no longer with us.

So yeah, there's always been a big disparity between the returns of Medallion, which is this highly secretive fund. No one really knows how much money it makes either, because there's no public information out there. And the returns are the funds that you and I can buy that Renaissance sells. And I remember them, they’re launching a CTA type strategy. I think it was called the Renaissance Institutional Futures Fund or something like that.

Niels: 00:05:52

Okay.

Rob: 00:05:53

I remember I had a catch acronym, which that would have been RIFF of course, and that did very badly. You know, it very much underperformed the various other kinds of benchmark CTA funds

So, you know, there are various explanations for this. Do they keep all the good stuff for themselves? Are they just good at one thing that has limited capacity and then the outside funds have to do other things? I don't know. But, yeah, I mean, 14% down in a month, It's a big number.

Niels: 00:06:21

I think, just to balance our conversation, I seem to remember that actually these funds have probably done pretty well the last few years. So, this is like, the journal is picking up on an outlier number. Let's be fair.

But I think you're right about the fact that they, at some point, also try to get into our industry. I haven't heard much about that ever since. But you know, we can all have an outlier month or an outlier year, without a doubt. So, we'll see whether it comes back.

The other thing that caught my attention, this is something I saw this morning. It was an article, and I don't even know how I found it. It was probably in one of those platforms that shows you all sorts of articles regarding or relating to our industry. It was an article on a webpage called CTOL. I never heard about it.

The headline was Volatile Commodity Markets Test Quantitative Trading Funds Performance Claims. And that got my attention. One of the first sub headlines was something like, Performance Falls Short of Marketing.

ring turbulent times, like in: 2022

nd they obviously added it in: 2023

But then it went on to something that kind of really caught my attention. And this kind of warms up to next week's conversation, which will be with Andrew, because it also mentions the ETF offerings that, of course, have done very well this year. And it talks about, and I'm just quoting here, a newer category of managed futures exchange traded funds has performed better than many established CTA funds. These ETF packages, similar strategies, in cheaper, more transparent structures, are accessible to retail investors and they have seen a lot of inflows, blah blah blah.

And then it goes on to say the irony is notable, products marketed less aggressively and with fewer institutional trappings have delivered much better results than prestigious legacy funds, blah blah blah. And I'm just going to say I don't think it's a fair comment to say that the ETF space is not being aggressive in their marketing. After all, we see them a lot, on all sorts of publications, including, of course, when Andrew is here.

So, I thought that was kind of funny if that's the impression they've left with this organization.

Rob: 00:09:25

Yeah, I mean I'm always getting content asking me to invest in CTAs, but I suspect that's the various algorithms identifying me as somebody who would potentially be interested in that.

r, I believe, in the past, in: 2022

Niels: 00:10:14

Yeah, I think, in fairness, again, just to balance the conversation here, I think I read something in the full article that at least one of these funds actually had a mandate to be not very correlated with equity.

So again, we can't translate that just because, so far this year, the S&P 500 is doing well, that this fund has to do well. We don't know.

Anyways, just a little bit of good news before we go on. And that is, of course, there is no AI bubble after all. And that is purely based on the fact that yesterday the world can stop worrying about an AI bubble, at least for now, because AI chip making linchpin Nvidia dropped a stellar earnings report yesterday that has, at least temporarily, eased concerns that the economy is on the verge of collapsing like the New York Mets in September, according to this article. I probably got it from Bloomberg, those nice words. Anyways, I have no idea about AI.

Rob: 00:11:14

I have no idea about the New York Mets either.

Niels: 00:11:17

Right, yeah, exactly.

Rob: 00:11:19

But I'm sure it'll be fine.

Niels: 00:11:22

The trend may continue a little bit longer. Anyways, speaking of trends, my trend barometer finished at 48 last night. That's neutral, I would say, but it is a bit weaker than it was 10 days ago. And I think performance, when we get to that in a second, reflects that. So far, in October, I was kind of optimistic after last week. I thought, pretty good start after all the first couple of days, not great in November, and then last week was actually decent. But this week, not so much. It's definitely a bit challenging, a bit of a headwind for managers, at least that's what I see on my side.

There are a couple of things that are standing out, I guess, so far this month. It's still, you know, okay to be long in precious metals. It's okay in terms of the softs, they're doing okay, maybe even some of the products, oil products. But equities is probably where the real challenge is.

So, depending on… And this is where also, you know, funds like ETFs and replicators and all of that stuff, it's all about exposure this year. It's all about how much equity have you got, or how much fixed income have you got, and so on, and so forth. So, to say conclusively that one approach is better than the other, I think is very dangerous this year. Because it's really depending on more, I think, your market universe than anything else right now. Maybe speed, of course, short-term has been challenged for sure. But market universe, this year, is really having a huge impact on returns.

Some of the smaller currencies, New Zealand dollar, Mexican pesos, still doing okay as far as I can tell. Other than that, that's kind of been our experience. Anything from you in terms of takeaways?

Rob: 00:13:13

Yeah, yeah, sort of a similar picture for me. So, I'm pretty much flat for November, I guess. Yeah, like most people I had few good days and have given some of that back recently. For the year I'm up about sort of 5%. So, it's okay. It's not too bad. So, down about 3% from sort of mid October when things were looking good.

And I'm still in a sort of drawdown of about, well, just over 10%, I'd say. In terms of risk, kind of looking forward, so, interestingly, my biggest short is in bitcoin, which is very satisfying because you know my thoughts on that subject. But obviously that's the system hating it, not me.

I'm sort of net long equities, although for example, I'm short DAX, but I'm actually long the Italian equities, so there's a fair bit of dispersion there. But yeah, I guess my risk sizing is probably in line with your trend parameter. It's sort of definitely a bit below average. It's not the sort of… We’re going to get some fairly decent trends, and performance was really picking up kind of coming into sort of September, October, but things have gotten a little bit volatile since then.

Niels: 00:14:28

You mentioned bitcoin, so I can't help asking you, have you ever tried to model this cycle, it has a halving cycle, to see if there's any truth to it?

Rob: 00:14:40

Well, as a quant, I mean, I'm not sure, off the top of my head, how many halvings there have been.

Niels: 00:14:45

There's been like four.

Rob: 00:14:46

Okay, so that's not really enough data points for a statistically significant model, is it? And you should know better than to even ask me that question, Niels.

Niels: 00:14:54

Well, anything to do with crypto, I have to kind of just poke a little bit.

Rob: 00:14:58

You're trying to make me hot. I know. You're trying to get me excited.

Niels: 00:15:00

Exactly.

Rob: 00:15:00

Trying to make me hot.

Niels: 00:15:01

Yeah, good, all right, good. All right, as of Tuesday, not so hot, I have to say, the performance numbers. Down 62 basis points for the BTOP 50, down 60 basis points for the year. SocGen CTA index down 69 basis points for the month, down 2.2% for the year. The SocGen Trend index is down 30 basis points, down 1.18% for the year. And the Short-Term Traders index down 88 basis points, down 5.16% for the year. Yesterday probably a mixed day, some up, some down, so it could be a little bit worse as of today.

MSCI World also down, down 2.79% so far this month and that is as of yesterday, Wednesday, and it's up 16.86% for the year. The S&P US Aggregate Bond Index down 10 basis points in November, up 6.5 for the year. And the S&P 500 down 2.82% as of last night, up 14.2% so far this year.

Now, we've got this wonderful topic that we're going to keep a little bit longer in terms of suspense, but we've got lots of great questions so we appreciate that. Thank you for sending them in. Now, I've not filtered them, I'm just going to take them as they came in in the order they came in and read them as best as I can.

All right, first one is from George. My question is what does a solid reliable robustness testing framework look like? I'm struggling in the ‘try different things out’ stage e.g. too robust, every strategy fails even though they look good on unseen OOS data. And if too lose we yield a fair few good looking strategies e.g. my current idea: build strategy on three to five years of data, depending on time frame, and therefore risk of regime change. Do a quick high precision test on the build data. Those that do significant worse bin them, then a slippage test. Do they still make money if slippage is increased?

I find this particularly harsh assuming you get slipped five pips on every trade. Monte Carlo testing, randomize trade order, permutate strategy parameters, not optimize but see if changing parameters by 10% up or down breaks the strategy. I believe this helps to avoid curve fitting in the mining process. Then we test on tick data.

By this point most strategies still in the running seem to survive this. Performance looks good and ranking of the best strategies changed slightly. Then the ultimate test is on unseen OOS tick data 1 to 3 years, depending on the data sample in the build stage.

Then we have, hopefully, a good few strategies to incubate which hope leads to profitable live trading. Please can you guys comment discuss the process. I find my robustness test may be too strict. All my strategies are just BEEP (I'm just going to say that wordless BEEP.) But I always have in mind, how do I know I filtered out the good strategies that will last. My process is strictly and yields a few excellent strategies. Thanks in advance.

Okay, that was a very long thing.

Rob: 00:18:35

Just to be clear, BEEP is a bad word. He doesn't think these are good strategies, so yeah.

There's actually a big piece of information missing from this, which is what your expected holding period is because that's going to make quite a big difference to whether this process makes sense. So, I would say, for example, if you were trading with very short time frames, so intraday, then the timeframes and the things in the process kind of make sense. Although, I would say, if you are doing that, then you should probably start with higher frequency data because he talks about build data and then tick data. So, if you're trading on a fast timeframe, you really ought to be starting with tick data; with data that's much faster than your holding period is.

On the other hand, if your holding period is more analogous to mine, which is about say an average of a month, then you don't need to go near tick data. There's absolutely no need to go near it at all. You can just do simple things to sort of check what the effect of your execution is on your performance.

And if you're holding periods a month, then even delaying your trades by a day (which is what I test in my backtester) shouldn't change your results that much. And if it does, you've got a problem somewhere. But on the other hand, if you are holding for ,say, a month, then I would say that three to five years probably, for me, is an insufficient amount of time to do sort of the calibration testing of strategies. And I know he then has another one to three years of out of sample. So, I think it feels to me like you might not be getting sort of robust results because of this sort of brief periods of time.

Just to say in advance, by the way, overall there's no kind of massive red flags in here; all the steps individually kind of make sense. I think it's more whether together they make sense, and again, whether the sort of time periods being used and the data being used matches the strategy that you're trading.

Anyway, so, putting that aside then, I think this is interesting stuff actually because (not the book I'm currently writing, but the book I'm going to write after this one) I'm going to write a book on backtesting because I think there's partly a misunderstanding, but partly there's a sort of deep conflict between what the point is of a backtest and how you should use the results of it.

Because backtests fulfill two functions. They fulfill the function of finding you a strategy that's going to work well in the future, and they also tell you how well you would have performed in the past. And ideally, they would do both. And this particular framework is not going to do that for you. It's not going to tell you how well he would have done in the past. Because you're basically selecting the strategies that did the best with the data that you've got.

And you've got this in sample and out of sample process. But that's only going part of the way to doing that. Because the real kind of gold standard way of doing this is you start with, say, a thousand strategies and you basically do a rolling process where you sub select from those strategies depending on the performance you've only seen in the past. And you'll be doing that over the eight year period. And at the start of that eight year period you've got no information. So, you just include all the strategies you back test, including ones that are BEEP as well as ones that are good.

And your performance initially would be pretty, I mean, let's say on average half your strategy is half is BEEP, then your performance, to begin with, will be flat because you'd have good performance and bad performance and they cancel each other out. Over time you would hopefully discover what the good strategies are and you would include more of those in your portfolio blend, and you'd end up with the set of strategies that you'd want to trade going forwards.

And then, if you look at that historic backtest to performance, that's telling you how well you would actually have done had you had no future information at the start of your backtest. And it's also going to give you, at the end of the back test, it's going to give you the mix of strategies that you should trade going forwards. But importantly, doing it this way means that it's much more likely that, if you're doing your sort of strategy selection in a statistically robust manner, you're much less likely to do something like (and it sounds like this guy's doing it) pick, say, 1% out of those thousand strategies; to pick just 10 that do the very best. Because it's extremely unlikely unless, as I said, you are trading at a very short time frame.

It's extremely unlikely that after 3, 4, 5, 6, 7, 8 years of time that enough time will have passed you to be able to say, with statistical confidence, that these 10 strategies are the best and you shouldn't trade anything else. It’s much more likely that you'd end up with… So, you know, you're kind of fitting weights to a thousand different strategies. Maybe half of them you'll kind of have enough evidence to get rid of entirely, but most of the rest would still be in there. Yes, your 10 superstars would have a higher weight than the other others, but, by no means, would they be filling the entire portfolio up.

And the issue here is this thing called the multiple testing problem. And essentially, even if you set a very high bar, let's say you say, well, I only want to see the top 1%, if you've got a thousand things, then just by luck, even if on average they're kind of flat performance, just by luck, ten of them are going to be amazing. And then you pick those 10 out. And yes, he's doing these other robustness tests that are good and will hopefully kind of weed out anything too crazy. But you know, I still think that potentially the main issue with this is that you're selecting a very small subset of strategies based on insufficient data. That's kind of the big red flag for me with this process as described.

But, you know, doing things like Monte Carlo testing, doing things like holding out a sample, that's all good, but you know… Sorry, the one other thing to say is as well, you know, he tests stuff and then tests to see what the effect is of adding trading costs. Trading costs should be in there from the very start. There's no point even considering things that you can't make money with before slippage.

So, you know, unless there's something about your back testing process that makes it very computationally expensive to include costs, and that's why you're doing this, I don't understand otherwise why you do that.

So, that's kind of my general thoughts. And, as I said, I could go on and you know, as I said, if I'm going to write a book about this, which will probably be at least 300 pages, then I could clearly go on for a long time to discuss this topic. But that's sort of my thinking about how you should backtest and the purposes of backtest.

And hopefully that gives you an idea of some of the changes you can make to this process to Improve it.

Niels: 00:25:46

It's all well and good to follow a process and you know, you end up deciding on something you feel comfortable with. But things change, data changes. How often would you go back and do this, and would you simply change your, say, models, parameters, whatever, on an ongoing basis in the future, based on future tests that you make?

Rob: 00:26:14

Well, basically, in theory, you run the process forward. So, if your back testing process consists of… So, if you've got a monthly holding period, then I think it's probably appropriate to do this re-optimization of weights to strategies about every year, say. There's no point in doing it more frequently than that. And that will then result in your weight slowly evolving. And depending on your time frame, you know, you will get to see a slow evolution. Some things will become better, some things will become worse. So, that, in theory, should just roll that forward.

ou know, my data goes back to: 1970

Now you could argue that you should weight more recent data more highly than that, but even so, if you've got long back tests and relatively slow trading, then actually your weights probably aren't going to change that much. And the truth is, for me personally, I've refitted my weights on average about every three years. So, you know, every year seems like a lot of work. So, normally only if I make other changes to my system I then go and also change the weights. Because, as I said, with such long holding periods, there's not really evidence to say you should do anything differently.

Niels: 00:27:45

No, I think that's a perfect follow up answer. Thanks for that.

All right, we move on to a question from Frederick. He writes, hello, thanks for the show. I've learned a lot from it. My question for Rob is if he has any thoughts or statistics around intraday versus end-of-day trading for a medium to long-term trend system. Would the faster entries and tighter risk control actually lead to better results?

Rob: 00:28:11

I am a bit confused by this question to be honest, because the two sentences to me don't seem related to each other. So, let's say you're trading medium to long-term trends, okay? So, you can trade that system in different ways.

So, one way you could trade it is, basically, a lot of people backtest their stuff on end-of-day data, which is completely fine if you're trading slowly enough, as we discussed the first question. So, one thing you can do is, basically, then, when you're trading, actually try and trade as close to the end of the day as possible. So, you sort of matching your backtest.

Now that might not be ideal. And it's funny actually because I've done consulting and a lot of people have got this sort of fixation with oh, I must trade at the end of the day because that's where my backtest data's from. And I said to them, well just try this experiment which I already mentioned, just delay your fills by a day in your backtest. Does it make it any difference? Does it not make any difference? Okay, well then you're fine.

Or you know, as long as you do something like what I do, which is essentially, you know, take the previous day's closing prices and then, at some point during the following day, do your trading, you'll know you're going to achieve performance somewhere between if you'd managed to do it exactly at the close of the previous day, which you can't, and the following day is closed, which is what I backtest, you're going to be somewhere between those two. And if those two things are close enough, then obviously, on a day-to-day basis, you might see some differences of prices have been a bit crazy but on average you'll be fine.

So, I would suggest… And the other advantage of that, of course, is if the close is not liquid, or is crazy, or there's some weird auction process, or whatever, you can focus your trading at other parts of the day that are more liquid. And, of course, if you're trading institutional size money, the last thing you want to do is do one massive trade every day. You want to spread your trades throughout the day and try and reduce your market impact as much as possible. So, if you're trading medium to long-term trends where, basically, you want to trade roughly once a day, then spreading that out is no bad thing.

The other thing you can do, if you have a system like mine which essentially dynamically looks at its positions sort of optimal versus what it actually has, I could actually run that thing throughout the day. And that would not increase the total amount of trading I did, but it would, again, result in a sort of smoothing process and it would allow me to react faster to very large intraday price movements which may or may not be a good thing because, obviously, if prices bounce back, then that's actually going to lose you money. So that's kind of my answer to what is, for me, quite a confusing question, to be honest.

And I don't really understand what faster entries and tighter risk control actually mean unless he's talking about trading much more quickly. So, he's actually talking about sort of intraday trend trading. So, you know, in which case, you know, I've got two big issues with people who trade trends intraday.

One is obviously costs. Costs are going to be higher because you're doing more trades, but also because it's harder to do your trades with sort of passive limit orders because you want to catch a trend that's only going to last a couple of hours. You don't want to sort of wait there for it to be passively executed. You need to jump in straight away.

And the other issue is if you actually look at the efficacy of trend systems, they tend to start to decline for holding periods shorter than about a month or a couple of weeks, depending on the asset class. And by the time you get down to intraday, it's less obvious that there are strong trend patterns there. And, actually, if you get down to sort of sub one hour, you tend to start to see mean reversion, which obviously is the opposite of trend. So, I'd be very careful about looking at intraday trend trading.

Faster entries and tighter risk control, whatever that means, aren't going to be a lot of help when you're paying those big trading costs.

Niels: 00:32:11

I'm thinking that, you know, if you are newer to the space, and you get maybe information from, you know, online platforms, people promoting stuff, etc., etc., I can understand why people might think that actually, oh, trading faster, as long as it's trend, it works.

And I even had a conversation on email from a nice guy in the US regarding using trend following just on a single asset class. They're doing it on equities. And I'm thinking, okay, it's great if it works for them. But it's just not my experience that, actually, trend following isn’t designed to work either on faster speeds or on a single asset class. It's really the diversification, and the longer holding period, and all that stuff, that makes it a little bit difficult, makes it a little bit uncomfortable, that makes it work. But I do take that people might have a different impression coming to the space and maybe getting a lot of the information initially from, say, more commercial outlets, let's call it that.

Anyways, we appreciate the question, Frederick.

ng, spanning from February of: 2015

Rob: 00:34:08

I mean, it's quite depressing that you can only manage 10 pages of… It's not clear whether it's my trading journal or my book, only 10 pages before he just grew so bored and just skipped.

Niels: 00:34:18

I think he did the right thing. He went straight to the source and asked his questions. I think he did.

Rob: 00:34:22

Yeah, well, if this is a ploy to try and avoid reading the rest of the book or the, or the trading journal, tough luck, you're still going to have to do that. So, that's my first piece of advice. Read till the end. It's all good stuff.

Although, to be fair, the trading journal is many thousands of posts long now, so, you know, it'll take a while. The book's only a few hundred pages though.

Yeah, it's an interesting question actually because, you know, what's changed over the last ten years?

It's actually… Where are we? It's about eleven and a half years since I started running my own automated trading platform. And it's nearly twenty years since I began working in the CTA industry. So, you know, quite a few things probably have changed in that time period. But I'm actually struggling. I mean, Niels, you've been around even longer than I have. Although people might not guess it because you still have a reasonable amount of hair. So, you probably look about ten years younger than me.

I mean, I don't know what you think, but I'm actually struggling to think how my advice would have changed from ten or even twenty years ago. I think, I probably say the same things. I mean, if I was to go and revisit that book I wrote, over 10 years ago now, I'm not sure very much in it would change. I mean, I'd probably put some stuff in for crypto, just that specific asset class that's kind of come out of nowhere. And my new book that I'm writing now is, has got some stuff on crypto just for that reason. But apart from that, you know, I'm not really feeling that there's been… you know… I think the same principles apply. I mean things we talked about like backtesting. I think those are principles that have applied and it's not like they're sort of new scientific discoveries.

The advice I normally give to people is things like keep things simple. As we've discussed, think about trading costs, think about leverage, optimal leverage. Don't take too much risk because the three things that kill systematic traders are spending too much on costs, taking too much leverage, and overfitting their trading systems. And those are the same three things I was warning about 10, 12 years ago.

And those are the same things that I think were a problem 20 years ago. So yeah, I'm going to hand this one back to you Niels, as you’re even more experienced than me, what do you think?

Niels: 00:36:43

So, first of all I would say… And I think this was your first book, if I'm not mistaken, Systematic Trading Strategies. Am I correct in saying that? I think that was, actually, the first time you were on the podcast was when we reviewed that book.

Rob: 00:36:58

It was, yeah.

Niels: 00:36:59

Which is a great book and everybody should really read it, and it is timeless, and I tend to agree with you that not a lot of change. Where I think things may have changed is that I don't necessarily feel that the way we try to identify entries would have changed. Of course we still adhere to the various golden rules of trend following that have lasted for decades.

I do think, maybe, the industry has evolved when it comes to how we manage risk. It's difficult for me to say specifically how each firm would have evolved, but if I look at what we do at Dunn, I would say we do things differently. We do it in a smarter way, in a sense.

I think, also, we are more open to differentiating speeds a bit, and I don't mean just deploying short term strategies. That's not what I mean. But you can design your systems to react differently from entries to exits. I think that that is something we do, and I think that has helped us. And as I said, overall risk management, I feel, has evolved as well. Maybe people like you wrote another blog post, which we're not going to talk about today, but that Katy plugged for you last week, was about predicting volatility. Of course, volatility…. Maybe we are using volatility differently today than we did 20 years ago and maybe we can use it better now in the way we manage the risk.

So, I generally agree with you that it's not massive changes. But I do think that we have evolved. I mean, there's a reason why we have these research teams who are very good at what they do. It doesn't mean that they can overcome bad market environments. And we've had some of that in the last 20 years for sure. And that's why people sometimes write that, oh, this strategy doesn't work as well anymore. I'm not so sure that's true.

At the end of the day, if you're starting out on this journey, I think it's really about committing to that journey, committing to these rules, because it won't be an easy journey at all for you, Abram, as we discuss every single week. I think that commitment and really wanting to do this and feeling completely comfortable with a strategy that is unpredictable in some ways, yet it's very predictable in the long run. It's unpredictable from a day-to-day basis. I think that's probably some of the things I would work on.

All right, let's move on to Carlo's question.

Carlo writes, I would like to submit a question for Robert Carver. How should a trend following investor think about position sizing when volatility is time varying? Okay, so now we're getting into some of the stuff.

In particular, I would be very interested in Robert's view on the following points: static versus dynamic exposure. Some trend followers argue maximizing the economic benefits of outlier hunting. (I think he's been listening to Rich too much, actually.) It is preferable to keep the numbers of contract unchanged throughout the trade so that only the volatility at entry matters. Others prefer a continuous volatility targeting framework based on Robert's backtesting experience. Is there empirical evidence that dynamic volatility targeting produces better results than a static exposure? Or is the choice mainly driven by investors objective function? For example, prioritizing Sharpe ratio optimization versus maximizing long-term CAGR, as if that is something you can guarantee just by using static position sizing (I just want to caveat that.) rebalancing frequency. How often does it make sense to adjust exposure as volatility changes? Nature of volatility, should a trend trader treat volatility differently depending on whether it supports the trend positive volatility or threatens it’s negative volatility. Thank you so much, from Carlo.

Rob: 00:41:06

This is a great question. I could almost have written it myself. So, actually, on the first question about the choice of dynamic or static volatility position sizing. Actually, let, Let me very quickly, because I'm sure 90% of the people listening to this know exactly what we're talking about, but it's probably maybe 10% who have just started listening. Maybe Abram's just started listening.

Niels: 00:41:27

Yep.

Rob: 00:41:28

So, static exposure is where essentially you put a trade on, and you size your position according to the volatility that's current at the time, and then you basically maintain that position to the same level. Okay, so, if the market gets riskier, you know, your position stays the same size. If the market gets less risky, your position stays the same size. You may adjust your position for other reasons, like if you're using some kind of pyramiding where you add as things have stronger trends or whatever.

Now the alternative is dynamic position sizing, and that's where you essentially measure the volatility throughout the life of the trade.

So, if the market suddenly gets riskier, you reduce your position. If the market gets safer, you increase your position. And this is sort of relates to…

Niels: 00:42:18

Oh, can I add one thing?

Rob: 00:42:20

You can.

Niels: 00:42:21

The dynamic position sizing may not only be influenced by volatility.

Rob: 00:42:25

I was about to say that, Niels, I was about to say that. Will you not butt in, please? I'm on a roll here. Come on, give me some space.

So, yeah, what I was going to say was, what I call, continuous trading which is basically where you constantly evaluate your positions according to things like volatility, strength of your trend, but also things like potentially, in fact, it could be even things like the size of your account as well. So, as you lose money, you'll be taking money off the table, and so on, and so forth.

Niels: 00:42:56

And correlations, Rob, and correlations.

Rob: 00:42:59

Yeah, potentially, depending on how your risk system works. In my particular system, correlations wouldn't affect things to a first order.

But if things get very correlated, then there is a sort of exogenous risk factor that kicks in. But yes, if you're running your system at a fixed volatility target, we always target the same vol every day. Then one thing that would affect things every day is correlations as well. So, absolutely.

Okay. Now I have tested these two things a few times on my blog. And one sort of important question is, yes, what, what do you care about as an investor? Do you care about maximizing your Sharpe ratio? Do you care about maximizing your geometric returns?

And the reason why that's important is that things that have positive skew may not have as good a Sharpe ratio, but may produce you a better geometric return. Things that have negative skew produce the opposite effect.

Now, if you're not using volatility targeting, then your skew profile is much more likely to be positive skew. And the reason for that is quite straightforward. If you're holding a position and the thing suddenly leaps in price, and let's think about cocoa a couple of years ago, because that's the example that I think still sticks in people's heads. Something leaps in price, if you're not changing your position according to volatility, that leap in price, which of course also increases volatility, you keep your position the same size, and that means you get this massive outsize positive outlier return.

And then that obviously will help improve your geometric return. Even though, from a Sharpe ratio perspective, it may actually look worse because your risk is going to be much more variable. And Sharpe ratio calculations don't like risk that moves around a lot.

If you've got a nice, steady, consistent set of returns, you're much more likely to have a higher Sharpe ratio. Now it's basically possible to look at the trade-off between… I won't go into too much detail because you will have to go and look on my blog to find the article, but especially you can look at the trade-off between skew and Sharpe ratio for maximizing your CAGR. So essentially there should be a certain amount of positive skew that you're willing to give up in return for higher Sharpe to get the same CAGR. So, there's sort of almost like a risk\reward trade off, except that it's skew versus Sharpe ratio trade-off.

And what I found is that basically (and this is not an empirical result by the way, this is actually theory, so you can't argue with it, essentially it's not just a quirk of my data), the amount of Sharpe ratio you should be prepared to give, up for the sort of improvements in skew that you see from using fixed vol scaling and not change your position size. The bottom line is it's not worth doing fixed vol position sizing because the additional skew you get does not pay you for the loss in Sharpe, which is substantial. So, you end up with a lower CAGR, basically. And that, you know…

Niels: 00:46:08

Just to clarify.

Rob: 00:46:09

Yeah, please go.

Niels: 00:46:09

Yeah. When you say fixed volume, what you just mean is you keep the position size static.

Rob: 00:46:16

Yeah, static position sizing, exactly.

So actually, no, we think about funds, some CTAs that have extremely good returns, very good CAGRs, and positive skews, and have this sort of outlier behavior, and their volatility is often very big. They sort of will tend to outperform a system that's more like mine, which has a higher Sharpe ratio, less positive skew, less outliers, because it's dynamic position sizing.

But basically, if I apply just a little bit of extra leverage to my system, I could very easily beat their geometric returns because my superior Sharpe ratio more than pays for the loss in positive skew that I have. So that kind of answers that question. It's quite a long-winded answer, but it is quite a complex subject. But yeah, go on my blog and there's an article about it.

Then there's a question about rebalancing frequency. Well, essentially this is the standard question about rebalancing. When you're doing rebalancing, you're trying to trade off two things: the benefits you'd get from having your position close to what your optimal position should be, and the cost of trading to get there. And the worst thing in the world is if you rebalance the wrong way.

So, let's suppose volatility is sort of jiggling around a bit, but basically falling. So, all the things being equal, I would increase my position size. if I was to rebalance, say every day, without fail, then some days I'd be buying, some days I'd be selling (I'm assuming everything else is fixed, of course), so I've been carrying a lot of extra trading costs.

So, you know, that would imply that my rebalancing frequency should be a bit slower because those extra trading costs are going to more than kill, you know, the extra benefit I might be getting. But there is a solution to this conundrum and that's to use either buffering or smoothing.

So, with buffering, essentially you only trade if your position is further away from the optimal position. And with smoothing, you basically take something like a moving average of what you want your position to be. And a lot of trading rules that we use, obviously incorporate moving averages in them already. So, with volatility, if you were using something like an extra weighted moving average of volatility estimates, that would be relatively smooth and you'd do fewer trades.

So, the answer to the question about rebalancing frequencies, to be honest, I can't tell you because it's going to depend on costs. But as I said, if you use these other strategies, then you can do rebalancing more frequently without actually incurring any trading costs. So, you're going to get the win there.

Now, the last question actually is the most intriguing because I don't actually have an answer for it. And that's about using an asymmetric volatility measure. So, if we think about cocoa as an example, then that was positive volatility, right? The price went up, we were long, Hooray! And maybe in those circumstances we shouldn't reduce our position.

Now, my concern with this is that returns are a coin flip pretty much. So, if you're a really good trader, you might make money for 51% of days and lose money 49% of days. So, if you've got a massive risk on because the thing's just gone up in a straight line and that's wonderful, and you just hold your position the same size, you're taking this huge amount of upside of risk. Your upside risk and your downside risk are the same. Okay, you're just a coin flip away from those massive profits becoming massive losses.

So overall, I'd say, no. I'd say you should treat both kinds of volatility the same. That's not to say, though, that assets do have different behavior on the upside and the downside. And there are interesting things we can do to model that. But this specific question, I suggest that you just treat risk as symmetrical.

Niels: 00:50:11

Well, that's definitely an option for you to include that in one of your next books, whether there is a difference in that subject.

Anyways, let's move on because we've still got a couple of questions before we get to the big secret topic of today. A question from Dario. Hi, Mr. Carver. I hope you're well. Thank you so much for your contributions to the investment community.

I'm curious about sentiment indicators used in low frequency algorithms. I wonder if you have ever tried trading off sentiment indicators? Are your skew signals a proxy for sentiment? Thank you, from Dario.

Rob: 00:50:51

I must say, I'm slightly uncomfortable with all these people calling me Mr. Carver. It's a bit formal for me, but anyway. Okay, so, sentiment is interesting because I don't actually use any sort of sentiment indicators myself. So, my skew signal, which he asked specifically about, that's something that's looking at historic levels of skew.

Now, you could argue that as a sentiment indicator. You could argue that if an asset has recently seen very violent falls in price, that suggests that most people would have a negative sentiment towards that asset. And in the case of my trading system, I would actually buy that asset because something that's got very heavy negative skew would tend to be underpriced because people don't like it. So, actually, in that specific example. I'm a buyer of negative sentiment, But generally speaking, partly because of resources, I tend to stay away from, say, weirder data, things like sentiment scores. Also, some of them tend to be, effectively, in sample fitted.

So, when I used to work for AHL, we had a classic thing where sell side traders would come in and say, look, we've got this amazing model that predicts this, do you want to buy it, or trade it through us and give us a commission, or whatever. And you'd say, how did you construct it?

And after a lot of plotting ,and prodding, and pulling, and asking your questions, and digging into details, you find out essentially it's massively in sample fitted and therefore of course it looks very good. And we, generally speaking, would prefer to do that job ourselves, and do it properly.

So, I'd be wary of kind of buying any or sort of random, third party sentiment indicators, but there are things that kind of indicate the sentiment of the market. So skew is one. There's sort of various options ratios as well you can use. You could just look at the level of the VIX and say, well, that's sort of a proxy for how scared people are. That's a measure of sentiment. So, all of these are good things that are potentially useful for predicting prices. I just tend not to use them myself. But that doesn't mean that it's not a valid research area.

Niels: 00:53:12

Sure, fine. Final question today is from Andreas, actually I know who Andreas is. So, he has two questions for you Rob, and he does call you Rob, by the way. In Rob's research, the relationship between ATR and standard deviation, he concludes with an empirical and theoretical solution. Question, A, is there a mathematical logical relationship between daily and weekly ATR? And, B, if a model is trading once a week only, is it advisable to use daily data or weekly data with respective to ATR? And then there's a follow up question, but let's do one at a time.

Rob: 00:53:47

Yeah, so, ATR (for those who aren't familiar) is Average True Range. So, the true range essentially is the difference in the highest and lowest prices you see in a time period like a day. So, it's sort of a bit like standard deviation in the sense that it's designed to measure how much market smoothing… Rather than just looking at single price per time interval, what technical analysts call a bar, it looks at the size of the bar itself as well.

Now, essentially, it's not possible to work out… So, you could look at the imperial relationships in daily and weekly ATRs, but it depends on essential autocorrelation. It depends on how the price return in one period influences a return in the following period. And this actually affects standard deviation estimates as well.

So, for example, returns tend to cluster, so good returns tend to be followed by other good returns, then generally speaking, if you compare, say, your daily standard deviation, your annual standard deviation, you'll find that there isn't the kind of theoretical square root of time relationship between the two. You'll find that… So, let's take an extreme example. Suppose prices go up by 1% and down by 1% every day of the year. If it's a leap year, there's an even number of days in the year, so the return for the year will be zero. So, if that happened forever, then the standard deviation of annual returns would be zero. But the standard deviation of daily returns is 1%.

So, that simple example where you've got very strong negative autocorrelation, the daily standard deviation is much higher than the annual standard deviation. If you had positive autocorrelation, then it would be the way around, basically.

And this is quite interesting for people who do things like… There's a paper by Andrew Lowe where he looks at hedge fund performance and Sharpe ratios, and concludes that he really wants to try and get sort of monthly, weekly, or daily data rather than just looking at annual because annual returns hide a lot of fun autocorrelation properties. But that's another story. So yeah, it's not really…

So, I think off the top of my head, the weekly to daily ATR relationship should behave the same as standard deviation. If you assume zero autocorrelation and probably some of the distributional assumptions, then it should follow the square root of time rule. And that means that if markets are trading on weekdays only, then there should be a sort of multiple of about square root of five between those two values, in theory. But yeah, it depends on the autocorrelation.

Niels: 00:56:31

And would you use weekly data if you only trade once a week?

Rob: 00:56:35

I mean, yeah, there's no reason not to. It was like the earlier discussion when we were saying, well, why use tick data if you're trading daily? What extra information are you going to get?

Now, having said that… Sorry, the question was about estimating the ATR. I would say yes, you always get a more accurate estimation of volatility if you use data that's more frequent, definitely. So yeah, I would use daily price changes to calculate my ATRs. But in terms of actual general backtesting of my system, then yeah, sure, weekly for weekly, daily to daily. There's no reason to do it faster.

Niels: 00:57:15

Yeah, that's important.

All right, final second part of the question. Has Rob done any research on using intraday stops versus using market orders at the next day open if the hard stop has been reached? How much additional room e.g. expressed in ATRs is advisable to add to an intraday stop?

Rob: 00:57:37

I mean, I don't use stop losses when I'm trading and I don't use ATRs either. So, I'm not sure how qualified I am to answer this question. The one thing I will say is that there is a nice paper by… I can't remember who it's by. It's inevitably going to be someone who works at AQR, I'll say that much. I don't think it's Anty and I don't think it's Lars. I think it might be Frank anyway. But the paper basically discusses a very neat way of combining slow momentum with shorter-term mean reversion is to do something really simple, which is, when you come to execute your trades the day after the previous day's close (as we've been discussing), only execute those that have moved in your favor. So, if you're buying, only execute if the price has dropped in the last closing price and vice versa.

So, that's a very, a very, quite a cheap way of getting potentially better execution costs by essentially combining your slow system with a fast system. Because normally the problem, as we've discussed, with faster systems that cost too much in trading costs. This way you're going to do that trade anyway. It's just which level you do it at.

So, the key issue there is whether that delay of one, two, maybe more days… We know that a delay of one day is not going to affect us too much. We discussed that already. But whether delaying by potentially more than one day, because you're waiting for the price to reach a nice level, whether that's an issue or not. And that's something I need to test because that's in my back book of things I need to research and implement, definitely.

I've not answered the question at all because I feel completely unqualified to. It's just that was a random idea that came out of my head that I think is sort of related to what he's talking about.

Niels: 00:59:29

,: 2026

Now, in the article that I found, which is not the FT article that you sent, Rob, it also goes on to say that under the TPA, the focus will be on which investments can best contribute to the performance of the entire CalPERS portfolio as opposed to achieving individual asset class targets that were periodically reviewed. Now, I'm going to shut up now pretty much because I want you to take us through what you found to be the interesting part.

But as I said last week, when I spoke with Katy, because of that sentence, that each component will be judged on what they add in terms of value to the portfolio, personally, I think that makes it very, very interesting from a trend following perspective.

Rob: 01:01:32

I think it does. Yeah.

I mean, although cynically I wonder whether this is just going to be used an excuse to jam more private equity, you know, or private debt, or other private…

Niels: 01:01:45

Anything private.

Rob: 01:01:45

That's because people love the private stuff at the moment, don't they? But now, let's be upbeat and positive and assume it's going to be a good thing for trend following.

Now, I have to say I was very intrigued by this subject for a number of reasons. One is that portfolio optimization is sort of my hobby, I suppose it's one of the big research area I keep coming back to and one I've done a lot of spending time and thinking about.

though it was invented in the: 1950

So, SAA or strategic asset allocation, is kind of the way that people have done asset allocation for almost, I would say, my entire lifetime, pretty much with some tweaks. And it does grow out of the Markovitz model. And the basic idea is this, it's simplest to think about it in an equity bond setup.

So, you think about your expectations for forward returns, risk and correlations between the two asset classes that you're considering, and then you set some target return. And that's for example, if you're a pension fund, you know that's going to be based on something like the average age of your members and you know how much money you need to sort of pay them out.

You know, a recent innovation has been these things called target date funds, where you dynamically change your allocation as people age effectively. And you know, in simple terms, that will put more into bonds and less into equities, which kind of makes sense.

And then you build this thing called the efficient frontier, which is the sort of the best portfolio of these two assets. You pick a point that has the highest Sharpe ratio and then, if you can use leverage, you basically construct a tangent. If you can't use leverage, you pick some point along the efficient frontier where you get, essentially, the lowest risk (that still meets your hurdle) return.

So, what that means in practice is if you're a retirement fund, but your members are quite young and you want to achieve a certain amount, you can sort of have a higher return to try and meet your obligations in the future. And that will mean a higher mix to equities. And obviously, if you've got a lot of old retirees, and this is particularly true of the UK… in the UK we've got a lot of so-called defined benefit pension funds where the investment risk is all on the manager and not the person, the retiree. And that means that they have a lot of money in bonds. And that's been particularly good for long-dated UK bonds, actually. That does weird things to the yield curve. That's another story.

Anyway, that's just the starting point. And then you’ve got your SAA or strategic asset allocation, and then, of course, you can add to that other things. So, they use this expression ‘sleeves’, which I've never really understood but you can have sleeves for hedge funds, you know, for equity market neutral hedge funds specifically, perhaps. You can have them for private credit and again for private equity. And of course you could have a sleeve for CTAs.

And then the idea is that you then sort of go down to the next level and you say, right, we need to have exposure to this asset class. And then we maybe make some determination about how much is passive, how much is active. Maybe we have some country weightings and then so on, and so forth. So, the key points here are that it's a very kind of structured top-down process, basically.

I like to think of it as a series of boxes. So you start off by saying, well, I've got this big box and in it I need to put asset classes in different sizes, and then I open up those boxes, and I put other stuff inside those boxes, and so on, and so forth.

And importantly, no one takes into view the fact that if, for example, let's say I happen to put into one of my boxes something that's highly correlated to something in another box, that does not enter into the decision process because no, no, no, we're just putting stuff in boxes now. We're not considering the total portfolio, the holistic portfolio. And that might mean, for example, that, well, let's take what I think is a pretty made-up example. Let's just think of it anyway.

Let's suppose you have a CTA that has amazing performance but for some reason has a very high correlation with the Danish equity market. (This is a completely made-up example.) And let's also suppose that, separately, you're the guy that manages your equity portfolio, really likes Danish equities. Well, now you've got a problem because you have a big exposure to Danish equities in two parts of your portfolio. And maybe somewhere there's a risk management team that will point this out to you. But in terms of the portfolio construction process, this sort of weird correlation hasn't been taken into account.

So, my understanding of total portfolio approach is that, rather than doing this kind of top-down bits and pieces, blah, blah, blah, that we look at the thing as a whole… And then we'd potentially make a decision along lines of saying, well, we've got this amazing CTA manager, but he's got this massive exposure to Danish equities and we've already got a big exposure to Danish equities. Well, does it make sense to add them to the portfolio? Does it make sense to dial down the long-only Danish equities and then add these guys in, or does it make sense to take out the Danish equities long-only and just put the CTA in?

So, you make those decisions on a more holistic basis. Now, I have to say that, to an extent, this whole…. Can we call it an industry yet? I don't know.

Niels: 01:08:14

Which part?

Rob: 01:08:14

Well, the TPA in industry, you know.

Niels: 01:08:16

Well, they are alike.

Rob: 01:08:16

There are people who are going to do this for a job.

Niels: 01:08:18

They quote like five or six sovereign wealth funds or big pension funds worldwide. I'm not so sure that qualifies it just yet.

Rob: 01:08:31

Let's call it a cult then, instead, because it's like maybe a few hundred people doing this globally. That's about the size of a cult.

Niels: 01:08:39

Or a group, we could just call them a group.

Rob: 01:08:40

A group. I don't know about cult, maybe, has connotations, do you think?

Anyway, one of the problems I found with this group is that it does seem to be something that's been invented by some management consultants and it's very vague and there's a lot of very nice charts and pictures and expressions like… (I'm going to read this out.) “TPA shifts the focus from rigid allocations to unified strategy where decisions are guided by the Fund's overarching goals, drawing on ideas from the entire investment portfolio, inducing the flexibility to adapt to emerging priorities such as sustainability, intergenerational equity, evolving economic conditions. The approach enhances governance, fosters collaboration, unlocks new opportunities and supports multidimensional 3D investing.”

Now, that sentence was definitely written by a management consultant and not by a quant, because a quant would say something like maximize utility function, where you input the correlation, the standard deviation, and the expected vector of mean returns, plus your constraints, and with that I've just described essentially the Markovitz optimization.

So it's a bit hard for me to kind of make judgments on this, generally speaking, because there's no real precise mathematical definition of what it is. And, you know, I really feel like I need that to hang on to. But, as a general rule, clearly, I think it makes a lot more sense to think about your portfolio in a holistic sense, rather than just putting things in little boxes. Because I think another point I would make is that one of the issues the CTA industry has is which box do we go in? Are we in the hedge fund box?

Rob: 01:10:18

Because we're quite different from most hedge funds, we've got quite a different correlation. Are we in some kind of nebulous tail protection box? Well, that's tricky because unlike proper tail protection funds, we can't guarantee that we'll make you money in a down market. We hope we will, but there's no guarantee of it. On the other hand, we've got a positive Sharpe ratio, which those guys don't have, so we don't fit in that box either.

So maybe, just maybe, this move away from a reductionist approach of putting everything into little boxes will also be good because people will be able to say, well, I've got my portfolio here. And if I add this weird thing here, it does improve the overall setup (as long as there's not any too many Danish equities in there, of course), and that's great.

So, that's kind of where I am. I'm hopeful. I'm a little bit skeptical because it does sound a little bit management consultancy. I'm also, as a quant, struggling with the fact there's not really any formal definition of how you do this. And there does seem to be a lot of hand waving going on. But that's my take on it. But I think you're right, this could be a game changer.

I mean, if CalPERS are doing it, they're big players. And if, for example, someone like ADIA starts looking at it, the quant team at ADIA, I think they employ some like 20% of all the quants of the world now. You know, there's some very famous people there. So, if they start doing it, then I think that really will be a game changer and maybe they'll actually write some papers and explain to us what it is, because I'm still struggling slightly.

Niels: 01:11:57

Yeah, I think you make a lot of great points. And I was thinking, if it wasn't a management consultant that wrote that text you read, it could have been chatGPT. And the reason I say that is you also sent me an article, and I have no idea what you were thinking, you sent me an article in French expecting me to read about the total portfolio approach in French. And, to be very open about it, I'm not very good at French.

Anyways, so I had to resort to ChatGPT just to make some sense of it. And what it did, I just asked for a summary and when it got to the TPA it wrote the following. (And that's why I'm thinking that it could be ChatGPT.) “One portfolio, one goal, maximize total return within a defined risk budget. Asset classes don't matter, only contribution to total outcome does. CIO lead, dynamic collaboration, performance equal fund objective, not asset class alpha, this is portfolio construction without walls.”

And then I asked, okay, so why does this work? And it says,”It empowers teams to act on the best opportunities, not just stay in their lane. It frees capital for macro, trend and uncorrelated strategies and puts risk at the center where it belongs. It favors adaptability over prediction.”

Rob: 01:13:33

Yeah, I don't know. It's becoming increasingly difficult to tell the difference between AI and non AI related content, especially if it comes from a management consultant, I have to say.

Niels: 01:13:41

It even went on to say, “Here's the TTU, the Top Traders Unplugged take. Okay, it says SAA is a map, TPA is GPS, and in this environment you need to navigate, not just rebalance. For trend followers TPA is the best chance we've ever had to be judged on what matters, our ability to deliver value at the portfolio level. This is our moment if we are ready to own it.”

I love ChatGPT. It's fantastic.

Rob: 01:14:22

I'm sure, in the not too distant future there's going to be just a blank screen next to you as you press play on Rob Carver…

Niels: 01:14:33

No, no, Mr. Rob Carver.

Rob: 01:14:34

Mr. Rob Carver, ChatGPT trained LLM, complete with voice, and yeah, off it will go. There'll be no need for me to keep signing in.

Niels: 01:14:46

The good news is that is not going to happen just now. You will be back soon.

Rob: 01:14:50

But yes Christmas episode.

Niels: 01:14:52

And, actually, there is a Christmas episode coming up which will be fantastic with all the co-hosts. I think maybe one won't be there.

But anyways, before we get to even any of that, I think that, hopefully, I'm hoping people will go to their terminals, find their favorite podcast platform, and say a big thank you to you by leaving a rating and review for this episode because you put a lot of hard work in answering all those questions. So, we really appreciate that, as well as reviewing the articles and information about the TPA. So that's great.

And if you have any questions, comments, otherwise, as usual, you can send them to info@toptradersunplugged.com, and especially if you have something for Andrew Beer because he's coming up next week. This will be fun. This will be interesting. It always is with Andrew. I think we may have a little bit of back and forth on some of the new stuff. He's going to be talking about some news that just came out, actually, in the last day or two. So, join us again next week when Andrew's here and send us some questions like you did for Rob this week.

Anyways, 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 always, take care of yourself and take care of each other.

Ending: 01:16:20

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 info@toptradersunplugged.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's a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions. Thanks for spending some of your valuable time with us and we'll see you on the next episode of the Systematic Investor.