218 Systematic Investor Series ft. Richard Brennan – November 13th, 2022

[00:00:00] Intro

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 strategies. Welcome to the Systematic Investor Series.

[00:00:22] Niels

Welcome, and welcome back to this week’s edition of the Systematic Investor Series with Richard Brennan and I, Niels Kaastrup-Larsen, where each week we take the pulse of the global markets through the lens of a rules based investor. If you’re new to the show, I hope that today’s episode will trigger your curiosity enough to check out the back catalog and listen to past episodes that you may have missed.

Like my conversation with Rob last week where we discussed the optimal lookback periods for trend following strategy, and also touched on how Rob can, essentially, replicate a much bigger market universe using a much smaller set of markets – kind of how some people replicate CTAs and hedge funds. 

Also, I would encourage you to listen to the midweek episode where Rob and I were joined by the head of Systematic Strategies at Goldman Sachs, Nick Baltas, to discuss a wide range of topics when it comes to quant strategies and also some of the great research papers that he has written, one of which is about why the media often blame risk parity strategies and CTAs when something big happens in the markets. (That’s actually not the name, but that’s what it’s about.) So, head over and check them out if you are curious, after you’re done listening to Rich and I today. 

Rich, as always, such a pleasure to have you back this week. How are things, how are things going down under? 

[00:01:40] Rich

Yeah, very well, Niels. It was an absolutely magnificent day today.

Quite unusual, but the weather was perfect and I was just enjoying the entire day. On other things, I was absolutely wrapped when I heard that Kherson had been liberated. I was giving a big thumbs up to that move. Of course yesterday it was a bit of a different day with a bit of frenetic activity with the portfolio.

So the battleship was getting hammered yesterday. 

Anyway, how are you? 

[00:02:10] Niels

Yeah, doing well. It has been a busy week, and I’ll try and summarize some of the headlines now. I think this will be a fun conversation because it’s just full of things moving around in the portfolio. I think we’re going to have a lot of fun today. 

The week, of course, turned out to be all about CPI and FTX. Finally, we had some downward bias in the consumer price index in the US. That’s not to say that prices are contracting. In fact, taking it at face value, the inflation numbers are still too high, but the rate of increase is falling which is welcome news for consumers. 

Core CPI, the measure that excludes food and energy, rose 6.3% year and year, falling from a year on year increase of 6.6% last month. On the month over month basis, the measure rose 0.3%, down from 0.6% last month. That is a welcome improvement and comes just in time for the Fed, I think. 

Coincidentally, the tightening of monetary policy has seen the ‘rising tide floats all boats’ adage play out in reverse. Before this week’s CPI melt up, equities had been under selling pressure, for sure, most of this year. The selling has been most apparent in the darlings of the retail markets such as the FANG stocks. All are down double digits in 2022 with Meta, the parent of Facebook, down 69% from its peak. And the best performing of the group is Apple with a year-to-day loss of only 27%. 

Topping the FANG losses we also have Bitcoin, the darling of the more sophisticated retail investors. That’s down about 76% since last December, which of course is better than those who have lost more than 90% of their value in the last year.

And with a well known crypto exchange blowing up this week, we are once again reminded that with high returns, as seen in the past few years for crypto investors and ARK investors, comes high risk. Interestingly enough, the old “boring” Dow Jones Index is getting close to a new all time high, only another 8% to go.

The impact of the better than expected CPI, to the markets, with Swift (since the hawkish FOMC meeting), there has been substantial selling pressure on the front end of the treasury curve. The two year note touched 4.72% on Monday as traders were braced for another 75 basis points hike in December. But within minutes of the CPI print, that bearish niche evaporated and the two year note finished the week around 4.33% as traders recalibrated their rate hike expectations.

Maybe this week CPI does feel like it was the beginning of the end for inflation, but we shouldn’t take much comfort in the market reaction. You only get market moves like that in a bear market, which suggests we’re still in a bear market, but perhaps we should stop worrying about inflation now and start worrying about corporate earnings and deflation. Only time will tell. 

Anyway, Rich, let me bring you back here just to talk about what has caught your attention. Maybe it’s all been the last week but how are things going with your portfolio and the battleship? It seems like there’s been a little bit of high seas in the last few days.

[00:05:40] Rich

Yes, it’s been a turbulent last few days. The prior month I was slightly down, but the last few days have certainly compounded that a bit. I’d been riding some very long-term powerful trends, particularly in the yen pairs such as the Swiss/Yen and the USD/yen.

Obviously, there was a lot of giveback with my unrealized equity in those trades. Despite the givebacks, there was a bit of blue sky with Bitcoin, because I was nicely short Bitcoin with the FTX scandal. There’s obviously some downside pressure on Bitcoin, which I’ve been taking advantage of.

But apart from that, Niels, it’s been quite a volatile session. After this spectacular year to date result, you’ve just have to accept that these times occur. Obviously, when we have these spectacular trending environments we are highly committed to those trends with a lot of our systems.

So, when the giveback occurs it usually is quite pronounced. You just have to accept that, I think. I’ve experienced that before when we talked, on this podcast, about the Black Friday event last year, and all of these different events. Yeah, it’s just part and parcel of the game we play, but it’s always a bit painful.

Yeah it’s dealing with what I refer to as ‘my unrealized equity’. I treat it a bit less seriously than I do my realized equity, which is a bit different to a lot of people. They like to treat equity all evenly. I’ve got a different philosophy there because I like betting a lot of my unrealized equity. So, that’s what I’m doing to bet for (hopefully cross my fingers), the enduring trends using that unrealized equity. So, it’s just part and parcel of my game. 

[00:07:40] Niels

Yeah, I completely agree, of course. I was curious, you obviously do things a little bit different to the way we do it. You have multiple systems, for example, that’s part of your diversification. Did you see (I’m just curious) a lot of these different types of systems actually exiting positions during a week like this? Or was it mostly just, as you say, giving back some open profits, but nothing really in terms of changing? 

[00:08:07] Rich

Some of them were quite severe, like I think on spaces last night I mentioned that there were a few remaining Japanese/Yen trades on. The reality is, when I closely looked at everything, I’m out of Japanese yen, so it was quite a significant move in Japanese yen and it nailed all of my six systems.

But, in a way, I do love the fact that we have these trailing stops because I tell you what, I would hate to be in a buy and hold position with Japanese yen now, in a position where, your drawdown has significantly accelerated away from you and you haven’t released any of that risk and you’re still holding it.

So if that adverse position perpetuates you could get yourself in severe, nasty water without those risk release valves of our stops that get us out of these positions. Yeah, we took the hit yesterday. In that process yesterday, I view this as a bit like releasing steam from a pressure cooker. All of the gas was released yesterday. The portfolio, now, when I look at my portfolio, I’ve lost about 25% of my positions. I’m sitting on about 75%, now. It’s ‘degassed’ is what I refer to it as. Yeah, see where it goes from here, 

[00:09:31] Niels

Oh yeah, absolutely. I mean it’s completely normal in our world to see this, even though you could say that you knew that [might happen] when the CPI print would come out better than expected. There’s been a few attempts on this kind of trade of a release of the increases of interest rates in the US. When would they pivot? When would they stop? When would they pause? Whatever. 

I have to say, myself, I wonder if the market reaction would give the Fed more ammunition to say, “Hang on, nothing has changed (from our point of view) and we’re going to go back,” because I noticed there’s like 10 FOMC people speaking next week. So I have a feeling that we might hear something different. 

[00:10:25] Rich

Isn’t it interesting how, when we get an event yesterday, which is effectively, I think, off the back of the CPI. You realize how correlated our portfolios actually are. So, you see these moves and a lot of these trending series, which you think are unrelated, but inevitably they probably are correlated with this impact of the CPI. Yeah, it always surprises me. 

[00:10:53] Niels

You’re absolutely right. This week we saw a lot of correlation after the event, but actually I think this time around things are maybe a little bit different because I think the theme this year has been one theme, and that’s inflation.

That’s been the theme that all the trends have reacted to, whether it’s been bonds, or currencies (to some extent), and so on, and so forth. I think that’s probably why we saw such a coordinated (can you use that word), coordinated reaction. 

So anyways, from my side I would say that, of course (and we’ve already talked about it), when the outlook for inflation changes, like it did this week, and when it has been the dominant driver, then of course the direction of many trends will happen or will change.

That obviously will need to lead to losses for trend followers. That’s just how it is. When we say losses, maybe we should just say we’re giving back some of the open profits, as you rightly put it. As far as I can tell, from the numbers that I follow (and where we have a little bit of access on daily numbers), these givebacks were well in proportion to the profits generated so far this year.

Of course when you drill down, as you can do, as I can do, it’s probably something that occurs across most markets in the portfolio. This correlation, of course, the effect was pretty amazing. I will say, and you focus on currencies which we don’t, but I will say I think the currencies probably stood out, especially the Yin, the Swiss Franc, things like that.

The Yin and the Swiss Franc, they rallied 6% for the week against the dollar. That is pretty significant. There should be a lot of losses from that. Equities, I would imagine that most trend followers are short equities. I think that’s probably another source of giveback.

Of course, fixed income. We know that if you’re a longer-term trend follower, medium to longer-term, you will be short, probably, the whole gamut. That will be another area. It doesn’t change positions or direction, I think. We’ll see when the headlines come out in financial media saying, “Oh, but now trend followers are starting to go long bonds.” I don’t think that’s going to happen anytime soon, anyways.

Now, energy markets are kind of interesting, right? Because they sold off, and I think it’s a little bit of a question mark whether you’re long or short energies at this point. It probably depends just purely on your lookback period. I think longer-term managers are still long.

There could be some shorts in shorter-term managers. The same with metals. I think a lot of metals had the opposite reaction, moving higher. I think a lot of trend followers probably are short these markets at the moment. Anyway, big reversal, completely normal. We’ll see how it all plays out. 

My trend barometer was getting weaker during the week actually already. It finished the week at 30, which is weak. It’s not surprising that we would expect trend followers to be down for the month. If I look at the numbers as of Thursday. Friday, I don’t know, I don’t think Friday was a big day.

It was really Thursday where all the action was. Friday was also, by the way, a holiday. I don’t know if that had an impact in the US. Anyways,BTOP50, as of Thursday, was down 2.13% for the month, still up 17.3%; SocGen CTA Index down 2.6% for the month, up 23.36%; Trend Index down 3%, roughly up 1.5% for the year; the Short Term Traders Index down 0.4%, but up 12% for the year. 

Of course, equities are having a good month. MCI World up almost 5%, and down 17% for the year; World Government Bond Index had, finally, a little bit of good news, so they’re up now 80 basis points for the month, but still way in the red for the year; the S&P Total Return Index down about 1% for the month only, down 19.68% for the year. As I mentioned, which surprised me, I didn’t know this until I saw it this morning, the Dow Jones actually is only like 8% away from its all time high. That’s pretty good.

All right, we have a couple of questions. One of them from down under so let’s start with that. Daniel writes in, “Thanks for the work you and your team do on the podcast. I look forward to it every week. I also enjoyed the interview with Annie Duke, and I love her new book. Now for the question, the panel has talked many times about trading different speeds and using different lookback periods.

Can I ask how these different speeds relate to Rich’s pant sizing? I think he mentioned that the position sizing and stops were unchanged with the different lookbacks. i.e. the pants were the same size. I trade three different lookbacks, but I have looser pants for the longer lookbacks, wider stops for smaller position size. The question is, should the pants be the same for all lookbacks, or should the pants be tighter or looser for different lookbacks?” 

All I just want to say, before you answer Rich, is if someone comes to the podcast for the first time and hears a question like this, where we talk about pant sizes, they must be confused what the hell this podcast is all about.

But there we are. I think hopefully most of our listeners will know that when we talk about pants, it’s back from this quote we had from Perry Kaufman, that loose pants fits all. 

[00:17:00] Rich

Yeah, it’s not an episode of Weight Watchers. 

[00:17:03] Niels

Right? Yeah, that’s exactly right. Yeah, alright.

[00:17:07] Rich

Yeah, good question from Daniel and good to see another Aussie putting in the questions. Look, in relation to the pants size, I think Daniel might have been a bit confused because let’s say that I have six trading systems, all with different lookbacks. I might be spanning in my lookbacks from say, a 50 day lookback up to about a 300 day lookback.

There are lots of different types of trend following models. That’s a genuine lookback period I engage in to have what I regard in my sort of ‘speak’, my short-term, my medium-term, and my long-term holds. Daniel, you are right. Each of those models has loose pants. 

What I mean by that is that the parameters that are used for each of those trend following models are few in number. I’ll have a couple of parameters used for my entry criteria. I’ll have a parameter for my stop. I’ll have a parameter for my trailing stop. In general I’m using about four to five parameters, at maximum, for each system.

The less parameters, or less variables you use for your system, the greater you can respond to degrees of freedom in price. With less variables, the less you constrain the way price needs to move to stay within the constraints of your system. If you could imagine, if you go back to the mathematics class and you look at the simplest form of curve, that’s where we’ve got three variables and it’s expressed by that equation. Y = MX + C, three variables there. That allows a great degree of freedom because of the simplicity of the variables. 

As you add parameters, you effectively are developing a much more complex parameter space, such as a polynomial distribution, or a different sort of curve. If you can imagine, when you require your system, that trades into the future, to adopt to a complex curve or to a complex function, because if that future doesn’t exactly replicate the past, there is a very large error function associated with that future if you use a highly complex, many variable system.

So that’s why my loose pants relates to my simplicity of my systems, but each of the systems I deploy, with these varying range of lookbacks, has only a few variables in it, which is this loose pants phenomenon. You are right. 

With what I regard as my shorter-term systems, my initial stop and my trailing stop, are both using ATR based measures. The reason I use ATR for my stops and my trailing stops is that it allows me to normalize that trading system to any liquid market. That means I can trade Bitcoin the same way as I trade a currency, is the same way as I trade an ETF, is the same way as I trade another thing. We are normalizing the system using ATR as a means to normalize it to any market. So, I can trade every market the same way with my models. 

If I use a short-term lookback model, the ATR based initial stop, and the ATR based trailing stop are typically tighter than if I’m using my medium to long-term models, which have a much wider initial stop and trailing stop.

The combination of those three different models are targeting different facets of a trend from short-term momentum impulses to more wandering trend cycles that have a great degree of freedom in their expression. All of these different loose pants models are effectively trying to capture different aspects of trend, which we know can occur in many different forms.

So, that’s about it from me. How about you, Niels? 

[00:21:20] Niels

Yeah, I think it’s a great explanation, but also I just want to say that some listeners might ask, why do we get so many questions about position sizing? Actually the next question is about position sizing as well, but from a different angle.

I just want to say that I think this is one of the most important parts of what we do, both at its entry, or during the lifetime of the trade. I think it sometimes comes as a surprise to people, at least when I talk to them, about what’s the main thing we worry about and do. That is we are risk managers. 

We don’t worry about the performance of our systems because we don’t control the performance, but we do have influence on the risk, and that’s why we spend so much time thinking about what are the best ways of managing these different types of risks that we have, as someone with 50, 60, 100, 200 different positions at any one time.

In a sense I like the fact that people are picking up on the importance of position sizing and asking questions about it, because it is really critical to the success of any type of investment, by the way. 

Let’s move on to the next question. That might be an Ausie asking you a question.

I can’t say if the next question comes from a fellow Dane. He’s called Henrik, which is the same name as my father actually. He could be Danish, but I don’t know. 

Henrik writes, “Niels, thanks for the excellent podcast. Here’s a question for you and your guest, especially Richard, whenever it’s a natural topic to discuss.

It’s on position sizing. In general, I very much support the notion of pursuing wide diversification, in terms of traded instruments, and in doing so, increasing the possibility of catching outliers. However, when investigating the effect of different position sizing schemes on my own system, it seems as though my main trend following system, which is a moving average system with some statistical bells and whistles, performs the best that it has the highest CAGR. When I implement a Kelly sizing scheme, half Kelly or quarter Kelly are also better than the regular Top 50 scheme I employ. 

It actually also performs the best in terms of risk adjuster returns with a full Kelly sizing scheme. I find this a little bit puzzling. I should note, I only trade stocks. How do your guests think about the trade off between optimal sizing, Kelly variation to catch big fish, and using a wider net or wide net to catch many small fish?”

Okay, Professor Brennan. It’s up to you now to explain what Henrik means by Kelly before I think you answer the question. 

[00:24:35] Rich

Yeah. It’s a very interesting question from Henrik. The Kelly Criterion is a very clever mathematical formula that effectively optimizes position sizing or optimizes your bet to maximize your geometric returns.

It’s looking at the compound annual growth rate, how to maximize that through the most effective way through a probability sizing application, through the application of this formula – the Kelly Criterion formula. It’s based on a couple of underlying assumptions. 

The assumptions are, because it’s determining the optimal bet for geometric returns over the long-term, it demands stable returns. It demands an exact knowledge of expected returns.  The method used to construct the Kelly formula is a bit like the expectancy formula. It uses your historical win percentage, your loss percentage, your return to risk relationship for each of your trades. It’s requiring those statistics to be very stable. 

With the stability of those statistics, then with a degree of confidence, you can apply this aggressive position sizing formula to maximize your geometric returns. Now, because it does maximize your geometric returns, it is a very aggressive formula, so there is no room error in that formula.

In other words, if there is a degree of wandering between your win rates and your return to risk relationships, over the course of time, the Kelly Criteria can get you into deep water when that stability of returns is not delivered. During periods of uncertainty where the relationship between those statistics used in the formula alters, or during what we call the adaptive markets where there is non-stationarity and the regimes change, those historical values (that are used to calculate Kelly) are also going to change. What that will do is it’ll put you into high risk territory because you’re using a very aggressive position sizing method to maximize your returns. 

A lot of people therefore say, “I’ll compensate for that by using a half Kelly application.” In other words, reduce the result by a half, to water down the degree of leverage achieved by position sizing using Kelly. For trend followers like me, where we are dealing with returns out in, what I refer to as, the tail region of the distribution of returns, in that region there aren’t things such as a stable mean. There aren’t things such as a stable standard deviation. Things get quite chaotic out in those tail regions, and that’s what leads to these anomalies, which defies statistical reason. So, when you are targeting those particular edges in that particular region of the distribution of returns, the Kelly Criteria (or application of it) gets you into severe deep water. So, I avoid that entirely. 

The way I, therefore, adopt my position sizing method is a standard, very small risk bet for every single return stream in my portfolio. They’re all equally weighted, according to how I do it, with a very small bed size. I’m using more the magnification abilities of the portfolio, itself, to do all of my lifting or my compound annual growth rates over the long-term.

The way that my portfolio hunts for these outliers and improves my risk adjusted returns is at the portfolio level (that it’s achieving that) rather than at the discreet position sizing level that Kelly’s dealing with. That’s the way I deal with it, but I hope I’ve explained that a bit.

[00:28:50] Niels

Yeah. Maybe we can still address the question from Henrik to see why he might get these better results. Let me just ask you one question. I’m really just curious here, could you, with Kelly, just increase the “lookback” of the Kelly calculation so that those values that you input, such as win/loss ratios and whatever, become more stable and less volatile?

Kind of a little bit like with volatility, right? If we use too short a volatility measure, we can have quite big differences, but then that’s probably why we also use a longer-term volatility measure so, when things go out of whack, we don’t get too little leverage… We could get too little leverage actually, because if the shorter-term is higher, we would use that one.

If volatility goes too low, like it did in say fixed income markets during this last five years (at some points we had almost zero volatility in some of these short-term contracts), it would be wiser to say, “I always use this longer-term level of volatility as my floor, so I don’t end up with silly position sizing.”

So I was just curious, could you think about Kelly in the same way? 

[00:30:11] Rich

Look, I’d say no. The reason is this: let’s say we increase the sample size to a 30 year period where we think that we’ve got a sufficient sample size, that it offers me stability. It gives me these single statistics that I input into the Kelly Criterion formula, such as a win rate, et cetera.

It’s not recognizing the regime changes that occur during that 30 year sample. In these different regimes, you get a variation away from the average of the entire result. Because it’s a highly leveraged position size formula, that deviation from the total or the average of the entire sample size is enough to really get risk of ruin happening.

I don’t do it that way. It’s because of this sort of non-stationary markets. These regime changes fundamentally change the character of the markets to produce different ratios, effectively. Even though you think, with a large sample size, it starts approximating a mean, a standard deviation, et cetera, that’s not really the way real markets work if they’ve got this leptokurtic nature of these tail regions and these peaks. 

If it was normally distributed, not a problem. But because we know that markets aren’t normally distributed, and they have these extreme variations and different regimes, it causes problems in using formulas such as this. Even though it’s a very clever formula, it’s an issue. 

[00:31:46] Niels

We need to label this episode: “Don’t try this at home.” 

[00:31:51] Rich Don’t try this at home, yes. 

[00:31:53] Niels Okay, Good. All right. 

Now, since Henrik asked that he’s surprised by his result, what you’re saying is that maybe he shouldn’t be surprised because if you do it over, say maybe a certain asset class, it could be also the fact that he’s doing it over a certain period of time that it just happens to have worked really well. Or is there something else? 

[00:32:18] Rich:

There’s something else. 

[00:32:19] Niels

Okay, there’s something else.

[00:32:20] Rich

Yeah. What’s happening is we’ll get into this discussion a bit in this episode, but with convergent types of trading activities or convergent trading styles, they are looking for these very predictable, stable regimes. It is very useful, if you’re using a convergent methodology, to apply things such as the Kelly Criteria, but you do have to be careful when that regime changes. Over extended periods of time, during periods of market stability, the Kelly Criteria is an excellent position sizing method to use for convergent methods.

I’m just saying for trend following methods that are targeting the chaotic region of that tail region, where there is no stability, don’t do it. I do say use it possibly for convergent or variations. 

[00:33:20] Niels

Henrik writes, specifically, that he does do trend following. So, he’s using a divergent strategy, as far as I can tell. He’s only applying it to equity. I don’t know if that has an impact. Again, if you were looking at this, maybe for the last 10 years, where equities generally were going up most of the time, I don’t know. I think he’s surprised, or at least that’s what he writes. We know what that has to do with the universe. He’s trading, I don’t know.

[00:33:55] Rich

Yes, when people look at the equities markets, in the season, things like that, a lot of people say that they have a mean reverting character. I do agree with that. In general, there is a lot of mean reversion in equity markets. Maybe it’s a feature of that. I don’t know. I’m not sure about what model he’s using and whether he had classified trend following, like ours, or different or whatever.

[00:34:18] Niels

He says a trend following system, moving average system with some statistical bells and whistles. Again, of course, it’s impossible for us to answer specifically for Henrik because we don’t know his system.

So I appreciate your views and your insights and I hope Henrik will take what he can from that and, of course, understand that we can’t answer specifically when we don’t know how the system is designed.

All right, let’s move on because you brought along some, as usual, some great topics that we’re going to dive into, of course, trend following related. I’m going to just give you the floor and hopefully I’ll keep up with you, and maybe I have a comment or two along the way. I know you have it all planned and laid out. Over to you, Rich. 

[00:35:17] Rich

Okay, so Niels, I’d like to go back to the Alex Greyserman, Katie Kaminski story in their great book Trend Following With Managed Futures: The Search for Crisis Alpha. I know in the discussions that we’ve had over the many months since that great book was released, we have questioned this ability for trend following to offer Crisis Alpha.

We tend to say that we know that it’s uncorrelated. That’s about as far as we can go. What I’d like to do in this is dig into at least the last 22 years of equity market performance and bond market performance, et cetera, to look at how trend following performed. So we’ll use the top Traders Unplugged Trend Following Index as a proxy for general trend followers, and we’ll compare and contrast that to periods of adversity in the stock market using the US stock market as a proxy, S&P 500 Total Return Index, and also periods of boom time because something is really interesting that comes out of that. In general, we know that trend following, by virtue of its extensive diversification, is typically uncorrelated to any single asset class.

That’s understandable because, are we correlated to equities? No. We invest in equities, we invest in commodities, we invest in bonds, we invest in fixed income, or currencies, et cetera. We invest in cryptocurrencies even, to a degree. 

[00:36:59] Niels:

Some of us, some of us.

[00:37:00] Rich:

Yeah, some of us.

We have wide asset class diversification and also we diversify with our systems. Are we correlated to any single asset class? No, we’re not. By virtue of this extensive diversification, we can certainly say, over the long term, that we are uncorrelated. If I take the last 22 years, and I look at the level of correlation of the TTU Index against, say, the S&P 500, when I look at it now, I see that we are at the minus 0.1 correlation to the S&P 500 – a very, very low correlation. 

We say it is uncorrelated, but one of the things is, remember that is a single statistic. It’s saying over that 22 year period, it is uncorrelated. When we drill down into these periods of adversity and these periods of boom time, we see something funny going on.

In periods of adversity, we see what we refer to as negative correlation going on. During these sustained periods of adversity, like when we look at the S&P 500, we saw the tech collapse, (I think it was 2002, 2003 or whatever), so we saw the equity markets declining significantly.

At that same time, the TTU Trend Following Index was rising sharply, so it was negatively correlated to that relationship. The next major event we had was the 2008 GFC. Once again, we see the TTUTF Index being negatively correlated to equities as equities were being smashed during that period, we saw significant growth.

Also, over that 22 year period there’s really only about five key periods of adversity for the equities market. We’ve had a stellar run for the equities market. Back in, I think, 2018, we had the Trump trade wars. Just before that we got a little blip, which we refer to as Volmageddon, back in February, 2018.

We don’t really take that into account because of the significance of that on the equity market (even though on the day it was a short, sharpe move), it was not significant in terms of looking back now, and the impact it had on the equity market. We also saw, of course, a covid decline in the equity market.

The most recent one has been this magnificent decline we’ve had leading to building drawdowns from January through the current day, associated with the Ukraine War, China slow down, massive rising inflation, et cetera. We’ve got this period now of declining equity returns, even though in the last couple of days, with CPI coming off the heat a bit, we see a significant snap-back, to a degree.

In general, the last six months have been very unfavorable for equities. In all, there were five instances there. Now, when we look at the performance of the TTU Trend Following Index during those periods of adversity, we see that four out of the five periods, we had negative correlation – clear negative correlation.

The good thing is that of course, this year, this calendar year, we’ve had a significant negative correlation to the equity market. Hence why trend following programs have received some of their biggest returns they’ve had in their history over this year-to-date performance. That’s a story about adversity.

So we see that in four out of five of these instances, we actually were negatively correlated. Even though the total statistic is saying we are uncorrelated, we know that during these periods of adversity, four out of five were negatively correlated. Where does the necessary positive correlation come from that lifts that ratio back to an uncorrelated relationship? 

We see that in the boom periods of the equity market. When we go to the returns over the boom, and so a good example is when the equity market was significantly increasing, after the tech bubble collapsed, from 2003 up to 2008, trends were also there. Trend following programs were also increasing. We were beneficiaries as well as the equity markets were beneficiaries post the .com collapse up to the GFC. Another significant period of growth, both where equity markets and TTU Trend Following Index was positively correlated as opposed to negatively correlated, was in 2011 – basically during the resurgence after the GFC. 

Then we see, in 2014 to 2015, we had a significant positive correlation with the stock market. There was good growth in the stock market, but really good growth in the TTU Trend Following Index up to 2014. Then more recently, everything up until December of 2021 – so the period from around about 2020 to December 2021 (this is before the adversity started impacting the equity markets) both the equity markets and the TTU Trend Following Index were experiencing significant growth. 

In this case, we talked about in periods of adversity we had four out of five instances. Here we had, in the periods of boom, we had four clear instances where there was a positively correlated relationship. Even though we say that statistically it’s uncorrelated, there is something going on in the markets themselves, during these periods of significant boom and significant bust, where there is a degree of causal correlated impacts going on between the trend following performance and the equities market.

That’s what we want to dig into. What is the reason for this? We not only provide protective cushioning for periods of adversity, it’s been demonstrated four out of five times in 22 years. That’s great. Bonds have also provided a degree of protective cushioning until the breakdown of the cushioning relationship, which occurred in this financial year, where we saw the decoupling and now the positive correlation between bonds and equities.

Prior to that, over the last 20 years, there was a degree of cushioning by bonds, but we have to remember this, bonds as an asset class, have predictable returns and their returns don’t offer significant (what I call) lifting power for the portfolio. They do provide a cushioning effect. At least they have demonstrated that over the last 20 years, but now they are no longer even providing the cushioning effect. They don’t provide any lifting power. 

Where I say trend following is superior (at least in my opinion), as an allocation measure with equities, they not only provide this cushioning effect, they come to the rescue when they’re most needed(during when equity markets are in adversity), but they offer bonanzas during boom periods where the fear of missing out is making everyone die for the markets. We are also experiencing those strong trending regimes. Because of that positive story, or the positive correlated relationship during boom times, and the negative correlated relationship during adversity, in total, trend following offers this big lifting power to the overall portfolio. 

It goes one step further to what bonds have traditionally been doing. It’s not only provided this cushioning impact, but it’s also lifted the equity curve. So far, what do you think? 

[00:45:24] Niels

I think that’s a great description. It’s important to understand. I’ve always referred to it as conditional correlation. You could say that, actually with bonds, that the condition is pretty simple. It’s probably focused around where inflation is heading. You’re right with trend following, because we do have exposure to many of these other wonderful types of assets and markets. They have more strings to play on, so to speak, in delivering both the cushion and the lift.

I think that’s a great way of explaining it. Actually it’s a really good thing. Maybe we do ourselves a disservice by just saying, “Oh, but we are uncorrelated.” Maybe we should explain much more about how we’re negatively correlated when you need that, and we’re positively correlated when you need that.

The funny part is that very interestingly, and here there has been talk about, and I think some managers have done this in their programs where they say, “I’m not going to go long equities because my investors, they already have that long exposure.”

Of course I don’t agree with that. We should design the best product we can design. To your point actually, that includes that we need to be positively correlated from time to time with the rest of someone’s portfolio. There’s nothing wrong with that. The other thing we’ve talked about in the past, Rich, is that we actually also, even though we talk about diversification, we also need to accept that when we make the most money, like 2022, we are actually somewhat concentrated in our bets because they’re all driven by one or two themes, and that’s okay as well.

It’s because we have all the flexibility, because we have the ability to adapt. Which a bond doesn’t have, it has no ability to adapt. It’s just the bond. It’s the whole reason why you and I have loved this space so much as we do. It has this amazing power to be a different helper with different powers, let’s call it that, with different powers at different times. 

[00:48:01] Rich

Yes, that’s right. Look, the reason we’ve set upon this story, so far, in this podcast is I want to set in trail this narrative to explain why there is this positively correlated relationship during boom times, and why there’s a negatively correlated relationship during adversity.

This is where we start going back to the old Jean Philippe Bouchaud phenomenon of trader impact. There is something that is occurring, during these periods of extreme. When we are talking about these periods of boom, we are talking about the significant fear-of-missing-out syndrome. 

We’re talking about behavioral tendencies that change the nature of the distribution in terms of fear and greed. Something happens, outside of what I call stable, predictable markets. Something occurs with the nature of the market itself in these periods of extreme. That’s why we have this positive and negative correlated relationship because, as we’ve talked about, I believe the most edge we are getting in our trend following programs is associated with the tail behavior of those market distribution of returns.

If we understand what we mean by that, we’re talking about the extremities, the left and right tails – the extremities of price action. There’s something going on which is giving these significant trending regimes, which we are therefore capitalizing on. Of course, we are talking about what is causing this enduring nature of trends out in these periods of extremity.

There’s something going on there that’s different to the nature of trends that occur in much more stable, predictable regimes – there’s something driving the impetus and the momentum of trends out in these extreme areas. What is it? Before we look at what it is and why it occurs, the first thing we’ve have to understand is the systems we deploy are specifically configured to target directional trends.

Our trading rules are that we cut losses short and let profits run through design statements. The way we do that is through our small initial bet, our initial stop, and our trailing stop, which are effectively design constraints that, therefore, say price needs to move within these design constraints to be classified as a trend by our system.

We might deploy lots of different systems and each one of our systems are having their own definition of trend. We are standing back saying, “Let the systems work that out. That short-term system can call that a short-term trend. This medium-term system can call it that.”

But these design constraints configure how that trend is defined and when price moves outside those constraints, it closes that trend definition down. Then other trend definitions of our different systems might still be alive, or closed, or whatever, dependent on how price moves. We also trade both long and short with our systems. 

That’s incredibly important because we are saying that when we are dealing with left and right tail events, we are saying that they are agnostic as far as the directional significance. They can be long or they can be short. So, capitalize on both opportunities because it’s not only beneficial for your wealth building process, but it’s also very beneficial for some correlation benefits, at the portfolio level, when you are trading both long and short. That’s at the portfolio level. At the moment we’re talking about the system design stage. 

Capital protection is a priority. That’s why we always cut losses short and we don’t deploy these profit targets. At least I don’t. I only use these trailing exits, which means that, potentially, any one of my systems can ride that directional trend for as long as it takes until the system says that trend is over. 

Potentially, you leave yourself open to unlimited upside with that design consideration. That’s the first thing. Trend followers shine during adversity and during periods of boom, if there are trends in these regimes. The significant anomalies, or outliers, in these regimes of fear and greed, dominated by fear and greed can be significant. Our systems are deliberately configured to capture that directional nature of those trending series. 

What that design statement does is it produces positive skew at the trade distribution level. Typically what we find, we cut losses short, so we never allow ourselves to be victims of what we call ‘a left tail event’ as far as our trades. We are skewing our distribution of trade returns to lots of small losses. We know we get lots of small losses, but leaving it open to potential unlimited upside. That skews our trade distribution to have this big positive skew. Now that is a symptom of our design statement that we impregnate into our systems.

We also find that when we look at the trade histogram of returns, so when we look at a histogram of trade results produced by our trend following systems, we find that the bulk, we might get a 60% loss rate. We have lots of small, what I refer to as linear losses – lots of small losses. That, therefore, creates this significant impact close to the mean of the distribution of our trade returns, but we get a lot of small whipsaws in our trade distribution.

What we find, because of the unlimited upside potential, we find that even though we have a say a 40% win rate, we find that the real big things that move the needle of our total wins is about 5% to 10% of our trades. The rest of the wins are effectively offsetting those many small losses.

About 90% of our trades are what’s necessary to keep us afloat – to stop us sinking. The 5% and 10% of those big outliers are what really moves the needle and what gives lifting power. When I was talking about what is the lifting power of the Top Traders Unplugged Trend Following Index, it’s this impact of outliers in our trade distribution. 

That’s the system side of it. The next step is not only do we have these systems that are deliberately configured for capturing trends, but now we’ve have to move to the second very important aspect of trend following, which is diversification.

Really diversification is essential for trend followers. Now, we all know the benefits that diversification brings in the ability to bring together uncorrelated return streams. The offsets produced by that ensemble of return streams produces a smoother equity curve at the portfolio level – when you consolidate all of those individual return streams.

We have to remember that with our trend following systems applied to each individual market, we might be trading many markets, say 60 markets or so, these outliers are infrequent and few and far between. But, we might have two to three outliers in one return distribution, maybe no outliers in another return distribution, five in another four in another, or whatever.

When you do this across 60, you find that the outliers in the ensemble of return streams, they are not what I call coincident. In other words, they’re not all in the same location of the portfolio distribution of returns. We’ve got, say, the Euro offering outliers at a particular location, say in June, 2014. 

We’ve got London sugar, we’ve got commodities. All of these outliers that occur in these series, we don’t want them to be all located at the same location in time and space in our portfolio distribution. We actually want them to be widely distributed throughout the entire time series of our portfolio.

What that does, that lifting power we are talking about, therefore allows optimal compound growth over time because it’s continuously offering lifting power rather than being concentrated in locals across the time series. Whilst we do find there is this concentration effect in periods such as 2008, where we find that there’s a lot of correlated markets all doing the same thing.

We find that our portfolios massively increase in equity because all of the outliers are coming at the same time – we often find that in periods of adversity. But in periods of boom time we often find that the outliers are quite distributed throughout the time series. The combination of this is, therefore, giving us the ability to not only catch outliers, which is our modus operandi as increased diversification allows us to increase the frequency of outliers and our distribution. There’s no question about that. Three outliers here, five outliers here, four outliers here, three outliers here. When you look at that over a 60 market portfolio, you’ve got a large number of outliers. You’ve hunted well for those outliers, but also, by virtue of the systems we deploy and the way markets work, a lot of them are quite well distributed throughout the time series. 

That’s why we find that when we compile our diversified portfolios, we find it quite smooth and we get a false impression of how easy it’s been. We see this magnificently rising equity curve that we think there’s really been no problems throughout that entire time series.

The reality, when we get into the detail of each of the individual markets, we see it’s been a bumpy, volatile ride. That’s the nature of this game. It’s a bit deceptive, when we bring it together as a portfolio, because it hides a story of the pain that’s embedded in that story, but it’s there, trust me.

Before I get onto that next stage I’ll stop here. What do you think so far? 

[00:59:07] Niels

Yeah, absolutely. I think another way of thinking about this is just that when (and we’ve talked about this over the years), and that is that sometimes we see markets that don’t do anything for us for a long time.

For example, currencies, I think for the last 15 years, before this year, were probably not really producing any returns (depending on your system and to lookback) for long-term trend following over currencies, because the regime was not that conducive for currencies. We know a market like Coco is notorious for things, but then you could have said exactly the same about short-term interest rates for the last five years prior to 2022 because interest rates were pretty much zero in the developed world. So, there were no real opportunities, per se, for a trend follower. 

Then you look at how much money has been made in short-term interest rates, from a trend following perspective, in 2022. You think, “Yeah, that’s exactly why I never consider taking these markets out of my portfolio.” What you’ve explained, very elegantly, we see play out all the time really when you take a step back.

[01:00:26] Rich

Yes. Look, what I’d like to bring up is something that I think is particularly attributable to trend following, and you might not necessarily see it with other techniques. We all know about this ability of correlation offsets at the portfolio to improve risk adjusted returns.

This is because they’re attacking the drawdowns of the individual return streams and there’s offsets being provided that reduce the impact of those drawdowns to create this smoothness. We know that is a fundamental principle of portfolio diversification. Why we like to bring together uncorrelated return streams is because it produces this dampening effect of volatility and smooth returns.

That’s why the 60/40 portfolio has chosen to use bonds, which doesn’t have much lifting power, but it has been a good cushioning impact for the last 20 years, to dampen the volatility of the US equity portfolio. We know this principle, but something that is I think particularly attributed to trend following is that (not many people know this) trend following programs, because they’re attacking outliers, it’s the difference between these nonlinear outliers that are many orders of magnitude greater than the linear losses. What it’s doing by increasing the relative frequency of outliers on nonlinear anomalies in our distribution, it’s also increasing the compound annual growth rate of our diversified portfolios.

When you go back to the typical use of Markowitz with the efficient frontier, you typically find that CAGR is never improved through diversification. What is improved is volatility. What you find with trend following, because it’s attacking these nonlinear anomalies, is that it is, actually, also improving the outliers, or the lifting power.

That’s why we are seeing the TTU Trend Following Index lifting well above the S&P 500. That’s what’s giving it this lifting power. It’s these nonlinear anomalies through this technique that’s hunting for these outliers. We’re getting now to the real reason for what it is during periods of uncertainty, during periods of boom, that’s actually causing trends.

We know we’ve got systems and diversification principles that can capture these trends, but what is it that’s causing markets to trend? That’s where we’re going to tie this back to this feature of markets being not normally distributed. 

I think we accept that any of our liquid markets, over the long term, when we look at the nature of that sample using a histogram, we find that while being close to a normal distribution, it typically has these left and right tails, and it has this peak. Now, the nature of this leptokurtic distribution that is produced by these liquid markets. When people say, as a trend follower, what is your prime thing?

One of the things is we must trade liquid markets. That’s because when we look at these very large data samples, we see this leptokurtic signature embedded in all of our liquid markets. There’s something in the behavioral tendency of participants in the market that creates these features, that create this correlation impact in the market data that makes it non-normal in nature.

When we look at a normal distribution, let’s think of a game of chance like flipping a coin, an unbiased coin. When we flip that coin, heads or tails, and we are doing the count of the number of consecutive tails or the number of consecutive heads that flipped coin gets. When we plot that through a histogram, because each event is independent in nature and not causally connected to the prior event, we find that the sequence of consecutive heads or tails, actually plots a normal distribution. 

A normal distribution implies that markets are totally random. There is no causal correlation in the market data, and they are efficient in nature. We know that there are periods of time where markets shift from efficient markets out into more extreme regimes, such as we’ve experienced over the last six months with uncertainty. 

When uncertainty prevails we find that we get these tail events occurring outside of the region of the normal distribution, and we also find that there are periods of time, like we’ve experienced with the Great Fed put from 2000, following the GFC right up to 2019, 2020. We are also experiencing this correlated regime which creates these peaks around the equilibrium of the distribution.

What this does is it signifies that there are, in quantitative terms, just looking at price, so we’re not looking at the edge through fundamental investing. We’re not looking at the edge through lots of different factors. What we’re doing here is saying what is the edge available through price action?

There are really two forms of edge. There’s an edge from convergent practices, which is based on the principle of targeting the edge that resides around the equilibrium of the distribution of market returns, where we get this peak that extends beyond the normal distribution. We’re talking about divergent techniques that are focused on the left and right tails. 

The way it boils down, when we say to ourselves, “Alright let’s accept the fact that there are two broad techniques to extract this edge from price data, convergent to divergent.” The question is, in this zero sum game, we call speculation in these markets, why is it that trend followers do exceptionally well during these periods of uncertainty?

What I’d like to say is this: because convergent techniques are based around the premise that price reverts to an equilibrium, the vast majority of trading techniques and investment techniques deployed by participants are convergent in nature. 

A good example is, fundamental investors. They use this concept called the intrinsic price. They calculate the intrinsic price level. They then look at where price currently is in relation to that intrinsic price level. Their assumption is it will revert to that intrinsic price level. That is a convergent technique. Warren Buffet, with his value analysis, is effectively applying convergence here on the principle that he knows where price theoretically should be, where it is now, it’s going to revert to that. That’s a convergent premise. 

It’s a predictive premise in that it’s assuming that where price is now is not going to be where it’s going to be in the future. It’s going to be back at this equilibrium point. Other forms of convergent technique, the majority of technical analysis is based on the principle of repeating patterns in the market.

The reason for these repeating patterns in the market is because of the predictable nature of the market itself. Now what this predictability is all concerned with is this period, this position of equilibrium in the market, this period of stability in the market where there are these regular, repetitive oscillations that create these patterns that we know are indicative. If we see these patterns, we know what ultimately price should do in response to those patterns. 

Once again, a large proportion of technical analysis uses convergent methods based on the principle of market stability, predictability, these oscillations that they try to exploit. Also, high frequency trading is another technique that is essentially a mean reverting technique based on extreme activity. Extracting this exploitable arbitrage from this predictable nature of the market. 

Of course, we know central banks have been deploying this mean reversion regime where they’ve been buying the dips and selling the tips with their practices to try and stabilize the market around an equilibrium. They’re trying to prevent the market from going out into these tail regions. They’re trying to suppress volatility, make it more predictable, and make it more stable. Of course, due to the significant impact of central bank intervention, it’s therefore encouraging this huge growth and blossoming of convergent models.

So many of the models out there are convergent. When we fundamentally boil down all of the different trading and investment practices, we realize that about 90% of participants are convergent participants. They all exert what we call a convergent force to the market. 

When markets are stable and predictable, and operate around an equilibrium, we get a lot of these predictive methods that are exploiting the arbitrage from that stability. Now, all of those competing pressures are coming from lots of different directions, and if you can imagine all of these competing forces working together, it actually stabilizes things from the competing forces that are coming together. It’s forcing things towards an equilibrium. 

This is why, this is what we are referring to as this convergent premise. The trader impacts of their convergent styles is creating this market behavior. It’s forcing it, but at the same time it’s moving towards this equilibrium. 

Sitting in the tails is a much less popular technique. It’s one that is non predictive in nature. It’s price following. It’s reactive in nature. Only a few types of traders and investors specialize in this. Those that play with options such as buying calls and buying puts, and the trend followers, the diversified systematic trend followers. About 10% of the participants are participating in the divergent tail region.

During normal market stability, we’ve got this inequality in participant behavior, 90% doing convergence, 10% doing divergence. Now, what happens when the markets start becoming unpredictable? All of those convergent participants who have been relying on the predictable nature and the stability of the market find that their models no longer work.

Now this is the key, because what happens here when their models no longer work, they are forced to exit their position. What that does is whilst they had a convergent method, in exiting their position it forces them into a divergent impact. In other words, when they’re exiting from their position, it’s amplifying trends.

Also, we find some of the participants refuse to exit their position and they say to themselves, “The market is going to return back to predictability. I’m going to average down with my technique and I’m going to increase the leverage because I know, I’m convinced that markets are going to return to that past predictable state.”

Now, this is where leverage starts increasing the toxicity of this environment, because as they’re embedding leverage into their decisions, ultimately you might find that they come to Margin Call City, and during a margin call, or being forced to exit their position, once again, the impact on the leverage is even more significant than the prior traders who took the loss and just accepted that they were out.

What we find is that the participant behavior that was 90% to 10% starts being 80%, 70%, 60%. Meanwhile, on the other side, the impacts of divergence is gained 10%, 20%, 30%, 40%. In this zero sum game, we see this wealth flow occur from convergence to divergence. Now, if you can imagine, with trend following programs with their stops, they’re always cutting losses short, so they’re never getting into this position where they’re becoming convergent traders.

You don’t get this same move from trend following 10% to become convergent. They stay there 10%. During this period where their predictive models break, and periods of uncertainty arise, we get this wealth flow occurring from convergent participants over to divergent participants. When you look at 2008, during the GFC, you say to yourself, “Who was on the other side of those trades when the equity market was tanking?” You say, “The trend followers.” They’re the beneficiaries of that wealth flow.

Who was on the other side of the trade during 2022 during the inflation, the significant increase in inflation for the first half of the year? Trend followers were on the other side of the trade. What was causing this magnificent amplification of trends was this altering participant behavior, which was not instantaneous.

Prices are sticky. The behavior is in a gradual way, but as it’s occurring, it’s creating non-linear amplification because we’re going from 90%, coming over to 10%. The impact of those 90% suddenly becoming divergent is actually putting a non-linear power law into those trends. It’s amplifying those trends, and of course, the major beneficiaries of all of that are the trend following programs.

Therefore, in a summary, why do we do well during booms and busts? The bottom line is that trading behavior alters when the market refuses to play ball. In other words, when traders and investors find that their models no longer work, they are forced to turn away from their models. They’ve got therefore no guideposts to how they should trade, and they revert to their behavioral tendencies. 

Good, or whatever evolution then comes to the fore, fear and greed, fear of missing out, all of these behavioral tendencies start influencing their decision making as opposed to their predictive models that were saving them from that decision making hiatus that’s now occurred because of the behavioral change. 

That’s how I’d like to leave it Niels, and wrap it up. What do you think?

[01:15:53] Niels

I think that was beautiful. Absolutely fantastic. Great lesson, masterclass as usual. No, this is important stuff.

Even though we do this every week, there’s just sometimes where we get into the flow, usually it’s the guests getting into the flow, and just explain things, lay them out so clearly, so simply. I hope people will go back and maybe rewind part of this episode and just listen to that explanation one more time, because if you can really internalize what you just said and laid out, trend following makes perfect sense.

More and more people, we hope (that’s why we do this every week), we hope will see this. It’s easy enough to see the results. It’s easy enough to see that a 60/40 portfolio has been devastated this year. Trend followers are doing great. That’s easy. It doesn’t mean that you’re necessarily going to say, “Oh, yeah, I’d definitely need that in my portfolio.”

If you understand why it makes sense, and why trend following doesn’t only make money when there is an issue like this year, but actually also makes money when there are boom periods around, it makes even more sense. I think it is human nature that we need to understand ‘the why’ to take the jump, take the leap.

I think you’ve laid out a really convincing case for that today. I’m very grateful, and I think it’s a perfect place to wrap up our conversation, which already is a long one today, but powerful one as well. 

Thank you Rich for doing that. 

Now, I would say that deserves a rating and review in iTunes, Amazon, and Spotify. I hope all of you listening will agree with that. Head over and leave a rating and review, if you wouldn’t mind. 

Also, Rich referred many times to the TTU Trend Following Index. Of course, you can find a lot of information in the blog post section where we write up every month a beautiful explanation of how to look at this, and obviously what’s going on. Head over to that. 

You’ll also see some new stuff, actually some new long-form blog posts that come out every Sunday, where I try to summarize key takeaways from the midweek episodes – the more broad based conversations that we have. This is brand new. You should go and check it out.

I will eventually also find a way to put up on the website a way where you can just opt into “the blog post,” because I do send them out, now, to most of the people that have opted in, historically, in an email every Sunday. There are those however I should say, if you’re not receiving it, maybe you just need to go and opt in for something on the website again, because we try to be very prudent with the way we send out emails. We don’t send out emails to everyone, just because they, five years ago, opted into something.

We want people to be engaged with the website, with Top Traders, with the content we produce, because that is good practice. It’s not good practice to just send out willy-nilly, to everyone who you’ve ever come across in your life. We’ll leave that to other people. If for some reason you’re not receiving an email from me on a Sunday, just go and opt into something, a book or a guide.

By the way, I’m excited because the new Ultimate Guide is very close to being published, and there’s some really good stuff in that. I think we are above 350 books and some other secret stuff, resources that I will announce in that as well. Of course, Rich and I still have a little book to finish, but that’s probably more on me than on Rich.

Anyways, we’re working through all of these ideas that we get into our head along the way. I can assure you it’s not because of lack of effort and time. There is so much going on. Of course we couldn’t do it without you, the listener. We really appreciate all the support we get, all the questions we get, and, of course, all the rating and reviews and the sharing of the content, that means so much to us and we are so grateful.

From Rich and me, thanks ever so much for listening. I should say, by the way, Cem is back, he should have been here this week, but Rich kindly stepped up on short notice as Cem had to change his recording with me. So, I appreciate that again Rich. Anyways, he’s back next week, so send in your questions if you have any to info@TopTradersUnplugged.com. 

From Rich and me, thanks so much for listening. We look forward to being back with you next week. In the meantime, as usual, take care of yourself and take care of each other.

[01:21:15] Ending

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.