Is the degradation of trend following performance a cohort effect, instrument decay, or an environmental problem?

It's probably bad luck to say this, but the most recent poor performance of CTAs and trend following managers this year appears to have been reversed. My own system is up over 12% since the nadir of the summer drawdown, and is now up for year; admittedly by only by 5.5%. 

Nevetheless, it's true to say that trend following performance appears to have been degrading over the last few decades. If I can literally talk my own book (the book in question being Advanced Futures Trading Strategies - AFTS), then in one chapter I note:

Having said that, it does look like the returns from strategy nine are falling over time. The inflationary 1970s were particularly strong, with a total non-compounded return of over 500% over the decade (if we had been compounding, our returns would have been even more spectacular). We then made over 200% in each of the next three decades. But since 2010 our average return has roughly halved.

But where the better returns in the 1970's (and to a lesser extent 80's, 90's and 00's) because we had a better environment for trend following, or because we had better instruments, or because over time instrument performance decays?

Let me explain. Back in 1972 when my backtest begins, there were just a few instruments. In the first few years only 11 instruments were around, out of the 100 or so in my usual list of liquid instruments I use for testing. And they were weird: seven were agricultural commodities, two are currencies and two metals. Perhaps the better performance of trend following in the 1970's was because the instruments we had then were just better at trend following? Or, perhaps it's just that when an instrument is first traded it does very well, because there aren't many other smart people hanging around to extract 'alpha'?

So, let's see which of these three explanations is most likely.

I'm going to start with the fastest EWMAC 2,8 crossover I use. In AFTS I noted that the two fastest crossovers have suffered particularly bad degeneration in performance since about 1990. These returns are before costs, so the after cost performance would be even worse.

There are quite a few of these graphs in this format, so let me explain. Each line is a different cohort of instruments. So the blue line for example, is all the instruments that began trading in the period 1971-1980 inclusive. On the y-axis is the average SR for those instruments in the five year period beginning in the date on the x-axis. So for example, for the instruments that began trading in the first ten years, from 1981-1985 their average SR was around 0.30; a little higher than the next cohort of instruments that just came in. 

* Important note. The date an instrument starts trading is the earliest point I have data for it. It may have been trading long before then.

Let's now review three possible explanations for the reduction in trend following p&l in the fifty years or so, and see which most represents the empirical results.

First of all we have a cohort effect explanation. Instruments which enter the dataset later have worse performance, so they drag down the average performance. This would look something like this:

You can see that the older an instrument is, the better it's performance. To combat this effect we could just avoid trading newer instruments.

Next we have an instrument lifetime decay effect. Each instrument does well when it first enters the dataset, but then it's performance decays as it ages. As a result, as more instruments are old and fewer newer, and the average performance falls. An extreme version of that effect would look something like this (I've drawn the horizontal lines slightly apart for clarity, they should be on top of each other): :

To deal with this we'd have to be constantly adding new instruments that haven't yet been affected by the influx of sophisticated traders looking for alpha. This is the opposite of what we'd do with a cohort effect.

Finally we have the general enviroment effect. All instruments have roughly similar performance in each period, which gets worse over time. This would result in something a bit like this (I've drawn these lines slightly apart for clarity, they should be on top of each other): 

There is no solution here. We are buggered. We need to get out of the trend following game. Or at least hope that this is just a temporary setback; after all people have tested trend following rules over hundreds of years so a few decades of bad performance is nothing to worry about.

Looking back at the EWMAC 2,8 graph, there is no support for the cohort effect. It looks like there is some evidence of an instrument decay, most strikingly for the 1991 cohort. However this only contains 7 instruments, so it's one of the smallest cohorts, so the significance is questionable. Overall though, this does look an enviroment effect with noise. 

Let's step down the speed to EWMAC4,8:

Looks like a very similar picture. How about EWMAC8,16?

Again apart from the 1991 cohort, this looks pretty much like a gradual decline due to enviromental effects. Now let's turn to EWMAC16,64, which readers of AFTS will know is the best following of my momentum indicators:

Feels like we're watching the same film over and over again, doesn't it?

EWMAC32,128

EWMAC64,256

Not quite as clear, but we're trading very slowly now so would expect more noise.

To summarise then, it looks like the decay of momentum performance has been solely down to  general enviromental effects, rather than a cohort effect, or the decay of instrument performance. This is bad, because it means we can't do anything about it by restricting ourselves to older or newer instruments. This is good, because instrument diversification really is our best chance of being profitable traders and making the most out of a weakening signal. I wouldn't want to suggest that you should do anything different than trading all the instruments you can get your hands on.

And to reiterate, let's hope this is a temporary situation. For me personally, as someone who isn't tied into a CTA box, I'm happy to continue trying to trade as many different risk / return factors as possible.

Bonus postscript: Here's carry!

This does look a little bit more like a decaying instrument story...

PCA analysis of Futures returns for fun and profit, part #1

 I know I had said I wouldn't be doing any substantive blog posts because of book writing (which is going well, thanks for asking) but this particular topic has been bugging me for a while. And if you listened to the last episode of Top Traders Unplugged you will hear me mention this in response to a question. So it's an itch I feel I need to scratch. Who knows, it might lead to a profitable trading system.

Having said all that, this post will be quite short as it's really going to be an introduction to a series of posts.

Given factor analysis

So at it's heart this is a post about factors. Factors are the source of returns, and of risk. This concept came from the land of equities, specifically the long short factor sorts beloved of Mssrs Fama and French; and it also spawned an entire industry: the modern equity market neutral hedge funds (although Alfred Winslow Jones actually implemented the whole hedge fund idea whilst Fama and French were still in high school). 

At it's core then we have the idea of the APT risk model which is basically a linear regression:

r_i,t = a_i + B_1_i*r_1_t + ..... + e

Where r_i,t is the return on asset i and time t, a_i is the alpha on asset i (assumed to be zero), B_1_i is the Beta on the first risk factor of asset i, r_1_t is the return of the first risk factor, there are more terms like this, and e is an error term with mean zero. Strictly speaking the returns on both i and the risk factor should be excess returns with risk free rate deducted, but we're futures traders so that detail can be safely ignored.

In it's simplest form with a single factor that is 'the market',  this is basically just the OG CAPM/EMH, and B_1 is just Beta. In a more complex form we can include things like the sorted portfolios of Fama and French. Notice that risk and return are intrinsically linked here. The factor is assumed to be some kind of risk that we get paid a price for exposure to. That price is the B_N term. 

(Should B_N be estimated in a time varying way? Perhaps. Although if you vol normalise everything first, you will find your B_N are much more stable, as well as being more interpretable).

Note that for both the market and the Fama French factors (FFF), the factors are given. To be precise, in both cases the factors consist of portfolios of the underlying assets, with some portfolio weights. For the market portfolio, those portfolio weights are (usually) market cap weights. For the FFF they are the +1 for top quartile, -1 for bottom quartile sort of thing. 

What can we do with factors?

Many things! The dual nature of factors as risk and return drivers leads them to multiple uses. So for example, we could own the factors. They are just portfolios, and going long if you think the factor will earn you a risk premium is not a bad idea. If you buy an S&P 500 ETF, well congratulations you have gone long the equity market beta factor. With the ability to go long and short we can own FFF as easily as the market factor. Indeed there are funds that allow you to get exposure to FFF factors or similar, though sometimes only on the long side. 

We could also trade the factors. My own work in my previous book, AFTS, suggests that 70% of the returns of a momentum portfolio come from trading an asset class index. That is an equal vol weighted rather than market cap weighted portfolio, but the overall effect is similar. Trading, i.e. market timing, the FFF or similar is a little more difficult and if you try to do it Cliff Asness will turn up at your house and hit you repeatedly with a stick.

If we treat the factors as risk we don't want, and we don't buy the idea of an efficient market, then we can buy high alpha / sell low alpha. If a stock looks like it has excess return, over and above what that market and FFF say it should have, then maybe it is a good bet? Although financial economists will scoff at you and say you are exposed to a risk that is not in your regression for which you are earning a risk premium, you can just point to your porsche and explain in great detail how you don't care.

Perhaps we believe in the efficient market hypothesis in the long term, but not in the short term. We wouldn't trust those alphas to be persistent as far as we could throw them. But if we take the residual term, e, well that will most likely show a lovely mean reverting pattern when cumulated. So we can mean revert the residual. Big upward swings away from efficiency that we can short the asset on, and lovely downward pulls we can go long on.

There are more esoteric things people do with factors, mainly to do with risk management. You can for example use them to construct robust correlation matricies, hedging portfolios and what not. Risk management isn't my principal concern here, but that is still good to know.

PCA factor analysis

This is all lovely, especially in equities, but in futures things are a bit more mysterious. For starters, we can do things at an asset class level (which is closer in spirit to the equity market neutral world, although we're still at a level higher as our components are e.g. equity indices, not individual equities); but we can also uniquely do a 'whole market' look by considering futures as a whole.

We could probably take a stab at creating an 'asset class' factor in each market that would be like Beta, and indeed I did that in AFTS with my equal risk weighted index. We know that there are certain bellweather markets like the S&P 500 that we could use as proxies for 'the market' in individual asset classes. 

But for futures as a whole, things are much harder. Is the 'market' really just long everything? Even VIX/VSTOXX where we know the risk premium is on the short side? My gut feeling is that our most important factor will be some kind of risk on/off, but then there will be times like 2022 when it would plausibly have been more inflation related. And what would the second factor be?

So we will switch tactics, and rather than use given factors, we will use discovered factors. The idea here is that data itself can tell us what the main latent drivers of returns are, if we just look hard enough. Sure in many cases that will give us the first factor as basically the market portfolio, but the subsequent factors will be more interesting. And in the specific case of futures, where we don't know what the likely factors are, it's going to be quite intruiging.

We use a PCA to discover these factors, with vol normalised returns as the starting point. For each factor we end up with a set of portfolio weights (can be long or short), which can then be helpful to interpret the factor. Note the weights are on vol normalised returns, which are more intuitive.

Sidebar: PCA meta factor analysis (on strategy returns)

Just as a brief note, as I don't intend to cover this here, but it was touched on in the podcast. If we started with the returns of trading eg momentum on a bunch of instruments, rather than the underlying returns themselves, then that might be useful for someone was thinking about replicating a hedge fund index or risk managing a CTA, or perhaps constructing a CTA where they have hedged out the principal component(s) of CTA risk. I've written about replication before, and I've already said I'm not really concerned with risk management here, so I won't talk about this again.

Some nice pictures

This won't be a long post, as I said, as I won't be looking at how to use the PCA returns now I have them. Instead I'm going to focus on visualising the PCAs and interpreting them. Which will be a bit of fun anyway. Methodological points, I used vol normalised returns and one year rolling windows to estimate my PCA. There is a debate to be had as to whether a year is best at compromising between having enough data and a stable result, or whether we need to adapt quicker to changing market conditions. 

I estimated at least two, and up to N/2 PCA depending on how many markets N had data. I used 100 liquid futures markets with daily data back to 1970 where possible.

Let's start with the contribution to variance. This is how important each PCA is.

We can see that the first PCA for the last 12 months at least explains 18% of the variance, the second 12.5% and so on. In contrast if we did this for US equities we'd find the first PCA explained 50%, and for US bonds it would be 70%. There is a lot more going on here.

If we look at how the first two factors contributions vary over time:

... we can see that there has been a bit of a downward trend as more factors arrive in the sample, but more generally the first PCA does hover around 20% and the second around 10%. There are exceptions like 2008 where I would imagine a big risk off bet drove the market. The same was true doing COVID.

What is the first PCA? Well currently it looks like this:

For clarity I've only included the top 20 and bottom 20 instruments by weight. Still you may be struggling to read the labels. The top markets are pretty much all stocks, with European equities getting a bigger weight. The S&P does just sneak into the top 20. The bottom 20 starts with VIX and VSTOXX, but mostly the weights here are quite small. So the first PCA right now is "Equity Beta, with a tilt towards Europe".

What about the second PCA?

The first 6 positive instruments are all US bonds, and nearly all the rest are government bonds of one flavour or another. Only EU-Utility stocks get to crash this party (interest rate sensitive?). On the short side we have some FX and quite a few energy futures. So this second factor is "Long bonds / Short energies"

PCA 3 is long a whole bunch of FX, which means it's short USD, and also some metals and random commodities. On the short side it's short EU-Health equities, CNHUSD FX and a whole bunch of European bonds. Feels a bit trade related. Shall we call this the Trump factor?

Anyway I could continue, but more intuitive would be to understand how these factors have changed over time. We'll pick some key markets with lots of history. We will then plot the weight each has in a given PCA over time. 

Here is the S&P 500:

We can see that is mostly positive on PCA1 and negative on PCA2,3 but there are periods when that is not the case. The sharp drops in weighting suggest that perhaps we ought to run at something longer than a year, or use an EWMA of weights to smooth things out.

Here is US10 year:

Again, this mostly loads positive on PCA2 but not always. You can see the increase in correlation of bonds and equities happening as PCA1 creeps up in the last few years.

Here is the first PCA weighting in June 2004, one of those interesting periods.

You can see that it was all about currencies in that period; plus silver and gold, various other metals, bonds and energies. So very much a short Dollar, long metals trade.

We're nearly done for today. Last job is to plot the factors. Here are the cumulated returns for PCA1:

That looks a lot like a vol normalised equity market; note the drops in 2009 and 2020.

And here is PCA2:

Again that could plausibly be bonds, mostly up with the exception of the post 2022 period.

This suggests another research idea which is to use the S&P 500 and US interest rates as 'given factors' which might be more stable than using PCA. Still that would mean missing out on times like 2004 when other things were driving the market. 

What's next

Next step would be to look at some of those opportunities for factor use and misuse outlined above, and see if there is profit as well as fun in this game!

What exactly is a CTA?

When I use a word... it means just what I choose it to mean

CTA. An industry standard term, that seems straightforward to define: Commodity Trading Advisor. We can all name a bunch of CTAs - I used to work for one. There are indices for them, such as this one or this one (also this, and this, oh and this, plus this, and these guys have one, as do these guys, oh and dont' forget this one ... I probably missed some but I'm bored now).  But in practice, the term CTA is a somewhat ill defined term with multiple overlapping meanings. Let's dive in.

Advising and managed accounts

The most intriguing word in the term CTA is the final one: advisor. CTA's are not fund managers in the normal sense of the word. 

Fund managers normally do this: They take your cash and comingle it with others in a legal vehicle, selling you shares in the vehicle in exchange. Then they go out and buy assets with the cash. You don't legally own the assets - the fund does. There's normally a second legal vehicle which is responsible for actually managing the fund - the fund manager. You don't own shares in that. 

The CTA is a bit like the second legal vehicle in a standard fund structure - it advises but doesn't actually own the assets. But in a traditional CTA structure the assets are not comingled but kept seperate. So it works as follows: you open a managed account and put some cash into it. The CTA fund then makes decisions about what that account buys or sells. Importantly, you still own the assets in the account. If something happened to another customers assets, it wouldn't affect you.

Note that the advisory term is a bit misleading as it implies that you have full trading discretion on the account, and the CTA ocasionally rings you up and advises you about what you might think about trading. That may have been the case in the 1970s when CTAs started to become popular, but nowadays CTAs almost always trade customers accounts for them, often with automated execution algos that are a world away from requiring someone to pick up the phone. 

Choice of instrument

In theory any asset can be put inside a managed account, but they have normally been used to trade futures. The combination of a managed account and futures trading brings us to the term managed futures which is often used interchangeably with the designation CTA. Since CTA is a term of US legal art, this definition is safer and can be used across geographical regions.

An important implication of this is that you need a fair bit of money to have a managed account. To hold a properly diversified portfolio of futures with anything less than a few tens of millions, without running into discretisation issues is tricky, as I've discussed at length on this blog. There are also admin and operational fixed costs associated with having a number of managed accounts.

Over time many CTAs have gradually increased the minimum required to hold managed accounts, or have switched to using non managed account structures, which also allow them to trade assets that aren't futures. 

The asset class 

You might think that commodity trading advisors manage commodities: things like Wheat and Crude Oil. The easiest way to manage such things, especially in a managed account type setup, is through futures contracts.

However it's pretty rare for a CTA to only trade commodity futures. Nearly all of them also trade financial futures like the S&P 500 equity index or US 10 year bonds (there's perhaps an argument about whether metals like Gold are commodities or not).

Over time, CTAs have begun to trade non futures instruments. For example, it's hard to get adequate diversification in FX with just the available futures contracts. Adding FX forwards makes a lot of sense in this context, as they are very similar to futures although obviously OTC rather than exchange traded. This has implications for the legal structure of the CTA, since you can't easily put these in managed accounts.

The legal / regulatory definition

CTA is a US regulatory term. From the horses mouth:

Commodity Trading Advisor (CTA) Registration

A commodity trading advisor (CTA) is an individual or organization that, for compensation or profit, advises others, directly or indirectly, as to the value of or the advisability of trading futures contracts, options on futures, retail off-exchange forex contracts or swaps.

There's quite a bit to unpick there. The most obvious is this: the advisor is expected to advise, suggesting a managed account structure rather than a traditional structure. Secondly, and contrary to what has been stated before, you can be a CTA and manage non futures assets. This is somewhat alien to the concept of managed futures, but reflects the reality of the modern CTA industry. 

Trading style and speed

Until now I haven't discussed exactly how the CTA trades, only what it trades, and the legal structure set up to do that trading. In theory a CTA could be anything from a high frequency futures trader, up to a slow moving risk parity fund. They could be doing pure trend following, or some dangerous combination of carry, mean reversion, and a systematic short position in VIX.

Generally though when people refer to CTAs or managed futures they are mostly expecting such funds to trade medium to slow speed trend following. This is pure path dependence and tradition - there is nothing in the CFTC or NFA definition that says you have to trade like this. But because CTAs have been around for a while, and because trading costs where higher in the past, and because trends work well in futures particularly during the inflation ridden era when CTAs came to prominence, and because trends work better for holding periods between a few months and a year... for all of these reasons the CTA industry grew up as fairly slow trend followers.

(Remarkably this is true of both the US and European 'wings' of the CTA industry; even though the latter grew up fairly independently.)

There is a degree of self reinforcement here. CTAs have done very well in several market downturns, leading to the mythical status of 'crisis alpha'; something that is both uncorrelated and yet provides a positive return, plus nice skew properties. Some of that is just maths - a trend following strategy will have the payoff function of a long straddle and positive skew when judged at the right time horizon - and some of it may be myth, luck, or due to secular trends and correlations that may not hold in the future. But as a marketing story it's certainly become popular in certain circles, which means that many clients expect CTAs to have a certain trading style to fit within their particular style box.

However the fact that there are now seperate 'SG CTA', 'SG CTA Trend', and 'SG Short term' CTA indices suggests there is now more to the industry than slothful trend followers.

Note: The HFRI index family does not use the term CTA, but does have the following confusing set of indices:

• HFRI Macro: Systematic Diversified Index 
• HFRI Macro: Systematic Directional Index 
• HFRI Trend Following Directional Index 

Systematic or discretionary

The very first CTAs can really only have been discretionary; men (back then, always men!) in green eye shades in darkened rooms looking at point and figure charts. However the use of trend following naturally lends itself to a systematic process. Systematisation also allows maximal diversification across numerous futures instruments, something that will improve expected performance.

The term CTA has thus become synonymous with systematic trend following. 

Notice that systematic is not the same as automated. It's possible in theory to have a fully systematic process which is hand cranked, or done in spreadsheets or with a calculator. The original turtles operated in such a fashion. But the easy availability of computer power means that the generation of trades in the vast majority of CTAS is now done in an automated fashion.

The modern CTA

A modern CTA may well still have some legacy managed accounts, or accounts opened for particularly large clients who want that traditional structure, but they are more likely to have morphed into a more typical hedge fund setup.

Instead of opening a managed account clients will buy shares in various legal investment vehicles, and there is usually a good offering of alternative jurisdictions (both onshore and offshore), currencies and types of fund (eg UCITS). In many cases a single legal vehicle may have different share classes, offering different currencies or degrees of leverage. 

(Another possibility is that the CTA uses a master-feeder structure. The client puts money into a feeder, which in turn is invested in various master funds. For example, a CTA might offer a variety of options that blend between a more traditional futures trend following fund, and an alternative that holds OTC assets.) 

These various funds will then have futures accounts opened them. Arguably these are also managed accounts, but the legal owner of the assets within them are funds not the final clients, so this is different from the traditional setup. 

However the investment funds can also go out shopping for assets that aren't futures. These can include other on exchange assets, like options on futures, or equities; or OTC assets like FX forwards, interest rate swaps or cash bonds.  This gives the clients access to a more diviersified set of instruments, something that would be extremely difficult in a traditional managed account structure. Consider for example the hassle of setting up 50 ISDA agreements for 50 $20 million managed accounts, rather than a single $1 billion fund.

It's likely that a modern CTA is fully systematic and automated up to the point of trade generation, although many funds will outsource the execution of their trades to in house human traders or external brokers. 

The variety of trading strategies is probably the area where the industry remains most heterogeneous. 

Many CTAs probably still have some medium speed trend following at their core, and may offer funds that are purer, but will have plenty of other signals such as carry and mean reversion. They may also offer funds that are very different but leverage off their experience, such as leveraged risk parity or equity market neutral. But this is a generalisation, and there are plenty of CTAs that have stuck more to their traditional knitting of medium speed trend following "plus nothing". We can even have a further debate about what does, or does not, constitute the correct use of the word trend following - but perhaps we should not do that today!

Conclusion

In summary, when you say CTA you may mean something quite different from what I mean. There is no such thing as a 'pure' or 'true' CTA. This is most true when it comes to defining the trading style. CTA is a poorly defined term, but it seems we are stuck with it (after all, 'managed futures' is not much better!).