nebulous intricacies

In the last post we looked at the Turtle Trading system and saw that its performance went from outstanding for a long period of time to flat for 20 years. This opens a can of worms:

Does Trend Following work, is it dead, do markets change, does trend following rules need to adapt to these changes?

Let’s look at the different points of view.

The EMH Crowd

The EMH crowd does not believe in anything else than the Random Walk and by definition discards any profit-generating mechanical strategy.

As much as the Efficient Market Hypothesis (EMH) is a cornerstone of most modern financial theory, it has proven to be wrong partly because of some of its assumptions (not all actors in the market are rational, market prices are not fully random and normally distributed, etc.).

One great book that discredits the EMH approach and their descendant theories (CAPM, etc.) is by Mandelbrot: The (mis)behavior of the markets. It lays the arguments against the EMH in an approachable (ie not too much maths) way.

Trend Following is dead/does not work

Curtis Faith declared that “every few years trend following traders experience a period of losses and inevitably some expert will announce the end of trend following.”

Mike Covel also has a similar quote in his Trend Following book: “every 5 years some famous trader blows up and everyone declares trend following to be dead. Then 5 years later some famous trader blows up and everyone declares trend following to be dead, etc. ”

One of the main proponents of the argument against Trend Following is infamous trader Vic Niederhoffer (who “blew up” twice). He has been highly vocal about it, declaring Trend Following as one of the top Stock Market con.
Here is another link from his website to read more about it.

Arguments like this, despite the empirical evidence against it – in the form of Trend Following Wizards success, can be taken as a motivation for healthy skepticism and push you to strengthen your statistical research.

Are markets changing? (and must Trend Following change too?)

The self-professed “Trend Following poster boy” (a.k.a. Michael Covel) authoratively declares that this is a specious argument.

Occasionally, someone trying to promote something or start a debate will argue that trend following rules must always change due to changing market conditions. This is nonsense. It is a specious argument.

This is at best ambiguous. Covel likes to cite Bill Dunn who:

proffered that his basic system rules have not changed since 1974

Now, that is seducing: it seems to sell you the idea that you can develop a system, implement it and trade it for life. However this is not strictly true. As mentioned in this interview:

Dunn annually adjusts the parameters of trading signals and each markets weighting. In February – just as the grains were about to take off – he dumped the entire grain sector. But Dunn has no regrets.

Dunn is also known to have collaborated with Robert Pardo, a strong proponent of Walk-Forward testing (see below: a constant system adjustment).

To clarify: although Trend Following principles will never change, the rules/parameters of a Trend Following system might need to be adjusted to changing market conditions.

How can a Trend Following strategy adapt to the ever-changing markets?

In an Active Trader article, Anthony Garner attempts to discuss:

Do markets change? Is it necessary to undertake continued research and development and adapt a trend-following system to maintain its profitability over the years?

The article is only available for the magazine subscribers, but the result of the equity curve can be found on the Trading Blox forum. By “tuning up” the Turtle system, Garner manages to obtain interesting stats (MAR=2.26, CAGR=35.28%). The main change to the system is the use of a longer-term timeframe.

Practically

I know I would not be happy trading the original Turtle System in the last 20 years and get a 0% return. If you started trading this system “back then”, when and how would you think it is time to switch to a revised system?

Let’s look at options:

1. Walk-Forward

Walk-Forward testing‘s principle is to keep running (in simulation) a “pool” of systems using different rules/parameters. At regular interval, you evaluate what systems are best (performance, robustness, etc.) and trade those until the next re-evaluation. The potential risk with this approach is that you might end up like a dog chasing your tail.

However, this approach would have you switched from the original Turtle system to the new one a while ago.

2. Alternative Walk-Forward

Markets exhibit some degree of inefficiency – and Trend Following is a strategy designed to profit from these inefficiencies. I am still refining my theoritical understanding and explanation of it, but I believe Trend Following’s performance is mostly the result of fat-tailed distributions (distribution kurtosis) and possibly autocorrelation.

If one can associate the evolution of these characteristics to the performance of Trend Following systems it might be possible to adapt the system rules/parameters to the values and evolution of the price distribution characteristics. This is a topic I’d like to investigate further.

3. Mixing Systems

Finally, and this seems to be a strategy adopted by many professionals: mix different systems and different timeframes. Here the rationale is that we cannot predict what systems are going to under/over perform and mixing several ones together will smooth out the equity curve.

some Autocorrelation representation by kylemcdonald@flickr (CC)

Kurtosis only?

In an earlier post, I discussed how fat-tails are a reason for Trend Following success (or in technical terms: the excess kurtosis of price distributions).

However, there is something unsatisfying in that explanation: if the kurtosis was the sole source of Trend Following success:

• Random entries should work as well as any other entries
• Strategies such as buying Out-of-The-Money (OTM) options (think Nassim Taleb for example) should exhibit similar performance to Trend Following (with the advantage of being a rather simpler strategy)

Something Extra?

I recently came across this paper (PDF) explaining that Trend Following and OTM options buying are strategies exhibiting similar performance profiles. However, the conclusion of this paper was that Trend Following showed superior performance.

Additionally, there is definitely a measurable edge to Trend Following entries (such as this Donchian breakout e-ratio calculation shows). Random entries would not show such an edge.

So, there must be something extra to the kurtosis story explaining Trend Following success…

Autocorrelation

One hypothesis that I want to investigate further is autocorrelation (also referred to serial correlation).

One of the main principles of Trend Following entries – in the face of conventional wisdom – is:

Buy High and Sell Low

Well, it should really say “Buy High, Sell Higher and Sell Short Low, Buy Back Lower”. The point is that Trend Following entries are made at extremes, in the direction of the extremes.

If market exhibit positive autocorrelation at extremes, it can be derived that following the direction of the extreme moves should provide an edge (positive expectancy). This would explain why Trend Following entries perform better than random entries and why Trend Following is a superior strategy to buying Out-of-The-Money options.

Calculation Project

Now, this sounds all well and fine in theory but does this stack up to verification?

To check this, I am planning to run some calculations on historical prices and see if markets exhibit such autocorrelation at extremes. Another aspect that will be interesting to look into is whether this autocorrelation evolves over time and whether these autocorrelation levels are autocorrelated themselves (ie is there some degree of predictability in the autocorrelation evolution).

Now, please note that I am stepping out of my comfort zone here: my “heavy maths” days are quite far behind me and I know that using statistics can be a minefield (because it is so easy to use it in an incorrect manner). For example, the “standard” correlation calculation (Pearson’s correlation coefficient) only determines linear dependence – although market data is non-linear. Might set myself up for some hardship but as we say in French: “Qui ne risque rien n’a rien” (no pain, no gain).

I am also thinking of getting one or two Econometrics books to give me a headstart on this. But if any of you clever readers have any suggestions or tips on any of the above, please let me know.

Please bear with me and stay tuned.