I believe yes. Because markets are non-stationary (changing all the time), their characteristics – including those at the root of Trend Following profits – are changing too.
Understanding these market characteristics is a first step towards being able to identify and measure them. This, in turn should be a step to linking Trend Following performance to the state of these market characteristics. Finally, this might be a step towards devising a way for a Trend Following strategy to adapt to these changing market characteristics (this last point makes a very big assumption: market characteristic changes can be predicted with some degree of accuracy).
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)
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…
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.
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).
Please bear with me and stay tuned.