Jez – Yes. That is correct. Non-directional states tend to outnumber trends which can often lead to ruin.

Stops are a function of the strategy, not the market. Cutting your losses short is a great saying but I think it’s devoid of any real meaning. It’s way too general. Performing a simulation with random entries is a great exercise. Use a stop, run 10,000 simulations of about 300 random entry trades. Perform it on real data and simulated data. With the simulated data, you can alter the drift and volatility.

There is direction and there is volatility. Step one is quantifying them. Step 2 is then determining if there a relationship between them. Correlation is good place to start but it only measures linear dependency. Don’t forget that correlation isn’t causation. Also, if the relationship is non-linear, correlation won’t capture that.

Trading is statistics and time series analysis. I’m amazed how many “trading books” fail to cover these relevant topics.

For me, one of the main characeristics of trend following is that it is a strategy that cuts your losses short and let your winners run. From that point of view only (ie ignoring the “trend entry” part of trend following, if you will), a high kurtosis will ensure that large or very large winners occur at a “higher than normal” frequency (ie fat-tail ends of return distributions). Looking at this argument only, random entries with SL but no TP should provide profitable returns.

Note that I have not verified that theory by doing a proper analytical research but this sounds logical to me (and seems confirmed by Trend Followers themselves).

However, this makes a big assumption: that the losses that might result from whipsawing in a trend following system do not outweigh the gains from venturing in the fat-tails. And I believe this is the point you are trying to make with the importance of the direction of volatile moves: if they tend to “move back and forth at very high levels” this will have a detrimental effect to the TF performance system.

Maybe, an additional calculation of the Hurst coefficient/fractal dimension in the time series would help quantify the whipsawing?

Happy to continue the discussion (and hear if you think that does not make sense) as it helps refine my understanding.

One of the well known characteristics of volatility is clustering. This means that there are periods of high volatility and periods of low volatility. Therefore the excess kurtosis that you’re observing in this histogram, both the left and right hand side, tend to occur in the same state, i.e. a state of high volatility. If a market is moving back and forth at very high levels, then this is very bad for a trend following system. It gets whipsawed to death.

Histograms tell you size and frequency but they don’t tell you structure. Structure is what you really want to know. Once you identify a particular structure within a market, you can then build a trading system around it.

@Trey I personally see kurtosis/fat-tail “at a higher timeframe” the source of trend following, ie if you trade a TF system on EOD data, the fact that monthly price data exhibits a leptokurtic distribution will profit your system, because each outlier monthly return move should generate a trend for your daily system – and the fat-tails mean these moves are more frequent than normal.

TF systems need drift in order to profit. Many time series don’t actually have a drift, but many random drifts, similar to streaks in a coin toss.

The regression in that papers appears to suffer from multi-collinearity, which if present renders the results meaningless. Each independent variable is multiplied by stddev. This induces dependency among those variables. The extremely high R2 is evidence of multi-collinearity as well.

There is much to learn from randomly generated data. You’ll see many patterns that you see in the market, which are no more real than the constellation Orion.

But it is good to have this kind of papers to flow around to discourage more people come into LTTF. That’s why most people don’t make money in the market. They listen to the so-called academic research based on fake data.