I came across a comment on something the Turtle traders had done, which was to stay out of the market in the next trading period following a 2x size win or loss. Interesting idea. They must have made the following observations for their strategy:
- unusually large winning trades were followed by losing trades (negative serial correlation or mean reversion for big winners)
- unusually large losing trades were often followed by another losing trade (positive serial correlation for large losing trades)
My main strategy is multi-asset so probably doesn’t lend itself as well to a “rule” like this. Interesting thought though. I should do some analysis on the pattern of winners and losers and see whether there is a consistent pattern.
Certainly for a single asset, it is not unusual to see mean-reversion following a ramp up in price.
One of my strategies uses a ML technique to find patterns in the distribution of returns across a portfolio. Conditioned on the pattern is a highly skewed marginal distribution for next period returns. The + skew is important and a very good thing, pointing to much more + returns than negative returns.
I had a theory that for this particular pattern, I would see higher negative serial correlation in the bigger winners. If true would allow further amplification of winners or better selection within. Indeed it did work out that more negative serial correlation produced higher next period returns on average.
Further, there was another factor that appeared to be relevant in the mean returns. Was easy to visualize / examine with the rgl package in R:
The is clustering quite visible in 1 corner. This is good. I’m sorry, but I can’t go into the background of what this is conditional on. Thought I’d give a plug for rgl and also note that autocorrelation can be a useful tool in predicting return bias.