Serial Correlation

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.

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2 Comments

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2 responses to “Serial Correlation

  1. Derek

    Is there a reason you didn’t use simple kernel density estimation for the conditional probability? You mentioned a ML method which makes me thing supervised learning (SVM, etc).

  2. tr8dr

    Actually the main component of the strategy is in identification of a pattern using a ML approach. Conditioned on the pattern and acf, found that acf was an important filter for whether to enter the trade or not.

    With regard to kernel density, I could use on the distribution, but wasn’t really needed. The + returns were nicely clustered above certain threshold. So did not need to determine the envelope of the distribution.

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