I have done some analysis of various pairs of tickers on the NYSE. I did a brain dead algorithm to come up with all combinations of pairs and then checked all pairs for cointegration/stationarity.

I found one pair that backtested to 40% returns over the last year. But the components of the pair are totally unrelated, not even in the same sector, I cannot dream up a scenario that explains the cointegration. So I guess it's down to luck and I should leave it alone.

How should I have gone about my search? Start with a particular sector? Is there a systematic way of finding candidate pairs?

  • $\begingroup$ This in an example of the high chance of making one or more false discoveries when you test a large number of hypotheses/experiments. Statisticians speak of a high "family wise error rate" (FWER) even though each test/experiment has low error rate. It is not easy to deal with this problem, which shows up whenever you do massive testing. $\endgroup$
    – nbbo2
    Apr 18, 2021 at 19:36
  • 1
    $\begingroup$ Another point to keep in mind, it depends upon the confidence of your test. Say you find 20 cointegrated pairs with a 5% confidence intervals. This tells us that 1 of these pairs will be a false positive and no significant cointegration relationship actually exists. $\endgroup$ Apr 19, 2021 at 0:09
  • $\begingroup$ I have extended my program by the following. I would love some feedback. I use PCA on the returns of the bunch of stock tickers, this will throw up proportionally how much each stock is contributing to the returns. I then fed that into k-means clustering in order to form clusters of similar profile stocks (similar - in terms of their contribution to returns). I then picked stocks that are part of the same cluster. I then did my cointegration/stationarity tests on these similar stocks only. Is this a better approach? $\endgroup$
    – brownie74
    Apr 21, 2021 at 5:51


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.