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I'm building a pattern recognition model for my master thesis. The idea is to build a framework with some Macro variables (long/short term rates; rates differential; equity; fx; vix) in order to find wich asset class (or investment style or strategy) would perform better on the current period, based on similarities with historical data. For that purpose I am using the K-nearest neighbour algorithm. I would like to ask sugestions regarding not only the quantitative method (KNN) but also the most significant macro variables to use.

I also would like to ask if you know any relevant literature regarding this or any similar theme?

Thanks in advance

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  • $\begingroup$ Why do you call it analog? Anyhow I don't think kNN is a good tool for this purpose as the variables will have different distributions the distance metric you will use will have very unintended results. $\endgroup$ Sep 18, 2014 at 9:33
  • $\begingroup$ Hi! Thank you for your answer. I call it analog since I will be trying to compare patterns across different regions. In order to avoid the problems you've mentioned I have standardized the data in order to avoid unintended results $\endgroup$
    – goncalogc
    Sep 18, 2014 at 9:36
  • $\begingroup$ Standardization will help if they are from the same type of distribution but with different parameters. If the distributions are of different type standardization will be ad-hoc. Also most of the time there will be variables which will have to be excluded. But kNN includes all of the variables. To test this you can simply generate a bunch of random numbers and introduce these as new variables and you will see that they degrade performance. How do you know that your candidate variables are all useful? $\endgroup$ Sep 21, 2014 at 17:01

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Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.

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  • $\begingroup$ Thank you very much Richard! I'll take a look on that for sure $\endgroup$
    – goncalogc
    Sep 18, 2014 at 13:52

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