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I have a dataset of stock prices and I want to group stocks that share similar characteristics together using cluster analysis. I'm interested in following the evolution of each cluster over time, but since stocks have very different behaviours (roughly 50% of the time a stock will change cluster the week after), I was wondering what would be a statistically sound approach. Is it a good idea to train a clustering algorithm every week and look backwards at the weekly evolution of each cluster?

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  • $\begingroup$ Statistically sound? I think it is more a question of what makes economic sense. If your clusters are this unstable over time, they are probably useless. Aren't they? So you need to come up with a better (or at least more stable) definition of your clusters. $\endgroup$ – g g Oct 8 '19 at 11:12
  • $\begingroup$ Too many considerations to give a 'good' answer, but some initial thoughts: (1) what's your ultimate objective? (2) what features are you using to create the clusters? (3) assuming returns are one of them, what return intervals are you considering? $\endgroup$ – Chris Oct 8 '19 at 22:43

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