Selecting Correlated Stocks with Pair Ranking or Optimization
Summary
The document discusses choosing a fixed number of highly correlated stocks from a larger universe. One suggested procedure is to calculate pairwise correlations, rank the pairs, and add stocks from the strongest pairs until the desired count of distinct names is reached. This is a simple way to impose a target count, though it does not define a unique portfolio objective or address how to resolve ties and overlap among pairs.
A second approach is to formulate selection as an optimization problem, allowing the researcher to specify an objective and constraints. The discussion also stresses that the right selection rule depends on the purpose: representing the broad market may call for a market index, while arbitrage research requires caution because historical correlations can weaken when economic conditions change. The document gives conceptual suggestions rather than empirical comparisons, and does not specify a correlation window, robustness procedure, or a precise optimization criterion.
Key ideas
- Rank pairwise stock correlations and add names from the strongest pairs until reaching the target universe size.
- An optimization formulation can make the selection objective and constraints explicit.
- The appropriate selection method depends on whether the goal is representation, arbitrage, or another use.
- Historical correlation can break down when economic conditions change.
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# Select top $n$ most correlated assets in universe # Select top $n$ most correlated assets in universe I know this questions is a bit ambiguous, but I guess that's natural. To put it simply: I have a universe of around 600 stocks. How do I find the top $n$ "most correlated" assets? At the moment I'm using a spectral coclustering technique to pick the cluster with the highest average correlation. This works fine, but it doesn't give me any sort of control over the number of assets that I want to pick. It just doesn't feel right. Is there a better way to do this? Edit: in principle I am looking at absolute correlation, but in my case almost all assets have a positive correlation. Thanks ## Answer by danuker (score 1) https://quant.stackexchange.com/a/65403 Given the question text, my reply would be: - Compute the correlation between all (or just a random subset, if 600^2 computations is too much) pairs of stocks. - Sort the pairs, and choose stocks from the top until you have n distinct ones. It would help to state what you are trying to achieve. - Is it to pick stocks representative of the market? If so, you might want an index fund, being cheaper to trade. - Are you trying to do arbitrage? Keep in mind that correlations can break down, especially when economics change (like during this pandemic). ## Answer by Enrico Schumann (score 0) https://quant.stackexchange.com/a/63780 You could treat your problem as an optimization problem, which would give you control over what to optimize and what constraints to add. Personally, I'd use an optimization heuristic to solve it. The downside is that you may have to do some programming yourself. See for instance this tutorial about heuristics (which I have written). A code example for a similar question is here: Find k of n assets that "minimize" the correlation matrix You would only need to adjust the objective function.
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