Identify asset clusters

Use case
Imagine you are working with a large matrix representing pairwise correlations between thousands of assets. You're trying to find assets that behave similarly. How can you use the concept of matrix "similarity" and orthogonal transformations to identify these asset clusters?



Matrix similarity and orthogonal transformations can be applied to group or cluster assets based on their pairwise correlations:
  1. Matrix Similarity: Two matrices and are said to be similar if there exists an invertible matrix such that . Similar matrices represent the same linear transformation but in different bases.
  2. Orthogonal Diagonalization: If the correlation matrix is symmetric (which it should be), you can use orthogonal diagonalization. This involves finding an orthogonal matrix (i.e., ) such that is diagonal. The columns of Β are the eigenvectors of .
  3. Clustering Using Eigenvectors: The eigenvectors corresponding to the largest eigenvalues of the correlation matrix capture the most significant patterns in the data. By examining the elements of these eigenvectors, one can identify which assets behave similarly. Large (either positive or negative) elements in the same position across these dominant eigenvectors indicate assets with similar behavior.
  4. K-means on Eigenvectors: Another approach is to perform a k-means clustering on the leading eigenvectors (associated with the largest eigenvalues). The clustering results can identify groups of assets with similar correlation structures.
  5. Interpreting Clusters: Once assets are clustered, it's essential to examine and interpret the clusters to understand the underlying similarities. For instance, assets in the same cluster may belong to the same industry, be influenced by similar macroeconomic factors, or have similar trading patterns.
  6. Regular Reassessment: As with many financial analyses, relationships can evolve over time. It's crucial to periodically re-cluster the assets and check for any shifts in correlations or behaviors.
Using matrix similarity and orthogonal transformations in this way can provide valuable insights into the relationships between assets, helping to inform investment decisions, risk management strategies, or even the creation of synthetic instruments.