Hierarchical Clustering for Market Analysis and Portfolio Grouping
Summary
The guide explains hierarchical clustering as an unsupervised method that builds nested groups and displays their relationships in a dendrogram. It contrasts clustering with supervised classification and compares hierarchical clustering with K-means: K-means requires a cluster count in advance, while a hierarchical tree can be cut at a chosen level. It also introduces cluster similarity and agglomerative and divisive approaches, and describes Python implementation and possible trading uses.
In finance, the examples include grouping stocks by characteristics or price relationships, exploring sectors, constructing portfolios, and finding candidates for pairs trading. The guide presents clustering as an exploratory aid for diversification, allocation, and market-structure analysis, not as a standalone signal that guarantees profitable trades. It notes that hierarchical methods can be computationally demanding on large datasets. Cluster quality depends on the chosen features, distance measure, and interpretation; any trading strategy built from clusters still needs independent validation and backtesting.
Key ideas
- Hierarchical clustering organizes observations into nested groups that can be inspected with a dendrogram.
- Unlike K-means, hierarchical clustering does not require choosing the final number of clusters before building the hierarchy.
- Clustering is unsupervised and discovers groupings without predefined labels, while classification predicts known labels.
- Stock clusters can support sector analysis, portfolio grouping, and the search for pairs-trading candidates.
- The method can be computationally intensive, and cluster-derived trading ideas require validation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.