How does K-means clustering work and when is it appropriate for financial data?

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AcadiFi · Accepted Answer

K-means is an unsupervised learning algorithm that partitions n observations into K clusters by iteratively minimizing within-cluster squared distance to centroids. The algorithm initializes K centroids, assigns each point to its nearest centroid, recomputes each centroid as the mean of its members, and repeats until assignments stabilize. For equity style segmentation at a fund such as Ravenswood Quant, features might include P/E, P/B, 12-month momentum, beta, and size. Before running K-means, standardize features because the algorithm uses Euclidean distance and is scale-sensitive. An analyst choosing K equal to 4 might discover clusters resembling value, growth, momentum, and defensive buckets. Evaluate the choice of K using the elbow method (plot within-cluster sum of squares against K) or silhouette score. K-means assumes clusters are roughly spherical and of similar variance, which is often violated in financial data where some style pockets are dense and others diffuse. It is also sensitive to initialization; use K-means++ seeding to reduce this. Outliers can distort centroids, so winsorizing or removing extreme observations helps. ```mermaid graph LR A[Data] --> B[Scale] B --> C[Init K
centroids] C --> D[Assign] D --> E[Update] E --> D E --> F[Clusters] ``` For CFA Level II candidates, K-means is the workhorse partitional method and pairs with hierarchical clustering when the number of natural groups is unclear.

How does K-means clustering work and when is it appropriate for financial data?

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