Unsupervised Learning for Equity Clustering and Feature Reduction
Summary
This overview explains how unsupervised learning finds structure in unlabeled data, contrasting it with supervised prediction. It presents K-means clustering and principal component analysis (PCA) through equity examples. K-means groups stocks using scaled features such as return on equity and beta; the article describes choosing a cluster count with an inertia elbow plot. It also shows how PCA transforms correlated stock returns into orthogonal components ordered by explained variance, allowing a smaller feature set to feed a supervised market-direction model.
The examples report that the illustrated stock clusters broadly separated utilities from higher-growth technology names, and that four components retained about 90% of the variance in a seven-stock example. These are demonstrations on particular samples, not evidence of durable trading returns or out-of-sample predictive power. The article also mentions hidden-state models for regime detection and association rules, while stressing that unlabeled outputs lack a single objective performance measure and require interpretation. Clustering and dimensionality reduction can support research workflows, but feature selection, scaling, and validation remain important.
Key ideas
- Unsupervised methods identify patterns in inputs without target labels, unlike classification and regression.
- K-means repeatedly assigns observations to nearby centroids and recalculates those centroids.
- Scaled equity characteristics can produce exploratory stock groupings, with inertia helping assess cluster count.
- PCA creates orthogonal components ordered by explained variance and can reduce feature dimensions.
- Unlabeled results require interpretation and do not provide a straightforward universal measure of model performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.