Unsupervised learning techniques can be introduced incrementally. Clustering can enhance asset grouping in portfolio construction or signal classification; anomaly detection can complement existing risk monitoring systems; and dimensionality reduction methods, such as PCA, can improve model interpretability or data preprocessing. Crucially, they can augment rather than replace existing models, making integration more feasible and less disruptive. For investment practitioners, these methods enable tasks including regime detection, portfolio diversification, signal classification, and anomaly detection by revealing complex relationships and latent factors often invisible to traditional approaches.
This chapter begins by introducing clustering methods including k-means, spectral clustering, and hierarchical clustering, highlighting their use in grouping assets, detecting market regimes, and constructing diversified portfolios. Notable use cases include De Prado’s Hierarchical Risk Parity framework and applications of spectral clustering for macro regime classification. The chapter then discusses dimensionality reduction techniques such as PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and ICA as methods for simplifying high-dimensional datasets.
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