 Iterative Complement-clustering PCA: Uncovering latent industry structures in stock returnsDaning Bi, Le Chang and Yanrong Yang Economics Letters, 2025, vol. 256, issue C Abstract: Principal component analysis (PCA) is a widely implemented statistical method for dimension reduction, but struggles to identify group-specific patterns (sub-homogeneity), such as the latent industry structures in high-dimensional stock return data. We propose an Iterative Complement-clustering PCA (ICcPCA) that jointly estimates homogeneity (market-wide effects) and sub-homogeneity (industry-specific risks), where a Leave-one-out principal component regression (LOO-PCR) clustering approach is developed to iteratively cluster variables (stocks) into disjoint multidimensional subspaces (groups). Simulations show that the ICcPCA outperforms the conventional PCA in both estimating the number of principal components and recovering the data. In analyzing stock returns of 160 firms across 8 industries, ICcPCA with LOO-PCR can separate market-wide effects from industry-specific risks, achieving higher clustering accuracy and lower recovering errors. Applications in portfolio optimization demonstrate that ICcPCA-based minimum variance portfolios can attain lower volatility and higher profitability than PCA-based portfolios. Keywords: Principal component analysis; Clustering; Stock returns; Portfolio optimization (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:256:y:2025:i:c:s0165176525004483 DOI: 10.1016/j.econlet.2025.112611 Access Statistics for this article Economics Letters is currently edited by Economics Letters Editorial Office More articles in Economics Letters from Elsevier |
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