Maximum Covariance Analysis in Python
Principle Component Analysis (PCA) with varimax rotation.
Hierarchical clustering and partitional clustering with exploratory factor analysis on chocolate quality.
Connectivity-based parcellation
Statistical properties of entromin as an orthogonal rotation criterion
Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.
📊 Analyze portfolio risk using PCA on daily returns of 20 large-cap US equities to reveal hidden factors and enhance interpretability.
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