Chapter 14: Latent Factor Models

Random Matrix Theory for PCA in Finance advanced

In a high-dimensional return panel, a large eigenvalue may reflect latent structure, or it may just be the geometry of estimation noise.

In a high-dimensional return panel, a large eigenvalue may reflect latent structure, or it may just be the geometry of estimation noise.

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References

FenTechSolutions/CausalDiscoveryToolbox
(2025)
The Elements of Quantitative Investing
Giuseppe A. Paleologo (2025) — John Wiley & Sons
Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis
Eric Zivot, Donald W. K. Andrews (1992)
Predictive learning via rule ensembles
Jerome H. Friedman, Bogdan E. Popescu (2008) — The Annals of Applied Statistics
Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
Jinho Baik, Gérard Ben Arous, Sandrine Péché (2005) — The Annals of Probability
Test Assets and Weak Factors
Stefano Giglio, Dacheng Xiu, Dake Zhang (2021)