A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.
arXiv:1808.03889v1 , year=
3 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
3
Pith papers citing it
12
external citations · Pith
abstract
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.
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Post-Screening Portfolio Selection
A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.
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Incrementally Updated Spectral Embeddings
Warm-starting eigensolvers with the previous subspace estimate maintains extremal eigenspaces in essentially O(1) iterations per small data perturbation.