Machine Learning and Factor-Based Portfolio Optimization

Thomas Conlon, John Cotter and Iason Kynigakis
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Iason Kynigakis: Smurfit Graduate Business School, University College Dublin

No 202111, Working Papers from Geary Institute, University College Dublin

Abstract: We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to covariance and portfolio weight structures that diverge from simpler estimators. Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in terms of risk minimization. These effects are amplified for investors with an increased sensitivity to risk-adjusted returns, during high volatility periods or when accounting for tail risk. Covariance matrices with a time-varying error component improve portfolio performance at a cost of higher turnover.

Keywords: Autoencoder; Covariance matrix; Dimensionality reduction; Factor models; Machine learning; Minimum-variance; Principal component analysis; Partial least squares; Portfolio optimization; Sparse principal component analysis; Sparse partial least squares (search for similar items in EconPapers)
JEL-codes: C38 C4 C45 C5 C58 G1 G11 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2021-03-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf, nep-ore and nep-rmg
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Related works:
Working Paper: Machine Learning and Factor-Based Portfolio Optimization (2021)
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