Machine Learning and Factor-Based Portfolio Optimization

Thomas Conlon, John Cotter and Iason Kynigakis

Papers from arXiv.org

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.

Date: 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-isf and nep-rmg
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http://arxiv.org/pdf/2107.13866 Latest version (application/pdf)

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Working Paper: Machine Learning and Factor-Based Portfolio Optimization (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2107.13866

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