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Machine Learning and Factor-Based Portfolio Optimization

Author

Listed:
  • Thomas Conlon

    (Smurfit Graduate Business School, University College Dublin)

  • John Cotter

    (Smurfit Graduate Business School, University College Dublin)

  • Iason Kynigakis

    (Smurfit Graduate Business School, 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.

Suggested Citation

  • Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  • Handle: RePEc:ucd:wpaper:202111
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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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