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Machine Learning Based Portfolio Selection Under Systemic Risk

Author

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  • Weidong Lin
  • Abderrahim Taamouti

Abstract

This paper aims to enhance the classical mean-variance portfolio selection by using machine learning techniques and accounting for systemic risk. The optimal portfolio is solved through a three-step supervised learning model. Firstly, the Smooth Pinball Neural Network is employed to predict return distributions of individual assets and the market. Secondly, we use copula to model dependence between assets and the market, based on which we simulate return scenarios. Lastly, we maximize an ex-ante conditional Sharpe ratio conditioning on systemic events. We run a large-scale comparative study using nearly 600 US individual stocks over 37 years. Our set of predictors includes 94 firm characteristics, 14 macroeconomic variables, and 74 industry dummies. The backtesting results demonstrate the superiority of our proposed approach over popular benchmark strategies including a GARCH-based model. This outperformance is statistically significant and robust to the inclusion of transaction costs.

Suggested Citation

  • Weidong Lin & Abderrahim Taamouti, 2023. "Machine Learning Based Portfolio Selection Under Systemic Risk," Working Papers 202311, University of Liverpool, Department of Economics.
    • Handle: RePEc:liv:livedp:202311
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    File URL: https://www.liverpool.ac.uk/media/livacuk/schoolofmanagement/departmentofeconomics/workingpapers/a-systematic-test-of-the-independence-axiom-near-certainty.pdf
    File Function: First version, 2023
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    More about this item

    Keywords

    portfolio optimization; systemic risk; neural network model; scenario analysis; forecasting;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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