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Machine Learning for Socially Responsible Portfolio Optimisation

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  • Taeisha Nundlall
  • Terence L Van Zyl

Abstract

Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor's risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsible (SR) investors. In response to this problem, the MV model must consider Environmental, Social, and Governance (ESG) scores in optimisation. Based on the prominent MV model, this study implements portfolio optimisation for socially responsible investors. The amended MV model allows SR investors to enter markets with competitive SR portfolios despite facing a trade-off between their investment Sharpe Ratio and the average ESG score of the portfolio.

Suggested Citation

  • Taeisha Nundlall & Terence L Van Zyl, 2023. "Machine Learning for Socially Responsible Portfolio Optimisation," Papers 2305.12364, arXiv.org.
    • Handle: RePEc:arx:papers:2305.12364
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    References listed on IDEAS

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    1. Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Mufhumudzi Muthivhi & Terence L. van Zyl, 2022. "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers 2203.05673, arXiv.org.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Abdessamad Ouchen, 2022. "Is the ESG portfolio less turbulent than a market benchmark portfolio?," Risk Management, Palgrave Macmillan, vol. 24(1), pages 1-33, March.
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