Machine Learning for Socially Responsible Portfolio Optimisation
This addresses the need for socially responsible investors to build competitive portfolios that balance financial returns with social and environmental goals, though it is incremental as it modifies an existing model.
The study tackled the problem of Mean-Variance portfolio models not incorporating Environmental, Social, and Governance (ESG) scores for socially responsible investors by amending the model to include these constraints, resulting in a trade-off between the Sharpe Ratio and average ESG score.
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.