Machine Learning for Socially Responsible Portfolio Optimisation
Taeisha Nundlall Terence L Van Zyl
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.