TY - GEN
T1 - Machine Learning for Socially Responsible Portfolio Optimisation
AU - Nundlall, Taeisha
AU - van Zyl, Terence L.
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/4/23
Y1 - 2023/4/23
N2 - 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.
AB - 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.
KW - ESG
KW - machine learning
KW - portfolio optimisation
KW - social responsibility
UR - http://www.scopus.com/inward/record.url?scp=85168917265&partnerID=8YFLogxK
U2 - 10.1145/3596947.3596966
DO - 10.1145/3596947.3596966
M3 - Conference contribution
AN - SCOPUS:85168917265
T3 - ACM International Conference Proceeding Series
SP - 1
EP - 6
BT - ISMSI 2023 - 2023 7th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence
PB - Association for Computing Machinery
T2 - 7th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2023
Y2 - 23 April 2023 through 24 April 2023
ER -
