The research aims to apply Artificial Intelligence (AI) and sustainability to investment portfolio construction.
The study calculates the annualised average return and volatility of five randomly selected New York Stock Exchange (NYSE) stocks across different sectors to inform portfolio diversification. The calculation is performed using the Excel Solver application, a portfolio optimisation tool that validates investors’ Environmental, Social and Governance (ESG) requirements by utilising AI to optimise portfolio weight allocation.
The findings indicate that the optimised Moderate ESG performed better with portfolio growth of 31%, portfolio volatility of 9% (well below the set maximum of 15%), and a portfolio ESG score of 42.40.
The study is novel in combining three aspects, i.e., AI-based portfolio optimisation, ESG investing, and behavioural finance, into a cohesive investment governance model. The research presents an approach that allows investors to flexibly set ESG preference ranges while ensuring efficient visualisation of portfolios with the desired risk profile.
This study offers practical implications for institutional and individual investors seeking to integrate computational investment efficiency with a sustainable portfolio approach.
Bolarinde, O and Haque, R. (2026): AI-Driven Portfolio Optimisation with Human Behavioural Adjustments for Sustainable Investment Performance. In Ahmed, A. (Ed.): World Sustainable Development Outlook 2026, Vol. 22, pp.XX-XX. WASD: London, United Kingdom.
