# Machine Learning for Socially Responsible Portfolio Optimisation

arXiv: https://arxiv.org/abs/2305.12364 · HTML: https://mlsanity.com/paper/2305.12364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 3 of 3)

- [ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection](https://mlsanity.com/paper/2107.02121/) (2021)
- [A Framework for Online Investment Algorithms](https://mlsanity.com/paper/2003.13360/) (2020)
- [Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization](https://mlsanity.com/paper/2203.05673/) (2022)

## Abstract (arXiv)

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.