Machine Learning for Socially Responsible Portfolio Optimisation

ArXiv ID: 2305.12364 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Portfolio Optimization, ESG (Environmental, Social, and Governance), Mean-Variance Model, Socially Responsible Investing, Sharpe Ratio, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper primarily relies on standard Mean-Variance optimization and basic machine learning algorithms like Random Forest, which are established and computationally dense rather than mathematically advanced. However, it demonstrates high empirical rigor through detailed data preprocessing, use of real-world datasets (Yahoo Finance, VettaFi), backtest-ready procedures, and evaluation of forecast errors.

  flowchart TD
    A["Research Goal: Extend Mean-Variance Model<br>with ESG Constraints"] --> B["Data & Inputs"]
    B --> C["Methodology"]
    C --> D["Computational Process"]
    D --> E["Key Findings/Outcomes"]

    subgraph B ["Data/Inputs"]
        B1["Multi-Asset Historical Data"]
        B2["ESG Scores per Asset"]
        B3["Investor Risk Tolerance"]
    end

    subgraph C ["Methodology"]
        C1["Classical Mean-Variance Optimization"]
        C2["Constrained Optimization<br>with ESG Score Weights"]
    end

    subgraph D ["Computational Process"]
        D1["Calculate Covariance Matrix"]
        D2["Solve for Efficient Frontier<br>max Return per Volatility"]
        D3["Iterate for ESG-Weighted Solutions"]
    end

    subgraph E ["Key Findings/Outcomes"]
        E1["Competitive SR Portfolios"]
        E2["Trade-off Analysis:<br>Sharpe Ratio vs. Avg ESG Score"]
        E3["Practical Framework for<br>Socially Responsible Investors"]
    end

    B1 --> C1
    B2 --> C2
    B3 --> C2
    C1 --> D1
    C2 --> D2
    D1 --> D2
    D2 --> D3
    D3 --> E1
    D3 --> E2
    D3 --> E3