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Esg rating

As explained in Build a Robo-Advisor with Python (From Scratch)

A quantitative assessment of an investment's environmental, social, and governance characteristics.

An ESG rating is a quantitative assessment of an investment’s environmental, social, and governance characteristics. Ratings combine applicable metrics across the three pillars into an overall ESG quality score. section 4.4.2

Common rating inputs include:

  • Environmental factors such as carbon emissions, water and land usage, toxic emissions, waste, and renewable-energy involvement. section 4.4.2
  • Social factors such as labor management, health and safety, product safety, privacy, security, and community relations. section 4.4.2
  • Governance factors such as executive pay, business ethics, and tax transparency; governance metrics apply across industries. section 4.4.2
  • Providers may apply different metrics according to industry materiality, so ratings are methodology-dependent rather than universal measures. section 4.4.2

Portfolio construction can treat each ETF’s ESG rating as a score vector ε and maximize εᵀw, the weighted average rating, while constraining weights to be nonnegative, sum to one, and remain close to a reference portfolio. section 10.3.4

ESG frontier

A reference portfolio scored 6.02 with zero tracking error. Increasing the tracking-error allowance to 2.50% raised its overall ESG quality score to 8.13, while predicted volatility stayed near 12.8%. section 10.3.4

Table 4.6 Examples of ESG factors
Environmental Social Governance
Carbon emissions Labor management, health, and safety Executive pay
Water and land usage Product safety, privacy, and security Business ethics
Toxic emissions and waste Community relations Tax transparency
Involvement in renewable energy

Use the factor table to inspect concrete environmental, social, and governance inputs behind an overall rating. section 4.4.2

Table 10.3 Results from the ESG frontier
Tracking error Volatility ESG quality score
0.00% 12.87% 6.02
0.25% 12.84% 6.34
0.50% 12.83% 6.66
0.75% 12.80% 6.97
1.00% 12.78% 7.27
1.25% 12.75% 7.52
1.50% 12.74% 7.68
1.75% 12.75% 7.81
2.00% 12.76% 7.92
2.25% 12.77% 8.03
2.50% 12.82% 8.13

Use the frontier table to compare tracking error, volatility, and portfolio ESG quality across optimization targets. section 10.3.4

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Explains metric categories, industry materiality, score aggregation, and constrained optimization, but does not derive provider scoring methodologies.
Examples
Uses carbon emissions, labor safety, executive pay, ETF ratings, and an ESG frontier ranging from 6.02 to 8.13.
What it assumes
Builds from ETF returns, covariance estimates, portfolio weights, benchmark portfolios, and tracking-error constraints.
What you can do after
Apply via constrained optimization to build portfolios with higher average ESG ratings and specified tracking-error limits.
How it connects
Connects ESG ratings with socially responsible investing, ETFs, portfolio optimization, tracking error, covariance matrices, and efficient frontiers.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.