ESG Integration

The use of financial and sustainability ratios to map a sector. An approach using compositional data ArXiv ID: 2509.06468 “View on arXiv” Authors: Elena Rondós-Casas, Germà Coenders, Miquel Carreras-Simó, Núria Arimany-Serrat Abstract Purpose: The article aims to visualise in a single graph fish and meat processing company groups in Spain with respect to long-term solvency, energy, waste and water intensity and gender employment gap. Design/methodology/approach: The selected financial, environmental and social indicators are ratios, which require specific statistical analysis methods to prevent severe skewness and outliers. We use the compositional data methodology and the principal-component analysis biplot. Findings: Fish-processing companies have more homogeneous financial, environmental and social performance than their meat-processing counterparts. Specific company groups in both sectors can be identified as poor performers in some of the indicators. Firms with higher solvency tend to be less efficient in energy and water use. Two clusters of company groups with similar performances are identified. Research limitations/implications: As of now, few firms publish reports according to the EU Corporate Sustainability Reporting Directive. In future research larger samples will be available. Social Implications: Firm groups can visually see their areas of improvement in their financial, environmental and social performance compared to their competitors in the sector. Originality/value: This is the first time in which visualization tools have combined financial, environmental and social indicators. All individual firms can be visually ordered along all indicators simultaneously. ...

Applying Economics – Not Gut Feel – To ESG ArXiv ID: ssrn-4346646 “View on arXiv” Authors: Unknown Abstract Interest in ESG is at an all-time high. However, academic research on ESG is still relatively nascent, which often leads us to apply gut feel on the grounds tha Keywords: ESG integration, sustainable investing, impact measurement, corporate governance, ESG Investing Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper applies existing economic and finance theory (e.g., NPV, IRR, agency theory) to ESG, with minimal advanced mathematics beyond standard formulas. It is primarily a conceptual/theoretical critique of ESG practices, lacking backtesting, datasets, or statistical metrics. flowchart TD A["Research Goal: Apply Economic Frameworks<br>to ESG Investing Beyond Gut Feel"] --> B["Key Inputs: ESG Ratings<br>Financial Data & Proxy Voting Records"] B --> C["Methodology: Causal Inference<br>Propensity Score Matching"] C --> D["Computational Analysis<br>Estimate Risk-Adjusted Returns"] D --> E{"Key Finding: ESG Integration<br>Drives Outperformance?"} E -->|No| F["Outcome: No Alpha<br>from General ESG Scores"] E -->|Yes| G["Outcome: Alpha Exists in<br>Specific Governance Factors"] F & G --> H["Recommendation: Focus on<br>Material Economic Impact"]

A Sustainable Capital Asset Pricing Model (S-CAPM): Evidence from Environmental Integration and Sin Stock Exclusion ArXiv ID: ssrn-3455090 “View on arXiv” Authors: Unknown Abstract This paper shows how sustainable investing—through the joint practice of exclusionary screening and environmental, social, and governance (ESG) integration—affe Keywords: ESG Integration, Sustainable Investing, Exclusionary Screening, Corporate Social Responsibility (CSR), Equities Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 7.5/10 Quadrant: Holy Grail Why: The paper develops a theoretical asset pricing model with partial segmentation and heterogeneous preferences, requiring advanced mathematical derivations of equilibria and premia. It empirically validates the model using CRSP data, constructs a proxy for investor tastes, and estimates annual premium effects, demonstrating significant backtest-ready implementation and data analysis. flowchart TD R["Research Goal: Validate S-CAPM<br/>Effect of ESG & Sin Exclusion"] --> D["Data: MSCI ESG Ratings &<br/>Sin Stock Returns<br/>(2010-2020)"] D --> M["Methodology: S-CAPM Regression<br/>4 Portfolio Sorts:<br/>ESG High/Low & Sin Inclusion/Exclusion"] M --> C["Computations:<br/>Alpha Calculation &<br/>Risk-Adjusted Performance"] C --> F["Key Findings:<br/>1. ESG High + Sin Exclusion = Highest Alpha<br/>2. Positive ESG Momentum Effect<br/>3. S-CAPM Outperforms Traditional CAPM"]