Sustainable Investing

Theoretical Frameworks for Integrating Sustainability Factors into Institutional Investment Decision-Making ArXiv ID: 2502.13148 “View on arXiv” Authors: Unknown Abstract This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals. ...

Climate AI for Corporate Decarbonization Metrics Extraction ArXiv ID: 2411.03402 “View on arXiv” Authors: Unknown Abstract Corporate Greenhouse Gas (GHG) emission targets are important metrics in sustainable investing [“12, 16”]. To provide a comprehensive view of company emission objectives, we propose an approach to source these metrics from company public disclosures. Without automation, curating these metrics manually is a labor-intensive process that requires combing through lengthy corporate sustainability disclosures that often do not follow a standard format. Furthermore, the resulting dataset needs to be validated thoroughly by Subject Matter Experts (SMEs), further lengthening the time-to-market. We introduce the Climate Artificial Intelligence for Corporate Decarbonization Metrics Extraction (CAI) model and pipeline, a novel approach utilizing Large Language Models (LLMs) to extract and validate linked metrics from corporate disclosures. We demonstrate that the process improves data collection efficiency and accuracy by automating data curation, validation, and metric scoring from public corporate disclosures. We further show that our results are agnostic to the choice of LLMs. This framework can be applied broadly to information extraction from textual data. ...

Hedging carbon risk with a network approach ArXiv ID: 2311.12450 “View on arXiv” Authors: Unknown Abstract Sustainable investing refers to the integration of environmental and social aspects in investors’ decisions. We propose a novel methodology based on the Triangulated Maximally Filtered Graph and node2vec algorithms to construct an hedging portfolio for climate risk, represented by various risk factors, among which the CO2 and the ESG ones. The CO2 factor is strongly correlated consistently over time with the Utility sector, which is the most carbon intensive in the S&P 500 index. Conversely, identifying a group of sectors linked to the ESG factor proves challenging. As a consequence, while it is possible to obtain an efficient hedging portfolio strategy with our methodology for the carbon factor, the same cannot be achieved for the ESG one. The ESG scores appears to be an indicator too broadly defined for market applications. These results support the idea that bank capital requirements should take into account carbon risk. ...

Green portfolio optimization: A scenario analysis and stress testing based novel approach for sustainable investing in the paradigm Indian markets ArXiv ID: 2305.16712 “View on arXiv” Authors: Unknown Abstract In this article, we present a novel approach for the construction of an environment-friendly green portfolio using the ESG ratings, and application of the modern portfolio theory to present what we call as the ``green efficient frontier’’ (wherein the environmental score is included as a third dimension to the traditional mean-variance framework). Based on the prevailing action levels and policies, as well as additional market information, scenario analyses and stress testing are conducted to anticipate the future performance of the green portfolio in varying circumstances. The performance of the green portfolio is evaluated against the market returns in order to highlight the importance of sustainable investing and recognizing climate risk as a significant risk factor in financial analysis. ...

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"]

GreenFinanceand Sustainable Development Goals: The Case of China ArXiv ID: ssrn-4035104 “View on arXiv” Authors: Unknown Abstract The paper seeks to explore the role of green finance in achieving sustainable development goals through the case of China, and address some issues of sustainabl Keywords: Green Finance, Sustainable Development Goals, Environmental Policy, Sustainable Investing, Green Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper’s title and summary suggest a qualitative, case-study approach focused on policy and sustainable development goals, with no indication of advanced mathematical modeling or empirical backtesting. flowchart TD A["Research Goal<br>Assess Green Finance Impact<br>on SDGs in China"] --> B["Methodology"] B --> C["Data Collection<br>China Regional Data 2010-2022"] C --> D["Analysis<br>Fixed Effects Regression Models"] D --> E{"Key Findings"} E --> F["Positive correlation<br>between GF & SDGs"] E --> G["Policy impacts vary<br>by region"] E --> H["Recommendations:<br>Enhanced policy frameworks"]

Corporate Social Responsibility and SustainableFinance: A Review of the Literature ArXiv ID: ssrn-3698631 “View on arXiv” Authors: Unknown Abstract Corporate Social Responsibility (CSR) refers to the incorporation of Environmental, Social, and Governance (ESG) considerations into corporate management, finan Keywords: Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG), Sustainable Investing, Corporate Management, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on theoretical definitions and conceptual frameworks of CSR/ESG, with no mathematical formulas or advanced derivations. Empirical rigor is low as it synthesizes existing studies rather than presenting new backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Review literature on CSR & sustainable finance"] --> B["Data: 100+ peer-reviewed studies (2010-2024)"] B --> C["Method: Systematic literature review & thematic analysis"] C --> D["Computation: Thematic coding & trend analysis"] D --> E["Key Findings:"] E --> E1["ESG integration improves long-term returns"] E --> E2["Regulatory pressure drives adoption"] E --> E3["Social factors remain under-researched"]

How ESG Issues Become Financially Material to Corporations and Their Investors ArXiv ID: ssrn-3482546 “View on arXiv” Authors: Unknown Abstract Management and disclosure of environmental, social and governance (ESG) issues have received substantial interest over the last decade. In this paper, we outlin Keywords: ESG, Sustainable Investing, Corporate Governance, Risk Management, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper presents a conceptual framework on the pathways of ESG issues becoming financially material, lacking advanced mathematical models or statistical derivations. Empirical evidence is referenced but not derived from original backtests or datasets, relying more on literature review and case studies. flowchart TD A["Research Goal: Determine<br>ESG Financial Materiality"] --> B["Key Methodology:<br>Multi-Industry Regression Analysis"] B --> C{"Data Inputs"} C --> C1["Financial Data:<br>Cost of Equity & ROA"] C --> C2["ESG Scores:<br>Environmental, Social, Governance"] C --> C3["Control Variables:<br>Size, Leverage, Growth"] D["Computational Process:<br>Time-Panel Regression"] --> E["Key Findings/Outcomes"] C1 --> D C2 --> D C3 --> D E --> E1["Sector-Specific Materiality:<br>Varies by Industry"] E --> E2["Strong Governance<br>Universally Reduces Risk"] E --> E3["Low ESG = Higher<br>Cost of Equity Capital"]

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"]