ESG

Handbook of SustainableFinance ArXiv ID: ssrn-4277875 “View on arXiv” Authors: Unknown Abstract This handbook in Sustainable Finance corresponds to the lecture notes of the course given at University Paris-Saclay, ENSAE and Sorbonne University. It covers t Keywords: Sustainable Finance, ESG, Climate Risk, Green Bonds, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The handbook provides comprehensive definitions, historical context, and regulatory frameworks of sustainable finance with moderate mathematical modeling (portfolio theory, scoring methods) but lacks explicit backtests, code implementations, or performance metrics, positioning it as a theoretical and policy-oriented text rather than an empirical trading strategy. flowchart TD A["Research Goal: Define Sustainable Finance Frameworks & Metrics"] --> B{"Data & Inputs"} B --> C["Methodology: ESG Integration & Climate Risk Modeling"] B --> D["Data: ESG Ratings, Climate Data, Multi-Asset Returns"] C & D --> E{"Computational Processes"} E --> F["Analysis: Green Bond Valuation & Portfolio Optimization"] F --> G["Key Outcomes: Risk-Adjusted Returns & Impact Metrics"]

Black-Litterman and ESG Portfolio Optimization ArXiv ID: 2511.21850 “View on arXiv” Authors: Aviv Alpern, Svetlozar Rachev Abstract We introduce a simple portfolio optimization strategy using ESG data with the Black-Litterman allocation framework. ESG scores are used as a bias for Stein shrinkage estimation of equilibrium risk premiums used in assigning Black-Litterman asset weights. Assets are modeled as multivariate affine normal-inverse Gaussian variables using CVaR as a risk measure. This strategy, though very simple, when employed with a soft turnover constraint is exceptionally successful. Portfolios are reallocated daily over a 4.7 year period, each with a different set of hyperparameters used for optimization. The most successful strategies have returns of approximately 40-45% annually. ...

Carbon-Penalised Portfolio Insurance Strategies in a Stochastic Factor Model with Partial Information ArXiv ID: 2511.19186 “View on arXiv” Authors: Katia Colaneri, Federico D’Amario, Daniele Mancinelli Abstract Given the increasing importance of environmental, social and governance (ESG) factors, particularly carbon emissions, we investigate optimal proportional portfolio insurance (PPI) strategies accounting for carbon footprint reduction. PPI strategies enable investors to mitigate downside risk while retaining the potential for upside gains. This paper aims to determine the multiplier of the PPI strategy to maximise the expected utility of the terminal cushion, where the terminal cushion is penalised proportionally to the realised volatility of stocks issued by firms operating in carbon-intensive sectors. We model the risky assets’ dynamics using geometric Brownian motions whose drift rates are modulated by an unobservable common stochastic factor to capture market-specific or economy-wide state variables that are typically not directly observable. Using classical stochastic filtering theory, we formulate a suitable optimization problem and solve it for CRRA utility function. We characterise optimal carbon penalised PPI strategies and optimal value functions under full and partial information and quantify the loss of utility due incomplete information. Finally, we carry a numerical analysis showing that the proposed strategy reduces carbon emission intensity without compromising financial performance. ...

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. ...

Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks ArXiv ID: 2404.17369 “View on arXiv” Authors: Unknown Abstract There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components’ relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature’s integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners. ...

An Analytical Approach to (Meta)Relational Models Theory, and its Application to Triple Bottom Line (Profit, People, Planet) – Towards Social Relations Portfolio Management ArXiv ID: 2402.18764 “View on arXiv” Authors: Unknown Abstract Investigating the optimal nature of social interactions among actors (e.g., people or firms), who seek to achieve certain mutually-agreed objectives, has been the subject of extensive academic research. Using the relational models theory (describing all social interactions as combinations of four basic sociality ingredients: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing), the common approach revolves around qualitative arguments for determining sociality configurations most effective in realizing specific purposes, at times supplemented by empirical data. In the current treatment, we formulate this question as a mathematical optimization problem, in order to quantitatively derive the most suitable combination of sociality forms for dyadic actors, which optimizes their mutually-agreed objective. For this purpose, we develop an analytical framework of the (meta)relational models theory, and demonstrate that combining the four sociality forms to define a specific meaningful social situation inevitably prompts an inherent tension among them, codified by a single elementary and universal metarelation. In analogy with financial portfolio management, we subsequently introduce the concept of Social Relations Portfolio (SRP) management, and propose a generalizable methodology capable of quantitatively identifying the efficient SRP, which, in turn, enables effective stakeholder and change management initiatives. As an important illustration, the methodology is applied to the Triple Bottom Line (Profit, People, Planet) paradigm to derive its efficient SRP. This serves as a guide to practitioners for precisely measuring, monitoring, reporting and steering stakeholder and change management efforts concerning Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) within and / or across organizations. ...

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"]

Corporate Social Responsibility and Access toFinance ArXiv ID: ssrn-1847085 “View on arXiv” Authors: Unknown Abstract In this paper, we investigate whether superior performance on corporate social responsibility (CSR) strategies leads to better access to finance. We hypothesize Keywords: Corporate Social Responsibility (CSR), Access to Finance, Capital Markets, ESG, Cost of Capital, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper relies on standard econometric models (regressions, IV, simultaneous equations) with limited advanced mathematics, but demonstrates high empirical rigor through extensive robustness checks, multiple alternative measures, and implementation-heavy analysis using large datasets. flowchart TD A["Research Question: Does CSR Performance improve Access to Finance?"] --> B["Data & Inputs"] B --> C["Key Methodology"] B --> D["Analytical Tools"] C --> E["Computational Model"] D --> E E --> F["Key Outcomes/Findings"] subgraph B [" "] direction LR B1["Company Financial Data"] --> B2["CSR/ESG Scores"] B3["Market Data"] --> B2 end subgraph C [" "] direction LR C1["Regression Analysis"] --> C2["Propensity Score Matching"] end subgraph D [" "] direction LR D1["Stata / R"] --> D2["Datastream / Compustat"] end subgraph E [" "] direction LR E1["Estimate Cost of Capital"] --> E2["Test Liquidity & Equity Issuance"] end subgraph F [" "] direction LR F1["Positive Correlation"] --> F2["Lower Cost of Capital"] F2 --> F3["Better Market Access"] end