ESG Investing

Evaluating the resilience of ESG investments in European Markets during turmoil periods ArXiv ID: 2501.03269 “View on arXiv” Authors: Unknown Abstract This study investigates the resilience of Environmental, Social, and Governance (ESG) investments during periods of financial instability, comparing them with traditional equity indices across major European markets-Germany, France, and Italy. Using daily returns from October 2021 to February 2024, the analysis explores the effects of key global disruptions such as the Covid-19 pandemic and the Russia-Ukraine conflict on market performance. A mixture of two generalised normal distributions (MGND) and EGARCH-in-mean models are used to identify periods of market turmoil and assess volatility dynamics. The findings indicate that during crises, ESG investments present higher volatility in Germany and Italy than in France. Despite some regional variations, ESG portfolios demonstrate greater resilience compared to traditional ones, offering potential risk mitigation during market shocks. These results underscore the importance of integrating ESG factors into long-term investment strategies, particularly in the face of unpredictable financial turmoil. ...

Asset management with an ESG mandate ArXiv ID: 2403.11622 “View on arXiv” Authors: Unknown Abstract We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negative ESG premium arises. A result that is supported by empirical data. The negative ESG premium is due to the ESG constraint imposed on institutional investors and is not associated with a risk factor. ...

Navigating Uncertainty in ESG Investing ArXiv ID: 2310.02163 “View on arXiv” Authors: Unknown Abstract The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning (RL) model, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing the Double-Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-adjusted Capital Asset Pricing Models (CAPMs) to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings, facilitating more informed investment decisions. ...

Unraveling the Trade-off between Sustainability and Returns: A Multivariate Utility Analysis ArXiv ID: 2307.12161 “View on arXiv” Authors: Unknown Abstract This paper proposes an expected multivariate utility analysis for ESG investors in which green stocks, brown stocks, and a market index are modeled in a one-factor, CAPM-type structure. This setting allows investors to accommodate their preferences for green investments according to proper risk aversion levels. We find closed-form solutions for optimal allocations, wealth and value functions. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction in order to increase green investments. Secondly, we propose a parameterization to capture investors’ preferences for green assets over brown or market assets, independent of performance. The paper uses the RepRisk Rating of U.S. stocks from 2010 to 2020 to select companies that are representative of various ESG ratings. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors; this holds even as the overall level of pecuniary satisfaction is kept unchanged. ...

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"]

Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows ArXiv ID: ssrn-3016092 “View on arXiv” Authors: Unknown Abstract Examining a shock to the salience of the sustainability of the US mutual fund market, we present causal evidence that investors marketwide value sustainability. Keywords: Sustainability, Mutual funds, Investor preferences, Fund flows, ESG investing Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on econometric analysis (difference-in-differences, local linear plots, fixed effects) rather than advanced mathematics, but is exceptionally data-heavy, using a large-scale natural experiment on $8 trillion in assets with precise flow measurements and experimental validation. flowchart TD A["Research Goal:<br>Do investors value sustainability?"] --> B["Methodology:<br>Natural experiment from sustainability ranking shock"] B --> C["Data/Inputs:<br>US mutual fund flows & sustainability scores"] C --> D["Computation:<br>Difference-in-differences analysis"] D --> E["Key Findings:<br>Investors increase flows to<br>higher sustainability funds post-shock"]

Carbon Risk ArXiv ID: ssrn-2930897 “View on arXiv” Authors: Unknown Abstract We investigate carbon risk in global equity prices. We develop a measure of carbon risk using industry standard databases and study return differences between b Keywords: carbon risk, climate finance, ESG investing, portfolio pricing, equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper uses standard asset pricing regressions and portfolio sorts but lacks heavy mathematical derivations; however, it demonstrates strong empirical rigor through the use of multiple industry-standard ESG databases, a constructed factor-mimicking portfolio (BMG), and extensive backtesting across regions and time periods. flowchart TD A["Research Goal<br>How does carbon risk affect<br>global equity returns?"] --> B["Data Collection<br>Refinitiv ESG, CRSP, Compustat"] B --> C["Methodology<br>Portfolio Formation &<br>Regression Analysis"] C --> D["Computation<br>Carbon Risk Score &<br>Alpha Calculation"] D --> E["Key Finding 1<br>High-carbon firms earn<br>significant positive returns"] D --> F["Key Finding 2<br>Carbon risk is priced<br>in global markets"]