A Study on Financial Modelling for Sustainable Investing Decisions of Individual Investor

DOI:10.34047/MMR.2025.12210

Abstract

In recent years, environmentally conscious investments grew dramatically as individual investors in as many ways include factors related to the environment, society, and governance (ES into the choices they make regarding their processes. This shift has necessitated the development of sophisticated financial modeling techniques that incorporate ESG metrics into traditional valuation frameworks, such as discounted cash flow (DCF) analysis, Monte Carlo simulations, and real options valuation. This study systematically explores data-driven approaches to financial modeling for sustainable investing, leveraging quantitative ESG rating systems, carbon footprint analysis, and sustainability-weighted risk-adjusted return metrics to evaluate investment viability. It highlights key financial tools, including Bloomberg ESG Data Services, MSCI ESG Ratings, and Sustainalytics Risk Ratings, which provide granular insights into the long-term performance implications of sustainable assets. Case studies illustrating the impact of integrating ESG factors on risk-adjusted returns reveal that companies with high ESG scores outperform their lower-rated counterparts by 3–5% CAGR over a 10-year period, underscoring the financial materiality of sustainability considerations. This research also delves into the computational complexities of incorporating ESG-adjusted beta coefficients and stochastic modeling techniques into portfolio allocation models, enabling investors to optimize risk-return trade-offs in line with sustainability objectives. Advanced machine learning algorithms, such as natural language processing (NLP) for ESG sentiment analysis and AI-driven scenario forecasting, further enhance predictive accuracy in sustainable investing. The study provides a comparative analysis of traditional versus ESG-integrated financial models, demonstrating that portfolios constructed with carbon-adjusted risk factors yield superior Sharpe ratios, outperforming standard benchmarks by 15–20 basis points per annum. By presenting empirical evidence from real-world investment cases, this research offers a rigorous methodological framework for individual investors seeking to align financial objectives with sustainable impact, ensuring robust, data-backed investment decision-making.