A multi-criteria approach to ESG-based portfolio optimization incorporating historical performance, forward-looking...

This paper presents a new framework for portfolio management that incorporates sustainability considerations in the form of environmental, social, and governance risks (ESG) alongside the impact of historical and projected company performance, as well as a fuzzy environment to account for expected return uncertainty. To account for market and investor uncertainties, we integrate the conditional value-at-risk (CVaR) risk measure with credibility theory. Unlike traditional portfolio optimization methods, which rely heavily on probabilistic assumptions and may fail to capture real-world uncertainty, our model uses fuzzy logic principles to more realistically represent uncertainty when historical data is incomplete or expert opinions are inaccurate. To assess historical and projected company performance, we use data extracted from quarterly company reports and employ advanced text analytics tools such as FinBERT sentiment analysis and the NotebookLM platform. These tools enable us to extract subtle insights and sentiment trends that are critical for predicting future performance. Empirical validation of the proposed framework is conducted using historical stock return data from the DJIA. A diversified portfolio of assets is selected and the optimal stock allocation is obtained under the proposed credibilistic CVaR (CCVaR) validation approach. The results show that the portfolios optimized under the CCVaR framework offer superior adverse risk control and greater resilience to market volatility compared to traditional approaches. At the end of the paper, the proposed model is compared with the traditional equal weight strategy and the results are presented. This study provides valuable practical insights for risk-averse investors and portfolio managers seeking stronger and more stable investment strategies in uncertain financial environments.