Portfolio Optimization Based on Multi-Criteria ESG: Combining Historical Performance, Forward-Looking Insights, and Trustworthy CVaR

A Multi-Criteria Approach to ESG-Based Portfolio Optimization Incorporating Historical Performance, Forward-Looking Insights, and Credibilistic CVaR: A Case Study on the DJIA

Introduction

In the current uncertain global capital market, constructing a robust investment portfolio no longer relies solely on traditional probability-based optimization models. With the increasing importance of Environmental, Social, and Governance (ESG) factors and growing uncertainty in market information, traditional risk assessment methods often struggle to fully capture real-world complexity. Based on a study in *Scientific Reports*, this paper proposes an innovative portfolio management framework that combines ESG considerations, historical performance, forward-looking insights, and Conditional Value at Risk (CCVaR), a measure incorporating credible theory, providing a more adaptive optimization path for risk-averse investors.

Market Background

Global financial markets are facing challenges involving structural transformation and systemic risk simultaneously. The volatility of the macroeconomic environment, geopolitical uncertainties, and long-term trends like climate change often make market information ambiguous or incomplete. Traditional investment models typically depend on deterministic predictions of future returns, which is difficult to achieve in highly dynamic financial environments. Therefore, institutional investors need analytical tools that are more resilient and capable of handling such uncertainty.

Current Capital Flows

Global capital is shifting from purely pursuing high returns to the refined management of risk-adjusted returns. Institutional investors, including pension funds and sovereign wealth funds, are increasingly viewing ESG factors as key indicators of long-term value creation rather than mere compliance requirements. Capital flows are accelerating towards assets that can effectively incorporate sustainability risks into financial forecasts. Especially when facing climate change and regulatory changes, investment strategies that can anticipate and quantify ESG risks in advance are gaining institutional favor.

Investment Logic Analysis

The fundamental driving force behind capital flows is the redefinition of "true risk."### Investment Logic Analysis

The fundamental driver of capital flow is the redefinition of "true risk." Research indicates that relying solely on historical data to predict future returns has limitations, especially during periods of market volatility. Therefore, introducing the concept of Fuzzy Set Theory allows the model to create a more realistic representation of imprecise, fuzzy market information, overcoming the oversimplification of reality by pure probabilistic models. By integrating Conditional Value at Risk (CVaR) and Credibility Theory, risk measurement shifts from depending on potentially erroneous probability distributions to depending on more "credible" cognition and fuzzy information, greatly enhancing the model's robustness in handling financial uncertainty.

This methodological shift marks a transition in investment strategy from simply "maximizing expected returns" to "achieving stable returns under credible risk constraints." Institutional investors are no longer focused on a single perfect prediction, but on ensuring the survival capability and controllable downside risk of the portfolio under various uncertainty scenarios—this is the core value provided by the CCVaR framework.

Risk Factors

Despite the significant improvements this framework offers, investors still need to pay attention to several key risks. First is the balance between the complexity and interpretability of the model itself. Introducing fuzzy logic and CCVaR enhances the model's adaptability to uncertainty but may also increase dependence on model parameters and input information, requiring portfolio managers to possess a high level of expertise. Second, although ESG is taken into consideration, ESG ratings themselves possess subjectivity and inconsistency, which may introduce new information asymmetry risks. Finally, although CCVaR aims to control extreme downside risk, the model's effectiveness against extreme shocks from "black swan" events in the market still needs to be validated through rigorous stress testing.

Long-Term Outlook

Looking ahead to the next decade, this multi-criteria, multi-dimensional optimization approach will become the mainstream paradigm for institutional portfolio management. As regulatory bodies increase their mandatory requirements for climate risk and sustainable finance, ESG will evolve from a "bonus" into a "hard constraint" for investment decisions. The innovation of combining CCVaR with fuzzy theory is expected to help investors achieve a closer integration of risk control and long-term value growth in rapidly changing markets. Institutional investors will continue to seek advanced tools that can effectively merge qualitative insights (like ESG) with quantitative risk measures (like CVaR) to achieve true long-term capital allocation goals.