Sustainable Multi-Manager Portfolio Optimization under Factor Model Uncertainty
提出一种无需调参的可持续基金配置方法，通过控制风格风险并提升超额收益，在考虑因子模型不确定性的情况下，使组合表现优于多种被动ESG指数及主动策略。
Beyond environmental and ethical objectives, the performance of sustainable funds has been shown to vary significantly over time due to their exposure to identifiable investment factors such as growth and quality. The authors introduce a straightforward allocation method of sustainable funds that smooths performance over market cycles by capping unwanted style risks and boosting genuine outperformance (alpha). This is accomplished through an empirical strategy that accounts for uncertainty in factor models when estimating fund sensitivities and abnormal returns, combined with an optimization program that requires no tuning parameters and limits portfolio rebalancing. Empirical applications to a European universe—including Sustainable Finance Disclosure Regulation Articles 8 and 9 environmental, social, and governance (ESG) funds—demonstrate that the proposed active strategy outperforms many widely used passive ESG indexes, as well as competing active and smart beta approaches. These results also hold over an extended universe of both sustainable and nonsustainable funds, underscoring the robustness of the methodology.