论文标题
用于构建低碳共同基金投资组合的三标准模型：一种基于偏好的多目标遗传算法方法
Tri-criterion model for constructing low-carbon mutual fund portfolios: a preference-based multi-objective genetic algorithm approach
论文作者
论文摘要
可持续财务，该财务将金融决策的环境，社会和治理（ESG）标准整合在一起，这取决于应将金钱用于良好目的的事实。因此，预计金融部门将发挥更重要的作用来使全球经济脱碳。为了使财务流与通往低碳经济的途径保持一致，投资者应该能够将其纳入其财务决策中，超出收益和管理气候风险的风险之外的其他标准。我们提出了一个三级投资组合选择模型，以扩展经典的Markowitz均值方差方法，以便在投资组合碳风险敞口上包括投资者的偏好作为附加标准。为了近似3D Pareto前沿，我们采用了一种有效的多目标遗传算法，称为EV-MOGA，该算法基于电子优势的概念。此外，我们引入了一种后验方法，将投资者的偏好纳入解决方案过程中，内容涉及其可持续性偏好通过碳风险暴露和他/她的损失态度来衡量的。我们在欧洲SRI开放式基金的横截面中测试了拟议算法的性能，以评估根据投资者的喜好将与气候相关的风险嵌入投资组合中的程度。
Sustainable finance, which integrates environmental, social and governance (ESG) criteria on financial decisions rests on the fact that money should be used for good purposes. Thus, the financial sector is also expected to play a more important role to decarbonise the global economy. To align financial flows with a pathway towards a low-carbon economy, investors should be able to integrate in their financial decisions additional criteria beyond return and risk to manage climate risk. We propose a tri-criterion portfolio selection model to extend the classical Markowitz mean-variance approach in order to include investors preferences on the portfolio carbon risk exposure as an additional criterion. To approximate the 3D Pareto front we apply an efficient multi-objective genetic algorithm called ev-MOGA which is based on the concept of e-dominance. Furthermore, we introduce an a posteriori approach to incorporate the investor's preferences into the solution process regarding their sustainability preferences measured by the carbon risk exposure and his/her loss-adverse attitude. We test the performance of the proposed algorithm in a cross section of European SRI open-end funds to assess the extent to which climate related risk could be embedded in the portfolio according to the investor's preferences.
