Optimal Impact Portfolios with General Dependence and Marginals

Andrew W. Lo (), Lan Wu (), Ruixun Zhang () and Chaoyi Zhao ()
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Andrew W. Lo: MIT Sloan School of Management, Cambridge, Massachusetts 02142; Laboratory for Financial Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; Santa Fe Institute, Santa Fe, New Mexico 87501
Lan Wu: School of Mathematical Sciences, Peking University, Beijing 100871, China; Laboratory for Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, China
Ruixun Zhang: School of Mathematical Sciences, Peking University, Beijing 100871, China; Laboratory for Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, China; Center for Statistical Science, Peking University, Beijing 100871, China; National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China
Chaoyi Zhao: School of Mathematical Sciences, Peking University, Beijing 100871, China

Operations Research, 2024, vol. 72, issue 5, 1775-1789

Abstract: We develop a mathematical framework for constructing optimal impact portfolios and quantifying their financial performance by characterizing the returns of impact-ranked assets using induced order statistics and copulas. The distribution of induced order statistics can be represented by a mixture of order statistics and uniformly distributed random variables, where the mixture function is determined by the dependence structure between residual returns and impact factors—characterized by copulas—and the marginal distribution of residual returns. This representation theorem allows us to explicitly and efficiently compute optimal portfolio weights under any copula. This framework provides a systematic approach for constructing and quantifying the performance of optimal impact portfolios with arbitrary dependence structures and return distributions. Funding: Research funding from the China National Key R&D Program [Grant 2022YFA1007900], the China National Natural Science Foundation [Grants 12271013, 72342004], the Fundamental Research Funds for the Central Universities (Peking University), and the MIT Laboratory for Financial Engineering is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.0400 .

Keywords: Financial Engineering; impact investing; environmental; social; and governance (ESG); induced order statistics; copula; representation theorem; portfolio theory (search for similar items in EconPapers)
Date: 2024
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