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On the trade-off between ESG integration and tracking quality of index tracking portfolios in developed financial markets

Authors

  • Thomas R. Holy, Ernst-Abbe-Hochschule, 07745 Jena, Germany
  • Mario Brandtner, Ernst-Abbe-Hochschule, 07745 Jena, Germany
  • Daniel Rodenburger, Friedrich-Schiller-Universität, 07743 Jena, Germany

Abstract

The growing demand for passive and environmental, social, and corporate governance (ESG) investing has led to the development of cost-effective exchange-traded funds (ETFs) that integrate ESG criteria into their investment decisions. This paper provides insights into the trade-off between ESG integration and tracking quality measured by tracking error and portfolio diversification under real-world conditions. To this end, we propose a two-stage approach for optimized sampling, combining feature selection from machine learning to identify tracking portfolio components and portfolio optimization to determine the components’ portfolio weights. The approach is designed to construct tracking portfolios efficiently, considering ESG integration and financial constraints. By applying our method to a unique dataset that covers six financial markets and spans different market phases, we provide empirical evidence of an economically significant negative relationship between increasing ESG integration and tracking quality (i.e. increasing tracking error and decreasing portfolio diversification) when exceeding a certain ESG score threshold. Our findings significantly impact researchers and ETF sponsors seeking to develop efficient and practical ESG tracking portfolio construction solutions.