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The ESG ETFs in the UK

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Abstract

This study examines the performance of 49 so-called ESG ETFs in the UK. These funds apply environmental, social and governance criteria in their investing strategies. Raw and risk-adjusted returns are estimated with standard methodology including the Capital Assets Pricing Model, the Fama and French (Journal of Financial Economics 116:1–22, 2015) Five-Factor Model, and the Sharpe and Treynor ratios. On average terms, no significant alpha is achieved by ESG ETFs in the UK, whereas there are not differences in Sharpe and Treynor ratios between ETFs and their benchmarks. However, some empirical evidence obtained indicates that ESG ETFs outperform the FTSE 100 Index, which stands as a proxy for the UK stock market. Along with performance, we examine whether investors award responsible ETFs by entrusting more money to them. However, no significant relationship is found between the ESG rating of ETFs and their assets. On the contrary, it is revealed that the return of ETFs is negatively related to their ESG metrics.

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Notes

  1. For a detailed report on the global landscape of sustainable funds refer to “Passive Sustainable Funds: The Global Landscape 2020”, available on: https://www.morningstar.com/lp/passive-esg-landscape (accessed on 3/1/2021).

  2. Refer to “The MSCI Principles of Sustainable Investing”, available on: www.msci.com/documents (accessed on 3/1/2021).

  3. We note that data on the Fama and French factors for the UK are provided publicly by the University of Exeter. However, this data are only available up to December 31, 2017. Our study period spans from the inception of ESG ETFs in the UK up to December 31, 2020 (the iShares Global Clean Energy UCITS ETF USD (Dist) was the first ESG ETF to be launched in the UK on July 6, 2007). The site of Kenneth French provides updated data on the necessary market factors for Europe as a whole rather than specifically for the UK. We calculated the correlation of the Fama and French factors between the available data for the UK from the University of Exeter and the European data provided by Kenneth French, which is about 0.60. We deemed this figure satisfactory enough in order to use the European data in our empirical analysis. This data are available on: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  4. The information presented here along with the ESG profiles of ETFs that will be discussed in the following section have been found on: https://www.ishares.com/uk.

  5. This index is designed to measure the performance of the large and mid-cap segments across 27 Emerging Stock Markets that are relevant to Islamic investors. Among others, the countries covered by this index are Argentina, Brazil, Chile, China, Colombia, Egypt, Greece, India, Taiwan, Thailand, Turkey and the United Arab Emirates.

  6. We have run a simple linear regression model of the logarithm of assets on the age of ESG ETFs and we have obtained a positive but insignificant slope. The positive estimate indicates that the oldest a fund is, the more assets it attracts. However, the lack of statistical significance means that this assumption cannot be verified in statistical terms.

  7. It should be noted that daily returns have been selected due for data availability purposes. In particular, in the sample of the 49 ETFs examined, 43 have been launched in 2016 or later. Therefore, we had to use daily returns in order to have sufficient data to perform our tests.

  8. We have performed a linear cross-sectional regression of ETFs’ cumulative tracking error on their expense ratios finding no significant correlation. However, we have applied another regression model of ETFs’ cumulative tracking error on a dummy variable taking zero value when the ETF implements full replication and unity when an ETF adopts optimized techniques. In this case, we have received a slope of 6.20, which is significant at 5%. This estimate verifies our hypothesis about the increase in the tracking error of an ETF that can be triggered by its choice to track its benchmark through optimized techniques.

  9. It should be noted that, given the passive nature of the ESG ETFs in the sample, each fund has its own benchmark, whose performance seeks to replicate. For instance, the iShares Global Clean Energy UCITS ETF USD (Dist) tracks the S&P Global Clean Energy Index, the iShares MSCI World Islamic UCITS ETF USD (Dist) tracks the MSCI World Islamic Index and so on. In our analysis, we use the benchmark of each ETF to assess the ability of ESG ETFs to track their benchmarks, but we also use the FTSE 100 Index to evaluate the ability of the ESG ETFs in the UK to beat the overall stock market.

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Authors and Affiliations

  1. Department of Economics, Athens University MBA, National and Kapodistrian University of Athens, 81A Karaiskaki Str, Glyka Nera, Athens, Greece

    Gerasimos G. Rompotis

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Rompotis, G.G. The ESG ETFs in the UK. J Asset Manag 23, 114–129 (2022). https://doi.org/10.1057/s41260-021-00251-z

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