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European Green Mutual Fund Performance: A Comparative Analysis with their Conventional and Black Peers

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Abstract

We conduct the first comparative analysis of the financial performance of European green, black (fossil energy and natural resource) and conventional mutual funds. Based on a unique dataset of 175 green, 259 black and 976 conventional mutual funds, the investigation contrasts the financial performance of the three dissimilar investment orientations over the 1991–2014 period. Over the full sample period, green mutual funds significantly underperform relative to conventional funds, while no significant risk-adjusted performance differences between green and black mutual funds could be established during the same period. Environmentally friendly investment vehicles display a significant exposure to small cap and growth stocks, while black funds are more exposed to value stocks. Remarkably, the green funds’ risk-adjusted return profile progressively improves over time until no difference in the performance of the green and the conventional classes could be discerned. Further evidence suggests that the green funds are beginning to significantly outperform their black peers, especially over the 2012–2014 investment window.

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Notes

  1. This approach has already been well established in the literature (see as examples, Climent and Soriano 2011; Ito et al. 2013; Ziegler et al. 2007).

  2. Please note that the congruence of the portfolio holdings of the three distinct mutual fund classes has not been tested due to limited information on portfolio stock contents. However, while it is possible that the diversified conventional mutual funds include stocks contained in both the green and black mutual funds, it is highly unlikely that the congruence will be significant enough to bias our results. This is because each conventional mutual fund usually contains about 100–150 stocks on average and the inclusion of one or two black and green stocks each will be enough to capture the gains of diversification. The diversification benefits in the funds should ensure that no one sector dominates returns. Furthermore, given the commonality in the two non-conventional mutual fund classes, fund returns are unlikely to be significantly boosted in the long-term by including several stocks from the same industry in one conventional mutual fund.

  3. The EEA includes EU countries and also Iceland, Liechtenstein and Norway. Membership of this economic bloc allows the non-EU countries access to the EU’s single market. However, Switzerland is neither a EU nor an EEA member but is also part of the single market based on other subsisting treaties.

  4. The official fund documents such as the ‘Key Investor Information Document’ (KIID), the prospectus, the sales brochure or the annual/half-year reports are scanned to identify the true investment objective of each and every single ethical mutual fund. In addition, in the case that uncertainty persists, the concerned fund’s portfolio composition and top holdings are revised so as to increase the level of confidence of the fund class allocation. The documents are received from the publicly available Morningstar or Fundsquare mutual fund database. In the case that the official mutual fund investor documents have only been available in a language other than English, French or German, the translation of the relevant text passage has been performed with the help of Google Translate, and the subsequent green inclusion/abandon decision trusts the tool to yield the correct translation of the generic meaning of the information. We also obtain information from the issuers themselves. The mutual fund emitters’ declarations are believed to be accurate.

  5. The fund names of all primary European equity mutual funds are browsed for keywords such as ‘renewable’, ‘green’, ‘eco’, ‘efficiency’, ‘water’, ‘solar’, ‘wind’, ‘biomass’, ‘environment’ and ‘climate’ as well as their respective synonyms and counterparts in other European languages.

  6. The Lipper Global Classification Schemes screen is used to sift for funds classified under the following categories: ‘Commodity Energy’, ‘Equity Sector Natural Resource’, ‘Equity Sector Utilities’, ‘Commodity Industrial Metals’ and last but not least ‘Commodity Precious Metals’.

  7. The sample is defined to solely comprise mutual funds with a diversified investment strategy through the application of the Lipper filters ‘Equity Europe’, ‘Equity Eurozone’ and ‘Equity Global’.

  8. This exclusion controls for short selling and other alternative trading strategies. Short selling restrictions were implemented in Europe, if at all, for very limited periods and would have the highest impact only on these excluded funds. As summarised by Beber and Pagano (2013), for the period of the global financial crisis, most short selling constraints were lifted within several months following their imposition. Furthermore, it is generally accepted that the majority of open-ended mutual funds engage in long only investment strategies.

  9. The European benchmark is employed for robustness. We aim to account for potential distortions, and also confirm the appropriateness of use and the results of the global benchmark.

  10. In the same spirit, the single-factor computations are repeated employing the S&P Global Natural Resources Index; the results are not presented but are available on request. While the force of expression of all previous indices on the black mutual fund returns is relatively low, the natural resources benchmark notably improves the black class’s \(R^{2}_{\text{ADJ}}\). Nonetheless, it should be noted that the amelioration might be ascribed to the considerably reduced time span for which index data is available (2009–2014). In line with prior estimations, no performance differences between the black fund portfolio and the applied market proxy can be identified. An inter-class interpretation of the regression outcomes is avoided due to the aforementioned line of reasoning.

  11. Nevertheless, we also conduct an analysis based on the matched-pair approach using a reduced sample of funds, with no qualitative difference in the inferences drawn. This is not surprising because the sample size of each of the investigated three mutual fund classes allows for correction of possible distorting fund characteristics such as fund size, age, management and investment policy. If they exist, the biases are expected to average out. Thus, as suggested by the matched-sample robustness analysis, the significance of the study outcomes is not affected. The results from this additional analysis are not presented but are available on request.

  12. Further analysis, conducted to examine whether regional orientation of funds is a factor influencing performance, suggests that the orientation is not a significant factor for our samples. This is not surprising given the high degree of commonality among global financial markets.

  13. We also conduct narrower estimations using mutual funds with only global investment focus for both the 1-factor and multi-factor models. The results obtained are qualitatively similar. This is not surprising since most of the funds have a global investment focus.

  14. We also conduct narrower estimations using only mutual funds with a European investment focus for both the 1-factor and multi-factor models. Although the statistical significance of coefficients obtained from the estimations is generally reduced on account of smaller sample sizes, the overall inferences drawn from the results are qualitatively unchanged. Furthermore, using the Kenneth R. French data library European market portfolio does not yield qualitatively different results; however, the \(R^{2}_{\text{ADJ}}\) values are slightly lower.

  15. \(\beta_{\rm HML}\): −0.51*** (−4.33)—Please note that these results are not shown in any of the tables included in this paper.

  16. We also conduct a Chow-type parameter stability test by obtaining the residual sum of squares for the panel regressions; the results suggest that there are no breakpoints at all the periods tested.

  17. See “Centrica buys into Cuadrilla’s Lancashire fracking licence” in the June 13, 2013 edition of Financial Times.

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Acknowledgments

We thank an anonymous referee and the Finance Section Editor, Gary S. Monroe, for their constructive and helpful feedback. We are grateful to Jo Danbolt, Wenxuan Hou, Kenneth Amaeshi and Francisco Ascui for their helpful comments. All remaining errors are our own.

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Authors and Affiliations

  1. University of Edinburgh Business School, 29 Buccleuch Place, Edinburgh, EH8 9JS, UK

    Gbenga Ibikunle

  2. Centre for Responsible Banking and Finance, University of St Andrews, St Andrews, UK

    Gbenga Ibikunle

  3. Fondazione European Capital Markets Cooperative Research Centre (ECMCRC), Pescara, Italy

    Gbenga Ibikunle

  4. University of Edinburgh, Edinburgh, UK

    Tom Steffen

  5. Macquarie University, Sydney, Australia

    Tom Steffen

  6. Capital Markets Cooperative Research Centre (CMCRC), Sydney, Australia

    Tom Steffen

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  1. Gbenga Ibikunle
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Correspondence to Gbenga Ibikunle.

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Ibikunle, G., Steffen, T. European Green Mutual Fund Performance: A Comparative Analysis with their Conventional and Black Peers. J Bus Ethics 145, 337–355 (2017). https://doi.org/10.1007/s10551-015-2850-7

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