Abstract
Purpose
The aim of this paper is to evaluate the impact of the sustainable financial disclosure regulation (SFDR) on the environmental, social and governance (ESG) performance and risk scores of sustainable funds (SFs) from a multi-regional perspective.
Design/methodology/approach
This research involves conducting a comparative study between self-labeled SFs and conventional funds of the same mutual fund company matched using a five-step process. Using the SFDR publication as a natural study, this study uses panel data methodology on a portfolio ESG score database before SFDR implementation and three to six months post-SFDR Level 1 requirement to measure the impact.
Findings
The findings provide evidence of a clear reduction in ESG risk and an improvement in ESG performance across all samples and ESG dimensions following the SFDR regulation. In addition, the results reveal a positive spillover effect of the regulation on conventional funds following its implementation.
Research limitations/implications
The study can be helpful for fund managers, investors and regulators as it provides insights into the impact of mandatory ESG disclosure regulation on the global fund investment market. The study is limited by data availability due to the restrictive matching approach used, which starts with fund pairs from the same fund management company.
Practical implications
The study can be helpful for fund managers, investors and regulators as it provides insights into the impact of mandatory ESG disclosure regulation on the global fund investment market.
Originality/value
To the best of the authors’ knowledge, there is a lack of research papers that analyze the impact of the SFDR mandatory regulation as a driving force on the ESG scores of the fund market using the same fund management matched pair approach. This paper tests the importance of the investment area through a multi-regional approach to study potential “spillover” effects.
Keywords
Citation
Martinez-Meyers, S., Ferrero-Ferrero, I. and Muñoz-Torres, M.J. (2024), "The European sustainable finance disclosure regulation (SFDR) and its influence on ESG performance and risk in the fund industry from a multi-regional perspective", Journal of Financial Reporting and Accounting, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFRA-03-2024-0150
Publisher
:Emerald Publishing Limited
Copyright © 2024, Susana Martinez-Meyers, Idoya Ferrero-Ferrero and María Jesús Muñoz-Torres.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Sustainable investment funds (thereafter SF) have experienced substantial growth in the past decade due to strong social demand and regulatory support. Fund management companies are increasingly repurposing funds or “greening” them by introducing changes in the name to include words such as sustainable, environmental, social and governance (ESG), green and impact (Ghoul and Karoui, 2020). The growth and the repurposing have raised greenwashing concerns among academics and practitioners.
Investors may not be able to discriminate between greenwashers and genuine ESG investors (Liang et al., 2021). This could be attributed to the lack of harmonized ESG disclosure regulation that creates distortions (Muñoz et al., 2021), as well as the costs associated with sustainability assessment (Darnall et al., 2018). Investors complain about the lack of comparable and verifiable information, which results in the use of time and effort to detect greenwashers, especially among passive funds. Voluntary nonfinancial disclosure has been associated with cherry-picking, the fabrication of positive information (Monciardini et al., 2020; Ferrero-Ferrero et al., 2020) and facilitating the diffusion of greenwashing (Gatti et al., 2019). On the other hand, we are experiencing an increase in mandatory disclosure, which is enforced or promoted by governments and other stakeholders to urge corporations toward sustainability (Ho and Park, 2019). Mandatory ESG disclosure has been associated with improved accurateness of analysts’ forecasts and reduced dispersion (Krueger et al., 2021) and has led to a higher prevalence of sustainable investing (Aghamolla and An, 2021).
The European Union (EU) has been a driver and leader in terms of sustainability-linked regulation and is considered the leader in the implementation of ESG practices (Kaiser, 2020). After adopting the 2015 Paris Climate Agreement, the EU announced the Action Plan on Financing Sustainable Growth (European Commission, 2018; European Commission Press Release, 2018) and the EU Green Deal (European Commission, 2019a, 2019b). These plans had, among others, the objectives of reorienting capital flows toward sustainable investments and enhancing transparency and disclosure in the financial sector. Among these initiatives, our research focuses on the sustainability-related disclosure in the financial services sector regulation (thereafter SFDR), which was published on November 27, 2019, and came into effect on March 10, 2021. The abovementioned EU initiatives are linked and work toward a coordinated effort searching to increase transparency and accountability in the financial system. The SFDR addresses the challenges brought by SF investment approaches, such as the lack of comparability in disclosure that could potentially distort the investors’ decisions when considering these instruments.
1.1 Overview of the sustainable finance disclosure regulation
Sustainability risk is defined in Article 2 of the SFDR as an “environmental, social or governance event or condition that, if it occurs, could cause an actual or a potential material negative impact on the value of the investment” (European Commission, 2019a, 2019b). SFDR will require financial market participants and advisers to follow mandatory disclosures on integrating these sustainability risks, consider adverse sustainability impacts (principal adverse impacts, thereafter PAIs) and make disclosures measurable and comparable (Folqué et al., 2021).
The initial application of the SFDR required that at a product level fund managers must disclose in the precontractual and periodical reports the categorization of their funds into three categories: funds with environmental or social characteristics (Article 8, also called “light green”), funds with sustainable investment objectives (Article 9 or also called “dark green”) and funds that do not meet the requirements of the previous categories, which imply they do not integrate or if so in a small degree sustainability into their investment process (Article 6). Cherry-picking of metrics has been an issue in the sector; therefore, these labels can help investors reduce information and research costs linked to SRI fund investing (Gutsche and Zwergel, 2020). In 2022, the SFDR Level 2, also known as Regulatory Technical Standards (RTS), was introduced as a draft to provide more detailed technical standards and specific disclosure requirements. It included a reference of 14 mandatory indicators (nine indicators of the environmental pillar and five for social and governance) and 46 additional indicators. The final report on the draft of the RTS was published in December of 2023 after a consultation process that included changes to PAI and an extension of the social PAI indicators.
A growing volume of academic papers study the link between ESG disclosure and firm performance (Huang, 2021); however, there is a research gap in the link between mandatory ESG disclosure regulation and ESG score performance of funds. Our research addresses this gap by testing if SFDR has motivated the market to a stronger commitment and has influenced ESG fund scores. We hypothesize that the passage of the SFDR regulation may have increased awareness and incentivized portfolio managers not only of self-labeled SFs but also of conventional funds (CF) from the same fund management companies. To test this hypothesis, following Belghitar et al. (2017), we perform a matched pair approach of SF vs CF from the same fund management firm using SFDR publication as a natural experiment to analyze the effects of this intervention. Amir and Serafeim (2018) pointed out that sustainability regulation and interpretation of ESG as part of fiduciary duty differ depending on the region. Thus, we would test for significant geographical differences in the results depending on the area of investment of the funds.
Our research paper contributes to the growing literature on the impact of mandatory ESG regulations on ESG fund performance. This topic has attracted numerous debates and concerns among practitioners, academics and regulators, such as in the EU (the ESMA and the EU Commission), the UK and the USA (the SEC). First, to the best of our knowledge, there is a lack of papers that analyze the effect of the SFDR mandatory regulations on the ESG scores of self-labeled ESG funds using the same fund management matched pair approach. We have conducted a test on ESG performance from two perspectives: ESG performance from Refinitiv and sustainability risk from Morningstar. Second, as the SFDR is a European regulation and the area of investment is critical, we have used a multiregional approach to observe ESG commitment differences per region and to address potential “spill-over” effects. Our results will contribute to the theoretical understanding of the impact of mandatory ESG disclosure regulations in the global fund investment market. Our findings indicate that the SFDR regulation has led to a clear decrease in ESG risk and an improvement in ESG performance across all samples and for all three pillars. In addition, our research suggests that the SFDR regulation had a positive spillover effect on CFs.
2. Literature review and hypothesis development
2.1 Prior literature
SFDR and previous ESG disclosure regulations have played a crucial role in influencing investor behavior and improving transparency and accountability. We have organized our literature review into two groups: research on ESG disclosure regulation prior to the SFDR and research specifically studying the impact on ESG integration and performance of the SFDR.
2.1.1 Research on environmental, social and governance disclosure regulation prior to the sustainable finance disclosure regulation.
Grewal et al. (2019) used the publication of the nonmandatory financial disclosure regulation of the EU to examine the reaction of the equity markets. They observed that firms with strong ESG disclosure before the entry of force of the regulation exhibit less negative and even positive returns. This suggests that the market perceives the benefits of strong nonfinancial performance and disclosure, which positively influences investor behavior. Santamaria et al. (2021) evaluated the impact of NFRD regulation on the ESG scores of 31 publicly quoted Italian companies and observed an increase in their scores, which highlights the effectiveness and strategic relevance of disclosure.
A different perspective of academic studies is to observe if sustainability disclosure has influenced behavior and acted as a catalyst for change in the sectors or markets in which it was introduced. Mésonnier and Nguyen (2021) studied the impact of the 2016 new French mandatory regulation that required institutional investors to disclose their climate-related exposure and climate change mitigation policy. They observed that these new disclosure requirements reduced financing to fossil fuel energies by 40% vs the control group, thus pointing out that mandatory disclosures can become a driver for change for a whole industry. Jouvenot and Krueger (2019) studied the UK law that mandated publicly listed companies to disclose for the first time their greenhouse emissions in a standardized way and found evidence of firms reducing their emissions by approximately 16% in response to the new disclosure requirements of the regulation. Tomar (2021) observed a similar effect in GHG emissions reductions in the USA in 2010 as a reaction to mandatory reporting of GHG for manufacturing facilities, showing how disclosure and accountability can spur emission reductions. Chen et al. (2018) found that disclosure of mandatory CSR activities in China generated positive externalities for stakeholders (lower CO2 emissions and industrial wastewater).
2.1.2 Research on environmental, social and governance integration and performance of the sustainable finance disclosure regulation.
Moving to academic research related to the SFDR, most studies have observed a positive impact of the SFDR regulation in terms of flows. Becker et al. (2021) studied the impact of the SFDR regulation on funds’ ESG scores with a comparative regional perspective. The authors use a difference-in-differences methodology to compare EU funds vs US-based mutual funds using a 1:1 nearest neighbor to see whether the global universe of European funds experienced an increase in their ESG score. Their results showed an increase in ESG scores and fund net inflows for the EU fund group after the policy announcement vs the USA. Emiris et al. (2023) researched the impact of the implementation of SFDR on portfolio allocation. They observed increased flows to ESG funds after the regulation, which were higher in countries with stronger environmental preferences.
Academic research has also concentrated on analyzing the significance of SFDR labels. Ferriani (2022) conducted a study comparing the informativeness of ESG risk metrics provided by Morningstar Globes versus SFDR levels. The study observed that regulation-induced labels were not relevant in explaining flow heterogeneity, except for Article 9. Cremasco and Boni (2022) conducted a similar study comparing SFDR Article 9 funds versus Article 6 and found surprisingly that both categories were behaving in similar terms from a financial and sustainability perspective.
2.3 Hypothesis development
The literature suggests that ESG corporates may be pressured to live to the expectation that their actions are appropriate within societal forms to obtain legitimacy (Suchman, 1995). In this context, corporations could be using environmental disclosure to legitimize their actions (Guthrie and Parker, 1989). Voluntary CSR disclosure may be used as a proactive tactic to maintain legitimacy, showing the link between sustainability reporting and sustainability practices (Esterhuyse, 2019). However, voluntary disclosure has also been seen as a way of manipulating society so scrutiny will relax and be satisfied by symbolic environmental actions (Mobus, 2005). Jiang et al. (2023) found evidence from 2012 to 2015 of a sample of S&P 500 that companies were using carbon emissions disclosure as a legitimizing tool, specifically in high carbon intensity sectors. Therefore, companies’ motivations for disclosing ESG information may not be straightforward. Certain companies may use public disclosure to reduce social pressure in case of failure to comply with the “social contract” (Tamimi and Sebastianelli, 2017), as in the case of controversial industries that dedicated a higher ratio of their reports to social and community than environmental trying to legitimate their operations (Byrd et al., 2017). This is aligned with Grougiou et al.'s (2016) findings that “sin industries” were more likely to release a CSR report.
The EU directive 2014/95 of nonfinancial information (NFRD) has provided credibility and legitimacy to corporate nonfinancial disclosure (Mazzotta et al., 2020). Applying legitimacy theory to the SFDR regulation could improve the legitimacy of funds after the concerns raised by practitioners and the financial press. Furthermore, SFDR will increase the perception of accountability for fund managers, which can become a driver for sustainable finance (Jansson and Biel, 2011). Given the evidence from the literature and the link between ESG disclosure and the legitimacy theory, we want to test the spillover effect of the SFDR regulation over the entire fund sector. Therefore, we wish to contribute to the growing debate about the impact of mandatory ESG disclosure regulation by testing the following hypothesis:
The application of SFDR regulation has positively impacted the ESG scores of funds and its pillars.
Voluntary ESG disclosure may be used by corporations to signal future financial prospects of the firm and “good news” (Rezaee, 2016), which relates to the signaling theory (Grinblatt and Hwang, 1989). Companies with superior ESG performance may have a higher incentive to engage in voluntary disclosure (Dainelli et al., 2013) as there may be an association between CSR investments and future firm performance (Lys et al., 2015) and to signal their sustainability achievements as good corporate citizens through their sustainability reports (Christensen et al., 2021). Certifications can increase the credibility of product claims, build trust and reduce information asymmetry (Erdem and Swait, 1998).
The signaling theory may provide a solid framework for interpreting ESG disclosure of funds. SFs have a commitment to the clients through the investment mandate (Dolvin et al., 2019) and failing to meet this obligation could potentially lead to agency conflict (Gangi and Varrone, 2018). We expect that the self-labeled SF group may have felt the pressure of the new regulation and might have been incentivized to make a stricter commitment to sustainability. The SFDR regulation categories could become a signaling tool for the commitment of SF funds to transparency (Ioannou and Serafeim, 2014) in a market that has been occasionally associated with greenwashing and mis-selling:
The “self-declared or published” SF have experienced a higher increase in their ESG scores than their conventional counterparts.
The literature has identified that institutional and geographical contexts can determine sustainability disclosure. Organizations respond to pressure from their institutional environments (DiMaggio, 1988). Institutional theory views organizations as operating within a framework that has assumptions about what constitutes appropriate behavior and that influences their structure and practices (Carpenter and Feroz, 2001). The institutional theory can serve as a third framework for our research, as institutional backgrounds from different countries can also affect accounting and CSR practices (Deegan, 2009), the reliability of ESG disclosure (Yu et al., 2020) and ESG performance (Ortas et al., 2015).
The perception of sustainability and fiduciary duty is different depending on the region. Amir and Serafeim (2018) point out that a bigger percentage of US investors believe that ESG information is irrelevant than European investors. Code law countries have shown a higher commitment to CSR as part of stakeholder commitment vs common law countries like the USA (de Villiers and Marques, 2016) and show a higher adoption of mandatory ESG regulation (Krueger et al., 2021). Higher levels of CSR disclosure could be related to countries with higher regulation and investor protection, where managers must show greater conformity with social norms (de Villiers and Marques, 2016) or countries with a stronger environmental agenda (Glennie and Lodhia, 2013). Accordingly, we expect fund management companies with headquarters in the EU to have felt more strongly the pressure of the SFDR Regulation:
The portfolios of the “self-declared or published” SF funds with headquarters in the EU have experienced a stronger relative increase in their ESG scores as a result of SFDR regulation.
3. Data and methodology
3.1 Data collection
The choice of the funds is based on self-labeled SFs, where their fund name includes terms such as ESG, SRI, sustainable, social, ethic, green, clean, carbon, SDG, climate, responsibility, sustainability or ethical (related to the approach used by Takahashi and Yamada, 2021). Through self-labeled SF names, the funds voluntarily demonstrate their commitment to address ESG considerations (Joliet and Titova, 2018). Within the investment universe, we focus on active (vs passive) equity (vs fixed income or alternative asset classes) as this specific group represents the biggest part of the fund investment world.
We use the fund management tools offered on Morningstar’s website for our data collection. Once we arrive at a selection of “self-labeled” SFs during the first period of the sample, we match them using a matched pair approach introduced by Mallin and Saadouni (1995). Our matching starts with funds from the same fund management company, as studies have shown it plays a major role (Belghitar et al., 2017). We use a one vs one approach and the following matching criteria: same management fund company, geographical area of investment, investment size and style according to the nine-grid box from Morningstar, and finally, fund age and size. We do not use the most recent proposed matching approaches of propensity score matching (Alda, 2018; Ammann et al., 2019; Bilbao-Terol et al., 2017; Day et al., 2016; Ghoul and Karoui, 2020; Joliet and Titova, 2018) as our paper focus on differences in funds by the agents (fund management co) reacting to the disclosure requirements proposed by the SFDR. For the whole universe screened, we arrive at a sample of 71 matched pairs of funds (a total of 142 funds), of which 56 funds are categorized as Article 8 (39.4% of the total) and 12 funds are categorized as Article 9 (8.5% of the total). The rest are categorized as Article 6 (25 funds – 17.6% of total) or 49 funds (34.5% of total), which are not categorized because they are based on areas that did not require categorization.
After the matching process, we gather ESG fund scores from different providers for each of the pairs at three different time points. ESG fund scores transmit information on sustainability performance to investors. There is evidence that investors value sustainability positively, with causal evidence that demand for funds is affected by sustainability ratings (Hartzmark and Sussman, 2019). We use two different ESG fund score providers due to the ongoing debate around the divergence of ESG rating agencies (Berg et al., 2019, 2020; Dimson et al., 2020). ESG scores are low-frequency observation data points and contain a higher degree of qualitative data. Due to the different publication frequencies, we use Morningstar as a reference for our time points. We allow for a ±2-month difference for the Eikon database compared to Morningstar data. We obtained three data points for the ESG scores: the first data is from October 2020 (approximately six months before the entry into force of the SFDR), the second point is from June 2021 (approximately three months after the entry into force of SFDR Level 1) and our last data point is October 2021 (six months after the entry in to force SFDR). These three points represent the time dimension that we include in the panel.
3.2 Description of variables and methodology
This study tested the hypothesis using two different statistical methods with the aim of providing more robust results. First, this study applies an analysis of variance (ANOVA) method due to the categorical nature of independent variables (Dey et al., 2018): SF vs CF, before the entry into force of the SFDR vs after, headquarters located in EU vs other regions and their interactions. This study conducts separate analyses for sustainability performance, sustainability risk and each dimension of sustainability.
Second, this study estimates the linear regression model shown in equation (1) with the aim of testing the abovementioned hypotheses. In this case, the study uses panel data methodology with the aim of addressing the existence of latent unobservable effects specific to each fund. In particular, this study has applied the generalized least square random effect (RE) technique. This study has selected RE as the model includes dummy variables that do not change during the sample period and cannot be estimated under the fixed effect technique. This estimator uses the variation within data, and therefore, the variables that are constant along the sample period are always zero, i.e. they are colinear, and thus, they are removed from the estimation sample (Pulido Pavón, 2015). We compare the pooled OLS and REs using the Breusch–Pagan LM test (Tables 4 and 5) (Azam et al., 2021; Grozdić et al., 2020). The results show that the use of the RE technique is appropriate at a 1% significance level. Moreover, this study analyzes the robustness of the results using a model without constant dummy variables and using the FE technique.
In addition, the potential problem of multicollinearity has been explored by means of the variance inflation factors (VIF). The VIF values are below 10; therefore, multicollinearity is not a concern (Allison, 2012; Ferrero-Ferrero et al., 2016):
The variables included in equation (1) are consistent with previous research (Becker et al., 2021). The dependent variable of this study describes the sustainability rating or ESG score of the fund i at time frame t. For the ESG score, as mentioned before, we use two providers of information. ESG performance Refinitiv score, which is the higher the score, the better (used by Gangi and Varrone, 2018; Nitsche and Schröder, 2018; Madhavan et al., 2021). Sustainability risk from Morningstar is based on Sustainalytics data, and for this score, the lower, the better, as it measures risk (used by Alda, 2020; Becker et al., 2021; Joliet and Titova, 2018; Kim and Yoon, 2020; Nitsche and Schröder, 2018).
The independent variable is SELF SF, which is a dummy variable that takes the value 1 if the fund is self-labeled as sustainable finance. The variable POST used to test the first hypothesis is a dummy variable, which will be 1 if the information is after the entry in force of the SFDR regulation (March 2021) and 0 if it is before the entry in force of the SFDR. To test the second hypothesis, we use the dummy variable SELFSF*POST, which equals 1 if the fund i is a self-labeled SF fund and for data points after the entry in force of the SFDR. To test the third hypothesis of geographical differences, we include the dummy variable EUHEAD*POST, which equals 1 if the fund i has headquarters in the EU and it is affected by the entry in force of the SFDR legislation. This study also introduces the dummy variable SELFSF*EUHEAD*POST to explore if, in the previous relationship, the funds that are self-labeled as sustainable finance could have an additional influence on the dependent variable.
The study includes several control variables that are included in similar models presented in previous studies (Martínez Meyers et al., 2024). We include the variable investment size (mid-size is included, large size is the dummy variable omitted) and style (growth style and value style are included; blend style is the dummy variable omitted), which has been observed to have explanatory value for ESG scores mainly for environmental scores (Madhavan et al., 2021; Ramos et al., 2023). The performance control variable measures one-year performance. We also include a control variable for size, measured as a log of total net assets of a given fund i under the fund’s management at a point as fund size used in other studies as size has been proven to be a significant variable affecting fund performance (Chen et al., 2004; Ding et al., 2021). This study has also included a dummy regarding the time frame that refers to the second point: June 2021. Note that we have not included any additional time dummy for addressing multicollinearity problems. The data for the control variables are obtained for all funds on the same dates for the three-time frames using the Refinitiv database.
4. Results
Table 1 displays the descriptive statistics of the variables included in the empirical analyses. The average ESG performance (sustainability risk) of funds is 68.3310 (21.1869). The environmental pillar has the lowest contribution to the combined sustainability risk. Governance is the pillar that shows the lowest dispersion (standard deviation) in both approaches, which could be related to the higher consensus on governance measurements due to regulatory standards that resulted from the financial crisis (Gibson et al., 2020). Table 2 contains Pearson’s pair-wise correlation matrix for the variables presented in two subtables, given the nature of the relationships depending on whether they focus on sustainable risks or ESG performance. The number of observations for each variable is different depending on the availability of information and the different sources of information: Morningstar for sustainability risks and Refinitiv for ESG performance and control variables.
Tables 3 and 4 present the results of the ANOVA analysis and Tables 5 and 6 display the regression analysis results using panel data analysis (see Tables Annex 1 and Annex 2 for robustness analysis). Focusing on H1, both analyses (ANOVA and panel data) find evidence to support it and reveal a clear reduction of ESG risk (an increase in ESG performance) after the entry into force of the SFDR regulation for all the samples analyzed and for the three dimensions of the ESG. We could argue that SFDR may have worked as a driver for the fund industry as a whole, and thus, this study supports H1.
Focusing on H2, the results of the ANOVA do not find a significant difference in terms of the ESG score between self-labeled SF and CF after the entry into force of SFDR regulation. Surprisingly, the panel data analysis shows that in aggregated terms for all the periods studied, the SELF SF has a higher sustainability risk (lower sustainability performance). Contrary to our initial expectations, after the application of the SFDR regulation, the “self-labeled” SF affects sustainability risk (performance) with a lower intensity than CF. The decrease in scores of SELF SF POST funds (after the entry of force of the SFDR) could be interpreted as CF experiencing a relative decrease in their ESG risk (increase in their ESG performance scores). The SFDR legislation may have increased awareness of ESG risks and affected the rest of the funds. Jouvenot and Krueger (2019) observed a similar effect where firms that were higher emitters due to additional transparency coming from the disclosure regulation pushed managers to reduce GHG emissions. We could argue that “self-labeled” SF funds improved their ESG scores (reduced their ESG risk) as our first data point (October 2020) reacting ahead of the entry into force of the SFDR and as a reaction to the announcement and then have relaxed (decrease of ESG scores post-SFDR), whereas CF in the same company have benefited from this increased awareness and implementation of new internal procedures that are required by Fund Management companies to be able to comply with the requirements. Becker et al. (2021) pointed out that the fund managers had time since 2019 to adjust and increase the ESG alignment of their portfolios.
Considering the relationship of EU headquarters, this study shows that funds invested in Europe show a lower sustainability risk score (higher sustainability performance) than their counterparts. However, if this study focuses on the third hypothesis, exploring the differences between self-labeled SF funds and CF after the entry into force of SFDR regulation, the results show that there are no statistical differences (ANOVA analysis). With respect to the panel data, we do not find that “self-declared” SF with headquarters in the EU experience a stronger increase in their ESG scores post-SFDR in terms of risk. Nonetheless, in terms of ESG performance, this study has found slight evidence regarding a positive effect on ESG score after the SFDR of those funds from the EU and “self-declared” SF. Therefore, regarding H3, this study obtains mixed results. This study does not support the hypothesis in terms of sustainability risks; however, in the panel data, we have obtained slight evidence regarding using sustainability performance and in the environmental and social dimensions. The results of the ANOVA analysis are not consistent with these findings. Thus, it does not allow us to state the relationships examined with a high level of certainty. These mixed results contribute to the doubt about the effectiveness of the SFDR regulation on the SF in the EU market and open new questions to explore this effect in more detail in future studies with more longitudinal data.
The findings regarding the three hypotheses are consistent with the results obtained from the robustness analysis (Table Annex 1 and Annex 2).
5. Discussion
Our findings for H1 indicate that mandating ESG disclosure through SFDR led to a reduction in the overall risk on ESG scores and an increase in ESG performance for all markets. This resulted in a greater prevalence of sustainable investing, producing a positive spillover effect (Admati and Pfleiderer, 2000). New disclosure requirements not only result in information for investors and stakeholders but may influence real firm decisions and how the agents allocate resources (Kanodia and Sapra, 2016). A shift from voluntary to mandatory environmental regulation has resulted in greater ESG activity (Jouvenot and Krueger, 2019) and even the anticipation of future regulation can motivate a response (Tomar, 2021). Our results are consistent with Dario et al. (2021), who stated that ESG regulation will pressure companies with lower disclosure and allow for better comparability vs the best-practice firms.
In regard to H2, contrary to our expectations, we saw relatively better behavior from CF. We could argue that the regulation is acting on CF as they have a lower incentive to deviate from market expectations as they are forced to additional disclosure (Grewal et al., 2019) and cannot hide poor ESG performance under a lack of available information (Aghamolla and An, 2021). The SFDR reporting mandate includes disclosure requirements at the firm level that may also push their CF. Fund sale distributing platforms are shifting toward sustainability products because of investment constraints from institutional investors (Hartzmark and Sussman, 2019). Jouvenot and Krueger (2019) observed that the prospect of lower intuitional ownership served as an incentive to reduce GHG emissions. It is possible that self-labeled SF reacted in anticipation of SFDR’s entry into force in March 2021, as the announcement was made in November 2019, and our first data point is from October 2020.
Our results show no significant difference in the ESG score between self-labeled SF and CF after the entry into force of SFDR. This may be linked to the lack of clarity and fuzziness between the different fund categories (Cremasco and Boni, 2022). This may have resulted in some fund managers overstating their sustainability commitments, whereas others may understate their sustainability commitments due to the high disclosure costs or the “greenness” they want to signal to the market (Emiris et al., 2023). Brito-Ramos et al. (2024) observed that signals from labels for SFs were not always coherent, which could jeopardize their role. These arguments are also consistent with the results of H3; although this study finds that the investment funds with EU headquarters show a lower sustainability risk score (higher sustainability performance), this relationship is not intensified by the entry into force of SFDR regulation or by those funds that are self-labeled SF. This result is supported by Martínez Meyers et al. (2024), who observed that those areas with strong institutional support exhibited better ESG fund performance.
Our research provides implications for the multiple stakeholders needed to address the sustainability challenge. The EU SFDR has proven to be a driver and a catalyst for ESG score improvement in the fund sector. However, our mixed findings support the call for clearer regulation to reduce potential greenwashing (Gatti et al., 2019). The lack of difference between categories may indicate that the system is failing to help investors differentiate between the more sustainable alternatives. Our suggestion of clearer guidelines for defining “sustainable investment” and linking it to the EU taxonomy definitions (Quirici, 2023) is in line with the report published in 2024 by ESMA, “Guidelines on fund´s names using ESG or sustainability-related terms”. The regulation establishes a minimum threshold of at least 80% of the investment that must meet ESG or sustainability objectives to use ESG or sustainability-related names. These proposed new EU steps align with the SEC “Names Rules” and the UK Sustainability Disclosure Requirements and aim for increased transparency and accountability. The investors’ trust needs to be protected and these steps will help reduce ambiguity and potential greenwashing in the globalized fund industry.
6. Conclusion
We have seen that with the growth of SFs, concerns about lack of disclosure, transparency and potential cases of greenwashing have increased. The EU has decided to tackle this issue with the SFDR legislation that requires mandatory disclosure of PAI and categorization of funds. In our study, we addressed the effect that the mandatory ESG disclosure regulation (SFDR) had on the ESG investment behavior of fund managers. Our study highlights that SFDR has worked as a driving force for sustainable development in the funds market, as the results show a clear reduction of ESG risk. SF may have anticipated the effect of SFDR as the time of the announcement of the legislation, and we have seen a spillover effect of the regulation on CF, which experienced better relative performance on their ESG scores after the entry of force of SFDR regulation. Our findings may interest regulators who are considering mandatory ESG regulations and their consequences (Leuz and Wysocki, 2016), as well as the need for clear standards for self-labeled funds.
Several limitations apply to our results. First, as we describe, the same fund management company matching approach affects our final sample size. However, we believe the approach is justified for the research’s purpose and the variable’s significance (Belghitar et al., 2017). Second, our results may be affected by the timing of the sample. COVID-19 created notable turbulence in the financial markets. As Christensen et al. (2021) point out, new regulation does not occur in a vacuum, and results of event studies may be affected by confounding events and economy-wide shocks. To extrapolate the findings to other markets a more extensive longitudinal sample could be conducted. There are interesting new avenues of research, including analyzing the reasons behind the category fuzziness and the disclosures from the PAI.
A mandatory regulation will decrease greenwashing (Gatti et al., 2019; Seele and Gatti, 2017); however, for this move to work, we acknowledge the need for harmonized global regulation as currently there is no standardized reporting framework where information is scalable and comparable (Christensen et al., 2021; Dario et al., 2021). Verification (del Giudice and Rigamonti, 2020) and labels will reduce information-related barriers (Gutsche and Zwergel, 2020) that will help maintain the trust and confidence of the investment community that may have been damaged by the abovementioned concerns.
Descriptive statistics
| Obs. | Mean | S.D. | 25th P. | 50th P. | 75th P. | Skw. | Kurt. | |
|---|---|---|---|---|---|---|---|---|
| SUS_RISK | 421 | 21.1869 | 2.0310 | 19.79 | 21.08 | 22.45 | 0.4209 | 3.0139 |
| ENVI_RISK | 421 | 3.8364 | 0.8467 | 3.28 | 3.75 | 4.3 | 0.7571 | 4.2031 |
| SO_RISK | 421 | 8.9311 | 0.9991 | 8.32 | 8.96 | 9.62 | −0.2895 | 3.4293 |
| CG_RISK | 421 | 7.3225 | 0.7765 | 6.87 | 7.22 | 7.72 | 0.4818 | 3.9177 |
| ESG_PERFORM. | 369 | 68.3310 | 6.6329 | 64.3873 | 68.8360 | 72.8615 | −0.7268 | 4.2007 |
| ENVI_PERFORM. | 369 | 64.7537 | 9.0183 | 58.9850 | 65.5673 | 70.6177 | −0.6095 | 3.5524 |
| SO_PERFORM. | 369 | 72.8063 | 7.2084 | 68.3407 | 72.9508 | 77.9117 | −0.6859 | 3.9594 |
| CG_PERFORM. | 369 | 64.5321 | 5.2157 | 61.6566 | 65.0913 | 68.0206 | −1.0329 | 6.7371 |
| SELF SF | 426 | 0.5 | 0.5006 | 0 | 0.5 | 1 | 0 | 1 |
| POST | 426 | 0.6667 | 0.4720 | 0 | 1 | 1 | −0.7071 | 1.5 |
| EUHEAD | 426 | 0.4085 | 0.4921 | 0 | 0 | 1 | 0.3725 | 1.1388 |
| GROWTH STYLE | 426 | 0.3099 | 0.4629 | 0 | 0 | 1 | 0.8223 | 1.6766 |
| VALUE STYLE | 426 | 0.1338 | 0.3408 | 0 | 0 | 0 | 2.1513 | 5.6282 |
| MID SIZE | 426 | 0.0352 | 0.1845 | 0 | 0 | 0 | 5.0435 | 26.4365 |
| FINANCIAL_PERF. | 364 | 3.7364 | 8.9353 | 0.1032 | 0.2035 | 1.9327 | 2.3076 | 9.8846 |
| SIZE | 368 | 16.4537 | 2.9439 | 15.3598 | 17.0223 | 18.3572 | −1.1652 | 4.5522 |
The variables are sustainability risk (SUS_RISK), environmental risk (ENVI_RISK), social risk (SO_RISK), corporate governance risk (CG_RISK), ESG performance (ESG_PERFORM.), environmental performance (ENVI_PERFORM.), social performance (SO_PERFORM.), corporate governance performance (CG_PERFORM.), fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), growth style (GROWTH STYLE), value style (VALUE STYLE), investment mid-size (MID SIZE), financial 1-year performance (FINANCIAL_PERF.), fund size (SIZE). Data provided for risk variables from MORNINGSTAR, for performance and control variables from REFINITIV
Source: Authors’ own creation
Correlation matrix – sustainability risk
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) SUS_RISK | |||||||||||
| (2) ENVI_RISK | 0.55*** | ||||||||||
| (3) SO_RISK | 0.54*** | 0.06 | |||||||||
| (4) CG_RISK | 0.73*** | 0.34*** | 0.59*** | ||||||||
| (5) SELF SF | −0.32*** | −0.18*** | −0.30*** | −0.22*** | |||||||
| (6) POST | −0.41*** | −0.09** | −0.32*** | −0.26*** | 0.00 | ||||||
| (7) EUHEAD | −0.13*** | 0.12** | −0.04 | −0.06 | −0.00 | −0.00 | |||||
| (8) GROWTH STYLE | −0.08* | −0.27*** | −0.23*** | −0.17*** | 0.00 | −0.00 | −0.22*** | ||||
| (9) VALUE STYLE | 0.15*** | 0.36*** | 0.14*** | 0.13*** | −0.15** | 0.00 | 0.26*** | −0.26*** | |||
| (10) MID SIZE | −0.02 | 0.10** | −0.23*** | −0.27*** | 0.04 | 0.00 | −0.00 | 0.20*** | −0.08 | ||
| (11) FINANCIAL_PERF. | 0.20*** | −0.18*** | 0.23*** | 0.09* | 0.03 | −0.55*** | −0.18*** | 0.25*** | −0.21*** | −0.02 | |
| (12) SIZE | −0.02 | −0.04 | 0.02 | −0.02 | −0.14*** | 0.11** | −0.01 | 0.02 | −0.16*** | −0.00 | −0.01 |
| Correlation matrix – sustainability performance | |||||||||||
| (1) ESG_PERFORM. | |||||||||||
| (2) ENVI_PERFORM. | 0.97*** | ||||||||||
| (3) SO_ PERFORM. | 0.98*** | 0.95*** | |||||||||
| (4) CG_ PERFORM. | 0.89*** | 0.83*** | 0.80*** | ||||||||
| (5) SELF SF | 0.18*** | 0.19*** | 0.18*** | 0.13** | |||||||
| (6) POST | 0.15** | 0.14** | 0.15** | 0.13** | 0.00 | ||||||
| (7) EUHEAD | 0.34*** | 0.34*** | 0.33*** | 0.32*** | −0.00 | 0.04 | |||||
| (8) GROWTH STYLE | −0.46*** | −0.47*** | −0.46*** | −0.42*** | 0.02 | −0.05 | −0.32*** | ||||
| (9) VALUE STYLE | 0.15** | 0.17*** | 0.13** | 0.15** | −0.12** | −0.08 | 0.32*** | −0.25*** | |||
| (10) MID SIZE | −0.19*** | −0.17*** | −0.22*** | −0.09 | 0.02 | 0.00 | 0.01 | 0.18*** | −0.07 | ||
| (11) FINANCIAL_PERF. | −0.42*** | −0.44*** | −0.39*** | −0.35*** | 0.02 | −0.53*** | −0.23*** | 0.26*** | −0.20*** | −0.04 | |
| (12) SIZE | 0.00 | −0.00 | −0.00 | 0.01 | −0.16** | 0.18* | 0.01 | −0.00 | −0.18*** | −0.06 | −0.02 |
Notes: The table shows the Pearson’s pair-wise correlation matrix. Statistically significant at 1% (***), 5% (**) and 10% (*). The variables are sustainability risk (SUS_RISK), environmental risk (ENVI_RISK), social risk (SO_RISK), corporate governance risk (CG_RISK), fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), growth style (GROWTH STYLE), value style (VALUE STYLE), investment mid-size (MID SIZE), financial one-year performance (FINANCIAL_PERF.), fund size (SIZE). Data was provided for risk variables from MORNINGSTAR and control variables from REFINITIV. Correlation among control variables slightly differs from Table 2A to 2B due to sample sizes.
The table shows the Pearson’s pair-wise correlation matrix. Statistically significant at 1% (***), 5% (**) and 10% (*). The variables are ESG performance (ESG_PERFORM.), environmental performance (ENVI_PERFORM.), social performance (SO_PERFORM.), corporate governance performance (CG_PERFORM.), fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), growth style (GROWTH STYLE), value style (VALUE STYLE), investment mid-size (MID SIZE), financial 1-year performance (FINANCIAL_PERF.), fund size (SIZE). Data provided for performance and control variables from REFINITIV. Correlation among control variables slightly differs from Table 2A to 2B due to sample sizes
ANOVA estimates for sustainability risk – Morningstar
| Explanatory variables | Sustainability risk | Environmental risk | Social risk | Corporate governance risk |
|---|---|---|---|---|
| SELF SF | 51.35*** | 16.35*** | 38.35*** | 17.68*** |
| POST | 95.72*** | 3.73* | 50.03*** | 31.48*** |
| EUHEAD | 7.31*** | 6.54** | 0.67 | 0.96 |
| SELF SF*POST | 0.22 | 0.23 | 0.43 | 0.09 |
| EUHEAD*POST | 0.41 | 0.08 | 0.04 | 0.85 |
| SELF SF*EUHEAD*POST | 0.09 | 1.43 | 1.67 | 0.82 |
| Model | 23.59*** | 4.09*** | 14.65*** | 8.17*** |
| N. obs. | 421 | 421 | 421 | 421 |
The table shows the F-statistic from the ANOVA estimation of the data is provided from Morningstar.
The explanatory variables are fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POST).
Statistically significant at 1% (***), 5% (**) and 10% (*)
ANOVA estimates for sustainability performance – Refinitiv
| Explanatory variables | Sustainability performance | Environmental performance | Social performance | Corporate governance performance |
|---|---|---|---|---|
| SELF SF | 8.82*** | 10.24*** | 8.42*** | 3.57* |
| POST | 5.99** | 4.68** | 5.29** | 4.68** |
| EUHEAD | 33.14*** | 35.41** | 31.67*** | 27.50*** |
| SELF SF*POST | 0.02 | 0.13 | 0.01 | 0.22 |
| EUHEAD*POST | 0.01 | 0.07 | 0.01 | 0.25 |
| SELF SF*EUHEAD*POST | 0.84 | 1.31 | 0.67 | 1.24 |
| Model | 7.77*** | 8.20*** | 7.30*** | 6.19*** |
| N. obs. | 268 | 268 | 268 | 268 |
The table shows the F-statistic from the ANOVA estimation of the data is provided from Refinitiv.
The explanatory variables are fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POST).
Statistically significant at 1% (***), 5% (**) and 10% (*)
Source: Authors’ own creation
Regression results – sustainability risk – Morningstar
| Explanatory variables | Sustainability risk | Environmental risk | Social risk | Corporate governance risk |
|---|---|---|---|---|
| SELF SF | −1.4741***(0.3255) | −0.4470***(0.1453) | −0.8052***(0.1588) | −0.4307***(0.1348) |
| POST | −2.2044***(0.1601) | −0.3758***(0.0859) | −0.8621***(0.0966) | −0.5876***(0.0671) |
| EUHEAD | −0.6384*(0.3412) | −0.0156(0.1524) | −0.1157 (0.1667) | −0.0963(0.1413) |
| SELF SF*POST | 0.3236*(0.1848) | 0.2400**(0.0987) | 0.2021*(0.1109) | 0.1205(0.0774) |
| EUHEAD*POST | 0.1389(0.2174) | 0.1637(0.1161) | −0.0107(0.1304) | −0.0220(0.0911) |
| SELF SF*EUHEAD*POST | −0.3239(0.2815) | −0.1948(0.1485) | −0.0300(0.1664) | −0.1111(0.1179) |
| GROWTH STYLE | −0.3770(0.3494) | −0.4511***(0.1526) | −0.4616***(0.1661) | −0.2182(0.1444) |
| VALUE STYLE | 0.5762(0.4715) | 0.6101***(0.2060) | 0.1804(0.2243) | 0.0954(0.1950) |
| MID SIZE | 0.0287(1.0100) | 1.0301**(0.4408) | −0.8205*(0.4800) | −0.9977**(0.4176) |
| FINANCIAL PERFORMANCE | −0.0148**(0.0060) | −0.0058*(0.0032) | 0.0016(0.0036) | −0.0052*(0.0025) |
| SIZE | −0.0067(0.0406) | −0.0037(0.0193) | 0.0173(0.0212) | −0.0071(0.0170) |
| TIME DUMMY | 0.3420***(0.0868) | 0.0367(0.0468) | 0.1706***(0.0527) | 0.1237***(0.0364) |
| CONSTANT | 23.7420***(0.7687) | 4.3793***(0.3613) | 9.7035***(0.3972) | 8.1641***(0.3196) |
| R2 overall | 0.3237 | 0.2646 | 0.3127 | 0.2062 |
| WALD χ2 statistic | 669.45*** | 67.18*** | 326.04*** | 283.84*** |
| Spec. testBreusch–Pagan | 212.33*** | 154.91*** | 159.38*** | 219.24*** |
| N. obs. | 317 | 317 | 317 | 317 |
The table shows the results of the GLS-RE estimation. The risk variables data is provided from MORNINGSTAR and control variables data is provided from REFINITIV.
The explanatory variables are fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POST), growth style (GROWTH STYLE), value style (VALUE STYLE), investment mid-size (MID SIZE), financial one-year performance (FINANCIAL_PERF.), fund size (SIZE).
Standard errors are in brackets. Statistically significant at 1% (***), 5% (**) and 10% (*)
Source: Authors’ own creation
Regression results – ESG performance –Refinitiv
| Explanatory variables | ESG performance | Environmental performance | Social performance | Corporate governance performance |
|---|---|---|---|---|
| SELF SF | 3.7134***(1.1207) | 4.9783***(1.5306) | 3.8519***(1.2892) | 1.8712**(0.8290) |
| POST | 3.4624***(0.6114) | 4.0518***(0.8744) | 3.6227***(0.6810) | 1.7981**(0.7438) |
| EUHEAD | 2.5558**(1.1924) | 3.6345**(1.6284) | 2.7060**(1.3717) | 1.4590(0.8845) |
| SELF SF*POST | −2.8857***(0.7015) | −4.2587***(1.0027) | −2.6763***(0.7816) | −1.9494**(0.8453) |
| EUHEAD*POST | −0.6901(0.7931) | −1.6104(1.1336) | −0.6393(0.8836) | 0.6929(0.9542) |
| SELF SF*EUHEAD*POST | 1.9670*(1.0359) | 3.5973**(1.4776) | 2.3801**(1.1557) | 1.0691(1.1979) |
| GROWTH STYLE | −4.9812***(1.1988) | −6.7650***(1.6329) | −5.2392***(1.3813) | −3.3120***(0.8431) |
| VALUE STYLE | 1.8648(1.7846) | 2.9191(2.4311) | 1.9162(2.0561) | 0.7123(1.2574) |
| MID SIZE | −2.3216(3.7257) | −3.0145(5.0663) | −3.9251(4.2977) | −0.4038(2.5375) |
| FINANCIAL PERFORMANCE | −0.0460**(0.0208) | −0.0887***(0.0297) | −0.0622***(0.0232) | −0.0323(0.0245) |
| SIZE | 0.1367(0.1360) | 0.1883(0.1897) | 0.0847(0.1541) | 0.0771(0.1170) |
| TIME DUMMY | −0.7514**(0.3457) | −0.6247(0.4950) | −1.1071***(0.3847) | −0.9030**(0.4300) |
| CONSTANT | 64.3218***(2.5114) | 59.7146***(3.4897) | 69.5816***(2.8526) | 62.7478***(2.1133) |
| R2 overall | 0.3387 | 0.3539 | 0.3158 | 0.3164 |
| WALD χ2 statistic | 169.75*** | 146.72*** | 172.49*** | 78.11*** |
| Spec. testBreusch–Pagan | 92.23*** | 82.06*** | 118.94*** | 55.44*** |
| N. obs. | 189 | 189 | 189 | 189 |
The table shows the results of the GLS-RE estimation. The data is provided from REFINITIV.
The explanatory variables are fund self-labeled as sustainable finance (SELF SF), period post-entry in force of the SFDR regulation (POST), fund with EU headquarter (EUHEAD), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POST), growth style (GROWTH STYLE), value style (VALUE STYLE), investment mid-size (MID SIZE), financial one-year performance (FINANCIAL_PERF.), fund size (SIZE).
Standard errors are in brackets. Statistically significant at 1% (***), 5% (**) and 10% (*)
Source: Authors’ own creation
Regression results – sustainability risk – Morningstar – robustness analysis
| Explanatory variables | Sustainability risk | Environmental risk | Social risk | Corporate governance risk |
|---|---|---|---|---|
| POST | −2.2073***(0.1615) | −0.3480***(0.0859) | −0.8929***(0.0979) | −0.5879***(0.0679) |
| SELF SF*POST | 0.2909(0.1889) | 0.2187**(0.1027) | 0.2063*(0.1146) | 0.1221(0.0794) |
| EUHEAD*POST | 0.1370(0.2217) | 0.1307(0.1205) | 0.0397(0.1344) | −0.0098(0.0932) |
| SELF SF*EUHEAD*POST | −0.2795(0.2947) | −0.1413(0.1602) | −0.1187(0.1787) | −0.1406(0.1239) |
| FINANCIAL PERFORMANCE | −0.0159**(0.0062) | −0.0047(0.0033) | 0.0005(0.0037) | −0.0048*(0.0025) |
| SIZE | −0.0053(0.0609) | −0.0102(0.0331) | 0.0448(0.0369) | −0.0037(0.0256) |
| TIME DUMMY | 0.3334***(0.0868) | 0.0271(0.0471) | 0.1791***(0.0526) | 0.1185***(0.0365) |
| CONSTANT | 22.6958***(0.9930) | 4.2296***(0.5396) | 8.6517***(0.6021) | 7.7845***(0.4175) |
| R2 overall | 0.1479 | 0.0066 | 0.1013 | 0.0664 |
| F-statistic | 91.93*** | 3.49*** | 40.46*** | 37.48*** |
| N. obs. | 317 | 317 | 317 | 317 |
The table shows the results of the FE estimation. The risk variables data is provided from Morningstar and control variables data is provided from Refinitiv.
The explanatory variables are period post-entry in force of the SFDR regulation (POST), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POS), financial one-year performance (FINANCIAL_PERF.), fund size (SIZE).
Standard errors are in brackets. Statistically significant at 1% (***), 5% (**) and 10% (*)
Source: Authors’ own creation
Regression results – ESG performance –Refinitiv – robustness analysis
| Explanatory variables | ESG performance | Environmental performance | Social performance | Corporate governance performance |
|---|---|---|---|---|
| POST | 3.9499*** (0.5976) | 4.7957*** (0.8555) | 4.0756*** (0.6753) | 2.7673*** (0.7600) |
| SELF SF*POST | −2.9910*** (0.6899) | −4.4835*** (0.9876) | −2.7714*** (0.7795) | −2.2489** (0.8772) |
| EUHEAD*POST | −1.1534 (0.7793) | −2.3767** (1.1156) | −1.0300 (0.8805) | −0.3160 (0.9909) |
| SELFSF*EUHEAD*POST | 1.9267* (1.0407) | 3.7523** (1.4898) | 2.3542** (1.1759) | 1.0424 (1.3233) |
| FINANCIAL PERFORMANCE | −0.0213** (0.0209) | −0.0532* (0.0299) | −0.0409* (0.0236) | 0.0179 (0.0265) |
| SIZE | 0.2759 (0.1881) | 0.3526 (0.2692) | 0.1963 (0.2125) | 0.3923 (0.2391) |
| TIME DUMMY | −0.6953** (0.3339) | −0.5211 (0.4780) | −1.0487** (0.3773) | −0.7716* (0.4246) |
| CONSTANT | 62.7578*** (3.0661) | 58.2893*** (4.3893) | 68.3563*** (3.4645) | 57.2971*** (3.8989) |
| R2 overall | 0.0079 | 0.0106 | 0.0244 | 0.0011 |
| F-statistic | 18.46*** | 14.16*** | 19.08*** | 4.96*** |
| N. obs. | 189 | 189 | 189 | 189 |
The table shows the results of the FE estimation. The data is provided from Refinitiv.
The explanatory variables are period post-entry in force of the SFDR regulation (POST), fund self-labeled as sustainable finance and after the entry in force of the SFDR (SELF SF*POST), fund with EU headquarter and after the entry in force of the SFDR (EUHEAD*POST), fund self-labeled as sustainable finance with EU headquarter and after the entry in force of the SFDR (SELF SF*EUHEAD*POS), financial one-year performance (FINANCIAL_PERF.), fund size (SIZE).
Standard errors are in brackets. Statistically significant at 1% (***), 5% (**) and 10% (*)
Source: Authors’ own creation
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Further reading
Benson, K.L., Brailsford, T.J. and Humphrey, J.E. (2006), “Do socially responsible fund managers really invest differently?” Journal of Business Ethics, Vol. 65 No. 4, doi: 10.1007/s10551-006-0003-8.
ESMA (2024), “Guidelines on fund´s names using ESG or sustainability-related terms”, available at: www.esma.europa.eu/sites/default/files/2024-05/ESMA34-472-440_Final_Report_Guidelines_on_funds_names.pdf
European Commission (2023), “Final report on draft regulatory technical standards”, available at: https://ec.europaeu/
Fernando, S. and Lawrence, S. (2014), “A theoretical framework for CSR practices: integrating legitimacy theory, stakeholder theory and institutional theory”, Journal of Theoretical Accounting, Vol. 10 No. 1, pp. 149-178.
Grozdic, V., Maric, B., Radišic, M., Šebestová, J. and Lis, M. (2020), “Capital investments and manufacturing firms’ performance: panel-data analysis”, Sustainability, Vol. 12 No. 4, p. su12041689, doi: 10.3390/su12041689.
Morningstar (2023), “SFDR Article 8 and Article 9 Funds: Q1 2023 in review”, Morningstar Manager Research, pp. 1-34, available at: www.morningstar.com
Acknowledgements
Funding: This paper is supported by the project CIAICO/2021/090 (Generalitat Valenciana).
Corresponding author
About the authors
Susana Martinez-Meyers earned her PhD in the field of Sustainable Finance at University Jaume I (2022). She currently works as an Adjunct Professor at the IE Business School and the University, where she teaches courses on Sustainable Finance, Finance and Financial Accounting. She has more than 20 years of experience in the area of Finance with expertise in Financial Valuation and Investment Banking. She holds a double degree in BBA from Northeastern University (Boston) and an Icade (E4), a Master in Sustainability and CSR, (Uned–Uji) Spain and a CFA designation from the CFA Institute and CFA ESG. Her research interests primarily lie in Sustainable Finance, SRI funds, ESG scores and the impact of disclosure and ESG regulation on financial products.
Idoya Ferrero-Ferrero earned her PhD in Business Management (2012) from the Universitat Jaume I. Currently, she works as Senior Lecturer at the Finance and Accounting Department of the Universitat Jaume I, Spain. Her current research interest is focused on corporate governance, sustainability assessment and reporting. She has published scientific papers in high-impact international academic journals and is involved in several externally funded research projects such as “Towards a New Zero Food Waste Mindset Based on Holistic Assessment” (ToNoWaste) (2022-2026), funded by the EU’s framework programme for research and innovation Horizon-Europe. She is also a member of the Sustainability of Organizations and Social Responsibility Management–Financial Markets Research Group.
María Jesús Muñoz-Torres earned her PhD in agricultural economics from the Polytechnic University of Valencia (1994). She is professor in finance in the Department of Finance and Accounting at the Jaume I University and Coordinates ToNoWaste Project. Her research currently focuses on socially Circular Economy, Responsible Investing, sustainable efficiency of public financial support to companies and Sustainable Business Models. She has published scientific papers in high-impact international academic journals and is involved in several externally funded research projects such as the funded by the EU’s framework programme for research and innovation Horizon Europe Project “Towards a New Zero Food Waste Mindset Based on Holistic Assessment” (ToNoWaste) (2022-2026) or the Horizon-2020 Project Sustainable Market Actors for Responsible Trade (SMART) (2016-2020).