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The literature on the impact of ESG engagement on the firms’ financial performance or return on investment provides mixed results, some come up with positive impact, some with negative impact, some with different impact during different economic swings, and some report no impact. Most authors have studied individual companies’ financial performance versus ESG ratings and not diversified portfolios. In this research study, we examined the separate impacts of Sustainalytics -Morningstar E, S, G, ESG, and carbon risk scores on two financial performance indicators (return on invested capital and sales growth) and two risk-return performance indicators (Jennsen Alpha and Sharpe Ratio) of 100 randomly selected U.S. based equity ETFs. We applied the path analysis method of structural equation modeling (SEM) to analyze the data. Our findings showed that whereas the distinct metrics E, S, and G had mixed impacts on the selected performance metrics, the overall ESG risk score had significant impacts on all the financial and risk-return performance indicators. The findings of this research might encourage investors to increase the share of low ESG risk ETFs in their portfolios which in turn pushes the companies to improve their ESG engagement, a win for the environment and entire society.

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Introduction

The literature on the impact of environmental social and governance (ESG) investing on the firms’ financial performance or return on investment provides mixed results. Some come up with positive impacts, some with negative impacts, some with different impacts during different economic swings, and some report no impact. One reason for these mixed results in the literature could be that most authors have studied individual companies’ financial performance versus ESG ratings and not portfolios, such as ETFs, index funds, or active mutual funds. Individual companies face unpredicted idiosyncratic risks that might overshadow the potential positive impacts of their ESG engagement.

Exchange-Traded Funds (ETFs) have gained significant popularity as investment vehicles in recent years. Global ETF’s assets under management rose from $204 billion in 2003 to over $10 trillion in 2022, that is, an increase of almost 50 times (Joshi & Dash, 2024). This surge in ETF investor preferences is attributed to several factors, including high daily transparency of underlying holdings, cost-effectiveness, and high liquidity since, unlike mutual funds that can be bought from or sold to the issuer at the close of the market, ETFs can be traded during market hours. Additionally, by investing in a portfolio of different companies, ETFs like other mutual funds and investment companies, diversify away most of the idiosyncratic and non ESG related risks of individual companies. Simultaneously, there has been a growing interest in Environmental, Social, and Governance (ESG) considerations among investors, leading to the integration of ESG criteria into investment strategies (Camilleri, 2021). The purpose of this research is to examine the impact of ESG integration on financial performance and risk-return performance of U.S.-based equity ETFs, as the integration of ESG criteria into ETFs is a relatively recent phenomenon, and there is little literature studying the performance of ETFs with consideration of their ESG engagement.

The rest of this paper is organized as follows: we begin by having an overview of how ESG emerged out of previously socially responsible investment doctrines, followed by a discussion of the operationalization of ESG construct by some ESG rating entities, the theoretical basis of ESG, literature review, population and sample, our model, data analysis, results, and interpretation. In the final part of the paper, we have the conclusion and will suggest areas for further research.

Emergence of ESG

The idea of environmental, social, and governance investment (ESG) has emerged out of many other ethical and socially responsible investment practices. The ethical approach to investment in publicly traded companies goes back to the early twentieth century when, in 1921, the Pioneer Group mutual fund excluded tobacco, gambling, and alcohol (the sins) from its investment portfolio. Subsequently, in the 60s, military-producing companies were added to the list of sin stocks, and certain investors excluded these companies’ stocks from their portfolios (Caplanet al., 2013). In 1971, Pax World Management LLC was created as the first mutual fund to have modern socially responsible investment (SRI) as its investment philosophy. In 2018, Impax Asset Management Group plc acquired Pax World Management LLC, which was subsequently renamed Impax Asset Management LLC. With the investment philosophy of: “We invest in companies and assets that are well positioned to benefit from the transition to a more sustainable economy” (IMPX, 2018,  https://impaxam.com/investment-philosophy/).

Growing investors’ interest in ESG investing and substantial growth in funds investing in assets with ESG rating is accompanied by regulators mandating or considering mandating ESG-related disclosure requirements. In 2006, the United Nations declared the establishment of its Principles for Responsible Investment (UNPRI/PRI), consisting of an international network of investors, which adhered to considering companies with strong ESG characteristics for their portfolio investment rather than focusing on the exclusion of those with bad ESG performance (UNPRI, 2006). In 2010, The Global Sustainable Investment Alliance GSIA was established through a partnership of sustainable investment organizations in seven countries with the mission of deepening the impact and visibility of sustainable investment organizations at the global level. The current members as of September 2023 were:

  • US SIF: The Forum for Sustainable & Responsible Investment (United States)
  • Eurosif: The European Sustainable Investment Forum (Euro Zone)
  • RIAA: Responsible Investment Association Australasia (Australia)
  • RIA Canada: Responsible Investment Association Canada (Canada)
  • UK SIF: UK Sustainable Investment & Finance Association (United Kingdom)
  • VBDO: Dutch Association of Investors for Sustainable Development (Netherlands)
  • JSIF: Japan Sustainable Investment Forum ( https://www.gsi-alliance.org/alliance-members/)

Since its establishment, the GSIA has been publishing biennial Global Sustainable Investment Reviews (GSIR) following the development and growth of sustainable investment throughout the globe. According to the GSIA (2020), sustainable investment by the member countries increased from $22.8 trillion in 2014 to $30.7 trillion in 2016 and then to $35.3 in 2020. The upward trend has also been the case in the share of sustainable investment in the total assets under management (AUM). Whereas in 2014 sustainable investments constituted 27.9% of AUM, they increased to 33.4% of AUM in 2016 and 35.9% of AUM in 2020.

In 2014, the European Commission (EC) issued a Non-Financial Reporting Directive (NFRD) that requires ESG-related disclosure by large, listed companies. This was followed by their 2019 Sustainable Finance Disclosure Regulation (SFDR) and subsequent 2022 supplementing regulations, which require ESG-related disclosures about products and investees for investment managers (EU, 2022).

The U.S. Security and Exchange Commission (SEC) started soliciting comments about climate change-related disclosure in February 2021 and established an enforcement task force focused on climate and ESG issues in March 2021. Subsequently in May 2022, the SEC announced that it “is considering a proposal to improve disclosures by certain investment advisers and funds that purport to take Environmental, Social, and Governance (ESG) factors into consideration when making investing decisions” (SEC, 2022,  https://www.sec.gov/newsroom/speeches-statements/gensler-statement-esg-disclosures-proposal). Most recently, in their March 6, 2024 press release, the SEC announced they have “adopted rules to enhance and standardize climate-related disclosures by public companies and in public offerings” (SEC, 2024,  https://www.sec.gov/newsroom/press-releases/2024-31).

As the SRI continued to become popular with more investors, a growing concern started to emerge that these exclusionary investment policies do not capture the impact of some important factors such as energy efficiency, sustainability, carbon emissions, toxic waste treatment, workplace safety, employee relations, community assistance programs, and corporate governance on the financial/investment performance of the companies. The studies addressing these issues formed the theoretical basis of ESG investing (Gieseet al., 2019).

In November 2020, the leading independent investment research company, Morningstar Inc., announced they are formally incorporating ESG factors in their rating of funds, stocks, and asset managers. For this purpose, they use the ESG ratings provided by Sustainalytics’ ESG risk ratings (Morningstar Inc., 2020). Sustainalytics, which was acquired by Morningstar Inc. in July 2020, measures a company’s exposure to material ESG risks, evaluates the probability those risks materialize and the associated valuation impact, and then assigns a Risk Rating to the company ( https://www.sustainalytics.com/esg-data).

The Charted Financial Analyst Institute (CFA Institute) encourages the integration of ESG data into portfolio managers’ investment process so that investors can make informed decisions. In September 2021, the CFA Institute sent a survey to 30,000 of its members and CFA Chartered holders about ESG integration and sustainability reporting and received 710 responses with a 93% completion rate. On integration of ESG factors in the investment process, the majority (48%) of the respondent stated that only those aspects of ESG that are financially material to the investment process should be integrated. Nearly 80% of the respondents believed ESG integration should be decided by the client in consultation with the investment manager and should not be government-mandated, and nearly two-thirds of the respondents said ESG-style claim should be verifiable and thus, the need for some globally consistent disclosure standards (CFA Institute, 2021).

Operationalization of ESG Constructs

With the growth of investors and funds’ interests in ESG investing, several entities, including Sustainalytics, Morgan Stanly Capital International (MSCI), Thomson Reuters, CSRHub, and ISS ESG, are now providing ESG ratings on the overall ESG performance of companies and funds and some on individual components E, S, and G as well as. The E score measures the firm commitments to the environment, such as emission reduction, eco-sustainable product development, use of renewable energy, and so on. The S score measures social and workforce-related issues, such as the firm commitment to the community, the health and safety of the workforce, product safety and quality, and more. The G score measures issues such as board structure, executive compensation, shareholder rights, corporate governance practices, and so on. There are quite some differences between the entities that provide ESG scores regarding what and how many features should be included in each of the E, S, and G metrics, as well as how the overall composite ESG score should be calculated.

Since in this research we are using Sustainalytics, which is now owned by Morningstar, ESG scores, we provide a brief description of how they measure their ESG metric. Sustainalytics ESG risk ratings measure a company’s unmanaged material risk, which includes unmanageable risk and risk that can be potentially addressed by the management (management gap). Therefore, the lower the risk rating, the better the company is managing its risks. Material risk is defined as an issue that can potentially have “impact on the economic value of a company and, hence, its financial risk-and return profile from an investment perspective” (Sustainalytics, 2021, p. 3). They provide risk ratings both for individual E, S, and G, as well as for the overall ESG. A point that has been overlooked by some authors in the literature we reviewed is that the E, S, and G scores are not part of the overall Sustainalytics ESG risk ratings architecture. The overall ESG risk rating is calculated through measuring unmanaged risks in three building blocks, namely, corporate governance block, material ESG issues block, and idiosyncratic block (Sustainalytics, 2021). Sustainalytics measures the magnitude of a company’s unmanaged ESG risks through a quantitative score and a risk category. The risk categories and scores defined/measured by Sustainalytics are shown in Table I.

Unmanaged risk Score
Negligible 0–10
Low 10–20
Medium 20–30
High 30–40
Severe 40+
Table I. Sustainalytics Risk Categories and Scores

Theoretical Basis of ESG

The theoretical justification of ESG stems from the seminal work of Freeman (1984) about the stakeholder theory. The theory posits that a firm should create value for all stakeholders and not just for the shareholders. Since the stakeholders contribute to the value creation of the firm and since the stakeholders’ well-being is impacted by the activities of the firm, the firm should, in return, align the interests of various stakeholder groups with the long-term goals of the company. Each of the E, S, G, and overall ESG includes multiple constructs that assess the way a firm interacts with its different stakeholders, including, of course, with the environment. Therefore, firms initiate ESG activities to address stakeholders’ requirements, which leads to improved efficiency, increased competitiveness of the company, reduced environmental and social risks to the company, and eventually enhanced the firm’s long-term growth (Daugaard & Ding, 2022). Other theories cited in the literature for theoretical justification of ESG construct include sustainability theory, value-creation theory, cost-of-capital reduction theory, and resource-based view (RBV) theory of a firm in arguing that delivering superior ESG performance positively impacts a firm’s financial and market performance (Gillanet al., 2021).

Literature Review

As the concept and definition of ESG and its individual components are continually evolving, and as this article is not a meta-analysis or systematic literature review approach to the topic, we will try to focus our literature review on the most recent peer-reviewed articles. The literature on the impact of ESG engagement on the firm’s financial performance, return on investment, or market value provides mixed results; some come up with a positive impact, some with a negative impact, and some report no impact, though more recent papers point to a positive relationship.

In the positive camp, Antuneset al. (2023) examined the relationship between financial distress and ESG disclosure and performance. They applied the DEA Model to a sample of 645 U.S.A listed companies for the period 2008–2021 using Sustainalytics ESG score ratings and found that firms with lower ESG risk scores are less likely to be exposed to bankruptcy and governance scores are negatively correlated with financial distress efficiency. Baueret al. (2023) studied a database of 7,415 private shareholder engagements on ESG issues with 2,465 publicly listed firms worldwide from 2007 to 2020 and found that firms in which major shareholders engage in their material ESG activities increase their average MCSI ESG scores by 3.76% and MSCI environmental scores by 3.44% relative to peers. They also found that firms with successful material engagements outperform their peers by 2.5% over the next 14 months and that material engagements are associated with better profitability and cost ratios than immaterial engagements. Ademi and Klungseth (2022) studied the relationship between ESG performance and financial performance of 150 companies included in the S&P 500 index over the period 2017–2020 across several industries. They found that firms delivering superior ESG performance yield better financial and market performance during times of crisis. According to the authors, the ESG rating score is correlated with both return-on-capital-employed as a proxy for financial performance and Tobin’s Q as a proxy for the market valuation of a company. Concaet al. (2021) studied a sample of 57 European-listed companies (EU28) in the agri-food sector for the period 2010–2018 and reported the existence of a positive relationship between profitability and disclosure practices of environmental (E) and social (S) information and a negative effect between company market value and disclosure practices relating to governance (G). Alessandrini and Jondeau (2019) provided evidence on the firms which are included into MSCI All Countries World Index. The study covers the period between January 2007 and December 2018. ESG screening and smart beta strategies are utilized as portfolio construction methods. The results indicate that both the passive ESG strategy, which holds portfolios unchanged until new scores are published, and the smart beta strategy produce abnormal returns (Alessandrini & Jondeau, 2019). Similar results were reported by Rufet al. (2019). Brooks and Oikonomou (2018) conducted a comprehensive literature review in the British Accounting Review journal spanning over 45 years and concluded that there is a positive and statistically significant link between ESG and financial performance at the firm level and that the risk-adjusted performance of ESG integrated funds and indexes is statistically indistinguishable from that of conventional funds and indexes. Amel-Zadeh and Serafeim (2018) conducted a survey study of mainstream investment organizations across the globe (not funds) to explore why and how investors use reported ESG information. With an average response rate of 9% across survey questions, they received 413 responses from senior investment professionals. Amongst their findings were the majority of mainstream investors are motivated by financial reasons rather than ethical reasons in using ESG data and that they regard ESG information to be material for financial performance.

Some other studies providing positive evidence include Gillanet al. (2021), Behlet al. (2022), Bansalet al. (2021), Hwanget al. (2021), Shahbazet al. (2020), Liet al. (2018), Gutscheet al. (2017), and Mervelskemper and Streit (2017). Less recently, Gunnaret al. (2015) reviewed 2,200 studies that reported empirical findings and revealed that about 90% of studies found a nonnegative relation between ESG and corporate financial performance. Moreover, they reported that based on the literature reviewed, the majority of studies reported positive findings and that the positive ESG impact on corporate financial performance appears stable over time.

In the negative or no effect camp, Aldieriet al. (2023) examined the first 25 (by market cap weight) of S&P 500 stock index components for the period 2020–2022 to assess if ESG score of companies are related to the company’s risk-adjusted performance. Their findings were that risk-adjusted market performance does not depend on high or low ESG rates. Camilleri (2021) conducted a comprehensive literature review of SRI development in the past few decades and concluded that investors are increasingly directing their funds toward companies with positive impact and sustainable investment. The author further argued that the market for responsible investing has led to an increase in the number of stakeholders, including contractors, non-governmental organizations, and research firms, who are engaged in critical appraisal of the firms’ ESG behaviors. Landauet al. (2020) examined the impact of voluntary integrated reporting (IR), that is, combined reporting of financial and ESG, on market valuation of companies comprising the Euro SOXX Index for the period 2010–2016 and found that IR reporting had a negative impact on market valuation of those companies. Nonetheless, the authors claimed the negative effect is mitigated by the quality of the reports. Duque-Grisales and Aguilera-Caracuel (2019) examined the relationship between ESG score and financial performance of 104 Latin American countries using Thomson Reuters Eikon™ database and found negative relationship between financial performance on overall ESG scores and each of the E, S, and G scores.

Other authors finding negative or no impact of ESG rating on corporate financial performance or return on investment include Hsuet al. (2021), Buchananet al. (2018), Duurenet al. (2016), Halbritter and Dorfleitner (2015), Junkus and Berry (2015), Masulis and Reza (2015), Di Giuli and Kostovetsky (2014), and Servaes and Tamayo (2013).

The literature related to the impact of ESG ratings of mutual funds, and in particular of exchange-traded funds (ETFs), on financial and return performance is limited and also inconclusive. Guimaraes and Malaquias (2023) examined the risk-adjusted performance of 3840 equity mutual funds in the period January 2006 to December 2020 and reported that ESG-related funds had higher risk-adjusted returns during periods of financial constraints, including during COVID 19 period. Noman (2023) considered daily and weekly data from September 2020 through August 2022 to examine and compare tracking error, premium, discount, and performance of core ETFs and ESG ETFs. They reported that on a risk-adjusted basis, core ETFs delivered better performance than their ESG counterparts during the studied period. On the other hand, Raghunandan and Rajgopal (2022) study of a large sample of U.S. mutual funds during 2018–2020 found ESG funds financially underperformed as compared to other funds within the same asset manager and year. Kanuri (2020) examined the risk and return features of ESG ETFs for the period February 2005 to July 2019 in comparison to USA and the global equity indexes and found that ESG ETFs outperformed the indexes in some periods, though during the entire period, indexes outperformed the ESG ETFs. Daset al. (2018) studied the relationship between risk-adjusted return performance of ESG oriented mutual funds and their ESG scores. They found mutual funds with high ESG scores performed better than funds with medium and low scores during periods of economic downturns.

As the review of the literature indicates, there is still room to examine how and to what extent ESG scores/ratings of entities impact their financial and return performance. The debate on this issue is, therefore, open, and there is a need for additional analytical, empirical research to strengthen one side or the other of this debate. We think more research should be done on the impact of ESG on corporate financial and return performance so that we have adequate current research for a meta-analysis study to estimate the effect size of ESG. We have decided to contribute to the literature by examining the ESG integration of U.S.-based equity ETFs and its impact on their financial and risk-adjusted return performance. As we will explain in the model development section of this research, we have contributed to the literature by applying the sophisticated methods of structural equation modeling (SEM) for data analysis, and to our knowledge, there is no research examining ESG integration of ETFs using SEM techniques.

Population and Sample

The population for this study is the equity ETFs that trade in the U.S. market and the issuers are also located in the United States. As of November 2023, the universe of U.S.-located issuer ETFs consisted of 3173 equity, bond, commodity, hybrid, and other ETFs, out of which 1,183 were equity ETFs (VettaFi LLC website,  www.etfdb.com). We first cleaned the equity ETFs dataset for those ETFs that did not have an ESG rating. This left us with an equity ETF population of 960 ETFs. To control for size, we sorted the data based on assets under management (AUM) from the highest AUM to the lowest. We labeled the first 480 ETFs as large and the second 480s ETF as small. To determine the appropriate sample size, we will be applying structural equation modeling (SEM) techniques for our statistical data analysis, and we will follow recommendations in the SEM literature. Sample size determination in SEM is a complicated matter and depends on the complexity of the model (Kline, 2016; Whittaker & Schumacker, 2022). One rule of thumb as frequently referred to in the literature, is that minimum sample size in SEM is determined in terms of the N:q rule (rule of thumb), which is the ratio of the number of cases (N) to the number of model parameters that require statistical estimates (q). Ratios ranging from 5:q to 20:q are stated in the literature depending on the complexity of the models. Since our model does not include any latent or mediating variables, we consider it as the least complex model and go with the 5:q option which gives us the minimum sample size of 100 since we have 20 parameters to estimate in our model. This sample size is consistent with the seminal work of Hairet al. (2010), which states that in SEM models with less than three latent constructs, the minimum sample size should be 100. We applied the simple random sampling feature of SPSS software and randomly selected 50 ETFs from the large-cap population and 50 ETFs from the small-cap population. This gave us our required random sample size of 100.

Model Specification

Guided by the theoretical foundation of ESG and a review of the literature discussed in previous sections, we specify our model in terms of hypotheses I and II to examine the impact of ESG integration on two sets of performance measures, namely, financial performance measures and investment risk-return performance measures of the sample ETFs.

Financial Performance Measures

Financial performance measures are accounting-based metrics derived from the financial statements of the companies, which include return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), sales growth (SG), net margin ratio, etc. As stated by Inc.et al. (2015), the two main financial performance measures that ultimately create value for the firm over time are ROIC and sales growth. Following Inc.et al. (2015), we pick ROIC and sales growth as measures of financial performance and posit the following hypothesis for this study: Hypothesis I: The Sustainalytics-Morningstar scores for Environmental (E), social (S), governance (G), overall ESG, and carbon risks are predictors of financial performance.

Hypothesis I will be tested through the following two linear multiple regressions: where the βs are the linear regression coefficients, the subscript i refers to the ETFs in the sample, and ε is the error term. Note that we are examining the separate effects of E, S, and G as well as the impact of overall ESG. Carbon risk is measured as a separate risk factor by sustainability rating companies, and we are, therefore, including it in our model.

R O I C i = β 0 + β 1 × E i + β 2 × S i + β 3 × G i + β 4 × E S G + β 5 × C a r b o n R i s k i + ε i
S a l e s G r o w t h i = β 0 + β 1 × E i + β 2 × S i + β 3 × G i + β 4 × E S G + β 5 × C a r b o n R i s k i + ε i

Risk-Return Performance

Risk return performance measures determine how well an investment has performed, given the level of the risk inherent in the investment. The two portfolio investment performance measures commonly used by scholars are Jensen’s Alpha and Sharpe Ratio (Bodie, 2023). Jensen’s Alpha (α), also referred to as abnormal return, measures how well an investment has performed compared to its expected return, with the expected return derived from applying the capital asset model (CAPM) to the investment. Sharpe ratio (SR), also known as risk-adjusted return, measures the investment return per unit of standard deviation (total risk) of returns. For risk-return measures, we posit the following nondirectional hypothesis: Hypothesis II: The Sustainalytics-Morningstar scores for Environmental (E), social (S), governance (G), overall ESG, and carbon risk are predictors of an investment’s risk-return performance.

Hypothesis II will be tested through the following two linear multiple regressions:

A l p h a i = β 0 + β 1 × E i + β 2 × S i + β 3 × G i + β 4 × E S G + β 5 × C a r b o n R i s k i + ε i
S h a r p R a t i o i = β 0 + β 1 × E i + β 2 × S i + β 3 × G i + β 4 × E S G + β 5 × C a r b o n R i s k i + ε i

In each of the two hypotheses stated above, we have the same independent variables that predict more than one outcome variable. If we estimate the multiple regression equations (1)(4) separately, then a 5% type I error will be elevated to 1 − (1%–5%)4 = 18.55%. To avoid type I error surge, the four multiple regression equations should be solved simultaneously. One approach is to conduct a multivariate multiple regression. However, the multivariate multiple regression model assumes the residuals are multivariate normal. As we will show in the coming pages, the data that we are working on does not pass the test of multivariate normality. To simultaneously solve regression equations (1)(4)and avoid the multivariate normality assumption, we use path analysis, which is one of the structural equation modeling (SEM) testing techniques. Another benefit of path analysis as compared to multinomial multiple regression is that the path solution reports the covariances between dependent variables residuals and their statistical significance.

SEM can test many theoretical models, including confirmatory factor analysis (CFA), exploratory factor analysis (EFA), latent growth curve, and path analysis. CFA, EFA, and latent growth models, by definition, use observed variables to define latent (unobserved) constructs, while path models can use latent and observed or only observed variables (Whittaker & Schumacker, 2022).

We will be using STATA software for our data analysis. The default estimator for SEM models in STATA software is the maximum likelihood (ML) technique, which requires multivariate normality. However, the STATA gives you the option of selecting a nonparametric estimator called asymptotic distribution free (ADF), also called the weighted least squares (WLS) estimator, which does not make any assumptions about the distribution of variables, and we chose this option for our SEM data analysis. WLS has demonstrated better efficiency with non-normal variable distributions relative to ML (Whittaker & Schumacker, 2022). In the language of SEM, independent variables are called exogenous variables, and the dependent variables are called endogenous variables, and we will use these terms interchangeably. The error terms of the endogenous variables (disturbances) are considered exogenous latent variables; they represent unknown factors that impact the endogenous variables.

Data Analysis and Results

We retrieved data for the exogenous variables E, S, G, ESG, and Carbon risk and for the endogenous variables ROIC, Sale Growth, Alpha, and Sharpe Ratio from Morningstar Investor website (to which we subscribed) during November–December 2023. Sample data were analyzed through the path model depicted in Fig. 1. The results are reported in four parts: parameter estimation and their significance, endogenous variables covariance matrix, overall goodness of fit, and equation level goodness of fit. The overall goodness of fit and the equation level goodness of fit will be discussed under the model identification section. First, we test for multivariate normality of the model’s variables using STATA software.

Fig. 1. The predictive path from exogenous variables to endogenous variables and endogenous residual’s covariance path.

Test for Multivariate Normality

As can be seen, the Doornik-Hansen test statistic for multivariate normality is significant, and thus, the null hypothesis that the variables are multivariate normal is rejected. Therefore, we use the asymptotic distribution free (ADF) estimator in SEM of STATA.

The path model to estimate the parameters is depicted in Fig. 1.

Parameter Estimates

Solving the path model using ADF estimator, we get the following estimates for the model’s parameters, as shown in Table II. Since Morningstar-Sustainalytics metrics measure the degree of unmanaged risk rather than managed risk, that is, the higher the risk score, the higher the sustainability risk, we should expect negative regression coefficients.

Structural Coefficient Standard error z P > |z| [95% conf.interval]
Alpha
  E 0.20 0.31 0.65 0.52 −0.41 0.81
  S −0.92 0.42 −2.18 0.03 −1.74 −0.09
  G 1.96 0.70 2.83 0.01 0.60 3.33
  ESG −1.45 0.50 −2.88 0.00 −2.43 −0.46
  CarbonRiskScore 1.50 0.23 6.58 0.00 1.05 1.95
  Cont. 11.67 10.50 1.11 0.27 −8.90 32.25
SharpRatio
  E −0.01 0.01 −0.77 0.44 −0.02 0.01
  S −0.01 0.02 −0.49 0.63 −0.04 0.02
  G 0.06 0.02 2.89 0.00 0.02 0.10
  ESG −0.06 0.01 −4.80 0.00 −0.09 −0.04
  CarbonRiskScore 0.05 0.01 9.02 0.00 0.04 0.06
  Cont. 0.96 0.27 3.55 0.00 0.43 1.50
ROIC
  E −0.48 0.35 −1.38 0.17 −1.17 0.20
  S −0.19 0.38 −0.50 0.62 −0.92 0.55
  G 2.39 0.60 3.98 0.00 1.21 3.56
  ESG −2.79 0.57 −4.89 0.00 −3.91 −1.67
  CarbonRiskScore 1.55 0.30 5.15 0.00 0.96 2.14
  Cont. 50.33 10.05 5.01 0.00 30.64 70.03
SalesGrowth
  E 1.14 0.34 3.33 0.00 0.47 1.81
  S −0.68 0.75 −0.90 0.37 −2.14 0.79
  G 0.23 0.76 0.31 0.76 −1.25 1.72
  ESG 1.76 0.26 6.72 0.00 1.25 2.28
  CarbonRiskScore −0.60 0.18 −3.33 0.00 −0.96 −0.25
  Cont. −21.90 5.18 −4.23 0.00 −32.05 −11.74
Table II. Parameter Estimate of the Path Model

We now review and interpret the output of Table II for each of the sustainability risk factors. The environmental risk factor E impacts on Alpha, Sharpe Ratio, and ROIC are not statistically significant. However, the effect of E on SalesGrowth is statistically significant, but the regression coefficient is positive (b = 1.14 p < 0.001), implying that for each unit increase in E risk, sales will be predicted to grow by 1.14%. The social risk factor S does not have a statistically significant impact on Sharpe Ratio, ROIC, and SalesGrowth, but has a statistically negative significant impact on Alpha (b = −0.92, p = 0.03), which means for each unit decline in S risks, alpha will increase by 0.92%. The governance risk factor G does not have a statistically significant impact on SalesGrwoth, though its impacts on Alpha, Sharpe Ratio, and ROIC are statistically significant. However, the regression coefficient of G is positive for all these three endogenous variables, which is against expectation. The interesting outcome is although E, S, and G provided mixed results for their impacts on the endogenous variables, the overall ESG risk factor impacts on every one of the endogenous variables are statistically significant, and except for the SalesGrowth the regression coefficients are negative, supporting the idea that sustainability efforts have positive impact on financial performance and investment risk-return performance. To be specific, for the impact of ESG on Alpha, we have b = −1.45, p < 0.001, meaning for each unit decrease in ESG risk, Alpha will increase by 1.45%. For the impact of ESG on the Sharpe Ratio, we have b = −0.06, p < 0.001, implying that for each unit decrease in ESG risk, the Sharpe Ratio increases by 0.06. For the impact of ESG on ROIC, we have b = −2.79, p < 0.001, meaning for each unit decrease in ESG risk, ROIC increases by 2.79%. However, for the impact of ESG on SalesGrowth we have b = 1.76, p < 0.001 implying that as ESG risk increases by one unit SalesGrowth increases by 1.79%, an outcome contrary to the expectation. As for the CarbonRisk score, its impact on the four endogenous variables is statistically significant (p < 0.001), but except for SalesGrowth, the regression coefficients for the other three endogenous variables are positive, meaning the higher the carbon risk, the higher the performance. A visual summary of significance and the slope sign of the path parameters are exhibited in Table III.

Parameters E S G ESG CarbonRisk
Alpha NonSig/+Slope Sig/−Slope Sig/+Slope Sig/−Slope Sig/+Slope
SharpRatio NonSig/−Slope NonSig/−Slope Sig/+Slope Sig/−Slope Sig/+Slope
ROIC NonSig/−Slope NonSig/−Slope Sig/+Slope Sig/−Slope Sig/+Slope
SalesGrowth Sig/+Slope NonSig/−Slope NonSig/+Slope Sig/+Slope Sig/−Slope
Table III. Significance and the Slope Sign of Path Parameters

A sig/−slope means the risk factor is a predictor of the related endogenous variable, which implies less risk and higher performance; A sig/+slope means the risk factor is a predictor of the related endogenous variable, which means more risk and higher performance, and a nonsig/−or +slope means the risk factor is not a predictor of related endogenous variable.

Residuals’ Covariance Matrix

The endogenous variables’ residual covariance matrix provides information about the existence of some latent exogenous variables, in addition to the model’s observed exogenous variables, that can account for common variations in the observed endogenous variables and thus demand further research to identify other possible exogenous variables. This is a distinguishing feature of path analysis as compared to traditional multiple regression models. The covariance matrix of endogenous variables residuals is depicted in Table IV.

Endogenous variables Coefficient Standard error z P > |z| [95% conf. interval]
var(e.Alpha) 53.66 15.68 30.26 95.16
var(e.SharpRatio) 0.05 0.01 0.03 0.06
var(e.ROIC) 57.77 9.28 42.16 79.14
var(e.SalesGrowth) 42.32 5.57 32.69 54.79
cov(e.Alpha,e.SharpRatio) 1.19 0.28 4.32 0.00 0.65 1.73
cov(e.Alpha,e.ROIC) 21.79 5.90 3.69 0.00 10.22 33.35
cov(e.Alpha,e.SalesGrowth) −0.76 5.05 −0.15 0.88 −10.66 9.14
cov(e.SharpRatio,e.ROIC) 0.89 0.18 5.00 0.00 0.54 1.24
cov(e.SharpRatio,e.SalesGrowth) −0.01 0.17 −0.07 0.94 −0.35 0.32
cov(e.ROIC,e.SalesGrowth) 9.38 5.56 1.69 0.09 −1.51 20.27
Table IV. Endogenous Variables’ Residual Covariance Matrix

As can be seen from Table IV SalesGrowth residuals do not have statistically significant covariances with any of the other three endogenous variables residuals (p > 0.05), but the residual covariances between Alpha and Sharpe ratio, Alpha and ROIC, and Sharpe Ratio and ROIC are statistically significant (p < 0.001).

Model Identification

Model Identification deals with how well the estimated model fits the data, and it consists of overall goodness of fit and equation level goodness of fit.

Overall Goodness of Fit

The overall goodness of fit commonly is assessed through χ2 test statistic of the model and several other model fit indices. In SEM there are two χ2 tests, one is model versus saturated (ms), and the other model versus baseline (mb). In both ms χ2 and mb χ2 tests the null hypothesis is that the population covariance matrix is equal to the model implied covariance matrix. Therefore, in the ms χ2 a nonsignificant χ2 would be desired for a good fit. In the mb χ2 test all of the observed variables, including the endogenous variables, are treated as exogenous (independent) and only the variances of these variables are estimated in the model. This means the model implied covariance matrix consists of variances in the diagonal and zeros off the diagonal. Therefore, for a good fit, we desire to reject the null hypothesis. Other model fit indices commonly used include root mean squared error of approximation (RMSEA), comparative fit index (CFI), Tucker–Lewis index (TLI), and standardized root mean squared residual (SRMR). For a detailed description of these indices, we refer the readers to Whittaker and Schumacker (2022). Results for the overall goodness of fit test of our model, calculated using STATA software, is reported in Table V. The last column of Table V shows the criterion for goodness of fit interpretation of each metric quoted from Whittaker and Schumacker (2022).

Fit statistic Value Description Good fit criteria (Whittaker & Schumacker, 2022)
Discrepancy
  chi2_ms(1) 0.096 Model vs. saturated
  p > chi2 0.757 Nonsignificant chi2 ms reflects good model fit
  chi2_bs(26) 147.451 Baseline vs. saturated
  p > chi2 0 Significant chi2 bs reflects good model fit
Population error
  RMSEA 0.00 Root mean squared error of approximation 0.05 or less close fit
  90% CI, lower bound 0.00
  Upper bound 0.18
  pclose 0.78 Probability RMSEA <= 0.05
Baseline comparison
  CFI 1.00 Comparative fit index Value greater than 0.90 reflects good model fit
  TLI 1.19 Tucker–Lewis index Value greater than 0.90 reflects good model fit
  Size of residuals
  SRMR 0.01 Standardized root mean squared residual 0.05 or less good fit
  CD 0.82 Coefficient of determination
Table V. Test of Overall Goodness of Fit of the Model

As we see in Table V, our model passes all the overall goodness of fit tests.

Equation-Level Goodness of Fit

The equation-level goodness of fit tests shows, measured by R-squared numbers, the percentage variations of each endogenous variable explained by all the exogenous variables taken together. Equation-level goodness of fit is reported in Table VI.

Dependent variables Fitted Variance predicted Residual R-squared mc mc2
Observed
Alpha 127.14 73.48 53.66 0.58 0.76 0.58
SharpRatio 0.10 0.06 0.05 0.56 0.75 0.56
ROIC 99.40 41.63 57.77 0.42 0.65 0.42
SalesGrowth 63.39 21.07 42.32 0.33 0.58 0.33
Overall 0.82
Table VI. Equation-Level Goodness of Fit Test

All the R-squared represent strong effect size, with the overall R-square being 82%, indicating strong predictability power of the exogenous variables.

Conclusions

The idea of environmental, social, and governance investment (ESG) has emerged out of many other ethical and socially responsible investment practices. The ethical approach to investment in publicly traded companies goes back to the early twentieth century when, in 1921, the Pioneer Group mutual fund excluded tobacco, gambling, and alcohol (the sins) from its investment portfolio. Growing investors’ interest in ESG investing and substantial growth in funds investing in assets with ESG rating is accompanied by regulators mandating or considering mandating ESG-related disclosure requirements.

The literature on the impact of ESG engagement on the firms’ financial performance or return on investment provides mixed results; some come up with a positive impact, some with a negative impact, some with a different impact during different economic swings, and some report no impact. Most authors have studied individual companies’ financial performance versus ESG ratings and not diversified portfolios. The literature related to the impact of ESG ratings of mutual funds, and in particular of exchange traded funds (ETFs), on financial and return performance is limited and also inconclusive.

In this research study, we examined the separate impacts of Sustainalytics-Morningstar E, S, G, ESG, and carbon risk scores on two financial performance indicators (return on invested capital and sales growth) and two risk-return performance indicators (Jensen Alpha and Sharpe Ratio) of 100 randomly selected U.S. based equity ETFs. We applied the path analysis method of structural equation modeling (SEM) to analyze the data. We specified our model on the basis of theoretical foundations of ESG engagement, and the data analysis results of the model met all the identification criteria of SEM.

Our findings showed that whereas the distinct metrics E, S, and G had mixed impacts on the selected performance metrics, the overall ESG risk score had significant impacts on all the financial and risk-return performance indicators. CarbonRisk factor impact was significant on all performance indicators, but except for its impact on SalesGrowth, the regression coefficient sings were positive, an outcome that needs further research in separate studies. The study showed significant covariances between all endogenous (independent) variables, which implies there are some other exogenous (independent) factors, in addition to the ones selected in this study, that must be included in an enhanced model, another area for further research.

Overall, we have contributed to the literature by applying the sophisticated methods of structural equation modeling (SEM) for data analysis, and to our knowledge, there is no research examining ESG integration of ETFs using SEM techniques. The findings of this research might encourage investors to increase the share of low ESG risk ETFs in their portfolios, which in turn pushes the companies to improve their ESG engagement, which is a win for the environment and the entire society.

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