Introduction

Investment solutions considering environmental, social, and governance (ESG) criteria, so-called sustainable investments, have experienced substantial demand and supply side growth in recent years. According to the Global Sustainable Investment Alliance (GSIA), the volume of sustainable investments in Europe, the USA, Canada, Australia/New Zealand, and Japan increased from 22.84 trillion U.S. dollars at the beginning of 2016 to 35.30 trillion U.S. dollars at the beginning of 2020, representing an increase of 65% (GSIA 2020). According to these numbers, sustainable investments account for more than one third of global assets under management (Boston Consulting Group 2020; GSIA 2020).

The growth of sustainable investments is accompanied by a large number of studies that examine the effect of ESG criteria on the risk-return profile of investments (Friede et al. 2015; Wallis and Klein 2015). The vast majority of those studies focus on listed equity, although fixed-income instruments’ share is almost 40% of all sustainable investments (GSIA 2018). Furthermore, studies analyzing the impact of ESG criteria on the risk-return profile of fixed-income instruments focus on (i) (abnormal) returns of sustainable mutual bond funds (Derwall and Koedijk 2009; Henke 2016) (ii) (abnormal) returns of synthetic bond portfolios (Pereira et al. 2019; Polbennikov et al. 2016) or (iii) credit risk at company-level (Graham and Maher 2006; Klock et al. 2005; Oikonomou et al. 2014; Stellner et al. 2015). However, to the best of our knowledge, there is no research which systematically investigates the effects of ESG criteria on corporate bond portfolios’ credit risk.

This study aspires to fill this research gap by analyzing whether ESG criteria are a means to manage the credit risk of bond portfolios. To do so, we compare the credit risk exposures of sustainable and non-sustainable bond portfolios using the two-factor model by Fama and French (1993) and a four-factor model by Elton et al. (1995). Our analysis covers approximately 5000 corporate bonds and the 2013-2020 period. We derive the portfolio constituents for the sustainable (non-sustainable) portfolio based on an ESG-best-in-class (worst-in-class) approach with varying ESG score percentile thresholds. Thus, the sustainable portfolio consists of bonds with the highest ESG scores, while the non-sustainable portfolio consists of bonds with the lowest ESG scores. We control for industry and regional allocation, option-adjusted duration, and credit rating to minimize the impact of other bond characteristics on the portfolios’ credit risk exposure. As proposed by Chatterji et al. (2016), we conduct our analysis using ESG ratings from two different ESG rating providers to control for differences in ESG ratings across different providers.

We find a significantly adverse relation between the bond portfolios’ sustainability and their credit risk. Furthermore, a clear relationship between the level of reduction in credit risk and the severity of the ESG-best-in-class (worst-in-class) approach indicates that ESG criteria can be used to systematically manage the credit risk of a bond portfolio. These findings are robust to the use of different asset pricing models and ESG ratings.

The contributions of this study are threefold. First, it is the first study which systematically investigates the effect of ESG criteria on the credit risk of corporate bond portfolios. Hence, this study adds a new perspective to the risk-return debate inherent to sustainable investments. It clearly shows that the consideration of ESG criteria in the portfolio allocation of bonds can significantly reduce a portfolio’s systematic credit risk. Second, it also provides important insights into the usability of ESG ratings. While a large body of literature indicates that (some) ESG ratings are subject to various biases, such as the sustainability rating agencies’ understanding of sustainability (Berg et al. 2022; Chatterji et al. 2016; Dimson et al. 2020; Dorfleitner et al. 2015) or the size (Drempetic et al. 2020), the rewriting history (Berg et al. 2020), a mean reverse (Gidwani 2020), and a quantity bias (Chen et al. 2021), the results of this study suggest that ESG ratings are still a suitable means for managing bond portfolios’ credit risk. Third, the findings of this study support investment professionals in finding an efficient solution to effectively manage credit risks in their corporate bond portfolios by integrating ESG criteria into their investment process.

The remainder of this study is structured as follows: The next section briefly reviews the related literature. The section is followed by the description of the data and methodology of the empirical analysis. We then present the results and highlight the main findings of the analysis, followed by a confirmation of the results using different robustness checks. Finally, this paper concludes with a summary of the key findings and gives an outlook of further research needs.

Literature review

Research that assumes a link between sustainability and credit risk usually argues with one of the two following theories. First, the risk mitigation view (Goss and Roberts 2011) states that companies with a superior sustainability performance are less risky, since they are, e.g., less prone to reputational losses induced by negative incidents. Second, the overinvestment view (Goss and Roberts 2011) regards investments in sustainability as a waste of scarce resources, which increases fixed costs and earnings’ volatility and thus companies’ credit risk (e.g., Frooman, Zietsma and McKnight 2008). Literature that deals with the effect of ESG criteria on the risk-return profile of fixed-income instruments primarily focuses on (i) (abnormal) returns of sustainable mutual bond funds, (ii) (abnormal) returns of synthetic bond portfolios or iii) credit risk at company-level. Thus, there is no literature that systematically analyzes the effect of ESG criteria on the risk characteristics of bond portfolios.

Concerning returns of sustainable mutual bond funds, Derwall and Koedijk (2009) measure the performance of ESG fixed-income funds in the U.S. for the 1987 to 2003 period by using multi-index performance evaluation models. They find that the performance of average ESG bond funds does not differ compared to conventional bond funds. Henke (2016) uses a five‐factor model to explain returns of bond funds, showing that ESG bond funds in the U.S. and the Eurozone outperform conventional funds during the 2001 to 2014 period. This finding especially occurs during recessions or bear market periods. He attributes the outperformance to a systematic effect of social screening on financial performance caused by the hypothesized lower risk of high-ESG companies in combination with ESG bond funds’ exclusion of irresponsible corporate bond issuers. Leite and Cortez (2018) investigate the performance of ESG bond funds domiciled in France and Germany from 2002 to 2014 using conditional multifactor models that allow for both time-varying risk and performance. They find that ESG bond funds significantly outperform their conventional counterparts, which might be attributable to the government bonds and not to the corporate bonds in the portfolios. Madhavan and Sobczyk (2020) find a strong negative relation between a fund’s total return and its holding-based ESG score for an active U.S bond fund sample in the 2015 to 2020 period. The authors explain this observation by the fact that funds with higher ESG scores have an exposure to high quality bonds, which are less volatile.

Considering the return patterns of synthetic bond portfolios, Polbennikov et al. (2016) show that portfolios consisting of high-ESG bonds have a slightly higher performance than their low-ESG counterparts after controlling for systematic risk factors such as duration, spread, and DTSFootnote 1 by sector. Moreover, high-ESG bond portfolios have lower credit spreads on average. The first finding indicates the existence of a sustainability premium, which can explain a certain fraction of bond portfolio returns beyond the used systematic risk factors. Their second finding adds to the current academic discussion regarding the impact of sustainability on credit spreads. However, even though Polbennikov et al. (2016) account for systematic risk factors in their return attribution, they do not analyze possible differences in the exposure to systematic factors. Pereira et al. (2019) investigate the performance of high- and low-ESG bond portfolios of 189 Eurozone companies between 2003 and 2016 using a conditional multifactor model. They conclude that high-ESG bond portfolios outperformed at an early stage, with the outperformance disappearing over time. Moreover, their regression results indicate that high-ESG bond portfolios are significantly more exposed to credit risk than low-ESG bond portfolios. Pereira et al. (2019) try to explain their finding by arguing that since low credit rated bonds present high yields, issuers of speculative grade bonds can benefit the most in absolute terms from reductions in the cost of debt that may result from considering ESG criteria in business practices. Building active investment grade, high yield, and emerging market bond portfolios, Bahra and Thukral (2020) conclude that ESG scores can be used to enhance portfolio outcomes via lower drawdowns, reduced portfolio volatility, and, in some cases, even marginally increased risk-adjusted returns.

Finally, there is a large body of literature investigating the potential effects of the integration of ESG criteria in business activities on credit ratings, credit risk premiums, and the costs of debt of companies. The results are ambiguous. Some studies suggest better credit ratings, i.e., lower risks and costs of debt, for high-ESG companies (Bauer and Hann 2010; Bhojraj and Sengupta 2003; Chiesa et al. 2021; Oikonomou et al. 2014; Stellner et al. 2015). In contrast, other investigations point in the opposite direction (Izzo and Magnanelli 2012; Menz 2010).

Methodology

We investigate the impact of ESG criteria on the credit risk exposure of corporate bond portfolios using the following two-factor approach introduced by Fama and French (1993):

$${r}_{P,t}=\alpha +{\beta }_{1}*{\mathrm{TERM}}_{t}+{\beta }_{2}*{\mathrm{DEF}}_{t}+{\varepsilon }_{P,t}$$
(1)

This model explains excess returns (\({r}_{P,t}\)) of, in our case, a bond portfolio P with a term (\({\mathrm{TERM}}_{t}\)) and a credit risk (\({\mathrm{DEF}}_{t}\)) factor in month t. \({\mathrm{TERM}}_{t}\) represents the return resulting from price changes and the carry of treasury bonds, hence, capturing interest-rate risk. The factor return is calculated using the monthly return of a long-term U.S. treasury indexFootnote 2 in excess of the one-month U.S. treasury bill rate. The monthly U.S. treasury bill rate proxies for a risk-free investment opportunity. \({\mathrm{DEF}}_{t}\) explains the part of the return that is attributable to credit spreads, i.e., \({\mathrm{DEF}}_{t}\) captures credit risk. The monthly factor return is calculated by subtracting the long-term U.S. treasury index from long-term U.S. corporate bond indexFootnote 3. \({\varepsilon }_{P,t}\) denotes the error term. Despite being a rather simple model, Fama and French (1993) demonstrate the strong explanatory power of these two factors in the variation of investment-grade corporate bond portfolio returns. Hence, we use this straightforward approach due to its simplicity to analyze the differences in the systematic credit risk of sustainable and non-sustainable corporate bond portfolios by regressing the time series of different portfolios returns as well as the long-short portfolio returns for each level of sustainability. We test the robustness of our results by using (i) ESG scores from two different data providers and (ii) a more sophisticated four-factor model to explain the portfolio returns.

Portfolio construction

We derive our initial bond universe using the yearly constituents from the “Bloomberg Barclays US Agg Corporate Bond Index”Footnote 4, during the analyzed period from 2013 to 2020 covering 12,592 corporate bonds in total. The index comprises corporate bonds denominated in U.S. dollar. Bonds included in the index are restricted to a time to maturity of at least one year and a credit rating equal to investment grade. We exclude bonds without an ESG rating. The index provides an excellent foundation due to its broad coverage and its representativeness of the U.S. dollar bond market. We consider all bonds with a sustainability rating from MSCI ESG ResearchFootnote 5.

We group the bonds according to their industry sector, region of risk, credit ratingFootnote 6 , and duration using Bloomberg data. The grouping procedure enables us to minimize the influence from differences in exposure to systematic risk factors. Table 1 describes the variables and the possible characteristics used in the grouping procedure:

Table 1 Description of the Grouping Systematics for the Matching Procedure
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We first rank the bonds within each of the resulting 1050 groups with respect to their MSCI ESG score. Bonds with an ESG score equal to or higher than the 12.5% percentile (25% and 50% percentile, respectively) within each group are assigned to the High ESG portfolio, and conversely, bonds with scores equal to or lower than the 87.5% percentile (75% and 50% percentile, respectively) are assigned to the Low ESG portfolio. This procedure yields three pairs of portfolios with each pair consisting of one sustainable and one non-sustainable portfolio.Footnote 7 We account for changes in a bond’s option-adjusted duration, companies’ credit rating, and their sustainability performance (proxied by the ESG score) by revising the constituents of each portfolio in December of each year. All bonds within a portfolio are assigned an equal weighting.

Systematically investigating the impact of ESG criteria on credit risk using a portfolio approach rather than focusing on individual bonds comes with several advantages. First, the portfolio approach reduces bond specific idiosyncratic factors. Thus, it allows to gain more precise estimations of the exposure to the credit risk factor and, ultimately, the impact of ESG criteria and credit risk. Second, it enables us to differentiate between different levels of sustainability. And third, the findings are better tailored to investors.

Descriptive statistics

Table 2 provides statistics on High (Low) ESG portfolios’ yearly credit ratings and their option-adjusted duration based on the 12.5% ESG score percentile threshold for which the likelihood of significant differences in these characteristics is the highest. However, we expect differences in both characteristics to disappear with respect to our grouping procedure which allows us to assign differences in the High and Low ESG portfolios’ exposure directly to the differences in their ESG criteria. The descriptive analysis of the two characteristics at the portfolio level supports our expectation. We find consistently small, if any, differences between the High and Low ESG portfolios’ credit ratings (Panel A) and the option-adjusted duration (Panel B) with respect to their yearly median (column 3) and mean (column 4). Using a nonparametric Wilcoxon test (column 7) and a two-sided t test (column 8), we can confirm that those small differences are not statistically significant. We conclude that High and Low ESG portfolios do not differ with respect to their average credit rating (Panel A) and to the average option-adjusted duration (Panel B) in any of the given years. Thus, the grouping process is successful and differences in the portfolios’ credit risk can be attributed to differences in the degree of sustainability.

Table 2 Descriptive Portfolio Statistics
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Results

Table 3 presents the results of the Fama and French (1993) two-factor model regressions for the High and Low ESG corporate bond portfolios. In addition to the regression results for the return time series of the respective portfolios, return and risk factor differences between both portfolios are analyzed by means of a difference portfolio, going long in the Low and short in the High ESG portfolio (“Low–High”).

Table 3 Fama and French two Factor (1993) Model using MSCI ESG Data
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We observe that the explanatory power of the Fama and French (1993) two-factor model is quite high accounting for 93.4% to 97.4% in the Low and High ESG portfolios’ return variation. Unsurprisingly, we find significantly positive loadings on TERM, indicating a statistically significant interest-rate risk exposure in both portfolio types. Since the coefficient of the TERM factor does not significantly differ from zero in any of the Low–High difference portfolios, there is, however, no evidence for differences in interest-rate-risk exposure between the two portfolios. This finding again reflects a successful grouping of bonds before the regression. Furthermore, all Low and High ESG portfolios load significantly positive on the credit risk (DEF) factor. We also find a significantly positive coefficient for DEF in each of the three Low–High ESG portfolios, implying that Low ESG portfolios have a significantly higher credit risk than High ESG portfolios. Interestingly, the magnitude of DEF coefficients of the difference portfolio regressions is adversely related to the ESG score percentile threshold, raising from 0.049 with a 50% ESG score percentile threshold to 0.164 with a 12.5% ESG score percentile threshold. Thus, the difference in the exposure to credit risk between Low and High ESG portfolios raises with the differences in the average ESG scores suggesting that a portfolio’s overall credit risk significantly relates to its ESG performance. The more sustainable a corporate bond portfolio is, the lower its credit risk on average. This finding suggests that corporate bond portfolios’ credit risk can be purposefully managed by considering ESG criteria in the investment process.

We find evidence for a systemically lower exposure toward credit risk of ESG-best-in-class created sustainable corporate bond portfolios compared to their non-sustainable corporate bond peer portfolios. Hence, we can confirm that ESG score differences on the portfolio level are associated with a lower portfolio credit risk on average. Furthermore, the constants of our Low–High difference portfolios do not support a significant risk-adjusted outperformance of non-sustainable corporate bond portfolios compared to sustainable corporate bond portfolios. We can reject a performance penalty subject to the ESG-best-in-class approach. Based on these findings, we conclude that an ESG-best-in-class strategy can be used as an instrument to control the systematic credit risk exposure of a portfolio while the resulting ESG shift still allows to adequately diversify the sustainable portfolio.

Robustness checks

Our initial analysis has already revealed robust results for varying ESG score percentile thresholds. In addition to the application of those different thresholds, we run two further robustness checks integrating the findings of previous academic studies. Recent research, e.g., revealed inconsistencies between ESG ratings of different rating providers (Berg et al. 2022; Chatterji et al. 2016; Dorfleitner et al. 2015; Dumrose et al. 2022). Chatterji et al. (2016), therefore, propose using scores from different ESG rating providers to ensure the robustness of studies’ results. Following this proposal, we replace MSCI ESG scores with Refinitiv ESG scores and re-run the Fama and French two-factor model under consideration of the aforementioned ESG score percentile thresholds. Table 4 presents the results using Refinitiv ESG scores. This analysis confirms the results of our initial analysis revealing that the exposure to the credit risk factor increases adversely to the ESG score percentile threshold in the Low–High ESG portfolios. While the differences in the credit risk are statistically significant for all Low–High ESG portfolios when using MSCI ESG ratings, we only observe a statistically significant difference for the 12.5% ESG score percentile threshold. These results, however, underpin our conclusion stating that the differences in the credit risk rises with the difference in the average ESG scores of the corporate bond portfolios.

Table 4 Fama and French two Factor (1993) Model using Refinitiv ESG Data
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In our second test, we replace the Fama and French (1993) two-factor model with a more sophisticated four-factor model introduced by Elton et al. (1995). This model provides two major enhancements with respect to our findings. First, Elton et al. (1995) takes a slightly different approach in measuring the credit risk factor. The authors use the difference in returns from a U.S. high-yield corporate bond index and a U.S. intermediate treasury bond index as gauge for the systematic credit risk factor. And second, the four-factor model by Elton et al. (1995) covers not only systematic credit risk (DEF2) but also incorporates a risk factor related to the risk in the overall bond market (BOND), an optionality premium factor (OPTION) and a risk factor relating to the systematic equity risk (EQUITY)Footnote 8. Therefore, we apply the following regression model:

$${r}_{p,t}=\alpha +{\beta }_{1}*{\mathrm{EQUITY}}_{t}+{\beta }_{2}*{\mathrm{BOND}}_{t}+{\beta }_{3}*{\mathrm{DEF}2}_{t}+{\beta }_{4}*{\mathrm{OPTION}}_{t}+{\varepsilon }_{P,t}$$
(2)

Table 5 presents the results of the four-factor model. We do not find a significant exposure toward the EQUITY factor for either the Low or High ESG portfolio. However, both portfolios are symmetrically exposed toward the BOND, DEF2, and OPTION factor. In line with our expectations, the significant factor exposure does not persist for the BOND and OPTION factor in the Low–High ESG portfolio. However, the results of the four-factor model support our previous findings regarding significant differences in the Low–High ESG corporate bond portfolios’ exposure toward the credit risk factor. The regression coefficient is positive and statistically different from 0. A one percentage point increase in the return of the credit risk factor is associated with an increase in the Low–High ESG portfolio return by 0.13 percentage points on average. Thus, our finding of significantly different exposure toward the credit factor relating to large differences in portfolios’ ESG scores can be confirmed using the more sophisticated Elton et al. (1995) model.

Table 5 Elton et al. (1995) Model 12.5% Cut-Off Level
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Conclusion

The aim of this study is to give academics and investment professionals important insights on the impact of sustainability on the systematic credit risk exposure of corporate bond portfolios. Controlling for several portfolio characteristics, our findings highlight that the implementation of an ESG-best-in-class strategy significantly affects the credit risk exposure without any performance or diversification penalty. There is a clear adverse relation between a corporate bond portfolio’s sustainability and its credit risk: the higher the sustainability, the lower the credit risk. Hence, our results are consistent with the risk mitigation view (Goss and Roberts 2011) which states that companies with a superior sustainability performance are less risky. The findings of this study are robust to the usage of ESG ratings from different providers and different asset pricing models.

Our study further has implications for investment professionals. First, the consideration of ESG criteria in portfolio management is a suitable tool to systematically manage credit risk. Second, investment professionals have to account for the resulting bias in their portfolios to control their active risks. For instance, asset managers tracking a sustainable portfolio against a conventional benchmark need to overweight companies with lower credit ratings to compensate for the risk mitigating effect from higher sustainability to reduce systematic credit risk tracking error.

This is the first academic study that analyzes the effect of incorporating an ESG-best-in-class approach on corporate bonds, which noticeably contributes to the current academic discussion. Further research could investigate the stated relationships for different fixed-income segments, like high yield or emerging market, because the demand from clients in these segments is rising and the impact of sustainability could be more pronounced due to higher information-asymmetries and lower regulations.