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Analyzing the Performance of Ten Socially Responsible Mutual Funds

Mary H. Lesser

Charles M. Snipes College of Business and Economics, Lenoir-Rhyne University, Hickory, NC 28601, USA

Abstract: Studies have considered fund family effects on fund performance. This study examines fund family effects by building on the work of Kathman [1] comparing the performance of five funds with a broad SRI (or ESG) mandate that belong to a family of funds that specialize in this area to five funds that have focused mandates but do not belong to such families and are stand-alone products. The objective is to see if being part of a larger family or not affects the performance of the funds. The of the two groups of funds are analyzed using the CAPM, Fama-French, and four-factor models, as well as the tests required on the entire sample. Building on the work of Gil-Bazo et al. [3] and on that of Phillips et al. [3], this study further investigates whether there are differences between SRI funds based on the “family” to which they belong. The most interesting contribution of this study is the comparison between the two groups of funds. One might expect the funds from specialty firms to have some advantage due to economies of scale or returns on accumulated expertise. However, the Chow test indicates that there are significant differences between the two groups and analysis shows that the five funds from the non-specialty firms is the better performer.

Keywords: social conscience investing, mutual funds

Received September 17, 2022; Revised October 22, 2022; Accepted November 02, 2022

Citation: Mary H. Lesser, Analyzing the Performance of Ten Socially Responsible Mutual Funds. Journal of Finance and Economics, 10 (3). 68-74. January 2022. doi:10.12691/jfe-10-3-3

1. Introduction

When Amy Domini started investing with a focus on social conscience in 1990, it was a novel idea that one could link values with financial investment; that one could do “well” (financially) and at the same time do “good” work for the betterment of the world according to set goals. By 2012, it was estimated that approximately $1 of every $9 invested was in a mutual fund identified as “socially responsible”, otherwise known as a “social conscience fund.”

There is evidence that the trend continues. According to Morningstar, by December 2013, mutual funds and exchange traded funds (ETFs), for which managers applied “ethical” investing guidelines, controlled more than $115.6 bn in assets, not just in the United States but all over the globe [4]. It could be argued that an increase in social awareness in recent years has reinforced this trend, which will only strengthen, supported by recent evidence on investing behavior by Millennials [5].

The interpretation of “socially responsible” varies across such funds, but the unifying practice is that fund managers use some screening technique(s) that preclude investing in particular companies (or even entire sectors) because of the social impacts of the activities involved. To cite just two examples, the screening may include an assessment of a firm’s environmental practices, to determine whether or not the fund will acquire its shares. Or a strict explicit requirement may exist such that the fund will not invest in any firm that deals in some defined product, such as tobacco.

Some of the literature on this topic specifically addresses the question of whether investors have sacrificed financial returns to invest along “socially responsible” guidelines; that is, whether the results on such funds are significantly lower (given similar risk, etc.). Indeed, in some views of altruism the returns should be lower and investors should be sacrificing returns in tradeoff for social benefits.

Other researchers argue that the returns on such funds might be significantly different in a positive direction, as the screening eliminates firms whose activities lead to reduction in shareholder value. For example, a firm with weak environmental standards and practices has an increased risk of litigation and adverse judgments against it, which reduces the value of the firm for its owners.

The question then is whether a portfolio built around such screening generates returns that are significantly different on a risk-adjusted basis than one without the limitations on manager choice.

2. Review of the Literature

Edwards and Samant [6] studied the performance of socially responsible mutual funds in the period of 1991-2001 with the stated purpose of evaluating risk-adjusted returns using the modern portfolio theory. The research provides investors who would consider such funds with objective criteria to guide their choices. Another contribution of their analysis is the use of the “M Squared” is the use of the “M Squared” risk adjusted performance measure (developed by Modigliani & Modigliani, 1997) [6].

Statman [7] investigates the characteristics of socially responsible companies by comparing the content of four indices of socially responsible companies to that of the S&P 500. The study finds that, in general, SRI indices outperformed the S&P in the boom of the 1990s but underperformed in the downturn of the early 2000s [7].

Geczy et al. [8] investigate the cost of social responsibility investment (SRI) constraints on investors seeking the highest possible Sharpe ratio. Their study is structured much like the one outlined for this project. They employ the capital asset pricing model (CAPM) and find that an investor who rules out managerial skill (stock picking) the cost of the SRI restriction is a few basis points. They then expand the analysis to a four-factor model (the Fama-French model plus the Carhart momentum factor) and find that the SRI constraint costs at least 30 basis points per month [8].

Ter Horst et al. (2007) make four major contributions. They survey the literature on socially responsible investments and identify both the various interpretations and the associated screenings. They also review the literature on corporate social responsibility (CSR), which is defined as a combination of good corporate governance practices, sound environmental standards, and care of stakeholder returns. In particular, they review the literature on the performance of mutual funds, again using both the CAPM and four-factor models, as well as other methodologies. Last, they present empirical findings that the risk-adjusted returns for SRI funds are not different from those of “conventional funds” in the United States and the United Kingdom, but that SRIs in Europe and the Asia-Pacific region significantly underperformed [9].

Gil-Bazo et al. (2008) examine the effect of fees on SRI mutual fund performance. It is intuitively appealing that fund managers who have to apply SRI constraints, in addition to more “conventional” financial constraints, would be charging more. Indeed, all of the funds cited for this project make very clear statements in the prospectuses that the social constraints are in addition to seeking out firms that are undervalued, etc. They find that in the period 1997-2005, SRI funds in the United States had significantly higher fees than conventional funds with similar characteristics, and the U.S. SRI funds had better performance than the conventional funds, both before and after fees are considered. They note that the differences can be attributed to SRI funds that are run by management companies that specialize in SRI [2].

3. Hypotheses

This study analyzes the performance of SRI funds using the CAPM, Fama-French, and four-factor models, as well as the tests required on the entire sample. However, building on the work of Gil-Bazo et al. [2] and on that of Phillips et al. [3], this study further investigates whether there are differences between SRI funds based on the “family” to which they belong. The academic literature has provided mixed evidence regarding whether there are diseconomies of scale in the mutual fund industry as a whole. Phillips et al. [3] provide results indicating that fund size does not appear to matter to fund performance though their work indicates that there may be an indirect relation as a result of preferential allocation of investment strategies to smaller funds within fund families.

As discussed by Kathman [1], many of the “best-known” SRI funds come from management firms described as “ESG” shops (Environmental, Social, Governance). This list includes Calvert Small Cap Fund (Class A Shares), CCVAX; Parnassus Fund, PARNX; Domini Social Equity Fund (Investor Shares) DSEFX; and Pax World Growth Fund (Individual Investor Class), PXWGX; and Walden Social Balance Fund, WSBFX. Due to the nature of the Walden holdings, part of this analysis is to add Walden’s data (CRSP #30850) to the sample, run the results for that fund individually and compare them to the analyses of the other funds, amend the total sample to include this as a tenth fund and rerun both the equal-weighted and value-weighted regressions.

The other five funds considered are categorized variously by Kathman: Appleseed Fund (APPLX) is from a smaller specialty firm, Neuberger Berman Socially Responsive Fund, Investor Class Shares (NBSRX) and Dreyfus Premier Third Century Fund, Class A Shares (DTCAX) are from non-specialty firms (the fund families offer these SRIs as part of a range of products), Ariel Appreciation Fund (CAAPX) is described as “SRI Lite” because of its relatively few restrictions (specifically tobacco, nuclear power, and handguns), and Green Century Equity Fund (GCEQX) is from what could be called a single-priority shop (as the fund name implies, the environment is the focus for management).

This study concludes by dividing the ten funds into two groups of five funds each and use a Chow Test to examine whether there is a significant difference in the performance of the funds from specialty firms (called “Five Large”) compared to those of the five funds from the non-specialty firms (called “Five Other”).) As per Phil.

However, an indirect relation manifests as a result of preferential allocation of investment strategies to smaller funds within fund families.

4. Analysis of the Ten Funds, Three Different Model Specifications

Table 1 shows the results of estimating the Capital Asset Pricing Model (CAPM) [10], the Fama-French Model [11], and the Fama-French plus the Carhart momentum factor for each of the 10 funds. The models are specified as follows:

• the 1-factor model (CAPM, or Jensen's alpha),]

• the 3-factor model (Fama-French alpha),

• and the 4-factor model (FF plus Carhart),

Table 1. Results from Ten Funds, Three Models

In all three models, the significance of the coefficients is tested by deriving a t-statistic using the estimated value of the coefficient divided by the estimated standard error of that coefficient. For the variable “Excess Return on the Market”, the hypothesis tested is whether the beta for that coefficient is 1; for all others, the test is whether the value of the coefficient is zero. Critical t values are shown for three levels of significance: 0.01, 0.05, and 0.10. It should be noted that the degrees of freedom change for each model specification, but that did not result in different critical values of t.

Reviewing the results, it was found that in none of the 30 regressions (3 model specifications for 10 funds) is the intercept coefficient significant, suggesting no positive alpha or excess return, and in all of the regressions, the F test shows a significant relationship as a whole.

In all of the regressions, the coefficient on Excess Return on the Market{1} is positive, although it is not always significantly different than 1. The coefficient is significantly different than 1 on the higher side for some funds, but for most funds, it is different on the lower side. That is, the fund beta is less than one, indicating that the firm is taking less risk than the overall market.

Comparing the CAPM and the Fama-French specifications, it appears for most funds that the Excess Return on the Market variable loses significance as the other factors are added. The Excess Return on the Market or “Beta” in the Fama-French is analogous to that in the CAPM, but they are not equal. Therefore, the observed decrease in significance of the Excess Return on the Market variable is not surprising, as the point of the Fama-French specification is to add detail to the CAPM. One would expect influences captured by the Excess Return on the Market variable in the simpler specification to include influences from the other factors added by Fama and French.

The two added factors in the Fama-French specification are “SMB”, which represents “Small Minus Big,” a measure of the excess return of small cap over large or large cap stocks, and “HML,” which represents “High Minus Low,” a measurement of excess returns on value stocks (high book-value-to-price) compared to growth stocks (low book-value-to-price). By these definitions, the closer the estimated value of the coefficient on SMB is to 1, the greater the fund behaves like a small-cap portfolio (and vice versa, for closer to 0), and the closer the estimated value of the coefficient on HML is to 1, the greater the fund behaves like a value fund (and vice versa, for closer to 0).

Looking at the results in Table 1 across the ten funds in the Fama-French specification, the only significant coefficients for SMB are positive and that is for only four of the ten funds (again, this is a test for significantly different than zero). There is a range of values on this coefficient, from approximately 0.26 (for Neuberger Berman, NBSRX) to a high of 0.86 (for Calvert, CCVAX). These findings are generally consistent with how the funds are described (e.g., CCVAX is Calvert Small Cap Fund).

The coefficient on HML is only significant for Ariel, CAAPX, where it is positive and relatively small in value. This is not surprising given that the fund self-identifies as the Ariel Appreciation Fund.

Looking across all three model specifications, the adjusted R square is highest in the four-factor specification that includes the momentum variable. From this, one can conclude that the addition of the variable adds to the power of the model. As noted above, the intercept is not significant in the four-factor specification for any of the ten funds. The coefficient on the Excess Return on the Market is always positive, although it is not always significantly different from 1. The coefficient is significantly different than 1 on the higher side for some funds, but for most funds, it is different on the lower side; that is, the fund beta is less than one, indicating that the fund manager is taking less risk than the overall market. By itself, this would explain lower returns as the result of less risk taking, consistent with the idea that these funds invest in companies with less exposure to various adverse circumstances, as discussed above.

As in the Fama-French specification, the only significant coefficients for SMB in the Four-Factor Model are positive and that is for only four of the ten funds, and the range of values on this coefficient, from approximately 0.26 (for Neuberger Berman, NBSRX) to a high of 0.86 (for Calvert, CCVAX), is essentially unchanged.

The coefficient on the momentum variable is not significant for all funds. However, when it is significant, the value is negative. Recall that in an article published in 1997, Mark Carhart showed that a fund's preference for holding stocks that had the best recent performance, as opposed to those that had the worst recent performance, was also a significant factor explaining returns [12]. The negative values suggest that managers of these funds are not selecting stocks that have been winners over time, a finding consistent with the idea that an SRI by definition has an emphasis on other priorities. It is suggestive that the screening may in some cases reduce the returns to the shareholders.

It is worth noting at this point that the tenth fund, Walden (WSBFX), does not appear significantly different in its behavior than the other nine. Having noted the differences above it would have been desirable to find a breakdown between the two groups of funds, but this is not apparent. The test of the difference between the two groups of funds as constructed is reserved for a later section of this paper.

5. An Analysis of Average Monthly Returns

The same three model specifications used above are next applied to the equal weighted and the value weighted average monthly returns for the entire sample. As described earlier in the paper, the analyses are conducted for the sample with and without the addition of the tenth fund, Walden. Table 2 on Pages 12 and 13 shows the results.

As before, color coding is used in the table to highlight which coefficients are significant at each of the three specified levels of significance; those left without highlighting are not significant at 0.10.

Reviewing the results, it is found that in none of the 12 regressions (3 model specifications for 4 average return constructs, equal-weighted and value-weighted, for 9 funds and 10 funds) is the intercept coefficient significant, suggesting no positive alpha or excess return, and in all of the regressions, the F test shows a significant relationship as a whole.

The coefficient on Excess Return on the Market is noticeably more often significant when the value-weighted construct is used and its value changes considerably from the CAPM to the other two models. The coefficient on the “SMB” variable is always positive and significant. The estimated value of the coefficient on SMB is notably smaller when the value-weighted average is used. The HML variable is significant only in the equal-weighted four-factor model and only for two funds, but it is negative across all specifications, again pointing to this factor as a possible cause of lower excess returns.

The momentum variable is significant and negative across all four formulations of the weighted average monthly return in the four-factor model. It is again noted that the value of the adjusted R square increases from the CAPM to the four-factor model, suggesting that the added factors increase the model’s explanatory power. The addition of Walden to the sample appears to have no impact.

Table 2. Analysis of Average Monthly Returns: Equal Weighted and Value Weighted, 9 Fund and 10 Fund

6. Comparing the Two Groups of Funds

The final investigation in this study is whether there is a significant difference between the two groups of funds. As discussed earlier, one group is the “best-known” SRI funds, which are Calvert Small Cap Fund (Class A Shares), CCVAX; Parnassus Fund, PARNX; Domini Social Equity Fund (Investor Shares) DSEFX; and Pax World Growth Fund (Individual Investor Class), PXWGX, along with Walden Social Balance Fund, WSBFX. The other five funds, which can be categorized variously, are Appleseed, APPLX; Neuberger Berman, NBSRX; Dreyfus, DTCAX; Ariel, CAAPX; and Green Century Equity Fund, GCEQX.

Table 3 shows the results for the three model specifications for the two groups of funds.

Table 3. Comparative Results, Two Groups, Three Models

All highlighted are significant at 0.01

Based on the results of the analyses above, the Chow Test is performed for the model specification with the strongest performance, the four-factor model using the value-weighted monthly average construction. The null hypothesis of the Chow test is that all paired coefficients are equal between the two groups; that is, the coefficient on the Excess Return on the Market is equal for the Five Large Funds and for the Five Other; similarly for the coefficients on SMB, HML, and Momentum, the coefficient on each for the Five Large group would be the same as that for the Five Other group. The test assumes that the model errors are independent and follow identical normal distributions with unknown variance.

The test statistic is constructed{2} as:

The results from the ANOVAs are shown in Table 4 below.

Table 4. Analysis of Variance and Chow Test

Using the data to construct the Chow Test F statistic, we have:

The critical value of F is at the 0.01 level of significance with k=5 and n-2k=51 degrees of freedom. The value of the calculated F statistic allows us to reject the null hypothesis and conclude that not all pairs of coefficients are equal.

This result is not surprising when one reviews the results in Table 3. While there is a match in the coefficients that are significant for the two groups, there are clear differences in the values of those coefficients.

Looking specifically at the four-factor model using the value-weighted monthly average construction, it is noted that the intercept terms (neither significant) differ in sign. The Excess Return on the Market coefficient for the Five Other Groups is almost twice that for the Five Large Groups (both are positive and significant). The SMB coefficient for the Five Other Groups is almost five times that for the Five Large Groups (both are positive and significant), and the Momentum factor for the Five Other Groups is almost two-and-a-half times that for the Five Large Groups (both are negative and significant).

The results lead to the conclusion that the Five Other funds, as a group, would be expected to perform better than the Five Large funds. While this may seem counterintuitive given that the Five Large funds are from “specialty” shops, it again raises the issue of how size affects performance for this type of mutual fund.

7. Conclusion

Reviewing the results, in none of the 30 regressions (3 model specifications for 10 funds) is the intercept coefficient significant, suggesting no positive alpha or excess return, and in all of the regressions, the F test shows a significant relationship as a whole. This alone indicates that there is no basis to conclude that these firms outperform or underperform in the market as a whole.

The coefficient on the Excess Return to the Market variable is significantly different than 1 on the higher side for some funds, but for most funds, it is different on the lower side. That is, the fund beta is less than one, indicating that the fund is taking less risk than the overall market. Returns would be expected to be consistent with risk taking.

The results on the SMB and HML variables indicate that the funds are acting in a manner consistent with what they state in their prospectuses, and the momentum factor, which is negative when it is significant, indicates that social screening may override the “picking a winner” tendency for these funds.

The most interesting contribution of this study is the comparison between the two groups of funds. One might expect the funds from specialty firms to have some advantage due to economies of scale or returns on accumulated expertise. However, the Chow test indicates that the Five Other group is the better performer.

This finding connects this study to the literature on fund size and performance, particularly the work of Phillips et al. [3], which considers the possibility of preferential allocation of investment strategies among funds in the same family, with the “best” investment strategies going to the smaller funds. The better performance of the Five Other group in this study may be because those funds studied are the only (or one of the few) SRI funds in their fund family, and thus received the best ideas consistent with the screening and performance criteria. This seems to be a promising area for further investigation, in particular, to replicate some of the studies on fund size and performance for SRIs as a group.

Acknowledgements

The author wishes to thank Prof. Paul Bolster of Northeastern University for the course project that started this research.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The Author declares that there is no conflict of interest.

Notes

{1} It should be noted that the Fama-French dataset revised the market return used to measure Rm-Rf in the US. “It is now the value-weight return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ that have a CRSP share code of 10 or 11 at the beginning of month t, good shares and price data at the beginning of t, and good return data for t. Previously the CRSP NYSE/AMEX/NASDAQ Value-Weighted Market Index was used as the proxy for the market return. The set of firms in the new series is more consistent with the universe used to compute the other US returns.” http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

{2} http://en.wikipedia.org/wiki/Chow_test.

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