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Virtue Remains After Removing Sin: Finding Skill Amongst Socially Responsible Investment Managers

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Abstract

We examine the investment skill of socially responsible investment (SRI) fund managers. Prior studies use the ‘alpha’ from standard asset pricing models as a proxy for management skill. However, implicit in the use of such models is that managers operate under no investment constraints. In the SRI context, this is patently false and can lead to biased alpha estimates and false conclusions about the existence of skill. We introduce a novel three-factor Fama–French asset-pricing model with the aim of estimating alpha more accurately and hence investment skill, without bias. This model excludes SRI-prohibited industries such as defense, alcohol, tobacco and gambling in the construction of the Fama–French market, value and size risk factors. We show that the exclusion of the SRI-prohibited industries leads to subtle and complex changes to the risk factors that drive SRI returns. When we re-estimate alpha using the new model we find, in contrast to the conventional Fama–French model, evidence of statistically and economically significant alpha. Furthermore, the risk loadings on the new risk factors are similar to those of the original Fama–French model suggesting that changes in risk loadings are not responsible for the finding of significant skill.

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Notes

  1. From its inception in 2002 the Vice fund has invested only in SRI-prohibited industries (www.vicefund.com). Interestingly, when compared with the Ave Maria Catholic Values funds, since 2002, the cumulative return performance is very similar. They also both outperform the S&P500 index by about 30 % over that time. (http://finance.yahoo.com/q/bc?t=my&s=VICEX&l=on&z=m&q=l&c=avemx&c=%5EGSPC).

  2. Statman and Glushkov (2009) claim that by using ‘best-of’ SRI screening in the construction of their portfolio ‘accepted minus shunned’ that they account for industry effects.

  3. More information is available at www.census.gov and www.standardandpoors.com.

  4. To replicate accurately, we used the Fama and French (1993) published papers, and to replicate in C++ we used the Fama and French asset pricing model. We used some SAS code provided in one of the WRDS forums (see Vora and Palacios 2010) as a guide.

  5. We cannot simply rely on the information provided by French on his web-site since as we needed to recreate the constitute HML/SMB portfolios accurately so that we may be able to remove the SRI-prohibited stock and create the new SRI factors.

  6. While we also include a momentum factor, its addition does not add much significance to the findings. Thus, the analysis focuses on results using the recast Fama–French model.

  7. When using product screens, SRI funds use the following three methods of screening:

    1. (i)

      Negative screening, which excludes companies based on their involvement in what are commonly known as SRI-prohibited industries. These include alcohol, tobacco, gambling and defense stocks.

    2. (ii)

      Positive screening involves seeking out companies that enhance SRI practices, such as those with good labor relations or a good history of community involvement. Companies must fulfill criteria set by SRI researchers to be included in a fund.

    3. (iii)

      Best-of screening is similar to negative screening; however, it does not take such an extreme position when rejecting companies. For example, a negative screen will completely exclude a company if any of their profits are derived from a SRI-prohibited industry, whereas a best-of screen will still consider companies that derive a small portion of their earnings from such industries.

  8. This raises issues too; for example, it is hard to imagine any tobacco company that best conforms to SRI goals.

  9. The Security monthly database (SECM) from Compustat was changed in 2006. The data stopped after 2006 and was retrospectively made quarterly by Compustat around 2009. Our C++ program relied on this database to find the monthly market capitalization of each company (i.e. PRCC*CSHO).

    Even though the SECM database was monthly initially Compustat changed, the frequency of common shares outstanding (CSHO) to quarterly (CHSOQ) around 2007.

    Thus, we were restricted to focus on our downloaded data which ended in 2006.

    Source: ‘Compustat Xpressfeed North American in WRDS—The security Monthly Table’ (http://wrds-web.wharton.upenn.edu/wrds/support/Data/).

  10. A robustness test which splits the data into tow equal halves, and estimates Fama–French regressions on each subsample is reported in Appendix 1.

  11. One equity fund was eliminated from the data sample as it did not use product screens.

  12. Hong and Kacpercyzk (2009) find that tobacco stocks might be driving some of the high returns of sin stock due to litigation risk and subsequently good cash flow news.

  13. While we also report and discuss results using SICS and NAICS filtering and the two larger samples as mentioned in the methodology, the analysis focuses on the GICS filtered benchmark and sample 1.

  14. When the SICS and NAICS benchmarks are used, very similar results are found.

  15. Consistent with the notion that (due to their large returns) once removed from the investment set, sin stocks reduce the market premium and are, in turn, responsible for the increase of the beta co-efficient in about two thirds of the funds in data sample 1. However, this does not translate to a statistically significant difference between the beta co-efficients of unfiltered and filtered risk premium.

  16. It is possible that the alpha increase is due to additional excluded risk factors.

  17. The fund numbers for period two funds are identical with those of the entire sample. However, see the authors for details about comparisons between period one funds and the period two/entire sample.

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Acknowledgments

The authors would like to thank Rohan Fletcher and the Australian Center for Financial Studies for a research grant supporting this work.

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Authors and Affiliations

  1. Department of Accounting and Finance, Monash University, Clayton, VIC, 3800, Australia

    Elizabeth Ooi & Paul Lajbcygier

  2. Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, 3800, Australia

    Paul Lajbcygier

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  1. Elizabeth Ooi
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  2. Paul Lajbcygier
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Correspondence to Paul Lajbcygier.

Appendix 1: Robustness Tests

Appendix 1: Robustness Tests

This appendix reports a robustness test conducted on subsamples of the data. We divided the data into two (near-) equal time periods 1984–1995 and 1996–2006. We then re-estimated the Fama–French regressions for all the funds in each period.

Only 19 funds existed in the first period, whereas 66 funds existed in the second period. For a fund that existed in both periods, we estimated separate Fama–French regressions for both periods.

Overall, the robustness test shows that changing the time-frame of the analysis does not affect the results greatly. Consistent with the conclusion of the paper, we still find that standard Fama–French regressions provide little evidence of out-performance of SRI funds in each period in contrast to Fama–French factors estimated without ‘sin’ stock, which provide ample evidence of out-performance in each time period. Furthermore, the outperformance is broadly consistent with the results on the entire sample provided in the main body of the paper.

Tables 6, 7, 8, 9, 10 and 11 contain the Fama–French regression results, divided into two periods. Tables 6, 7 and 8 shows the first period: 1984–1995 for samples 1, 2 and 3, respectively. Tables 9, 10 and 11 shows the second time period: 1996–2006 for samples 1, 2 and 3, respectively. Similar to the results presented in the body of the paper. The SRI funds are divided into three samples depending upon type of screening employed (i.e. sample 1: negative, 2: a mix of negative/best-of and, 3: best of screening).

Table 6 Robustness test—sample 1 (1984−1995)
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Table 7 Robustness test—sample 2 (1984−1995)
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Table 8 Robustness test—sample 3 (1984−1995)
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Table 9 Robustness test—sample 1 (1996−2006)
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Table 10 Robustness test—sample 2 (1996−2006)
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Table 11 Robustness test—sample 3 (1996−2006)
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Based on these results we conclude that the Fama–French regressions estimated on data in each time period are similar with those of the entire period (provided in Tables 1, 4 and 5). The majority of funds (i.e. 4/7) with significant alpha in sample one (see Table 6A) also have a significant alpha in period two (see Table 9A) and the entire sample (see Table 1).Footnote 17 Also, the magnitudes of the sample one alphas are very similar between periods. Finally, we find that irrespective of whether GICS, SICs or NAICs are used to filter firms from the investable universe the results are similar.

To conclude, the robustness check in this Appendix 1 shows that changing the time-frame of the analysis does not greatly affect the results.

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Ooi, E., Lajbcygier, P. Virtue Remains After Removing Sin: Finding Skill Amongst Socially Responsible Investment Managers. J Bus Ethics 113, 199–224 (2013). https://doi.org/10.1007/s10551-012-1290-x

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