Do responsible utility companies outperform their peers?

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University of Amsterdam, Amsterdam Business School

Master in International Finance

Do Responsible Utility Companies Outperform

Their Peers?

September 2014

Abstract

This paper investigates the relationship between environmental, social and governance (ESG) factors and financial performance for a sample of publicly traded US Utility companies. Using the Sustainalytics database on several E, S and G dimensions in the 2009-2013 period, and weighting those dimensions according to prominence in the utilities sector, the study constructed and evaluated several portfolios that differed in ESG characteristics. The portfolio from which low rated companies were excluded and the “best-in-class” provided substantially higher average returns than the high rated, low rated, and “long-short” portfolios over the 2010-2014 period. This performance differential could not be explained by differences in market sensitivity and investment style.

Author:

Rajiv Bajoria

Thesis Supervisor:

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1. Overview of Research

1.1 Introduction

Socially responsible investment (SRI) is attracting a lot of attention, both in academia and in practice. A growing number of fund managers are incorporating Environmental, Social and Governance (ESG) factors while making investment decisions. According to the US Social Investment Forum (2012), almost one out of nine dollars under professional management in the United States - $3.74 trillion or more was invested according to SRI strategies.

Mainly driven by climate change and human rights concerns, environmentally and socially responsible business practices are becoming indispensable for companies. For investors, the scope of responsible investment can cover not only environmental issues and climate change mitigation but also the effects of businesses on a broad range of social and ethical concerns. The paper investigates the implications of this strategic shift in the allocation of resources towards such concerns for the performance of utility companies in the United States. Specifically, it aims to assess whether there is a link between ESG ratings of large utility companies and their stock performance.

The utilities sector comprises a wide range of services, including electricity, water, gas and public sanitation management. The companies in this sector has a direct impact on various environmental and social issues. As major emitters of carbon dioxide (CO2) and other air emissions, energy-related utilities have significant environmental impact such as climate change. Further, since utility companies operate close to local communities, their operations can have large negative consequences, such as the Fukushima nuclear disaster.

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Such incidents can cause significant reputational, operational and regulatory risks to companies. As a result of these risks, the utilities industry is not quite what it used to be. Once considered the quintessential safe widow-and-orphan stocks, electricity companies are undergoing big change as they respond to regulatory changes, demand fluctuations, price volatility and new competition. In this changing scenario, it is becoming all the more important to assess company’s environmental preparedness and social practices in order to make investment decision. A survey conducted by EIRIS in 2006 analyzed the response from over 40 mainstream and SRI managers in which 15 sectors were identified where ESG issues have the most significant financial impact. Investors consider ESG issues to have the greatest impact in the Energy and Utilities sector.

1.2 Literature Review

In the last two decades, there has been a plethora of studies on the relationship between ESG factors and financial/stock performance of companies. Overall, current theory about the impact of ESG on corporate financial performance is ambiguous (e.g., Waddock and Graves, 1997). In other words, positive, negative as well as neutral effects are found. Alexander Kempf and Peer Osthoff (2007) conducted a research to identify whether a trading strategy in stocks using past SRI ratings lead to abnormal performance. They used KLD ratings which covered six qualitative criteria: community, diversity, employee relations, environment, human rights, and product, each one having multiple sub –criteria. Two portfolios with high and low ratings were studied over the period 1992-2004, while measuring outperformance using the Carhart (1997) model. The results show that the high rated portfolios perform better than the low rated portfolios and a best-in-class strategy

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yields a positive alpha of up to 8.7% per year. Derwall et al. (2005) constructed and evaluated two equity portfolios that differed in “eco-efficiency”, which can be thought of as the economic value a company creates relative to the waste it generates. The result showed that, after considering industry effect, in combination with the Carhart (1997) model, a long-short portfolio provided a 6.04% alpha. Gompers et al. (2003) used 24 different provisions to build a “Governance Index” for about 1,500 firms per year. They concluded that, an investment strategy which bought the firms in the lowest decile of the index (strongest shareholder rights) and sold the firms in the highest decile (weakest shareholder rights) would have earned 8.5 % per year over the period 1990-1999. These results including several other studies suggest that intangibles are not fully valued in the market and that incorporating ESG or SRI screens can improve portfolio returns. On the other hand, some studies show that SRI underperforms a conventional strategy or has no significant impact on portfolio returns.

According to modern portfolio theory, excluding low ESG ranking or “sin stocks” from a portfolio decreases diversification opportunities in terms of forgone returns. This is because the excluded stocks receive a return premium (Fama and French, 2007). Hong and Kacperczyck (2007) find that there is a general practice to not invest in companies that are involved in the business of tobacco, weapons, adult entertainment etc, hence some institutional investors such as pension funds, charitable foundations have to forego the return premium from these stocks. Bauer et al., 2005 conducted a research on 103 German, UK and US ethical mutual funds. Using Carhart (1997) model and after controlling for investment style, little evidence is found of significant differences in risk-adjusted returns

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between ethical and conventional funds for the 1990-2001 period. There is an extensive empirical literature examining the impact of ESG on financial performance. Unsurprisingly, it has produced a mixed array of findings (for reviews see Orlitzky, Schmidt and Rynes, 2003; Margolis, Elfenbein and Walsh, 2007; van Beurden and Goessling, 2008; Horvathova, 2010). While a detailed review of this literature is outside the scope of this paper, it is clear that the topic is filled with problems due to potential publication bias, differences in sampling periods and contested statistical procedures.

However, some of the more recent studies indicate that majority of the evidence supports the existence of a positive relationship between ESG factors and stock returns. One of the reasons might be that earlier reviews included many papers from 1970-1990 when ESG issues had low socio-political importance as suggested by Van Beurden and Goessling (2008). The other important consideration while analyzing the relationship between ESG and financial performance is sector differences. Since different industry sectors are impacted differently by environmental, social and governance issues, studies done on many sectors can mask sector specific effects (Griffin and Mahon, 1997). Hence, Chand (2006) suggests using industry type as boundary condition for assessing the relationship between ESG factors and financial performance. It will eliminate all the environmental differences that organizations in different industries face that tend to cloud this relationship.

With regards to the utilities sector, there is little evidence of a study of the impact of ESG factors on the stock performance of companies. Besides, the utilities sector is directly impacted by the growing number of environmental issues, mainly climate change

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regulations. These changes have been forcing companies in this space to make structural shifts in their operating strategy in order to survive and maintain their revenues and market share. As a result, companies are focusing more and more on environmental and social issues. In such a scenario, it is anticipated that companies which are better prepared with strong policies and programs will be rewarded in terms of better financial performance and investors in terms of increased stock returns. Hence, the current study aims to uncover the relationship between the ESG ratings of utility companies in United States and their stock returns.

1.3 Thesis Structure

The second part of the thesis will be an introduction to the sustainability data as well as the financial data used in this research, including the features of the data and sample. The third part of the thesis will be the explanation of the methodology of this research. After which the results are presented. The conclusion of the thesis is in the fifth part.

2. Data Analysis

2.1 Introduction to Sustainalytics ESG Database

Sustainalytics is a leader in ESG research services and the data used in this paper are taken from their database. It offers environment, social and governance scores that are consolidated into an ESG overall score per company.

The scores of the sub-indicators are given between 0% and 100%. The highest value for the indicator means that it is scored fully. For example, the social indicator “Health and Safety Programmes” with a score of 100% means that programmes on health and safety are in place. A health and safety indicator analyses whether a company has employee health

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& safety programmes and related targets. This indicator is only used for certain high-risk industries such as energy and utilities. Sustainalytics utilizes approximately 150 ESG indicators to measure a company’s sustainable practices, which are consolidated on an overall Environment, Social and Governance level. Approximately 30 indicators pertains to governance, 55 indicators to social and 54 indicators to environment. If an indicator is not related to the industry or sector, that ESG indicator is weighted a zero. For the purpose of this study, which is based only on the utilities sector, only those sub-factors relevant to it will be considered in the overall ESG score. Refer to appendix for a detailed overview of all the ESG sub factors pertaining to the utilities industry.

The final score given to a company is arrived at by Sustainalytics as the weighted score of its scores at E S and G levels. The weighting criteria is flexible to be modified by the user as per his assessment of the risk of each indicators and levels. In the current study, the default weights set by Sustainalytics for the utilities sector were used, which are 45% for Environment, 30% for Social and 25% for Governance. The data on the aggregated level of environment, social, governance scores are available on a yearly basis.

2.2 US Sample Summary

For the current research, only US companies of the database were considered; the database is expanding year by year as 11 and 53 US utility companies were in the database in the year 2007 and 2013 respectively. In total 34 companies were included in the research for the period 2009 to 2013. This sample consists of large to mid-size companies, with the largest company being Duke Energy with a market capitalization of USD 52.3 billion while the smallest company is Integrys Energy with a market capitalization of USD 5.4 billion. All these figures are as of 31st_{August 2014. This sample was compared with the utilities}

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sector companies in the S&P 500 to check the market representativeness of Sustainalytics database. It was found that these 34 companies provide an adequate coverage of the US utilities sector, both in terms of the number of companies and the market capitalization. The number of utility companies in the S&P 500 index is 33 with a total market capitalization of USD 575 billion, while the Sustainalytics database for US utility companies cover 34 companies with a market capitalization of USD 582 billion. The sample covers all major sub-industries including electric, gas, water, multi-utilities and independent power producers and energy traders.

For financial data, the monthly stock returns from January 2010 to June 2014 were taken CRSP database.

3. Methodology

In order to analyse the effects of ESG factors on financial performance, several different portfolios were formed including top 50%, bottom 50%, best-in-class etc. In this section, the portfolio construction method is described (Section 3.1) and the performance measurement of these portfolios is detailed in the next section (Section 3.2).

3.1 Portfolio Construction

At the end of year t - 1, Sustainalytics reports the rating of the companies. Based on these ratings, five different equal-weighted portfolios were formed at the beginning of year t and these portfolios were held unchanged until the end of year t. The first portfolio is called the “High-rated Portfolio” which consists of top 50% of the 34 companies according to

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overall ESG ratings, while the second portfolio is called the “Low-rated Portfolio” which consists of bottom half companies as per ESG score. The third portfolio is a “Long-Short” portfolio (long in the top 30% high-rated portfolio and short in the bottom 30% low-rated portfolio). The fourth portfolio is called “Excluding low rated” which consists of all the companies excluding the bottom 30% of the 34 companies. It resulted in the exclusion of 10 companies with the lowest scores. The fifth portfolio is called the “Best-in-class”, which contains the top 30% rated companies, resulting in the selection of top 10 companies out of 34. As Sustainalytics produces new scores every year, the portfolios are rebalanced using the scores as of the year end from 2009 to 2013. Further, ESG ratings are updated during the year after the reporting period, for example, the information for the year 2012 will be updated in the ESG reports prepared during the year 2013. So, the portfolios are constructed based on the availability of new ratings. For the current research, portfolios are built for the years from 2010 to 2014 based on ratings from 2008 to 2012. Hence, at the end of year t, we take the new ESG ratings and construct the portfolios to be held in year t + 1. This leads to a time series of monthly returns for the years from January 2010 to June 2014. Five separate portfolios as described above are formed based on these monthly returns.

3.2 Performance Measurement

The Carhart (1997) four-factor model is used to find out the outperformance of portfolios. Multi-factor models have evolved from the CAPM model, which prices securities according to their betas. However, the results of studies on cross-sectional stock returns brought about doubts regarding the ability of a single index model to explain mutual fund

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performance. Fama French three-factor model (1993) is considered to give better explanation to fund performance after taking into account two more factors size and value. However, it is unable to explain the cross-sectional variation in momentum-sorted portfolio return. Therefore, Carhart four-factor model extended Fama French model by adding one more momentum factor to capture the effect of momentum:

𝑅_𝑖𝑡 − 𝑅_𝑓𝑡 = 𝛼_𝑖+ 𝛽_0𝑖(𝑅_𝑚𝑡− 𝑅_𝑓𝑡) + 𝛽_1𝑖𝑆𝑀𝐵_𝑡+ 𝛽_2𝑖𝐻𝑀𝐿_𝑡+ 𝛽_3𝑖𝑀𝑜𝑚_𝑡+ 𝜀_𝑖𝑡

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Where 𝛼𝑖 is the abnormal return based on Carhart model (1997), used to examine portfolio outperformance; 𝑆𝑀𝐵𝑡 is the return gap between a small and a large cap stock portfolio at time t; 𝐻𝑀𝐿_𝑡 is the return gap of a portfolio consisting of high book-to-market stocks and low book-to-market stocks; 𝑀𝑜𝑚_𝑡 is the difference in return between a portfolio of past 12 months outperformers and a portfolio of past 12 month underperformers at time t (Mark M. Carhart (1997)). 𝛽0𝑖 is the market risk of portfolios. The Carhart factors data are taken from Kenneth R. French-Data Library. Since, the CAPM model is still a popular model amongst the investor community, the portfolio outperformance was also determined using the CAPM single factor model.

𝑅_𝑖𝑡− 𝑅_𝑓𝑡 = 𝛼_𝑖 + 𝛽_0𝑖(𝑅_𝑚𝑡− 𝑅_𝑓𝑡) + 𝜀_𝑖𝑡 (2)

Another method to measure the risk-adjusted return is by using the Sharpe ratio. Equation (1) is the formula used to calculate Sharpe ratio, which is the equity’s excess return per unit of risk. The Sharpe ratio can tell whether a higher return is brought about by better investment strategy or by taking excess risks.

Sharpe raio =𝐸(𝑟𝑝) − 𝑟𝑓

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Where 𝐸(𝑟_𝑝) the expected portfolio is return; 𝑟_𝑓 is the risk free rate and 𝜎_𝑝 is portfolio standard deviation.

4. Results

In this section, it is analyzed whether investors can improve performance by using ESG ratings in constructing portfolios. We examine this question based on different portfolios and models for performance measurement.

4.1 CAPM Model

Table 1 summarizes the results of the CAPM single factor model for all the five portfolios. We first look at the high and low rated portfolios. The table shows that the alpha for the high-rated portfolio (5.46%) is slightly higher than the alpha of the low-rated portfolio, however, the difference in beta is quite big (0.29 for high-rated vs 0.44 for low rated). This shows that a high ESG score may not result in higher returns but it helps to reduce portfolio risk. We now turn to the long-short portfolio in which the top 30% rating companies were bought while the bottom 30% companies were sold. This resulted in a positive alpha of 2.98%, while a beta of -0.10, which implies that this portfolio is negatively correlated with market returns. Finally, we look at the two most striking results. First, the “excluding low rated” portfolio, which excluded investing in the worst 30% ESG rated companies. It resulted in a positive alpha of 10.81% and a beta of 0.34, which indicates that this portfolio is riskier than the high-rated portfolio (beta of 0.34 vs 0.29), but, the alpha is significantly

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higher. One possible explanation is that the returns are enhanced by selecting some average low rated companies by only slightly increasing the risks. This might be because the average low rated companies have weaker policies and programs but have little or no controversies. Regarding, the best-in-class portfolio, the alpha is 9.63%, while the beta is 0.29, which means that by selecting only the best practice companies, the risk of the portfolio can be reduced by sacrificing some returns. Further, only the “excluding low rated” and “best in class” portfolios alpha were significant, while all others were not significant. Overall, by employing the CAPM model, the alphas seem to be quite high.

4.1 Carhart Model

As it was empirically established that the single-factor CAPM framework is not efficient, the Carhart multi-factor model was employed to evaluate the portfolio returns. Table 2 shows the results of the Carhart multi factor model for all the five portfolios. Overall, the results look similar, relatively, in comparison to the CAPM, but, the alphas are significantly lower in the Carhart framework. This is expected as the other factors such as HML, SMB and MOM contribute to the returns. Again, the most significant positive alpha was attained by the “excluding low rated” portfolio (7.80%), which is significant at 10% level. While,

Portfolio Alpha Beta Rm-Rf

High-rated Portfolio 5.46% 0.29 1.34%

Low-rated Portfolio 4.52% 0.44 1.34%

Long-Short 2.98% -0.10 1.34%

Excluding low rated 10.81% 0.34 1.34%

Best-in-class 9.63% 0.29 1.34%

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all the other portfolios give positive alphas, these are not significant at 5% or 10% levels.

A snapshot of the descriptive statistics is shown in table 3 below.

The Sharpe ratio, which shows the risk adjusted returns was also calculated. The results are shown in table 4.

Again, the best portfolio turn out to be the “excluding low rated” followed by the “best-in-class”.

Portfolio High-rated Portfolio Low-rated Portfolio Long-Short Excluding low rated Best-in-class

Aphla 2.25% 1.87% 1.98% 7.80% 6.32% (t-statistic) 0.5072 0.3983 0.7099 1.7897 1.4001 ßeta 0.5056 0.6162 -0.0531 0.5228 0.4922 SMB -0.9031 -0.7676 -0.2318 -0.8142 -0.8902 HML 0.1032 0.1343 0.0569 0.1619 0.1761 MOM 0.4472 0.3780 0.1706 0.4578 0.5081

Table 2: Multi-factor Regression Results for ESG Portfolios, January 2010–June 2014

Mean 1.22% 1.34% 0.11% 1.36% 1.20%

Std.Dev 3.14% 3.39% 1.68% 3.22% 3.29%

Maximum Monthly Return 8.05% 7.57% 3.70% 8.31% 8.67%

Minimum Monthly Return -7.85% -8.20% -3.23% -7.62% -8.09%

Skewness -0.74 -0.53 -0.10 -0.75 -0.69

Kurtosis 0.66 0.14 -0.70 0.60 0.63

Table 3: Descriptive Statistics for ESG Portfolios, January 2010–June 2014

Excess return 10.15% 11.59% 1.30% 16.24% 14.30%

Std. Devn. 11.15% 12.02% 5.82% 11.14% 11.40%

Sharpe ratio 0.9106 0.9643 0.2236 1.4576 1.2537

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5. Conclusion

An increasing number of investors incorporate ESG factors into their investment process. This raises the question of how ESG performance of companies affects the financial performance of these portfolios. In this paper we analyze whether investors can increase their returns, both absolute and risk-adjusted, by following a simple trading strategies based on ESG ratings. The stocks were screened based on a variety of scenarios and the performance was measured using the Carhart (1997) model. We got the following main results:

Investors can earn remarkably high abnormal returns by following a simple strategy of excluding the bottom 30% ESG rated companies from their portfolios, or, by following a best-in-class strategy, where, only the top 30% rating companies are included in a portfolio. This return behavior can be explained by the fact that the companies which are involved in significant environmental, social or governance incidents and/or controversies usually have the lowest ESG ratings. Because controversies have a significant impact on a company’s performance, its weighting is very high in the calculation of an ESG score. Hence, it can be concluded that excluding the lowest ESG rated companies can significantly enhance the portfolio returns, while also reducing risk. To reduce risk, the strategy should be to invest only in best-in-class companies, since they have lower ESG risks.

Overall conclusion is that ESG ratings have valuable information for investors. These results show remarkably high abnormal returns. This immediately raises the question of where this extra returns come from. Does it result from a temporary mispricing in the

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market or does it compensate for an additional risk factor? Answering this question seems to be a promising avenue for future research.

6. References

Bauer, R., Koedijk, K. and Otten, R., ‘International evidence on ethical mutual fund performance and investment style’, Journal of Banking and Finance, Vol. 29, 2005, pp. 1751–67.

Chand M. 2006. The Relationship between Corporate Social Performance and Corporate Financial Performance: Industry Type as a Boundary Condition. The Business Review 5(1): 240–245.

Carhart M. (1997) “On Persistence in Mutual Fund Performance.” The Journal of Finance, Vol. 52, No. 1 (Mar. 1997), 57-82

Derwall J. Guenster N. Bauer R. and Koedijk K. (2009). “The Eco-efficiency Premium Puzzle.” Financial Analysts Journal, Vol. 61, No. 2 (Mar. - Apr., 2005), pp. 51-63

Fama, E. F. and K. R. French. (1993) "Common risk factors in the returns on stocks and bonds." Journal of Financial Economics, 33 (1): 3-53.

Gompers P., Ishiiand J., Metrick A. (2003). “Corporate Governance and Equity Prices.” Quarterly Journal of Economics, 118(1), February 2003, 107-155

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Griffin J, Mahon J. 1997. The Corporate Social Performance and Corporate Financial Performance Debate: Twenty-Five Years of Incomparable Research. Business and Society 36(1): 5–31.

Hong H., Kacperczyk M. (2009). “The price of sin: The effects of social norms on markets.” Journal of Financial Economics, 93(2009)15–36

Horthathova E. 2010. Does Environmental Performance Affect Financial Performance: a meta-analysis? Ecological Economics 70(1): 52–59.

Kempf A. and Osthoff P. (2007). “The Effect of Socially Responsible Investing on Portfolio Performance.” European Financial Management, Vol. 13, No. 5, 2007, 908–922

Margolis, J. D., H. A. Elfenbein and J. P. Walsh. (2007) "Does it pay to be good? An analysis and redirection of research on the relationship between corporate social and financial performance." (Working Paper, Harvard University), Available:

http://stakeholder.bu.edu/Docs/Walsh,%20Jim%20Does%20It%20Pay%20to%20Be%20 Good.pdf.

Orlitzky, M., F. L. Schmidt and S. L. Rynes. (2003) "Corporate Social and Financial Performance: A meta-analysis." Organization Studies, 24 (3): 403-441.

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Van Beurden P, Goessling T. 2008. The Worth of Values – A Literature Review on the Relation between Corporate Social and Financial Performance. Journal of Business Ethics 82(2): 407–424.

Waddock, Sandra A., and Samuel B. Graves 1997 ‘The corporate social performance financial performance link’. Strategic Management Journal 18: 303–319.

7. Appendix

4.1 Sustainalytics - ESG Sub factors

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Social (Weight – 30%)

Environment - Preparedness

Environmental Policy

Environmental Management System EMS Certification

Hazardous Waste Management Air Emissions Programmes Water Management Programmes GHG Reduction Programmes Renewable Energy Programmes Green Procurement Policy

Supplier Environmental Programmes Supplier Environmental Certifications

Environment - Disclosure

Environmental Reporting CDP Participation

Scope of GHG Reporting Water Intensity

Environmental Fines & Penalties Carbon Intensity

Carbon Intensity Trend Renewable Energy Use

Sustainable Products & Services Energy Mix

Environment - Qualitative Performance - Controversies

Operations Incidents

Environmental Supply Chain Incidents Product & Service Incidents

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Governance (Weight – 25%)

Social - Preparedness

Freedom of Association Policy Discrimination Policy

Diversity Programmes Health & Safety Programmes Health & Safety Certifications Scope of Social Supplier Standards Supply Chain Monitoring

Customer Eco-Efficiency Programmes Community Involvement Programmes Access to Basic Services

Philanthropic Guidelines Corporate Foundation

Social - Disclosure

Collective Bargaining Agreements Employee Turnover Rate

Top Employer Recognition LTIR Trend

Employee Fatalities

Activities in Sensitive Countries Cash Donations

Social - Qualitative Performance - Controversies

Employee Incidents

Social Supply Chain Incidents Customer Incidents

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4.1 List of 34 US Companies

Governance - Preparedness

Bribery & Corruption Policy Whistleblower Programmes Global Compact Signatory ESG Governance

ESG Performance Targets Gender Diversity of Board Separation of Chair & CEO Board Independence

Audit Committee Independence Non-Audit to Audit Fee Ratio

Compensation Committee Independence Political Involvement Policy

Lobbying and Political Expenses

Governance - Disclosure

Tax Disclosure

ESG Reporting Standards Verification of ESG Reporting Board Remuneration Disclosure Board Biographies Disclosure

Governance - Qualitative Performance - Controversies

Business Ethics Incidents Governance Incidents Public Policy Incidents

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Alliant Energy Corp Ameren Corporation

American Electric Power Co., Inc. American Water Works Company, Inc. Calpine Corp.

Center Point Energy CMS Energy Corp. Consolidated Edison Dominion Resources Inc. DTE Energy

Duke Energy Corporation Edison International Entergy Corporation Exelon Corporation FirstEnergy Corporation Integrys Energy Group, Inc. MDU Res. Group Inco. Next Era Energy, Inc. NiSource

Northeast Utilities NRG Energy, Inc. OGE Energy Corp. ONEOK Inc. Pepco Holdings PG & E Corp.

Pinnacle West Capital Corp. PPL Corporation

Public Service Enterprise Group Scana Corp.

Sempra Energy Southern Company The AES Corporation Wisconsin Energy Corp. Xcel Energy