Social Responsible Investing versus Sin Investing:
the Importance of Morality in Investment Decisions
Ornella Lupoi - 11040815
January 2018
Study program: Economics and Business Specialization: Finance and Organization Number of credits thesis: 12 credits Supervisor: Liang Zou
Abstract
In the past decades, the question of whether investors should be following morals or not for investment decisions has resulted in a growing interest among scholars. This paper measures the extent to which SRI funds and Sin funds are able to beat the returns of the market. The performance of a sample of 23 US equity SRI mutual funds and of 23 US equity Sin mutual funds is analyzed through the use of the CAPM and Carhart regression models. The results show that neither of the two types of funds is able to earn positive abnormal returns.
Statement of Originality
This document is written by Student Ornella Lupoi who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating
it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents
1. Introduction ……….4
2. Theoretical Framework………5
2.1 Socially Responsible investing………...5
2.2 Screening methods………..6
2.3 On the performance of SRI funds………...7
2.4 Sin Investments………...9
2.5 On the performance of Sin funds………..10
2.6 SRI and Sin mutual funds performance during crisis………...10
3. Literature review ………...11
3.1 Socially responsible funds performance studies………...11
3.2 Sin stocks performance studies……….13
4. Methodology………..14 5. Sample………15 6. Data………16 7. Results………18 8. Conclusion……….21 9. References………..24 10. Appendix………..27
1. Introduction
The majority of the financial theories that are present to this day are based on the conventional view that investors are rational and wealth-maximizing and that their decisions are based on self-interest. However, in the Theory of Moral Sentiments published by Adam Smith in 1759, the author addresses how fairness is important in underlying economic behavior. This old belief can be reflected in today’s new growing type of investing, that is Socially Responsible Investing or SRI.
SRI is an investment method that has a dual purpose, namely pursuing both financial returns and social good. Socially responsible strategies achieve the latter objective by investing only in those companies whose activities are considered to be environmentally friendly, that follow an ethical code or that actively address social issues. Bauer, Koedijk, and Otten (2005) report in their paper how in the past 10 years, controversies such as the environment, human rights, and nuclear power have caused an intensification of social awareness, also in the financial world. This is visible in the reports of the US Forum for Sustainable and Responsible Investments (USSIF): in 2015 Americans invested 8.72 trillion dollars according to SRI strategies. The prominent growth of this new type of investing is accompanied by an increasing academic interest (Renneboog, Ter Horst, Zhang, 2008b). Numerous scholars have conducted different types of research in order to determine what kind of financial outcomes SRI brings about. In other words, they have tried to verify how good it is to be “good”.
Many academics go beyond the arguments of the Modern Portfolio Theory school in order to support SRI. Instead, they follow the concepts of the Stakeholder Theory, claiming that efforts made to bring social good and more transparent operations within a firm are rewarded with good financial performance. This translates into positive returns for those funds who invest their money in these companies. Opponents carry forward the idea that the returns gained by following this investing strategy have to be lower than those of the market or of other conventional funds. The reason for this claim comes from the restricted possibility of SRI funds to diversify. Initial research has contradicted both lines of reasoning. The conclusion of many initial studies was indeed that SRI funds and conventional ones display the same financial performance.
Would it not be better to be “bad”? At the opposite side of SRI’s purpose investors have the possibility of pursuing financial returns in the so-called “sin stocks”. These investments are offered by industries discredited because they are said to make profits from the exploitation of human flaws. If seen from a profit-maximizing point of view, putting money into sin stock is believed to be a sound investment. The nature of these shares makes them attractive because of their inelastic demand. Furthermore, competitors are discouraged to enter the market because of the social and regulatory risks that these industries entail. This ensures a lower degree of competition that translates into solid profits.
In this research, the financial performance of a sample of US SRI equity mutual funds and of US Sin mutual funds will be assessed in periods of crisis and non-crisis in order to derive inferences regarding these investment strategies. The period identified as
crisis is the one from August 2007 until March 2009. This timeframe was picked by
following the price developments of the S&P500 index. The purpose of the paper is to determine to what extent socially responsible funds and sin funds are able to beat the market returns during bear and bull markets. The use of this study for the literature is twofold. Firstly, the research will be conducted during a timeframe that incorporates the latest information on the performance of both types of funds. Moreover, a simultaneous analysis of the two different investing strategies will be performed, contrary to other papers that examine them separately.
The remainder of the paper is organized as follows. Section 2 introduces and describes the theories relevant to comprehend the research problem. In section 3 I will summarize the findings of past research and present the hypothesis that will be tested. In the subsequent sections (4, 5 and 6) I will explain how I am going to test the hypothesis antecedently formulated, how the sample is composed and where I retrieve my data. Next, I will present and discuss the results. Conclusions are drawn in the last section.
2. Theoretical Framework
2.1 Socially responsible investing
Although more than one out of every five dollars under professional management in the US today is involved in SRI—22% of the $40.3 trillion in total assets— a unison belief on what
constitutes social responsible investing is still not present (Fabozzi, Ma, & Oliphant, 2008). Ethical investing has very ancient origins, as far back as the 12th century, where decisions were based on Jewish, Christian or Islamic traditions.
The roots of modern SRI can be traced to the early 1960s, where investors started to take financial actions based more on personal ethical beliefs than on religious convictions (Renneboog et al., 2008b). Hellsten and Mallin (2006) assert that since then, it has been difficult to decide on what is good or bad for society as a whole, because of the disparate value systems each culture has. Over time, the definitions of what constitutes socially responsible investing became more and more similar. In the table below, we may find some of these definitions.
2.2 Screening methods
Socially responsible funds try to make a worthwhile contribution to society by applying screening to their investment strategies. There exist two different types of screening criteria, namely positive and negative. Positive screening refers to the practice of selecting those companies that perform well on environmental, societal and governance (ESG) matters. It is considered to lead to superior Corporate Social Responsibility standards (Renneboog,
Table I
Definitions of SRI Mackanzie and
Lewis (1999)
“all kinds of investments that mix ethical with ordinary financial motivations or objectives”
Budde (2008) “those investments strategies that consistently and explicitly consider social factors as part of the investment process”
Renneboog, Horst, and Zhang (2008a)
“an investment process that integrates social, environmental, and ethical considerations into investment decision making”
Schuet (2003) “the process of integrating personal values, societal concerns, and/or institutional missions into investment decision making”
USSIF “Sustainable, responsible and impact investing (SRI) is an investment discipline that considers environmental, social and corporate governance (ESG) criteria to generate long-term competitive financial returns and positive societal impact”
Horst, & Zhang, 2011). Negative screening, the most basic SRI strategy, follows a different method: managers of funds that apply this type of screens avoid to invest in “non-ethical” businesses, such as tobacco, alcohol or defense companies, or in those that have a low level of corporate governance. Renneboog et al. (2008b) divide the twenty-one most widely used screening criteria into four categories, which are Sin, Ethical, Corporate Governance and Social, Environmental.
The Forum for Sustainable and Responsible Investment (2014) reports in its statement on US Sustainable, Responsible and Impact Investing Trends that the different screening practices are also applied to shape some specific types of investments. For instance, Best-In-Class investing is a financial strategy with the objective to pursue returns in sectors, companies or projects selected for positive ethical performance in comparison to industry peers. Impact investing refers to those investments that are mostly undertaken in private markets; its main goal is to solve specific social or environmental issues. Lastly, Sustainability Themed Investing indicates the selection process of those assets that are pointedly related to sustainability in single-themed or multi-themed funds.
2.3 On the performance of SRI funds
The efficient market hypothesis (EMH) states that, it is not possible to “beat the market”, i.e. earn abnormal risk-adjusted returns. The logic behind this is that share prices always reflect all available information because of market efficiency. However, even if stocks supposedly trade at their fair value, different conflicting theories are brought up by scholars in relation to the possible out- or under-performance of SRI funds. Academics belonging to the Modern Portfolio school claim that efforts related to social responsibility negatively affect performance. On the other hand, supporters of these ethical practices emphasize on the competitive advantage created thanks to these efforts.
The most criticized aspect of SRI is that screening practices confine the investment range. Markowitz (1952) asserts that over the long run ethical portfolios must underperform conventional ones because they are only a subset of the market portfolio. As a consequence, these funds face a diversification problem. The issue goes against modern portfolio theorists, who assert that an investor’s main objective is to maximize gains while reducing
is impossible to invest optimally if socially responsible screens are adopted. Under the Markowitz (1952) assumptions, the limitations imposed on the investing strategy unfold into lower financial performance. Undeniably, fund managers voluntarily exclude sin firms from their holdings, and eventually they do not diversify assets as they should.
Other than not diversifying, managers could make suboptimal investment choices, as is shown by Renneboog et al. (2011) in the following example. The fund manager of a SRI fund and that of a non-SRI fund could invest in 4 types of companies. Company 1 and 2 yield both positive NPV, where one grants positive externalities to other stakeholders, while the other yields negative externalities, respectively. Company 3 and 4 both yield negative NPV, however, company 3 generates positive externalities for stakeholders, while company 4 generates negative ones. Clearly, both managers will invest in company 1 but not in company 4. Differences arise when they need to take decisions on company 2 and 3: the SRI fund manager will invest in company 2 and not 3, deciding to not invest optimally (like the non-SRI fund manager will do) because of ethical reasons. As a consequence, the formerly mentioned fund will underperform the latter, if the EMH holds.
Additionally, different scholars address the competitive disadvantage that results from the screening procedures. Laurel (2011) describes these operations as considerably expensive and time consuming. Apart from screening, the costs associated with monitoring have to be taken into account (Areal, Cortez, & Silva, 2010). In fact, SRI funds have to bear these additional expenses since funds’ managers have to control the activities of the companies that they invest in. Therefore, the ethical funds will contract higher costs that will be above the average, considering that conventional funds do not incur them. This will then cause a financial performance that is below the average.
Proponents of the higher performance of socially responsible investments base their motivation on the stakeholder theory. Introduced by Freeman (1983), the theory proposes that addressing morals in the management of organizations increases the value of the latter.
Renneboog et al. (2008b; 2011) propose two arguments that support the out-performance theory of SRI funds. The first is that judicious social and environmental performance is a positive signal for skilled managerial quality, which turns into favorable financial performance. For instance, these signals could improve a firm’s reputation as a provider of satisfactory quality products, or they could attract more dedicated employees.
The second argument is that ESG screening practices are able to reduce the costs associated with potential lawsuits, corporate social crises or environmental accidents. Portfolios that consist of firms with good levels of corporate governance, and that apply wise social or environmental criteria can outperform their benchmarks (Derwall, Bauer, & Koedijk, 2005).
Additionally, Grossman and Stiglitz (1980) theorize that there cannot be informationally efficient markets, otherwise, those who devote their time and resources to obtain information would not receive any sort of remuneration. On the same line of thought lies one of the findings or Renneboog et al. (2011). They found evidence that European SRI funds that employ an “in-house SRI research team” report an increased risk-adjusted return of 3% per annum. Therefore, it is justifiable to think that these costly screening processes create value-relevant information.
2.4 Sin Investments
The Random House Dictionary defines sin as “any act regarded as such a transgression, especially a willful or deliberate violation of some religious or moral principle”. This definition is adopted in the financial world to indicate a unique type of stock, namely “sinful stocks”, issued by companies associated with activities that are considered immoral. However, it is the definition of immorality that brings up complications when one investor decides to pursue this kind of investing strategy. Indeed, there cannot be a standard definition of what vice is, considering that this changes among societies and over time (Blitz & Fabozzi, 2017). Following previous studies of Hong and Kacperczyk (2009) and Salaber (2007), for this research the consumption of alcohol and tobacco, and gambling will be regarded as sinful behavior. Hence, the industries that provide these products and services will be regarded as “vice-companies”. Blitz and Fabozzi (2017) report in their paper that additional industries other than the aforementioned ones have been acknowledged as if they consist of sinful companies. This results from the companies’ involvement in non-environmentally sustainable activities, or from documented low levels of corporate governance. Therefore, companies that would be negatively screened out by SRI fund managers, i.e., with low ESG rating, will also be regarded as sinful companies.
2.5 On the performance of Sin funds
Existing literature has demonstrated that sin stocks realize greater market returns. The higher performance could be explained by the “shunned-stock hypothesis”. This carries on the idea that controversial stocks experience superior returns. The latter are caused by the investment decisions of value-driven investors (Derwall, Koedijk, & Ter Horst, 2011). These individuals intentionally shun unethical investments, and opt only for those that reflect their morals. This happens since they apply ethical motives other than financial ones in their investment strategies. Such behavior induces a decrease in price for the non-ethical investments, causing their expected return to increase.
Studies indicate that the stocks of unethical firms are neglected by investors because of social norms, regulatory scrutiny and litigation risk (Kim and Venkatachalam, 2011). In an academic research carried by Hong and Kacperczyk (2009), the two authors hypothesize two reasons for this anomalous behavior. They consider that the realized positive abnormal returns could be because of the higher litigation risk faced by the sinful companies or because of the neglect effect that results from the preference of investors to adhere to social norms. The neglect effect is the theory that discloses the propensity for lesser-known companies to outperform better known companies. This effect advocates that these “secondary” firms are able to bring about higher returns because they are less likely to be examined by market analysts. Hong and Kacperczyk (2009) find proof that the excess returns are due solely to the neglect effect.
Hamilton, Jo, and Statman (1993) oppose the previous theories with a different rationale. They argue that non-ethical firms have high chances of being affected by detrimental information, which can lead to high litigation costs. For instance, they make the example of an Oil firm experiencing an oil spill. As a consequence, the expected return of an immoral fund will be remarkably lower than, for example, that of an ethical fund.
2.6 SRI and Sin mutual funds performance during crisis
Distinct theories support the idea of the existence of a negative relationship between corporate social performance and financial risk. Oikonomou, Brooks, and Pavelin (2012) examine these concepts and test whether or not corporate socially responsible actions and
practices lead to lower levels of firm financial risk. They base their hypothesis on the perception that socially and environmentally responsible companies should, over time, benefit from less volatility in their share price because they reflect their ethical beliefs in their strategic stance. Oikonomou et al. argue that these benefits are associated with a lower probability of facing legal charges, less demanding regulatory controls and more stable affinity with the government and the society. One of their main findings is indeed that corporate social responsibility is negatively related to systematic firm risk. Moreover, they also report that corporate social irresponsibility is negatively and strongly related to financial risk. These findings are in line with those of Nofsinger and Varma (2014) and of Soler-Domínguez and Matallín-Sáez (2015). The former demonstrate that socially responsible mutual funds outperform the market during the global financial crisis. The latter, instead, carry out an analysis on the performance of the most well-known sin fund, the VICEX, and find that it underperforms when the economy is in distress.
3. Literature review
3.1 Socially responsible funds performance studies
Hamilton et al. (1993) review the performance of 32 SRI mutual equity funds compared to that of 320 conventional mutual equity funds in the United States for the period from January 1981 to December 1990. They measure the excess returns of each mutual fund using Jensen's alpha of the Capital Asset Pricing Model (CAPM), relative to the NYSE index. Then they divide the 32 SRI funds in two groups: those established before 1985 (17 in total) and those established after 1985 (15 in total). For the group of SRI funds with a longer history, they observe an average monthly alpha of -0.06%, higher than the average monthly alpha of -0.14% of the reciprocal conventional funds. Opposite results are found for the funds established after 1985. Hamilton et al. conclude that the market does not price social responsibility characteristics.
The outcomes of Bauer et al. (2005) are analogous to those of Hamilton et al. (1993). They use both the single index model (CAPM) and the Carhart four-factor model to assess the possible presence of abnormal returns of 103 SRI funds in the US, Germany and the U.K. They make use of several indices, including sustainability indices to investigate
whether these would give higher explanatory power. Their results show that German and American SRI underperform their market index, while the British funds outperform it although the alphas are not statistically significant.
Goldreyer et al. (1999) extend the research to other types of funds in addition to the equity portfolios, and include in their sample bond and balanced funds. For the period of 1981-1997, they examine a group of 49 SRI funds with the previously mentioned investment styles and compare it to a matched group of non-ethical funds. Using the CAPM model and 3 different market proxies (equity, bond, and balanced market proxies), they did not find alphas significantly different from zero. The authors also make a distinction between SRI funds that use positive screens from those that do not. What they find is significant evidence on the outperformance of SRI funds that use positive screens (α=-0.11%) in comparison to the other SRI funds (α=-0.81%). This sustains the hypothesis that screens affect the performance of sustainable investing. The hypothesis is also supported by the research of Barnett and Salomon (2006). They found a curvilinear relationship between the intensity of screening and returns, which increases when the intensity of screening is high.
Similar conclusions with regards to positive screening are also drawn by Nofsinger and Varma (2013). They examine the return alphas in crisis and non-crisis periods using a sample of US socially responsible funds compared to regular funds. Over the whole period from 2000 to 2011, the alphas observed for both types of funds are not significant. However, dissimilarities emerge if looking at the periods of non-crisis and crisis separately. In the first instance, SRI funds are outperformed by conventional portfolios, in the second the opposite is true. The authors argue that the mitigation of risk during crisis comes at the cost of low returns during non-crisis times. Further investigation proves that these abnormal returns are generated from those funds that apply positive screens.
An additional study that analyses the risk-adjusted returns of SRI funds during the financial crisis is the one of Nakai, Yamaguchi, and Takeuchi (2016). Using the Fama-French three-factor model, they estimate the average cumulative abnormal returns of two separate samples of Japanese SRI and traditional funds through an event study. Their results indicate the event increased the performance of SRI funds, with significance at 5% level.
With the theories mentioned throughout the theoretical framework, and given past studies, I present the following two hypotheses on US socially responsible mutual funds.
H1: US socially responsible mutual funds report positive Jensen’s alphas during the financial crisis period
H2: US socially responsible mutual funds report negative Jensen’s alphas after the crisis period.
3.2 Sin stocks performance studies
Fabozzi et al. (2008) define a sin portfolio by selecting stocks from unethical companies such as alcohol, defense, tobacco, gambling and adult services. These firms are taken from a global sample across 21 countries during the period from 1970 to 2007. They include a business in the portfolio only if the profits earned exceeded more than 30% of the firm’s total revenue. They observe that sin stocks perform better than the market by 3% per year on a raw basis, and by 6% on a beta-adjusted basis.
Hong and Kacperczyk (2009) narrow their sample to only US sin stocks, however they observe their performance over a longer period (1965-2006) compared to Fabozzi et al. (2008). They also use the same industries as Fabozzi et al., with the exception of adult entertainment. The models used in their research are the single-factor CAPM, three-factor and four-factor models. For all the models, they observe significant alphas of nearly 3% per year. Likewise, Statman and Glushkov (2009) conduct a similar type of analysis of US sin stocks over the period from 1992 to 2007. Consistent with Hong and Kacperczyk results, they find yearly alphas of 2% to 3% .
Salaber (2007) analyses the returns of sin stocks from tobacco, alcohol and gaming industries in different European countries in the time span of 1975-2006. His findings report that the returns for these stocks were dependent on the legal environment of the nation where the stocks were traded. To capture this, he measures the legal environment in terms of the extent of litigation risk. After adjusting for size and book to market ratio, he finds that vice stocks present an improved performance when their litigation risk is higher.
Soler-Domínguez and Matallín-Sáez (2015) analyze the performance of the VICEX, a widely known fund because the majority of its assets come from immoral industries. They observe the abnormal returns of the fund during economic expansions and during economic turmoil. Thus far, it is the only research that distinguishes the performance of these stocks during crisis and non-crisis periods. In line with the studies mentioned above, their findings show that the VICEX outperforms during periods bull markets, but it is unable to beat the market during bear markets.
Finally, latest research on sin stocks performance is carried out by Blitz and Fabozzi (2017). They provide evidence that abnormal greater returns of sin stocks can be illustrated by the new pricing factors introduced by Fama and French – profitability and investment. They demonstrate that if one controls for these factors, then there is no evidence of the presence of a premium relevant to sin stocks.
Again, following what was presented in the theoretical framework with regards to the sin funds and based on past literature, I present the following two hypotheses on US sin mutual funds:
H3: US sin funds report a negative alpha during the financial crisis period.
H4: US sin funds report a positive Jensen’s alpha after the crisis period.
4. Methodology
To derive inferences about the performance of US equity mutual funds the average Jensen’s alpha will be estimated for each sample. This is a measure of the risk-adjusted performance, and it represents the abnormal return of a portfolio of securities over the theoretical expected return. The two following financial models will be adopted:
Ri-Rf = ɑi - βi
R(mkt)-R(f)(Rmkt-Rf ) + εi
The first single index model is the Capital Asset Pricing Model (CAPM). Despite this being used in most research papers on mutual fund performance, the ongoing debate on the suitability of the aforementioned model in this kind of studies brings up the need to use a multi-factor asset pricing model (Bauer et al., 2005). As a result, I will estimate the funds’ performance by also employing Carhart's four-factor model. The latter is the extension of the Fama and French three factor model plus a fourth factor introduced to capture the momentum anomaly described by Jegadeesh and Titman (1993).
In both regression models, the constant αi reveals whether the funds are able to beat the market or not. Hence, the average alpha of the Socially Responsible fund sample and of the Vice fund sample will be tested for significance. The variable (Rmkt-Rf) represents the excess return on the market and is calculated as the value-weighted return on all stocks listed on the NYSE, AMEX, and NASDAQ minus the one-month Treasury bill rate. SMB (Small-Minus-Big) is the size return variable and is the difference between the average return on the three small-cap portfolios and the average return on the three big-cap portfolios at time t. The variable HML (High-Minus-Low) denotes value-growth return at time t and is the average return of the two small value and big value portfolios minus the average return of the two small growth and big growth portfolios. MOM (Momentum) is the difference in the returns of a portfolio which comprises the past-year high-performing companies minus the returns of a portfolio that consists of the past-year low-performing companies.
5. Sample
In line with research by Bauer et al. (2005) and Nofsinger and Varma (2012), the information provided by Morningstar will be used to construct the samples. The focus of this research is to study the performance of ethical and non-ethical mutual funds, with particular reference to the US equity market. As a matter of fact, the funds to be included in the two different samples are those that invest in large cap stocks (stocks in the top 70% of the capitalization of the U.S. equity market) and that fall under three different (but similar) Morningstar categories, namely “Large Growth”, “Large Blend” and “Large Value”. Funds that are classified as Large Value are those that invest primarily in US companies that are
growing more slowly compared to other large-cap stocks. On the contrary, Large Growth portfolios invest in those companies that are predicted to grow faster than other large-cap stocks. Finally, Large Blend funds represent the overall US stock market, and neither of the growth or value objectives prevails.
The funds selected are then differentiated in Socially Responsible or Vice according to the Morningstar Sustainability Rating. The rating is based on the Morningstar Portfolio Sustainability Score, that is computed by taking into account the environmental, social and governance scores of each company, and the controversy score, that indicates the company’s engagement in socially irresponsible activities. Therefore, the sustainability assessment is a measure of how the stocks in a portfolio are managing their ESG risks and opportunities with respect to their category peers. Based on their portfolio sustainability scores, funds are assigned absolute category ranks from “Low”, “Below average”, “Average”, “Above Average” and “High”. The portfolios that have a rank of “Low” or “Below average” are those that will make up the Vice funds sample, while those that have a rank of “Above Average” and “High” are those that will make up the Socially Responsible funds sample.
The final samples consist of 23 socially responsible mutual equity funds and 23 sin mutual equity funds.
6. Data
Data on the US equity mutual funds were collected from the Thomson Reuters’ database, Datastream. First, daily data on the total return index (RI) is obtained in order to calculate the mutual funds daily returns. The index shows a theoretical growth in value of a share holding over a specified period, assuming that dividends are re-invested to purchase additional units of an equity. Subsequently, the daily returns are computed using the following formula: (RIt-RIt-1)/ RIt-1 .
Elton, Gruber, and Blake (1996) report in their paper that inferences on funds’ performance may be overestimated if the sample used for that research solely consists of funds that have data for the entire period. The overestimation is a result of excluding from the samples those funds that were merged or liquidated, probably because of bad performance, hence causing survivorship bias. It is to point out that due to the lack of access to other databases such as the CRSP survivorship-bias free US mutual fund database, I was
not able to identify non-surviving funds, therefore I acknowledge that the two samples may suffer from survivorship bias. In a study conducted by Renneboog, Ter Horst, and Zhang (2007) it is shown that churn rates (number of funds leaving the market) are low in the ethical segment. Therefore, the misrepresentation might be small.
Daily data for the market risk premium variable, for the Fama and French factors (SMB and HML), and for Carhart’s momentum factor are retrieved from the Wharton Research and Data Services library. As stated in the previous paragraph, SMB is computed by subtracting the average returns of big companies from the average returns of small ones. Small and big companies are defined as such depending on whether their market capitalization is higher or lower than the median market-cap of the companies listed on the NYSE. The HML factor represents the difference in between a portfolio containing value stocks and a portfolio containing growth stocks. Value stocks are those that have a low book-to-market ratio, while growth stocks have a high book-to-market ratio. The partition is made by referring to the 30th and 70th percentiles: companies below the 30th percentile are value firms, while those above the 70th percentile are growth firms. The same reasoning is applied for differentiating the high performing firms from the low performing ones when calculating Carhart’s momentum variable.
7. Results
In this section, I will discuss and interpret the results of the empirical testing, which are shown in the following tables. The values reported are the coefficients of the variables.
*, **, and ***, indicate the variables significant at 10%, 5% and 1% significance level, respectively.
*, **, and ***, indicate the variables significant at 10%, 5% and 1% significance level, respectively Table II - SRI funds
CAPM CARHART (1) (2) (3) (4) (5) (6) 2007- 2017 2007-2009 2009-2017 2007-2017 2007-2009 2009-2017 Rmkt-Rf .9608*** .9548*** .9664*** .9558*** .9428*** .9688*** SMB -.0418*** -.0359** -.0581*** HML .0190*** -0.005 .0251*** MOM -.0107*** -0.0221 -.0138*** Alpha -0.00002 0.00007 -.0004** -.00001 0.00007 -.00004*** observations 2,893 399 1,994 2,893 399 1,944 R2adj 0.9888 0.9819 0.9944 0.9895 0.9821 0.9959
Table III - Sin funds
CAPM CHARART -1 -2 -3 -4 -5 -6 2007-2017 2007-2009 2009-2017 2007-2017 2007-2009 2009-2017 Rmkt-Rf .9167*** .9035*** .9276*** .9195*** .9060*** .9323*** SMB -0.0108** 0.0013 -.0297*** HML .0057 -.01725 .0110*** MOM .00944*** -0.003 .0077*** Alpha -.0000338* -.0002** -0.00002 -.0000339* -.00017** -0.00002 observations 2,893 399 1,944 2,893 399 1,944 R2adj 0.9945 0.9958 0.9946 0.9946 0.9958 0.9949
In both tables, regressions 1 to 3 refer to the CAPM model while regressions 4 to 6 refer to the Carhart four factor model.
For the crisis period, from August 2007 until March 2009, the first hypothesis argues that socially responsible mutual funds over-perform the market. By looking at the results, this hypothesis is rejected: both models report very small, positive but insignificant alphas. This evidence is not in line with the findings of Nofsinger and Varma (2009), who have found that SRI funds outperform both conventional funds and the market during the crisis. Furthermore, it is in contrast with Markowitz (1952), who have asserted how ethical portfolios must underperform traditional portfolios because of diversification problems. This indicates that markets are efficient to some degree. An explanation for this could be that the extra costs related to screening or restricted diversification are compensated because of the ‘ethicalness’ level. Indeed, Oikonomou et al. (2012) support the idea that SRI companies and funds benefit from sympathy from the government and society.
However, with regards to the sin funds, the results are in line with the expectations: both the second and fifth regressions (table III) report negative and significant average alphas. This means that during times of financial distress sin funds underperform the market. Hence, the third hypothesis is not rejected. The outcome supports the conclusion of Soler-Domínguez and Matallín-Sáez (2015), who have shown that the VICEX fund showed negative significant alphas during bear markets. The lower returns could be attributed to the slowdown of the gaming sector, that has been hard hit by the crisis. The underperformance could also be a consequence of litigation costs that have been rather high during the economic turmoil.
Some differences arise when taking into account the years after the crisis. The alphas of the sinful portfolio are very small and negative, however, they are not significantly different from zero. These results indicate that this type of investing strategy has not financially compensated an investor as it was predicted in the fourth hypothesis, which is consequently rejected. On the contrary, the alphas of the socially responsible sample in both models are negative and statistically significant. The difference between the regression coefficient of the CAPM and that of the Carhart model is that one is significant at the 5% significance level while the other is significant at the 1%, respectively. Because of these
underperform the market during periods of non-crisis. Contrary to what was said with regards to the first hypothesis, this outcome supports the analysis of Nofsinger and Varma (2009). The stability of the SRI funds during the crisis comes at the cost of low returns during non-crisis times. This higher cost could be due to the expensive fees these funds incur, as it was proposed by Laurel (2011) and Areal et al. (2010).
The first and fifth regression report the coefficients for the entire sample period (2007 to 2017). The results for the socially responsible funds shows negative yet not significant alphas. This indicates the absence of a difference in risk-adjusted returns between the market and these funds during the whole sample period. On the other hand, the Jensen’s alpha for the sin funds sample is negative and statistically significant at the 10% level under the single-factor and the four-factor regression model. These results convey that investors who invest their money ethically will neither be remunerated nor penalized to follow the aforementioned investment strategy, however, the same conclusion cannot be reached for those who invest their money in sin funds.
The comparison among the returns of the SRI fund portfolio, the Sin fund portfolio, and the Market it is vividly shown in the graph below. Overall, it seems like both types of funds follow the market trends, as none of the points indicating the returns of the funds is particularly distant from the market returns.
Inferences on the investment style of each portfolio can also be drawn from the table. During the time frame of this research, the ethical portfolio displays a positive beta on HML meaning that it has a positive relationship with the value premium. In other words, the socially responsible funds behave like those funds with exposure to value stocks. The same investment strategy is observed in the sin fund sample, but only from 2009 until 2017. In this period, the sinful funds behaved more like value stocks, as it is also shown in prior studies from Kim and Venkatachalam (2011) and Lobe and Walkshäusl (2011). The negative coefficient on SMB suggests that during the decade that is being analyzed, the ethical fund portfolio predominantly invests in large-cap stocks. Likewise, the vice funds follow the same investing-path as the ethical funds, but not in the crisis period: the insignificant coefficient in regression 5 of Table III shows that these type of funds mainly invested neither in small nor in large-cap stocks.
Furthermore, the table also reports the adjusted R-squared, which is a measure of power of the regression model. For both the SRI and Sin samples we observe a higher R-squared in the regressions 4 to 6: this implicates how the additional variables are essential in explaining risk-adjusted returns.
Lastly, inferences can be made on the coefficients of the Market Risk Premium variable (Rmkt-Rf) for both samples. Foremost, they are all significant at the 1% significance level. Moreover, these coefficients inform us on the volatility of the funds relative to the market. Taking into account the coefficients given by both models, the SRI sample reports a market beta of approximately 0.95, while the sinful sample shows a market beta of approximately 0.92. Given that both betas are less than 1, these values confirm that both portfolios are somewhat less volatile than the market. A singular difference is that the beta for the sin sample is lower than the beta for the SRI sample.
9. Conclusion
This paper aimed to determine the extent to which socially responsible mutual funds and sin funds are able to beat the market during economic recessions and expansions. According to the Efficient Market Hypothesis, share prices reflect all available information, hence it is not possible to earn abnormal risk-adjusted returns. Proponents of SRI argue that because of
screening procedures, ethical funds should outperform the market. On the contrary, sin funds should underperform because of high litigation costs. Opponents of these theories argue instead that because of the limited investing horizon, inefficient decision making and above average costs, ethical funds should report negative returns. On the other hand, sin funds, avoided by value-driven investors, should experience abnormal returns as a consequence of the neglect effect.
Building on prior research, a single and multiple regression analysis were employed to determine the financial performance of a sample of US SRI equity funds and of a sample of US sin equity funds. The analysis was conducted during a crisis period (2007-2009) and an after crisis period (2009-2017). The results obtained for the ethical funds portfolio in part corroborate the outcome of Nofsinger and Varma (2009). The Jensen’s alpha of both models used was positive and insignificant for the 2007-2009 timeframe. For the after-crisis period, we observe negative and significant alpha coefficients. This proves that the mitigation of risk during crisis comes at the cost of low returns during non-crisis times. The outcomes obtained for the sin funds follow in part the expectations. Indeed, the sample portfolio reports negative, significant alphas during the crisis, as it was shown by Soler-Domínguez and Matallín-Sáez (2015). Contrary to the findings of Fabozzi et al. (2008), Hong and Kacperczyk (2009) and Salaber (2007), the sin funds do not outperform (but neither underperform) the market. Taking into account these findings we conclude that neither SRI funds nor sin funds are able to beat the market. Furthermore, it cannot be said whether the performance of ethical funds is better or worse than that of sin funds since that is depending on the state of the economy.
Nevertheless, this research presents some limitations. Firstly, the samples used suffer from survivorship bias, since only surviving funds were included in the portfolios. Considering the study of Renneboog et al. (2007), who have found that few funds stop existing in the SRI industry, the bias might be moderate. Nontheless, it has to be acknowledged while interpreting the results. Furthermore, while analyzing the SRI sample, no differentiation was made in relation to the screening practices. Distinct results could have been possible if, for instance, the study would have differentiated between funds that apply negative screens from those that apply positive screens. Lastly, the performance of the funds could have been compared to that of an ethical index or a sin index instead of the weighted
returns of the NYSE, AMEX, and NASDAQ. Future research should focus on improving these limitations.
10. References
Areal, N., Cortez, M. C., & Silva, F. (2010). Investing in Mutual Funds: Does it Pay to be a Sinner or a Saint in Times of Crisis?.
Barnett, M. L., & Salomon, R. M. (2006). Beyond dichotomy: The curvilinear relationship between social responsibility and financial performance. Strategic Management Journal, 27(11), 1101-1122.
Bauer, R., Koedijk, K., & Otten, R. (2005). International evidence on ethical mutual fund performance and investment style. Journal of Banking & Finance, 29(7), 1751-1767. Blitz, D., & Fabozzi, F. J. (2017). Sin Stocks Revisited: Resolving the Sin Stock Anomaly. The
Journal of Portfolio Management, 44(1), 105-111.
Budde, S. J. (2008). Compelling returns: A practical guide to socially responsible investing. John Wiley & Sons.
Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2005). The eco-efficiency premium puzzle. Financial Analysts Journal, 61(2), 51-63.
Derwall, J., Koedijk, K., & Ter Horst, J. (2011). A tale of values-driven and profit-seeking social investors. Journal of Banking & Finance, 35(8), 2137-2147.
Elton, E. J., Gruber, M. J., & Blake, C. R. (1996). The persistence of risk-adjusted mutual fund performance. Journal of business, 133-157.
Fabozzi, F. J., Ma, K. C., & Oliphant, B. J. (2008). Sin stock returns. The Journal of Portfolio
Management, 35(1), 82-94.
Freeman, R. E. (1983). Strategic management: A stakeholder approach. Advances in strategic management, 1(1), 31-60.
Goldreyer, E. F., & Diltz, J. D. (1999). The performance of socially responsible mutual funds: incorporating sociopolitical information in portfolio selection. Managerial Finance, 25(1), 23-36.
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393-408.
Hamilton, S., Jo, H., & Statman, M. (1993). Doing well while doing good? The investment
performance of socially responsible mutual funds. Financial Analysts Journal, 49(6), 62-66. Hellsten, S., & Mallin, C. (2006). Are ‘ethical’or ‘socially responsible’investments socially
responsible?. Journal of Business Ethics, 66(4), 393-406.
Hong, H., & Kacperczyk, M. (2009). The price of sin: The effects of social norms on markets. Journal of Financial Economics, 93(1), 15-36.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of finance, 48(1), 65-91.
Kim, I., & Venkatachalam, M. (2011). Are sin stocks paying the price for accounting sins?. Journal of Accounting, Auditing & Finance, 26(2), 415-442.
Laurel, D. (2011). Socially responsible investments in Europe: The effects of screening on risk and the clusters in the fund space. Available at SSRN 1883427. p. 1-47
Lobe, S., & Walkshäusl, C. (2011). Vice vs. virtue investing around the world.
Mackenzie, C., & Lewis, A. (1999). Morals and markets: the case of ethical investing. Business Ethics Quarterly, 9(3), 439-452.
Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
Nakai, M., Yamaguchi, K., & Takeuchi, K. (2016). Can SRI funds better resist global financial crisis? Evidence from Japan. International Review of Financial Analysis, 48, 12-20.
Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal of Banking & Finance, 48, 180-193.
Oikonomou, I., Brooks, C., & Pavelin, S. (2012). The impact of corporate social performance on financial risk and utility: A longitudinal analysis. Financial Management, 41(2), 483-515. Renneboog, L., Ter Horst, J., & Zhang, C. (2007). The price of ethics: Evidence from socially
responsible mutual funds.
Renneboog, L., Ter Horst, J., & Zhang, C. (2008a). The price of ethics and stakeholder governance: The performance of socially responsible mutual funds. Journal of Corporate Finance, 14(3), 302-322.
Renneboog, L., Ter Horst, J., & Zhang, C. (2008b). Socially responsible investments: Institutional aspects, performance, and investor behavior. Journal of Banking & Finance, 32(9), 1723-1742.
Renneboog, L., Ter Horst, J., & Zhang, C. (2011). Is ethical money financially smart? Nonfinancial attributes and money flows of socially responsible investment funds. Journal of Financial Intermediation, 20(4), 562-588.
Salaber, J. M. (2007). The determinants of sin stock returns: Evidence on the European market. Schueth, S. (2003). Socially responsible investing in the United States. Journal of business ethics,
43(3), 189-194.
Soler-Domínguez, A., & Matallín-Sáez, J. C. (2016). Socially (ir) responsible investing? The performance of the VICEX Fund from a business cycle perspective. Finance Research Letters, 16, 190-195.
Links USSIF
https://www.ussif.org/sribasics
Report on US Sustainable, Responsible and Impact Investing Trends (2014)
https://www.ussif.org/Files/Publications/SIF_Trends_14.F.ES.pdf Morningstar Sustainability Rating
11. Appendix
Appendix 1: SRI funds sample
In the table below are reported information regarding the SRI funds sample.
SRI funds
Fund name Ticker Above
Average High Category Total assets US stock
1 Amana Income Investor AMANX x Large Blend 1.4 B 85.31%
2 American Century Sustainable Equity Inv AFDIX x Large Blend 231.0 M 99.2% 3 Ave Maria Rising Dividend AVEDX x Large Blend 985.0 M 88.4% 4 Parnassus Core Equity Investor PRBLX x Large Blend 16.2 B 94.69% 5 Calvert US Large Cap Core Rspnb Idx A CSXCX x Large Blend 982.2 M 98.87% 6 Domini Impact Equity Investor DSEFX x Large Blend 888.5 M 92.81% 7 Vanguard FTSE Social Index Inv VFTSX x Large Blend 3.7 B 97.74% 8 Pax ESG Beta Quality Individual
Investor PXWGX x Large Blend 212.3 M 98.13%
9 Walden Equity WSEFX x Large Blend 197.2 M 97.25%
10 State Street Instl US Equity Inv SUSIX x Large Blend 476.1 M 95.76% 11 ClearBridge Dividend Strategy I SOPYX x Large Blend 6.2 B 86.36%
12 Deutsche Core Equity S SCDGX Large Blend 3.7 B 95.38%
13 JPMorgan Intrepid Sustainable Equity A JICAX x Large Blend 20.4 M 94.29% 14 American Beacon Large Cap Value Instl AADEX x Large Value 7.4 B 85.78% 15 Vanguard Windsor™ II Admiral™ VWNAX x Large Value 49.2 M 88.21% 16 Sound Shore Investor !!! SSHFX x Large Value 2.2 B 80.57% 17 Invesco Diversified Dividend B LCEDX x Large Value 24.2 B 73.13% 18 BlackRock Equity Dividend Instk !!! MADVX x Large Value 21.7 B 77.34% 19 Thrivent Large Cap Value A AAUTX x Large Value 1.0 B 94.82%
20 Invesco Comstock Y ACSDX x Large Value 12.7 B 84.47%
21 American Trust Allegiance ATAFX x Large
Growth 25.9 M 75.84%
22 Parnassus PARNX x Large
Growth 1.1 B 87.92%
23 Green Century Equity GCEQX x Large
Appendix 2: Sin funds sample
In the table below are reported information regarding the Sin funds sample.
Sin Funds
Fund name Ticker Low Below
Averag e
Category Total
assets US stock 1 Fidelity Advisor® Diversified Stock C FDTCX x Large Blend 2.1 B 91.99%
2 Davis NY Venture B NYVBX x Large Blend 11.5 B 81.72%
3 Prudential QMA Defensive Equity A PAMGX x Large Blend 235 M 95.05% 4 American Century Adaptive Equity Inv AMVIX x Large Blend 106.0 M 96.04%
5 Clipper CFIMX x Large Blend 1.2 B 79.71%
6 Dreyfus Disciplined Stock DDSTX x Large Blend 614.3 M 98.44% 7 Prudential QMA Defensive Equity B DMGBX x Large Blend 235 M 95.5%
8 DFA Tax-Managed US Eq DTMEX x Large Blend 3.4 B 98.53%
9 Eaton Vance Stock A EAERX x Large Blend 103.1 M 93.35%
10 Nuveen Large Cap Select A FLRAX x Large Blend 64.1 M 97.57%
11 FPA US Value FPPFX x Large Blend 108.4 M 83.97%
12 Blue Chip Investor BCIFX x Large Value 39.6 M 84.03%
13 American Beacon Bridgeway Lg Cp
Val Inst BRLVX x Large Value 4.7 B 97.7%
14 Wells Fargo C&B Large Cap Value A CBEAX x Large Value 391.5 M 80.54%
15 Copley COPLX x Large Value 86.6 M 134.07%
16 Dreyfus Strategic Value A DAGVX x Large Value 1.9 B 99.2% 17 MassMutual Premier Disciplined Value
Svc DENVX x Large Value 206.4 M 98.35%
18 Alger Large Cap Growth I-2 AAGOX x Large
Growth 272.4 M 96.79%
19 AB Growth B AGBBX x Large
Growth 840.9 M 91.64%
20 Tanaka Growth TGFRX x Large
Growth 14.4 M 77.5% 21 American Funds Growth Fund of Amer
A AGTHX x Large Growth 176.2 B 76.02%
22 USA Mutual Vice Investor VICEX x Large Blend 242.2 M 62.43% 23 FIDELITY SLT.CSM.STAPLES
Appendix 3: S&P 500 index graph
Appendix 4: Sin funds regressions
2007-2017
! _cons -.0000338 .0000179 -1.89 0.059 -.0000688 1.22e-06 MktRf .9167004 .0026851 341.41 0.000 .9114351 .9219657 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00087 R-squared = 0.9950 Prob > F = 0.0000 F(1, 2389) > 99999.00 Linear regression Number of obs = 2,391 . regress RiRf MktRf, vce(robust)
! 2007-2009 ! ! _cons -.0000339 .0000177 -1.91 0.056 -.0000686 9.21e-07 Mom .0094446 .0025606 3.69 0.000 .0044234 .0144658 HML .0056985 .0055809 1.02 0.307 -.0052454 .0166423 SMB -.0108102 .0056086 -1.93 0.054 -.0218085 .000188 MktRf .9193274 .0033706 272.75 0.000 .9127178 .925937 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00087 R-squared = 0.9950 Prob > F = 0.0000 F(4, 2386) = 33489.53 Linear regression Number of obs = 2,391 . regress RiRf MktRf SMB HML Mom, vce(robust)
_cons -.0001661 .0000667 -2.49 0.013 -.0002972 -.000035 MktRf .9034641 .0044659 202.30 0.000 .8946843 .9122439 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00132 R-squared = 0.9958 Prob > F = 0.0000 F(1, 397) = 40925.82 Linear regression Number of obs = 399 . regress RiRf MktRf, vce(robust)
_cons -.000168 .0000666 -2.52 0.012 -.000299 -.0000371 MOM -.0030268 .0070927 -0.43 0.670 -.0169711 .0109175 HML -.0172583 .0110999 -1.55 0.121 -.0390807 .0045641 SMB .0013419 .0122502 0.11 0.913 -.022742 .0254257 MktRf .9060075 .0062863 144.12 0.000 .8936486 .9183664 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00131 R-squared = 0.9958 Prob > F = 0.0000 F(4, 394) = 10034.13 Linear regression Number of obs = 399 . regress RiRf MktRf SMB HML MOM, vce(robust)
2009-2017
!
!
Appendix 5: SRI funds regressions
2007-2017
_cons -.0000198 .0000165 -1.20 0.230 -.0000521 .0000125 MktRf .9276955 .0022368 414.74 0.000 .9233088 .9320822 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00073 R-squared = 0.9946 Prob > F = 0.0000 F(1, 1992) > 99999.00 Linear regression Number of obs = 1,994 . regress RiRf MktRf, vce(robust)
_cons -.0000202 .0000161 -1.26 0.209 -.0000517 .0000113 MOM .0076754 .0028761 2.67 0.008 .002035 .0133158 HML .0109058 .0039246 2.78 0.006 .0032091 .0186025 SMB -.02969 .0041397 -7.17 0.000 -.0378087 -.0215713 MktRf .9323182 .0024519 380.24 0.000 .9275096 .9371269 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00071 R-squared = 0.9949 Prob > F = 0.0000 F(4, 1989) = 56677.78 Linear regression Number of obs = 1,994 . regress RiRf MktRf SMB HML MOM, vce(robust)
_cons -.0000152 .0000279 -0.54 0.586 -.0000699 .0000395 MktRf .960813 .0027425 350.34 0.000 .955435 .9661909 RiRfSRI Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00138 R-squared = 0.9887 Prob > F = 0.0000 F(1, 2389) > 99999.00 Linear regression Number of obs = 2,391 . regress RiRf MktRf, vce(robust)
! 2007-2009 ! ! _cons -.000011 .000027 -0.41 0.683 -.000064 .0000419 Mom -.0107034 .002704 -3.96 0.000 -.0160059 -.0054009 HML .0190199 .0049957 3.81 0.000 .0092235 .0288163 SMB -.0418905 .0044069 -9.51 0.000 -.0505323 -.0332487 MktRf .9558372 .0030715 311.19 0.000 .9498141 .9618603 RiRfSRI Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00134 R-squared = 0.9894 Prob > F = 0.0000 F(4, 2386) = 36384.82 Linear regression Number of obs = 2,391 . regress RiRf MktRf SMB HML Mom, vce(robust)
_cons .0000668 .0001505 0.44 0.657 -.000229 .0003626 MktRf .9547545 .0056907 167.77 0.000 .9435668 .9659421 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .0029 R-squared = 0.9819 Prob > F = 0.0000 F(1, 397) = 28148.29 Linear regression Number of obs = 399 . regress RiRf MktRf, vce(robust)
_cons .0000695 .0001509 0.46 0.645 -.0002271 .0003661 Mom -.02208 .0070884 -3.11 0.002 -.0360159 -.0081442 HML -.0050159 .010282 -0.49 0.626 -.0252304 .0151985 SMB -.0359453 .010639 -3.38 0.001 -.0568615 -.015029 MktRf .9427834 .0065763 143.36 0.000 .9298544 .9557125 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00289 R-squared = 0.9822 Prob > F = 0.0000 F(4, 394) = 8313.66 Linear regression Number of obs = 399 . regress RiRf MktRf SMB HML Mom, vce(robust)
2009-2017 ! ! _cons -.0000373 .0000177 -2.11 0.035 -.0000719 -2.69e-06 MktRf .9664225 .0019454 496.77 0.000 .9626072 .9702378 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00078 R-squared = 0.9944 Prob > F = 0.0000 F(1, 1992) > 99999.00 Linear regression Number of obs = 1,994 . regress RiRf MktRf, vce(robust)
_cons -.0000407 .0000151 -2.69 0.007 -.0000703 -.000011 Mom -.0137837 .0029691 -4.64 0.000 -.0196065 -.0079609 HML .025112 .0041705 6.02 0.000 .0169331 .0332909 SMB -.058071 .0037256 -15.59 0.000 -.0653774 -.0507645 MktRf .968839 .0022977 421.66 0.000 .9643328 .9733451 RiRf Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .00067 R-squared = 0.9959 Prob > F = 0.0000 F(4, 1989) = 75076.80 Linear regression Number of obs = 1,994 . regress RiRf MktRf SMB HML Mom, vce(robust)
