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The impact of screening and portfolio ethicality on

socially responsible investment fund performance

Abstract

This paper investigates the relation between the ethicality of portfolios

and the fund performance of both socially responsible investment (SRI) and

conventional mutual funds. Specifically, we find that the increase in envi-

ronmental, social and governance scores leads to higher subsequent financial

performance in both SRI and conventional funds while the exclusion of sin

stocks from a portfolio results in lower subsequent period returns. The results

of the impact of ethical screens on the ethicality of SRI funds show that the

screens employed by SRI funds positively affect the ethicality of SRI funds.

Keywords: Socially responsible investment; SRI; ESG; Mutual funds; Screening intensity;Ethicality of portfolio

JEL classification: G11; G23

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1. Introduction

Over the past decade, socially responsible investment (SRI) has experienced significant

growth and has attracted considerable interest around the world. The US Social In-

vestment Forum (SIF) defines SRI as “an investment process that considers the social

and environmental consequences of investments, both positive and negative, within the

context of rigorous financial analysis.” Advocates of SRI believe that the only way for

businesses to achieve sustainable growth is to consider all facets of operations, including

environmental, social and governance (ESG) factors. Given that an increasing number

of firms are integrating ethical considerations into investment decision making, SRI is

gradually becoming a mainstream investment vehicle in many developed countries. In the

US, professionally managed SRI assets expanded from $3.74 trillion at the start of 2012

to $6.57 trillion at the start of 2014, an increase of 76% (SIF, 2014). Increasing demand

has resulted in the growth of SRI mutual funds (SRI funds hereafter).

In, Kim, Park, Kim, and Kim (2014) show that the increase in competition due the

recent growth in the number of SRI funds has had a positive impact on the performance

of SRI funds. One implication of this finding is that SRI funds are continuously attracting

new investors by differentiating themselves from conventional funds. The most distinctive

feature of SRI funds compared to conventional funds is that SRI funds integrate investors’

ESG concerns as well as personal values into their investment decisions. SRI funds are

largely classified into three different groups, based on how securities are selected, namely,

by shareholder advocacy, community investments, and investments with screening criteria.

Among the three different types, the screening approach has been the most popular so far

in the US.1 Many studies define an SRI fund as a fund that incorporates screening in their

stock selection. For example, Renneboog, Horst, and Zhang (2011) consider SRI funds if

their names are related to the terms ethical, socially responsible, ecology, Christian value,

or Islamic. The authors also use the definition of Standard & Poor’s, which classifies an

SRI fund as one whose prospectus specifies social, environmental, and ethical investment

goals. Nofsinger and Varma (2014) employ a similar method and find that SRI funds use

1According to SIF (2014), investments with a screening process were recorded as having assetsunder management of $2.51 trillion, followed by shareholder advocacy and community investing, withapproximately $1.5 trillion and $41 billion, respectively. In addition, overlapping portfolios exist that usea combination of ESG incorporation, shareholder advocacy, and community investing, with assets undermanagement of around $981 billion.

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different screenings including product-related ESG screens.2

The screening process results in different levels of ethicality between SRI and conven-

tional mutual funds, as measured by the ESG score.3 Kempf and Osthoff (2008) report

that SRI funds have significantly higher ESG scores and conclude SRI funds are not

conventional funds in disguise. This distinct feature of SRI funds leads studies to examine

the difference in performance and risk between SRI and conventional funds. Although not

conclusive, the general finding is either the underperformance of SRI funds or a lack of

difference in performance between SRI and conventional funds in the US market.4 From

a risk perspective, Rudd (1979) argues that restrictions on an investment opportunity

set could lead to inefficient diversification and increase the non-systematic risk of an

investment portfolio. However, Bello (2005) finds that SRI funds show no difference in

terms of the characteristics of assets held, portfolio diversification, and the impact of

diversification on performance.

Given the backdrop, the purpose of this study is to measure the level of ethicality

of SRI and conventional funds, and examine the relation between ethicality and fund

performance. Accordingly, this study has two main contributions. First, our study

utilizes two measures of fund ethicality, namely, ESG scores and controversial business

involvement (CBI) scores, to capture the different dimensions of fund portfolios’ ethicality.

Specifically, we begin by calculating the ESG and CBI scores of the individual firms in each

fund’s portfolio. We then aggregate these two measures of ethicality at the fund level by

calculating the value-weighted average of ESG and CBI scores for the fund portfolios. This

approach enables us to gauge the actual ethicality of fund portfolios in the two dimensions

(ESG and CBI) for both SRI and conventional funds which can be directly compared.

The division of a fund’s ethicality into these two dimensions is crucial in explicating the

disagreement on SRI fund performance since the two measures have different impacts on

fund performance. Empirical studies at the individual firm level suggest that while firms

2Nofsinger and Varma (2014) define product-related screens as restricting investment in firms thatproduce certain products related to alcohol, tobacco, gambling, weapons, nuclear technology, pornography,abortion or animal testing.

3The ESG score is constructed as the value-weighted average of a portfolio’s strength and concernscores in ESG issues; the mean value of the concern score is then subtracted from the strength score. Theprecise definition of the strength and concern scores, plus details of the calculation of the ESG score areprovided in Section 4.

4No performance difference is found by Hamilton, Jo, and Statman (1993), Goldreyer and Diltz (1999),Statman (2000), Bauer, Koedijk, and Otten (2005), and Bello (2005) amongst others. Girard, Rahman,and Stone (2007) find evidence of SRI fund underperformance.

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with higher ESG scores produce superior abnormal returns (see, e.g., Derwall, Guenster,

Bauer, & Koedijk, 2005; Kempf & Osthoff, 2008; Statman & Glushkov, 2009), firms

classified as sin stocks (i.e., firms with higher CBI scores) also provide higher expected

returns (Hong & Kacperczyk, 2009). Since ESG-related screens are aimed at increasing

ESG scores while exclusionary screens are designed to decrease CBI scores, these findings

indicate that ESG-related screens and exclusionary screens have opposite effects on fund

performance. ESG-related screens refer to environmental, social and governance related

screens, either negative or positive. Exclusionary screens refer to the process of excluding

securities from the investment opportunity set based on certain criteria, such as excluding

sin industries. Theoretically, the implementation of ESG-related screens should increase

the ESG score of the fund while the use of exclusionary screens should decrease the CBI

score of the fund.

Prior studies have also utilized ESG scores to measure the ethicality of SRI and

conventional funds. For example, Kempf and Osthoff (2008) calculate ethical rankings

by first averaging the ratings of subcategories of the MSCI STATS database and then

calculating the aggregate ranking for each fund. The difference between our calculation of

ethical rankings and Kempf and Osthoff (2008) is that the latter normalizes the portfolio

weights to sum up to one. Since data on reported portfolio holdings are not always

complete, simply normalizing the portfolio weight might distort the actual ethicality of

funds. For example, if a fund has a total reported weight of 30%, the normalization to

100% might not reflect the true ethicality of this fund. Hence, in our study, we use funds

that have less than 10% difference between the total equity position of each fund and

the total weight reported. In this way, we assume that cash and bond positions do not

contribute to the ethicality of funds. Kempf and Osthoff’s (2008) approach also gives

more weight to the social category of the total ESG score than the environmental and

governance categories since the MSCI STATS database has five subcategories under the

social category. For comparison purposes, Kempf and Osthoff (2008) rank the SRI and

conventional funds according to their aggregate rankings. However, the comparison of

rankings between the two fund groups might not be appropriate for drawing a conclusion

on the actual difference in the ethicality of portfolios since the rankings do not take into

account the magnitude of ESG scores. For example, a ranking difference between two

funds in the first quartile could be different from that in the third quartile.

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Our second contribution is that by appropriately calculating the ESG and CBI scores

of SRI and conventional funds, we are able to examine the effect of screening on ethicality

and determine how the value-relevant information contained within the two measures of

ethicality impacts on fund performance. Despite SRI funds’ lack of performance difference

(or even underperformance) relative to conventional funds, the SRI market continues to

grow while the conventional fund market has shrunk. Since SRI fund managers incorporate

ESG and product-related concerns into their investment decisions, the ethicality of SRI

portfolios is expected to be higher than that of conventional fund portfolios. One of the

critical assumptions in the studies that examine the relation between screening intensity

and fund performance is that the screening process directly impacts on fund performance.

The logic behind this assumption is that an increase in screening intensity leads to a

decrease in the investment opportunity set which deteriorates potential diversification

effects. In our study, we show that it is the value-relevant information contained in the

ethicality of fund portfolios that has a direct impact on fund performance. This issue is

empirically important because one of the reasons SRI investors invest in SRI stocks is

to obtain utility from non-financial factors. Thus, if the ethicality of SRI funds does not

differ from that of conventional funds, then SRI’s slogan of “doing well by doing good”

would be somewhat tarnished and could be regarded as another type of green-washing in

the context of fund management.5

The remainder of this paper is organized as follows. Sections 2 and 3 describe the

theoretical background and hypothesis development, respectively. Section 4 discusses the

sample data and the measures of portfolio ethicality. Section 5 presents the methodologies

used in this paper and Section 6 provides our empirical findings. Section 7 concludes the

paper.

2. Theoretical background

2.1. Related literature

Although no consensus has been formed on the performance of SRI funds relative to

that of conventional funds, a few studies have examined the relation between screening

5Walker and Wan (2012) define green-washing as the discrepancy between the substantive actions onenvironmental issues and the symbolic actions.

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intensity and fund performance. Barnett and Salomon (2006) investigate how screening

intensity affects the financial performance of SRI funds and find a U-shaped relation

between screening intensity and performance. Renneboog, Horst, and Zhang (2008) and

Lee, Humphrey, Benson, and Ahn (2010) argue that screening intensity negatively affects

performance because non-financial screens restrict investment opportunities, reduces

diversification efficiency, and thereby adversely affects performance. However, these two

studies do not find a U-shaped relation. Humphrey and Lee (2011) use negative and

positive screens as well as overall screening intensity as regressors and find no impact of

screening intensity on fund performance in Australian SRI funds.

One of the critical assumptions of the above studies is that the diminished investment

opportunity set due to the implementation of social screens is directly linked to fund

performance. The logic behind this argument is that an increase in screening intensity leads

to a decrease in the investment opportunity set which in turn results in the deterioration of

potential diversification effects. Consequently, SRI portfolios are systematically subject to

idiosyncratic risk which worsens the risk-adjusted performance of SRI funds. On the other

hand, stakeholder theory suggests that the screening process might be able to identify

stocks with superior returns and help avoid stocks that have poor stakeholder relations

(Cornell & Shapiro, 1987; Fombrun, Gardberg, & Barnett, 2000). However, a constrained

investment opportunity set does not necessarily imply that the actual portfolios of SRI

funds are highly ethical. There is also no guarantee that the intense use of screens for

any SRI fund would effectively produce a highly ethical investment opportunity set or

actual portfolio. Figure 1 illustrates this argument. Hence, it is important to investigate

whether SRI fund screening processes increase the degree of ethicality of their portfolios

since SRI fund investors not only require financial returns but also have an interest in

ethical investment.

[Insert Figure 1 here]

There has been little attempt to examine the actual role of screens in socially responsible

investments. The purpose of screens can be understood in two ways. The first purpose

is to promote ethical behaviour and to invest in more socially responsible companies so

that the company could have better finance resources or lower cost of capital (Ghoul,

Guedhami, Kwok, & Mishra, 2011). The other purpose is that SRI fund managers might

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use the screening process to maximize the wealth of investors. If managers believe that

ESG information is value-relevant, they would exert an effort to construct portfolios that

have high ESG scores while balancing the costs of sacrificing diversification. Thus, the

degree of ethicality of fund portfolios is a result of the implementation of time-invariant

screens. In our study, we focus on the relation between the ethicality of fund portfolios

with fund performance. Since the measures of ethicality employed in this paper (ESG

and CBI scores) can be constructed for both SRI and conventional funds, we are able to

compare how the ethicality of both types of funds impacts on fund performance.

Studies that examine the relation between screening intensity and fund performance

do not consider the different impacts of exclusionary screens and ESG-related screens

on fund performance. Hong and Kacperczyk (2009) report that a portfolio of sin stocks

earns 3.5% more abnormal return than a portfolio with comparable stocks in the US.

This result implies that an increase in the number of exclusionary screens would have a

negative impact on fund performance since it would reduce the number of sin stocks in the

fund portfolio. On the other hand, there is evidence that ESG scores bear value-relevant

information since recent studies have shown that ESG scores have a positive impact

on fund performance. Derwall et al. (2005) find that portfolios with high eco-efficiency

scores outperform those with lower scores by 6% per annum. Similarly, Kempf and

Osthoff (2007) report that a strategy of buying responsible stocks and selling irresponsible

stocks produces abnormal returns of up to 8.7% per annum. Nofsinger and Varma (2014)

propose that SRI portfolios perform better during financial crisis periods at the expense

of underperformance during non-crisis periods, in which investors who demand downside

protection would find merit. Statman and Glushkov (2009) study the returns of stocks

rated on social responsibility during 1992–2007. They find that the expected returns of

stocks of socially responsible companies are higher than those of conventional companies.

Similarly, Edmans (2011) analyzes the relation between employee satisfaction and long-run

stock returns and finds that firms with high employee satisfaction earn significantly higher

abnormal returns than industry benchmarks, even after controlling for firm characteristics

and industries.

We introduce two measure of ethicality: ESG and CBI scores. Although these two

dimensions of portfolio ethicality are not mutually exclusive, they are the result of different

screening processes. ESG scores are constructed from ESG-related screens and CBI scores

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are constructed from exclusionary screens. SRI funds that apply ESG-related screens aim

to either invest in stocks with high ESG performance (positive ESG screens) or not invest

in firms with poor ESG performance (negative ESG screens). Theoretically, the use of

ESG-related screens should increase the ESG scores. Exclusionary screens are those that

exclude securities from the investment opportunity set based on certain criteria. Although

negative ESG screens and exclusionary screens may overlap to some extent, they are

not identical. For example, many socially responsible investors filter out tobacco-related

businesses in their investment pool (an exclusionary screen), but this screen is not related

to ESG screens. To reduce the chance of overlap, during the construction of CBI scores,

we restrict our focus to businesses related to tobacco, alcohol, gambling, firearms, and

military or nuclear operations. These industries are less likely to be related to ESG

screens.

2.2. Predictions from the corporate social responsibility literature

The mean–variance efficient portfolio theory introduced by Markowitz (1959) suggests

that a portfolio’s risk-return relation can be described by an asset pricing model. The

cornerstone of modern asset pricing theory is that the value of an asset is equal to

the expected discounted payoff under the assumption that individuals aim to maximize

economic utility. Fama and French (2007) point out that the assumptions of standard

asset pricing models are unrealistic and show how disagreement and tastes for assets as

consumption goods can alter asset prices.

In Fama and French’s (2007) framework, socially responsible investors can be regarded

as misinformed (or as those who have tastes for assets). Their model predicts that the

impact of misinformed investors on asset prices is large if (1) the amount of invested

wealth is substantial; (2) they are misinformed or have a taste for many assets; (3) their

portfolios differ from the market portfolio; and (4) the returns of assets demanded by

misinformed investors are not highly correlated with those of the assets they underweight.

More specifically, if socially responsible investors have sizable assets under management,

the expected returns of socially responsible stocks will be lower than that of the assets the

investors underweight (i.e., irresponsible stocks). This taste-based hypothesis has been

empirically examined by Hong and Kacperczyk (2009). The authors find that sin stocks

are underpriced and produce positive abnormal returns after controlling for traditional

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risk factors. They also note that this effect is driven by institutional investors, who are

restricted from investing in sin stocks by social norms.

Heinkel, Kraus, and Zechner (2001) develop a calibrated equilibrium model in which

exclusionary investment strategies can reduce risk-sharing opportunities for polluting

firms, which leads to a share price drop. This limited risk-sharing hypothesis also predicts

that the expected returns of irresponsible stocks are higher than those of responsible

stocks to compensate for the limited risk-sharing opportunities of irresponsible stocks.

However, the implicit assumptions of the hypothesis, that the share of socially responsible

investors is sufficiently large and that the socially responsible investors are homogeneous

in exclusionary investment strategies, need further investigation.

In contrast to the aforementioned two hypotheses, Derwall, Guenster, Bauer, and

Koedijk (2011) present a competing hypothesis called the errors-in-expectations hypothesis.

The idea is that corporate social responsibility (CSR) contains value-relevant information

and financial markets do not completely reflect the aspects that could create opportunities

for socially responsible investors to generate abnormal returns. Derwall et al. (2011)

explain why the market might not fully understand the value of CSR. First, since CSR

is a multidimensional and partially subjective concept and its appropriate measurement

is difficult for investors, it is challenging to examine the relation between CSR and firm

fundamental value. Second, no accounting standards have formally used CSR, so there are

no sound evaluation tools to measure the value added of CSR to firm value. Consequently,

socially responsible stocks have higher risk-adjusted returns because the market is slow

to recognize the positive impact that strong CSR practices have on companies’ expected

future cash flows.

3. Hypotheses development

Since SRI funds incorporate social and environmental factors when making their invest-

ment decision, the ethicality of SRI funds should differ from that of conventional funds.

However, the empirical evidence in the literature is mixed. Kempf and Osthoff (2008)

provide evidence that SRI funds are not analogous to conventional funds since SRI funds

exhibit higher ESG rankings than their counterparts do. On the contrary, Utz, Wimmer,

Hirschberger, and Steuer (2014) find that SRI and conventional funds do not differ in terms

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of mean and maximum ESG scores, although SRI funds have slightly larger minimum

scores. If a difference in ethicality exists between the two groups of funds, then it should

be attributable to the screening process imposed by the SRI funds. If no difference exists,

it could be because either SRI funds’ screening processes do not help construct socially

responsible portfolios or conventional funds also consider ESG factors in their investment

process. For example, conventional fund managers could exclude firms with low ESG

scores from their opportunity set since these firms could be subject to higher litigation

risk and capital costs.

Therefore, it is important to determine whether the screening processes used by SRI

funds actually improve their ESG scores. Since exclusionary screens ban industries or

companies involved in products related to tobacco, alcohol, gambling, firearms, and

military and nuclear operations from the investment universe, the use of more exclusionary

screens is expected to decrease portfolios’ involvement in controversial businesses. Similarly,

the use of ESG-related screens should increase SRI funds’ ESG scores since SRI fund

managers are inclined to invest in stocks with high ESG score (positive screens) and avoid

stocks with low ESG scores (negative screens). Based on the aforementioned discussion,

our hypotheses are as follows:

Hypothesis 1a: SRI funds have higher ESG scores than conventional funds.

Hypothesis 1b: Exclusionary screens have a negative impact on SRI funds’ CBI scores.

Hypothesis 1c: ESG-related screens have a positive impact on SRI funds’ ESG scores.

While the impact of CSR on firm value has been examined theoretically and empirically

at the individual firm level, there has been little attempt to investigate such a relation at

the fund portfolio level. Theoretical models (Fama & French, 2007; Heinkel et al., 2001)

predict that the use of exclusionary screens will lead to a decrease in fund portfolios’ CBI

scores which in turn reduces fund performance. Other empirical studies argue that fund

portfolios with higher ESG scores outperform those with lower ESG scores (Kempf &

Osthoff, 2007). In reality, SRI funds may combine different investment screening strategies,

including the use of exclusionary and ESG-related screens, to cope with various investor

demands. This procedure results in different ESG and CBI scores across SRI funds.

Accordingly, whether the ethicality of fund portfolios leads to a performance difference

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between SRI and conventional funds is an empirical question. Although conventional funds

do not explicitly employ a screening process, their portfolios still have an inherent degree

of ethicality which can be captured by ESG and CBI scores if these measures contain

value-relevant information. Prior studies indicate that SRI funds do not underperform

conventional funds (Goldreyer & Diltz, 1999; Hamilton et al., 1993; Statman, 2000),

except for Girard et al. (2007), who do find evidence of underperformance. However, these

studies do not directly measure the relation between fund ethicality and performance. By

measuring the ethicality of both SRI and conventional funds, our study aims to fill this

gap in the literature. Accordingly, we state the following related hypotheses:

Hypothesis 2a: There is a positive relation between a fund’s CBI score and subsequent

performance.

Hypothesis 2b: There is a positive relation between a fund’s ESG score and subsequent

performance.

Several event studies have shown that stock prices react differently to positive and

negative CSR news. Most recently, Kruger (2015) shows that the market response to

environmental news is asymmetric. The author finds that the positive impact on stock

prices followed by positive news is smaller than the negative effect of bad news. The author

attributes the decrease in firm value in response to bad environmental news to the legal

penalties imposed by the government. The author also finds a statistically insignificant

relation between positive news and firm stock return. Based on these empirical findings,

Derwall et al. (2011) conclude that while investors may be fully aware of the negative

impact of low CSR on future cash flows, they may not be aware of the positive impact of

high CSR.

When we decompose the future cash flows of a firm into normal cash flows and cash

flows related to good and bad CSR practices, the stock price at time zero can be described

as follows:

P0 =∞∑t=0

Cashflowt + CSRgoodt − CSRbad

t

(1 + r)t, (1)

where P0 is the stock price at time zero, Cashflowt is a firm’s cash flow unrelated to its

CSR practices, and CSRgoodt and CSRbad

t are a firm’s cash flow associated with good and

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bad CSR practices, respectively. Given the empirical evidence that CSRgoodt is generally

insignificant and has a small impact, if the stock market does not account for value-relevant

CSR information to determine stock prices, it could overestimate a stock’s future cash

flows which would lead to lower future expected returns on unethical portfolios. When

fund managers apply sophisticated screening processes to minimize negative cash flows

from bad CSR practices and maximize cash flows from good CSR activities, their stock

prices will be higher than expected by the market which results in better performance of

ethical portfolios. Hence, increases in the ESG scores of portfolios increases the probability

of CSR adding positive value to fund portfolios.

Therefore, if a fund manager uses effective screening processes to select firms with

good CSR practices (measured by the strength of ESG scores) and rule out those with

bad CSR practices (measured by the concern of ESG scores), then it is likely that that

good CSR practices are capitalized and creates a positive impact on fund performance,

while poor CSR practices may not be priced which could lead to a smaller impact on fund

performance. Thus, our hypotheses are stated as follows:

Hypothesis 3a: There is a positive relation between the strength of ESG scores and

fund performance.

Hypothesis 3b: There is no relation between the concern of ESG scores and fund

performance.

4. Data

4.1. SRI funds

To obtain a list of US SRI mutual funds, we use multiple sources including Morningstar,

US SIF reports from 1999 to 2010, and the SRI World Group.6 A fund is classified as an

SRI fund if it is listed by at least one of these sources. Following Nofsinger and Varma

(2014), we also hand-collect some missing funds by searching keywords that frequently

appear in the names of SRI funds and verify whether the missing funds were, in fact, SRI

6The US SIF reports are available at www.ussif.org and the list of SRI funds provided by the SRIWorld Group is available at www.socialfunds.com. The list has been used by other SRI researchers,including Nofsinger and Varma (2014).

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www.ussif.org

www.socialfunds.com

funds, using their prospectuses and websites.7 We then exclude balanced, bond, money

market, stock index, and international equity funds and focus our analysis on domestic

US equity funds, whose holding information we obtain from the Center for Research in

Security Prices (CRSP) database. After checking each individual fund’s prospectus, we

selected all mutual funds that did not explicitly specify the incorporation of ethical screens

as a reference group. Our total sample consists of 300 SRI funds. The return data were

obtained from the CRSP Survivor-Bias-Free US Mutual Fund Database.

4.2. Ethicality measures

To evaluate the degree of portfolio ethicality, we first construct firm-level ESG scores

using data from MSCI STATS, formally known as the KLD database, which provides the

annual ESG ratings of over three thousand publicly traded firms in the US. The database

consists of three major ESG categories (environment, social, and governance) as well as six

controversial business involvement indicators.8 The social category has five subcategories

including community, human rights, employee relations, diversity, and customers. Except

for the business involvement indicators, each measure has strength (positive) and concern

(negative) ratings. For example, when a firm is environmentally friendly (environmentally

unfriendly), the strength (concern) measure is given a value of one.

There are a number of different ways to construct a firm-level ESG score, but we

employ an approach analogous to that of Deng, Kang, and Low (2013). The MSCI STATS

database itself provides the aggregate ratings in seven categories by simple summation,

however, this approach has a drawback.9 Since the number of strength and concerns

in each category varies over time, a comparison of aggregate ratings across years and

dimensions is not meaningful. Similar to Deng et al. (2013), we construct a measure of

ethicality for individual stocks by dividing the strength and concern measures of each

category by the respective number of strength and concern indicators within that category.

We then take the difference between the adjusted total strength score and the adjusted

total concern score. Finally, the overall ESG score of the firm is calculated by taking the

7We use the same keywords as Nofsinger and Varma (2014): Social, socially, environment, green,sustainability, sustainable, ethics, ethical, faith, religion, Christian, Islam, Baptist and Lutheran.

8This includes alcohol, gambling, firearms, military, nuclear power, and tobacco. These businesses aretypically excluded from the investment opportunity set for socially responsible investors by exclusionaryscreens.

9These categories refer to the environmental, governance and five social subcategories.

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average across the three major ESG categories. To calculate the firm-level CBI score, we

simply take an equal-weighted average of the controversial business involvement indicators.

After computing the firm-level ESG scores, we match the MSCI STATS data with the

CRSP’s fund holding information. We then construct equal-weighted and value-weighted

ESG and CBI scores for both SRI and conventional funds. The value-weighted approach

is based on the weights of securities in a fund’s portfolio at the time when the ESG and

CBI scores are calculated. When a portfolio has higher ESG (CBI) scores than other

funds, the portfolio is considered more ethical (unethical). Note that the CRSP mutual

fund holding information is largely incomplete from 2001 to 2002 and some funds do not

have complete stock holding data, even after the first two years.10 Hence, to ensure that

the ethicality of fund portfolios is calculated accurately, we only include funds that have

at least 90% of stock holding information available for the fund’s equity position. After

calculating the fund-level ESG and CBI scores, we match fund characteristics from the

CRSP database with the fund-level ESG and CBI scores which includes net fund flow

(Flow), total asset value (TNA), family total asset value (Family TNA), fund age (Age),

expense ratio (Expense), and turnover ratio (F.Turnover).

In addition, we calculate fund portfolio characteristics as the value-weighted averages of

individual stock characteristics in a mutual fund’s portfolio. Specifically, we calculate book-

to-market ratios (BM), firm size (Cap), leverage (Leverage), dividend yields (DivY ield),

return on assets (ROA), cash flow volatility (CFV olt), return volatility (RetV olt), share

turnover (Turnover) and the Amihud’s (2002) illiquidity ratio (Illiquidity). The book-

to-market ratio is the book value of equity divided by the market capitalization. Similar

to Fama and French (1993), we calculate book value at the end of the previous fiscal year

as the sum of common stockholder’s equity, deferred taxes and investment credits. Firm

size is the market capitalization measured in billions of dollars. Leverage is represented

by the debt-to-equity ratio. Dividend yield refers to the annual percentage dividend yield

calculated as a stock’s annual dividends (split-adjusted) divided by price (split-adjusted).

Return on asset is the net income divided by average total assets. Similar to Zhang

(2006), cash flow volatility is the standard deviation of the net cash flow from operating

activities over the previous five financial years (minimum three years), scaled by the

10Our sample starts from the year 2000, but due to the lack of portfolio holding information, ouranalysis covers the period from 2003 to 2012.

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average total assets. Return volatility is the standard deviation of daily excess returns

(excess over the CRSP value-weighted index) over the last calendar year. Share turnover

is calculated as the trading volume divided by shares outstanding over the last calendar

year. A higher turnover potentially indicates greater liquidity. Following Amihud (2002),

the illiquidity ratio is calculated as the daily absolute return divided by its trading volume,

and multiplied by a thousand for scaling purposes. A higher value for the illiquidity ratio

indicates low liquidity.

5. Methodologies

We first examine the difference in ESG scores between SRI and conventional funds

(hypothesis 1a). To do this, we begin by calculating the average ESG scores across SRI

and conventional funds. The mean and median ESG scores are then compared using

Wilcoxon/Mann-Whitney tests. Next, we run multivariate regressions to investigate

whether the exclusionary and ESG-related screens used by SRI funds affect the CBI

and the ESG scores, respectively (hypothesis 1b and 1c). Accordingly, we estimate the

following models:

CBIi,t = α + β1Exclusionaryi + β2Fund characteristicsi,t

+ β3Time dummies+ εi,t, (2)

ESGi,t = α + β1ESi + β2SSi + β3GSi + β4Fund characteristicsi,t

+ β5Time dummies+ εi,t, (3)

where CBIi,t is the CBI score of the SRI fund; Exclusionaryi is the intensity of exclu-

sionary screens used; and ESGi,t is the total ESG score of the SRI fund. ESi, SSi and

GSi are dummy variables that have a value of one if an SRI fund employs environmental,

social and governance screens, respectively, and zero otherwise.11 Time dummies are

included to control for time variation of the dependent variable. Fund characteristicsi,t

include a fund’s total net asset value, fund family total net asset value, age, expense and

turnover ratios.

Next, we run pooled OLS regressions to examine the impact of CBI and ESG scores

11We collect the information on the use of environmental, social and governance screens for SRI fundsfrom their prospectus.

15

on the subsequent period fund performance (hypothesis 2a and 2b).12

Ri,t+1 = α + β1ESGi,t + β2CBIi,t + β3Fund characteristicsi,t

+ β4Portfolio characteristicsi,t + β5Time dummies+ εi,t, (4)

where ESGi,t is a fund portfolio’s ESG score; CBIi,t is a fund portfolio’s CBI score;

Fund characteristicsi,t include net fund flow, total net asset value, fund family total

net asset value, age, expense and turnover ratios for fund i. Portfolio characteristicsi,t

are portfolio characteristics control variables which contain the book-to-market ratio,

market capitalization, leverage, dividend yield, return on asset, cash flow volatility, return

volatility, individual stock turnover and illiquidity.

Finally, to examine the relation between the strength and concern of ESG scores on

fund performance (hypothesis 3a and 3b), we run the following pooled OLS regression:

Ri,t+1 = α + β1ESG(+)i,t + β2ESG(−)i,t + β3CBIi,t + β4Fund characteristicsi,t

+ β5Portfolio characteristicsi,t + β6Time dummies+ εi,t, (5)

where ESG(+)i,t and ESG(−)i,t are a fund portfolio’s strength and concern ESG scores,

respectively. To further investigate if there is an asymmetric impact of ESG scores

on future fund performance, we consider the quantile estimation of Eq. (4). Quantile

regression is introduced by Koenker and Bassett (1978), which can be viewed as an

extension of the classical least squares estimation of conditional mean models to the

estimation of an ensemble of models for several conditional quantile functions.

There are several advantages to using quantile regressions over simple OLS regressions.

First, when data are heterogeneous, quantile regressions allow inferences about the influence

of regressors conditional on the distribution of the endogenous variable. OLS regression

models merely estimate the relation between covariates and the conditional mean of

the dependent variable. Quantile regression extends the regression model to conditional

12We compute standard errors that are clustered by both fund and time in the spirit of Thompson(2011) and Petersen (2009). Thompson (2011) argues that double-clustering is most important when the

number of firms and time periods are not too different. The variance estimate for an OLS estimator β isV (β) = Vfirm + Vtime,0 + Vwhite,0, where Vfirm and Vtime,0 are the estimated variances that are clustered

by firm and time, respectively, and Vwhite,0 is the usual heteroskedasticity-robust OLS variance matrix(White, 1980).

16

quantiles of the dependent variable. Because quantile regressions estimate conditional

quantile functions, they are appropriate when there is a significant degree of variation in

the data. Therefore, quantile regressions can capture information about the slope of the

regression line at different quantiles of the endogenous variable (fund performance) given

the set of exogenous variables (ESG, CBI, and fund/portfolio characteristics). Second,

since no distributional assumption is imposed on the error term, quantile regression

estimates exhibit strong model robustness. The conditional quantile regression analysis

developed by Koenker and Bassett (1978) and extended by Koenker and Hallock (2001)

accounts for the skewed distribution of fund performance, and can be used to draw

more appropriate inferences with respect to independent variables across the performance

distribution.13

6. Empirical findings

6.1. Difference in portfolio ethicality between SRI and conventional funds

We begin by examining the time series of ESG and CBI scores for SRI and conventional

funds. Table 1 shows the number of funds used in our analysis and the time-series of the

value-weighted ESG and CBI scores. Note that the number of SRI funds is much less than

the number of conventional funds. This is expected because the SRI fund market operates

as a niche market within the entire mutual fund market.14 In our sample, the number of

SRI fund is 182 as of 2011, which is comparable to the 184 SRI funds in Nofsinger and

Varma (2014).

Figure 2 graphically shows how equal-weighted ESG and CBI scores change over

time.15 In most years, the total ESG scores of the SRI funds are slightly higher than those

of the conventional funds. However, the total ESG score does not differentiate between

the three individual categories of ethicality. Examining the environmental, social, and

13Note that quantile regression is not equivalent to simply separating the unconditional distribution ofthe dependent variable into quantiles and then estimating the effects of independent variables using OLSfor each subset. This erroneous approach would lead to catastrophic results, particularly when the datahas outliers. In contrast, quantile regressions utilize all of the data to fit quantiles.

14According to the Investment Company Fact Book 2014, the total net asset value of the entire mutualfund market is $11,831.3 billion as of 2011 while the SRI mutual fund market is only $316.1 billion basedon the US SIF report 2011.

15We use equal-weighted ESG and CBI scores for Figure 2 since we want to compare SRI and conventionalfunds with the US market. For all other tables, we use value-weighted ESG and CBI scores.

17

governance scores separately shows that SRI funds typically have higher environmental

and social scores than conventional funds but exhibit very similar governance scores. This

result could be because conventional fund managers recognize that improvements in firm

governance leads to better future firm performance, thus, conventional fund portfolios could

contain more firms with better governance scores in order to maximize fund performance.

Furthermore, we observe that both SRI and conventional funds’ environmental and social

scores are above market average for all years but their governance scores are below market

average until 2009.16 Overall, it appears that the higher ESG scores of SRI funds relative

to conventional funds stem from their better environmental and social scores.

We also observe an increasing trend in ESG scores in Figure 2 which could be a result

of better ESG reporting over the years. As mentioned previously, the number of strength

and concern indicators reported by MSCI STATS varies each year and generally increases

over time. Thus, one might argue that the observed upward trend in ESG scores is an

artefact of the better reporting by MSCI STATS over the sample period. However, we have

taken two steps to mitigate this potential problem. First, we use the adjusted strength

and concern scores as in Deng et al. (2013) so that scores across years can be compared.

Second, if the upward trend observed in the ESG scores of both SRI and conventional

funds is due to the better reporting over time, then there should also be an upward trend

in the ESG score of the market portfolio. However, as shown in Figure 2, both SRI

and conventional funds’ ESG scores deviate significantly from the market average, which

indicates that the trend is not due to better reporting. We also observe a substantial

difference in the CBI scores between SRI and conventional funds which suggests that the

use of exclusionary screens could play pivotal role in reducing the proportion of sin stocks

in SRI portfolios.

[Insert Table 1 here]


One interesting pattern in Figure 2 is that there are sudden jumps in ESG scores

around the financial crisis periods. Both the environmental and total ESG scores exhibit

16We calculate the market ESG and CBI scores in the same way that is used for SRI and conventionalfunds. The coverage of the market portfolio is around 3000 stocks available in the MSCI STATS databasewhich includes the 3000 largest US companies and companies in the MSCI KLD 400 social index.

18

an abnormal increase in 2009 while the social and governance scores increase steadily

in 2010. For the market portfolio, the direction of the pattern is opposite, except for

the environmental score. The market portfolio’s social, governance and total ESG scores

declines during the crisis periods and then increases afterward. This observation could be

due to the majority of firms reducing operational costs and shrinking social benefits to

employees during the financial crisis period. The decoupling behavior around the crisis

period is in line with the shielding effect of socially responsible stocks against crisis (Kim,

Li, & Li, 2014) and the lower crash risk of socially responsible stocks during crisis periods

(Nofsinger & Varma, 2014).



To examine the impact of funds’ level of ethicality on their performance, we sort

the funds according to their ESG scores (Table 2) and CBI scores (Table 3) into three

groups and compute the corresponding fund returns. Rank 1 in Table 2 represents the

most ethical fund group while rank 3 represents the least ethical fund group based on

ESG scores. Across all ranks, the ESG scores for SRI funds are greater than those of

conventional funds. This finding is consistent with those in Table 1. The difference is

also formally tested by a mean difference t-test and a Wilcoxon/Mann-Whitney test for

the median (Panel C of Table 2). The results show that the ESG score differences are

statistically significant for both the mean and median at the 1% level. Our result is

consistent with Kempf and Osthoff (2008), but not with Utz et al. (2014). Utz et al. (2014)

find that SRI funds are similar to conventional funds in mean and maximum ESG scores,

but have higher minimum scores over time. The different results stem from three sources.

First, Utz et al. (2014) cover 27 SRI funds and use a sample period from 2003 to 2010. In

contrast, we have a much larger sample of SRI funds over a similar time period. Second,

the ESG scores of Utz et al. (2014) are computed using an inverse portfolio optimization

technique whereas we follow the approach of Deng et al. (2013). Lastly, our study uses

MSCI STATS database to compute ESG scores while Utz et al. (2014) uses Thomson

Reuters ASSET4.

Panels A and B in Table 2 show that portfolio groups with high ESG scores do not

necessarily exhibit low CBI scores. This result implies that the use of ESG-related screens

19

does not necessarily exclude sin stocks from portfolios. For example, if a tobacco company

(which is considered a sin stock) performs well in ESG-related areas, it could be included

in an SRI fund’s portfolio that does not use an exclusionary screen for the tobacco industry.

For the relation between ESG and fund returns, it is not apparent that higher ESG scores

lead to higher current or subsequent period returns, since neither Rt nor Rt+1 increases

with ESG scores for either fund type. The book-to-market ratio and market capitalization

do not show any obvious patterns in relation to the ESG score.

We now examine the relation between CBI scores and fund performance by sorting

both types of funds into high-, medium-, and low-CBI portfolios. Table 3 shows the

relation between CBI scores and the ESG scores, fund returns, the book-to-market ratio

and market capitalization of funds. Rank 1 (3) contains the portfolio with the highest

(lowest) CBI scores. Analogous to the ESG score, the mean and median differences of

the CBI scores between SRI and conventional funds are statistically significant at the

1% level, which suggests that SRI funds are more likely to exclude sin stocks from their

portfolios (Panel C of Table 3). In line with the findings of Table 2, CBI scores are not

strongly correlated with ESG scores reinforcing the idea that these two scores capture

different dimensions of fund ethicality. We observe that for both types of funds, CBI

scores are positively associated with both current and subsequent returns. This result

indicates that the proportion of sin stocks in portfolios has a considerable impact on fund

returns and is in line with the findings of Hong and Kacperczyk (2009) who show that sin

stocks have higher expected returns. We also see that funds with more sin stocks (i.e.,

greater CBI scores) tend to have stocks of greater value and market capitalization.

6.2. The impact of screens on the ethicality of portfolios

Next, we examine the relation between ESG scores and screens employed by SRI funds.

Although the screening process is the main tool that SRI funds use to differentiate

themselves from their conventional counterparts, there has been little attempt to investigate

whether these screens actually increase the ethicality of SRI fund portfolios. In principle,

when more intense exclusionary screens are employed (measured by the number of screens),

portfolio exposure to controversial businesses is expected to be reduced (i.e., a reduction

in the CBI score). If ESG-related screens are implemented, then the ESG score of fund

portfolios is expected to increase.

20


The first column of Table 4 demonstrates that the intensity of exclusionary screens has

a negative impact on SRI funds’ CBI scores significant at the 5% level. For the individual

ESG scores, all ESG-related screening processes have significant positive coefficients.

For example, in the second column of Table 4, environmental screens help improve the

environmental score of the SRI fund by 0.015. This result indicates that environmental,

social, and governance screens help improve the scores of the individual categories of

ethicality. When considering all ESG-related screens together in the regression specification

in column five, the significance of the screens disappears. This result could imply that

individual screens do not have any explanatory power on the total ESG score.17 Finally, in

the last column of Table 4, we see that when both exclusionary and ESG-related screens

are applied, the coefficient on exclusionary screens becomes insignificant, highlighting the

fact that the exclusionary screening process affects CBI scores and not ESG scores.

6.3. Impact of portfolio ethicality on fund performance

In this section, we test whether a fund’s level of controversial business involvement and

ethicality are related to its subsequent performance (hypotheses 2a and 2b). Table 5

shows the impact of ESG and CBI scores on subsequent fund returns. Columns (2) and

(5) of Table 5 show that funds with higher CBI scores have higher subsequent fund returns

for both SRI and conventional funds, which is consistent with hypothesis 2a. A test of

significance between the coefficients on the CBI variable for SRI and conventional funds

indicates that the magnitude of the CBI variable is significantly larger for SRI funds.

This result shows that since conventional funds do not apply exclusionary screens, their

fund portfolios would contain more sin stocks than SRI fund portfolios. Consequently, an

increase in the CBI score of conventional fund portfolios would lead to a smaller increase

in expected fund returns due to diminishing marginal returns. However, since SRI funds

apply exclusionary screens, which excludes sin stocks from their portfolios, the inclusion

of sin stocks in SRI funds’ portfolios would induce much larger financial gains.


17The lack of statistical significance of individual screens in explaining the total ESG score could alsobe because they are highly correlated. Specifically, their correlation varies approximately from 0.55 to0.65 with the total ESG score.

21

The ESG score also has an expected positive sign for both fund types as shown in

columns (1) and (4) of Table 5. For SRI funds, a 10% increase in ESG score results in a

1.72% growth in subsequent fund return, all other things constant. In columns (3) and (6)

of Table 5, we see that both CBI and ESG scores have a positive relation with subsequent

fund returns with the impact of CBI scores being stronger than that of ESG scores. This

result implies that for SRI funds there is a trade-off in financial benefits depending on

the screens applied. Specifically, since exclusionary screens are shown to decrease CBI

scores, the underperformance of SRI funds documented in the literature could be due to

the extensive use of exclusionary screens. On the other hand, since conventional funds are

not subject to any screening processes, they are able to maximize the expected returns of

their portfolio by choosing stocks with the highest CBI and ESG scores.


To ensure robustness of the results in Table 5, we also use risk-adjusted returns as

the dependent variable including excess fund returns over the risk-free rate and over the

market return, and the Sharpe ratio. The estimation results are presented in Table 6.18

The main findings in Table 6 are similar to those in Table 5, that is, both ESG and CBI

scores have a positive impact on subsequent risk-adjusted returns for SRI and conventional

funds. The significantly positive coefficient on the ESG score across all columns indicates

that an increase in ESG score sufficiently compensates the decrease in the potential

diversification risk as argued by Rudd (1979).


Next, we decompose the ESG scores into its subcategories and investigate the impact

of each subcategory on the fund returns of the subsequent period. Table 7 shows the

relation between individual ESG scores and subsequent fund returns. For SRI funds, the

environmental and social scores have a significantly positive impact on subsequent fund

returns, while the governance score has a insignificant coefficient. This result could be

due to the fact that the governance score is not directly related to the governance screens

18We do not use Jensen’s alpha or other regression based risk-adjusted performance measures since thedependent variable is the one-step ahead return which restricts us to using only one-year return series forthe estimation.

22

employed by SRI funds. In fact, Servaes and Tamayo (2013) point out that the governance

score does not fully reflect firms’ CSR practices, given that corporate governance is about

the mechanisms that allow principals (shareholders) to reward and exert control on agents

(managers). Inspecting the individual items which form the governance score shows that

there are two strength indicators (reporting quality and public policy) and four concern

indicators (reporting quality, public policy, governance structure controversies, and other

controversies). Most of these variables do not have sound definitions and are less clear

in practice. For example, the governance structure controversy indicator is defined as

measuring the severity of controversies related to a firm’s executive compensation and

governance practices. This may lead to more subjective decisions made on part of the

SRI fund when implementing governance screens.

When comparing the magnitudes of the coefficients between the environmental and

social scores, the social measure is almost twice as large as the environmental measure

for both SRI and conventional funds. Since an improvement in environmental aspects

usually requires some type of investment as well as constant monitoring effort, activities

that improve the environmental score would be more costly than those activities that help

improve the social score. Consequently, the impact of environmental factors on subsequent

returns is lower than that of social factors. For conventional funds, all three subcategories

appear to be significantly positive. The positive impact of CBI scores on subsequent

returns remains largely the same across all columns.


Table 8 presents the estimation results of the regression of subsequent fund returns on

the strength and concern of ESG scores for both SRI and conventional funds. Columns

(1) to (4) and (5) to (8) show the results for SRI and conventional funds, respectively. As

shown in columns (4) and (8), the total ESG strength score is significantly positive while

the total ESG concern score is insignificant. This result indicates that the good news

contained in the ESG strength score has value-relevant information which contributes

towards subsequent fund performance. For SRI funds, the environmental and social

strength scores are all positive and significant but their respective concern scores are

insignificant. Both the strength and concern scores for governance are insignificant. These

results imply that for SRI funds, the value-relevant information contained in the total ESG

23

strength score originates from the environmental and social categories. For conventional

funds, all of the individual ESG strength scores are positive and significant indicating that

conventional funds tend to capitalize on good CSR practices across all three categories of

ethicality to improve fund performance.

6.4. Asymmetric impact of portfolio ethicality

In this section, we examine whether portfolio ethicality has an asymmetric effect on

subsequent fund returns, that is, whether fund ethicality has a different impact on the

subsequent fund returns of high-performance portfolios compared to low-performance

portfolios. We run a quantile regression from the 0.05 quantile to the 0.95 quantile of the

subsequent period fund returns, with quantile increments of 0.05. Table 9 reports the

quantile regression estimation results at the 0.1, 0.3, 0.5 (median), 0.7, and 0.9 quantiles.

The median regression shows that the median coefficient estimates of both the CBI and

ESG scores are similar to the OLS estimates from Table 5. Figure 3 plots the point and

interval coefficient estimates of the ESG and CBI scores at all quantiles estimated. We see

that while CBI scores appear to have an asymmetric impact on subsequent period fund

returns for SRI and conventional funds, ESG scores do not. Specifically, for both types

of funds, the positive relation between CBI scores and subsequent fund returns is much

stronger (weaker) for high-performance (low-performance) funds which implies that the

financial performance of SRI and conventional funds largely depends on the inclusion of

sin stocks in their portfolios. Although the ESG score appears to have an inverse U-shape

relation with subsequent fund returns across quantiles for SRI funds, the magnitude of the

variation is minimal (coefficients range from 0.04 to 0.18) which means there is unlikely

to be any asymmetric effects. For conventional funds, the effect of the ESG score is fairly

flat, which implies that conventional funds are unlikely to utilize ESG criteria in their

investment decision making.



24

7. Conclusions

Socially responsible funds have become increasingly popular over the last two decades and

their assets under management are expected to grow further in the future. While several

studies have attempted to determine the financial performance differences between SRI

and conventional funds, differences in portfolio ethicality have received little attention,

despite the fact that SRI funds tend to charge higher fees than conventional funds primarily

due to the additional ethical research. We find that the exclusionary screens employed by

SRI funds help decrease investments in irresponsible companies and that the ESG-related

screens increase the corresponding individual ESG scores. We empirically examine the

ethicality of SRI and conventional funds and find that SRI funds have higher ESG scores.

Moreover, it appears that both types of funds have tilted their portfolios toward more

ethical stocks, especially around the recent financial crisis periods. As Nofsinger and

Varma (2014) argue, if responsible portfolios are less risky in crisis periods, it is sensible

to move from irresponsible to responsible portfolios during periods of turmoil, which leads

to an increase in fund ESG scores.

We also empirically study the financial performance of SRI and conventional funds by

examining the impacts of exclusionary and ESG-related screens simultaneously. Using

a sample of SRI and conventional funds from 2003 to 2012, we find that an increase in

ESG scores leads to higher subsequent returns, while a decrease in CBI scores leads to

inferior financial performance in the following period for both SRI and conventional funds.

These findings are consistent with the errors-in-expectation and taste-based hypotheses.

However, it appears that the impact of CBI scores on fund performance dominates that

of the ESG scores when the relative effects are compared. This result suggests that SRI

funds that only impose exclusionary screens are more likely to underperform conventional

funds, but funds with only ESG screens might be able to outperform conventional funds.

25

References

Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time series effects.

Journal of Financial Markets , 5 (1), 31–56.

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 and Finance, 29 , 1751–1767.

Bello, Z. Y. (2005). Socially responsible investing and portfolio diversification. Journal of

Financial Research, 28 (1), 41–57.

Cornell, B., & Shapiro, A. C. (1987). Corporate stakeholders and corporate finance.

Financial Management , 16 (1), 5-14.

Deng, X., Kang, J. K., & Low, B. S. (2013). Corporate social responsibility and

stakeholder value maximization: Evidence from mergers. Journal of Financial

Economics , 110 (1), 87–109.

Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2005). The eco-efficiency premium

puzzle. Financial Analysts Journal , 61 (2), 51–63.

Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2011). A tale of values-driven and

profit-seeking social investors. Journal of Banking and Finance, 35 (8), 2137–2147.

Edmans, A. D. (2011). Does the stock market fully value intangibles? Employee

satisfaction and equity prices. Journal of Financial Economics , 101 (3), 621–640.

Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and

bonds. Journal of Financial Economics , 33 (1), 3–56.

Fama, E. F., & French, K. R. (2007). Disagreement, tastes, and asset prices. Journal of

Financial Economics , 83 (3), 667–689.

Fombrun, C. J., Gardberg, N. A., & Barnett, M. L. (2000). Opportunity platforms

and safety nets: Corporate citizenship and reputational risk. Business and Society

Review , 105 (1), 85-106.

Ghoul, S. E., Guedhami, O., Kwok, C. C. Y., & Mishra, D. R. (2011). Does corporate

social responsibility affect the cost of capital? Journal of Banking and Finance,

35 (9), 2388–2406.

Girard, E., Rahman, H., & Stone, B. A. (2007). Socially responsible investments:

Goody-two-shoes or bad to the bone? Journal of Investing , 16 (1), 96–110.

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.

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),

26

62–66.

Heinkel, R., Kraus, A., & Zechner, J. (2001). The effect of green investment on corporate

behavior. Journal of Financial and Quantitative Analysis , 35 (4), 431–449.

Hong, H., & Kacperczyk, M. (2009). The price of sin: The effects of social norms on

markets. Journal of Financial Economics , 93 (1), 5–36.

Humphrey, J. E., & Lee, D. D. (2011). Australian socially responsible funds: Performance,

risk and screening intensity. Journal of Business Ethics , 102 (4), 519–535.

In, F., Kim, M., Park, R. J., Kim, S., & Kim, T. S. (2014). Competition of socially

responsible and conventional mutual funds and its impact on fund performance.

Journal of Banking and Finance, 44 , 160–176.

Kempf, A., & Osthoff, P. (2007). The effect of socially responsible investing on financial

performance. European Financial Management , 13 (5), 908-922.

Kempf, A., & Osthoff, P. (2008). Sri funds: Nomen est omen. Journal of Business Finance

and Accounting , 35 , 1276–1294.

Kim, Y., Li, H., & Li, S. (2014). Corporate social responsibility and stock price crash

risk. Journal of Banking and Finance, 43 , 1–13.

Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46 (1), 33–50.

Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic

Perspective, 15 (4), 143–156.

Kruger, P. (2015). Corporate goodness and shareholder wealth. Journal of Financial

Economics , 115 (2), 304–329.

Lee, D. D., Humphrey, J. E., Benson, K. L., & Ahn, J. Y. K. (2010). Socially responsible

investment fund performance: The impact of screening intensity. Accounting and

Finance, 50 (2), 351–370.

Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments. New

York: Wiley .

Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal

of Banking and Finance, 48 , 180–193.

Petersen, M. (2009). Estimating standard errors in finance panel data sets: Comparing

approaches. Review of Financial Studies , 22 (1), 435–480.

Renneboog, L., Horst, J. T., & Zhang, C. (2008). The price of ethics and stakeholder

governance: The performance of socially responsible mutual funds. Journal of

Corporate Finance, 14 (3), 302–322.

Renneboog, L., Horst, J. T., & 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.

Rudd, A. (1979). Divestment of south african equities: How risky? Journal of Portfolio

Management , 5 (3), 5–10.

Servaes, H., & Tamayo, A. (2013). The impact of corporate social responsibility on firm

27

value: The role of customer awareness. Management Science, 59 (5), 1045–1061.

Social Investment Forum. (2014). Report on US sustainable, responsible and impact

investing trends. US Social Investment Forum.

Statman, M. (2000). Socially responsible mutual funds. Financial Analysts Journal ,

56 (3), 30–39.

Statman, M., & Glushkov, D. (2009). The wages of social responsibility. Financial

Analysts Journal , 65 (4), 33-46.

Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm

and time. Journal of Financial Economics , 99 (1), 1–10.

Utz, S., Wimmer, M., Hirschberger, M., & Steuer, R. E. (2014). Tri-criterion inverse

portfolio optimization with application to socially responsible mutual funds. European

Journal of Operational Research, 234 (2), 491–498.

Walker, K., & Wan, F. (2012). The harm of symbolic actions and green-washing:

Corporate actions and communications on environmental performance and their

financial implications. Journal of Business Ethics , 109 (2), 227–242.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a

direct test for heteroskedasticity. Econometrica, 48 (4), 817–838.

Zhang, X. F. (2006). Information uncertainty and stock returns. Journal of Finance,

61 (1), 105–137.

28

Tables

Table 1Time-series of ESG and CBI scores.

Year # of funds E S G ESG CBI

Panel A: SRI funds2003 114 0.001 0.018 -0.139 -0.040 0.0062004 107 -0.009 0.023 -0.181 -0.056 0.0062005 121 0.014 0.013 -0.044 -0.006 0.0092006 125 0.001 0.009 -0.064 -0.018 0.0082007 129 0.017 0.002 -0.053 -0.011 0.0102008 160 0.020 -0.002 -0.073 -0.018 0.0082009 185 0.029 0.003 -0.074 -0.014 0.0082010 189 0.238 0.030 0.033 0.100 0.0222011 182 0.267 0.072 -0.005 0.111 0.0232012 187 0.206 0.224 0.194 0.255 0.024

Panel B: Conventional funds2003 7533 -0.011 0.031 -0.260 -0.080 0.0142004 7691 -0.035 0.032 -0.263 -0.088 0.0172005 7807 0.000 0.012 -0.067 -0.019 0.0242006 8035 -0.012 0.005 -0.107 -0.038 0.0222007 8313 -0.015 -0.011 -0.071 -0.032 0.0242008 9865 -0.006 -0.016 -0.114 -0.045 0.0192009 9114 0.005 -0.015 -0.099 -0.036 0.0152010 8922 0.212 0.008 0.020 0.080 0.0312011 8766 0.223 0.045 -0.045 0.074 0.0282012 8649 0.133 0.178 0.113 0.174 0.029

This table reports the number of SRI and conventional funds used in our analysis and the time seriesof value-weighted ESG and CBI scores. Panels A and B report the ESG and CBI scores for SRI andconventional funds, respectively. The variables E, S, and G refer to the individual environmental, social,and governance scores, respectively. We exclude the years 2001 and 2002 since the holding information ofSRI funds is largely incomplete.

29

Table 2The relation between ESG scores and fund performance.

Rank ESG CBI Rt Rt+1 BM Cap

Panel A: SRI fundsTotal 0.030 0.012 0.062 0.061 0.286 6.637

1 0.080 0.012 0.066 0.058 0.310 7.0072 0.031 0.011 0.058 0.073 0.248 6.2193 -0.024 0.013 0.062 0.051 0.296 6.664

Panel B: Conventional fundsTotal -0.001 0.022 0.065 0.041 0.310 7.960

1 0.042 0.021 0.063 0.047 0.291 8.1262 0.002 0.023 0.056 0.039 0.296 7.9993 -0.047 0.021 0.077 0.040 0.342 7.755

Panel C: Mean and median testsESG difference Mean Median

SRITotal-ConventionalTotal 4.470∗∗∗ 5.142∗∗∗

SRI1-Conventional1 5.088∗∗∗ 6.441∗∗∗



This table reports the CBI score, current and subsequent period fund returns, BM and Cap of SRI(Panel A) and conventional (Panel B) funds sorted according to the ESG score. Rank 1 (3) containsthe portfolio with the highest (lowest) ESG scores. ESG and CBI are the ESG and CBI scores of theportfolio, respectively. The variables Rt and Rt+1 are the current and subsequent period fund returns.BM is the book value of equity divided by the market capitalization. Cap is calculated as the marketcapitalization (in billions) at the end of the year. Panel C reports the mean and median difference test forthe ESG score between SRI and conventional funds. The t-statistic is provided under the column titled“Mean” and the Wilcoxon/Mann-Whitney test statistic is provided under the column titled “Median”. ∗∗∗

denotes significance at the 1% level.

30

Table 3The relation between CBI scores and fund performance.

Rank CBI ESG Rt Rt+1 BM Cap

Panel A: SRI fundsTotal 0.012 0.030 0.062 0.061 0.286 6.637

1 0.022 0.042 0.075 0.073 0.335 7.8312 0.010 0.041 0.059 0.056 0.275 6.6873 0.003 0.007 0.052 0.054 0.244 5.302

Panel B: Conventional fundsTotal 0.022 -0.001 0.065 0.041 0.310 7.960

1 0.036 0.008 0.068 0.051 0.319 8.4782 0.022 0.006 0.060 0.039 0.309 8.0983 0.008 -0.018 0.068 0.035 0.301 7.300

Panel C: Mean and median testsCBI difference Mean Median

SRITotal-ConventionalTotal -18.328∗∗∗ 20.2856∗∗∗

SRI1-Conventional1 -21.716∗∗∗ 19.8330∗∗∗



This table reports the ESG score, current and subsequent period fund returns, BM and Cap of SRI(Panel A) and conventional (Panel B) funds sorted according to the CBI score. Rank 1 (3) contains theportfolio with the highest (lowest) CBI scores. ESG and CBI are the ESG and CBI scores of the portfolio,respectively. The variables Rt and Rt+1 are the current and subsequent period fund returns. BM is thebook value of equity divided by the market capitalization. Cap is calculated as the market capitalization(in billions) at the end of the year. Panel C reports the mean and median difference test for the CBI scorebetween SRI and conventional funds. The t-statistic is provided under the column titled “Mean” andthe Wilcoxon/Mann-Whitney test statistic is provided under the column titled “Median”. ∗∗∗ denotessignificance at the 1% level.

31

Table 4Screening intensity and fund ethicality.

CBI E S G ESG ESG

Intercept -0.004∗∗ -0.015 -0.019 -0.141∗∗∗ -0.085∗∗∗ -0.059∗∗∗

(-2.19) (-1.19) (-1.62) (-7.08) (-4.36) (-4.67)Exclusionary -0.001∗∗ -0.001

(-1.98) (-1.39)ES 0.015∗∗∗ 0.007 0.000

(2.70) (0.71) (0.09)SS 0.015∗∗ 0.003 0.008

(2.52) (0.39) (0.82)GS 0.013∗ 0.008 0.012

(1.93) (0.73) (1.12)TNA -0.001∗∗∗ -0.008∗∗∗ -0.009∗∗∗ -0.009∗∗∗ -0.010∗∗∗ -0.009∗∗∗

(-4.14) (-2.94) (-3.22) (-3.52) (-3.56) (-3.05)Family TNA 0.001∗∗∗ 0.005∗∗∗ 0.005∗∗ 0.006∗∗∗ 0.006∗∗∗ 0.006∗∗∗

(7.52) (2.68) (2.52) (3.27) (3.31) (2.94)Age 0.006∗∗∗ 0.049∗∗∗ 0.050∗∗∗ 0.051∗∗∗ 0.056∗∗∗ 0.058∗∗∗

(7.51) (6.70) (6.88) (6.77) (7.26) (7.42)Expense 0.000∗∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗

(5.64) (14.77) (13.99) (14.00) (13.91) (13.98)F.Turnover 0.000 -0.028∗∗∗ -0.028∗∗∗ -0.027∗∗∗ -0.028∗∗∗ -0.029∗∗∗

(-0.12) (-4.56) (-4.50) (-4.40) (-4.51) (-4.60)

Adj. R2 0.111 0.073 0.070 0.069 0.088 0.100

This table presents the estimation results of the regression of portfolio ethicality on the screens usedby SRI funds. The first row denotes the dependent variable of each regression. ESG and CBI are theESG and CBI scores of the fund portfolio, respectively. The variables E, S, and G refer to the individualenvironmental, social, and governance scores, respectively. Exclusionary is the number of exclusionaryscreens used and ES, SS and GS are dummy variables that have a value of one if an SRI fund employsenvironmental, social and governance screens, respectively, and zero otherwise. TNA is the logarithm ofthe total net asset value of a fund and Family TNA is the logarithm of the total net asset value of afund’s family. Age is the logarithm of the age of a fund since its inception. Expense and F.Turnoverare the expense and turnover ratio of the fund. For all specifications, year fixed effects are included;t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% level,respectively.

32

Table 5The impact of CBI and ESG scores on subsequent fund returns.

SRI funds Conventional funds

(1) (2) (3) (4) (5) (6)

Intercept 0.138∗∗∗ 0.147∗∗∗ 0.152∗∗∗ 0.019 0.010 0.025(6.39) (6.76) (6.98) (0.61) (0.37) (0.84)

ESG 0.172∗∗∗ 0.141∗∗ 0.212∗∗∗ 0.188∗∗∗

(3.23) (2.50) (5.17) (4.11)CBI 1.384∗∗∗ 1.226∗∗∗ 0.783∗∗∗ 0.637∗∗∗

(3.61) (3.04) (5.49) (3.85)Flow -0.028∗∗ -0.029∗∗ -0.027∗∗ 0.000 0.000 0.000

(-2.46) (-2.56) (-2.46) (1.39) (1.51) (1.31)TNA -0.001 -0.001 -0.002 0.000 0.000 0.000

(-0.68) (-0.72) (-0.93) (-0.09) (-0.11) (-0.04)Family TNA -0.001 -0.002 -0.002 -0.001 -0.002 -0.001

(-0.90) (-1.61) (-1.47) (-0.62) (-1.09) (-0.85)Age 0.001 -0.001 0.000 0.007 0.006 0.006

(0.12) (-0.12) (0.04) (1.41) (1.37) (1.36)Expense 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.683 0.727 0.724

(3.25) (3.78) (3.26) (0.77) (0.81) (0.81)F.Turnover 0.001 -0.002 -0.003 0.000∗∗ 0.000∗∗∗ 0.000∗∗

(0.12) (-0.36) (-0.45) (2.27) (2.62) (2.42)BM 0.192∗∗ 0.203∗∗ 0.182∗∗ -0.040 -0.046 -0.041

(2.25) (2.22) (2.08) (-1.02) (-1.17) (-1.03)Cap 0.008∗ 0.008∗ 0.008∗ 0.012∗∗∗ 0.012∗∗∗ 0.010∗∗∗

(1.84) (1.72) (1.67) (3.66) (4.28) (3.26)Leverage -0.001 -0.002 -0.001 -0.001∗∗∗ -0.001∗∗∗ -0.001∗∗∗

(-0.47) (-0.98) (-0.54) (-3.47) (-3.31) (-3.21)DivY ield -3.816∗∗ -3.177∗∗ -3.532∗∗ -0.709 -1.300 -1.082

(-2.50) (-2.04) (-2.20) (-0.86) (-1.62) (-1.40)ROA -0.707∗∗ -0.691∗∗ -0.756∗∗ -0.663∗∗∗ -0.686∗∗∗ -0.648∗∗∗

(-2.11) (-2.12) (-2.28) (-4.97) (-5.44) (-4.90)CFV olt 0.000 0.000 0.000 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗

(-0.22) (-1.63) (-1.49) (-6.99) (-5.91) (-6.87)RetV olt -1.739 -2.660∗∗ -1.674 4.463∗∗∗ 3.744∗∗∗ 4.662∗∗∗

(-1.34) (-2.00) (-1.30) (7.97) (6.21) (8.26)Turnover -0.096 1.059 0.216 -3.153∗∗∗ -2.782∗∗∗ -3.048∗∗∗

(-0.03) (0.35) (0.07) (-3.63) (-3.23) (-3.48)Illiquidity 51.231 77.810∗∗ 49.394 -128.984∗∗∗ -108.261∗∗∗ -134.740∗∗∗

(1.37) (2.03) (1.33) (-7.96) (-6.14) (-8.24)

Adj. R2 0.834 0.835 0.835 0.428 0.428 0.429

This table presents the estimation results of the regression of subsequent fund returns on the ESG andCBI scores of both SRI and conventional funds. ESG and CBI are the ESG and CBI scores of the fundportfolio, respectively. Flow is the net fund flow. TNA is the logarithm of the total net asset valueof a fund and Family TNA is the logarithm of the total net asset value of a fund’s family. Age is thelogarithm of the age of a fund since its inception. Expense and F.Turnover are the expense and turnoverratios of a fund. BM is the book value of equity divided by the market capitalization. Cap is calculatedas the market capitalization (in billions) of the stock at the end of the year. Leverage refers to thedebt-to-equity ratio. DivY ield refers to the annual percentage dividend yield calculated for each stock asthe annual dividend divided by price. ROA is the net income divided by average total assets. CFV oltis the standard deviation of the net cash flow from operating activities over the previous five financialyears (minimum of three years), scaled by the average total assets. RetV olt is the standard deviation ofdaily excess returns (excess over the CRSP value-weighted index) over the last calendar year. Turnoveris calculated as the trading volume divided by shares outstanding and Illiquidity is Amihud’s (2002)illiquidity ratio. For all specifications, year fixed effects are included and standard errors are clustered byfund and time; t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗ indicate significance at the 10%,5%, and 1% level, respectively.

33

Table 6The impact of CBI and ESG scores on subsequent risk-adjusted fund returns.


(1) (2) (3) (4) (5) (6)

Intercept 0.148∗∗∗ 0.053∗∗ 5.838∗∗∗ 0.021 -0.073∗∗ 1.624∗∗

(6.79) (2.48) (12.10) (0.70) (-2.44) (1.98)ESG 0.141∗∗ 0.139∗∗ 4.989∗∗∗ 0.188∗∗∗ 0.187∗∗∗ 2.718∗

(2.50) (2.47) (4.53) (4.12) (4.08) (1.88)CBI 1.226∗∗∗ 1.232∗∗∗ 37.568∗∗∗ 0.637∗∗∗ 0.638∗∗∗ 15.335∗∗∗

(3.04) (3.05) (4.75) (3.85) (3.86) (3.09)Flow -0.027∗∗ -0.027∗∗ -0.344 0.000 0.000 0.001∗

(-2.47) (-2.43) (-1.46) (1.31) (1.31) (1.81)TNA -0.002 -0.002 -0.015 0.000 0.000 0.031

(-0.92) (-0.99) (-0.35) (-0.05) (-0.03) (0.56)Family TNA -0.002 -0.002 -0.106∗∗∗ -0.001 -0.001 -0.039

(-1.47) (-1.47) (-3.77) (-0.85) (-0.82) (-0.76)Age 0.000 0.001 -0.120 0.006 0.006 0.046

(0.03) (0.12) (-0.89) (1.36) (1.36) (0.37)Expense 0.001∗∗∗ 0.001∗∗∗ -0.007∗ 0.723 0.728 26.094

(3.26) (3.23) (-1.84) (0.81) (0.81) (0.89)F.Turnover -0.003 -0.003 -0.079 0.000∗∗ 0.000∗∗ 0.007∗∗

(-0.44) (-0.52) (-0.57) (2.42) (2.41) (2.04)BM 0.182∗∗ 0.185∗∗ 0.997 -0.041 -0.038 -1.170

(2.08) (2.12) (0.70) (-1.03) (-0.97) (-1.05)Cap 0.008∗ 0.008∗ 0.123 0.010∗∗∗ 0.010∗∗∗ 0.229∗∗∗

(1.67) (1.68) (1.46) (3.26) (3.19) (2.94)Leverage -0.001 -0.001 0.020 -0.001∗∗∗ -0.001∗∗∗ -0.019∗∗

(-0.54) (-0.53) (0.61) (-3.22) (-3.12) (-2.53)DivY ield -3.531∗∗ -3.547∗∗ -27.833 -1.079 -1.115 20.759

(-2.20) (-2.21) (-1.28) (-1.40) (-1.44) (0.93)ROA -0.757∗∗ -0.752∗∗ -4.949 -0.648∗∗∗ -0.650∗∗∗ -13.169∗∗∗

(-2.28) (-2.27) (-1.12) (-4.90) (-4.91) (-3.62)CFV olt 0.000 0.000 0.000 0.000∗∗∗ 0.000∗∗∗ -0.001∗∗∗

(-1.49) (-1.51) (-0.79) (-6.88) (-6.81) (-8.76)RetV olt -1.668 -1.750 -46.832∗ 4.678∗∗∗ 4.466∗∗∗ 55.176∗∗∗

(-1.29) (-1.37) (-1.94) (8.28) (8.04) (4.33)Turnover 0.215 0.232 -55.473 -3.060∗∗∗ -2.898∗∗∗ -29.097

(0.07) (0.08) (-1.11) (-3.49) (-3.33) (-1.26)Illiquidity 49.218 51.559 1378.295∗∗ -135.198∗∗∗ -129.104∗∗∗ -1602.669∗∗∗

(1.32) (1.40) (1.98) (-8.25) (-8.02) (-4.30)

Adj. R2 0.837 0.724 0.839 0.430 0.270 0.195

This table presents the estimation results of the regression of subsequent risk-adjusted fund returns onthe ESG and CBI scores of both SRI and conventional funds. The dependent variable is: (i) the excessfund returns over the risk-free rate in columns (1) and (4); (ii) the excess fund returns over the marketreturn in columns (2) and (5); and (iii) the Sharpe ratio in columns (3) and (6). ESG and CBI are theESG and CBI scores of the fund portfolio, respectively. Flow is the net fund flow. TNA is the logarithmof the total net asset value of a fund and Family TNA is the logarithm of the total net asset value of afund’s family. Age is the logarithm of the age of a fund since its inception. Expense and F.Turnoverare the expense and turnover ratios of a fund. BM is the book value of equity divided by the marketcapitalization. Cap is calculated as the market capitalization (in billions) of the stock at the end of theyear. Leverage refers to the debt-to-equity ratio. DivY ield refers to the annual percentage dividendyield calculated for each stock as the annual dividend divided by price. ROA is the net income divided byaverage total assets. CFV olt is the standard deviation of the net cash flow from operating activities overthe previous five financial years (minimum of three years), scaled by the average total assets. RetV oltis the standard deviation of daily excess returns (excess over the CRSP value-weighted index) over thelast calendar year. Turnover is calculated as the trading volume divided by shares outstanding andIlliquidity is Amihud’s (2002) illiquidity ratio. For all specifications, year fixed effects are included andstandard errors are clustered by fund and time; t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗

indicate significance at the 10%, 5%, and 1% level, respectively.

34

Table 7The impact of individual ESG scores on subsequent fund returns.


(1) (2) (3) (4) (5) (6)

Intercept 0.151∗∗∗ 0.151∗∗∗ 0.147∗∗∗ 0.021 0.014 0.028(6.99) (6.92) (6.64) (0.72) (0.48) (0.87)

Environmental 0.147∗∗∗ 0.152∗∗∗

(2.75) (4.50)Social 0.285∗∗∗ 0.275∗∗∗

(4.28) (5.43)Governance 0.000 0.113∗∗

(0.01) (2.32)CBI 1.253∗∗∗ 1.295∗∗∗ 1.383∗∗∗ 0.683∗∗∗ 0.780∗∗∗ 0.596∗∗∗

(3.14) (3.46) (3.46) (4.40) (5.60) (2.88)Flow -0.026∗∗ -0.028∗∗ -0.029∗∗ 0.000 0.000 0.000

(-2.40) (-2.44) (-2.56) (1.24) (1.41) (1.40)TNA -0.002 -0.002 -0.001 0.000 0.000 0.000

(-1.05) (-1.03) (-0.72) (-0.03) (-0.06) (-0.07)Family TNA -0.002 -0.002 -0.002 -0.001 -0.001 -0.001

(-1.50) (-1.59) (-1.59) (-0.89) (-0.86) (-0.90)Age 0.001 0.001 -0.001 0.006 0.007 0.006

(0.14) (0.11) (-0.12) (1.34) (1.39) (1.36)Expense 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.730 0.717 0.723

(2.86) (3.16) (3.82) (0.81) (0.80) (0.80)F.Turnover -0.003 -0.002 -0.002 0.000∗∗ 0.000∗∗ 0.000∗∗

(-0.42) (-0.29) (-0.36) (2.44) (2.51) (2.44)BM 0.190∗∗ 0.205∗∗ 0.203∗∗ -0.043 -0.033 -0.043

(2.18) (2.24) (2.26) (-1.10) (-0.88) (-1.10)Cap 0.005 0.005 0.008∗ 0.009∗∗∗ 0.007∗∗ 0.012∗∗∗

(1.25) (0.97) (1.66) (2.85) (2.33) (4.56)Leverage -0.001 -0.001 -0.002 -0.001∗∗∗ -0.001∗∗∗ -0.001∗∗∗

(-0.53) (-0.62) (-0.99) (-3.28) (-3.27) (-3.17)DivY ield -3.282∗∗ -3.633∗∗ -3.178∗∗ -1.010 -1.108 -1.201

(-2.13) (-2.33) (-1.97) (-1.32) (-1.36) (-1.55)ROA -0.693∗∗ -0.745∗∗ -0.691∗∗ -0.661∗∗∗ -0.616∗∗∗ -0.665∗∗∗

(-2.16) (-2.31) (-2.09) (-5.08) (-4.81) (-5.04)CFV olt 0.000∗ 0.000∗∗ 0.000 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗

(-1.66) (-2.02) (-1.58) (-6.66) (-6.60) (-6.60)RetV olt -1.500 -1.468 -2.658∗∗ 4.777∗∗∗ 4.765∗∗∗ 4.214∗∗∗

(-1.21) (-1.07) (-1.96) (8.49) (7.18) (7.59)Turnover 0.133 0.608 1.056 -3.341∗∗∗ -3.031∗∗∗ -2.744∗∗∗

(0.05) (0.21) (0.35) (-3.69) (-3.63) (-3.24)Illiquidity 44.372 43.459 77.745∗∗ -138.029∗∗∗ -137.640∗∗∗ -121.849∗∗∗

(1.25) (1.10) (1.99) (-8.46) (-7.10) (-7.56)

Adj. R2 0.837 0.838 0.835 0.430 0.429 0.429

This table presents the estimation results of the regression of subsequent fund returns on the individualESG scores of both SRI and conventional funds. The variables Environmental, Social, and Governancerefer to the individual environmental, social, and governance scores of the fund portfolio, respectively.CBI is the CBI score of the fund portfolio, respectively. Flow is the net fund flow. TNA is the logarithmof the total net asset value of a fund and Family TNA is the logarithm of the total net asset value of afund’s family. Age is the logarithm of the age of a fund since its inception. Expense and F.Turnoverare the expense and turnover ratios of a fund. BM is the book value of equity divided by the marketcapitalization. Cap is calculated as the market capitalization (in billions) of the stock at the end of theyear. Leverage refers to the debt-to-equity ratio. DivY ield refers to the annual percentage dividendyield calculated for each stock as the annual dividend divided by price. ROA is the net income divided byaverage total assets. CFV olt is the standard deviation of the net cash flow from operating activities overthe previous five financial years (minimum of three years), scaled by the average total assets. RetV oltis the standard deviation of daily excess returns (excess over the CRSP value-weighted index) over thelast calendar year. Turnover is calculated as the trading volume divided by shares outstanding andIlliquidity is Amihud’s (2002) illiquidity ratio. For all specifications, year fixed effects are included andstandard errors are clustered by fund and time; t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗


35

Table 8The impact of ESG strength and concern scores on subsequent fund returns.


(1) (2) (3) (4) (5) (6) (7) (8)

Intercept 0.153∗∗∗ 0.151∗∗∗ 0.147∗∗∗ 0.154∗∗∗ 0.050∗ 0.025 0.035 0.050∗∗

(7.16) (7.01) (6.63) (7.14) (1.71) (1.04) (1.18) (2.05)E(+) 0.180∗∗∗ 0.188∗∗∗

(2.70) (5.11)E(−) 0.073 0.059

(0.63) (1.16)S(+) 0.299∗∗∗ 0.319∗∗∗

(4.08) (5.28)S(−) -0.218 -0.126

(-1.25) (-0.91)G(+) -0.003 0.320∗∗∗

(-0.05) (6.69)G(−) -0.009 0.121

(-0.09) (1.11)ESG(+) 0.237∗∗∗ 0.298∗∗∗

(2.82) (8.80)ESG(−) 0.149 0.156

(1.01) (1.02)CBI 0.906∗ 1.247∗∗∗ 1.378∗∗∗ 0.996∗∗ 0.401∗∗ 0.740∗∗∗ 0.541∗∗∗ 0.462∗∗∗

(1.69) (2.88) (3.34) (2.19) (2.10) (5.89) (2.73) (3.22)Flow -0.026∗∗ -0.027∗∗ -0.029∗∗ -0.025∗∗ 0.000 0.000 0.000 0.000

(-2.41) (-2.35) (-2.49) (-2.27) (1.37) (1.45) (1.30) (1.40)TNA -0.002 -0.002 -0.001 -0.002 0.000 0.000 0.000 0.000

(-0.94) (-1.02) (-0.73) (-0.85) (0.00) (-0.02) (0.03) (0.04)Family TNA -0.002 -0.002∗ -0.002 -0.002∗ -0.001 -0.001 -0.001 -0.001

(-1.57) (-1.65) (-1.51) (-1.72) (-0.90) (-0.87) (-0.84) (-0.86)Age 0.001 0.001 -0.001 0.000 0.006 0.006 0.006 0.006

(0.08) (0.12) (-0.12) (0.05) (1.30) (1.35) (1.29) (1.27)Expense 0.001∗∗ 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗ 0.745 0.747 0.759 0.770

(2.14) (3.15) (3.76) (2.57) (0.83) (0.82) (0.84) (0.85)F.Turnover -0.003 -0.002 -0.002 -0.003 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗

(-0.48) (-0.29) (-0.37) (-0.43) (2.75) (2.62) (3.03) (2.91)BM 0.195∗∗ 0.205∗∗ 0.203∗∗ 0.185∗∗ -0.057 -0.035 -0.062 -0.057

(2.22) (2.24) (2.24) (2.11) (-1.47) (-0.91) (-1.45) (-1.33)Cap 0.004 0.004 0.008 0.002 0.005 0.004 0.003 0.001

(0.89) (0.68) (1.39) (0.40) (1.46) (1.34) (1.27) (0.43)Leverage -0.001 -0.001 -0.002 -0.001 -0.001∗∗∗ -0.001∗∗∗ -0.001∗∗ -0.001∗∗

(-0.32) (-0.63) (-1.00) (-0.51) (-2.58) (-2.93) (-2.03) (-2.14)DivY ield -3.540∗∗ -3.664∗∗ -3.188∗∗ -3.632∗∗ -1.419∗ -1.393∗∗ -1.307∗ -1.593∗∗

(-2.20) (-2.31) (-1.97) (-2.25) (-1.81) (-2.09) (-1.74) (-2.41)ROA -0.628∗∗ -0.725∗∗ -0.696∗∗ -0.647∗∗ -0.602∗∗∗ -0.605∗∗∗ -0.638∗∗∗ -0.597∗∗∗

(-2.04) (-2.21) (-2.05) (-2.00) (-4.51) (-4.93) (-4.95) (-4.85)CFV olt 0.000∗∗ 0.000∗∗ 0.000 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗

(-2.52) (-2.22) (-1.20) (-2.72) (-7.89) (-4.08) (-6.47) (-5.95)RetV olt -1.125 -1.405 -2.654∗ -1.276 5.029∗∗∗ 4.843∗∗∗ 4.734∗∗∗ 5.023∗∗∗

(-0.93) (-1.03) (-1.92) (-1.00) (8.82) (6.98) (7.60) (8.26)Turnover 0.423 0.784 1.008 1.001 -2.912∗∗∗ -2.665∗∗∗ -1.954∗∗ -2.296∗∗

(0.14) (0.27) (0.35) (0.34) (-3.29) (-2.68) (-2.03) (-2.28)Illiquidity 33.583 41.623 77.621∗ 37.890 -145.323∗∗∗ -139.893∗∗∗ -136.795∗∗∗ -145.146∗∗∗

(0.96) (1.06) (1.95) (1.03) (-8.80) (-6.90) (-7.54) (-8.20)

Adj. R2 0.838 0.838 0.835 0.837 0.430 0.429 0.431 0.430

This table presents the estimation results of the regression of subsequent fund returns on the strength and concern of ESGscores for both SRI and conventional funds. The variables E(+), S(+), G(+), and ESG(+) refer to the strength of theindividual environmental, social, governance, and total ESG scores, respectively, while E(−), S(−), G(−), and ESG(−)refer to the concern of the individual environmental, social, governance, and total ESG scores, respectively. CBI is theCBI score of the fund portfolio, respectively. Flow is the net fund flow. TNA is the logarithm of the total net asset valueof a fund and Family TNA is the logarithm of the total net asset value of a fund’s family. Age is the logarithm of the ageof a fund since its inception. Expense and F.Turnover are the expense and turnover ratios of a fund. BM is the bookvalue of equity divided by the market capitalization. Cap is calculated as the market capitalization (in billions) of the stockat the end of the year. Leverage refers to the debt-to-equity ratio. DivY ield refers to the annual percentage dividendyield calculated for each stock as the annual dividend divided by price. ROA is the net income divided by average totalassets. CFV olt is the standard deviation of the net cash flow from operating activities over the previous five financial years(minimum of three years), scaled by the average total assets. RetV olt is the standard deviation of daily excess returns(excess over the CRSP value-weighted index) over the last calendar year. Turnover is calculated as the trading volumedivided by shares outstanding and Illiquidity is Amihud’s (2002) illiquidity ratio. For all specifications, year fixed effectsare included and standard errors are clustered by fund and time; t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗


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Table 9Quantile regression of subsequent fund returns on ESG and CBI scores.


Quantile 0.1 0.3 0.5 0.7 0.9 0.1 0.3 0.5 0.7 0.9

Intercept 0.028∗∗∗ 0.114∗∗∗ 0.158∗∗∗ 0.194∗∗∗ 0.302∗∗∗ 0.013 0.090∗∗∗ 0.166∗∗∗ 0.190∗∗∗ 0.232∗∗∗

(24.06) (16.09) (15.91) (19.57) (28.28) (0.75) (8.38) (16.20) (15.51) (17.29)ESG 0.036∗∗∗ 0.124∗∗∗ 0.177∗∗∗ 0.051∗∗ 0.064∗∗ 0.247∗∗∗ 0.248∗∗∗ 0.252∗∗∗ 0.279∗∗∗ 0.310∗∗∗

(12.14) (6.82) (6.95) (2.00) (2.34) (11.99) (20.09) (21.38) (19.72) (19.99)CBI 0.106∗∗∗ 1.053∗∗∗ 1.224∗∗∗ 1.970∗∗∗ 2.214∗∗∗ 0.163∗ 0.574∗∗∗ 0.665∗∗∗ 0.722∗∗∗ 0.588∗∗∗

(5.47) (8.78) (7.28) (11.73) (12.26) (1.76) (10.33) (12.51) (11.33) (8.43)Flow -0.010∗∗∗ -0.010∗∗∗ -0.014∗∗∗ -0.005 -0.021∗∗∗ 0.000 0.000 0.000∗∗ 0.000 0.000∗∗

(-18.49) (-3.06) (-3.10) (-1.00) (-4.26) (1.41) (0.42) (2.26) (1.09) (2.00)TNA -0.002∗∗∗ -0.004∗∗∗ -0.002∗ 0.000 -0.001 0.001 -0.001∗∗ -0.002∗∗∗ -0.002∗∗∗ -0.002∗∗∗

(-15.22) (-6.66) (-1.95) (-0.32) (-1.24) (1.07) (-1.99) (-5.02) (-3.93) (-3.76)Family TNA 0.001∗∗∗ -0.002∗∗∗ -0.004∗∗∗ -0.004∗∗∗ -0.006∗∗∗ 0.004∗∗∗ 0.003∗∗∗ 0.003∗∗∗ 0.002∗∗∗ -0.001∗∗

(15.66) (-5.18) (-6.22) (-6.69) (-8.72) (7.13) (7.83) (8.31) (4.42) (-1.97)Age 0.004∗∗∗ 0.001 -0.001 -0.004 -0.018∗∗∗ -0.003 0.002∗ 0.005∗∗∗ 0.005∗∗∗ 0.006∗∗∗

(12.43) (0.43) (-0.28) (-1.18) (-5.48) (-1.50) (1.90) (4.65) (3.85) (4.05)Expense -0.001∗∗∗ 0.000 0.001∗∗ 0.001∗∗∗ 0.001∗∗∗ -1.233∗∗∗ -0.695∗∗∗ -0.621∗∗∗ -0.337∗∗ 0.368∗∗

(-26.45) (0.62) (2.32) (3.97) (4.23) (-5.29) (-4.97) (-4.65) (-2.11) (2.10)F.Turnover 0.003∗∗∗ -0.004∗∗ -0.001 -0.009∗∗∗ -0.003 0.000∗ 0.000 0.000∗∗ 0.000∗∗∗ 0.000

(7.98) (-2.20) (-0.29) (-3.35) (-0.92) (1.92) (1.56) (2.27) (3.43) (-0.29)BM 0.149∗∗∗ 0.003 0.110∗∗∗ 0.076∗∗∗ 0.024 0.032∗ 0.001 0.010 0.000 -0.051∗∗∗

(42.33) (0.13) (3.59) (2.49) (0.71) (1.69) (0.10) (0.95) (0.01) (-3.66)Cap 0.013∗∗∗ 0.006∗∗∗ 0.000 -0.004∗∗ -0.005∗∗∗ -0.002 -0.005∗∗∗ -0.007∗∗∗ -0.008∗∗∗ 0.002∗

(66.96) (4.96) (0.13) (-2.34) (-2.94) (-1.45) (-4.62) (-7.76) (-6.70) (1.95)Leverage 0.001∗∗∗ -0.001∗∗ 0.002∗ -0.003∗∗∗ 0.006∗∗∗ -0.003∗∗∗ -0.002∗∗∗ -0.001∗∗∗ -0.001∗∗∗ 0.000

(10.56) (-2.01) (1.69) (-2.77) (6.02) (-5.44) (-5.84) (-2.95) (-3.73) (0.05)DivY ield -1.717∗∗∗ -0.355 -2.100∗∗∗ -0.829∗∗ -2.003∗∗∗ -1.229∗∗∗ -1.356∗∗∗ -2.134∗∗∗ -2.218∗∗∗ -2.518∗∗∗

(-36.08) (-1.20) (-5.08) (-2.01) (-4.51) (-4.04) (-7.44) (-12.24) (-10.62) (-11.00)ROA 0.534∗∗∗ 0.220∗∗∗ -0.039 -0.469∗∗∗ -0.910∗∗∗ 0.044 -0.228∗∗∗ -0.271∗∗∗ -0.354∗∗∗ -0.848∗∗∗

(49.12) (3.27) (-0.41) (-4.97) (-8.97) (0.58) (-5.05) (-6.28) (-6.84) (-14.96)CFV olt 0.000∗∗∗ 0.000∗∗ 0.000∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗

(14.84) (-1.99) (-2.12) (-6.03) (-4.32) (-3.12) (-9.21) (-10.87) (-10.42) (-11.20)RetV olt -7.941∗∗∗ -1.690∗∗∗ -0.834 -1.511∗∗∗ 1.869∗∗∗ 3.900∗∗∗ 2.739∗∗∗ 2.103∗∗∗ 2.739∗∗∗ 5.178∗∗∗

(-118.06) (-4.05) (-1.43) (-2.59) (2.98) (8.04) (9.42) (7.56) (8.22) (14.17)Turnover -2.020∗∗∗ -4.356∗∗∗ -2.494∗∗ 4.850∗∗∗ 8.984∗∗∗ -6.885∗∗∗ -2.292∗∗∗ -1.722∗∗∗ -0.073 1.233∗∗

(-17.01) (-5.92) (-2.42) (4.71) (8.11) (-9.24) (-5.13) (-4.03) (-0.14) (2.20)Illiquidity 230.032∗∗∗ 49.673∗∗∗ 24.837 44.085∗∗∗ -53.232∗∗∗ -111.171∗∗∗ -78.576∗∗∗ -60.429∗∗∗ -78.990∗∗∗ -149.472∗∗∗

(118.77) (4.14) (1.48) (2.62) (-2.94) (-7.96) (-9.38) (-7.54) (-8.23) (-14.21)

This table presents the quantile estimation results of the subsequent fund returns on the ESG and CBIscores of both SRI and conventional funds. ESG and CBI are the ESG and CBI scores of the fundportfolio, respectively. Flow is the net fund flow. TNA is the logarithm of the total net asset valueof a fund and Family TNA is the logarithm of the total net asset value of a fund’s family. Age is thelogarithm of the age of a fund since its inception. Expense and F.Turnover are the expense and turnoverratios of a fund. BM is the book value of equity divided by the market capitalization. Cap is calculatedas the market capitalization (in billions) of the stock at the end of the year. Leverage refers to thedebt-to-equity ratio. DivY ield refers to the annual percentage dividend yield calculated for each stock asthe annual dividend divided by price. ROA is the net income divided by average total assets. CFV oltis the standard deviation of the net cash flow from operating activities over the previous five financialyears (minimum of three years), scaled by the average total assets. RetV olt is the standard deviation ofdaily excess returns (excess over the CRSP value-weighted index) over the last calendar year. Turnoveris calculated as the trading volume divided by shares outstanding and Illiquidity is Amihud’s (2002)illiquidity ratio. For all specifications, year fixed effects are included and standard errors are clustered byfund and time; t-statistics are shown in the parenthesis. ∗, ∗∗, and ∗∗∗ indicate significance at the 10%,5%, and 1% level, respectively.

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Figures

Fig. 1. The mechanism of screening intensity. This figure describes the mechanism of the screeningprocess. Existing studies have focused on the impact of the screening process on fund performance. Weargue that the screens used by SRI funds are designed to increase the ethicality of SRI funds. In addition,the screening process may not have a direct impact on fund performance since there is no guaranteethat increases in screening intensity leads to portfolios with higher ethicality. Our study focuses on therelation between ESG and CBI scores, which contains value-relevant information, and fund performance.

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Fig. 2. Time-series of ethicality measures for SRI funds, conventional funds, and the market portfolio.This figure plots the time-series of equal-weighted environmental, social, governance, total ESG, and CBIscores for SRI funds, conventional funds, and the US market portfolio.

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Fig. 3. Quantile regression of fund returns on CBI and ESG scores. This figure plots the quantileestimation results of the subsequent fund returns on CBI and ESG scores for SRI and conventional funds.The quantile regression is estimated from the 0.05 quantile to the 0.95 quantile of the subsequent periodfund returns, with quantile increments of 0.05.

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the impact of screening and portfolio ethicality on … annual meetings...1.introduction over the...

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