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Tilburg,11thofAugust2017

MASTERTHESISFINANCE

Tilburg School of Economics and Management

TheEffectofESGFactorsonEarningsForecastAccuracyandEarningsRelatedStockReturns

Short-termDynamicsofLong-termInvestments

Student: BastienFrançoisANR:SNR: MailAddress: Department: StudyProgram:ThesisSupervisor:SecondReader:

295985u1251496Finance,TilburgUniversityMasterFinance(General)dr.P.C.DeGoeijdr.M.R.R.VanBremen

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ABSTRACTThis thesis investigates the effect of firms’ ESG scores on stock price behavior around

earnings announcements using a dataset of 19,910 earnings announcements. First, using

an ordinary least squares regression, the empirical findings show a positive relationship

between the ESG score and the analysts’ forecast error. This effect is driven by positive

forecast errors and the social score. Second, using an instrumental variable

methodology, the empirical findings show that the cumulative abnormal returns are

more sensitive to negative earnings forecast errors for firms with a high ESG score.

This effect is driven by the environmental score and social score. The empirical findings

indicate, but do not show statistical evidence of, a larger sensitivity of the cumulative

abnormal returns to positive forecast errors for firms with a high ESG score. Third,

using the same instrumental variable methodology, the empirical findings indicate, but

do not show statistical evidence of, lower volatility in abnormal returns for firms with

a high ESG score. Finally, the instrumental variable methodology shows that the

industry averages are strong instrumental variables for the overall ESG score, the

environmental score, and the social score.

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ACKNOWLEDGEMENT

I would like to express my gratitude to Peter de Goeij for his suggestions and help

during the process of writing this thesis

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TABLEOFCONTENTS

1. Introduction 6

2. LiteratureReview 8

2.1. EarningsAnnouncements 8

2.1.1. Analysts’ForecastErrors 9

2.1.2. EarningsManagement 11

2.1.3. AnnouncementTiming 12

2.2. Environmental,SocialandGovernance(ESG)Factors 13

2.2.1. TheImportanceofESGFactors 14

2.2.2. TheImpactofESGFactorsonFirmValue 15

2.2.3. TheImpactofESGFactorsonFirmRisk 17

2.3. EarningsAnnouncementsandESGFactors 18

2.3.1. Analysts’ForecastAccuracyandESGFactors 18

2.3.2. PastPerformanceandESGFactors 19

2.4. HypothesesDevelopment 21

3. Data 23

3.1. DependentVariables 24

3.2. IndependentVariables 26

3.3. DescriptiveStatistics 28

3.3.1. Analysts’ForecastErrors 28

3.3.2. CumulativeAbnormalReturns 29

3.3.3. VolatilityinAbnormalReturns 31

3.3.4. ESGScores 32

3.3.5. ControlVariables 33

4. Methodology 34

4.1. Analysts’ForecastAccuracy 34

4.2. CumulativeAbnormalReturns 36

4.2.1. Endogeneity 38

4.3. VolatilityinAbnormalReturns 40

5. EmpiricalResults 42

5.1. PairwiseCorrelations 42

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5.2. Analysts’ForecastAccuracy 43

5.2.1. Robustness 44

5.3. CumulativeAbnormalReturns 46

5.3.1. Endogeneity 48

5.3.2. Robustness 50

5.4. VolatilityinAbnormalReturns 51

5.4.1. Endogeneity 52

5.4.2. Robustness 54

6. Conclusions 55

6.1. Discussion 55

6.2. Limitations 57

6.3. Recommendations 58

Bibliography 59

Appendices I

AppendixA-Data I

AppendixB–Methodology XII

AppendixC–Results–PairwiseCorrelations XIII

AppendixC.1–Results–Analysts’ForecastAccuracy XV

AppendixC.2–Results–CumulativeAbnormalReturns XVIII

AppendixC.3–Results–VolatilityinAbnormalReturns XXV

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1. IntroductionEarnings announcements are of particular academic interest in the financial literature

because of the large stock price reactions. During earnings announcements, more

information arrives at the market than at other times (Chambers & Penman, 1984). It is

accepted in the literature that stock returns are correlated with the sign of the earnings

forecast error (Kross & Schroeder, 1984). Beaver, Clarke, and Wright (1979) show that

this is also the case for the magnitude of the earnings forecast error. Boudt, De Goeij,

Thewissen, and Van Campenhout (2015) present an investment strategy with a long

(short) position in the most pessimistic (optimistic) forecasts, which yields a risk-

adjusted return of 16.56 percent per annum.

Environmental, social, and governance (ESG) information is defined by Bassen and

Kovács (2008) as “extra-financial material information about the challenges and

performance of a company on these matters” (p.184). Empirical studies on ESG factors

have already been conducted since the second half of the twentieth century (Renneboog,

Ter Horst, & Zhang, 2008). Multiple studies show a positive relationship between ESG

performance and a firm’s market value and/or financial performance. Verheyden,

Eccles and Feiner (2016) find that only a preliminary screening on ESG factors can be

beneficial for any investment strategy. Kotsantonis, Pinney, and Serafeim (2016)

summarize studies on ESG factors and show that, over the last fifteen years, studies

have shown a positive relationship between ESG factors and a firm’s financial

performance. This shows that, in general, the financial benefits of ESG activities are

well documented in the academic literature.

There are a few studies that relate ESG performance, or performance on ESG

constituents, to earnings announcements. These studies focus on the analysts’ forecast

error. Byard, Li, and Weintrop (2006) find that corporate governance leads to higher

analysts’ forecast accuracy. Becchetti, Ciciretti, and Giovannelli (2013) find equal

results for corporate social responsibility. Kim, Li and Li (2014) state that financial

disclosures are of higher quality and that there are less negative surprises in earnings

for firms that perform well on corporate social responsibility.

This thesis first replicates these studies to analysts’ forecast accuracy using the overall

ESG score. Additionally, this thesis investigates the relationship between ESG

performance and stock returns around earnings announcements. If there is a relationship

between ESG performance and analysts’ forecast accuracy, a different stock price

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reaction for firms with a high ESG score has significant implications on how to exploit

the advantages of lower forecast errors for both investors and firm managers. Bollen

(2007) finds a lower sensitivity to negative and higher sensitivity to positive past returns

for socially responsible investment funds compared to conventional funds. However,

the impact of ESG performance on the behavior of an individual firm’s stock price is

not investigated in the context of earnings announcements as a measure for a firm’s

past performance. This thesis is relevant for the academic literature as it is, to the

knowledge of the researcher, the first to explore this relationship. Furthermore,

Orlitzky, Schmidt, and Rynes (2003) find evidence of reversed causality between

corporate social responsibility and financial performance. Therefore, this research tests

the models that relate ESG performance to stock returns for endogeneity.

Practically, this thesis gives insights in the benefits of ESG factors to active investors,

who are interested in short-term benefits. The findings may show, for example, higher

returns or lower risk for ESG investments around short-term events, which are in this

case earnings announcements. Additionally, this thesis gives insights to firm managers

in how ESG performance influences the firm’s stock price through earnings

announcements. As a result, managers can make more informed decisions on both the

adoption of ESG practices and the way in which they announce earnings to the public.

In short, this thesis answers the following research question:

Research Question: What is the effect of firms’ ESG scores on stock price behavior around earnings announcements?

The research question is answered using a dataset of 19,910 earnings announcement

and ESG data from the Thomson Reuters Asset4 Database. First, the findings show a

positive relationship between the ESG score and the analysts’ forecast error. This effect

is driven by positive forecast errors and the social score. A causal effect cannot be

concluded, since this model is not controlled for endogeneity. Second, the empirical

findings show that the cumulative abnormal returns are more sensitive to negative

earnings forecast errors for firms with a high ESG score. This effect is statistically

significant and driven by the environmental score and social score. The empirical

findings indicate, but do not show statistical evidence of, a larger sensitivity of the

cumulative abnormal returns to positive forecast errors for firms with a high ESG score.

Both effects hold after controlling for endogeneity, using the industry average ESG

score as the instrumental variable. Third, the empirical findings show that a high ESG

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score causes the abnormal returns to be less volatile around earnings announcements.

After controlling for endogeneity, again using the industry average ESG score as the

instrumental variable, this effect loses its statistical significance. Finally, the

instrumental variable methodology shows that the industry average governance score

is a weak instrument for the governance score. The industry averages are strong

instruments for the overall ESG score, the environmental score, and the social score.

This thesis is structured as follows. Chapter 2 discusses the literature about earnings

announcements and ESG factors, and develops the hypotheses. Chapter 3 describes the

data. Chapter 4 discusses the empirical methodology that is used to answer the research

question. Chapter 5 tests the hypotheses by means of empirical analyses of the data.

Finally, this thesis is concluded in Chapter 6, which includes a discussion of the

empirical results, limitations, recommendations for future research and practical

recommendations. The Appendices contain figures and tables, which show the data and

empirical findings.

2. LiteratureReviewIn this chapter, the existing literature on earnings announcements and environmental,

social and governance (ESG) factors is discussed. First, the literature on earnings

announcements is reviewed in section 2.1 to give insights in the dynamics underlying

analysts’ forecast errors and stock price reactions to earnings announcements. Second,

the relevance of ESG factors in finance is discussed in section 2.2 to get an

understanding of the impact of these factors on firm value and a firm’s financial

performance. Third, the relationship between ESG factors and earnings announcements

is discussed in section 2.3, after which hypotheses are developed in section 2.4.

2.1. EarningsAnnouncementsInce and Trafalis (2006) state that stock prices reflect the market’s average

interpretation of information. On average, more information arrives at the market

during periods when earnings reports are released than at other times (Chambers &

Penman, 1984). Beaver (1968) empirically investigates prices and trading volumes

around annual earnings announcements and finds that earnings are the most important

information source for common stock investors. Ball and Brown (1968) show that

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annual abnormal return adjustments are greater for changes in earnings than for changes

in cash flows.

Kim and Verrecchia (1992) investigate information streams around earnings

announcements. They find that earnings announcements create information asymmetry

among investors through the activities of traders who process public announcements

into private information. This information asymmetry leads to higher bid-ask spreads

and lower liquidity in the stock market. However, public disclosures lead to more

informed opinions and an increase in trading volume, despite the reduction in liquidity.

According to Ince and Trafalis (2006), trading volume measures investor activity.

Hence, an increased trading volume indicates an increase in investor activity around

earnings announcements.

The importance of earnings information is shown by numerous studies that find stock

price reactions after changes in earnings. Earnings increases (good news) cause positive

stock price reactions and earnings decreases (bad news) cause negative stock price

reactions for firms in the United States (Ince & Trafalis, 2006). Bernard (1992)

mentions that a complete initial stock price reaction to earnings announcements stems

from the economic logic underlying the efficient market hypothesis. Patell and Wolfson

(1984) confirm this by finding an immediate response of the stock market, with the

largest returns occurring within 30 minutes of an announcement. Trueman, Wong and

Zhang (2003) show that investors’ attention picks up five trading days before an

earnings announcement and directly decreases the day after the announcement.

However, Ince and Trafalis (2006) state that the stock market is inefficient during

earnings announcement periods. Bernard (1992) summarizes evidence on stock price

reactions after earnings announcements. The evidence challenges the efficient market

hypothesis, as even after fully controlling for risk, cumulative abnormal returns

continue to drift up (down) for good news (bad news) firms. This long-term effect is

known as the post earnings announcement drift.

2.1.1. Analysts’ForecastErrorsResearch to analysts’ forecast errors is interesting because of the large stock price

reactions that result from forecast errors. This body of research investigates the effect

of analyst and firm characteristics on forecast accuracy and/or forecast bias. Coën,

Desfleurs and L’Her (2009) define forecast accuracy as the earnings forecast error,

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which is the difference between the consensus analysts’ earnings forecast and actual

earnings, and forecast bias as the relative forecast error between analysts.

Stock returns are correlated with the sign of the earnings forecast error (Kross &

Schroeder, 1984). Beaver et al. (1979) show that this is also the case for the magnitude

of the earnings forecast error. Lopez and Rees (2002) find that the market penalty for

missing analysts’ forecasts is, in absolute terms, significantly larger than for beating

analysts’ forecasts. However, beating analysts’ forecasts is rewarded as stock prices are

more sensitive to positive forecast errors. According to Payne and Robb (2000),

individual investors consider analysts’ forecasts as an influential source of information

for investment decisions. Analysts are able to monitor the credibility of management

disclosures with their direct management contact and their ability to combine economic

and industry specific information that affects firm value. Kross, Ro and Schroeder

(1990) empirically investigate the difference in earnings forecasts between a time series

model and analysts. They find that analysts’ forecasts are more precise because of an

information advantage. Payne and Robb (2000) find that both the earnings forecast and

the dispersion in analysts’ forecasts affect management’s financial reporting decisions.

First, within the literature on analysts’ forecast errors, there are studies that focus on

firm characteristics. Coën et al. (2009) compare country, industry and firm specific

effects to explain the variation in analysts’ forecast errors. They find that firm specific

effects are the main determinant of the accuracy and bias in analysts’ forecasts.

Especially the type (profit or loss) and the variation (increase or decrease) of earnings

are large determinants of analysts’ forecast accuracy. This statement is supported by

Boudt et al. (2015), who also state that analysts’ forecast accuracy is lower for firms

with high volatility in earnings. According to Lang and Lundholm (1996), firms with

more informative disclosure policies have more analysts following the firm, more

accurate analysts’ forecasts, less dispersion in analysts’ forecasts and less volatility in

analysts’ forecast revisions. Thomas (2002) finds that analysts’ forecast accuracy is

lower for diversified firms. Das, Levine, and Sivaramakrishnan (1998) find that

analysts’ forecasts are less accurate for firms with low earnings predictability.

Second, there is a body of research on analysts’ forecast errors that focuses on analyst

characteristics. Sinha, Brown, and Das (1997) replicate proof of an analyst effect, which

means that there is a significant difference between the forecasts of different analysts.

They do not find superior analyst performance over multiple years, but they do find a

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positive relation between forecast recency, which is the time that has passed since the

last forecast made by the analyst, and forecast accuracy. Bosquet, De Goeij, and Smedts

(2015) use a two stage model to assign a behavioral bias and a strategic bias to the

deviations from rationality in the decision-making process of analysts. They find that

the behavioral bias is significantly larger for men. Boudt et al. (2015) find that analysts

provide optimistic (less accurate) forecasts to facilitate access to information through

management. This is especially the case when competition between analysts is intense.

Das et al. (1998) provide anecdotal evidence of managers who punish analysts by

limiting contact based upon the content of their forecasts. Clement (1999) finds a

positive effect of analysts’ experience and employer size on forecast accuracy and a

negative effect of the number of industries followed by analysts on the forecast

accuracy.

2.1.2. EarningsManagementThe pressure that analysts’ forecasts put on stock prices incentivizes managers to meet

the expectations set by analysts’ forecasts. This results in earnings management, which

Tangjitprom (2013) investigates and describes as “the efforts of firm managers or

executives in manipulating the earning figures in financial reporting” (p.213). This

practice can be both beneficial and harmful to firm value, depending on the managers’

motives. On the one hand, earnings management techniques can be used to

communicate useful information and to smooth earnings to reduce volatility. On the

other hand, earnings management can be used to manipulate earnings reporting to

obtain compensation or to cover information that will otherwise be harmful, lowering

the informative value of earnings announcements. In general, earnings management has

a negative effect on firm value.

Payne and Robb (2000) show that managers especially aim to meet or beat analysts’

earnings forecasts when the dispersion in analysts’ forecasts is low. A lower dispersion

implies that the forecasts are more precise, resulting in larger stock price reactions when

there is a forecast error. The magnitude of earnings management is even influenced by

the height of the dispersion in analysts’ forecasts. DeFond and Park (1997) show that

management spreads reported income over accounting periods based on the earnings

expectations set by analysts for the next period.

Chai and Tung (2002) investigate the motives for earnings manipulation by

management. They find that managers use more negative accruals in bad times to make

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bad earnings announcements even worse. Firm management save positive accruals for

later periods to enhance future profits and bonuses in good times. Managers favor the

use of accruals to manipulate earnings, as it is difficult for outsiders to identify the

effects of discretionary accruals when only limited information is available. They also

find that late reports contain more income decreasing accruals, which implies that

managers delay announcements to manage earnings.

2.1.3. AnnouncementTimingAnother area of research around earnings announcements is announcement timing.

Kross and Schroeder (1984) show that late announcements convey more bad news.

More specifically, they find that report timing is correlated with the sign and magnitude

of the earnings forecast error. The main conclusion from their research is that early

quarterly announcements more often contain good news (and are associated with larger

abnormal returns) than late announcements. Furthermore, good news firms only

announce ‘moderately’ good news when they announce late. Similarly, bad news that

is announced early tends to be better than bad news that is announced late. The timing

effect persists regardless of good or bad news, annual or quarterly announcements, or

firm size.

Chambers and Penman (1984) show that the variability of stock returns is larger when

reports are released earlier than expected. This can be explained by the fact that other

sources of information allow the market to anticipate the earnings report. This empirical

finding is supported by Chai and Tung (2002), who find that a higher reporting lag

results in lower value of the announcement’s information content. Chambers and

Penman (1984) find that investors interpret the failure to report on time as an indication

of bad news. Their findings show that average abnormal returns at the expected date of

unexpectedly late reports are negative. Similarly, unexpectedly early good news reports

experience unusually high stock return variability. Finally, they find that annual

earnings announcements tend to have a higher reporting lag than interim reports.

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2.2. Environmental,SocialandGovernance(ESG)FactorsCherneva (2012) describes ESG factors as “public interest issues that affect human,

societal, and environmental well-being and that are increasingly relevant to business

and finance operations” (p.94). Kotsantonis et al. (2016) state that investors turn to ESG

data to get insights in sustainability in businesses, which they define as “the integration

of social and environmental considerations, such as climate change and income

inequality, into business strategy and practices” (p.10). Bassen and Kovács (2008)

define ESG information as “extra-financial material information about the challenges

and performance of a company on these matters” (p.184) and refer to it as additional

relevant information that allows investors to better assess risks and opportunities,

allowing for more differentiated investment judgments.

Empirical studies on ESG factors have already been conducted since the second half of

the twentieth century. The Pax World Fund, the first modern socially responsible

investment (SRI) mutual fund, was founded in 1971 in the United States. Since the early

1990s, the SRI industry has grown strongly in the United States, Europe and the rest of

the world (Renneboog et al., 2008). Patten (1990) is one of the first studies to find

investor reactions to ESG factors by showing that there is an increase in trading volume

around social responsibility information disclosures. Studies on the combination of

ESG factors under the name ‘ESG’ are relatively new, but studies on the separate ESG

constituents are more prevalent. For example, Kim et al. (2014) use the MSCI ESG

governance rating (G) for corporate governance and the MSCI ESG social rating (S)

for corporate social responsibility (CSR).

Kotsantonis et al. (2016) elaborate on the total amount of assets under management

(AUM) that considers ESG factors and data. In 2016, over 1,400 institutional investors

($60 trillion in AUM) had signed the United Nations Principles for Responsible

Investment (UNPRI), which commits its signatories at least theoretically to consider

ESG factors when allocating capital. However, this remains a misleading indicator of

the actual ESG integration. All signatories do not fully comply, are at different stages

of development in the ESG integration process or are not obliged to apply it to their

total AUM. As a result, only 16% of total AUM in the United States, equaling $6.2

trillion, explicitly incorporates ESG factors into investment analysis and decision-

making. Most of these assets are managed by negative screening, a technique that

excludes companies or countries that do not match certain standards or do not act in

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line with international treaties. Sector-based screening eliminates sectors such as

tobacco, alcohol and weapons because of their negative social consequences. Data

provided by Verheyden et al. (2016) shows that the amount of AUM is increasing. They

find that $21.4 trillion was managed using some form of socially responsible investing

at the start of 2014. This equaled 30% of the global AUM and is notably lower than the

$60 trillion mentioned by Kotsantonis et al. (2016) two years later. This growth was

foreseen by Renneboog et al. (2008), who show that, in 2005, European and United

States’ socially responsible AUM was only 10-15% and 10% of total AUM,

respectively.

2.2.1. TheImportanceofESGFactorsEvidence on the importance of ESG factors for companies has increased over the past

years. As a result, these issues are now more seen as central issues in the global finance

industry (Cherneva, 2012). Bassen and Kovács (2008) address the importance of ESG

factors by stating that financial professionals anticipate ESG factors to powerfully

change the economic landscape. They also relate this importance to the increased share

of intangible assets in the total value of companies, especially over the long term. A

full evaluation of ESG factors gives better insights in the risks and opportunities a

company faces and allows for enhanced risk management and security selection.

Additionally, it serves as a proxy for management quality and the company’s ability to

maintain a competitive advantage on the long term. Kotsantonis et al. (2016) find that

innovation can be the output of a strategy that is driven by sustainability. They also

state that valuation models only include the traditional economic factors and ESG

factors should therefore be excluded since they are perceived to be non-economic.

However, Kotsantonis et al. (2016) argue that ESG factors should be included in

valuation models, since research over the last fifteen years has shown that many ESG

factors can have a material impact on the financial performance of companies.

Because of this importance of ESG factors, awareness of environmental and social risk

increased, resulting in an increased demand from institutional and individual investors

for ESG investments (Mânescu, 2011). As a result, CFOs increasingly manage ESG

factors within corporations and investors increasingly react to negative ESG events over

time (Möller, Koehler, & Stubenrauch, 2015). The awareness of governance factors is

already more established since these issues constitute a classic examination field in

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corporate valuation, especially compared to social and environmental factors that have

only received marginal attention (Bassen & Kovács, 2008).

2.2.2. TheImpactofESGFactorsonFirmValueRenneboog et al. (2008) state that “firm value remains the single most important

performance measure for management” (p.1731). Still, even though ESG factors are

not integrated into most mainstream investment decision-making models, the effects

are visible in the stock market in terms of a correlation between ESG performance and

firm performance (Kotsantonis et al., 2016).

In their study, Renneboog et al. (2008) find that in the period 1997-2003 a portfolio of

firms with high scores on environmental factors outperforms a portfolio of firms with

low scores by 6% per year. Furthermore, they find a high correlation between firm

value, measured by Tobin’s Q (a firm’s market value divided by the replacement value

of a firm’s assets), and the governance index. Hermes Investment Management (2016)

mentions ESG factors as an inseparable component of best-practice investment

management for all investment strategies. Their data shows that firms with a low

governance score underperform their peers by 30 basis points per month. The same

holds for low environmental and social scores, but this results in lower

underperformance. Additionally, Verheyden et al. (2016) find that a preliminary

screening on ESG factors can already be beneficial for any investment strategy.

Verheyden et al. (2016) state that ESG performance also gives information on future

financial performance as multiple studies find significant positive correlation between

material ESG factors and financial performance. Of these studies, 88% find a positive

correlation between a company’s social responsibility and its operating performance

(e.g. operating income). Bassen and Kovács (2008) also mention that the extra-financial

ESG information can have both direct and indirect financial consequences for firms and

investors. Kotsantonis et al. (2016) split ESG data in material issues, which are value-

relevant, and immaterial issues, which are value-irrelevant. They find that companies

that invest in material ESG issues have higher growth in profit margins and higher risk-

adjusted stock returns. On the contrary, firms that invest in immaterial ESG issues see

average or even inferior performance.

Studies on the relationship between ESG factors and corporate financial performance

are subject to important limitations. Dowell, Hart and Yeung (2000) find a positive

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correlation between a firm’s environmental performance and a firm’s Tobin’s Q.

However, a causal relationship is not found. They state that it is possible that firms of

higher value avoid negative media coverage by applying higher environmental

standards. Orlitzky et al. (2003) conduct a meta-analysis and find implications of

reversed causality between corporate social and financial performance. More

specifically, they find higher correlation between CSR and backward looking measures

(accounting returns) than with forward looking measures (stock returns). Next to this,

corporate social reputation indices have a larger correlation with corporate financial

performance than other CSR measures.

Additionally, the effect of ESG factors on firm value is not always correctly

incorporated in stock prices. Mânescu (2011) only finds a positive risk-adjusted stock

return for companies that score high on community relations, which is only a

component of the social score within the total ESG score. Mânescu (2011) states that

this return is not a compensation for risk but a result of mispricing, which results from

investors underestimating the benefits and overestimating the costs of ESG factors.

Möller et al. (2015) mention the reporting on ESG performance as a key missing link

in delivering value through transparency and in leveraging the impact of sustainable

practices. The availability and quality of ESG data has improved over the past few

years, making ESG data more accessible to investors (Kotsantonis et al., 2016). Next

to this, better quantitative measures of ESG factors have been developed. Additionally,

investors increasingly pay attention to extra-financial information like ESG factors

(Bassen & Kovács, 2008). Hence, the impact of ESG factors on stock prices is expected

to increase over time.

Renneboog et al. (2008) provide two possible explanations for the higher returns of

firms with high ESG performance. First, it is possible that environmental performance

is correlated with a firm’s future cash flows through, for example, litigation costs or

compliance with environmental regulations, implying that investors react to a change

in cash flows. Alternatively, capital markets can internalize the external costs of a firm

through investors that are willing to pay for environmental performance. Mânescu

(2011) refers to these two explanations as the economic argument and the

discriminatory tastes argument, respectively.

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2.2.3. TheImpactofESGFactorsonFirmRiskVerheyden et al. (2016) find that, using Sharpe ratios, ESG screening reduces the

downside risk of stock portfolios. They are also able to challenge the (classical)

argument that portfolio diversification is sacrificed by ESG screening and find that the

specific risk of ESG screening is more than offset by the additional risk-adjusted returns

it provides. The evidence does not show a reduction in returns, but a reduction in risk.

This lower risk is supported by Mânescu (2011), Heinkel, Kraus and Zechner (2001)

and Möller et al. (2015). Mânescu (2011) finds that firms with poor employee relations

carry a premium for non-sustainability risk. Heinkel et al. (2001) find, using a

theoretical model, that firms with high ESG scores have a lower cost of capital when

there is a substantial share of socially responsible investors. Möller et al. (2015) state

that firms that disclose more ESG information are likely to enjoy a lower cost of capital.

Mânescu (2011) mentions three possible scenarios for the risk-adjusted returns of firms

with a high ESG score compared to firms with a low ESG score. First, the no-effect

scenario states that, adjusted for common risk factors, there is no difference. Second,

the mispricing scenario states that ESG factors have a value relevant impact on cash

flows, but this information is not efficiently incorporated in stock prices due to a lack

of available information. Third, the risk-factor scenario states that the expected returns

are higher for firms that score low on ESG issues because they carry a non-

sustainability risk premium. It is still possible that the expected returns are higher for

firms with a high ESG score because they carry a premium for a missing risk factor

aside from the traditional risk factors1. The latter has been used as an alternative

explanation to mispricing as firms that score high on ESG issues tend to have higher

risk-adjusted returns.

Hence, firms that score high on ESG factors enjoy lower downside risks and possibly

higher cash flows. As a result, ESG performance affects stock returns if information is

available and enough investors care about this information (Mânescu, 2011).

1 Beta, size, value, and momentum

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2.3. EarningsAnnouncementsandESGFactorsSeveral studies relate ESG factors to the dynamics around earnings announcements. In

this section, the relationship between ESG factors and analysts’ forecast accuracy is

discussed first. This gives a clear insight in how ESG factors influence analysts’

forecast errors. Afterwards, the relationship between ESG factors and stock price

reactions to earnings announcements is discussed to get an insight in the response of

investors to the past performance of firms with a high and low ESG score.

2.3.1. Analysts’ForecastAccuracyandESGFactorsMore informative disclosure results in more accurate analysts’ earnings forecasts (Lang

& Lundholm, 1996). Das et al. (1996) find that the demand for non-public information

is higher when earnings are harder to predict using a firm’s public information.

Dhaliwal, Radhakrishnan, Tsang and Yang (2012) state that analysts can gain more

useful insights from non-financial disclosures for financially opaque firms. They find

that analysts’ forecast errors are lower for firms that issue separate CSR reports. This

indicates higher earnings quality and more reliable disclosures. Bernardi and Stark

(2015) investigate the effect of transparency in ESG disclosures on analysts’ forecast

accuracy in South Africa. They find that this effect is significant only after a regulation

for integrated reporting is introduced.

Several studies investigate the effect of corporate governance on analysts’ forecast

accuracy. Byard et al. (2006) use four measures of corporate governance2 to test

whether corporate governance leads to higher analysts’ forecast accuracy as a proxy for

the quality of a firm’s information. They find that better governed firms provide better

information on future earnings (i.e. have a lower forecast error). However, higher

governance quality does not automatically lead to improved information for investors

and analysts. Bhat, Hope and Kang (2006) find that corporate governance leads to

higher analysts’ forecast accuracy when the level of financial disclosure is low. Their

reasoning why corporate governance leads to more accurate forecasts is twofold. First,

financial disclosures are more credible and integer. Second, governance disclosures

reduce uncertainty surrounding the future performance.

2 Independence of the board of directors, the presence of a CEO who is also the chair of the board, board size, and the presence of an independent audit committee

19

Kim et al. (2014) investigate the effect of CSR on firm crash risk and find that

companies that actively engage in CSR are more reluctant to hold back bad news.

Therefore, socially responsible firms behave more responsibly in financial reporting

and exhibit less evidence of earnings management. This leads to financial disclosures

of higher quality and less negative surprises in earnings. Multiple studies support the

findings of Kim et al. (2014). Gelb and Strawser (2001) find that socially responsible

firms disclose information more frequently, giving more insights in the firm.

Furthermore, Becchetti et al. (2013) investigate four CSR factors3 and find that all

factors have a significant effect on the absolute forecast error and its standard deviation,

supporting the hypothesis that good CSR practices lead to lower forecast errors.

Opposed to this view, Hemingway and Maclagan (2004) argue that companies adopt

CSR practices to gain or maintain a competitive advantage, to cover up the impact of

corporate misbehavior, to manage the environment in their favor, or that it is initiated

by individual managers to pursue their personal values. In many cases, they relate CSR

to corporate image management, which does not necessarily improve ethical behavior

or firm transparency. The leading example in their argument is Enron, which went

bankrupt as a result of unethical practices while they did have a reputable community

relations department. Additionally, Prior, Surroca and Tribó (2008) find a positive

relation between CSR practices and earnings management for regulated firms. Kim,

Park and Wier (2012) also argue that CSR can lead to more earnings management and

less forecast accuracy. Firms may use CSR as reputation insurance against earnings

management and managers may be very optimistic in the use of CSR practices.

However, their empirical findings show that firms with a high CSR score engage less

in earnings management and therewith have higher earnings quality.

2.3.2. PastPerformanceandESGFactorsTo the knowledge of the researcher, no studies have investigated the effect of ESG

factors on stock price reactions around earnings announcements so far. A study by

Siew, Balatbat and Carmichael (2016) investigates the impact of ESG disclosures and

institutional ownership on market information asymmetry. They find that ESG

disclosures decrease bid-ask spreads and that the presence of institutional investors

results in lower market information asymmetry. The effect of ESG disclosure on bid-

3 Accounting opacity, corporate governance, stakeholder risk and overinvestment in CSR

20

ask spreads is lower for relatively higher levels of institutional ownership, as these

investors tend to exploit private ESG information. According to Kim and Verrecchia

(1992), lower information asymmetry and lower bid-ask spreads imply larger liquidity

in the stock market and an increase in trading volumes.

The influence of past returns has been investigated more widely for SRI funds.

Renneboog et al. (2008) describe socially responsible investing as an “investment

process that integrates social, environmental, and ethical considerations into investment

decision making” (p.1723). Renneboog et al. (2008) define four different generations

of SRI screens. The first form is negative screening, which excludes firms that score

low on certain CSR metrics. The second is positive screening, which means a fund

selects the most superior firms on CSR metrics. The third is an integrated approach,

combining both positive and negative screens with economic, environmental and social

criteria. The fourth and last combines the third approach with shareholder activism.

Underlining the importance of past returns, Benson and Humphrey (2008) mention that

both old and new shareholders react to past performance and that past performance, for

individual investors, is one of the most important sources of information. Bollen (2007)

and Renneboog, Ter Horst and Zhang (2011) investigate the fund flows of SRI funds

based on past performance. Bollen (2007) finds that SRI funds are less sensitive to past

negative returns and more sensitive to past positive returns compared to conventional

funds. Renneboog et al. (2011) find that socially responsible investors from the United

States, the United Kingdom, continental Europe, Asia and the Pacific Rim region are

less influenced by negative returns.

Benson and Humphrey (2008) and Renneboog et al. (2011) find a clientele effect for

SRI funds. As a result of this clientele effect, these investors show a unique response to

past returns. Multiple studies find explanations for the different reactions of these

shareholders. Benson and Humphrey (2008) mention that socially responsible investors

may have higher searching costs because there are less alternatives so that they do not

switch between funds easily. Next to this, they mention that socially responsible

investors may have different investment horizons. Renneboog et al. (2008) conclude

that these investors may be willing to accept suboptimal financial performance to

pursue social and ethical objectives. Bollen (2007) states that these investors have a

multi-attribute utility function that includes both the standard risk-reward optimization,

and personal and societal values. The possibility for a clientele effect is also emphasized

21

by Kotsantonis et al. (2016), who identify three categories of institutional investors.

The first category are transients, which hold lots of stocks with high turnover. The

second category are quasi-indexers, which hold lots of stocks with low turnover.

Finally, the dedicated holders hold relatively few stocks for a long period of time.

Also on the firm level, the shareholder base is important as it influences the adoption

of ESG factors in the long-term strategy of the firm. Graafland (2016) provides

empirical evidence, based on a survey across twelve European countries, that corporate

social performance is encouraged by long-term orientation. As markets are sometimes

highly competitive, regulations need to be made to encourage a long-term planning

horizon. Hill and McDonnell (2015) show that activist investors can pressure

corporations to focus on short-term strategies. These investors pressure corporations to

pursue strategies that increase shareholder value on the short term. The other way

around, different management practices attract different types of investors, which

implies that the investor base of a firm can be shaped to be consistent with the

organization’s strategy (Kotsantonis et al., 2016).

2.4. HypothesesDevelopmentThe main variable of interest in this thesis is a firm’s ESG score. Earlier studies by Gelb

and Strawser (2001), Bhat et al. (2006), Byard et al. (2006), Kim et al. (2012), Dhaliwal

et al. (2012), Becchetti et al. (2013), and Kim et al. (2014) find that CSR, corporate

governance and/or ESG transparency lead to more accurate analysts’ forecasts. This

thesis tests this relationship using the overall ESG score to test whether high ESG

performance leads to more accurate earnings forecasts:

Hypothesis 1: Analysts’ earnings forecast errors are lower for firms with a high ESG score compared to firms with a low ESG score

Investors’ responses to analysts’ forecast errors may differ between firms with a high

and low ESG score. Siew et al. (2016) do not give a clear direction on the effect of ESG

factors on stock returns or trading activity around earnings announcements. When

looking at the studies to SRI funds, Bollen (2007) and Renneboog et al. (2011) find that

SRI fund flows are less sensitive to negative past returns than conventional funds.

Benson and Humphrey (2008) provide an explanation for this finding, namely higher

searching costs and longer investment horizons for investors that invest in SRI funds.

As this is assumed to be equal for individual firms, it is expected that the abnormal

22

stock return for firms with a high ESG score is less negative after a negative earnings

surprise. Bollen (2007) also finds that SRI funds are more sensitive to past positive

returns than conventional funds. Renneboog et al. (2011) do not find this effect. Still, it

can be expected that, if investors are willing to pay more for social and personal

objectives (Renneboog et al., 2008; 2011), the abnormal stock return for firms with a

high ESG score is more positive after a positive surprise in earnings. As data on ESG

factors is more accessible to investors (Kotsantonis et al., 2016) and investors

increasingly care about these factors (Bassen and Kovács, 2008), it is to be expected

that the stock reaction to earnings announcement differs between firms with a high ESG

score and firms with a low ESG score:

Hypothesis 2a: The abnormal stock return is less sensitive to a negative earnings forecast error for firms with a high ESG score than for firms with a low ESG score

Hypothesis 2b: The abnormal stock return is more sensitive to a positive earnings forecast error for firms with a high ESG score than for firms with a low ESG score

This thesis tests for endogeneity by using an instrumental variable methodology with

the industry average ESG score as the instrumental variable. Shareholders can both

influence the adoption of ESG factors (Renneboog et al., 2008) and shareholders can

choose to become a shareholder of a firm with a high ESG score (Kotsantonis et al.,

2016). Through their unique response to an earnings announcement, long-term

shareholders have an influence on the cumulative abnormal returns (Benson &

Humphrey, 2008). Kim et al. (2014) use the composition of the shareholder base,

namely long-term institutional ownership, as a control variable to mitigate the effect of

omitted variables in their research to the effect of CSR on firm crash risk. Since this

data is not available and of importance in establishing the relationship between the ESG

score and abnormal returns, an instrumental variable approach is necessary to test for

endogeneity. El Ghoul, Guedhami, Kwok and Mishra (2011) and Kim et al. (2014) use

the average industry CSR score as an instrumental variable to test for endogeneity bias

in the relationship between the CSR score and the cost of capital and firm crash risk,

respectively. Section 4.2.1 further elaborates on the suitability of the industry average

ESG score as an instrumental variable and the methodology to apply it to the

econometric model.

Siew et al. (2016) find that ESG disclosure results in lower bid-ask spreads. On the one

hand, the lower bid-ask spreads can be maintained around earnings announcements. On

23

the other hand, institutional investors may exploit public information more through

their additional private ESG information, which results in an even larger increase in

bid-ask spreads around earnings announcements. According to Bhat et al. (2006) good

corporate governance leads to better financial disclosures. Kim et al. (2014) state that

financial disclosures are of higher quality for firms that perform well on CSR.

Eventually, this results in less surprises in earnings. The possibility of larger bid-ask

spreads and the arrival of less new information may result in a lower increase in trading

volume (Kim & Verrecchia, 1992). On top of this, following Chambers and Penman

(1984), less new information results in lower variability in abnormal stock returns

around earnings announcements. Hence, it is expected that firms with a high ESG score

experience lower volatility in stock returns around an earnings announcement

compared to firms with a low ESG score:

Hypothesis 3: The volatility in abnormal stock returns around earnings announcements is lower for firms with a high ESG score compared to firms with a low ESG score

The lower volatility is expected to be partially caused by the lower forecast errors for

firms with a high ESG score compared to firms with a low ESG score. Therefore, the

model should control for the magnitude of the analysts’ forecast error. As Hypothesis

3 also concerns a relationship between the ESG score of a firm and its stock price

reaction, the same endogeneity tests apply as described above for Hypotheses 2a and

2b. Additionally, the level of institutional ownership has to be included in the model,

as Siew et al. (2016) find that institutional investors exploit private ESG information,

which decreases the effect of ESG disclosures on bid-ask spreads. Hypothesis 3 is

relevant for investors as lower stock price volatility around earnings announcements

means lower downside risks during times with relatively high information uncertainty

in the stock markets.

3. DataThis chapter provides an overview of the data that is used in this thesis. First, the

procedures to compute dependent and independent variables are explained in sections

3.1 and 3.2, respectively. Second, the descriptive statistics are discussed in section 3.3.

The descriptive statistics section also includes a first analysis of the data and a

motivation for data modifications in the control variables. Table 1 in Appendix A shows

a description of all the variables that are used in this thesis.

24

First, quarterly data is gathered. ESG data is gathered on all US companies in the

Thomson Reuters Asset4 Database between the first quarter of 2004 and the fourth

quarter of 2016. This results in a total of 1,746 firms with 41,651 observations. Second,

earnings announcement data is gathered from Worldscope and matched with stock

return data around the announcements from Wharton Research Data Services. In total,

24,662 observations are available from these databases. Finally, earnings forecast data

is gathered from I/B/E/S. Hereafter, data on 21,081 observations is available for the

analyses. Additional firm level data is gathered from COMPUSTAT and firm holdings

data is gathered from the Thomson Reuters Institutional (13f) Holdings database.

Following Thomas (2002), all observations with an absolute forecast error (|"#|) larger

than 100% of the share price at the time of the analysts’ earnings forecast, dispersion

in analysts’ forecasts ($%&') above 20% of the share price at the time of the analysts’

earnings forecast, and all earnings announcements with less than three forecast

estimates (#&() are dropped. Similarly, all absolute changes in earnings per share

(|∆#'&|) larger than 100% of the share price at the time of the analysts’ earnings

forecast are dropped. Outliers in the earnings-to-price ratio (#') are taken care of by

dropping the observations in the top and bottom percentile (Lopez & Rees, 2002; Bhat

et al., 2006). As a result, 19,910 observations remain to be used in this thesis. The final

dataset consists of observations on 940 firms and covers 51 quarters from the first

quarter of 2004 to the third quarter of 2016.

3.1. DependentVariablesThe first dependent variable of interest is the absolute analysts’ earnings forecast error.

The absolute forecast error is calculated as the absolute difference between the analysts’

consensus earnings forecast and actual earnings per share scaled by the share price at

the time of the earnings forecast, where the consensus forecast is the median value of

the most recent forecasts (Boudt et al., 2015; Thomas, 2002):

|"#|*,, =#'&*,, − "*,,

'*,,

Where |"#|*,, is the absolute forecast error for firm i at time t, #'&*,, is the earnings per

share for firm i at time t, "*,, is the most recent consensus forecast for firm i at time t,

(3.1)

25

and '*,, is the share price of firm i at the time of forecast "*,,. The real value of the

forecast error is used as an independent variable to test Hypotheses 2a and 2b. When

the real forecast error is used, it is denoted as"#*,,.

The second dependent variable of interest is the cumulative abnormal return (012)

around the earnings announcement. Similar to Thomas (2002), abnormal stock returns

are calculated using an estimation window that ranges from 210 days to 10 days before

the earnings announcement. The event window consists of the three days surrounding

the earnings announcement. The abnormal returns are calculated using a traditional

market model (Kross & Schroeder, 1984; Thomas, 2002; Trueman et al., 2003):

2*,, = 3* +5*26,, +7*,,

Where 2*,, is the stock return of firm i at time t, 3* and 5* are the intercept and the

covariance of firm i with the market portfolio, respectively, 26,, is the return of the

market portfolio at time t, and 7*,, is the error term. The normal or expected return for

firm i at time t is:

82*,, = 39 +5926,,

Where39 and 59 are the OLS estimates of the regression coefficients in equation 3.2,

and 26,, is the return of the market portfolio at time t. The residual of equation 3.2, or

the abnormal return, is calculated by subtracting the expected return from the actual

daily return:

12*,, = 2*,, − 82*,,

Where 12*,,is the abnormal return for firm i at time t. To compute the cumulative

abnormal return, the abnormal returns are summed from one day before to one day after

the earnings announcement:

012*,, (:, (; = 12*,,

<=

,><?

(3.2)

(3.3)

(3.4)

(3.5)

26

Where 012*,, (:, (; is the cumulative abnormal return for firm i at time t, from day (:

to day (; in the event window, 12*,,is the abnormal return for firm i at time t, and

(:and (;are -1 and 1, respectively.

According to Trueman et al. (2003), investors’ attention picks up five days before the

earnings announcement. Bernard and Thomas (1989) show that abnormal returns are

present up to 60 days after the earnings announcement, of which the largest portion

occurs in the five days following an earnings announcement. They find that this portion

is bigger for large firms and that it takes longer for small firms to fully incorporate

information from an earnings announcement in the stock price. Therefore, the volatility

in the abnormal returns (@(12)) is calculated as the standard deviation over the

abnormal returns from five days before to ten days after the earnings announcement:

@(12)*,,[(:, (;] =(12*,, − 112*,,)

;<=

,><?

(; − (: + 1

Where @(12)*,,[(:, (;] is the volatility in abnormal returns for firm i at time t, from

day (: to day (; in the event window, 12*,,is the abnormal return for firm i at time t,

112*,,is the average abnormal return for firm i at time t, from day (: to day (; in the

event window, and (:and (;are -5 and 10, respectively.

3.2. IndependentVariablesFollowing Chai and Tung (2002), announcement timing ((%F%8G) is measured by the

difference in the number of days between the expected report date and the actual report

date, where the expected report date is the announcement date in the previous year.

Following the method used by Lang and Lundholm (1996), the change in earnings per

share (|∆#'&|) is calculated as the absolute change in earnings per share, scaled by the

share price at the time of the most recent forecast:

∆#'& *,, =#'&*,, − #'&*,,H:

'*,,

Where ∆#'& *,,is the absolute change in earnings per share for firm i at time t,

#'&*,,is the earnings per share for firm i at time t, #'&*,,H:is the earnings per share for

(3.6)

(3.7)

27

firm i at time t-1, and '*,, is the share price of firm i at the time of the most recent

earnings forecast.

The dispersion in analysts’ forecasts ($%&') is also scaled by the share price at the time

of the most recent forecast (Lang & Lundholm, 1996). The earnings-to-price ratio (#')

is calculated by dividing the earnings per share over the previous calendar year by the

share price at the time of the most recent forecast. The earnings-to-price ratio is taken

instead of the price-to-earnings ratio to provide continuity through zero (Ou & Penman,

1989). The market-to-book ratio (FI) is calculated as the market value of equity plus

the difference between total assets and the book value of equity, divided by total assets

(Thomas, 2002). Firm leverage (J#KG) is calculated as the amount of long-term debt

and short-term debt in current liabilities divided by total assets. The value is set to one

in all cases where the value for leverage is larger than one (Thomas, 2002). Similarly,

the percentage of institutional shareholders (%8&() is set to one in these cases.

Market-adjusted buy-and-hold returns are calculated over two periods (2#(1; 2#(2),

following Boudt et al. (2015). In this thesis, the returns are calculated over the first and

second part of the estimation window:

2#(*,, = 1 + 2*,,

<=

,><?

− 1 + 26,,

<=

,><?

Where 2#(*,, is the market-adjusted buy-and-hold return for firm i from time (: to(;,

(1 + 2*,,)<=,><?

is the buy-and-hold return for firm i from time (: to (;, and (1 + 26,,)<=,><?

is the buy-and-hold market return from time (: to (;. The market-adjusted return is

calculated from 110 to 10 days before the earnings announcement (RET1) and from 210

to 110 days before the earnings announcement (RET2).

Finally, earnings management (#F) is measured by the discretionary accruals, which

are calculated based on the Jones (1991) model, consistent with previous studies on

earnings management. Payne and Robb (2000) perform the same method by applying

a cross-sectional variation of this model. First, the total accruals need to be calculated:

(1*,, = ∆01*,, − ∆0J*,, − ∆01&M*,, + ∆&($*,, − $1*,,

(3.8)

(3.9)

28

Where (1*,, is the total accruals for firm i at time t, ∆01*,, is the change in current

assets for firm i at time t, ∆0J*,, is the change in current liabilities for firm i at time t,

∆01&M*,, is the change in cash for firm i at time t, ∆&($*,, is the change in debt included

in current liabilities for firm i at time t, and $1*,, are depreciation and amortization

expenses for firm i at time t. Then, after calculating the total accruals, the following

equation is estimated using a panel data regression to find discretionary accruals:

(1*,,

1*,,H:= 3N

1

1*,,H:+ 5:

∆2#K*,,

1*,,H:+ 5;

''#*,,

1*,,H:+ 7*,,

Where (1*,, is the total accruals for firm i at time t, 1*,,H: is the total assets for firm i

at time t-1, ∆2#K*,, is the change in net revenues for firm i at time t, ''#*,, is gross

property, plant and equipment for firm i at time t, and 7*,, is the error term for firm i at

time t. The error term represents the portion of total accruals as a percentage of total

assets that is not explained by the model. Since the model depicts the normal operating

activities, the discretionary accruals are the residuals in the model. The residuals are

captured as the variable for earnings management (#F*,,). When the absolute value of

earnings management is used, the variable is denoted as|#F|*,,.

3.3. DescriptiveStatisticsTable 2 in Appendix A shows the number of observations, the mean, the standard

deviation, the minimum value, the maximum value and a set of percentiles for all

variables. Furthermore, to gain a first insight in the relationship between variables, the

ESG score is divided into five groups (quintiles) from lowest values (quintile 1) to

highest values (quintile 5). Similarly, five groups (quintiles) of the real value of the

forecast error are created, with quintile 1 denoting the lowest (most negative) value,

and quintile 5 denoting the highest (most positive) value. The ESG and forecast error

quintiles are used for graphical analysis of the data.

3.3.1. Analysts’ForecastErrorsTable 2 in Appendix A shows that of all the 19,910 analysts’ forecast errors (|"#|) in

the sample, 12,382 are positive ('"#) and 5,751 are negative (8"#). In 1,777 of the

cases, the forecast error equals zero. The average absolute forecast error is 0.30% of a

firm’s share price. Negative forecast errors are, on average, larger with a mean of 0.47%

(3.10)

29

compared to a mean of 0.26% for positive forecast errors. The standard deviation is

1.17% of a firm’s share price. Especially negative forecast errors have a large standard

deviation with 1.90% compared to positive forecast errors with 0.72%. These statistics

are in line with Chai and Tung (2002), who find that firm managers tend to make bad

announcements even worse by using negative accruals. Furthermore, the larger number

of (relatively small) positive forecast errors is supported by DeFond and Park (1997)

and Payne and Robb (2000), who both state that managers use positive accruals to meet

analysts’ earnings forecasts.

Figure 1 in Appendix A shows the average absolute forecast error for each ESG

quintile. The graph contains the full sample as well as a subsample of positive forecast

errors and a subsample of negative forecast errors. Table 3 in Appendix A shows the

data from the graph and gives insights in how statistically significant the values are

different from zero. It also provides insights in the division of the forecast errors over

the ESG quintiles. Figure 1 and Table 3 imply that forecast errors are smaller for firms

with a high ESG score compared to firms with a low ESG score. Firms in ESG quintile

1 have an average positive forecast error of 0.31% compared to 0.22% for firms in ESG

quintile 5. The average negative forecast error is 0.61% for firms in ESG quintile 1

compared to 0.27% for firms in ESG quintile 5. Overall, firms in ESG quintile 1 have

an average earnings forecast error of 0.39%, whereas firms in ESG quintile 5 have an

average earnings forecast error of 0.21%. This lower error is in line with Hypothesis 1

and earlier studies by, for example, Byard et al. (2006), and Becchetti et al. (2013).

Finally, the numbers of observations imply that firms with a higher ESG score have

more positive forecast errors, as 3,248 out of 4,845 observations in ESG quintile 5 have

a positive forecast error (67.04%), compared to 1,959 out of 3,512 observations in ESG

quintile 1 (55.78%).

3.3.2. CumulativeAbnormalReturnsTable 2 in Appendix A shows that the average cumulative abnormal return

(012[−1,1]) is 0.26%. The statistics show a large standard deviation of 6.20%. This is

also visible in the percentiles, where the 25th percentile has a CAR of -2.71% and the

75th percentile has a CAR of 3.18%.

The development of the cumulative average abnormal returns (CAAR) around the

earnings announcement date is graphically shown in Figure 2 in Appendix A. The

30

figure shows the daily development of the CAAR for both positive and negative

earnings forecast errors, and an overall case. A positive (negative) earnings forecast

error leads to a positive (negative) CAAR. The 95% confidence intervals are small and

statistically significant on both sides. The overall case is positive and statistically

significant, as there are more positive than negative earnings forecast errors in the

sample. The direction of a positive or negative forecast error is already slightly visible

two days before the earnings announcement. On the day before the announcement, the

CAAR becomes statistically significant. This is in line with expectations, as both

Thomas (2002) and Lopez and Rees (2002) use a three day event window to identify

the CAR. Finally, the magnitude of the positive CAARs is lower than the magnitude of

the negative CAARs. This is in line with Lopez and Rees (2002), who find larger

absolute market reactions for negative forecast errors compared to positive forecast

errors.

Figure 3 in Appendix A shows the average cumulative abnormal return (ACAR) for

each ESG quintile during the event window [-1,1], similar to Figure 1 for the forecast

errors. Also here, the sample is split in subsamples for positive and negative forecast

errors. The data from the graph is shown in Table 4 in Appendix A. The ACAR for

firms in ESG quintile 1 and ESG quintile 5 after a positive earnings forecast error is

2.03% and 0.89%, respectively. In case of a negative forecast error this is -1.43% and

-1.69%. Figure 3 shows the downward trend in the ACAR over ESG quintiles after a

positive earnings forecast error. After a negative earnings forecast error, the ACARs

are similar over all ESG quintiles. In the overall case, ACARs are positive and

statistically significant at the one percent level for ESG quintile 1, statistically

significant at the five percent level for quintiles 2 and 3, and statistically significant at

the ten percent level for quintile 4. The ACAR for ESG quintile 5 is also positive, but

not statistically significant. This graph does not give a clear direction for Hypotheses

2a and 2b as the interest of this thesis lies in the sensitivity of CARs to forecast errors

between different ESG groups. However, combining Figure 1 and Figure 3 in Appendix

A, the negative earnings forecasts are lower for firms in ESG quintile 5 than for firms

in ESG quintile 1, while the ACAR is almost equal between the groups. This implies

that firms with a high ESG score are more sensitive to negative earnings forecast errors

than firms with a low ESG score, which is the opposite of Hypothesis 2a. Both the

positive forecast error and the positive ACAR are lower for firms in ESG quintile 5

31

compared to firms in ESG quintile 1. Hence, this does not give a clear direction for

Hypothesis 2b.

3.3.3. VolatilityinAbnormalReturnsTable 2 in Appendix A shows that the average volatility in abnormal returns

(@ 12 [−5,10])is 1.81%. The standard deviation in the volatility of abnormal returns

is 1.25%. The 50th percentile is close to the mean value with 1.47%. The sample is

slightly skewed, as there are more low values than high values. This is shown by the

25th percentile, which has a value of 1.02% compared to the 75th percentile with a value

of 2.21%. The 99th percentile has a value of 6.36%. The minimum and maximum

volatility in abnormal returns are 0.26% and 20.51%, respectively. The maximum value

is high compared to the value of the 99th percentile. The value is kept in the sample

because it is still a realistic number, given the values of the CAR during the three day

event window.

Figure 4 in Appendix A shows the average volatility in abnormal returns for each ESG

quintile. Also here, the sample is split into subsamples for positive and negative forecast

errors. The data from the graph is shown in Table 5 in Appendix A. The graph shows

that the volatility is slightly higher after negative earnings forecast errors compared to

positive earnings forecast errors. Furthermore, for each type of forecast error the

volatility in abnormal returns shows a downward trend over the ESG quintiles. ESG

quintile 1 has an average volatility in abnormal returns of 2.18% compared to 1.48%

for ESG quintile 5. All values are statistically significant at the one percent level. Since

the volatility in abnormal returns is not expected to be equal for different forecast errors,

the average volatility in abnormal returns for each ESG group is graphically depicted

over forecast error quintiles in Figure 5 in Appendix A. This graph shows that volatility

increases with the magnitude of the forecast error. Additionally, the graph shows that

the volatility in abnormal returns decreases as the ESG score increases, regardless of

the forecast error, with the highest values for ESG quintile 1, and the lowest values for

ESG quintile 5. This is in line with Hypothesis 3 and studies by, for example, Heinkel

et al. (2001), Kim et al. (2014), Möller et al. (2015), and Verheyden et al. (2016) that

find lower risks for firms that score high on ESG factors or its constituents.

32

3.3.4. ESGScoresThe main variable of interest is the ESG dummy (#&G). Table 2 in Appendix A shows

descriptive statistics of both the dummy variable and the actual value of the ESG score

and its constituents E, S, and G. The ESG score is a measurement that shows the

percentage amount of a set of key performance indicators that a firm applies to.

Consistent with the method used by Tangjitprom (2013), the ESG score is transformed

into a dummy variable taking the value of one when a firm has an ESG score above the

median value and zero otherwise. With a mean of 0.5472 the amount of firms with a

high and low ESG score in the sample is balanced. This value is similar for E, S, and

G. The mean is not exactly equal to 0.5 since the dummy variable is generated based

on the median of the complete Thomson Reuters Asset4 Database. The standard

deviation in the ESG dummy is 0.4978. Also this value is very similar among the

constituents E, S, and G. On average, the firms in the sample have an ESG score of

53.76%. The score is lowest for the environmental component with 43.63% and highest

for governance with 71.45%. The standard deviation is 30.10% for the ESG score and

32.23%, 29.70% and 18.41% for the environmental, social and governance score,

respectively. These values confirm the statement of Bassen and Kovács (2008) that

governance is the most accepted practice in business.

Table 6 in Appendix A shows the difference in the mean of all the variables for firms

with a high ESG score (dummy equal to one) and firms with a low ESG score (dummy

equal to zero). All differences in the table are statistically significant at the one percent

level. In line with Hypothesis 1, the absolute analysts’ forecast error (|"#|) is lower for

firms with a high ESG score. The absolute difference is higher for negative forecast

errors (8"#) than for positive forecast errors ('"#). Firms with a high ESG score have

a lower CAR during the event window [-1,1]. This does not give a clear direction for

Hypotheses 2a and 2b, as this difference does not show the sensitivity of the abnormal

stock returns to forecast errors. Finally, the volatility in the abnormal returns around

(@ 12 [−5,10]) is lower for firms with a high ESG score, which is in line with

Hypothesis 3.

Concerning the control variables, firms with a high ESG score have a higher earnings-

to-price ratio (#'), lower market-to-book ratio (FI and lnFI), lower change in

earnings per share (|∆#'&|) and are on average larger (&%S# and ln &%S#) than firms

33

with a low ESG score. Furthermore, firms with a high ESG score are followed by more

analysts (#&() and have lower dispersion in analysts’ earnings forecasts ($%&'). Firms

with a high ESG score manage less earnings (|#F|) and, if they do, use less positive

accruals (#F). Interestingly, firms with a high ESG score make later announcements

((%F%8G) than firms with a low ESG score. Firms with a high ESG score have lower

stock returns (2#(1; 2#(2) and also lower stock volatility (KTJ). Finally, firms with

a high ESG score have less leverage (J#KG) and a lower level of institutional

ownership (%8&().

3.3.5. ControlVariablesFinally, Table 2 in Appendix A shows descriptive statistics of all the control variables.

The average market-to-book ratio (FI) of the firms in the sample is 2.12. The market

value of equity (&%S#) ranges from 91.15 million to 717 billion US dollars, with an

average firm size of 18.86 billion US dollars. The firms in the sample are, on average,

for 25.06% financed with debt (J#KG). The average earnings-to-price ratio (#') of the

firms in the sample is 0.0543, which equals an average price-to-earnings ratio of 18.42.

Some control variables are modified to obtain a better fit of the regression models.

Following Thomas (2002), the logarithms of the market-to-book ratio (lnFI) and firm

size (ln &%S#) are used in the analyses. The buy-and-hold returns (2#(1; 2#(2) are

Winsorized at the first percentile. These extreme observations are kept in the dataset

because the values are still correct. The extreme positive returns occur in the recovery

of the financial crisis in 2008. Announcement timing ((%F%8G) is changed to missing

when the announcement is more than 90 days too early or 90 days too late to keep the

amount of days within one quarter (Chai & Tung, 2002). Following Subramanyam

(1996), which uses the same cross-sectional method to compute discretionary accruals,

earnings management (#F) is changed to missing when an observation is three

standard deviations from the mean. The descriptive statistics of earnings management

are compared, and similar to, Subramanyam (1996). The qualitative results of this thesis

are unchanged when the outliers of these variables are included in the regression

analyses (see footnotes in sections 5.2.1, 5.3.2, and 5.4.2).

34

4. MethodologyThe method that will be used to answer the research question is an event study using a

cross-sectional OLS regression with panel data. Section 4.1 explains the model to test

Hypothesis 1. Section 4.2 explains the model to test Hypotheses 2a and 2b. Section 4.3

explains the model to test Hypothesis 3. The models are executed using the data

described in Chapter 3.

4.1. Analysts’ForecastAccuracyThe dependent variable of interest is the analysts’ earnings forecast error as a

measurement of analysts’ forecast accuracy, where a lower forecast error corresponds

to higher forecast accuracy. The independent variable of interest is the dummy variable

for a high ESG score. The regression controls for event, stock and firm characteristics

from earlier research to analysts’ forecast accuracy, and adds the ESG dummy. To test

Hypothesis 1, the following model is used:

|"#|*,, = 3N + 5:#&G*,, + 5;'"#*,,H: + 5U8"#*,,H: + 5V$%&'*,, + 5W#&(*,,

+ 5X(%F%8G*,, +5Y188*,, + 5Z|#F|*,, + 5[|∆#'&|*,,

+ 5:N ln &%S#*,,H: + 5::J#KG*,, + 5:;#'*,, + 5:U$J*,,

+ 5:V2#(1*,, + 5:W2#(2*,, + 5:XKTJ*,, + \:]* + ^:_, + 7*,,

Where |"#|*,,is the absolute analysts’ forecast error. 5:,the coefficient of the ESG

dummy (#&G*,,), tests Hypothesis 1. Analysts are expected to more accurately predict

the earnings of firms with a high ESG score. Hence, 5:is expected to be negative. The

null hypothesis (H1a) and alternative hypothesis (H1b) to test Hypothesis 1 are:

H1a:5: < 0

H1b:5: ≥ 0

Boudt et al. (2015) show that it is important to separate positive ('"#*,,H:) and negative

(8"#*,,H:) lagged forecast errors due to a difference in autocorrelation between the two.

Even though they do have an influence on analysts’ forecast accuracy, the direction for

5;and5Uis unclear. Next to this, Boudt et al. (2015) include dispersion ($%&'*,,) and

the number of analysts’ forecast estimates (#&(*,,) as proxies for information

uncertainty. As more dispersion between analysts’ forecasts indicates information

uncertainty, 5V is expected to be positive. More analyst coverage decreases information

(4.1)

(4.2)

35

uncertainty. Hence, 5W is expected to be negative. Kross and Schroeder (1984) state that

announcement timing ((%F%8G*,,) is related to the forecast error. Therefore, it is

included as a control variable even though the direction of 5X is unclear. Boudt et al.

(2015) include a dummy variable for annual reports (188*,,) because extraordinary

items and losses are more common in annual reports. As a result, 5Y is expected to be

positive. Payne and Robb (2000) investigate earnings management (|#F|*,,) and find

that managers align earnings to market expectations by using earnings management.

Hence, 5Z is expected to be negative. Lang and Lundholm (1996) and Coën et al. (2009)

find that the change in earnings per share (|∆#'&|*,,) influences forecast accuracy. The

larger the change in earnings per share, the higher the forecast error. Therefore, 5[ is

expected to be positive. Both Boudt et al. (2015) and Thomas (2002) include firm size

(ln &%S#*,,H:). As forecast accuracy increases with firm size, 5:N is expected to be

negative. Furthermore, leverage (J#KG*,,) increases volatility and uncertainty in

earnings (Thomas, 2002). Therefore, 5:: is expected to be positive. Boudt et al. (2015)

include the earnings-to-price ratio to identify value (growth) stocks, which are

characterized by lower (higher) earnings volatility. Adversely, Thomas (2002) uses an

R&D expense to sales ratio and an intangible assets to total assets ratio to identify firms

with larger growth opportunities. The earnings-to-price ratio is used in this thesis

because the availability of this data allows for more observations. As a high earnings-

to-price ratio (#'*,,) indicates a value stock and lower earnings volatility, 5:; is

expected to be negative. Bhat et al. (2006) include a loss dummy that takes the value of

one when a firm reports a loss and zero otherwise ($J*,,). As forecast accuracy is lower

for firms that report a loss, 5:U is expected to be positive. Boudt et al. (2015) include

past firm stock performance (2#(1*,,;2#(2*,,). Poor prior-period performance leads

to optimistic (less accurate) earnings forecasts by analysts. They do this to maintain

good relations with management. As low returns result in less accurate forecasts, both

5:V and 5:W are expected to be negative. Thomas (2002) argues that large stock price

volatility (KTJ*,,) indicates that much information arrived at the market in the period

preceding the earnings announcement. This makes it harder to accurately predict

earnings. Hence, 5:X is expected to be positive. Finally, firm (]*) and year (_,) dummies

are included to control for firm and year fixed effects. 7*,, is the error term in the model.

36

4.2. CumulativeAbnormalReturnsThe dependent variable of interest is the cumulative abnormal stock return (CAR) and

the independent variable of interest is the dummy variable for a high ESG score.

Primarily, the interest lies in the CAR for the event window [-1,1]. The regression

controls for factors from earlier research to earnings announcements as referenced

below and adds the ESG dummy. The method to test Hypotheses 2a and 2b is based on

Lopez and Rees (2002) and Bollen (2007), who both test sensitivity to past performance

on the firm level (CAR) and fund level (fund flows), respectively:

012*,, −1,1 = 3N + 5:#&G*,, + f:(8*,,×#&G*,,×"#*,,)

+ f;(8*,,×%. #&G*,,×"#*,,) + fU('*,,×#&G*,,×"#*,,)

+ fV('*,,×%. #&G*,,×"#*,,) + 5;#F*,, + 5U188*,,

+ 5V ln &%S#*,,H: + 5W%8&(*,, + \:]* + ^:_, + 7*,,

Where 012*,, −1,1 is the cumulative abnormal return for firm i at time t from one day

before, to one day after the earnings announcement. The intercept of firms with a high

ESG score (#&G*,,) is equal to3N + 5:, compared to an intercept of 3N for firms with a

low ESG score. The difference,5:, denotes the premium for firms with a high ESG

score, ceteris paribus. As firms with a high ESG score possibly have higher cash flows

and lower downside risk (Verheyden et al., 2016), and because higher earnings are

expected to result in more positive CARs, 5: is expected to be positive.

The interaction terms within brackets are between the variable8*,,, which takes the

value of one if the forecast error ("#*,,) is negative and zero otherwise, or the

variable'*,,, which takes the value of one if the forecast error ("#*,,) is positive and

zero otherwise, and the ESG dummy (#&G*,,) or the inversed ESG dummy (%. #&G*,,).

The latter takes the value of one for a firm with a low ESG score and zero otherwise.

The interaction terms are multiplied by the real value of the forecast error ("#*,,) to

model the effect of each scenario given a fixed magnitude in the forecast error. As a

positive forecast error is expected to lead to a positive CAR, and a negative forecast

error is expected to lead to a negative CAR,f:, f;, fU, and fV are all expected to be

positive (Beaver et al., 1979). Within the model, f: denotes the slope for firms with a

high ESG score and a negative forecast error, compared tof;, which denotes the slope

for firms with a low ESG score and a negative forecast error. Hypothesis 2a states that

firms with a high ESG score are less sensitive to negative earnings forecast errors.

(4.3)

37

(4.5)

Hence, Hypothesis 2a states thatf: is smaller thanf;. The null hypothesis (H2aa)

and alternative hypothesis (H2ab) to test Hypothesis 2a are therefore:

H2aa:f: − f; < 0

H2ab:f: − f; ≥ 0

Similarly, fU denotes the slope for firms with a high ESG score and a positive forecast

error, compared tofV, which denotes the slope for firms with a low ESG score and a

positive forecast error. Hypothesis 2b states that firms with a high ESG score are more

sensitive to positive earnings forecast errors. Hence, Hypothesis 2b states thatfU is

larger thanfV. The null hypothesis (H2ba) and alternative hypothesis (H2bb) to test

Hypothesis 2b are therefore:

H2ba:fU − fV > 0

H2bb:fU − fV ≤ 0

Both Hypotheses 2a and 2b as stated in equations 4.4 and 4.5 are statistically tested for

each regression using an F-test.

Model 4.3 also includes a set of control variables. Earnings management is used by firm

managers to meet earnings targets. Therefore, more positive earnings management

(#F*,,) is expected to result in higher returns around the earnings announcements

(Payne & Robb, 2000). Conversely, following Chai and Tung (2002), managers use

negative accruals in bad times to make these reports even worse. Hence, more negative

earnings management is expected to result in lower returns. Therefore, 5; is expected

to be positive. Kross and Schroeder (1984) point out the difference between interim and

annual reports. An annual report (188*,,) is expected to have a strengthening effect on

the CAR. As positive forecast errors are more prevalent, 5U is expected to be positive.

Small firms show larger price reactions to earnings announcements than large firms

(Chambers & Penman, 1984). Consequentially, 5V for firm size (ln &%S#*,,H:) is

expected to be negative. The level of institutional investors (%8&(*,,) is included as a

proxy for the shareholder base to lower the risk of omitted variable bias (Kim et al.,

2014). This is important since larger long-term ownership leads to lower levels of

investor turnover (Gaspar, Massa, & Matos, 2005), which influences the CAR. The

direction of 5W for institutional ownership is unclear. Finally, firm (]*) and year (_,)

(4.4)

38

dummies are included to control for firm and year fixed effects. 7*,, is the error term in

the model.

4.2.1. EndogeneityThe instrumental variable methodology is applied to test for endogeneity. The

relationship between the ESG score and the CAR can be a result of simultaneity or

reversed causality (Kim et al., 2014). This problem can be solved by turning the model

from a static to a dynamic model by including lagged variables of the ESG dummy

(#&G*,,H:). However, as stated by Kim et al. (2014), scores related to CSR are sticky

over the years, and this does not solve the simultaneity problem. As ESG scores are

updated annually, and quarterly data is used, this is also the case in this thesis.

Therefore, the instrumental variable methodology is used to test for endogeneity.

This method follows El Ghoul et al. (2011) and Kim et al. (2014), who use the industry

average CSR score as the instrumental variable to test the causal relationship between

CSR and the cost of capital and firm crash risk, respectively. A good instrumental

variable is correlated with the independent variable, and uncorrelated with the error

term (Verbeek, 2008). According to Cheng, Ioannou and Serafeim (2014) the CSR

score of a firm is influenced by the average score of industry peers. Additionally,

Ioannou and Serafeim (2016) state that this is also the case for ESG disclosures.

Therefore, there most likely is a relationship between the industry average ESG score

and the ESG score of individual firms (0mn(%8$#&G*,,, #&G*,,) ≠ 0). The identified

factor in the error term is long-term ownership. Gaspar et al. (2005) state that long-term

shareholders have a low turnover of shares. Kotsantonis et al. (2016) mention that some

screening strategies are related to specific industries. However, these strategies are

independent of firms’ ESG scores. Furthermore, the generations of screening strategies

presented by Renneboog et al. (2008) are aimed at individual firms. As a result, the

industry average ESG score is likely to be uncorrelated with the error term

(0mn(%8$#&G*,,, 7*,,) = 0). This is difficult to verify as 7*,, is unobserved. A reduced

form regression, where the industry average ESG score is added to model 4.3, is run to

give an indication on this assumption through the direct effect of the industry average

ESG score on the CAR. It is important to note that there may be unidentified factors in

the error term, which can still be correlated with the industrial average ESG score.

39

Normally, the instrumental variable approach can be executed using a simple two stage

least squares (2SLS) regression. However, as the ESG dummy is dichotomous, the

instrumental variable estimators are only consistent if the probit (probability unit) in

the first stage is correctly specified, which cannot be guaranteed (Williams, 2016). The

direct application of 2SLS to a probit is known as a “forbidden regression” because

only OLS residuals are guaranteed to be uncorrelated with the OLS fitted values

(Angrist & Pischke, 2008; Williams, 2016). A solution is to use not two, but three steps

to implement the instrumental variable method (Adams, Almeida, & Ferreira, 2009).

The main advantage of this methodology is that the first stage does not need to be

correctly specified to be consistent, while still taking the binary nature of the ESG

dummy into account. An additional advantage is that the standard errors are still

asymptotically valid. The first step in this approach is to estimate a probit of the

determinants of a high ESG score to obtain the fitted values for#&G9,,:

Pr(#&G*,, = 1|r, %8$#&G*,,)

= Φ(3N + 5:%8$#&G*,, + f:(8*,,×%8$#&G*,,×"#*,,)

+ f;('*,,×%8$#&G*,,×"#*,,) + 5;#F*,, + 5U188*,,

+ 5V&%S#*,,H: + 5W%8&(*,, + 7*,,)

Where Φ(∙) is the cumulative distribution function for a standardized normal random

variable, r is a vector of the control variables, and %8$#&G*,, is the industry average

ESG score, which needs to be statistically significant to support instrument relevance.

The fitted values of %. #&G9,, are predicted using the exact same regression model. The

second step is an OLS regression that regresses the respective regular ESG dummies

(#&G*,,;%. #&G*,,) on model 4.3 using the fitted values #&G9,, and %. #&G9,, as

instrumental variables to obtain the predicted values for#&G9,, and%. #&G9,,:

#&G*,, = 3N + 5:#&G9,, + f:(8*,,×#&G9,,×"#*,,)

+ f;(8*,,×%. #&G9,,×"#*,,) + fU('*,,×#&G9,,×"#*,,)

+ fV('*,,×%. #&G9,,×"#*,,) + 5;#F*,, + 5U188*,,

+ 5V&%S#*,,H: + 5W%8&(*,, + \:]* + ^:_, + 7*,,

Finally, #&G9,, and %. #&G9,, substitute #&G*,, and %. #&G*,, in model 4.3 to obtain the

IV estimates for5:, f:, f;, fU, and fV. The instrument relevance of the industry

(4.6)

(4.7)

40

average ESG score (0mn(%8$#&G*,,, #&G*,,) ≠ 0) can be tested by adding the industry

average ESG score to model 4.6. If the industry average ESG score is not equal to zero,

it shows that it has a relationship with the ESG score (Adams et al., 2009). Furthermore,

instrument relevance can be tested by conducting an F-test on the instrumental variable

(#&G9,,) in the first stage of a 2SLS, which is in this case the second stage as denoted

in model 4.7. If the value of the F-statistic is larger than 10, this supports instrument

relevance (Stock & Yogo, 2002). The endogeneity of #&G*,, cannot be tested with for

example a Durbin-Wu-Hausman test, because there are exactly as many conditions as

needed used for identification of the model (Verbeek, 2008). Therefore, since the

literature does not present a second suitable instrumental variable, the assumptions

made in the moment conditions are of identifying nature only and do not contain

statistical evidence that shows the validity of the industry average ESG score as the

instrumental variable.

According to Adams et al. (2009), it is important to investigate the inconsistency, or

bias, in the ordinary least squares estimator. The theoretical background and examples

on how this bias can be interpreted in this thesis are provided in Appendix B.

4.3. VolatilityinAbnormalReturnsThe dependent variable of interest is the volatility in the abnormal returns and the

independent variable of interest is the dummy variable for a high ESG score. The

regression controls for the identified factors of earlier research to earnings

announcements as referenced below and adds the ESG dummy:

@ 12 *,, −5,10

= 3N + 5:#&G*,, + 5; "# *,, + 5U$%&'*,, + 5V(%F%8G*,,

+ 5W188*,, + 5X|#F|*,, + 5Y ln &%S#*,,H: + 5ZJ#KG*,,

+ 5[ lnFI*,, + 5:N#'*,, + 5::$J*,, + 5:;KTJ*,,

+ 5:U%8&(*,, + \:]* + ^:_, + 7*,,

Where@(12)*,, [−5,10]is the volatility in the abnormal returns from five days before

to ten days after an earnings announcement for firm i at time t. According to Hypothesis

3, firms with a high ESG score (#&G*,,) release less surprising news at an earnings

announcement. Following Chambers and Penman (1984) this reduces the variability in

(4.8)

41

stock returns. Consequently, 5: is expected to be negative. The null hypothesis (H3a)

and alternative hypothesis (H3b) to test Hypothesis 3 are therefore:

H3a:5: < 0

H3b:5: ≥ 0

Beaver et al. (1979) find that stock returns are influenced by the magnitude of the

forecast error. Therefore, larger absolute forecast errors (|"#|*,,) are expected to lead

to higher volatility in abnormal returns. Hence,5; is expected to be positive. Dispersion

amongst analysts ($%&'*,,) measures uncertainty (Payne & Robb, 2000). More

uncertainty results in heavier stock price reactions. Hence, 5U is expected to be positive.

Chambers and Penman (1984) show that the variability in stock returns is higher when

reports are released early. Hence, coefficient 5V on announcement timing ((%F%8G*,,)

is expected to be negative. Boudt et al. (2015) include a dummy variable for annual

reports (188*,,) because extraordinary items and losses are more common. As these

extraordinary items and losses lead to larger stock price reactions, 5W is expected to be

positive. Payne and Robb (2000) find that firm managers use earnings management

(|#F|*,,) for optimistic reports, or as a tool to meet analysts’ forecasts. As lower

earnings surprises lead to lower volatility, 5X is expected to be negative. Additional

variables used by Thomas (2002), who investigates absolute abnormal returns, are also

included in model 4.8. These variables are firm size (ln &%S#*,,H:), leverage(J#KG*,,)

and the market-to-book ratio(lnFI*,,). The coefficients5Y,5Z, and 5[ on these

variables are expected to be negative, positive and positive, respectively. Firms with a

high earnings-to-price ratio (#'*,,) are value stocks, which show low earnings volatility

(Boudt et al., 2015). As a result, 5:N is expected to be negative. Forecast accuracy is

lower for firms that report a loss, which in turn leads to higher volatility in abnormal

returns. Therefore, 5:: on the loss dummy ($J*,,) is expected to be positive. Thomas

(2002) includes stock volatility to control for information that arrives at the market

during the estimation window. In this regression, volatility (KTJ*,,) controls for the

normal level of stock volatility. Volatile stocks are expected to stay volatile, which

corresponds to a positive value for5:;. The level of institutional investors (%8&(*,,) is

expected to increase bid-ask spreads, resulting in lower volatility in abnormal returns

(Siew et al., 2016). Hence, 5:U is expected to be negative. Finally, firm (]*) and year

(4.9)

42

(_,) dummies are included to control for firm and year fixed effects. 7*,, is the error term

in the model.

The ESG dummy in model 4.8 is subject to the same endogeneity problem as model

4.3. Hence, the same instrumental variable methodology is applied to this model.

5. EmpiricalResultsThis chapter discusses the results of the regression analyses using the data described in

Chapter 3 and the methodology described in Chapter 4. Section 5.1 describes the

pairwise correlations between the variables used in the analyses. Sections 5.2, 5.3 and

5.4 discuss the results to test Hypothesis 1, Hypothesis 2a and 2b, and Hypothesis 3,

respectively. Each section includes robustness tests. Sections 5.3 and 5.4 also include

endogeneity tests.

5.1. PairwiseCorrelationsTable 7 in Appendix C shows the pairwise correlations between all the variables used

in the regression analyses. Looking at the correlation between independent variables,

no alarming correlations above an absolute value of 0.7 are present. The only

correlations above 0.7 are between the ESG dummy (#&G*,,) and the ESG constituents,

#*,,, &*,,, and G*,,, and the industry average ESG scores (%8$#&G*,,;

%8$#*,,;%8$&*,,;.%8$G*,,). There are a few moderate cases of correlation with an

absolute value of 0.5 or higher. These correlations concern the change in earnings per

share (|∆#'&|*,,) and the lagged natural logarithm of firm size (ln &%S#*,,H:). The

change in earnings per share is moderately correlated with the lagged absolute value of

the negative forecast error (8"#*,,H:;0.5765) and dispersion in analysts’ earnings

forecasts ($%&'*,,;0.5499). The lagged logarithm of firm size is moderately correlated

with the number of analysts’ forecasts (#&(*,,;0.5179). Finally, there are a few variables

that are low to moderately correlated with an absolute value above 0.3. Here, the lagged

logarithm of firm size is negatively correlated with stock volatility (KTJ*,,;-0.3294) and

positively correlated with the ESG dummy and its constituents, #*,,, &*,,, and G*,,, with

the highest value for the ESG dummy (#&G*,,;0.4734). The change in earnings per share

is correlated with the lagged positive forecast error ('"#*,,H:;0.4315). Dispersion in

43

analysts’ forecasts is positively correlated with the lagged negative forecast error

(0.3685) and the loss dummy ($J*,,;0.3159). Finally, the loss dummy is negatively

correlated with the earnings-to-price ratio (#'*,,;-0.4026). All other correlations are

below an absolute value of 0.3. As all of the variables mentioned here are included in

model 4.1, and many of the variables are included in model 4.8, the variance inflation

factor (VIF) is of interest for these models to test for possible multicollinearity issues.

5.2. Analysts’ForecastAccuracyTable 8 in Appendix C.1 shows the output of model 4.1 with the absolute analysts’

forecast error (|"#|*,,) as the dependent variable. The regression in column 5 is run

using robust standard errors after conducting a Breusch-Pagan heteroscedasticity test.

The variance inflation factor (VIF) shows no concerning values for the independent

variables (>10), with the highest value for the absolute change in earnings per share

(|∆#'&|*,,) of 2.66. The adjusted R-squared in column 5 shows that the model explains

57.95% of the variation in the absolute analysts’ forecast error. This means that the

explanatory power of the model is rather high.

The ESG dummy (#&G*,,) is negative and statistically significant at the one percent

level up to column 3. From column 4 onwards, which includes firm and year fixed

effects, the coefficient becomes positive and statistically insignificant. When using

robust standard errors in column 5, the statistical significance increases slightly, but

remains insignificant. Here, a one standard deviation increase in the ESG dummy

results in a higher forecast error of 1 basis point. With a sample average (median) of 30

(11) basis points, this result is economically significant. These results imply that the

negative coefficient for a high ESG score in column 3 contains a downward bias

through its positive correlation with certain omitted firm characteristics, which decrease

the analysts’ forecast error (0mn #&G*,,, 7*,, < 0). After controlling for these firm

characteristics, a high ESG score increases the analysts’ forecast error. A possible

example of such a firm characteristic is the quality of financial disclosure that is

positively correlated with the social and governance component of the ESG score (Bhat

et al., 2006; Kim et al., 2014). The quality of financial disclosure is excluded from the

model due to unavailable data, which causes the aforementioned downward bias in

columns 1-3. This is in line with Coën et al. (2009), who find that firm specific effects

are the main determinant of forecast accuracy. Furthermore, Byard et al. (2006) and

44

Becchetti et al. (2013) find significant opposite results when investigating the effect of

specific factors within the social and governance components of the ESG score on

forecast accuracy. Therefore, the findings in Table 8 imply that the overall ESG score

is an indicator of certain firm characteristics that cause the analysts’ forecast error to

decrease, and that specific ESG measures are more informative. Additionally, the

dependent variable in the model includes both positive and absolute negative forecast

errors. According to Lopez and Rees (2002), some firms consistently outperform

analysts’ forecasts. The data in Chapter 3 shows that positive forecast errors are more

prevalent than negative forecast errors for firms with a high ESG score. As a result, the

positive coefficient may be driven by positive forecast errors of firms with a high ESG

score. Both possibilities are examined in section 5.2.1. Overall, based on this empirical

evidence, Hypothesis 1 is rejected.

Looking at the control variables in column 5, all variables have their expected signs

except leverage (J#KG*,,), the stock return during the first half of the estimation

window (2#(2*,,) and stock volatility (KTJ*,,), which are all statistically insignificant.

This implies that leverage does not influence earnings volatility enough to have an

impact on the forecast error. Next to this, given the coefficient of2#(1*,,, only recent

stock returns influence the interim forecasts by analysts. Finally, these results imply

that stock volatility is not a correct proxy for the amount of information arriving at the

market or that this does not influence the accuracy of analysts’ earnings forecasts.

5.2.1. Robustness4Table 9 in Appendix C.1 shows the results of model 4.1, replacting the overall ESG

score for the ESG constituent dummy variables#*,,, &*,,, and G*,,. The coefficients of

the environmental score dummy variable (#*,,) and the corporate governance score

dummy (G*,,) are positive and statistically insignificant. The coefficient of the social

4 Additional robustness tests are performed but not included in this report. This report only includes the most important findings. The additional tests include: a) Equal subsamples in time, which show that the positive effect is driven by the period 2004-2011

(0.0002 (0.59)). From 2012-2016, it is negative and statistically insignificant (-0.0000 (-0.02)) b) Running the regression on the real forecast error. This shows a positive coefficient of the ESG

dummy of 0.0005 (1.77), which is statistically significant at the ten percent level c) Including the outliers in announcement timing, earnings management and buy-and-hold returns,

which does not influence the qualitative result d) Controlling for time and industry fixed effects, which does not influence the qualitative result e) Subsample of only annual reports, which does not influence the qualitative result

45

score dummy (&*,,) is positive and statistically significant at the five percent level. A

one standard deviation increase in the social score dummy results in an increase in the

absolute forecast error of 2.49 basis points. This shows that the positive effect of the

ESG dummy on the analysts’ forecast error is driven by the social component. Similar

to the overall ESG score, this implies that other firm characteristics explain the lower

forecast error for firms with a high social score. This strengthens the implication that

the overall score on ESG factors is not as informative as more specific ESG measures.

Kotsantonis et al. (2016) find that material ESG issues are value relevant, compared to

immaterial ESG issues, which are value irrelevant. The overall scores also contain

immaterial issues that do not have an effect on firm value and possibly, in this case,

also not on the forecast error. As a result, Hypothesis 1 is still rejected. The results of

all the control variables are similar to column 5 in Table 8, which indicates that the

results are robust to using the different ESG constituents.

Table 10 in Appendix C.1 shows the results of model 4.1 on subsamples of absolute

positive and negative forecast errors. The ESG dummy is positive and statistically

significant at the ten percent level for positive forecast errors. Economically, a one

standard deviation increase in the ESG dummy results in a 1.49 basis point increase in

the positive forecast error. With a mean (median) of 26 (12) basis points, this result is

economically significant. When looking at the absolute negative forecast error, the ESG

dummy is positive and statistically insignificant. Economically, as the value is equal to

zero, the ESG dummy is insignificant. As a result, it can be concluded that the positive

coefficients in Table 8 and 9 are driven by the positive forecast error. The control

variables in Table 10 show some sign differences compared to Table 8. For example,

absolute earnings management (|#F|*,,) is positive and not statistically significant for

positive forecast errors, and negative and statistically significant at the five percent level

for negative forecast errors. This can be explained by the fact that firms use

discretionary accruals to meet analysts’ forecasts, while discretionary accruals are not

often used to further increase a positive forecast error (Payne & Robb, 2000). As these

sign differences result from the nature of the type of forecast error, it can be interpreted

that the results are robust to using subsamples of positive and negative forecast errors.

To conclude, the findings of this section show that it is important to analyze positive

and negative forecast errors separately. On the one hand, this different effect can be a

result of analysts underestimating the benefits and/or overestimating the costs of ESG

46

activities, similar to investors (Mânescu, 2011). However, if this were the case, the

coefficient of the ESG dummy should be negative for the subsample of negative

forecast errors. On the other hand, this different effect can result from higher financial

outperformance by firms with a high ESG score. This is in line with Verheyden et al.

(2016), who find a positive correlation between ESG performance and financial

performance. As the discussion in section 2.2.2 points out this does not prove a causal

relationship. This explains the low coefficient for negative forecast errors to the extent

that only firms that financially outperform benefit from, or invest in, ESG activities.

This model is not tested for endogeneity and hence a causal relationship cannot be

concluded. In any case, Hypothesis 1 is still rejected.

5.3. CumulativeAbnormalReturnsTable 11 in Appendix C.2 shows the output of model 4.3 with the cumulative abnormal

return (012*,, −1,1 ) as the dependent variable. The regressions in columns 4 and 5

are run using robust standard errors after conducting a Breusch-Pagan

heteroscedasticity test. Column 5 is the output of a regression using the instrumental

variable methodology. The adjusted R-squared in column 4 shows that the model only

explains 4.44% of the variation in the cumulative abnormal returns. Even though this

value is similar to Lopez and Rees (2002), this implies that the model has a low

explanatory power.

The ESG dummy (#&G*,,) is negative and statistically significant at the one percent

level in column 1 and statistically insignificant in column 2. After including firm and

year fixed effects the coefficient becomes positive, as expected, but statistically

insignificant. This remains the same after using robust standard errors in column 4,

indicating that there is no significant difference in the CAR for firms with a high ESG

score compared to firms with a low ESG score. Economically, a one standard deviation

increase in the ESG dummy results in a 9.46 basis points increase in the CAR. This

effect is economically also insignificant.

When looking at the interaction terms of the ESG dummy in column 4, all variables are

statistically significant at the one percent level. When the negative forecast error goes

up by one standard deviation, the CAR decreases with 125.95 basis points for firms

with a high ESG score (#&G*,,×8*,,×"#*,,), compared to 54.57 basis points for firms

47

with a low ESG score (%. #&G*,,×8*,,×"#*,,). This difference is statistically significant

at the ten percent level in column 4 and contradicts Hypothesis 2a. Hence, based on this

empirical evidence, Hypothesis 2a is rejected. Bollen (2007) finds that socially

responsible investment funds are less sensitive to past negative returns. This seems to

be the opposite for individual firms. Benson and Humphrey (2008) provide an

explanation for this by stating that investors in SRI funds have higher searching costs

and a longer investment horizon. The result in this thesis can be explained by the

abundance of alternatives on the stock market when it comes to firms with a high ESG

score. When the positive forecast error increases by one standard deviation, the CAR

increases with 82.88 basis points for firms with a high ESG score (#&G*,,×'*,,×"#*,,),

compared to 55.63 basis points for firms with a low ESG score (%. #&G*,,×'*,,×"#*,,).

This difference is statistically insignificant in column 4. Hence, based on this empirical

evidence, Hypothesis 2b is rejected. Bollen (2007) finds that SRI funds are more

sensitive to past positive returns, which is explained by the theory of a multi-attribute

utility function. If there is a difference, firms with a high ESG score also seem to be

more sensitive to positive forecast errors, which indicates that this multi-attribute utility

function may also be applicable to investors in the stock market. Taken together, firms

with a high ESG score seem to be more sensitive to both positive and negative forecast

errors. An additional explanation for this may be posed by Heinkel et al. (2001).

According to their study, firms with a high ESG score have a lower cost of capital. If

this is true, share prices of firms with a high ESG score should indeed be more sensitive

to changes in earnings, as earnings are discounted by the cost of capital in valuation

models. Finally, the coefficients of the interaction terms are higher for positive forecast

errors than for negative forecast errors, which is in line with the findings of Lopez and

Rees (2002).

Looking at the coefficients of the control variables in column 4, only earnings

management (#F*,,) does not have the expected sign. The coefficient is negative and

statistically significant at the ten percent level, which implies that positive (negative)

earnings management results in lower (higher) abnormal returns. The coefficient of the

percentage of institutional shareholders (%8&(*,,) is negative and statistically

insignificant after including firm fixed effects in column 3.

48

5.3.1. EndogeneityColumn 5 in Table 11 shows the results of a regression that uses the industry average

ESG score (%8$#&G*,,) as the instrumental variable. Table 11.1 in Appendix C.2 shows

the regressions that precede column 5 based on the methodology used by Adams et al.

(2009). First, the cumulative abnormal return (012*,,[−1,1]) is regressed on the entire

model including the industry average ESG score in column 1. The regression shows a

statistically insignificant coefficient. Furthermore, the adjusted R-squared does not

change after adding the industry average ESG score to the model. This strengthens the

assumption that 0mn(%8$#&G*,,, 7*,,) is equal to zero. Columns 2 and 3 show a probit,

which estimates the ESG dummy (#&G*,,) and the inversed ESG dummy (%. #&G*,,)

using model 4.3 with the industry average ESG score instead of the ESG and inversed

ESG dummies. Column 4 shows the exact opposite results of column 3, which is in line

with expectations as the inversed ESG dummy is the exact opposite of the ESG dummy.

The coefficients of the industry average ESG score have the predicted signs and are

statistically significant at the one percent level, which supports the theory of instrument

relevance. Furthermore, the coefficients of both variables have the expected signs.

Based on these results, it can be concluded that the instrumental variable is strong

(Adams et al., 2009). Finally, column 5 in Table 11 shows the F-statistic of the

instrumental variable in the second stage regression, which is 84.90 (>10). This further

strengthens the assumption that 0mn(%8$#&G*,,, #&G*,,) is not equal to zero.

The predicted values of the ESG dummy (#&G9,,) and the inversed ESG dummy

(%. #&G9,,) are used as instrumental variables in column 5 in Table 11. The coefficient

of the ESG dummy (#&G*,,) becomes more positive but is still statistically insignificant.

Economically, a one standard deviation increase in the ESG dummy now causes an

increase in the CAR of 179.21 basis points. This is of large economic significance.

Column 5 shows that the coefficients of the interaction terms do not change as much in

the instrumental variable regression. This may result from the fact that the coefficients

of the interaction terms are largely determined by the forecast errors, which are not

instrumented as these are assumed to be exogenous in the model. The coefficients of

the interaction terms for negative forecast errors move further apart. This difference is

now statistically significant at the five percent level. Economically, when the negative

forecast error goes up by one standard deviation, the CAR now decreases with 148.26

basis points for firms with a high ESG score (#&G*,,×8*,,×"#*,,), compared to 50.18

49

basis points for firms with a low ESG score (%. #&G*,,×8*,,×"#*,,). The coefficients of

the positive forecast errors both decrease and the difference remains statistically

insignificant. As a result, Hypotheses 2a and 2b are still rejected. Also in the control

variables, no notable changes occur as these variables are not instrumented. A crucial

assumption underlying the regression in column 5 is, therefore, that the other variables

in model 4.3 are exogenous.

Overall, the coefficients of the ESG dummies become larger compared to the inversed

ESG dummies. This is in line with the expected bias caused by the influence of long-

term shareholders. As discussed in section 2.4, firms with a high ESG score have more

long-term shareholders (Renneboog et al., 2008; Kotsantonis et al., 2016). Higher long-

term ownership possibly results in more stable, and hence, less positive and less

negative CARs around earnings announcements (Benson & Humphrey, 2008). The

coefficient of the ESG dummy shows a downward bias in column 4 compared to

column 5. This shows that 0mn #&G*,,, 7*,, < 0 in column 4, which implies that the

CAR is lower as a result of long-term ownership. When looking at the interaction terms,

the larger sensitivity to negative forecast errors for firms with a high ESG score shows

a downward bias, implying that long-term shareholders decrease this sensitivity

(0mn #&G*,,, 7*,, < 0). The opposite is true for the sensitivity to negative forecast

errors for firms with a low ESG score, implying an upward bias caused by short-term

shareholders (0mn #&G*,,, 7*,, > 0). Conversely, the lower sensitivity to positive

forecast errors for firms with a high ESG score shows an upward bias, implying that

long-term shareholders increase this sensitivity (0mn #&G*,,, 7*,, > 0). The sensitivity

of the CAR to a positive forecast error of firms with a low ESG score also shows an

upward bias in column 4, implying that short-term shareholders cause the same effect

for firms with a low ESG score (0mn #&G*,,, 7*,, > 0). Overall, as expected, these

findings show that firms with a high ESG score incur a higher reward for positive

forecast errors and a lower punishment for negative forecast errors caused by long-term

shareholders. It is important to note that the biases mentioned here can also be caused

by other factors in the error terms, which are not identified.

The quality of the instrumental variable estimators depends on the quality of the

instrumental variables (Adams et al., 2009). As the literature does not present many

suitable instrumental variables, it is important to note that the instrumental variable in

50

this thesis is used for the first time in this context, and is thus not proven to be robust.

Section 5.3.2 gives insights in the robustness of this procedure for the ESG constituents.

5.3.2. Robustness5Table 12 in Appendix C.2 shows the results of model 4.3 for the ESG constituent

dummy variables#*,,, &*,,, and G*,,. The results of all the control variables are similar

to columns 4 and 5 in Table 11, which indicates that the results are robust to using

different ESG explanatory variables. Tables 12.1, 12.2, and 12.3 in Appendix C.2 show

the results of the preceding regressions of the instrumental variables methodology.

Columns 1 in Tables 12.1, 12.2, and 12.3 show that the industry average score is

statistically insignificant in model 4.3 for the environmental score (#*,,), the social score

(&*,,), and the governance score (G*,,). Also the adjusted R-squared statistics show that

the industry averages add no additional explanatory power to the model. Columns 2

and 3 in these tables show that the instrumental variables are statistically significant at

the one percent level in the predicted directions. The industry averages of the

governance score and social score pose an issue in the probit estimations, as the

interaction variables predict the dummies with certainty. To solve this issue, the

industry averages are excluded from the interaction variables. The F-statistics of the

instrumental variables in the second stage regressions in Table 12 show instrument

relevance for the environmental score (53.32>10) and social score (86.11>10), but not

for the governance score (0.05<10). This implies that 0mn(%8$G*,,, G*,,) may be equal

to zero, which means that the industry average governance score is a weak instrument

for the governance score. This seems likely, as Bassen and Kovács (2008) state that

corporate governance is an accepted business practice. As a result, industry peers do

5 Additional robustness tests are performed but not included in this report. This report only includes the most important findings and uses the instrumental variable method. The additional tests include: a) Expanding the event window from a three day to a five day window. The coefficient of the ESG

dummy is positive and statistically significant at the ten percent level (0.0629 (1.80)). The difference in sensitivity to negative forecast errors loses its statistical significance

b) Equal subsamples in time, which shows that the results are driven by the period 2004-2011, which shows equal results. In the period 2012-2016, the difference after negative forecast errors is statistically insignificant, and the difference after positive forecast errors is statistically significant at the one percent level. The coefficients show higher sensitivity for firms with a low ESG score after both positive and negative forecast errors in this time period

c) Including the outliers in announcement timing, earnings management and buy-and-hold returns, which does not influence the qualitative result

d) Controlling for time and industry fixed effects, which does not influence the qualitative result e) Subsample of only annual reports. The difference after negative forecast errors is now statistically

insignificant

51

not have to influence a firm to adopt corporate governance practices. For this reason,

the results in column 6 have to be interpreted with caution, as this instrumental variable

regression may not be valid.

Similar to the ESG dummy, the coefficients of the constituents in Table 12 have a

downward bias in columns 1, 3, and 5. After controlling for endogeneity, the coefficient

of the environmental score (#*,,) is positive and statistically significant at the one percent

level. The coefficient of the social score (&*,,) remains negative and statistically

insignificant. The coefficient of the governance score (G*,,) remains positive and

statistically insignificant. Consistent with Table 11, firms with a high environmental

score, social score and governance score are more sensitive to both types of forecast

errors than firms with low scores on the ESG constituents. The difference is only

statistically significant (at the five percent level) for negative forecast errors for the

social score. After testing for endogeneity, this effect becomes statistically significant

at the one percent level. For the environmental score, after testing for endogeneity, the

difference becomes statistically significant (at the ten percent level) for negative

forecast errors. This shows that the results in Table 11 are driven by the environmental

score and social score. Economically, a one standard deviation increase in the negative

forecast error results in a 187.49 basis points decrease in the CAR for firms with a high

social score (&*,,×8*,,×"#*,,), compared to a 40.62 basis points decrease in the CAR for

firms with a low social score (%. &*,,×8*,,×"#*,,). Next to this, a one standard deviation

increase in the negative forecast error results in a 136.02 basis points decrease in the

CAR for firms with a high environmental score (#*,,×8*,,×"#*,,), compared to a 54.15

basis points decrease in the CAR for firms with a low environmental score

(%. #*,,×8*,,×"#*,,). This higher sensitivity is in line with the explanation that firms with

high ESG performance have a lower cost of capital (Heinkel et al., 2001) and that

investors in firms with a high environmental, social, and governance score have lower

searching costs and a shorter investment horizon compared to investors in SRI funds

(Benson & Humphrey, 2008). In any case, both Hypothesis 2a and Hypothesis 2b are

still rejected.

5.4. VolatilityinAbnormalReturnsTable 13 in Appendix C.3 shows the output of model 4.8 with the volatility in abnormal

returns (@(12)*,,[−5,10]) as the dependent variable. The regressions in columns 4 and

52

5 are run using robust standard errors after conducting a Breusch-Pagan

heteroscedasticity test. The variance inflation factor (VIF) shows no concerning values

for the independent variables (>10), with the highest value for the dispersion in

analysts’ forecasts ($%&'*,,) of 1.83. Column 5 is the output of a regression using the

instrumental variable methodology. The adjusted R-squared in column 4 shows that the

model explains 41.31% of the variation in the volatility in abnormal returns. This means

that the explanatory power of the full model is rather high.

The ESG dummy (#&G*,,) is negative and statistically significant in columns 1-4. In

column 4, the ESG dummy is statistically significant at the five percent level.

Economically, a one standard deviation increase in the ESG dummy results in a

decrease in the volatility in abnormal returns of 3.98 basis points. Given the mean

(median) of the volatility in abnormal returns of 181 (147) basis points, this effect is

economically insignificant. Overall, despite the low economic significance, the

alternative of Hypothesis 3 (H3b) is rejected. This finding is in line with the

expectations that less new information arrives at the market for firms with a high ESG

score, which results in lower volatility in abnormal returns around an earnings

announcement (Chambers & Penman, 1984). Furthermore, this is also in line with

studies that find a reduction in risk for firms with a higher ESG score (Heinkel et al.,

2001; Mânescu, 2011; Kim et al., 2014; Möller et al., 2015).

All coefficients of the control variables in column 4, except the earnings-to-price ratio

(#'*,,), have their expected signs. The earnings-to-price ratio has a positive coefficient,

which implies higher volatility in abnormal returns for value firms. The coefficient is

statistically significant at the ten percent level.

5.4.1. EndogeneityColumn 5 in Table 13 in Appendix C.3 shows the results of an instrumental variable

regression, using the industry average ESG score (%8$#&G*,,) as the instrumental

variable. Table 13.1 in Appendix C.3 shows the regressions that precede column 5

based on the methodology used by Adams et al. (2009). First, the volatility in abnormal

returns (@(12)*,,[−5,10]) is regressed on the entire model including the industry

average ESG score in column 1. The regression shows a statistically insignificant

coefficient. Furthermore, the adjusted R-squared does not change after adding the

industry average ESG score to the model. This strengthens the assumption that

53

0mn(%8$#&G*,,, 7*,,) is equal to zero. Column 2 shows a probit, which estimates the

ESG dummy (#&G*,,) using model 4.8 with the industry average ESG score instead of

the ESG dummy. The coefficient of the industry average ESG score is positive, as

expected, and statistically significant at the one percent level, which supports the theory

of instrument relevance. Based on these results, it can be concluded that the

instrumental variable is strong (Adams et al., 2009). Finally, column 5 in Table 13

shows that the F-statistic of the instrumental variable in the second stage regression is

equal to 139.99 (>10). This further strengthens the assumption that

0mn(%8$#&G*,,, #&G*,,) is not equal to zero.

The predicted values of the ESG dummy (#&G9,,) are used as the instrumental variable

in column 5 in Table 13. The coefficient becomes more negative, but statistically

insignificant. Economically, a one standard deviation increase in the ESG dummy

causes a decrease in the volatility in abnormal returns of 25.89 basis points. When

looking at the mean (median) of the volatility in abnormal returns of 181 (147) basis

points, this effect is now economically significant. Because the coefficient is no longer

statistically significant, Hypothesis 3 is rejected. The coefficients of the control

variables do not change, since these variables are not instrumented. An assumption

underlying the regression in column 5 is, again, that the other variables in model 4.8

are exogenous.

The coefficient of the ESG dummy becomes smaller, which shows that the endogeneity

problem causes an upward bias in the ESG estimator. This is not in line with the

expected bias caused by the influence of long-term shareholders. As discussed in

section 2.4, firms with a high ESG score have more long-term shareholders (Renneboog

et al., 2008; Kotsantonis et al., 2016). Higher long-term ownership results in a lower

share turnover and is therefore expected to result in lower volatility in abnormal returns

(Gaspar et al., 2005; Benson & Humphrey, 2008). As a result, 0mn #&G*,,, 7*,, < 0 in

column 4, which implies a downward bias in the ESG estimator. However, the findings

show an upward bias (0mn #&G*,,, 7*,, > 0). This implies that long-term ownership

results in higher volatility. An alternative explanation for this can be that, as opposed

to institutional investors, long-term shareholders do not increase bid-ask spreads (Siew

et al., 2016), resulting in higher market liquidity, a higher trading volume, and higher

volatility in abnormal returns (Kim & Verrecchia, 1992). It is important to note that the

54

biases mentioned here can also be caused by other factors in the error terms, which are

not identified.

Also here, the quality of the instrumental variable estimators depends on the quality of

the instrumental variables (Adams et al., 2009). Combining these empirical findings

with section 5.3, this procedure supports the robustness of the industry average ESG

score as a strong instrumental variable. Section 5.4.2 gives insights in the robustness of

this instrumental variable procedure for the ESG constituents.

5.4.2. Robustness6Table 14 in Appendix C.3 shows the results of model 4.8 for the ESG constituent

dummy variables#*,,, &*,,, and G*,,. The results of all the control variables are similar

to column 4 and 5 in Table 13, which indicates that the results are robust to using

different ESG explanatory variables. Tables 14.1, 14.2, and 14.3 in Appendix C.3 show

the results of the preceding regressions of the instrumental variables methodology.

Columns 1 in Tables 14.1, 14.2, and 14.3 show that the industry average score is

statistically insignificant in model 4.8 for the environmental score (#*,,), the social score

(&*,,), and the governance score (G*,,). Also the adjusted R-squared statistics show that

the industry averages add no additional explanatory power to the model. Column 2 in

these tables shows that also here, the instrumental variables are statistically significant

at the one percent level in the predicted directions. The F-statistics of the instrumental

variables in the second stage regressions in Table 14 show, similar to section 5.3.2, that

the instruments are valid for the environmental score (212.43>10) and social score

(151.09>10), but not for the governance score (0.51<10). Again, this implies that

0mn(%8$G*,,, G*,,) may be equal to zero, which means that the industry average

6 Additional robustness tests are performed but not included in this report. This report only includes the most important findings and uses the instrumental variable method. The additional tests include: a) Changing the event window to five days before and five days after the event window, following

the initial response window posed by Bernard (1992), which does not influence the qualitative result

b) Subsamples of positive and negative earnings forecast errors, which shows that the result is negative for both positive (-0.0074 (-1.74)) and negative (-0.0203 (-2.05)) forecast errors. The coefficient is statistically significant at the ten percent level for positive and at the five percent level for negative forecast errors

c) Equal subsamples in time, which shows equal results for the periods 2004-2011 and 2012-2016 d) Including the outliers in announcement timing, earnings management and buy-and-hold returns,

which does not influence the qualitative result e) Controlling for time and industry fixed effects, which does not influence the qualitative result f) Subsample of only annual reports, which does not influence the qualitative result

55

governance score is a weak instrument for the governance score. Again, according to

the explanation based on Bassen and Kovács (2008), who state that corporate

governance is an accepted business practice, it is possible that industry peers do not

influence a firm to adopt corporate governance practices.

Similar to the ESG dummy, the coefficients of the constituents in Table 14 have an

upward bias in columns 1, 3, and 5. After controlling for endogeneity, the coefficients

of the environmental score (#*,,), the social score (&*,,), and the governance score (G*,,)

are negative and statistically insignificant. Taking the values from columns 2, 4, and 6,

a one standard deviation increase in the environmental score, social score, and

governance score causes a decrease in the volatility in abnormal returns of 22.46, 24.43,

and 198.10 basis points, respectively. It is important to interpret the coefficient of the

governance score with caution as this instrumental variable methodology may not be

valid as a result of a weak instrumental variable. In the end, Hypothesis 3 is still

rejected, since none of the coefficients are statistically significant.

6. ConclusionsThis thesis explores the effect of ESG scores on the dynamics around earnings

announcements by answering the following research question:

Research Question: What is the effect of firms’ ESG scores on stock price behavior around earnings announcements?

The empirical findings to test the hypotheses and to answer the research question are

discussed in section 6.1, after which section 6.2 elaborates on the limitations of this

thesis. Section 6.3 provides recommendations for further research and practical

recommendations.

6.1. DiscussionFirst, using an ordinary least squares regression, the findings of this thesis show that a

high ESG score, and the social score in particular, is positively correlated with the

analysts’ forecast error. Even though the data shows that firms with high ESG scores

have lower analysts’ forecast errors, the regression analyses show that this is driven by

other firm characteristics. This is in line with Coën et al. (2009), who find that firm

specific effects are the main determinant of forecast accuracy. Overall, these findings

56

contradict with earlier research conducted by Byard et al. (2006) and Becchetti et al.

(2013), who find a negative and significant relationship between more specific ESG

measures and the analysts’ forecast error. An explanation for the different findings in

this thesis is that the general ESG score is used, which contains both material and

immaterial ESG issues (Kotsantonis et al., 2016). This general score is possibly merely

an indicator of certain firm characteristics that cause analysts to be better able to predict

earnings. An example of such a firm characteristic is the quality of financial disclosure

(Bhat et al., 2006; Kim et al., 2014). The robustness tests show that the positive

correlation is driven by positive earnings forecast errors. This finding can be explained

by Verheyden et al. (2016), who find that firms with a high ESG score outperform

financially. As previous research by Orlitzky et al. (2003) shows, this relationship is

subject to reversed causality. This model is not tested for endogeneity. Hence, a causal

effect cannot be concluded.

Second, using an instrumental variable methodology proposed by Adams et al. (2009),

the empirical findings of this thesis show that the cumulative abnormal returns around

earnings announcements of firms with a high overall ESG score, environmental score

and social score are more sensitive to negative earnings forecast errors than firms with

a low overall ESG score, environmental score, and social score. In this procedure, the

industry average scores are used as instrumental variables, following El Ghoul et al.

(2011), Cheng et al. (2012), and Kim et al. (2014). Firms with a high ESG score also

seem to be more sensitive to positive forecast errors. However, this difference is not

statistically significant. Following the explanation by Benson & Humphrey (2008), the

higher sensitivity to negative earnings forecast errors can be explained by the fact that

there is no difference in searching costs or investment horizons for investors that invest

in firms with high ESG scores. The higher sensitivity to positive earnings forecast errors

can be explained by a multi-attribute utility function, which causes investors to be

willing to pay more for firms with a high ESG score (Bollen, 2007). An alternative

explanation is that firms with a high ESG score have a lower cost of capital (Heinkel et

al., 2001), which causes larger sensitivity to changes in earnings. Additionally, the

findings show larger sensitivity of the cumulative abnormal returns to positive

compared to negative earnings forecast errors, which is consistent with the findings of

Lopez and Rees (2002).

57

Third, using the same instrumental variable methodology of Adams et al. (2009), the

empirical findings of this thesis show that the volatility in abnormal returns is lower as

a result of a higher ESG score. This result is equal for all ESG constituents and

consistent with the hypothesis that less information arrives at the market for firms with

a high ESG score, causing less volatility in abnormal returns (Chambers & Penman,

1984). This finding is consistent with earlier research that finds lower risks for firms

with a high ESG score (Heinkel et al., 2001; Mânescu, 2011; Kim et al., 2014; Möller

et al., 2015). However, after controlling for endogeneity, this effect loses its statistical

significance. Combined with the empirical findings from the previous model (4.3), the

instrumental variable methodology implies that the industry average governance score

is not a valid instrumental variable for the governance score of an individual firm.

Overall, the empirical findings show that a high ESG score is related to larger (positive)

forecast errors. The empirical findings also show a larger sensitivity of the CAR to

forecast errors for firms with a higher ESG score. However, the data shows that firms

with a high ESG score have lower forecast errors and a lower CAR. This implies that a

high ESG score is in itself merely an indicator of a type of firm with low forecast errors

and low CARs around earnings announcements. Furthermore, the empirical findings

show weak evidence that a high ESG score also causes a firm to have lower volatility

in abnormal returns around earnings announcements. All in all, when looking at a

specific set of firms, investing in firms with a high ESG score is beneficial for both the

abnormal returns and investment risk around earnings announcements.

6.2. LimitationsNext to the limitations mentioned in section 6.1, which are related to each specific

model, there are some limitations that concern all models in this thesis. First, there is a

possible sample bias, as this thesis only includes firms in the United States with ESG

data in the Thomson Reuters Asset4 Database and of which further data is available in

at least three other databases. As a result, this thesis only includes firms with large data

availability and, hence, larger transparency. Second, this thesis does not control for

autocorrelation or other time-series related biases in the estimators. As Lopez and Rees

(2002) show, some firms consistently outperform analysts’ expectations, which results

in a different behavior of the abnormal returns around earnings announcements. Finally,

the instrumental variable approach in the second and third model assumes that, except

58

for the ESG dummy, all other variables are exogenous, which may not be the case.

Furthermore, the instrumental variable estimators are only as good as the instrumental

variables that are used (Adams et al., 2009). Since only one instrumental variable is

used, no tests are conducted to test the validity of the industry average scores as

instrumental variables (Verbeek, 2008). Therefore, the validity of the industry averages

as instrumental variables only relies on qualitative identification.

6.3. RecommendationsPractically, this thesis poses implications for investors and firm managers. First, the

overall ESG score can be used as a practical indicator of firms with lower forecast errors

and lower risk around earnings announcements. Additionally, for active investors, this

thesis shows that it is beneficial to look at the ESG score to identify firms within certain

firm groups with more positive forecast errors, higher sensitivity to these forecast

errors, and lower risks. Furthermore, the comparison with other studies implies that it

is important for both investors and firm managers to identify more specific and material

ESG measures, as they may be more informative than overall ESG scores. Finally, firm

managers have to take into account that their environmental and social activities

influence (increase) the sensitivity of their firm’s share price to earnings information.

This thesis lays out possible fields for further research. First, this thesis shows that it is

important to find more instrumental variables for studies that investigate the effects of

ESG factors on forecast accuracy and stock price behavior. This will make it possible

to test the validity of the instrumental variables. Second, it is interesting to see whether

ESG scores also have an effect on trading volumes around earnings announcements to

directly measure investor activity. This will give more insights in the reaction of the

stock market to information announced by firms with high ESG scores. Third, this

thesis can be replicated by researchers who have access to more sophisticated

shareholder data, as it is an important influence on the findings in this thesis. Fourth,

this thesis shows that ESG scores have an effect on event related stock returns.

Therefore, it is interesting to include ESG scores in replications of other event studies

like merger and acquisition announcements. Finally, the endogenous character of the

ESG score in this thesis shows that it is important to replicate studies that are subject to

the same endogeneity problems. An example for this is the study by Bollen (2007),

whose findings and model formed the inspiration for this thesis.

59

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I

APPENDICESAPPENDIXA-DATATable 1. Variable Definitions. This table shows descriptions of the variables used in this thesis

Variable Description Dependent variables |FE| The absolute analysts’ forecast error computed as the absolute difference between the consensus

of the last earnings forecast and actual earnings per share scaled by the share price at the time of the forecast. The consensus forecast is the median value of the forecasts

PFE The value of |FE| in case of a positive forecast error (EPS>Forecast) NFE The value of |FE| in case of a negative forecast error (EPS<Forecast)

CAR[-1,1] The cumulative abnormal return, which is the sum of the abnormal returns from one day before to one day after the earnings announcement

@(AR)[-5,10] The volatility in the abnormal returns from five days before to ten days after the earnings announcement, expressed in percentages

Independent variables

FE The real value of |FE| ESG A dummy variable taking the value of one when a firm has an ESG score above the median and

zero otherwise E A dummy variable taking the value of one when a firm has an environmental score above the

median and zero otherwise S A dummy variable taking the value of one when a firm has a social score above the median and

zero otherwise G A dummy variable taking the value of one when a firm has a governance score above the median

and zero otherwise EP The earnings-to-price ratio computed as the earnings per share during the previous calendar

year divided by the share price at the time of the earnings forecast |∆EPS| The absolute change in earnings per share compared to the previous quarter scaled by the share

price at the time of the earnings forecast TIMING Announcement timing measured by the difference in the number of days between the date of

the earnings announcement and the date of the same announcement a year earlier, where a negative value means that the announcement is early

(ln_)MB (The logarithm of) the market-to-book ratio calculated as the market value of equity plus the difference between total assets and the book value of equity, divided by total assets

(ln_)SIZE (The logarithm of) the size of the firm measured by its market value in millions of US dollars |EM| Earnings management measured by the absolute value of the discretionary accruals as a

percentage of total assets EM The real value of |EM|

DISP Dispersion in analysts’ forecasts measured by the standard deviation of the earnings forecasts that are part of the consensus forecast, scaled by the share price at the time of the most recent forecast

VOL Stock volatility measured by the movement of a firm’s share price to a high and low from a mean price during the previous fiscal year, expressed in percentages

LEVG Leverage calculated by dividing the amount of long-term debt and the amount of short-term debt in current liabilities by total assets, expressed in percentages and maximized at the value of one when the outcome is larger than one

EST The number of earnings forecast estimates made by analysts following the firm ANN A dummy variable taking the value of one when the earnings announcement is an annual

announcement and zero otherwise DL A dummy variable taking the value of one when the firm reported a loss and zero otherwise RET1 The market-adjusted buy-and-hold return of a firm’s stock during the most recent half of the

estimation window (days -110 to -10), calculated as (1 + 2*,,)H:N,>H::N − (1 + 26,,)

H:N,>H::N

for each firm i, expressed in percentages and Winsorized at the first percentile RET2 The market-adjusted buy-and-hold return of a firm’s stock during the first half of the estimation

window (days -210 to -110), calculated as (1 + 2*,,)H::N,>H;:N − (1 + 26,,)

H::N,>H;:N for each

firm i, expressed in percentages and Winsorized at the first percentile INST The percentage of a firm’s shares that are owned by institutional investors. Maximized at the

value of one when the value is larger than one INDESG The industry average ESG score INDE The industry average Environmental score INDS The industry average Social score INDG The industry average Corporate Governance score

II

Table 2. Descriptive Statistics. This table shows descriptive statistics of all the variables in this thesis. The variables are as described in Table 1Variable N Mean St. Dev. Min Max 1p 5p 25p 50p 75p 95p 99p Dependent variables |FE| 19,910 0.0030 0.0117 0 0.5928 0 0 0.0004 0.0011 0.0026 0.0095 0.0303

PFE 12,382 0.0026 0.0072 0.0000 0.2513 0.0001 0.0002 0.0005 0.0012 0.0026 0.0084 0.0227 NFE 5,751 0.0047 0.0190 0.0000 0.5928 0.0001 0.0002 0.0005 0.0013 0.0035 0.0156 0.0550

CAR[-1,1] 19,910 0.0026 0.0620 -0.8574 0.6153 -0.1715 -0.0917 -0.0271 0.0015 0.0318 0.1000 0.1794 !(AR)[-5,10] 19,910 0.0181 0.0125 0.0026 0.2051 0.0049 0.0065 0.0102 0.0147 0.0221 0.0413 0.0636 Independent variables

FE 19,910 0.0003 0.0121 -0.5928 0.2513 -0.0198 -0.0049 -0.0003 0.0004 0.0016 0.0061 0.0165 ESG 19,910 0.5472 0.4978 0 1 0 0 0 1 1 1 1

ESG Score 19,910 53.76 30.10 3.57 98.31 6.57 10.19 25.92 51.81 85.09 95.45 97.07 E 19,910 0.5286 0.4992 0 1 0 0 0 1 1 1 1

E Score 19,910 43.63 32.23 8.59 97.47 8.97 10.00 12.93 30.29 79.12 93.63 95.99 S 19,910 0.5382 0.4985 0 1 0 0 0 1 1 1 1

S Score 19,910 46.78 29.70 3.76 98.93 6.46 8.18 18.67 42.86 74.73 93.42 96.64 G 19,910 0.5311 0.4990 0 1 0 0 0 1 1 1 1

G Score 19,910 71.45 18.41 3.06 97.44 14.95 33.87 60.92 75.54 85.39 94.06 96.10 EP 18,883 0.0543 0.0421 -0.3376 0.2017 -0.0940 -0.0058 0.0385 0.0551 0.0727 0.1152 0.1625 |∆EPS| 19,685 0.0062 0.0200 0 0.8123 0 0.0002 0.0009 0.0024 0.0059 0.0213 0.0575 TIMING 18,906 0.1187 6.6801 -87 56 -22 -8 -1 -1 2 8 19 MB 17,591 2.1161 1.5924 0.5427 29.5151 0.8746 0.9749 1.1895 1.6194 2.3972 4.9758 8.4595

ln_MB 17,591 0.5867 0.5210 -0.6112 3.3849 -0.1340 -0.0254 0.1735 0.4821 0.8743 1.6046 2.1352 SIZE 17,849 18,863.09 41,615.31 91.15 717,000.30 733.03 1,234.73 2,989.09 6,267.04 15,980.82 77,875.44 209,346.90

ln_SIZE 17,849 8.9038 1.2544 4.5126 13.4828 6.5972 7.1186 8.0027 8.7431 9.6791 11.2629 12.2518 |EM| 19,341 0.0552 0.0364 0.0000 0.2477 0.0010 0.0054 0.0260 0.0533 0.0773 0.1174 0.1694

EM 19,341 0.0316 0.0581 -0.1841 0.2477 -0.1262 -0.0704 -0.0081 0.0408 0.0743 0.1079 0.1642 DISP 19,910 0.0015 0.0036 0 0.1839 0 0.0001 0.0004 0.0007 0.0015 0.0046 0.0116 VOL 17,772 0.2624 0.0896 0.1041 0.6848 0.1231 0.1445 0.1967 0.2458 0.3118 0.4393 0.5268 LEVG 19,828 0.2506 0.1848 0 1 0 0 0.1134 0.2289 0.3555 0.5833 0.8306 EST 19,910 13.81 7.37 3 50 3 4 8 13 18 28 34 ANN 19,910 0.2407 0.4275 0 1 0 0 0 0 0 1 1 DL 19,910 0.0643 0.2453 0 1 0 0 0 0 0 1 1 RET1 19,910 0.0137 0.1660 -0.4037 0.6053 -0.4037 -0.2434 -0.0845 0.0049 0.0989 0.2967 0.6053 RET2 19,910 0.0223 0.1761 -0.3948 0.6733 -0.3948 -0.2416 -0.0829 0.0101 0.1082 0.3277 0.6733 INST 16,443 0.7250 0.1690 0 1 0.1247 0.4273 0.6394 0.7441 0.8387 0.9752 1 INDESG 19,910 51.97 14.33 11.70 86.21 25.16 30.94 39.31 49.43 63.03 74.13 86.21 INDE 19,910 42.56 16.90 11.16 82.73 13.50 21.73 26.80 39.69 57.15 70.28 82.12 INDS 19,910 45.25 14.03 8.53 83.98 16.79 24.80 35.53 43.86 54.97 65.89 80.82 INDG 19,910 70.17 6.98 42.71 85.12 52.83 58.62 65.63 71.10 74.52 79.77 83.47

III

Figure 1. Forecast Errors. This figure shows the average absolute forecast error (|FE|) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are shown for the full sample and for positive and negative earnings forecasts, separately. The data of this figure is presented in Table 3. The forecast error is shown on the y-axis in percentages and the ESG quintiles are shown on the x-axis

IV

Table 3. Forecast Errors. This table shows the average absolute forecast errors (|FE|) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are obtained by regressing the respective group of forecast errors (|FE|, PFE, or NFE) on the ESG quintile dummies, without a constant. The t-statistics of the means are shown in the third column. The last column shows the number of observations. The data in this table is displayed graphically in Figure 1

Dependent variable: FE ESG Group Coefficient (mean) t-statistic*** N All Forecast Errors (FE) Quintile 1 0.0039 19.78 3,512 Quintile 2 0.0037 18.77 3,582 Quintile 3 0.0028 15.26 4,000 Quintile 4 0.0028 14.81 3,971 Quintile 5 0.0021 12.60 4,845 Positive Forecast Error (PFE) Quintile 1 0.0031 19.38 1,959 Quintile 2 0.0030 19.45 2,091 Quintile 3 0.0024 16.69 2,471 Quintile 4 0.0026 18.86 2,613 Quintile 5 0.0022 17.40 3,248 Negative Forecast Error (NFE) Quintile 1 0.0061 11.39 1,238 Quintile 2 0.0059 10.53 1,162 Quintile 3 0.0047 8.36 1,151 Quintile 4 0.0039 6.65 1,025 Quintile 5 0.0027 4.90 1,175

*** All results are statistically significant at the 0.01 level

V

Figure 2. Cumulative Average Abnormal Returns. This figure shows the cumulative average abnormal returns (CAAR) for the sample from five days before [-5] to ten days after [10] the announcement date for all firms and all, positive and negative forecast errors. The y-axis denotes the ACAR in percentages and the x-axis denotes the respective day relative to the earnings announcement date

VI

Figure 3. Average Cumulative Abnormal Returns. This figure shows the average cumulative abnormal return (ACAR[-1,1]) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are shown for the full sample and for positive and negative earnings forecasts, separately. The data of this figure is presented in Table 4. The ACAR is shown on the y-axis in percentages and the ESG quintiles are shown on the x-axis

VII

Table 4. Average Cumulative Abnormal Returns. This table shows the average cumulative abnormal return (ACAR[-1,1]) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are obtained by regressing CAR[-1,1] on the ESG quintile dummies, without a constant, for each respective group of forecast errors (FE, PFE, or NFE). The t-statistics of the means are shown in the third column. The last column shows the number of observations. The data in this table is displayed graphically in Figure 3

Dependent variable: CAR[-1,1] ESG Group Coefficient (mean) t-statistic N All Forecast Errors (FE) Quintile 1 0.0061 5.79*** 3,512 Quintile 2 0.0023 2.21** 3,582 Quintile 3 0.0024 2.41** 4,000 Quintile 4 0.0019 1.91* 3,971 Quintile 5 0.0012 1.30 4,845 Positive Forecast Error (PFE) Quintile 1 0.0203 15.19*** 1,959 Quintile 2 0.0158 12.17*** 2,091 Quintile 3 0.0133 11.21*** 2,471 Quintile 4 0.0117 10.07*** 2,613 Quintile 5 0.0089 8.54*** 3,248 Negative Forecast Error (NFE) Quintile 1 -0.0143 -7.93*** 1,238 Quintile 2 -0.0175 -9.38*** 1,162 Quintile 3 -0.0184 -9.79*** 1,151 Quintile 4 -0.0184 -9.24*** 1,025 Quintile 5 -0.0169 -9.09*** 1,175

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

VIII

Figure 4. Volatility in Abnormal Returns. This figure shows the average volatility in abnormal returns (! AR [−5,10]) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are shown for the full sample and for positive and negative earnings forecasts, separately. The data of this figure is presented in Table 5. The volatility is shown on the y-axis in percentages and the ESG quintiles are shown on the x-axis

IX

Table 5. Volatility in Abnormal Returns. This table shows the average volatility in abnormal returns (! AR [−5,10]) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database. The averages are obtained by regressing!(AR)[−5,10] on the ESG quintile dummies, without a constant, for each respective group of forecast errors (FE, PFE, or NFE). The t-statistics of the means are shown in the third column. The last column shows the number of observations. The data in this table is displayed graphically in Figure 4

Dependent variable: AR(.)[-5,10] ESG Group Coefficient (mean) t-statistic*** N All Forecast Errors (FE) Quintile 1 0.0218 105.76 3,512 Quintile 2 0.0198 96.79 3,582 Quintile 3 0.0183 94.64 4,000 Quintile 4 0.0172 88.71 3,971 Quintile 5 0.0148 83.97 4,845 Positive Forecast Error (PFE) Quintile 1 0.0219 85.40 1,959 Quintile 2 0.0193 77.62 2,091 Quintile 3 0.0178 77.95 2,471 Quintile 4 0.0167 75.20 2,613 Quintile 5 0.0142 71.01 3,248 Negative Forecast Error (NFE) Quintile 1 0.0224 55.49 1,238 Quintile 2 0.0209 50.16 1,162 Quintile 3 0.0199 47.57 1,151 Quintile 4 0.0188 42.44 1,025 Quintile 5 0.0169 40.72 1,175

*** All results are statistically significant at the 0.01 level

X

Figure 5. Volatility in Abnormal Returns. This figure shows the average volatility in abnormal returns (! AR [−5,10]) calculated over five ESG quintiles, where quintile 1 denotes the group with the lowest 20% ESG scores, and quintile 5 denotes the group with the highest 20% ESG scores in the Thomson Reuters Asset4 US Database, and five forecast error (FE) quintiles, where quintile 1 denotes the group with the lowest 20% forecast errors, and quintile 5 denotes the group with the highest 20% forecast errors of all observations that are available for the entire Thomson Reuters Asset4 US Database. The volatility is shown on the y-axis in percentages and the FE quintiles are shown on the x-axis. Each line denotes a different ESG quintile

XI

Table 6. T-test by ESG score. This table shows the sample means by high and low ESG firms. The second and third column show the mean of each variable. The last column contains the difference in the mean values

Variable Mean Difference*** High ESG Low ESG

Dependent variables |FE| 0.0025 0.0036 -0.0012

PFE 0.0024 0.0030 -0.0006 NFE 0.0035 0.0058 -0.0022

CAR[-1,1] 0.0016 0.0038 -0.0022 !(AR)[-5,10] 0.0162 0.0204 -0.0042

Independent variables FE 0.0007 -0.0002 0.0008

EP 0.0612 0.0458 0.0154 |∆EPS| 0.0057 0.0069 -0.0013 TIMING 0.3889 -0.2050 0.5939 MB 1.9802 2.2758 -0.2956

ln_MB 0.5677 0.6089 -0.0412 SIZE 29,312.72 6,550.28 22,762.44

ln_SIZE 9.4469 8.2640 1.1829 |EM| 0.0507 0.0608 -0.0101

EM 0.0284 0.0355 -0.0072 DISP 0.0014 0.0016 -0.0003 VOL 0.2474 0.2829 -0.0355 LEVG 0.2427 0.2601 -0.0173 EST 15.23 12.10 3.12 RET1 0.0091 0.0194 -0.0103 RET2 0.0181 0.0274 -0.0093 INST 0.7137 0.7412 -0.0274 INDESG 57.54 45.25 12.29 INDE 48.90 34.89 14.02 INDS 50.55 38.84 11.70 INDG 72.51 67.34 5.17

*** All results are statistically significant at the 0.01 level

XII

APPENDIXB–METHODOLOGY

Interpretation of ordinary least squares inconsistency (Adams et al., 2009)

Only when the assumption holds that all the other variables in the model are exogenous,

the inconsistency in the ordinary least squares estimator can be interpreted by

comparing the ordinary least squares estimator to the instrumental variable estimator.

It is important to note that if some of the other variables in the model are endogenous,

it is not possible to sign the ordinary least squares inconsistency. For simplicity it is

assumed that all coefficients but 01234 are equal to zero. In that case:

01234 = 01 +789(:;<=,>, ?=,>)@AB(:;<=,>)

Where 01234 is the ordinary least squares estimator in model 4.3 and/or 4.8, and 01

denotes the true relationship between the ESG dummy and the CAR and/or volatility in

abnormal returns. The direction of the inconsistency in 01234 is determined by the sign

of 789(:;<=,>, ?=,>).

Example: Interpretation of the inconsistency for 01234 in model 4.3

For example, if firms with higher CARs invest more in ESG activities (reversed

causality), then 789 :;<=,>, ?=,> > 0. This results in an upward bias in 01234, which

then overestimates the true 01. When long-term ownership is positively correlated with

the ESG score, and these investors cause CARs to be lower around earnings

announcements (omitted variable bias), then 789 :;<=,>, ?=,> < 0. In turn, this results

in a downward bias in 01234, which then underestimates the true 01.

(B.1)

XIII

APPENDIXC–RESULTS–PAIRWISECORRELATIONSTable 7. Pairwise Correlations. This table shows the pairwise correlation matrix for all variables used in the regression analyses. The grey shades denote correlations that have an absolute value that is larger than 0.3

Variable |FE| PFEt-1 NFEt-1 CAR[-1,1] !(AR)[-5,10] FE ESG E S G EP |∆EPS| TIMING ln_MB ln_SIZEt-1

|FE| 1.0000 PFEt-1 0.1808 1.0000 NFEt-1 0.3411 -0.0196 1.0000

CAR[-1,1] -0.0127 -0.0060 0.0173 1.0000 !(AR)[-5,10] 0.2053 0.0522 0.1329 0.0339 1.0000

FE -0.5288 -0.0729 -0.0510 0.1145 -0.0887 1.0000 ESG -0.0501 -0.0105 -0.0484 -0.0174 -0.1666 0.0344 1.0000 E -0.0244 -0.0004 -0.0302 -0.0219 -0.1328 0.0304 0.7104 1.0000 S -0.0368 -0.0075 -0.0382 -0.0215 -0.1400 0.0301 0.7813 0.6184 1.0000 G -0.0191 -0.0137 -0.0257 -0.0210 -0.1023 0.0035 0.5458 0.4330 0.4648 1.0000 EP -0.1447 -0.0625 -0.1549 0.0326 -0.1457 0.0657 0.1826 0.1580 0.1766 0.1374 1.0000 |∆EPS| 0.6529 0.4315 0.5765 0.0025 0.1709 -0.2006 -0.0316 0.0000 -0.0206 -0.0051 -0.1320 1.0000 TIMING 0.0061 -0.0007 0.0045 -0.0236 -0.0441 -0.0251 0.0443 0.0281 0.0369 0.0454 0.0165 0.0014 1.0000 ln_MB -0.1318 -0.0700 -0.0912 0.0162 0.1188 0.0203 -0.0394 -0.0509 -0.0309 -0.0617 -0.2478 -0.1387 -0.0364 1.0000 ln_SIZEt-1 -0.1277 -0.0659 -0.0995 -0.0335 -0.2477 0.0260 0.4734 0.4349 0.4584 0.3467 0.1364 -0.1358 0.0203 0.2039 1.0000 |EM| 0.0205 0.0125 0.0139 -0.0085 0.0441 -0.0021 -0.1386 -0.1533 -0.1472 -0.1156 -0.0451 0.0121 -0.0086 0.0110 -0.0404

EM -0.0137 0.0000 0.0020 -0.0282 -0.0566 -0.0007 -0.0614 -0.0731 -0.0528 -0.0881 0.0157 -0.0273 -0.0008 -0.0305 0.0755 DISP 0.6042 0.2025 0.3685 0.0026 0.2545 -0.2686 -0.0356 -0.0038 -0.0228 -0.0054 -0.1431 0.5499 0.0030 -0.1896 -0.1614 VOL 0.1603 0.0936 0.1012 0.0165 0.4635 -0.0121 -0.1955 -0.1561 -0.1800 -0.0934 -0.2363 0.1511 -0.0212 0.0748 -0.3294 LEVG 0.0578 0.0316 0.0364 -0.0223 0.0102 -0.0088 -0.0466 -0.0116 -0.0336 0.0137 -0.0889 0.0701 0.0051 -0.1213 -0.0389 EST -0.0626 -0.0351 -0.0478 -0.0096 0.0038 0.0139 0.2109 0.1542 0.2339 0.1302 0.0128 -0.0852 0.0077 0.1801 0.5179 ANN 0.0214 -0.0008 -0.0112 0.0202 0.0092 -0.0135 0.0093 0.0090 0.0100 0.0104 0.0130 0.0179 -0.0063 -0.0153 0.0188 DL 0.2536 0.0751 0.1435 -0.0429 0.2380 -0.2385 -0.1520 -0.1057 -0.1273 -0.0849 -0.4026 0.2095 -0.0143 0.0018 -0.1907 RET1 -0.0511 0.0855 -0.0475 -0.0462 -0.0678 0.0635 -0.0309 -0.0284 -0.0379 -0.0152 -0.1107 -0.0293 -0.0122 0.0375 -0.0521 RET2 -0.0437 -0.0053 -0.0689 -0.0191 0.0197 0.0356 -0.0263 -0.0304 -0.0352 -0.0212 -0.1251 -0.0576 -0.0409 0.1857 0.0503 INST 0.0101 0.0043 -0.0009 0.0235 0.1165 0.0047 -0.0799 -0.0813 -0.0622 0.0104 0.0045 0.0009 -0.0125 0.1207 -0.1972 INDESG -0.0501 -0.0178 -0.0430 -0.0095 -0.0847 0.0267 0.4269 0.4578 0.4005 0.3046 0.1532 -0.0271 0.0087 0.0315 0.2430 INDE -0.0432 -0.0139 -0.0398 -0.0119 -0.0670 0.0295 0.4129 0.4812 0.3704 0.2823 0.1149 -0.0175 0.0070 0.0483 0.2316 INDS -0.0607 -0.0235 -0.0486 -0.0064 -0.0735 0.0275 0.4153 0.4341 0.4105 0.2948 0.1389 -0.0370 0.0064 0.0919 0.2685 INDG -0.0246 -0.0100 -0.0303 -0.0142 -0.0814 0.0273 0.3686 0.3976 0.3474 0.3400 0.1370 -0.0048 0.0095 -0.0264 0.1814

(continues)

XIV

Table 7. Pairwise Correlations (continued). Variable |EM| EM DISP VOL LEVG EST ANN DL RET1 RET2 INST INDESG INDE INDS INDG |EM| 1.0000

EM 0.4470 1.0000 DISP 0.2265 -0.0096 1.0000 VOL 0.2569 0.0283 0.2313 1.0000 LEVG 0.0692 -0.0306 0.1121 -0.0068 1.0000 EST -0.0384 0.0050 -0.0665 0.1222 -0.1890 1.0000 ANN -0.0244 -0.0784 0.0246 -0.0020 0.0008 0.0458 1.0000 DL 0.1563 -0.0216 0.3159 0.2711 0.1169 -0.0625 0.0071 1.0000 RET1 0.0273 0.0018 -0.0792 0.0611 -0.0252 -0.0482 -0.0274 -0.0234 1.0000 RET2 0.0254 0.0226 -0.0847 0.0965 -0.0373 -0.0208 -0.0227 -0.0435 0.0340 1.0000 INST -0.0618 -0.0623 0.0159 0.2497 -0.0073 0.0126 0.0054 0.0288 0.0234 0.0382 1.0000 INDESG -0.1665 -0.1366 -0.0440 -0.1543 -0.0467 -0.0127 0.0154 -0.1146 -0.0128 -0.0125 -0.0809 1.0000 INDE -0.1527 -0.1297 -0.0335 -0.1264 0.0202 -0.0456 0.0148 -0.0913 -0.0079 -0.0079 -0.1037 0.9581 1.0000 INDS -0.1596 -0.1307 -0.0585 -0.1451 -0.0449 0.0167 0.0148 -0.1065 -0.0103 -0.0062 -0.0700 0.9705 0.9161 1.0000 INDG -0.1633 -0.1830 -0.0036 -0.1380 -0.0063 -0.0193 0.0149 -0.0822 -0.0184 -0.0252 0.0101 0.8554 0.7778 0.8034 1.0000

XV

APPENDIXC.1–RESULTS–ANALYSTS’FORECASTACCURACYTable 8. OLS Regressions. This table shows estimates of ordinary least squares regressions. The dependent variable is the absolute forecast error, expressed in percentages. The regressions in columns 4 and 5 include firm and year fixed effects. The regression in column 5 is run using robust standard errors. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: |!"|$,& Regression Model

Ind. Variable (1) (2) (3) (4) (5) '()*,+ -0.0012***

(-7.07) -0.0005***

(-3.51) -0.0005***

(-3.22) 0.0002

(0.72) 0.0002

(0.87) ,-'*,+./ 0.0756***

(12.89) -0.1690***

(-26.01) -0.2142***

(-32.74) -0.2142***

(-3.41) 0-'*,+./ 0.1729***

(23.16) -0.2084***

(-23.41) -0.2751***

(-30.87) -0.2751**

(-1.99) 12(,*,+ 1.7521***

(85.42) 1.2129***

(55.49) 1.073***

(43.71) 1.073***

(4.53) '(3*,+ -0.0000**

(-2.28) 0.0000

(0.16) -0.0001***

(-3.77) -0.0001***

(-2.98) 32420)*,+ 0.0000

(0.88) 0.0000

(1.18) 0.0000

(1.48) 0.0000

(1.45) 500*,+ 0.0003**

(2.01) 0.0000

(0.02) 0.0000

(0.20) 0.0000

(0.20) |'4|*,+ 0.0017

(0.85) -0.0050**

(-2.02) -0.0050*

(-1.82) |∆',(|*,+ 0.3664***

(70.21) 0.3805***

(71.65) 0.3805***

(4.80) ln (2:'*,+./ 0.0000

(0.08) -0.0004

(-1.62) -0.0004

(-0.63) ;'<)*,+ -0.0016***

(-3.95) -0.0007

(-0.68) -0.0007

(-0.50) ',*,+ -0.0124***

(-6.58) -0.0248***

(-10.21) -0.0248**

(-2.55) 1;*,+ 0.0018***

(5.27) 0.0037***

(9.24) 0.0037***

(3.17) ='31*,+ -0.0005

(-1.06) -0.0008*

(-1.75) -0.0008*

(-1.04) ='32*,+ 0.0007

(1.72) 0.0003

(0.60) 0.0003

(0.28) <@;*,+ -0.0008

(-0.82) -0.0031

(-1.24) -0.0031

(-0.74) Constant 0.0036***

(29.41) 0.0005***

(3.12) 0.0008

(1.02) 0.0057

(1.09) 0.0057

(1.19) Firm FE No No No Yes Yes Year FE No No No Yes Yes Robust SE No No No No Yes Adj. R2 0.0025 0.3904 0.5517 0.5795 0.5795 N 19,910 18,860 15,275 15,275 15,275

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XVI

Table 9. OLS Regressions. This table shows estimates of ordinary least squares regressions. The dependent variable is the absolute forecast error, expressed in percentages. This table is a replication of column 5 in Table 8, using the ESG constituents E, S, and G in place of the ESG dummy. All regressions include firm and year fixed effects. All regressions are run using robust standard errors. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: |!"|$,& Regression Model

Ind. Variable (1) (2) (3) '*,+ 0.0001

(0.62) (*,+ 0.0005**

(2.10) )*,+ 0.0002

(0.10) ,-'*,+./ -0.2141***

(-3.41) -0.2141***

(-3.42) -0.2141***

(-3.41) 0-'*,+./ -0.2751**

(-1.99) -0.2751**

(-1.99) -0.2751**

(-1.99) 12(,*,+ 1.1077***

(4.53) 1.1069***

(4.53) 1.1079***

(4.54) '(3*,+ -0.0001***

(-2.99) -0.0001***

(-2.94) -0.0001***

(-2.99) 32420)*,+ 0.0000

(1.46) 0.0000

(1.45) 0.0000

(1.45) 500*,+ 0.0000

(0.20) 0.0000

(0.19) 0.0000

(0.19) |'4|*,+ -0.0049*

(-1.81) -0.0050*

(-1.82) -0.0049*

(-1.81) |∆',(|*,+ 0.3805***

(4.80) 0.3805***

(4.80) 0.3804***

(4.80) ln (2:'*,+./ -0.0004

(-0.62) -0.0004

(-0.65) -0.0004

(-0.62) ;'<)*,+ -0.0007

(-0.50) -0.0008

(-0.54) -0.0007

(-0.50) ',*,+ -0.0247**

(-2.55) -0.0247**

(-2.55) -0.0247**

(-2.55) 1;*,+ 0.0037***

(3.16) 0.0037***

(3.17) 0.0037***

(3.17) ='31*,+ -0.0008

(-1.04) -0.0008

(-1.02) -0.0008

(-1.04) ='32*,+ 0.0003

(0.29) 0.0003

(0.29) 0.0003

(0.29) <@;*,+ -0.0031

(-0.75) -0.0030

(-0.70) -0.0032

(-0.75) Constant 0.0056

(1.18) 0.0059

(1.23) 0.0055

(1.17) Firm FE Yes Yes Yes Year FE Yes Yes Yes Robust SE Yes Yes Yes Adj. R2 0.5795 0.5795 0.5795 N 15,275 15,275 15,275

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XVII

Table 10. OLS Regressions. This table shows estimates of ordinary least squares regressions. The dependent variables are the absolute positive and negative forecast error, expressed in percentages. This table is a replication of column 5 in Table 8. All regressions include firm and year fixed effects. All regressions are run using robust standard errors. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dep. Variable A!"$,& B!"$,&Regression Model

Ind. Variable (1) (2) '()*,+ 0.0003*

(1.73) 0.0000

(0.04) ,-'*,+./ -0.0170

(-0.43) -0.4006***

(-5.08) 0-'*,+./ -0.3804***

(-4.47) 0.2676

(1.32) 12(,*,+ 0.4340**

(2.46) 0.9478***

(4.71) '(3*,+ -0.0000

(-0.74) -0.0002***

(-2.70) 32420)*,+ 0.0000

(0.66) -0.0000

(-0.07) 500*,+ 0.0001

(0.70) -0.0000

(-0.06) |'4|*,+ 0.0005

(0.35) -0.0129**

(-2.18) |∆',(|*,+ 0.3902***

(6.75) 0.5688***

(6.86) ln (2:'*,+./ -0.0014***

(-3.85) 0.0025**

(2.38) ;'<)*,+ 0.0009

(0.67) -0.0058**

(-2.03) ',*,+ -0.0122**

(-2.48) -0.0406***

(-3.01) 1;*,+ -0.0029***

(-3.08) 0.0017

(1.18) ='31*,+ -0.0007

(-1.13) -0.0008

(-0.65) ='32*,+ 0.0003

(0.61) 0.0018

(0.95) <@;*,+ 0.0019

(0.69) -0.0006

(-0.07) Constant 0.0108***

(3.80) -0.0157*

(-1.94) Firm FE Yes Yes Year FE Yes Yes Robust SE Yes Yes Adj. R2 0.6341 0.7121 N 10,798 5,792

XVIII

APPENDIXC.2–RESULTS–CUMULATIVEABNORMALRETURNSTable 11. Regressions. This table shows estimates of ordinary least squares and instrumental variables regressions. The dependent variable is the cumulative abnormal return, expressed in percentages. The regressions in columns 3 and 4 include firm and year fixed effects. The regression in column 4 is run using robust standard errors. The regression in column 5 shows the results of an instrumental variable regression. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: CDE$,&[−H, H]

Ind. Variable Regression Model

(1) OLS (2) OLS (3) OLS (4) OLS (5) IV '()*,+ -0.0026***

(-2.86) -0.0015

(-1.23) 0.0019

(0.97) 0.0019

(0.87) 0.0360

(1.11) '()*,+×0*,+×-'*,+

0.6998*** # # #

(7.50) 0.6437*** # # #

(6.71) 0.6629*** # # #

(6.52) 0.6629*** #

(3.35) 0.7803*** # #

(3.54)

2. '()*,+×0*,+×-'*,+

0.3511*** # # # (7.49)

0.2943*** # # # (6.05)

0.2872*** # # #

(5.53) 0.2872*** #

(3.33) 0.2641*** # #

(2.97)

'()*,+×,*,+×-'*,+

1.3482*** # #

(10.89) 1.1656*** #

(8.89) 1.1511*** # #

(8.24) 1.1511***

(2.99) 1.1447***

(2.89)

2. '()*,+×,*,+×-'*,+

0.9712*** # # (10.23)

0.8444*** # (8.22)

0.7726*** # #

(6.90) 0.7726***

(2.75) 0.7476***

(2.63)

'4*,+ -0.0188** (-2.06)

-0.2762** (-2.06)

-0.2762* (-1.87)

-0.0279* (-1.89)

500*,+ 0.0032***(2.67)

0.0031*** (2.60)

0.0031*** (2.62)

0.0032*** (2.74)

ln (2:'*,+./ -0.0011** (-2.25)

-0.0200*** (-11.98)

-0.0200*** (-8.16)

-0.0218*** (-7.15)

20(3*,+ 0.0077** (2.45)

-0.0092 (-1.28)

-0.0092 (-1.13)

-0.0071 (-0.85)

Constant 0.0028*** (4.15)

0.0058 (1.15)

0. 2157***(5.88)

0. 2157***(5.03)

0.2414*** (4.84)

Firm FE No No Yes Yes Yes Year FE No No Yes Yes Yes Robust SE No No No Yes Yes 2nd stage F-statistic (IV)

84.90

Instrumental Variable

201'()*,+

Adj. R2 0.0173 0.0184 0.0444 0.0444 - N 19,910 14,234 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively # # #, # #, and # denote the statistical significance between the interaction variables at the 0.01, 0.05, and 0.10 level, respectively

XIX

Table 11.1. IV Procedure. This table shows estimates of an ordinary least squares regression and probit estimations to give insights in the process to include the instrumental variable in column 5 in Table 11. The regression in column 1 is an ordinary least squares regression with the cumulative abnormal return as the dependent variable. This regression is a replication of column 4 in Table 11 and includes the industry average ESG score as an additional independent variable. The control variables are not reported. The regressions in columns 2 and 3 are probit estimations, which estimate the ESG dummy and the inversed ESG dummy using the model in Table 11 with the industry average ESG score instead of the ESG and inversed ESG dummies. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in columns 2 and 3

Dep. Variable CDE$,&[−H, H] "LM$,& N. "LM$,&

Ind. Variable

Regression Model (1) OLS (2) Probit (3) Probit

201'()*,+ 0.0027 (0.98)

0.0439*** (43.36)

-0.0439*** (-43.36)

201'()*,+×0*,+×-'*,+ -0.0531**(-2.23)

0.0531**(2.23)

201'()*,+×,*,+×-'*,+ 0.3198*** (7.02)

-0.3198*** (-7.02)

'4*,+ -1.1001*** (-4.94)

1.1001*** (4.94)

500*,+ -0.0311(-1.07)

0.0311(1.07)

ln (2:'*,+./ 0.6173*** (47.16)

-0.6173*** (-47.16)

20(3*,+ 0.1213 (1.62)

-0.1213 (-1.62)

Constant 0.0555 (0.28)

-7.5435*** (-51.65)

7.5435*** (51.65)

Unreported Controls Yes No No Adj. R2 0.0444 - - Pseudo R2 - 0.3031 0.3031 N 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XX

Table 12. Regressions. This table shows estimates of ordinary least squares and instrumental variable regressions. The dependent variable is the cumulative abnormal return, expressed in percentages. This table is a replication of Table 11, using the ESG constituents E, S, and G. Columns 1, 3, and 5 show the results of ordinary least squares regressions. Columns 2, 4, and 6 show the results of instrumental variable regressions. All regressions in this table include firm and year fixed effects. All regressions in this table are run using robust standard errors. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: !"#$,&[−), )]

Ind. Variable Regression Model

(1) OLS (2) IV (3) OLS (4) IV (5) OLS (6) IV +,,- -0.0032

(-1.41) 0.1668***

(3.32) .,,- -0.0017

(-0.77) -0.0009

(-0.03) /,,- 0.0002

(0.11) 0.2547

(0.66) +,,-×1,,-×2+,,- 0.6062***

(3.49) 0.7159*** #

(3.87) 3. +,,-×1,,-×2+,,- 0.2886***

(3.22) 0.2850*** #

(2.83) +,,-×5,,-×2+,,- 1.2048***

(3.65) 1.1753***

(3.46) 3. +,,-×5,,-×2+,,- 0.6663**

(2.25) 0.6318**

(2.18) .,,-×1,,-×2+,,- 0.7601*** # #

(4.34) 0.8880*** # # #

(3.94) 3. .,,-×1,,-×2+,,- 0.2705*** # #

(3.34) 0.1848* # # #

(1.89) .,,-×5,,-×2+,,- 1.0085***

(3.04) 1.0174***

(2.86) 3. .,,-×5,,-×2+,,- 0.8394***

(2.65) 0.8281**

(2.23) /,,-×1,,-×2+,,- 0.4275***

(2.68) 0.5569*

(1.87) 3. /,,-×1,,-×2+,,- 0.2901***

(3.87) 0.3718*

(1.94) /,,-×5,,-×2+,,- 1.2080***

(3.37) 1.5350***

(2.63) 3. /,,-×5,,-×2+,,- 0.7267**

(2.50) 0.9912*

(1.82) (continues)

XXI

Table 12. Regressions (continued).

Ind. Variable

Regression Model (1) OLS (2) IV (3) OLS (4) IV (5) OLS (6) IV

+6,,- -0.0282* (-1.91)

-0.0223 (-1.51)

-0.0275* (-1.86)

-0.0276* (-1.76)

-0.0280* (-1.90)

-0.0308** (-2.04)

711,,- 0.0031*** (2.60)

0.0039*** (3.25)

0.0032*** (2.68)

0.0032*** (2.65)

0.0030** (2.53)

0.0030** (2.50)

ln .3;+,,-<= -0.0197*** (-8.06)

-0.0262*** (-7.88)

-0.0200*** (-8.15)

-0.0201*** (-7.93)

-0.0198*** (-8.14)

-0.0283** (-2.17)

31.>,,- -0.0090 (-1.10)

-0.0123 (-1.49)

-0.0093 (-1.13)

-0.0096 (-1.13)

-0.0091 (-1.12)

-0.0493 (-0.80)

Constant 0.2112*** (4.91)

0.3634*** (5.67)

0.2136*** (4.98)

0.2144*** (4.53)

0.2137*** (4.98)

0.3091** (2.04)

Firm FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Robust SE Yes Yes Yes Yes Yes Yes 2nd stage F-statistic (IV) 53.32 86.11 0.05 Instrumental Variable(s) 31?+,,- 31?.,,- 31?/,,- Adj. R2 0.0447 - 0.0446 - 0.0438 - N 14,234 14,234 14,234 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively # # #, # #, and # denote the statistical significance between the interaction variables at the 0.01, 0.05, and 0.10 level, respectively

XXII

Table 12.1 IV Procedure. This table shows estimates of an ordinary least squares regression and probit estimations to give insights in the process to include the instrumental variable in column 2 in Table 12. The regression in column 1 is an ordinary least squares regression with the cumulative abnormal return as the dependent variable. This regression is a replication of column 1 in Table 12 and includes the industry average environmental score as an additional independent variable. The control variables are not reported. The regressions in columns 2 and 3 are probit estimations, which estimate the environmental score dummy and the inversed environmental score dummy using the model in column 1 in Table 12 with the industry average environmental score instead of the environmental and inversed environmental dummy variables. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in columns 2 and 3

Dep. Variable !"#$,&[−), )] +$,& ,. +$,&

Ind. Variable

Regression Model (1) OLS (2) Probit (3) Probit

./012,3 0.0040 (1.10)

0.0424*** (48.38)

-0.0424*** (-48.38)

./012,3×/2,3×512,3 -0.1739***(-4.86)

0.1739***(4.86)

./012,3×62,3×512,3 0.6161*** (9.22)

-0.6161*** (-9.22)

172,3 -0.6672*** (-3.00)

0.6672*** (3.00)

8//2,3 -0.0376(-1.29)

0.0376(1.29)

ln <.=12,3>? 0.5717*** (44.52)

-0.5717*** (-44.52)

./<@2,3 0.0892 (1.18)

-0.0892 (-1.18)

Constant -0.0079 (-0.03)

-6.6720*** (-47.68)

6.6720*** (47.68)

Unreported Controls Yes No No Adj. R2 0.0447 - - Pseudo R2 - 0.2868 0.2868 N 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXIII

Table 12.2 IV Procedure. This table shows estimates of an ordinary least squares regression and probit estimations to give insights in the process to include the instrumental variable in column 4 in Table 12. The regression in column 1 is an ordinary least squares regression with the cumulative abnormal return as the dependent variable. This regression is a replication of column 3 in Table 12 and includes the industry average social score as an additional independent variable. The control variables are not reported. The regressions in columns 2 and 3 are probit estimations, which estimate the social score dummy and the inversed social score dummy using the model in column 3 in Table 12 with the industry average social score instead of the social and inversed social dummy variables. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in columns 2 and 3

Dep. Variable !"#$,&[−), )] A$,& ,. A$,&

Ind. Variable

Regression Model (1) OLS (2) Probit (3) Probit

./0<2,3 0.0024 (1.09)

0.0409*** (40.00)

-0.0409*** (-40.00)

/2,3×512,3 2.7346***(2.92)

-2.7346***(-2.92)

62,3×512,3 11.7529*** (6.61)

-11.7529*** (-6.61)

172,3 -0.8142*** (-3.74)

0.8142*** (3.74)

8//2,3 -0.0166(-0.58)

0.0166(0.58)

ln <.=12,3>? 0.5680*** (45.48)

-0.5680*** (-45.48)

./<@2,3 0.1320* (1.80)

-0.1320* (-1.80)

Constant 0.1067 (0.81)

-6.7081*** (-48.97)

6.7081*** (48.97)

Unreported Controls Yes No No Adj. R2 0.0446 - - Pseudo R2 - 0.2868 0.2868 N 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXIV

Table 12.3 IV Procedure. This table shows estimates of an ordinary least squares regression and probit estimations to give insights in the process to include the instrumental variable in column 6 in Table 12. The regression in column 1 is an ordinary least squares regression with the cumulative abnormal return as the dependent variable. This regression is a replication of column 5 in Table 12 and includes the industry average corporate governance score as an additional independent variable. The control variables are not reported. The regressions in columns 2 and 3 are probit estimations, which estimate the corporate governance score dummy and the inversed corporate governance score dummy using the model in column 5 in Table 12 with the industry average corporate governance score instead of the corporate governance and inversed corporate governance dummy variables. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in columns 2 and 3

Dep. Variable !"#$,&[−), )] B$,& ,. B$,&

Ind. Variable

Regression Model (1) OLS (2) Probit (3) Probit

./0C2,3 0.0044 (1.01)

0.0601*** (32.83)

-0.0601*** (-32.83)

/2,3×512,3 3.4894***(3.54)

-3.4894***(-3.54)

62,3×512,3 3.4864** (2.01)

-3.4864** (-2.01)

172,3 -1.1301*** (-5.40)

1.1301*** (5.40)

8//2,3 -0.0140(-0.53)

0.0140(0.53)

ln <.=12,3>? 0.3614*** (34.55)

-0.3614*** (-34.55)

./<@2,3 0.5203*** (7.38)

-0.5203*** (-7.38)

Constant -0.0954 (-0.28)

-7.6523*** (-45.52)

7.6523*** (45.52)

Unreported Controls Yes No No Adj. R2 0.0438 - - Pseudo R2 - 0.1560 0.1560 N 14,234 14,234 14,234

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXV

APPENDIXC.3–RESULTS–VOLATILITYINABNORMALRETURNSTable 13. Regressions. This table shows estimates of ordinary least squares and instrumental variables regressions. The dependent variable is the volatility in abnormal returns, expressed in percentages. The regressions in columns 1-4 are ordinary least squares regressions. The regressions in columns 3 and 4 include firm and year fixed effects. The regression in column 4 is run using robust standard errors. Column 5 shows the results of an instrumental variable regression. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: D("#)$,&[-5,10] Regression Model

Ind. Variable (1) OLS (2) OLS (3) OLS (4) OLS (5) IV 1<C2,3 -0.0042***

(-23.83) -0.0005**

(-2.10) -0.0008***

(-2.64) -0.0008**

(-2.40) -0.0052

(-1.20) |51|2,3 0.0715***

(8.07) 0.0623***

(7.54) 0.0623**

(2.48) 0.0619**

(2.48) 0.<62,3 0.3644***

(11.45) 0.3287***

(10.60) 0.3287***

(2.91) 0.3402***

(2.90) @.7./C2,3 0.0000

(0.44) -0.0000***

(-3.22) -0.0000***

(-3.13) -0.0000***

(-2.83) 8//2,3 -0.0000

(-0.22) 0.0001

(0.77) 0.0001

(0.81) 0.0001

(0.75) |17|2,3 0.0032

(1.22) -0.0022

(-0.73) -0.0022

(-0.57) -0.0015

(-0.39) ln <.=12,3>? -0.0012***

(-12.96) -0.0024***

(-6.77) -0.0024***

(-4.04) -0.0021***

(-3.18) H1IC2,3 0.0009*

(1.68) 0.0126***

(9.36) 0.0126***

(7.18) 0.0125***

(7.17) ln7J2,3 0.0033***

(15.79) 0.0016***

(2.81) 0.0016**

(2.07) 0.0014*

(1.78) 162,3 0.0218***

(8.68) 0.0097***

(3.42) 0.0097*

(1.92) 0.0105**

(2.01) 0H2,3 0.0034***

(7.40) 0.0025***

(5.15) 0.0025***

(3.07) 0.0023***

(2.72) IKH2,3 0.0524***

(42.66) 0.0261***

(8.29) 0.0261***

(5.66) 0.0258***

(5.56) ./<@2,3 -0.0017***

(-2.87) -0.0029**

(-2.40) -0.0029**

(-2.06) -0.0033**

(-2.26) Constant 0.0204***

(157.52) 0.0122***

(11.60) 0.0305***

(5.06) 0.0305***

(6.26) 0.0271***

(4.51) Firm FE No No Yes Yes Yes Year FE No No Yes Yes Yes Robust SE No No No Yes Yes 2nd stage F-statistic (IV)

139.99

Instrumental Variable(s)

./01<C2,3

Adj. R2 0.0277 0.2579 0.4131 0.4131 - N 19,910 13,453 13,453 13,453 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXVI

Table 13.1. IV Procedure. This table shows estimates of an ordinary least squares regression and a probit to give insights in the process to include the instrumental variable in column 5 in Table 13. The regression in column 1 is an ordinary least squares regression with the volatility in abnormal returns as the dependent variable. This regression is a replication of column 5 in Table 13 and includes the industry average ESG score as an additional independent variable. The control variables are not reported. The regression in column 2 is a probit, which estimates the ESG dummy using the model in Table 13 with the industry average ESG score instead of the ESG dummy. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in column 2

Dep. Variable D("#)$,&[-5,10] +AB$,&

Ind. Variable

Regression Model (1) OLS (2) Probit

./01<C2,3 -0.0000 (-0.06)

0.0450*** (41.56)

|51|2,3 -1.3352(-1.07)

0.<62,3 26.8687*** (6.10)

@.7./C2,3 0.0042* (1.81)

8//2,3 -0.0452(-1.49)

|17|2,3 -2.4440*** (-6.45)

ln <.=12,3>? 0.6821*** (44.60)

H1IC2,3 -0.2159*** (-2.93)

ln7J2,3 -0.5296*** (-17.50)

162,3 1.1874*** (3.43)

0H2,3 -0.1268** (-2.02)

IKH2,3 -0.3359** (-2.02)

./<@2,3 0.2596*** (3.14)

Constant 0.0316** (2.05)

-7.7482*** (-44.27)

Unreported Controls Yes No Adj. R2 0.4131 - Pseudo R2 - 0.3260 N 13,453 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXVII

Table 14. Regressions. This table shows estimates of ordinary least squares and instrumental variables regressions. The dependent variable is the volatility in abnormal returns, expressed in percentages. This table is a replication of columns 4 and 5 in Table 13, using the ESG constituents E, S, and G. Columns 1, 3, and 5 show the results of ordinary least squares regressions. Columns 2, 4, and 6 show the results of instrumental variables regressions. All regressions in this table include firm and year fixed effects. All regressions in this table are run using robust standard errors. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses

Dependent variable: D("#)$,&[-5,10] Regression Model

Ind. Variable (1) OLS (2) IV (3) OLS (4) IV (5) OLS (6) IV 12,3 -0.0006

(-1.53) -0.0045

(-1.09) <2,3 0.0002

(0.44) -0.0049

(-1.20) C2,3 -0.0001

(-0.05) -0.0397

(-0.70) |51|2,3 0.0624**

(2.48) 0.0625**

(2.50) 0.0624**

(2.49) 0.0633**

(2.52) 0.0624**

(2.49) 0.0641**

(2.55) 0.<62,3 0.3280***

(2.91) 0.3384***

(2.84) 0.3263***

(2.89) 0.3331***

(2.90) 0.3266***

(2.90) 0.3925***

(3.08) @.7./C2,3 -0.0000***

(-3.18) -0.0000***

(-3.19) -0.0000***

(-3.18) -0.0000***

(-3.09) -0.0000***

(-3.18) -0.0000

(-0.24) 8//2,3 0.0001

(0.80) 0.0001

(0.65) 0.0002

(0.82) 0.0002

(0.83) 0.0002

(0.82) 0.0001

(0.33) |17|2,3 -0.0023

(-0.59) -0.0020

(-0.51) -0.0023

(-0.61) -0.0020

(-0.51) -0.0023

(-0.60) -0.0011

(-0.26) ln <.=12,3>? -0.0024***

(-4.02) -0.0021***

(-2.97) -0.0025***

(-4.12) -0.0023***

(-3.81) -0.0025***

(-4.13) 0.0001

(0.02) H1IC2,3 0.0125***

(7.17) 0.0124***

(7.12) 0.0125***

(7.17) 0.0129***

(7.25) 0.0126***

(7.17) 0.0159***

(3.19) ln7J2,3 0.0016**

(2.03) 0.0011

(1.24) 0.0017**

(2.12) 0.0016**

(2.04) 0.0017**

(2.12) -0.0006

(-0.19) 162,3 0.0096*

(1.91) 0.0106**

(1.99) 0.0095*

(1.89) 0.0099*

(1.94) 0.0095*

(1.89) 0.0173

(1.48) 0H2,3 0.0025***

(3.13) 0.0026***

(3.23) 0.0025***

(3.12) 0.0025***

(3.10) 0.0025***

(3.12) 0.0034**

(2.19) IKH2,3 0.0261***

(5.66) 0.0258***

(5.58) 0.0262***

(5.71) 0.0238***

(4.62) 0.0261***

(5.66) 0.0138

(0.75) ./<@2,3 -0.0029**

(-2.02) -0.0029**

(-2.03) -0.0028**

(-2.00) -0.0033**

(-2.29) -0.0029**

(-2.01) 0.0046

(0.43) Constant 0.0307***

(6.24) 0.0267***

(4.08) 0.0313***

(6.37) 0.0283***

(5.21) 0.0312***

(6.40) 0.0148

(0.63) Firm FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Robust SE Yes Yes Yes Yes Yes Yes 2nd stage F-statistic (IV)

212.43 151.09 0.51

Instrumental Variable(s)

./012,3 ./0<2,3 ./0C2,3

Adj. R2 0.4129 - 0.4128 - 0.4127 - N 13,453 13,453 13,587 13,453 13.587 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXVIII

Table 14.1. IV Procedure. This table shows estimates of an ordinary least squares regression and a probit to give insights in the process to include the instrumental variable in column 2 in Table 14. The regression in column 1 is an ordinary least squares regression with the volatility in abnormal returns as the dependent variable. This regression is a replication of column 1 in Table 14 and includes the industry average environmental score as an additional independent variable. The control variables are not reported. The regression in column 2 is a probit, which estimates the environmental dummy using the model in Table 14 with the industry average environmental score instead of the environmental dummy. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in column 2

Dep. Variable D("#)$,&[-5,10] +$,&

Ind. Variable

Regression Model (1) OLS (2) Probit

./012,3 -0.0001 (-0.16)

0.0452*** (48.42)

|51|2,3 -0.6078(-0.53)

0.<62,3 25.3072*** (5.82)

@.7./C2,3 0.0005 (0.22)

8//2,3 -0.0624**(-2.05)

|17|2,3 -2.6997*** (-7.07)

ln <.=12,3>? 0.6603*** (43.96)

H1IC2,3 -0.3238*** (-4.37)

ln7J2,3 -0.6591*** (-21.39)

162,3 0.7979** (2.30)

0H2,3 0.0646 (1.03)

IKH2,3 0.3182* (1.89)

./<@2,3 0.3333*** (3.97)

Constant 0.0336* (1.82)

-7.2478*** (-42.81)

Unreported Controls Yes No Adj. R2 0.4129 - Pseudo R2 - 0.3416 N 13,453 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXIX

Table 14.2. IV Procedure. This table shows estimates of an ordinary least squares regression and a probit to give insights in the process to include the instrumental variable in column 4 in Table 14. The regression in column 1 is an ordinary least squares regression with the volatility in abnormal returns as the dependent variable. This regression is a replication of column 3 in Table 14 and includes the industry average social score as an additional independent variable. The control variables are not reported. The regression in column 2 is a probit, which estimates the social dummy using the model in Table 14 with the industry average social score instead of the social dummy. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in column 2

Dep. Variable D("#)$,&[-5,10] A$,&

Ind. Variable

Regression Model (1) OLS (2) Probit

./0<2,3 -0.0001 (-0.31)

0.0423*** (38.43)

|51|2,3 -0.8238(-0.70)

0.<62,3 22.0927*** (5.68)

@.7./C2,3 0.0036 (1.58)

8//2,3 -0.0395(-1.33)

|17|2,3 -2.8868*** (-7.78)

ln <.=12,3>? 0.6502*** (44.19)

H1IC2,3 -0.2807*** (-3.89)

ln7J2,3 -0.5085*** (-17.46)

162,3 1.5950*** (4.73)

0H2,3 0.0851 (1.42)

IKH2,3 -0.0885 (-0.55)

./<@2,3 0.2882*** (3.53)

Constant 0.0342*** (3.57)

-7.1496*** (-42.81)

Unreported Controls Yes No Adj. R2 0.4128 - Pseudo R2 - 0.2995 N 13,453 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively

XXX

Table 14.3. IV Procedure. This table shows estimates of an ordinary least squares regression and a probit to give insights in the process to include the instrumental variable in column 6 in Table 14. The regression in column 1 is an ordinary least squares regression with the volatility in abnormal returns as the dependent variable. This regression is a replication of column 5 in Table 14 and includes the industry average governance score as an additional independent variable. The control variables are not reported. The regression in column 2 is a probit, which estimates the governance dummy using the model in Table 14 with the industry average governance score instead of the governance dummy. The definition of the dependent and independent variables can be found in Table 1 in Appendix A. The t-statistics are denoted in parentheses in column 1, and the z-statistics are denoted in parentheses in column 2

Dep. Variable D("#)$,&[-5,10] B$,&

Ind. Variable

Regression Model (1) OLS (2) Probit

./0C2,3 -0.0001 (-0.26)

0.0578*** (31.04)

|51|2,3 0.0618(0.05)

0.<62,3 11.9560*** (2.96)

@.7./C2,3 0.0038* (1.83)

8//2,3 -0.0146(-0.54)

|17|2,3 -1.5393*** (-4.51)

ln <.=12,3>? 0.3860*** (32.54)

H1IC2,3 0.2829*** (4.24)

ln7J2,3 -0.2927*** (-11.03)

162,3 1.2317*** (3.89)

0H2,3 0.0192 (0.34)

IKH2,3 0.2415 (1.58)

./<@2,3 0.5727*** (7.42)

Constant 0.0389 (1.37)

-7.7157*** (-41.00)

Unreported Controls Yes No Adj. R2 0.4127 - Pseudo R2 - 0.1601 N 13,453 13,453

***, **, and * denote the statistical significance at the 0.01, 0.05, and 0.10 level, respectively