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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