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Corporate Social Responsibility Report Narratives and Analyst Forecast Accuracy

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Abstract

Standalone corporate social responsibility (CSR) reports vary considerably in the content of information released due to their voluntary nature. In this study, we develop a disclosure score based on the tone, readability, length, and the numerical and horizon content of CSR report narratives, and examine the relationship between the CSR disclosure scores and analyst forecasts. We find that CSR reporters with high disclosure scores are associated with more accurate forecasts, whereas low score CSR reporters are not associated with more accurate forecasts than firms who do not issue CSR reports. The findings are robust to controlling for firm characteristics including CSR activity ratings and financial narratives. The findings are driven by experienced CSR reporters rather than first-time CSR reporters. Together, our findings suggest that the content of CSR reports helps to improve analyst forecast accuracy, and this relationship is more pronounced for CSR reports with more substantial content.

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Notes

  1. These reports take different names such as (corporate) sustainability reports (Ioannou and Serafeim 2012; Simnett et al. 2009), accountability reports (Ramanna 2013), and responsibility reports (Corporate Register).

  2. For example, a study by Governance & Accountability Institute (the U.S. data partner of Global Reporting Initiative) shows a large contextual variation in CSR reports across firms and industries (See http://www.ga-institute.com/research-reports/2014-sustainability-what-matters.html).

  3. GRI has pioneered a comprehensive CSR reporting framework that is used worldwide. GRI seeks to improve comparability, credibility, and relevance of CSR information disclosed by different firms and thus to improve users’ understanding of sustainability-related risks and opportunities.

  4. KLD provides social screenings and performance rating of firms via its reports and socially screened mutual funds. KLD’s CSR performance database is used widely in the literature (e.g., Hillman and Keim 2001; Chatterji et al. 2009; Dhaliwal et al. 2011; Kim et al. 2012).

  5. Cho et al. (2010) and Plumlee et al. (2014) are notable exceptions. Cho et al. (2010) develop an index of optimism bias and uncertainty in 10-K narratives pertaining to environmental disclosure. Consistent with the GRI disclosure framework, Plumlee et al. (2014) develop a disclosure score of voluntary environmental disclosures for a sample of U.S. firms in five industries. Specifically, Plumlee et al. (2014) show that voluntary environmental disclosure score is associated with firm value. They further partition the disclosure score by disclosure type (hard/objective and soft/subjective) and disclosure nature (whether the hard/soft disclosure is related to positive/neutral/negative environmental issues). In contrast to these studies, our disclosure score includes all CSR activities reported on the standalone CSR reports.

  6. GRI is the most successful attempt to standardize CSR reporting. The latest GRI guidelines (GRI4) divide CSR reporting into economic, environment, and social categories, with social category further divided into sub-categories of labor practices and decent work, human rights, society, and product responsibility. Furthermore, auditing standards for CSR reporting have recently been developed. For example, the U.K. Institute of Social and Ethical Accountability developed AA1000 Assurance Standard, and the International Auditing and Assurance Standards Board developed the International Standard on Assurance Engagements 3000. Given the lack of an enforced standard CSR reporting, the auditing standards attempt to verify processes.

  7. While a universal notion of disclosure quality does not exist, the conceptual frameworks of the International Accounting Standards Board (IASB) and Financial Accounting Standards Board (FASB) point to various desirable aspects of disclosure such as understandability, relevance, reliability, and comparability (Botosan 2004). There are some notable attempts to measure disclosure score. For instance, Beretta and Bozzolan (2008) use concepts of width (i.e., coverage and dispersion of different topics that qualify a firm’s business model) and depth (i.e., insights related to performance) of disclosure besides quantity of disclosure.

  8. Through our review of analysts’ research reports, we do not find explicit references to disclosure quality of firms’ CSR reports. However, we find frequent references to firms’ CSR activities, especially environmental activities. Analysts likely gather information about firms’ CSR activities from firms’ disclosures, including CSR reports.

  9. When we examine the association between narratives in CSR reports and analysts’ forecast dispersion, we find results that are qualitatively similar to that of forecast accuracy. Specifically, analysts’ forecast dispersion is lower among firms with high CSR disclosure scores.

  10. These lists have been increasingly used in the accounting and finance studies. Li (2010a) suggests that alternative lists, such as Diction, General Inquirer, and the Linguistic Inquiry and Word Count, do not work well for corporate filings. Given that we examine a capital market consequence of CSR reports, we believe the financial tone of the narratives is more appropriate than using a more general list.

  11. We use other readability measures (Li 2008), i.e., Fog, Flesch-Kincaid, and Flesch reading ease indices, and find similar results to those reported.

  12. KLD rates CSR performance of a large number of firms by using surveys, corporate reports, and news articles. KLD rates CSR performance on seven categories: corporate governance, community, diversity, employee relations, environment, product, and an exclusionary screen for firms deriving revenues from “sin activities” such as alcohol, gambling, and tobacco. When summing up all the positive and negative indicators, we do not consider the corporate governance dimension of the KLD ratings because information transparency is part of that score, and including this dimension could induce a mechanical association between our disclosure score and analyst forecasts accuracy. In robustness tests, we include the corporate governance dimension and find similar results.

  13. Companies in three of the Fama–French 48 industries do not publish any CSR reports. A total of 173 observations from those industries are dropped from the selection model because of perfect prediction; therefore, the sample size used in multivariate analyses drops from 24,020 to 23,847.

  14. Although year-fixed effects control for time-invariant characteristics, the 2008 financial crisis may introduce bias. All our inferences remain the same when we repeat our analyses in the pre-crisis period.

  15. The report entitled “2012 Corporate ESG/Sustainability/Responsibility Reporting-Does It Matter? Analysis of S&P 500 Companies’ ESG Reporting Trends and Capital Markets Response” is available at http://www.ga-institute.com/research-reports/2012-corporate-esg-sustainability-responsibility-reporting-does-it-matter.html.

  16. We use our Java based textual analysis algorithm to calculate 10-K optimism, pessimism, readability, and length scores. Since this requires extensive resources, we limit the sample period to years 2000–2009. We do not consider numerical and horizon content in the 10-K, because part of numerical and horizon content is mandated (e.g., tables). Appendix 1 includes definition of variables.

  17. CSR-S Monitor is a Weissman Center of International Business Project at Baruch College, City University of New York, New York.

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Acknowledgements

The previous title of this paper was “Measuring Corporate Social Responsibility Report Quality Using Narratives.” We acknowledge the helpful comments of seminar participants at the 2014 AAA annual conference, 2014 Journal of Business Ethics conference, National Taiwan University, Fudan University, University of Exeter, University of Bristol, and Imperial College.

Author information

Authors and Affiliations

  1. University of Houston, Houston, TX, USA

    Volkan Muslu

  2. Kennesaw State University, Kennesaw, GA, USA

    Sunay Mutlu

  3. University of Texas at Dallas, Richardson, TX, USA

    Suresh Radhakrishnan

  4. Canadian School, York University, Toronto, ON, Canada

    Albert Tsang

Authors
  1. Volkan Muslu
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  2. Sunay Mutlu
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  3. Suresh Radhakrishnan
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  4. Albert Tsang
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Corresponding author

Correspondence to Albert Tsang.

Appendices

Appendix 1: Textual Analysis

Procedure

  1. 1.

    We match CSR reports with firm identifiers in the Compustat and CRSP databases, i.e., CUSIP, PERMNO, TICKER, and GVKEY, resulting in 1796 firm-years with CSR reports.

  2. 2.

    We format CSR reports to txt format and use a Java code to analyze narratives in the reports.

  3. 3.

    Some firms publish CSR reports every 2 or 3 years. We assume that firms have the same CSR report narratives in non-report years as in their most recent CSR reports. For example, if a firm issued CSR reports in 2008 and 2010 (but not in 2009 and 2011), we fill CSR data in 2009 with that in 2008, and CSR data in 2011 with that in 2010. This forward-filling procedure increases our sample from 1796 to 2462 firm-years.

Aspects of CSR Report Disclosure Score

Based on prior literature, we consider a CSR report to have a high disclosure score if (1) it includes fewer optimistic words, (2) it includes more pessimistic words, (3) it is readable, (4) it is long, (5) it includes numerical information, and (6) it includes horizon-related information. CSR report disclosure score (DSCORE) is composed of the following six independent components:

1. Optimism (RATIO_OPT):

Number of financial positive words divided by total number of words in the CSR report (Loughran and McDonald 2011; http://www3.nd.edu/~mcdonald/Word_Lists.html).

2. Pessimism (RATIO_PES):

Number of financial negative words divided by the total number of words in the CSR report. (Loughran and McDonald 2011; http://www3.nd.edu/~mcdonald/Word_Lists.html).

3. Readability (SMOG):

The Smog (Simple Measure of Gobbledygook) index is based on the number of years of formal education a reader of average intelligence would need to read and understand the text. It is defined as 1.043 * [(number of polysyllables) * (30/(number of sentences))]1/2 + 3.1291. Polysyllables are words that have more than three syllables.

4. Length (RESWORDS):

We first measure the length of a CSR report by the logarithm of the total number of words (WORDS). We then orthogonalize WORDS relative to its obfuscation component (SMOG). RESWORDS is defined as the residual from the regression WORDS = α + β*SMOG + ε, which is estimated for each year and Fama and French (1997) industry.

5. Numerical Content (RATIO_NUM):

Number of Arabic numerals and numerical words divided by the total number of words in the CSR report (Muslu et al. 2015). Numerical words are the following words: “first,” “second,” “third,” “fourth,” “fifth,” “sixth,” “seventh,” “eighth,” “ninth,” “tenth,” “eleventh,” “twelfth,” “thirteenth,” “fourteenth,” “fifteenth,” “sixteenth,” “seventeenth,” “eighteenth,” “nineteenth,” “twentieth,” “half,” “quarter,” “double,” “triple,” and “quadruple.”

6. Horizon Content (RATIO_HOR):

The number of references to future years and horizon words divided by the total number of words in the CSR report (Muslu et al. 2015). 2012 (2007) is (is not) a future year reference for a CSR report issued in 2008. Horizon words include short-horizon and long-horizon words. Short-horizon words are the following words: “short term,” “short-term,” “current fiscal,” “current quarter,” “current year,” “months,” “coming month,” “coming period,” “coming quarter,” “following month,” “following period,” “following quarter,” “incoming month,” “incoming period,” “incoming quarter,” “next month,” “next period,” “subsequent month,” “subsequent period,” “subsequent quarter,” “upcoming month,” “upcoming period,” “upcoming quarter.” Long-horizon words are the following words: “k years” where k is from 2 to 20 in numbers and from “two” to “twenty” in writing; “century,” “decade,” “foreseeable future,” “long-term,” “long term,” “coming year,” “following year,” “incoming year,” “next year,” “subsequent year,” and “upcoming year.”

Descriptive Statistics of the Six Aspects of DSCORE

The table below provides the descriptive statistics of the six components of DSCORE:

*1000 except SMOG and RESWORDS

Mean

Std. Dev.

Min

Q1

Q2

Q3

Max

RATIO_OPT

15.9

6.0

0

13.0

15.8

19.0

36.0

RATIO_PES

7.9

4.2

0

5.3

7.5

10.1

22.6

SMOG

20.3

13.3

4.5

17.5

18.6

19.7

128.4

RESWORDS

0.0

1.0

−3.6

−0.6

0.1

0.8

2.2

RATIO_NUM

38.0

23.9

0

23.0

34.4

46.8

160.4

RATIO_HOR

1.6

1.3

0

0.8

1.3

2.0

7.1

  1. Average RATIO_OPT is higher than RATIO_PES, consistent with managerial optimism in CSR disclosures. Average SMOG implies that an average reader needs to have a graduate level degree to understand a CSR report. Average RESWORDS is by definition zero. Average RATIO_NUM and RATIO_HOR suggest that firms frequently use numerical and horizon content in CSR reports

The table below reports Pearson (Spearman) correlations of the six components below (above) the diagonal. *, **, *** show statistical significances at the 10, 5, and 1% levels, respectively.

 

RATIO_

RATIO_

SMOG

RES

RATIO_

RATIO_

OPT

PES

WORDS

NUM

HOR

RATIO_OPT

 

0.10***

0.01

−0.10***

−0.07**

0.20***

RATIO_PES

0.90***

 

0.02

0.20***

0.20***

0.20***

SMOG

−0.10***

−0.10***

 

0.03

0.10***

0.08***

RESWORDS

−0.07***

0.02

−0.00

 

0.10***

0.10***

RATIO_NUM

0.40***

0.40***

−0.03

−0.06**

 

0.40***

RATIO_HOR

0.40***

0.40***

−0.05*

−0.05*

0.80***

 
  1. RATIO_OPT and RATIO_PES are positively correlated. RATIO_OPT is negatively correlated with both RESWORDS and RATIO_NUM, whereas RATIO_PES is positively correlated with both components. This indicates that, in contrast to pessimistic tone, optimistic tone is less supported by other aspects of DSCORE. RESWORDS and SMOG have zero correlation by design. RATIO_NUM and RATIO_HOR are positively correlated, suggesting that companies strategize in making more credible disclosures. In general, we observe positive, albeit at times insignificant, correlations among the aspects, indicating that these aspects likely capture a common construct of disclosure score

Computing DSCORE

  • DSCORE: The sum of decile ranks (scaled between 0.1 and 1) of RATIO_PES, RESWORDS, RATIO_NUM, RATIO_HOR, and inverse decile ranks (scaled between 0.1 and 1) of RATIO_OPT and SMOG.

  • DSCORE ranges between 0.6 and 6. We define the following indicator variables using DSCORE:

  • LowDSCORE: Indicator variable that is one if DSCORE is less than 3.3 (sample median).

  • MidDSCORE: Indicator variable that is one if DSCORE is greater than or equal to 3.3 and less than or equal to 3.9 (75th percentile of the sample).

  • HighDSCORE: Indicator variable that is one if DSCORE is greater than 3.9.

  • LowRATIO_HOR: Indicator variable that is one if RATIO_HOR is less than or equal to the 75th percentile of the sample.

  • HighRATIO_HOR: Indicator variable that is one if RATIO_HOR is greater than the 75th percentile of the sample.

  • LowRATIO_NUM: Indicator variable that is one if RATIO_NUM is less than or equal to the 75th percentile of the sample.

  • HighRATIO_NUM: Indicator variable that is one if RATIO_NUM is greater than the 75th percentile of the sample.

  • LowRESWORDS: Indicator variable that is one if RESWORDS is less than or equal to the 75th percentile of the sample.

  • HighRESWORDS: Indicator variable that is one if RESWORDS is greater than the 75th percentile of the sample.

  • LowRATIO_PES: Indicator variable that is one if RATIO_PES is less than or equal to the 75th percentile of the sample.

  • HighRATIO_PES: Indicator variable that is one if RATIO_PES is greater than the 75th percentile of the sample.

  • LowRATIO_OPT: Indicator variable that is one if RATIO_OPT*(−1) is less than or equal to the 75th percentile of the sample.

  • HighRATIO_OPT: Indicator variable that is one if RATIO_OPT*(−1) is greater than the 75th percentile of the sample.

  • LowSMOG: Indicator variable that is one if SMOG*(−1) is less than or equal to the 75th percentile of the sample.

  • HighSMOG: Indicator variable that is one if SMOG*(−1) is greater than the 75th percentile of the sample.

Defining 10-K Disclosure Variables

We control for narrative features of 10-K reports. This required us obtain 10-K reports for firm-years with non-missing CSR reports and define the following narrative features of the 10-K reports below:

  • 10KRATIO_OPT: Number of financial positive words divided by the number of words in the 10-K report (Loughran and McDonald 2011).

  • 10KOPT: Decile rank of 10KRATIO_OPT, between 0.1 and 1.

  • 10KRATIO_PES: Number of financial negative words divided by the number of words in the 10-K report (Loughran and McDonald 2011).

  • 10KPES: Decile rank of 10KRATIO_PESS, between 0.1 and 1.

  • 10KSMOG: Smog index of the 10-K report.

  • 10KREADABLE: Inverse decile rank of 10KSMOG, between 0.1 and 1.

  • 10KWORDS: Logarithm of total number of words in the 10-K report.

  • 10KRESWORDS: The residual from the regression 10KLOGWORDS = α + β*10KSMOG + ε, which is estimated for each year and Fama and French (1997) industry.

  • 10KLONG: Decile rank of 10KRESWORDS, between 0.1 and 1.

Examples

Year

Company

Inverse decile rank RATIO_OPT

Decile rank RATIO_PES

Inverse decile rank SMOG

Decile rank RESWORDS

Decile rank RATIO_ NUM

Decile rank RATIO_ HOR

DSCORE

High DSCORE

2010

United Technologies

0.8

1

1

1

1

1

5.8

2005

Newmont Mining

0.9

1

0.8

1

0.8

0.8

5.3

2005

IBM

0.9

0.7

1

1

0.9

0.7

5.2

2008

UPS

0.7

0.6

0.9

1

0.9

1

5.1

2003

Parker-Hannifin

0.9

1

0.8

0.5

0.9

0.9

5.0

Mid DSCORE

2001

IBM

0.5

0.7

0.7

1

0.8

0.2

3.9

2002

Ford Motor

0.4

0.7

0.3

1

0.6

0.9

3.9

2002

P&G

0.2

0.6

0.9

0.5

1

0.7

3.9

2005

Starbucks

0.5

0.3

0.8

1

0.5

0.8

3.9

2007

OfficeMax

0.8

0.4

1

0.7

0.8

0.2

3.9

Low DSCORE

2000

Halliburton

0.1

0.9

0.4

0.5

0.4

0.9

3.2

2002

Home Depot

0.1

0.4

0.8

0.7

0.4

0.8

3.2

2002

Intel

0.1

0.5

0.4

0.8

0.7

0.7

3.2

2003

Kimberly Clark

0.4

0.5

0.3

0.3

0.7

1

3.2

2004

Coca Cola

0.8

0.5

0.3

0.7

0.6

0.3

3.2

Appendix 2: Variable Definitions

CSR report variables

CSRREPORT

An indicator variable that is one if the firm issued a corporate social responsibility (CSR) report in a given year

SubsLow(Mid)

[High] DSCORE

An indicator variable that is one if the firm-year is in the Low(Mid)[High] CSR reporting disclosure score group for the second year in a row

FirstCSR

An indicator variable that is one if the CSR report is the first CSR report of the firm

FirstLow(Mid)

[High] DSCORE

An indicator variable that is one if the first firm-year observation with a CSR report is in the Low(Mid)[High] DSCORE group

KLD rating variables

KLDSTRENGTH

KLDCONCERN

CSR Strength score issued by KLD for the main categories of community, employee relations, environment, human rights, product, and diversity

CSR Concern score issued by KLD for the main categories of community, employee relations, environment, human rights, product, and diversity

Analyst forecast variables

FERROR(X)

The average absolute value of all forecast errors, multiplied by 100 and scaled by the stock price at the beginning of the year. X = 0,1,2 stand for contemporaneous, one-year ahead and two-year ahead forecasts, respectively

DISPERSION

The standard deviation of analyst forecasts for current year earnings, divided by the year-end stock price

Control variables

AGE

Natural logarithm of the number of years since a firm’s first appearance in CRSP

ANALYST

Number of analysts following the firm in a given year

ASSURANCE

An indicator variable that is one if the CSR report is audited by an external auditor

BLUE STATE

An indicator variable that is one if the firm’s headquarter is located in a blue state. A state is defined as a blue state if the state is carried by the Democratic Party in at least three of four presidential elections in 2000, 2004, 2008 and 2012

CAPX

The level of capital expenditures scaled by total assets

DJINDEX

An indicator variable that is one if the firm is included in the Dow Jones Sustainability Index. The coverage period of this data is 2002–2008. For years 2000 and 2001, we assume the same firms in 2002 are included in the index. For years 2009 through 2011, we assume the same firms in 2008 are included in the index

FFIN

Measure of financial transparency based on industry-year-adjusted scaled accruals. Scaled accruals are calculated as the absolute value of a firm’s accruals averaged over the past three years scaled by total assets of the last year. Scaled accruals are computed as follows: ΔCA − ΔCL − ΔCASH + ΔSTD − DEP + ΔTP, where ΔCA (ΔCL) is change in total current assets (liabilities); ΔCASH is change in cash; ΔSTD is change in the current portion of long-term debt; DEP is depreciation and amortization expense; and ΔTP is change in income taxes payable. FFIN takes the value of 1 if a firm has higher than industry-year mean of scaled accruals, and 0 otherwise (Bhattacharya et al. 2003)

FHORIZON

Forecast horizon, calculated as the median number of days between analyst forecasts and earnings announcement

GREEN

Newsweek Magazine’s green ranking based on environmental impact, initiation of green policies, and reputation. This rating, which is between 1 and 100, is available for 500 large firms. We assume the minimum score for firms that do not have Newsweek green rating

LEV

Long-term debt scaled by total assets

LOSS

Indicator variable that is one if the firm reports negative earnings at year end

MILLS

The inverse Mills ratio from the first-stage probit model as described in Appendix 3. It is used to control for the selection bias, i.e., the decision to publish a standalone CSR report

MKTSHARE

The firm’s fraction of sales in its Fama and French (1997) 48 industry

R&D

Research and development expenditures scaled by total sales

RELIGIOUS

Religion ranking of the state in which the firm’s headquarters is located, which ranges between 1 and 51. The ranking is based on the ratio of the number of religious adherents in the firm’s state to the total population in that state in 2000 (The Association of Religion Data Archive)

ROA

Net income scaled by lagged total assets

ROAVOL

Earnings volatility, computed as the standard deviation of previous five years’ ROA. At least three non-missing annual observations are required to calculate earnings volatility

SIZE

Natural logarithm of firm size, computed as common shares outstanding multiplied by fiscal year-end price

Alternative CSR report disclosure scores

KLD_REPORT

STRENGTH

An indicator variable that is one if KLD evaluates a firm’s reporting on social responsibility (CSR)/sustainability efforts to be high. Factors affecting this evaluation include, but are not limited to, the completeness and specificity of a firm’s reporting, setting of specific goals for CSR efforts, and quantitative measurement of progress toward these goals. The strength indicator shows that the company is particularly effective in reporting on a wide range of social and environmental performance measures, or is exceptional in reporting on one particular measure

KLD_REPORT

CONCERN

An indicator variable that is one if KLD evaluates a firm’s reporting on social responsibility (CSR)/sustainability efforts to be low. Factors affecting this evaluation include, but are not limited to, the completeness and specificity of a firm’s reporting, setting of specific goals for CSR efforts, and quantitative measurement of progress toward these goals. The concern indicator shows that the company is distinctly weak in reporting on a wide range of social and environmental performance measures

GRI

An indicator variable that is one if the CSR Report follows the GRI reporting format

Appendix 3: Self-Selection

DSCOREs can only be defined in companies with CSR reports. Several factors—some unobservable—determine a firm’s decision to provide CSR reports (Dhaliwal et al. 2012). If unaccounted for, these factors could lead to erroneous conclusions about the relationship between DSCORE and analyst forecast accuracy. We address this selection issue by using the Heckman (1979) procedure. Following Dhaliwal et al. (2012), we estimate the following first-stage probit model of a firm’s decision to issue a CSR report in a year:

 

P(CSRREPORT = 1)

Coefficient

z-stat

BLUE STATE

0.06

0.67

RELIGIOUS

−0.00

−0.85

DJINDEX

0.91***

6.17

GREEN

0.01***

4.72

ANALYST

−0.00

−0.07

SIZE

0.40***

8.54

ROAVOL

−2.00***

−2.70

FFIN

0.09

0.63

LEV

0.13

0.58

ROA

−1.17***

−3.03

R&D

0.28

0.37

CAPX

−0.82

−0.96

AGE

0.31***

6.04

MKTSHARE

2.06

1.22

Year and industry fixed effects

Yes

Pseudo R2

48.1%

Observations

23,847

The variables are defined in Appendix 2. Robust z-statistics clustered at the firm level are reported next to the coefficient estimates. *, **, *** indicate statistically significant coefficient estimates at 10, 5, and 1% levels. Following Deng et al. (2013), we use BLUE STATE and RELIGIOUS as instruments because they are likely to capture a firm’s attitude toward CSR activities, and still they are unlikely to be related to analysts’ forecast accuracy. Following Dhaliwal et al. (2012), we include DJINDEX and GREEN as additional instruments because they are likely to capture the firm’s CSR performance. Firms with better social performance are more likely to make disclosures to differentiate themselves from other companies and gain competitive advantage. These instruments represent unobservable factors that are likely to be correlated with the firms’ decision to provide CSR disclosures. The coefficient estimates are consistent with Dhaliwal et al. (2012), with the exception of ROA. We find a negative association between ROA and the likelihood of issuing CSR reports, while Dhaliwal et al. (2012) find a positive association. We calculate the inverse Mills ratio (MILLS) from the first-stage model and use MILLS as an additional control variable in the subsequent empirical tests to control for the factors that lead to firm’s decision to provide CSR reports.

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Muslu, V., Mutlu, S., Radhakrishnan, S. et al. Corporate Social Responsibility Report Narratives and Analyst Forecast Accuracy. J Bus Ethics 154, 1119–1142 (2019). https://doi.org/10.1007/s10551-016-3429-7

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