20-8-2018
CSR-washing,
analyst coverage
and stock price
crash risk
ABSTRACT: The literature on CSR and financial
performance is still growing, however we found no papers
regarding CSR-washing in relation with stock price crash
risk, this study tried to fill this gap. We measured
CSR-washing as the difference of the ESG scores collected from
the Bloomberg and Asset4 database. We measured stock
price crash risk as the down to up volatility of the stock
returns, since this measure is the least to be influenced by
abnormal returns. Our sample exists of 3235 globally listed
firms, between 2010-2016. This paper used regression
analysis to find results. Since, in the best of our knowledge,
this paper is the first to examine this relation we used this
sample for an enhanced external validity. We found no
significant effect. This paper also included analyst coverage
as a moderating variable due their ability to report firm
specific information.
Mehmet Çig
S2036576
Supervisor: N. Hussain
Faculty of business and Economics
Master Thesis Accountancy
Total words: 7525
20-08-2018
CONTENTS
1. Introduction 3
2. Theoretical Framework 6
3. Methodology 11
4. Results 15
5. Discussion & Conclusion 19
I. Introduction
Corporate Social responsibility (hereafter CSR) has become a dominant theme in the business world (Kim, Li, and Li, 2014). Crane, Matten, and Spence (2008) transcend this claim by considering that CSR has become a core area of management like accounting, finance and marketing. CSR also have emerged to improve the reputation of a firm, since it is about complying with external pressure (Porter and Kramer, 2011).
Enterprises use CSR to comply with external pressure by covering up human rights, labour conditions, environmental impacts, increased transparency, health issues and numerous other social and environmental causes (Hopkins and Cowe, 2004). Enterprises respond to these external pressures by e.g. disclosure, branding, partnerships, forming board committees and training (Hawn and Ioannou, 2016). Clarkson (1998) mentioned in his research that responding to stakeholder pressure is a key determinant in long time firm-performance.
More and more number of studies examined the underlying link between CSR and financial performance and the effect on firm risk, financial reporting and cost of capital in order to legitimize CSR on a economical base (Lee and faff, 2009; Dhaliwal, Li, Zhang, and Yang, 2011; Gelb and Strawser, 2001). Lee and Faff (2009) examined the relation between corporate social performance and corporate financial performance. Kim, Li, and Li (2014) examined the effect of corporate social responsibility on stock price crash risk. The mainstream of CSR studies in relation to financial performance shows positive relations, however there is also literature which shows negative relations between CSR and financial performance (Barnett and Salomon , 2012).
One of the central aspects in CSR-literature is the distinction between “walking” and “talking” CSR ( Berliner and Prakash, 2015). CSR could also be used as a tool to cover up unethical behaviour of managers and enterprises (Hemingway and Maclagan, 2004). By using corporate social responsibility, managers are able to divert attention from their misbehaving activities. CSR will then be used as a tool to pursuit the self-interest of managers (Mcwilliams, Siegel, and Wright, 2006). Some researches only focused on the environmental walking and talking aspect of CSR in order to clarify to effect on financial performance (Walker and Wan, 2012). Other studies are only based on the governance walking and talking aspect of CSR in order to examine the effect on financial performance (Westpal and Zajac, 1998). Schons and Steinmeier (2016) focused on the symbolic and substantive actions on the social domain, in order to clarify the relation between CSR-washing an financial performance.
To the best of this papers’ knowledge, there is no attention paid in literature on CSR-washing, in an holistic view, where Environmental social and governance aspects are all included in relation to financial performance. The effect of CSR-washing in relation with a specific financial performance measure, stock price crash risk, is unclear and has not been researched so far. This paper will try to fill this research gap but also trigger a debate, just like the ongoing debate on the relation between CSR and financial performance, where analyst coverage is added as a moderating variable. The determinants of stock price crash have also received an increased attention from investors, regulatory and policy makers. stock market opaque’s, financial report opaque’s, corporate tax avoidance and the value of CFO option portfolios have been used by prior scholars as determinants of the stock price crash risk (Jin and Myers, 2006; Hutton, Marcus, and Tehranian, 2009; Kim, Li, and Zhang, 2011(1),(2)). Again, in the best knowledge, CSR-washing have never been used as a determinant for stock price crash risk. The basic of these above mentioned papers is that the determinants of stock price crash risk are based on withholding / biased information and so is CSR-washing.
Many scholars, stakeholders and NGO are in the believe that multiple enterprises are benefitting from insincere CSR claims, what is seen as “CSR-washing” (Mattis, 2008). Pope and Waeraas (2016) defined CSR-washing as false CSR claims to improve company’s competitive standing, which are successfully implemented. This paper focusses on Environmental, Social and Governance aspects of greenwashing based on different metrics. Scholars prefer to make distinctions in different types of CSR-washing. Greenwashing, for example is seen as a deliberate disinformation published by an enterprise to represent an environmentally aware enterprise (Gillespie, 2008; Ramus and Montiel, 2008; Seele and Gatti, 2017). Blue-washing, another perspective of CSR-washing, indicates washing in humanitarian issues such as human rights and poverty (Seele, 2007). We found CSR-washing by comparing Environmental social and governance (ESG) scores of the asset4 and Bloomberg database.
The moderating variable is collected in the I/B/E/S database, and is measured by the number of recommendations. Several researches emphasized the role of analyst coverage in relation with firm value, since the information produced by an analyst is very important in order to asses firm value (Jo and Harjoto, 2014). This paper includes analyst coverage, since it has the potential to function as a monitoring mechanism in order to discipline misbehaving analysts (Yu, 2008). Recent corporate scandals, like Xerox and Enron, enhanced the attention in stock price crash risk (Chang, Chen, and Zolotoy, 2017). Hiding information from their stakeholders is the main
to hide the real debts of Enron until 2001. The business journal Fortune published an article where they doubted the profits of Enron, the SEC started an investigation after this article and the bubble exploded1. Enron went bankrupt after this scandal. This paper uses financial
information from Compustat, in order to determine the Down to up Volatility, since DUVOL is an important predictor of the possibility of stock prices to crash.
In this paper we used a sample of 3235 observations of firms around the world to conduct a regression analyses. We found a very small positive relation between CSR-washing and stock price crash risk, which is not significant. The moderating variable, analyst coverage, shows also a small positive effect, but is also not significant. Our study makes several contributions. First, our study adds to the growing literature of CSR. As discussed before a large number of research has been made on CSR. Many of these researches mentioned the effect of positive (negative) CSR on a dependent variable like stock price and corporate financial performance (Margolis and Walsh, 2001). Second, this study adds to previous researches that attempted to predict the stock price crash risk (Chen, Hong, and Stein, 2001; Hong and Stein, 2003; Jin and Myers, 2006). Second, until now there has no research been made in the effect of CSR-washing on stock price crash risk. This paper extents the literature by adding the CSR-washing effect of organizations on stock price crash risk. We extend previous research by adding the effect of the communication gap about CSR on stock price crash risk.
These contributions are important, since Enterprises frequently carry out claims in order to comply with institutional demands, where they often do not comply to (Bromley and Powel, 2012; Meyer and Rowan 1977; Zajac and Westphal 2004). As examples, Marquis, Toffel, and Zhou (2016) found in different researches that enterprises created their own corporate governance instead of complying with existing standards, developed self-regulations instead of complying with existing regulations. There is a lot of evidence that enterprises often respond symbolic to the needs of stakeholders, but there is a literature gap on what the effect is on firm performance.
This paper will add to the debate of literature on the effect of CSR-washing and financial performance, where CSR-washing will have a positive or negative relation with firm performance. we will try to clarify what the consequences will be of the distinctions in their walking and talking. Organizations who perpetrate CSR-washing in environmental social and governance aspects, intentionally or unintentionally, will be aware of the influence off
washing on the stock price crash risk. Investors who intend to invest in an organization will be aware of the potential stock price crash risk. They will be aware of the effect CSR-washing on stock price risk. Depending on the results of this paper (CSR-washing has a positive/negative/neutral on stock price crash risks) investor could anticipate on stock price crashes. When the effect is positive (when there is CSR-washing the stock price crash risk will be higher) investor will take this fact in consideration in their willingness to invest. The negative effect of CSR-washing on stock price crash risk (more CSR-washing will lead into a lower stock price crash risk) will also affect the investors willingness to invest in a company. When the effect is neutral (there is no effect of greenwashing on stock price crash risk) investors could ignore this effect considering the crash risk. This research is also important as we look at the Volkswagen scandal. Volkswagen reduced, as part of their think blue program, the usage of water and energy. They aimed to reduce their share in environmental pollution by 25% by 2018 compared to 20102. Four trading days after the scandal broke on, Volkswagen Lost 33% market
value on their outstanding shares (Benlemlih and Girerd-Potin, 2017).
The remainder of this paper proceeds as follows. Section 2 discusses the theoretical framework of this research, the third section explains the research methodology, in Section 4 the results are discussed and in the fifth and last section a conclusion is drawn.
II. Literature review
Prior literature that tries to explain why enterprises conduct CSR, is based on the economic perspective, the institutional perspective and the ethical perspective, where these three aspects are not mutually exclusive (Aguilera, Rupp, Williams, and Ganapathi, 2007; Bansal and Roth, 2000). The first, economic, perspective disputes CSR in an perspective where CSR is used in order to raise in financial performance or competitive advantage ( Husted and Salazar, 2006; Tang, Hull, and Rothenberg, 2012). The second, institutional, perspective focuses on external pressure. Enterprises take external expectations into consideration and tries to follow socially acceptable and legitimate strategies ( Mitchell, Agle, and Woord, 1997; Campbell, 2007; Chiu and Sharfman, 2011). Enterprises use CSR in order to meet up with external expectations (Hopkins and Cowe, 2004). The third, ethical, perspective considers that the reason of
conducting CSR is based on the ethical concerns of for instance managers (Donaldson and Dunfee, 1994; Hemingway and Maclagan, 2004).
Friedman (1970) is one of the first authors who examined corporate social responsibility as an agency problem. The agency theory deals with the relational problems between principals and agents, caused by the unaligned goals between an agent and principal. A key concept of the agency theory is information asymmetry, where one party has an information advantage over the other party (An, Davey, and Eggleton, 2011). Dhaliwal, Li, Zhang, and Yang (2011) considered CSR as an agency problem which occurs due the separation of ownership and control. Information asymmetry and agency cost, which is part of the agency theory, can hurt the shareholder value, and so the stock prices (Jensen and Meckling, 1976). Engaging in CSR-reporting will reduce the information asymmetry between the enterprise and his possible Investors/community, since CSR-reporting is a form of voluntary disclosure where more information is disclosed (Marquardt and Wiedman, 1998; Michaels and Grünig, 2017).
The signalling theory could also be used to approach the use of CSR. This theory is closely related with the agency theory, Since it is concerned with signalling information from seller to buyer to influence the market price of a good (Watson, shrives, and Marston, 2002). Both theories are based on information asymmetry between two parties. The information communicated to society is often complex and subject to change, this makes it difficult for stakeholders and stockholders to figure out what is said (Carlson, Grove, and Kangun, 1993). The signalling theory provides signals to the receiver to reduce this information asymmetry. But CSR is not always used to reduce information asymmetry. combining the relevance of the information for stakeholders and the information asymmetry between the enterprise and stakeholders, are enterprises in a position of signalling false information to enhance their legitimacy (Seele and Gatti, 2017).
Fleming and Jones (2013) mentioned that CSR is used in perspective of the legitimacy theory. Enterprises use CSR to legitimate their enterprise, based on their social contract with the community (legitimacy theory). This theory considers CSR as a tool to manage the legitimacy and reputation to respond to the external pressure of stakeholders (Michelon, 2011). Companies must respond to external since stakeholder can influence the profitability and therefore the continuance of the enterprise (Coebergh, 2011). Legitimacy is defied by Matejek and Göosling (2014) as the actions that are acceptable based on social norms, values, definitions and beliefs. CSR can be seen as a mechanism to offer assurance and to build a good image.
Another theory where CSR research mostly on based, is the impression management theory. Piechocki (2004) examined that the impression of an enterprise is based on the expectations of their stakeholders. This theory is closely linked with the legitimacy theory, since enterprises use CSR to influence the perception of the stakeholders (Hooghiemstra, 2000). Godfrey, Craig, and Jared (2009) noticed that CSR is functioning like an insurance protection for companies. Combining with the research of Sharfman and Fernando (2008), which states that improved risk management is associated with a lower cost of capital, there can be assumed that CSR affects the value of an organization.
As CSR has been given more and more attention in literature, also the economic impact of CSR has become more important (e.g. Kim and Statman, 2012; Roman, Hayibor, and Agle, 1999). Margolis and Walsh (2003) showed that the relation between CSR and firm performance is mixed. A number of studies looked beyond the link of CSR and financial performance. Lee and Faff (2009) for example focussed on firm risk, where El Ghoul, Guedhami, Kwok, and Mishra (2011) focussed on cost of capital in relation with CSR. We will add to literature by focusing on the stock price crash risk, and look beyond financial performance. Some studies also pursue and agency view on CSR, where a negative view is expressed on managerial motivations in order to conduct CSR (Friedman, 1970, Carroll, 1979). These studies mainly focus on that firms use CSR in order to prompt self-interest. One of the central aspect in the literature of CSR, is the distinction between “Walking” and “Talking” ( Berliner and Prakash, 2015). This paper will add to literature by focusing on this aspect, CSR-wash, in relation to stock price crash risk.
HYPOTHESES DEVELOPMENT
Lee and faff (2009) found in their research that firms which practice higher corporate social performance exhibits lower idiosyncratic risk. Vice versa, firms who practice lower corporate social performance exhibits higher idiosyncratic risk. A strong CSR portfolio reduces specific business risk (e.g. brand and reputation damage, lawsuits and boycotts), what results in higher profitability of an enterprise (Boutin-Dufresne and Savaria, 2004). Moreover, Healy and Palepu (2001) has shown that voluntary disclosure, such as CSR-reporting, is likely to increase shareholder value and enhances shareholder value. In these cases there is no CSR-washing included.
Marquis, Toffel, and Zhou (2016) considered disclosures where enterprises do not comply to as selective disclosure, which is seen as symbolic strategy to gain legitimacy by disclosing beneficial performance to obscure real performance. Acting symbolically permits an enhanced
internal flexibility than conformity while still profiting from the benefits of legitimacy (Suchman, 1995). As we mentioned before the mainstream literature found an positive relation between CSR and financial performance. This positive effect is mostly, in particular in the environmental aspect, due gaining regulatory advantages, competitive advantage and reduced cost. This competitive advantage is due differentiation (Porter and van der Linde, 1995). Even though these symbolic action are able to enhance legitimacy, eventually it will negatively influence the financial performance. This is due the fact that an enterprise will not benefit from the above mentioned reduced costs (Hart and Ahuja, 1996).
When detected, symbolic actions will lead in a decreasing loyalty towards an enterprise, which leads eventually in a lower financial performance (Walker and Wan, 2012). So in an social perspective, when an enterprise develops policies in order to enhance employee support activities but never follows these this will decrease the financial performance.
Some scholars make a distinction between blue-washing and greenwashing (Pearson, 2010), where some scholars prefer to use the term greenwashing what embraces social and economic issues (Bazillier and Vauday, 2009). In this paper we take every aspect of CSR in consideration while discussing CSR-washing.
CSR-washing is based on false claims, which are not implemented. The inconsistency between the communication and substantive actions is a result of the freedom to signal, or not to signal, the significant actions of an enterprise. Connelly, Serto, Ireland, and Reutzel (2011) claimed in their research that every enterprise has the freedom to communicate or not to communicate its true value to outsiders. Enterprises do not walk their talk by communicating selective information and create disinformation. An important predictor of the crash risk is the managerial bias to withhold information from investors (Jin and Meyers, 2006). The reason for withholding this information is the incentive and career concerns a manager has. Another reason is that bad news could cause a domino-effect for other bad news. The amount of hidden information will reach a certain point that all information will be exposed. All the bad news will come out once, what mostly results in a stock price crash. as mentioned above, This means that there is information hidden in the communication to the society.
If we look at stock prices, the greater part of the movements in stock prices in financial markets are downwards. Chen, Hong, and Stein (2001) indicates that this is due stock markets are more prostrate to melt down than to melt up. This claim can be enhanced by the fact that the past nine
of the ten biggest price movement in financial markets in the S&P 500 since 1947 were downwards.
Combining with that, withholding information is an predictor of stock price crash risk also that information will reach a tipping point followed by a domino-effect and that stock prices are more prostate to melt down than to melt up, we hypotheses the following:
Hypothesis (1): CSR-Washing will result in an increased stock price crash risk.
This thesis also included the moderating variable analyst coverage. Few studies linked analyst coverage and stock price crash risk, Even though there is wide literature on these aspects. Analyst coverage is the role of financial analysts as mandates of the information what is publicly available (Stagliano, Rocca, and Gerace, 2017). Analysts are information intermediaries, who use technical and fundamental measures to provide recommendations in order to advise stakeholders to purchase, sell or hold securities (Lang and Lundholm, 1996; Healy and Palepu, 2001). Analysts channel information in the form of earnings forecasts, recommendations and detailed reports (Xu, Jiang, Chan, and Yi, 2013). Analysts have an important role in the literature, because of their ability to influence firm value (Jo and Harjoto, 2014). Ivkovic and Jegadeesh (2014) mentioned that analysts are well trained and skilled in obtaining private information that is not publicly available. This privilege makes analysts better in valuing a firm. Chung and Jo (1996) suggested that analysts function as an information intermediary who can reduce agency cost. This function is very important since CSR-washing is possible because of information asymmetry. The number of analysts plays a vital role in attracting investors. Analysts have a potential to be an additional monitoring aspect on misbehaving managers, who do not act ethically (Yu, 2008). Berk and Demarzo (2011) mentioned that analysts have an important role in producing information and thereby a role in narrowing the information gap. The information transparency of an firm increases when analysts reveal especially bad news. Kim, Li, and Zhang (2011) mentioned that a lack of transparency increases the stock price crash risk by the possibility of hiding and accumulating bad news.
Analyst are able to produce firm specific information, Liu (2011) finds that the production of firm-specific information will enhance with the idiosyncratic volatility of the firm. Analyst producing firm specific information have strong incentives to enhance the crash possibility of stock prices, since it reveals hidden unethical information.
Therefore we hypotheses the next:
Hypothesis (2): analyst coverage increases the positive effect of CSR-washing on Stock price crash risk
We conceptualize the following model:
III. Methodology
Data and Sample
We construct our dependent, independent and control variables by using several data bases. For our independent variable, CSR-washing, we used two different databases, namely the
Asset4 and Bloomberg database. The Asset4 database is an relative new database, which has
been validated in prior researches (Cheng, Ioannou, Serafeim, 2014; Ioannou and Serafeim, 2012). The Tomson Reuters Asset4 database is specialized in producing auditable, objective, relevant and systematics corporate social responsibility information. Trained researches collect information on 900 evaluation aspects of each firm, these aspects are objective and publicly available. Stock exchange fillings annual reports (financial and non-financial), websites and news are sources of information for gathering data for the database. These raw data is converted into consistent data, what enables quantitative analysis. Converting the data provides z-scores that benchmark the focal firm against the other firms in the database. The CSR information is based on three aspects, namely Environmental Social and Corporate governance metrics (ESG) and over 400 metrics. The environmental database involves information on for example energy
used, water recycled, waste recycled, greenhouse gas emission, and pollution controversies. The social aspects involves information on for example employee turnover, training hours, number of women employees, and health & safety aspects. The last aspects, governance, involves information on for example board compensation, board experience and board diversity.
The Bloomberg database includes ESG data for more than 10.000 firms which are publicly listed over the world. The Bloomberg database rates companies annually based on the disclosure of quantitative and policy related Environment, social and governance data. The Bloomberg database also use third rating companies. The annual ratings are based on CSR-reports, web-sites but also direct contact with companies. The ESG data of the Bloomberg database covers 120 different metrics, including emissions, pollution, climate, human rights, executive compensation, takeover defences and independent directors. The Bloomberg database penalizes companies for missing data.
There is also accounting data collected from DataStream and analyst coverage data from I/B/E/S. The sample of this paper includes 3235 observations of firms between 2010-2016.
Measurement of the variables Independent variable
As mentioned before, to determine CSR-washing, this paper used Environmental social and governance from the Asset4 database and Bloomberg database. The asset4 database contains ESG scores on over 7000 different enterprises globally. It includes over 400 metrics over ethical screening and sustainability performance. The data is manually collected from public available information (https://financial.thomsonreuters.com).
The Bloomberg database contains ESG scores on enterprises globally. The Bloomberg data contains also internal information gathered from a Bloomberg survey, websites and disclosures. The Asset4 database contains only public available information (external disclosures) and the Bloomberg data contains also internal data mostly gathered from the Bloomberg survey (internal actions) (https://cfaboston.org). Based on our research we could say that CSR-washing is actually communicating CSR but not acting consistently with this communication. This paper follows Hawn and Ioannou (2016) in order to measure CSR-washing. The above mentioned paper defined greenwashing as a set of more external actions than internal actions. This paper has a more holistic view on greenwashing and examines not only environmental, but also social
and governance aspect So, we could measure CSR-washing by subtracting the internal actions from the external actions. When this distinction is positive we could conclude the firm is “CSR-washing”. The higher this score is, the higher CSR-washing is.
Analyst coverage, the moderating variable, which is collected from the I/B/E/S database, represents the number of recommendations made for a firm by an analyst over one specific year, prior to the stock price crash risk year.
Dependent variable
In order to measure the dependent variable, stock price crash risk, we will follow Chen, Hong, and Stein (2001) and Kim and Zhang (2013). DUVOL and NCSKEW are two different methods in order to measure the stock price crash risk of a firm. NCSKEW (negative conditional skewness) is measured by the following formula:
In words: negative conditional skewness is measured as the inverse of the third moment of weekly returns. Followed by normalizing this with the standard deviation.
Where Wi,T,t stands for the weekly firm specific stock return in t, and n stands for the weekly returns in year t. the negative sign in front of the formula explains that a higher NCSKEW is consistent with more negative skewed stock return distribution, a higher crash risk.
The second measure to determine stock price crash risk is the down to up volatility. This down to up volatility is measured by the log of the standard deviation of monthly returns for up months divided by standard deviation of monthly return for down months. An “up month” is a month where the monthly return is above the annual mean. Vice-versa, a “down month” is a month where the monthly return is below annual mean. A higher down to up volatility represents a higher stock price crash risk.
NCSKEW = - [n(n-1)3/2 ∑-&./(#$,&,'( #*$,'),]/ [(n-1)(n-2)(∑-&./(#$,&,'( #*$,')0),/0]
The difference between DUVOL and NCSKEW is that DUVOL does not include third moments. DUVOL is less likely to be overly influenced by abnormal monthly returns. In this research we used the DUVOL to determine the stock price crash risk, since it is less influenced by the abnormal monthly returns.
Control Variables
We use several control variables to clarify the relation between the independent and depended variable. First, turnover (TURNOVER) is used as a control variable by following Chen, Hong, and Stein (2001), who mentioned that trading volume a predictor is of stock price crash risk. Turnover can also be seen as the intensity of different opinions of investors. By taking the average monthly share turnover (at t=0) subtracted with the average monthly turnover (at t-1), the turnover of a firm is calculated. The second control variable is past returns (RET), what is calculated by the average of specific monthly returns over a year. The above mentioned paper also addressed the importance of past returns in calculating the crash possibility. High past returns are signals of a bubble which can result in a large price deduction. The third control variable, market-to-book-ratio (MB), is used for the same reason as the past returns. Fourth, the size (SIZE) of the firms is used as a control variable, as the size of an enterprise has a high predictive power (Harvey and Siddique, 2000). Stock Volatility is the fifth control variable (SIGMA), it represents the standard deviation of the monthly returns over a year. Higher volatility enhance the crash risk (Kim, Li, and Li, 2014). The sixth control variable in this paper is the financial leverage (LEV), computed as debts divided by the assets. The last control variable return on assets (ROA), is used to control for the profitability of the firm.
With the collected data we conduct the following regression analysis: Crash_Riskt = ß0 + ß1(CSR_WASHt-1) + ß2(ANALYST_COVt-1)
+ ß4(TURNOVERt-1) + ß5(RETt-1) + ß6(MBt-1) + ß7(SIZEt-1)
IV. Results
Table 1 Descriptive statistics N Mean Std. Deviation DUVOL 3235 -0.0585 0.2456 CSR-wash 3235 37.1540 17.5442 Analyst coverage 3235 11.9447 8.8928 Turnover 3235 5.4926 0.7798 Past return 3235 0.7430 0.5715 Market-to-book ratio 3235 0.2210 0.1471 Firm size 3235 6.7325 0.6121 SIGMA 3235 2.0542 0.8687 Return on assets 3235 5.4652 6.0340 Leverage 3235 26.3741 15.6395In table 1 the descriptive statistics are mentioned the dependent variable, down to up volatility, has an average of -0.0585. The CSR-wash score, based on the difference between the Bloomberg ESG scores and asset4 ESG score, has an average of 37.1540. Analyst coverage, what is the moderating variable in our model has an average of 11.9447. The moderating effect which is the multiplication of the z-scores of CSR-wash and analyst coverage, has an average of 0.0531. The control variables Turnover, Past return, Market-to-book ratio, firm size, SIGMA, return on assets and leverage have an average of, respectively, 5.4926, 0.7430, 0.2210, 6.7325, 2.0542, 5.4652 and 26.3741. The control variables turnover, Past return, Firm size and SIGMA are based on a logarithmic scale. In order to deal with outliers the dependent, independent, moderating variable and all of the control variables are winsorized. First the interquartile range is determined by subtracting the value of the first quartile from the value of the third quartile. Next the upper bound and lower bound are determined. For the upper bound the interquartile range is multiplied by 1.5 and added to the value of quartile. This is also done for the lower bound, but now the value of the interquartile range multiplied by 1.5 is subtracted from the value of the first quartile.
Table 2 shows the outcomes of correlation analysis. It shows the correlation between de dependent variable, independent variable, and all control variables. Since none of the correlation value is higher than 0.7, there is no evidence found for multicollinearity.
Table 3 Regression results (1) (2) (3) CSR-wash 0.007 0.007 Analyst coverage 0.001 Turnover -0.012** -0.012* 0.013** Past return -0.042** -0.043*** -0.042*** Market-to-book ratio -0.105*** -0.106*** -0.115*** Firm size -0.001 -0.004 -0.005 SIGMA -0.022*** -0.023*** -0.023*** Return on assets 0.004*** 0.004*** 0.004*** Leverage -0.001** -0.001** -0.001** R2 0.017 0.018 0.019 F-value 8.178 7.385 6.277
Note: ***, ** and * coefficients are statistically significant on, respectively, 0.01, 0.05 and 0.1 (based on two sided testing)
Table 3 shows the three different regression analyses that is computed in order to determine the effect of CSR-wash on the stock price crash risk, with analyst coverage as an independent variable. The first regression, (1) in the table above, includes de dependent variable down to up volatility and all the control variables, turnover, past returns, market-to-book-ratio, firm size, SIGMA, return on assets and leverage. The second regression, (2) in table 3, includes the dependent variable, all control variables an de independent variable CSR-wash. The third and last regression analyses is computed by running a regression with the dependent variable, the independent variable the control variable and analyst coverage as an moderating variable. The first regression has an significance of 0.000. the beta (β) values for the seven control variables, , turnover, past returns, market-to-book-ratio, firm size, SIGMA, return on assets and leverage are respectively, -0.012, -0.042, -0.105,-0.001, -0.022, 0.004 and -0.001. We see that turnover has a negative relation with the stock price crash risk, what is significant on a level of 0.05. the past return shows also a negative relation with the stock price crash risk, which is also significant on a level of 0.05. The market-to-book-ratio shows the biggest negative relation in the first regression and has the highest significance, namely on a 0.01 level. Firm size shows a very small negative relation with the dependent variable, and is in contrast with the previous control variables not significant. SIGMA shows also an negative relation, and is just like the
market-to-book-ratio significant on a level of 0.01. Return on assets is the only control variable in this regression which shows an positive relation with the stock price crash risk measure, DUVOL. The significance of this relation is just like SIGMA and Market-to-book-ratio on a 0.01 level. The last control variable leverage shows a very small negative relation with DUVOL, and is significant on a level of 0.05.
Regression (2) has also a significance of 0.000. The beta values mentioned above changed, but not a lot. The beta value of the turnover did not change, the turnover is now significant at a level of 0.1. The past returns went from 0.042 to 0.043 and is now significant at a level of 0.01. The significance of the market to book ratio did not change but the beta value went from -0.105 to -0.106. Relatively, the firm size is the most changed control variable. It went from -0.001 to -0.004. The significance of sigma stayed at a level of 0.01 and the beta went from -0.022 to 0.023. The last control variable leverage stayed the same.
Regression (3) includes the moderating variable analyst coverage. This model has a significance of 0.000. The regression results compared to regression (2) did not very altered. The beta of the turnover went from -0.012 to -0.013 and the significance from 0.05 again to 0.1. The control variable past return went from -0.043 to -0.042 while the significance stayed the same. Market-to-book-ratio went from -0.106 to -0.115 and stayed at the same significance level. The last control variable which has changed is firm size, what went from -0.004 to -0.005. Analyst coverage is the new variable in this analyses and shows a value of 0.004 and no significance at any level. The moderating variable shows a positive effect on the relation between CSR-wash and stock price crash risk, but this effect is not significant.
V. Discussion & Conclusion
Many scholar studied the link between CSR and financial performance. Mainstream studies found an positive link between CSR and financial performance. CSR-washing, a central aspect of CSR is the distinction between walking and talking CSR, i.e. CSR-wash. In the introduction we mentioned the ongoing research on the stock price crash risk. In the best of our knowledge, a literature gap is found since there is until now no research made between the relation of CSR-washing and stock price crash risk. This study contributed as being the first study which examined this relation, and hope to trigger a discussion just like ongoing discussion in the relation of CSR an financial performance.
As mentioned in the literature review, CSR enhances the legitimacy of an enterprise as a result of responding to the external pressure of stakeholders (Michelon, 2011). This study also mentioned that enterprise must respond to external pressure, since stakeholders are able to influence the profitability and so the continuance of an enterprise (Coebergh, 2011). But enterprises do not always walk their talk, and commit CSR-washing by not performing their communication. We found in literature that enterprise, because of the information asymmetry, have the freedom to communicate in their preferences (Connelly, Serto, Ireland, and Reutzel, 2011).
This paper found a small positive relation between CSR-wash and the stock price crash risk, but this relation is not significant. Therefore, we reject our first hypotheses which states that there is a positive relation between CSR-washing and the stock price crash risk. We will now explain why, in contrary to our hypotheses, why there is no positive and significant relation. As there is mentioned before enterprises use CSR to narrow the legitimacy gap between the enterprise and their stakeholders. Enterprises are able to enhance their allegoric actions, despite the falsehood of these actions (Palazzo and Scherer, 2006). In an environmental perspective, Patriotta, Gond, and Schultz, 2011 mentioned that manager are able to enhance their corporate legitimacy by managing false and intended actions. This legitimacy is important because it enables improved resources, improved workforce and enhances stronger relationships with business partners (Aldrich and Fiol, 1994; Oliver, 1991; Walker and Wan, 2012). Maybe the most important aspect of these false claims is that it enhances firm reputation, decreases the firm risk and eventually results in higher turnovers (Baum 2012; Dekhili and Achabou, 2013; Laroche, Bergeron, and Barbaro-Forlei, 2001)
BP spoiled in 2010 a large amount of oil in the Gulf of Mexico. Although Shell spilled a greater amount of oil in the last 50 years, the indignation towards BP was higher than the indignation towards Shell (Nossiter, 2010). Non-governmental organisations (NGO) and the media are mostly the parties who accuse enterprises of communicating symbolic actions. These accusations very important since CSR-washing mostly exist when an enterprise is blamed by the stakeholders, NGO or media (Seele and Gatti, 2007). This claim is consistent with previous studies where corporate behaviour is seen as social irresponsible only when the observers mention it like that (Lange and Washburn, 2012).
So, based on prior researches we could clarify, in contrast to our hypotheses 1, the insignificant effect of CSR-wash on the stock price crash risk. This insignificance could be explained due the previous papers that mentioned that, despite the falsehood of actions enterprises are able to enhance legitimacy. This eventually will enhance the reputation and decrease the stock price crash risk. We found also that accusation is an important determinant in order to reveal CSR-washing.
In regression (3) we also implemented the moderating variable analyst coverage. We found that analyst coverage has a small increasing influence on the relation between CSR-wash and Stock price crash risk. The moderating relation is however not significant, so we have no evidence to accept our second hypotheses therefore we reject it.
Although analyst convert private information into public information (Ivkovic and Jegdeesh, 2014) shows anecdotal evidence that financial analysts do not see CSR as value enhancing (Adhikari, 2016). A recent study from Ernst & Young3 mentioned that environmental and social
policies are the lowest are the lowest ranked non-financial aspects what influences the predictions of analysts. Campbell and Slack (2011) found in their research evidence that often analysts do not even read environmental reporting. These reports were considered as immaterial to analysts and had no influence on their decisions. So we could assume from these studies that analyst do not pay incremental attention to CSR.
Analysts provide information to investors and stakeholders in form of forecast recommendations and reports in detail. A firm’s transparency will increase, which will lower the stock price crash risk since it reveals firm specific hidden and or bad information (Hutton, Marcus, and Tehranian, 2009; Kim, Li, and Zhang, 2011). However, analysts have also their
own incentives, Beyer, Cohen, Lys, and Walther (2010) mentioned in their research that analysts are not fully committed to report all available information accurate and complete. Generally analyst are prompted to communicate optimistic earnings and recommendations. When analyst communicate optimistic earnings forecasts and recommendations, the negative information of the enterprises will not be revealed. So, analysts also report based on their self-interest. Generally, analysts are prompted to issue optimistic information about enterprise. Mola and Guidolin (2009) found that only four percent of all recommendations from 1995 until 2001 were rated to sell. The distinction of communicating truthfully is based on the cost and benefits trade-off for an analyst.
However analyst research is important to investors (Womack, 1996; Jegadeesh, Kim, Krische, and Lee, 2004), some scholars designate that analyst are rather industry experts than firm experts. So the firm specific information, coming in earnings forecast and recommendations, they reveal is actually based on the industry that an analyst covers rather than the enterprise (Chan and Hameed, 2006; Piotroski and Roulstone 2004). Based on these papers, we could assume that analysts do not process firm specific information in the stock prices, but industry specific information and the prices will move with the industry market.
Adhikari (2016) examined the influence of analysts on CSR and found that enterprises which have a bigger analyst coverage are less socially responsible. This finding is based on the view that CSR activities is based on an agency problem. CSR activities are done by managers with other people’s money in order to do good. Analysts function as an control mechanism and forces managers to reduce discretionary spending on CSR-activities.
So, based on prior researches we could clarify, in contrast to our hypotheses 2, the insignificant moderating effect of analyst coverage. In the hypotheses development we discussed that analysts are able to convert firm specific information into public available information, in case of CSR-washing analyst would reveal hidden information of the firm and enhance the stock price crash risk. We found that this could be not the case, since analyst do not found CSR as value enhancing and do not pay substantial attention to CSR. We found also that analyst communicate on behalf of their own interest. Since analysts do not benefit form negative information, analysts are prompted to lack in negative communication where CSR-washing is based on. So, this could be the second explanation why we did not found an significant relation. The last explanations are that analyst are behaving as market analyst rather than firm specific analysts and that an bigger analyst coverage reduces spending on CSR activities. These could also explain the non-significant moderating effect.
As there is explained in the methodology we have used the CSR-washing scores one year prior to the measurement of the stock price crash risk. There is a probability that one year is not a plausible period to reveal all the disinformation and enterprise communicates or an analyst communicates. If we look at the Volkswagen case, Volkswagen started cheating on pollution with some of their since 2009, but the market price responded to this wash in 2015.
The overall conclusion of this paper is that there is no significant relation between CSR-washing and stock price crash risk. Also, analyst coverage does not have an significant moderating effect on the relation between.
Future recommendations and limitations
The most important limitation of this paper is hidden in the determination of our independent variable CSR-washing. The two databases, Asset4 and Bloomberg, used different metrics in order to determine the Environment Social and governance score where CSR-washing is based on. The Asset4 database uses 400 different metrics in order to determine the ESG score, where the Bloomberg database uses 120 different metrics in order to determine the ESG score. This could be most important limitation of our paper.
This paper mostly followed Kim, Li, and Li (2014) in their research methodology. The last mentioned paper consist a sample of 12,978 firms. In this paper, due matching available data in two different databases, the available sample dropped to 3235. A lower sample could influence our outcomes and will results in a less representative outcome, what is the second limitation of our paper. This paper used firms globally which enhances the generalizability of this paper and so enhances the external validity. The external validity refers to the generalizability of internally valid causal claims (Campbell, 1957). Fogarty (2006) emphasizes the importance of using hand collected data. This especially when using archival data from databases. By collecting at least one variable with hand this paper would not have to depend on the confidentiality of the used databases. Limited time is the reason why this paper decided to trust on archival data and not to hand collect. Campbell (1957) considers internal validity in how confident the association between an intervention and outcome is causal. Not using hand collected data, decreases the internal validity.
In this paper no attention is paid to the media coverage, we already mentioned the importance of NGO and media who play an vital role in the subjective interpretation of corporate social irresponsibility. Kolbel, Busch, and Leonhardt(2017) Examined the effect of corporate social irresponsibility with media coverage as an intermediary on financial risk. They found that
media coverage is an important intermediary when it comes to examining irresponsible behaviour of enterprises on the effect of financial. So future papers could implement media coverage as an intermediary.
In the best of this papers knowledge no paper examined the willingness to buy after CSR-washing has been revealed. Willingness to buy could be included in the paper by for example conducting an survey under investors. This is important, since the market share of the Volkswagen polo’s only lost 0,3 percent market share.
Ali, Frynas, and Mahmood (2017) examined the determinants of corporate social responsibility disclosure and made a distinction between developed and developing countries. By conducting an literature review they found that there are different factors what determines the CSR disclosure of an company. This paper is important since it shows the predeterminants of corporate social responsibility disclosure, since washing is mostly done by disclosure. For example, company size has a significant positive relationship with CSR-disclosure in developed countries (Bouten, Everaert, Liederkerke, de Moor, and Christiaens, 2011), where systematic risk has an negative relation. An future paper could examine the same variables, but collecting data from an specific industry to see if the stock price crash risk is different with groups with similar firm characteristics to fill this research gap and enhance the discussion in CSR-washing and Stock price crash risk.
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