All that glitters is not gold: CEOs’ celebrity beyond media content Marco Caiffa Università di Roma “Tor Vergata” Via Columbia 2, 00133 Roma, Italy Tel: +390672595751 [email protected]Vincenzo Farina Università di Roma “Tor Vergata” Via Columbia 2, 00133 Roma, Italy Tel: +390672595903 [email protected]Lucrezia Fattobene* Università di Roma “Tor Vergata” Via Columbia 2, 00133 Roma, Italy Tel: +390672595931 [email protected]*Corresponding author
Transcript
All that glitters is not gold: CEOs’ celebrity beyond media content
Marco Caiffa Università di Roma “Tor Vergata” Via Columbia 2, 00133 Roma, Italy
All that glitters is not gold: CEOs’ celebrity beyond media content
Abstract
Mass media are known to be powerful in directing the public’s attention towards specific issues and socially shape individual’s opinions. In this study, we focus on Italian listed companies’ CEOs, Chairmen and Vice-Chairmen over a time span of 16 years to observe how the content of newspaper articles, the visibility of directors, and the mention by the press of celebrity (Top10) and not famous (Last_d) directors, influence investors’ reaction to news. Results reveal that celebrity status is not necessarily associated with positive emotional responses: besides the impact of the content of the news, visibility and celebrity are rather associated with a negative impact on investors’ opinion; on the other hand, scarce visibility does not drive any additional effect on stock market prices.
Keywords: stock market, investor sentiment, news, text analysis, celebrity CEOs
JEL: D53; G14; G34
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1. Introduction
For a world that is “out of reach, out of sight, out of mind” (Lippmann, 1922) for most of the
people, mass media are relevant in shaping their opinions and in influencing their perception of the
reality. In the mass media era, since the emergence of film star studies, a cultural phenomenon has
been deeply examined: celebrity (Dyer 1979; 1986). If public’ attention is considered a scarce
resource, social actors compete for attention (attention economy), and thus, someone’s celebrity
can be defined as is ability to grab more attention than other social actors (Van Krieken, 2012). In
the last decades, with the increase in the number of shareholders due to financial liberalization, the
interest of journalists in covering information about companies has also increased. Moreover, in a
scenario where media are invasive and have encroached personal lives, the status of celebrity “is
artificially producible and produced, and the celebrity’s ‘well-knownness’ a saleable and sold
commodity” (Gamson, 1994), making therefore interesting to explore the triangle “celebrity –
audience - brokers of information” in financial markets. Given that Chief Executive Officers
(CEOs) are considered the highest authority in the firms, a growth in journalists coverage of CEOs
has been registered, contributing to the creation of ‘CEO celebrity’. The role of mass media in
constructing celebrity and reputation has gained much attention in the recent literature (Chen and
Meindl, 1991; Hayward et al., 2004; Rindova et al., 2006). Previous studies have analysed CEO
celebrity in relationship with companies’ performance (Ketchen, Adams, Shook, 2008; Treadway
et al., 2009), CEOs’ compensation (Wade et al., 2006), directors’ compensation (Graffin et al.,
2008), managerial hubris (Hayward et al., 2004), CEO dismissal (Park, Kim and Sung, 2014) and
so on. Celebrity status has generally been defined as a positive circumstance (Hayward et al., 2004)
but researchers have finally also recognized the possibility that celebrity CEOs are negatively
viewed (Ketchen, Adams, Shook, 2008). In this paper we avoid to classify ex-ante directors as
good or bad, as celebrity or infamous, given that these attributions may vary along a continuum of
possible behaviours. Previous economic studies underline the impact on the stock market of the
language used by media in news concerning economic and financial issue (Ferguson, 2015;
Carretta et al., 2011; Tetlock et al., 2008; Tetlock, 2007). Anyway, no previous studies have
disentangled the effect of the content from that of celebrity status and separately quantified their
impact on investors’ perception. We aim to take advantage of the linguistic computational
techniques to extract the temporary sentiment expressed through mass media and then observe the
additional effect on stock market prices of visibility and celebrity status. In particular, we observe
the impact on shares’ prices of the proportion of news which mention the director (number of
newspapers percentage, n_of_newsper) and the impact of being either one of the most ten
mentioned director (Top10), or one of the last mentioned director (Last_d), the directors mentioned
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in less than 10 news over the 16-year time span. Results reveal that celebrity status itself does not
elicit always positive reactions but rather being in the line of fire of the press generally entails a
negative view from firms’ shareholders. Top 10 directors, in fact, negatively impact on stock
market prices, and visibility has been found to be associated with investors’ negative perception,
probably because this coincides with the feeling of the presence of more relevant information
hidden in the articles, which in turn leads to higher perceived uncertainty about the future and
therefore in a worse evaluation of the companies’ shares value.
The layout of this paper is as follows. In Section 2 we conduct a brief review of related research on
CEO celebrity and mass media communication. Section 3 and Section 4 present the sample and the
methodology, respectively. In Section 5 we describe the main findings and we discuss them and
suggestions for future research in Section 6.
2. Literature Review
In a constitutional democracy, power is divided in three branches: legislative, executive, judiciary.
“The press” or journalism has been defined the “Fourth Power”1, given its important role in
influencing public opinion. One way in which media might express their power is the possibility of
creating celebrities. The existence of celebrities related to the companies is not a new phenomenon:
everyone knows Ford, Rockfeller and Vanderbilt and their leadership in their companies. But now,
in the era of mass media, with the Web 2.0 revolution, the CEO celebrity phenomenon has gained
more attention.
Generally, those considered celebrities are social actors able to earn large-scale public attention and
who have a profit-generating value (Gamson, 1994). According to Hayward et al. (2004) “celebrity
arises when journalists broadcast the attribution that a firm’s positive performance has been caused
by its CEO’s actions”, while Rindova et al. (2005) refers to the combination of “high level of
attention” and “positive emotional responses from stakeholder audiences”. Several studies
document a positive relationship between celebrity CEOs and firms’ performance; Ranft et al.
(2006) report that hiring or developing a celebrity CEO may increase stock price, enhance outside
company’s perception and improve the morale of employees and other stakeholders. Wade et al.
(2006) document that firms enhance their credibility to stakeholders by employing celebrity CEOs
and therefore the firms are viewed more positively from the audience. Koh (2011) observes that
firm performance improves after celebrity CEOs win awards and also that celebrity CEOs engage in
more conservative accounting practices and are less likely to engage in opportunistic earnings
1 This expression was probably coined by Edmund Burke in a parliamentary debate in 1787.
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management to meet short-term earnings target. Besides these and other studies reporting a positive
effect of celebrity CEOs on firms’ performance, organizational and behavioural finance research
has found that CEO certification can cause overconfidence and managerial hubris, that, in turn, are
detrimental for firms performance (Hayward and Hambrick, 1997; Malmendier and Tate, 2005). In
a following study, Malmendier and Tate (2009) found negative cumulative abnormal returns and a
decline of accounting practices after the assignment of an award to a CEO; they also suggest that
superstar CEOs over-emphasize their personal careers focusing on short-term personal goal.
After several recent findings have revealed both positive and negative consequences associated with
CEO celebrity (Hayward et al.,2004; Ranft et al., 2006; Wade et al., 2008), a more complex
framework of the phenomenon includes a possible negative view from the audience (Ketchen,
Adams, Shook, 2008). In our study, we aim to observe investors’ reaction to visibility, to news
which mention Top10 directors and Last_d directors in the 16-year time horizon, after having
extracted and taken into account the content of each newspaper article.
The relevance of media in shaping opinions is the consequence of their ability to set the agenda for
public debate, deciding which issue to report (Chen and Meindl, 1991), to offer consolidated
assessments, to report other intermediaries’ evaluations (Deephouse, 2000), to provide common
knowledge which is perceived credible, and so on. Several recent studies, using textual analysis
technique, have extracted qualitative information from newspapers (Carretta et al., 2011; Tetlock,
2008; Antweiler and Frank, 2006), stock message boards (Antweiler and Frank, 2004), Twitter
(Sprenger et al., 2014; Bollen, Mao and Zeng, 2011; Bollen, Pepe, and Mao, 2009), Facebook
(Karabalut, 2013), Google (Preis et al., 2013), Wikipedia (Moat et al., 2014), and so on, brightening
some features on the link between financial information disseminated through media and stock
market prices. The intuition that relies behind this work is that the visibility itself of the person
mentioned in the news influences the opinions’ formation process of investors, aside from the
sentiment of the market. Media are, in fact, not only providers of redundant information, but
participants in the social construction process (Deephouse, 2000). Deciding which information to
report, to what extend emphasize it, which light – positive or negative and the many configurations
in the middle - to attribute, journalists influence stakeholder impressions ((Deephouse 2000;
Pollock and Rindova, 2003, Wade et al., 2006).
In this study, we try to extract qualitative information from newspaper articles to capture the overall
sentiment conveyed through media and then observe the effect, besides the content of the news, of
the different level of visibility, identifying: i) Top 10 CEOs, ii) Last_d, and iii) the proportion of the
press coverage. Specifically, we try to test if after taking into account the content of the news -
which is the main determinant of the direction of the stock market reaction - there is an additional
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impact of media on shares’ prices driven by the visibility or the credibility of the director mentioned
in the press. From this perspective our hypothesis is: the effect of the press on stock market prices
depends on visibility and on the celebrity status.
3. Sample
We decide to explore the above mentioned triangle “celebrity – audience – brokers of information”
in financial market using a sample of newspaper articles, considering that stakeholders, to form
their evaluation about firms’ and their leaders, have been found to rely on information
intermediaries such as the media and financial analysts (Deephouse, 2000; Zuckerman, 1999). In
particular, we choose newspapers because they are considered more credible than information
disseminated through the Web, are a way to obtain information very easy to understand with respect
to other typologies of information such as reports or financial statements, and at a very low price,
and provide common knowledge (people learn about something and also about the fact that other
people learn about something) which, in turn, influence stock prices (Morris and Shin, 2002).
The sample consists of all the news related to the more important members of the boards of
directors (BoD) of all Italian listed companies during the period 1998-2013. We select the Italian
country for the relevance of retail direct shareholding as in terms of capitalization holding and
trading participation individual investors’ represent relevant actors in the Italian Stock market,
making it a singular case in an international scenario (Coraggio and Franzosi, 2008). Moreover the
Italian context is characterized by a group of really powerful directors, well known from investors
and therefore able to impact on their opinions (Santella et al., 2007). The members of the BoD taken
into account are Chairman (C), Vice-chairman (VC) and Chief executive officer (CEO), as they are
supposed to be known from investors and therefore able to shape their opinions. The names are
obtained from Consob website in the section Emittenti - Società quotate - Organi sociali. From this
sample we derive two subsample of directors, the most ten mentioned directors (Top10) and the
group of last mentioned ones (Last_d), which are all those directors mentioned in less than 10 news,
to observe the effect of being recurrently mentioned, in the first case, or rarely mentioned, in the
second case, from the press. The news published on the Italian newspapers are downloaded using
LexisNexis™ Academic. The number of *.rtf files downloaded is 2,858. Each file contains a
number of articles that varies from 1 to 500. The final number of news processed is about 60,0002.
Datastream.
2 The initial number of relevant news extracted was 190,000 but the amount dropped after the merger with
companies’ performance database because of missing value. In a following version of the study we aim to retrieve
missing data and consider all the other news that in this version have not been analysed.
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< Insert Table 1,2,3,4 >
Table 1 shows the number of observations for year, over different industries classified according to
Datastream ICBIN industry code. The total number of observations is 4,361. Table 2 summarizes
the total number of listed companies, total directors and average board sizes, during the time period
considered. The number of listed companies varies from a minimum of 238 in 1998 to a maximum
of 301 in 2001. Table 3 illustrates for each year of the time span considered the total number of C,
VC, and CEO, the number of them excluding people who sit on more boards and the number of
them who sit on more than one boards. Table 4 exhibits some summary statistics about these
variables. The total number of C, VC, and CEOs for the whole sample period is 11,767 but because
of the phenomenon of interlocking directorship the amount of name of directors whose articles are
downloaded drops to 2,153. The number raises to 3,123 if each name is associated with the different
companies in whose board he sits. Of this directors, 1,108 are C, 994 are VC and 1,021 are CEO.
This is in line with previous studies who detect a small group of interlocked directors which are
remarkably stable over time (Santella et al., 2007), defined as the Lord of Italian stock market.
< Insert Table 5 >
Table 5 shows the list of the Top10 directors in the time horizon analysed.
4. Methodology
Different methodologies are applied to investigate the theoretical framework according to which
after an event occur, the different forms or channels of information dissemination impact on the
investors’ attention level and produce different effects on the financial market.
First, the text analysis (Stone et al., 1966) is used to classify the content and it is based on the “bag
of words” model according to which a pre-determined list of words is matched with the documents
(press news). In this study, the Linguistic Inquiry and Word Count (LIWC) for the Italian language
is used. It is a text analysis program which counts word in psychologically meaningful categories
(Tausczik and Pennebaker, 2010). The content can be defined as the degree to which news have
positive and negative meaning and it is computed by scaling the positive and negative words for the
total length of the article, following the formula P/Length and N/Length, where P is the number of
words considered positive, N the number of words considered negative, and Length is the total
number of words, in each single article. The members of the BoD to whom the news are related
taken into account are C, VC and CEO. Each name of the board’s member is associated with the
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company to be sure the news is economically relevant and no namesakes are considered. If a
member sits on more than one board, different companies are separately associated to him. So
different analysis are run for the same person which sits on the boards of different companies. To
extract all the information and avoid to lose observations, different names (like acronyms or short
name) for the same companies are considered3. For each news which refers to the person and its
company, is then extracted the publication date, the total number of words, positive and negative,
categories. The publication date extracted is exactly the date the news has been published and not
the “load-date” provided by the database Lexis Nexis. This is because, in some cases, a lag has been
found between the actual data of articles’ publishing and the data the news has been uploaded on
the database.
This rigorous approach is needed as to investigate if after a piece of news is published there is a
stock market reaction, and in a second step, the direction and the magnitude of this reaction, event-
study methodology is applied. This commonly used methodology to measure the stock market
reaction to the announcement of a particular event (Dodd and Warner, 1983; Brown and Warner;
1985) is based on the Efficient Market Hypothesis (EMH) (Fama et al., 1969; Fama, 1970) that
define a market efficient if “prices fully reflect all available information”. The aim is to observe if
after the news is published at the announcement time (t), Abnormal Return (AR), the difference
between the Actual Return on a stock i and the Expected Return on the stock i, is displayed over
various event windows. The announcement time is considered as exact event date instead of the
firm’s communication for different reason: i) investors often base their decision making process of
buying, holding and selling stocks, on second hand information rather than observing the actual
activity of the company (Tetlock et al., 2008; Antweiler and Frank, 2004; Coval and Shumway,
2001); ii) information considered is also not financial and related to personal behaviour of the
member of the BoD and firms do not communicate this type of information.
Next, Cumulative Abnormal Return (CAR) are computed between any two dates T1 and T2 as:
���� ��1, �2� = ∑ ���������� ,
where i is the stock and t the time.
The last step of the methodology is to specify an econometric model to investigate the link between
press news and stock market returns. The dependent variable is CAR while the independent
variables includes the variables related to the press news, variables related to company’s 3 For instance the bank Monte dei Paschi di Siena is searched in the articles in the following ways: Monte Paschi, MPS,
Monte dei Paschi di Siena.
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performance, and variable related to market’s performance. Among the variables computed from
the textual analysis we have i) those related to the content, positive or negative, ii) Top10 (dummy
variable) which refers to the mention in the news of one of the most 10 mentioned directors, iii)
Last_d (dummy variable) which instead includes all the directors which have less than 10 news, and
iv) number of news percentage which is a quantitative variable that refers to the proportion of press
coverage for each director.
Among variables related to company’s performance there are: return on equity (ROE), financial
leverage (LEV_PER), dividend yield (DY_PER), earnings per share (EPS), market to book value
(MTBV).
The following Table details the variable definition.
< Insert Table 6 >
The two following linear model are specified. Model 1 expresses the relationship between ARs and
CARs, the individual visibility, synthetized through the personal proportion of news over the time
horizon, and textual analysis variables (negative and positive sentiment). Model 2 takes into
account celebrity status, expressed through the Top10 variable, the effect of directors with poor
visibility, (Last_d), and textual analysis variables (negative and positive sentiment).
In this section we discuss the findings concerning the relationship between visibility, celebrity,
sentiment of the news, and stock market reaction.
< Insert Table 7 >
As displayed in Table 7, negative news have a strong, negative and statistically significant impact
on stock market prices, while positive news have a strong, positive (but less statistically
significant) impact on securities’ prices. In particular, the negative impact of negative content
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varies from a minimum of 0.168 (p < 0.001) for CAR [0;2] to a maximum of 0.326 (p < 0.001) for
CAR [-2,0], with an average value of 0.229 and a standard deviation of 0.059, while the positive
magnitude for the positive content ranges from a minimum of 0.169 (p < 0.05) for AR and CAR[-
1,0] to a maximum of 0.458 (p < 0.001)for CAR[-2,2], with an average value of 0.26 and a
standard deviation of 0.10. The variable number of newspapers , which refers to the proportion of
articles mentioning a single director, can be considered a measure of the visibility of each director
in the time span. Results reveal a negative impact of this variable on stock market prices,
suggesting that besides the content, which has been captured by the positive and negative sentiment
indicators, the greater the visibility of the actor, the worse the impact on shares’ prices. In fact,
regression coefficients are negative and statistically significant for all the event windows
considered, ranging from a minimum of 0.0097 for the AR and the CAR [-1,0] (p < 0.05) to a
maximum of 0.0342 for CAR [-2,2] (p < 0.001).
< Insert Table 8 >
Table 8 displays the results from Model 2 which takes into account the impact on stock market
prices of the press, differentiating the effect of the content, positive and negative, and of the
mention of Top10 directors or less visible ones. The analysis confirms the positive impact of
positive news and the negative impact of negative news; the magnitude of the coefficients is always
relevant and the results are stable and statistically significant. The variable which captures the
additional effect of being a celebrity director displays a stable, statistically significant (p < 0.001
for all the coefficients) negative impact on securities’ price, which ranges from a minimum of -
.0011 for AR and CAR[-1,0] to a maximum of 0.0025 for CAR[-2,2]. The average value of this
variable, -0.00186, is definitely lower than the average value for the content, suggesting that the
main impact on shares’ prices is driven by the actual content of the news, and celebrity or visibility
is an additional variable to consider, which can exacerbate the negative investors’ perception, or
soften the positive one. One possible interpretation is that this negative impact coming from high
visibility can be the result of investors’ perception of relevant information hidden in the articles,
which leads to higher perceived uncertainty about the future and therefore in a worse evaluation of
the companies’ shares value.
No statistically significant effect has been detected for directors with scarce visibility, revealing
that being not famous does not influence investors’ opinions beyond the sentiment of the press.
Overall, these results challenge the traditional view of celebrity as a trait which generates positive
feelings for the stakeholders and underpins the need to better explore the difference between
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celebrity, visibility and reputation and that between short-term and long-term impact on
companies’ (financial) performances.
5.1 Robustness
Regression analysis takes into account stock abnormal returns in different period around the event
date; however, the effect of different independent variables considered in our study may vary
considering CARs in other periods far from the event date. We re-estimate the regressions
considering different event windows and results are reported in Tables 9 and 10.
< Insert Table 9 and 10 >
As it is displayed, results are generally maintained; the effect detected is even stronger when we
considered other periods far from t0 which represents the event date.
Moving from the announcement date, robustness check analysis show us a stronger impact on stock
returns of positive and negative content of the news and a stronger negative impact of visibility and
celebrity. On the other hand, scarce visibility keeps maintaining no statistically significant impact
on shares’ prices.
The robustness test provides a certain stability to our results giving even more importance to the
research and more strength to the assumptions made.
6. Discussion
The contribution of this study relies in the exploration of visibility and celebrity in relationship to
the media content. We try to extract qualitative information from newspaper articles to capture the
overall sentiment conveyed through media and then observe the effect, besides the content of the
news, of the different level of visibility, identifying: i) Top 10 directors, ii) less mentioned directors
(Last_d), and iii) the proportion of the press coverage. Shortly, while reputation attains to the
public recognition of high quality, celebrity is the result of high level of visibility and positive
emotional responses from the audience (Rindova et al., 2010). Some scholars, on the contrary have
pointed out that celebrity can be associated also with a negative view. On this premise we aim to
study the impact of news which mention celebrity directors, taking into account the media content,
which has the power of shaping stakeholders’ opinion. Our results reveal that celebrity status itself
does not drive positive reaction but it rather negatively impact on stock market prices. In particular,
positive content correlates with an increase of shares’ price and negative content with a decrease of
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it. Visibility, measured as the number of news for each director, and celebrity, represented by the
ten most mentioned directors over the time span, both entail a negative impact on stock market
prices, furtherly challenging the view of celebrity CEOs always able to elicit positive emotional
responses. While being in the line of fire is negatively perceived by investors, being not very
visible, as in the case of the directors who are rarely mentioned in the press, does not yield
additional effect beyond that of the content of the articles. Further development of this research
may also deepen the content and try to extract the temporal focus of attention of the newspaper
articles, to observe if the temporal orientation of the journalists differently influence investors’
reaction. Moreover it would be interesting to investigate how the market incorporates news that
refer to celebrity directors, but taking into account firms’ fundamentals and eventually considering
also celebrity firms’.
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References
Antweiler, W., and Frank, M. Z., (2006). ‘Do US Stock Markets typically Overreact to Corporate News Stories?’, Working Paper.
Antweiler, W., Frank M.Z., (2004). ‘Is all that talk just noise? The information content of internet stock message boards’, The Journal of Finance, 59, 3, 1259-1293.
Bollen, J., Mao, H., Pepe, A., (2009). ‘Modeling public mood and emotion: twitter sentiment and socio-economic Phenomena’, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.
Bollen, J., Mao, H., Zeng, X., (2011). ‘Twitter mood predicts the stock market’, Journal of Computational Science, 2, 1, 1-8.
Brown, S., Warner, J., (1985). ‘Using daily stock returns: the case of event studies’, Journal of Financial Economics, 14, 3-31.
Carretta, A., Farina, V., Fiordelisi, F., Martelli, D., Schwizer, P., (2011). ‘The impact of corporate governance press news on stock market returns’, European Financial Management, 17, 100-119.
Chen, C., Meindl, J. R., (1991). ‘The construction of leadership images in the popular press’, Administrative Science Quarterly, 36, 521–551.
Chen, C.C., Meindl, J.R., (1991). ‘The construction of leadership images in the popular press: the case of Donald Burr and People Express’, Administrative Science Quarterly, 36, 4, 521-551.
Coraggio, V., Franzosi, A., (2008). ‘Household portfolio and demand for equity: an international Comparison’, BItNotes, 19, May.
Core, J. E., Guay, W., Larcker, D. F., (2008). ‘The power of the pen and executive compensation’, Journal of Financial Economics, 88, 1, 1–25.
Coval, J. D., Shumway T.,( 2001), ‘Expected Option Returns’, Journal of Finance, 56, 983- 1009.
Deephouse, D. L., (2000). ‘Media reputation as a strategic resource: An integration of mass communication and resource-based theories’, Journal of Management, 26, 1091–1112.
Dodd, P., Warner, J. B., (1983). ‘On corporate governance: a study of proxy contests’, Journal of Financial Economics, 11, 401-438.
Dyck, A., Zingales, L., (2003). ‘Asset Prices and the Media’, working paper.
Dyer, R., (1979). ‘Stars’. London: BFI Macmillan.
Dyer, R., (1986). ‘Heavenly Bodies.’ London: BFI Macmillan.
Fama, E. F., (1970). ‘Efficient capital markets: a review of theory and empirical work’, Journal of Finance, 25, 2, 383–417.
Fama, E., Fisher, L., Jensen, M., Roll, R., (1969). ‘The adjustment of stock prices to new information’, International Economic Review, 10, 1-21.
Ferguson, N., Philip, D., Lam, H., Guo, J., (2015). ‘Media Content and Stock Returns: The Predictive Power of Press’, Multinational Finance Journal, 19, 1, 1–31.
13
Gamson, J., (1994). ‘Claims to fame: Celebrity in contemporary America.’ Berkeley: University of California Press.
Graffin, S.D., Wade, J.B., Porac, J.F., McNamee, R.C., (2008). ‘The impact of CEO status diffusion on the economic outcomes of other senior managers’, Organization Science, 19, 457-474.
Hayward, M. L. A., Hambrick, D. C., (1997). ‘Explaining the premiums paid for large acquisitions: Evidence of CEO hubris’, Administrative Science Quarterly, 42, 103–127.
Hayward, M.L.A., Rindova, V.P., Pollock, T.G., (2004). ‘Believing one’s own press: the causes and consequences of CEO celebrity’, Strategic Management Journal, 25, 7, 637-53.
Karabulut, Y., (2013), ‘Can Facebook predict stock market activity?’, Working paper
Ketchen, D.J., Adams, G.L., Shook, C.L., (2008). ‘Understanding and managing CEO celebrity’, Business Horizons, 51, 529-34.
Kevin, Koh, (2011). ‘Value or Glamour? An empirical investigation of the effect of celebrity CEOs on financial reporting practices and firm performance’, Accounting and Finance, 51, 2, 517-547.
Lippmann, W., (1922). ‘Public opinion’, New York: Macmillan.
Malmendier, U., Tate, G., (2005). ‘Does overconfidence affect corporate investment? CEO overconfidence measures revisited’, European Financial Management, 11, 649–659.
Malmendier, U., Tate, G., (2009). ‘Superstar CEOs’, The Quarterly Journal of Economics, 124, 1593–1638.
Moat, H. S., Curme C., Avakian, A., Kenett, D. Y., Stanley, H. E., Preis, T., (2014). ‘Quantifying Wikipedia Usage Patterns Before Stock Market Moves’, Scientific Reports, Nature.
Park, J. H., Kim, C., Sung, Y. D., (2014). ‘Whom to dismiss? CEO celebrity and management dismissal’, Journal of business research, 67, 11, 2346 – 2355.
Preis, T., Moat, H. S., Stanley, H. E., (2013). ‘Quantifying trading behavior in financial markets using google trends’, Scientific reports, 3.
Ranft, A. L., Zinko, R., Ferris, G. R., Ronald Buckley, M., (2006). ‘Marketing the image of management: The costs and benefits of CEO reputation’, Organizational Dynamics, 35, 3, 279-290.
Rindova, V. P., Williamson, I. O., Petkova, A. P., (2010). ‘Reputation as an Intangible Asset: Reflections on Theory and Methods in Two Empirical Studies of Business School Reputations’, Journal of Management, 36, 3, 610–619.
Rindova, V. P., Williamson, I. O., Petkova, A. P., Server, J. M., (2005). ‘Being good or being known: An empirical examination of the dimensions, antecedents, and consequences of organizational reputation’, Academy of Management Journal, 48, 1033–1049.
Rindova, V.P., Pollock, T.G., Hayward, M.L.A., (2006). ‘Celebrity firms: the social construction of market popularity’, Academy of Management Review, 31, 1, 50-71.
Santella, P., Drago, C., Polo, A., Gagliardi, E., (2007). ‘The Italian Chamber of Lords sits on listed company boards: an empirical analysis of Italian listed company boards from 1998 to 2006’, MPRA paper, n. 2265.
14
Santella, P., Drago, C., Polo, A., Gagliardi, E., (2007). ‘The Italian Chamber of Lords sits on listed company boards: an empirical analysis of Italian listed company boards from 1998 to 2006’, MPRA paper, n. 2265
Sprenger, T. O., Andranik T., Philipp G. S., Isabell M. W., (2014). ‘Tweets and Trades: The Information Content of Stock Microblogs’, European Financial Management, 20, 5, 926-957.
Stone, P. J., Dunphy, D. C., Smith M. S., and Ogilvie, D.M., (1966). ‘The General Inquirer: a Computer Approach to Content Analysis’, MIT Press, Cambridge.
Tausczik, Y. R., Pennebaker, J. W., (2010). ‘The psychological meaning of words: LIWC and computerized text analysis methods’, Journal of Language and Social Psychology, 24-54.
Tetlock, P. C., (2007). ‘Giving content to investor sentiment: the role of media in the stock market’, Journal of Finance, 62, 3, 1139-68.
Tetlock, P. C., Saar-Tsechansky M., Mackassy, S., (2008). ‘More than words: quantifying language to measure firms fundamentals’, Journal of Finance, 63, 3, 1437-67.
Treadway, D. C., Adams, G. L., Ranft, A. L., Ferris, G. R., (2009). ‘A meso-level conceptualization of CEO celebrity effectiveness’, The Leadership Quarterly, 20, 554-570.
Wade, J.B., Pollock, T.G., Porac, J.F., Graffin, S.D., (2006). ‘The burden of celebrity: the impact of CEO certification contests on CEO pay and performance’, Academy of Management Journal, 49, 4, 643-60.
Table 3. Distributions of total number of C, VC and CEO over years
Year Total C, VC, CEO Except namesakes Namesakes
1998 617 543 74
1999 640 562 78
2000 728 650 78
2001 784 704 80
2002 763 692 71
2003 749 684 65
2004 726 668 58
2005 719 645 74
2006 751 676 75
2007 810 746 64
2008 816 752 64
2009 780 723 57
2010 769 712 57
2011 734 684 50
2012 695 645 50
2013 665 621 44
TOT 11746
Table 4. Summary Statistics of C, VC, and CEO
Total C, VC, CEO Except namesakes Namesakes
Mean 734.13 669.19 64.94
St. deviation 57.05 58.30 11.35
Min 617.00 543.00 44.00
Max 816.00 752.00 80.00
Mediana 741.50 680.00 64.50
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Table 5. List of the Top 10 directors.
Rank Name Press citations
1 Profumo Alessandro 6.696 2 Conti Fulvio 4.719 3 Confalonieri Fedele 3.695 4 Bazoli Giovanni 3.629 5 Mussari Giuseppe 3.404 6 Ghizzoni Federico 2.067 7 Tronchetti Provera Marco 1.35 8 Palenzona Fabrizio 1.321 9 Bernheim Antoine 1.318
10 Mazzotta Roberto 1.238
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Table 6. Regression variables
Variable Variable Type Description
>?@ABCD Dummy Variable It assumes value 1 if the piece of news mentions one of the ten most
mentioned directors, 0 otherwise
EFGD_HCD Dummy Variable It assumes value 1 if the piece of news mentions one of the less
mentioned directors (10 or less news), 0 otherwise
n_of_newsper Quantitative Variable Indicates the number (as a percentage of the total number) of news
recorded for each director.
IJGCD Quantitative Variable Abnormal Returns computed at different time
KIJGCD Quantitative Variable Cumulative Abnormal Returns computed at different time
ELMNODCD Quantitative Variable Number of words for article
@PG_CMHCD Quantitative Variable Degree to which an article has a positive meaning
QLN_CMHCD Quantitative Variable Degree to which an article has a negative meaning
J?RCD Quantitative Variable Is the amount of net income returned as a percentage of shareholders’
equity
ERS_@RJCD Quantitative Variable It is computed as debt (loans) scaled by common equity
TU_@RJCD
R@VCD
Quantitative Variable
Quantitative Variable
Market evaluation of dividend policy
Is the portion of a companies’ earnings allocated to each share of
common stock
W>XSCD Quantitative Variable It is computed as market capitalization scaled by the book value
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Table 7. Regression Analysis which considers textual analysis variables and the number of newspaper for director. Years and sectors not reported. Dependent variables ARs and CARs
AR AR1 CAR (0,1) CAR (-1, 0) CAR(0,+2) CAR (-2,0) CAR (-1,1) CAR (-2,2)
BIC -237705.2 -239628.4 -209965.1 -237705.2 -198104.2 -206674.8 -209965.1 -181527.3 * Winsorized variables, p(0.1). The symbols *, **, and *** represent significance levels of 10%, 5% and 1% respectively.)
21
Robustness check
Table 9. Regression Analysis which considers textual analysis variables and the number of newspaper for director. Years and sectors not reported. Dependent variables ARs and CARs
