Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Corporate Accessibility, Private Communications and
Stock Price Crash Risk 
Michael Firth, Sonia Man-lai Wong and Xiaofeng Zhao
This study examines how private communications is related stock price crash risk
in emerging markets where public information environment is weak. To capture
the extent and quality of private communications, we construct a corporate
accessibility measure for publicly listed firms in China based on their responses to
attempted communications with them (telephone, email, and on-line discussion
forum), and examine whether corporate accessibility is associated with lower
stock price crash risk at the firm-level. We find robust evidence that the
distribution of stock returns of accessible firms is less negatively skewed and the
stocks have a lower probability of extreme negative firm-specific returns. The
results are more pronounced in firms with low analyst coverage, high forecast
earnings dispersion, employment of non-Big 4 auditors, and greater earnings
management. We also find that, relative to non-accessible firms, accessible firms’
crash risk declines less when short-selling constraints are removed, and
accessible firms have lower market synchronicity. Lastly, accessible firms tend to
have more corporate site visits made by market participants than is the case for
non-accessible firms, and it is more likely for accessible firms that site visits
reduce crash risk. Overall, our results suggest that corporate accessibility is a
good measure of private communication and it complements public information
sources in reducing corporate asymmetry and ultimately stock price crash risk.
JEL Classification: G19, D89
Keywords: Corporate accessibility, Private communications, Crash risk
1. Introduction
Following the work of Jin and Myers (2006), scholars have turned their
attention to examining the determinants of stock price crash risk1. This body of
research has built on the premise that a lack of corporate transparency enables
corporate insiders to hoard and accumulate bad news, which subsequently
increases future stock price crash risk once the bad news is forced out.
Consistent with this notion, prior studies show that stock price crash risk is
lowered when firms adopt more conservative accounting standards, file more
voluntary disclosures, increase disclosures following the adopting of IFRS, and
have better earnings quality (Hutton et al. 2009; Kim et al. 2011; DeFond et al.
2014; Kim et al. 2014; Kim & Zhang 2015).
Many studies on corporate transparency and crash risk focus on the
accounting and disclosure practices adopted by firms. However, recent studies on

Prof. Firth is from Lingnan University, 8 Castle Peak Road, Tuen Mun, N.T., Hong Kong. Email:
mafirth@ln.edu.hk, Phone: (852) 2616-8950. Dr. Wong is from Lingnan University, 8 Castle Peak Road,
Tuen Mun, N.T., Hong Kong. Email: soniawong@ln.edu.hk, Phone: (852) 2616-8159. Mr. Zhao is from the
Chinese University of Hong Kong, No. 12 Chak Cheung Street, Shatin, N.T., Hong Kong. Email:
xiaofeng@baf.cuhk.edu.hk, Phone: (852) 6237-5342.
1
Existing studies have documented that the distribution of individual stock returns exhibits negative
skewness. That is, large negative stock returns occur more often than large positive stock returns (Chen et
al. 2001; Hong & Stein 2003).
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
corporate transparency have shown that corporate transparency is determined
not only by publicly available firm-specific information (which is generated from
corporate accounting and disclosures made by firms as well as recommendations
and reports produced by financial intermediaries) but also by information from
private communications between corporate insiders and outsiders (such as verbal
and written communications and/or conducting corporate visits and interviews)
(Bowen et al. 2002; Agarwal et al. 2008; Green et al. 2012; Cheng et al. 2013). In
this paper, we turn our attention from accounting and disclosure practices to the
private communications initiated by outsiders and examine whether the private
communications can reduce stock price crash risks in China, a market operating
under weak public information environment and that has hitherto been ignored in
the stock price crash risk literature.
China is an ideal setting for our research because crash risks in emerging markets
such as China tend to be more profound and vary a lot across the publicly listed firms.
For example, Piotroski et al. (2011) show that the negative skewness in daily excess
returns in China is significantly greater than the global average documented in Jin and
Myers (2006). More importantly, while China has many laws and regulations on
corporate governance, the enforcement of accounting rules and disclosure standards is
weak and the level of expertise and sophistication of financial intermediaries are less
developed. As a result of this, the quality of publicly available firm-specific information
is relatively low (Aharony et al. 2000; Chen & Yuan 2004; Liu & Lu 2007; Kao et al.
2009; Jian & Wong 2010; Piotroski et al., 2011). Thus, the market participants (individual
investors, fund managers, analysts, journalists) face great difficulties in obtaining
accurate information from publicly available sources. Participants operating in such a
weak public information environment will have incentives to seek additional information
directly from companies by initiating private communications with corporate
management . During private communications, market participants can ask corporate
management to clarify ambiguities in the publicly available information, which can help
them to make sense of the otherwise confusing public information (Green et al. 2012). In
addition, market participants can also obtain new firm-specific information through
directly observing the operation of the firms and/or the responses/tones of the managers
(Cheng et al. 2013).
Realizing the importance of private communications in enhancing corporate
transparency, the CSRC (China Securities Regulatory Commission) issued a
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
regulation-―The Provisions on Strengthening the Protection of the Rights and Interests of
the General Public Shareholders‖ (No. 118 [2004] of the CSRC) in 2004 to facilitate
direct communications between investors and firms2. The provision requires listed firms
in China to offer a variety of communication channels to outside investors. However, as
the CSRC has not set up a strict mechanism to enforce the regulation, we expect to see
substantial variations among firms in terms of actual accessibility and this makes China a
good testing venue for our investigation.
Unlike
corporate
accounting
reporting
and
disclosure
practices,
private
communications initiated by outsiders are difficult to observe by third parties.
Furthermore, the
information generated
depends
on the
quality of private
communications but this is hard to measure. In this study, we construct a novel measure
to capture the amount and the quality of private communications by looking at the ease
with which outsiders can effectively contact corporate insiders through publicly available
communication channels (via telephone, email, and on-line discussion forum) (hereafter
referred as corporate accessibility). We believe that corporate accessibility is a good
proxy for private communication because corporate outsiders have to contact the insiders
in some way or other before any communication can occur. Thus, corporate accessibility
is expected to be related with the frequency of private communication, especially those
initiated by outsiders who have no personal contracts with the firms. Furthermore, the
willingness of insiders to set up public communication channels for unconnected
outsiders could signal the hard-to-observe keenness of the insiders to engage in
meaningful communications with outsiders. Thus, corporate accessibility should be
positively related to the quality of the private communications.
Using an experimental approach similar to Chong et al. (2014), we individually
contact listed firms in China and construct a corporate accessibility measure for them
2
This provision was released in December 7, 2004. It is in the spirit of Provision No. 3 [2004] of the State
Council that calls for effectively protecting the rights and interests of investors, especially the general
public investors. The provision requires listed firms to improve investor relations management and
information disclosure quality. In specific terms, listed firms should open an investor relation section in
their webpages, establish dedicated investor telephone consultation, and promptly respond to concerns
raised by public investors.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
based on their responses to our approaches3. We focus on three communication channels
provided by a firm on its website: namely, on-line discussion forum, email, and telephone
communications. We consider a firm to be accessible if we can successfully communicate
with the firm using at least one of the three channels. Among the 1555 listed firms that
we investigate, we find that 26% of them are publicly accessible. The relatively low
accessibility rate is consistent with the lack of effective enforcement of the regulation and
listed firms‘ low incentive to enhance corporate transparency (Aharony et al. 2000; Chen
& Yuan 2004; Liu & Lu 2007; Kao et al. 2009; Jian & Wong 2010)). Using the measures
constructed, we then examine how corporate accessibility is related to future stock price
crash risk.
Following prior studies (Chen et al. 2001; Hutton et al. 2009; Kim et al. 2011; Kim
& Zhang 2015), we use two measures of firm-specific crash risk: (a) the negative
skewness of future firm-specific weekly returns and (b) the likelihood of occurrence of
future extreme negative firm-specific returns. We use corporate accessibility measured in
2010 to explain the stock price crash risk in the next 3 years (2011-2013). By doing so,
we can explore whether corporate accessibility indeed captures information that can
predict the stock price crash risk in the future. After controlling for various traditional
proxies of corporate transparency, we find that accessible firms are associated with lower
stock price crash risk in the future. In particular, relative to the firms that have no
accessible channels, the subsequent negative skewness of firms having at least one
accessible channel (of the three channels, telephone, email, and forum) declines by
around 36%, and the likelihood of experiencing extreme negative firm-specific returns is
reduced by 26% in the following three years. These findings provide us with basic
evidence that accessible firms suffer from less stock price crash risk.
As private communications may help corporate outsiders to understand the otherwise
confusing public information, we examine whether they play a more important role in
3
Chong et al. (2014) examine the government efficiency across countries by mailing letters to non-existent
business addresses and measuring whether they come back to a return address. They find that the number of
days before the letters are returned is associated with a set of variables that measure government efficiency,
and they argue that a simple and universal post office service could be used as signal for detecting the
quality of government.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
reducing crash risk when the public information environment is noisy (more opaque). We
use earnings management, employment of Big 4 auditors, analyst coverage, and analyst
forecast dispersion as transparency variables as they are widely used in prior studies to
measure the quality of the public information environment (e.g. Lang & Maffett 2011;
Lang et al. 2012). We find that the explanatory power of corporate accessibility increases
with opaqueness. Specifically, we find that the relation between corporate accessibility
and stock crash risk becomes more negative for firms that engage in greater earnings
management, hire non-Big 4 auditors, followed by fewer analysts, and when the analyst
earnings forecasts are more dispersed. Our results are robust when we use a matching
research design that pair accessible firms with characteristics-similar non-accessible
firms. Overall, our findings suggest that private communications initiated by outsiders are
complementary to the public information in reducing crash risk.
Next, we examine whether accessible firms actually accumulate less firm-specific
news and whether they are more likely to establish private communication with outsiders.
First, if corporate accessibility facilitates the transmission of firm-specific information
from corporate insiders to outsiders, accessible firms would have higher idiosyncratic risk
and their stock price would co-move less with the market (Morck et al. 2000; Durnev et
al. 2003). As expected, we find that accessible firms have a lower level of market
synchronicity than do non-accessible firms. In addition, the negative relation between
accessibility and market synchronicity is concentrated in firms with poorer accounting
and disclosure quality, confirming the importance of private communications in
mitigating the information asymmetry problems in these firms.
Second, prior research largely supports the theoretical view that constraining short
sales hinders price discovery because some negative information fails to be fully
incorporated into stock prices (Miller 1977; Diamond & Verrecchia 1987). Chang et al.
(2007) show that stock price crash risk is significantly reduced when short-sales
restrictions are lifted. If accessibility facilitates information transfer from corporate
insiders to outsiders and reduces the hoarding of negative information, we would expect
that the removal of short-sale constraints in accessible firms will be followed by a smaller
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
reduction in stock price crash risk than in non-accessible firms. Using a list of designated
stocks that are allowed to be sold short during the period of 2011-2013, we find that the
stock price crash risk is reduced after the short-sale constraint is removed and the
reduction is significantly larger for non-accessible firms than for accessible firms.
Third, since 2007, the Shenzhen Stock Exchange requires listed firms to disclose
data on the company site visits made by various outsiders (including financial analysts,
fund managers, public media, institutional investors, individual investors, etc.). This
provides us with the chance to test whether accessible firms actually have more and better
quality communications with the listed firms. We find that accessible firms have more
corporate site visits from individual investors, financial analysts, media, and other
outsiders. In addition, we find that corporate visits are negatively related to crash risk and
the negative relation is enhanced in accessible firms relative to non-accessible firms,
which suggests corporate accessibility might capture the real attempt of insiders to
engage in effective private communications with outsiders.
Skeptics may argue that our findings are driven by the existence of different types of
shareholders that invest in accessible and non-accessible firms, with accessible firms
being dominated by investors that are more rational and less myopic and non-accessible
firms being dominated by investors that are more speculative, overconfident, and
short-term. As a result of this, the stock prices of non-accessible firms are more likely to
suffer crash risk than accessible firms. To mitigate this concern, we examine the
characteristics and trading activities of investors in accessible and non-accessible firms.
We find that institutional investors in the two types of firms have similar investment
horizons. In addition, there is no difference in the proportions of short term, or myopic,
institutional investors across the two types of firms. In fact, the fraction of long-term
investors in accessible firms is significantly lower than in non-accessible firms. Thus, we
do not observe a pattern where institutional investors, which may be perceived as being
sophisticated investors, are more likely to invest in accessible firms. In addition, we use
the growth of shareholder numbers and trading turnover to measure the overconfidence of
investors. We find that the number of shareholders in accessible firms grows faster than
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
in non-accessible firms. Furthermore, accessible firms have higher trading turnover and
higher liquidity than non-accessible firms. These results suggest that non-accessible firms
are not dominated by overconfident investors.
Overall, our study provides systematic evidence to show that corporate accessibility
is associated with lower future stock price crash risk in China. Prior studies show that
earnings management, tax avoidance, accounting conservatism, corporate social
responsibility, and the adoption of IFRS, are significantly related with stock price crash
risk (Hutton et al. 2009; Kim et al. 2011; DeFond et al. 2014; Kim et al. 2014; Kim &
Zhang 2015). However, these studies are based on publicly listed firms in developed
markets and focus on corporate disclosure practices undertaken by company insiders. Our
study focuses on non-public information sources using private communications between
corporate insiders and outsiders. We test our conjectures and hypotheses in China, the
largest emerging market in the world where stock prices are often subject to crashes. We
find strong evidence that corporate accessibility has incremental power in explaining
future stock price crashes.
We also contribute to the literature by documenting that corporate transparency can
be improved not only through information provided by corporate insiders via disclosure
and information intermediary channels but also by the efforts of outsiders to actively seek
information from firms. The later type of information production is particularly important
for firms operating in markets where corporate disclosure standards are low and public
information sources are poor. We construct measures of corporate accessibility and use
them to signal the quality of overall private communications. Future studies can examine
detailed types of insider-initiated activities such as private verbal and written exchanges
and questions posted on corporate forums and examine whether these activities also allow
corporate outsiders to be better informed about firms.
Our study has practical relevance for investors. Prior studies, such as Yan (2011)
and Sunder (2010), suggest that the risk of extreme losses can be reduced through
screening but not though diversification. Thus, our corporate accessibility measure
provides investors with a screening technology to help avoid investing in firms that have
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
higher chances of future stock price crashes.
2. Data and Variables
To measure corporate accessibility, we manually conduct a survey based on all
non-financial firms listed on China‘s two stock exchanges as at the end of June 2010. We
exclude firms whose websites are newly published within the most recent one year 4 and
whose web address provided by the China Stock Market and Accounting Research
(CSMAR) database is invalid. To examine whether corporate accessibility is related with
future stock price crash risk, we construct two measures of crash risk based on the sample
period from 2011 to 2013. The information on firm stock returns and firm characteristics
is collected from CSMAR. After merging our datasets and excluding firms that have
missing financial information, we eventually have 4654 firm-year observations, with
1555 distinctive firms. The filtering process is presented in Table 1.
[Table 1 about here]
2.1. IR survey and measures of corporate accessibility
The details of our survey are provided in Appendix B and Appendix C. We briefly
discuss our key procedures here. First, we obtain the website addresses of all the firms
listed on China‘s two stock exchanges from CSMAR. If the address is missing or
infeasible, we search for the company‘s main page on Baidu.com (Chinese google). After
entering the website, we check whether there is an investor relations section (IR section
or IR subpage) identified on it. If yes, we then proceed to that section and check for the
following information: 1) Whether there was an on-line discussion forum with records of
communications between investors and the firm. If yes, then FORUM is set equal to 1
(accessible), and 0 (non-accessible) otherwise. 2) Whether the IR section offers an email
contact. If yes, we then contact the listed firm to confirm its accessibility. We send an
4
Newly launched websites generally need a period for testing, during which the information provided may
be incorrect and thus create a bias in identifying corporate accessibility.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
email to the firm asking a general question—the major locations where the firms have
their business operations. We carefully chose this question because it is meaningful but
not a question that involves sensitive information that may lead to the problem of
selective disclosure. If the firm actually replied to us with an answer, then EMAIL is set
to 1 (accessible), and 0 (non-accessible) otherwise. 3) Whether the IR section offered
telephone contact information. To confirm a firm‘s accessibility by telephone, we made a
direct call to a firm and enquired whether shareholders can pay a visit to the firm 5. This
question is selected because the 2004 regulation issued by the CSRC recommends that
listed firms provide minority shareholders with the opportunity to visit their firms. If we
can talk effectively with the company on this issue6, we set TEL to 1 (accessible), and 0
otherwise (non-accessible). In addition, we create another dummy variable IRACS. It is
set to 1 if at least one of the above three communication channels (telephone, email, and
forum) is accessible, and 0 if none of them is accessible7. We also create an IR score
index, IRSCORE. It adds up the number of communication channels that are accessible
(the maximum score is 3). This data-collection process took us 3 months from July to
September in 2010, covering all firms listed on the two Chinese stock exchanges as at the
end of June 2010.
An overview of our corporate accessibility measures is presented in Table 1. Among
the 1555 firms that we investigate, 67% of them have an IR section under their website
main page. In firms with an IR section, about 85% (64%, 28%) of the firms provide
telephone (email, forum) communication channels and about 20% (15%, 85%) of these
5
We use two different questions in the email and telephone survey in order to mitigate the potential biases
in firms‘ responses caused by the questions themselves (i.e., firms may be more inclined to answer certain
types of question by email and other types of question by telephone). We intentionally use a question that
relates to an established firm policy and that is capable of a simple answer (e.g., Yes or No) in the telephone
survey. We do this because some firms may refuse to talk just because the answer to an inquiry may be
considered to be too complex and difficult to communicate over the phone rather than reflecting their
unwillingness to communicate with outside shareholders.
6
An effective talk can be either a case where the company affirmatively accepted our request or a case
where it rejected our request for some acceptable reason. For details, see Appendix B. Our objective is to
gauge the ease with which outside shareholders are able to communicate with corporate insiders to obtain
information. As a result, we consider effective talking rather than the actual answers provided by a firm as
an indicator of accessibility.
7
The four accessibility measures (IRACS, TEL, EMAIL, and FORUM) take the value of 0 (non-accessible)
if there is no IR section on the firm‘s website. In a robustness test, we restrict our sample to those firms
with an IR section, and the test results are similar to those reported in this paper.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
channels are actually accessible. Taken together (see the last column), about 93% of firms
with an IR section provide at least one communication channel. Among these firms, 41%
(or 26% in terms of the total sample of 1555 firms) of them are accessible either by
online discussion forum, email, or by telephone.
2.2. Measures of stock price crash risk
We calculate the measures of stock price crash risk and the likelihood of large
negative returns based on firms‘ weekly stock returns from 2011 to 2013. We first
compute the firm-specific abnormal weekly return by running a market model. The model
is specified as:
𝑅𝑖,𝑤 = 𝛼𝑖 + 𝛽1,𝑖 𝑅𝑚,𝑤−2 + 𝛽2,𝑖 𝑅𝑚,𝑤−1 + 𝛽3,𝑖 𝑅𝑚,𝑤 + 𝛽4,𝑖 𝑅𝑚,𝑤+1 + 𝛽5,𝑖 𝑅𝑚,𝑤+2 +
𝜀𝑖,𝑤
(1)
Where 𝑅𝑖,𝑤 is the stock return of firm i in week w and 𝑅𝑚,𝑤 is the value-weighted
return on the Chinese stock market in week w. The residuals 𝜀𝑖,𝑤 from model (1) are
highly skewed. We transform them to a roughly symmetric distribution by defining the
firm-specific abnormal weekly return 𝐴𝑅𝑖,𝑤 as the log of one plus the residual, that is,
𝐴𝑅𝑖,𝑤 = 𝐿𝑜𝑔(1 + 𝜀𝑤 ).
Our first measure of stock price crash risk is the negative skewness of the
firm-specific abnormal weekly returns. In particular, for firm i in year t, the negative
skewness (𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 ) is computed as:
3
2 3/2
𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 = −[𝑛(𝑛 − 1)3/2 ∑ 𝐴𝑅𝑖,𝑤
]/[(𝑛 − 1)(𝑛 − 2)(∑ 𝐴𝑅𝑖,𝑤
) ]
(2)
Our second measure of stock price crash risk is the likelihood of the occurrence of a
large negative firm-specific return. Following Kim et al. (2011) and Hutton et al. (2009),
a firm is defined to have experienced a stock price crash in a year if it has a firm-specific
abnormal weekly return(s) that is (are) 3.2 standard deviations below the mean of the
firm-specific abnormal weekly returns in the entire fiscal year (𝐶𝑅𝐴𝑆𝐻𝑖,𝑡 =1). The
deviation of 3.2 standard deviations from its mean is chosen to generate a frequency of
0.1% in the normal distribution. The statistics in Table 2 show that the average negative
skewness is -0.229 and the standard deviation is 0.76. On average, 11.6% of the firms in
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
our sample have experienced a stock price crash.
[Table 2 about here]
3. Empirical Results
3.1. Stock price crash risk
To test the relation between corporate accessibility and stock price crash risk, we
regress the negative skewness on corporate accessibility using an OLS model, and regress
the indicator of crash on corporate accessibility using a logistic model. The two models
are specified as follows:
𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 = 𝛼0 + 𝛼1 𝐴𝐶𝑆𝑖 + 𝛼2 𝑋𝑖,𝑡−1 + 𝜀𝑖,𝑡
(3)
𝐶𝑅𝐴𝑆𝐻𝑖,𝑡 = 𝛼0 + 𝛼1 𝐴𝐶𝑆𝑖 + 𝛼2 𝑋𝑖,𝑡−1 + 𝜀𝑖,𝑡
(4)
The dependent variables in models 3 and 4 are 𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 and 𝐶𝑅𝐴𝑆𝐻𝑖,𝑡 ,
respectively. Our key explanatory variable is 𝐴𝐶𝑆𝑖 , which is one of our corporate
accessibility measures, IRSCORE, IRACS, TEL, EMAIL, and FORUM. We include a set
of control variables 𝑋𝑖,𝑡−1 that may have an effect on stock price crash risk. They are:
stock trading turnover (𝐷𝑇𝑈𝑅𝑁𝑖,𝑡−1), negative skewness (𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡−1 ), firm-specific
weekly returns volatility (𝑆𝐼𝐺𝑀𝐴𝑖,𝑡−1 ), average firm-specific weekly returns (𝑅𝐸𝑇𝑖,𝑡−1 ),
firm size (𝑆𝐼𝑍𝐸𝑖,𝑡−1 ), market-to-book ratio (𝑀𝐵𝑖,𝑡−1 ), financial leverage (𝑀𝐵𝑖,𝑡−1 ),
profitability (𝑅𝑂𝐴𝑖,𝑡−1), and discretionary accruals (𝐴𝐶𝐶𝑀𝑖,𝑡−1). These variables have
been used in prior stock price crash risk research (Kim et al. (2011); Hutton et al. (2009).
Detailed definitions of these variables can be found in Appendix A. They are lagged by
one year. The summary statistics of variables used in the models are presented in Table 2.
We include industry, province, and year fixed effects in the model. The regression results
are reported in Tables 3 and 4.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
In Table 3, the dependent variable is negative skewness. We find that corporate
accessibility measures are significantly negatively related with stock price crash risk. In
column 1, the coefficient on IRSCORE is -0.07. This means, on average, for each one unit
increase in the accessibility score, the negative skewness will be reduced by 0.07. This is
economically significant as it implies the negative skewness for an average firm will be
reduced by about 31% (the mean of NCSKEW is -0.229). In column 2, the coefficient on
IRACS is -0.083. This suggests that the skewness in accessible firms will be 0.083 lower
than that in non-accessible firms, or a decline of 36% (0.083/0.229) for firms having an
average level of negative skewness. We also find that the coefficients on TEL, EMAIL,
and FORUM are negative and significant. Overall, the results suggest that accessible
firms have lower stock price crash risk than non-accessible firms.
[Table 3 about here]
For the control variables, we find that firms with higher crash risk in the previous
period will also have higher crash risk in the current period. In addition, large firms and
firms with a high M/B ratio tend to have higher price crash risk. Lastly, we find that firms
that actively engage in earnings management are more likely to suffer from stock price
crash risk, which is consistent with the finding of Frank et al. (2009).
In Table 4, the dependent variable is a dummy variable indicating whether a firm
experiences a stock price crash (has an extreme negative return) or not. The table shows
that the coefficients on the accessibility measures are negative and statistically
significant. For example, in column 2, the coefficient on IRACS is -0.146 and significant
at the 1% level. To estimate the likelihood of price crash for accessible firms and
non-accessible firms, we first compute the predicted probability for each observation
1
using the function of 𝑃 = 1+𝑒 −𝑧 where z is the predicted value from model 4. We then
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
average the predicted probabilities for accessible firms (IRACS=1) and non-accessible
(IRACS=0) firms. We find that the average predicted probability of a stock price crash for
non-accessible firms is 13.1% and the probability for accessible firms is 9.7%. The
difference is 3.4% and significant at the 1% level. This means a decline of 26% in the
likelihood of a stock price crash when non-accessible firms become accessible. Thus, our
results suggest that accessible firms are less likely to experience a stock price crash.
[Table 4 about here]
We also find that firms with a higher level of trading turnover, greater negative
skewness in the previous period, higher valuation, lower profitability, and more earnings
management are more likely to experience stock price crash. These results are consistent
with the findings of Kim et al. (2011).
3.2. Robustness tests
Prior studies show that managers‘ tendencies to hide bad news and accelerate good
news recognition in audited financial statements could be offset by the asymmetric
verifiability requirement of conservative accounting policy (Kothari et al. 2010; Kim &
Zhang 2015). To deal with the concern that accessible firms could be more conservative
in recognition of earnings, we control for accounting conservatism in our model. In
specific, we adopt Khan and Watts (2009) firm-year conservatism measure (they call it
CSCORE in their paper). It measures the incremental timeliness of earnings in
recognizing bad news relative to good news and is expressed as linear functions of
firm-year-specific characteristics. Firms with a higher CSCORE are considered of more
conservative.
The results are reported in Panel A and Panel B of Table 5. As expected, we find that
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
conservative firms indeed have lower stock price crash risk. The results are highly
significant at 1% level. However, the coefficients on our measures of corporate
accessibility are quite stable and have no much change from those in Table 4. It suggests
our findings are robustness to the controlling of accounting conservatism.
[Table 5 about here]
We use a binary variable to capture the degree of accessibility. However, the quality
of accessibility could vary within the group of accessible (non-accessible) firms. We do a
robustness test by introducing four continuous variables to capture the variations in the
quality of corporate accessibility. These variables are: 1) Tel_Attitude, a rating (taking the
value of 0, 1, 2, 3, 4, or 5) given by our telephone interviewers on their perceptions of the
attitude and sincerity of the person who answered the phone call, with 0 being the worst
and 5 being the best. 2) Email_Timely, the logarithm of the number of days it took to
receive the firm's returned email. This variable measures the timeliness of firms in
responding to outsiders, with a low value indicating a better quality of accessibility. 3)
Email_Length, the logarithm of the number of characters in the replied email text. This
variable measures the effort made by a firm to respond to the outsiders, with a high value
indicating a better quality of accessibility. 4) Forum_PostN, the logarithm of the number
of postings on the online discussion forum. This variable measures the frequency of
interactions between firms and outsiders, with a high value indicating a better quality of
accessibility. We repeat the analysis in equations 3 and 4 using these accessibility quality
measures. The results are reported in Panel C and D of Table 5.
We find the coefficient on Tel_Attitude is significantly negative, suggesting firms
with higher quality of accessibility as measured by better attitude in answering our survey
questions are less likely to suffer stock price crash risk. In addition, firms that reply to our
email in a more timely manner and with more detailed content have significantly lower
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
stock price crash risk. Lastly, we find that firms that more frequently communicate with
outsiders on the online forum are associated with lower stock price crash risk. These
results suggest that firms that have a higher quality of accessibility are more likely to
have lower stock price crash risk.
3.3. Complementarity between corporate accessibility and public information
Facing noisy public information, market participants may complement those
otherwise confusing pieces of public information by using private communications. Thus,
private communication is more valuable in firms where public information is weak. In
this section, we examine whether the relation between corporate accessibility and stock
price crash risk becomes stronger when the public information environment is weak.
Following Lang et al. (2012) and Lang and Maffett (2011), we use four proxies that
are widely used in existing literature to measure corporate opaqueness. These are analyst
coverage (𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖,𝑡−1 ), analyst forecast dispersion (𝐷𝐼𝑆𝑃𝑖,𝑡−1 ), the employment of big
4 auditors (𝐵𝐼𝐺4𝑖,𝑡−1), and earnings management (𝐴𝐶𝐶𝑀𝑖,𝑡−1 ). Firms are believed to be
more transparent when they are followed by more financial analysts, have a lower analyst
forecast dispersion, hire Big 4 auditors, and have a lower level of earnings management
(see, Fan & Wong 2002; Fan & Wong 2005; Yu 2008). We augment models 3 and 4 by
introducing interaction terms between corporate accessibility and the transparency
measures. The results are reported in Tables 6 and 7.
In Table 6, the dependent variable is NCSKEW. In Panel A, we report the coefficients
on corporate accessibility, analyst coverage, and the interaction terms between them. We
find that the coefficients on the accessibility variables continue to be significantly
negative and the coefficients on the interaction terms are significantly positive. This
suggests that the negative relation between accessibility and stock price crash risk is
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
inversely impacted by the analyst coverage. The effect of corporate accessibility in
reducing stock price risk is greatest when firms are not followed by financial analysts. In
Panel B, the transparency measure is analyst forecast dispersion. We find that the
coefficients on the interaction terms between accessibility and forecast dispersion are
significantly negative. However, the negative coefficients on the standalone accessibility
variables disappear and in fact they even become positive. This result implies that the
effect of corporate accessibility in reducing stock price crash risk is concentrated in firms
that have large analyst forecast dispersion.
[Table 6 about here]
In Panel C, the transparency measure is the employment of Big 4 auditors. We find
that the relation between corporate accessibility and stock price crash risk is greatest
when firms hire non-big 4 auditors. In Panel D, we find corporate accessibility is more
significantly negatively related with stock price risk when firms engage intensively in
earnings management. Our results thus suggest that the effect of corporate accessibility in
reducing stock price crash risk is more pronounced in firms with poor financial reporting
quality. .
In Table 7, the explained variable is the stock price crash dummy. We find that when
firms are covered by fewer financial analysts, have dispersed analyst earnings forecasts,
hire non-Big 4 auditors, and extensively manipulate their earnings, being accessible will
have the most impact on lowering the probability of stock price crash. Overall, our results
provide us with evidence that corporate accessibility plays a complementary role to
public information sources and thus has the strongest power in explaining the stock price
crash risk of firms with poor public information sources.
[Table 7 about here]
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
3.4. Matching accessible firms with non-accessible firms
To provide robust evidence that accessible firms have lower stock price crash risk
than non-accessible firms, we directly compare these two types of firms by matching
accessible firms with characteristics-similar non-accessible firms, and then examine the
difference of stock price crash risk between them.
Specifically, we first run a logistic model where the dependent variable is IRACS and
the RHS variables are the set of control variables from models 3 and 4. With the
propensity score estimated from the logistic model, we find the best matched control firm
(IRACS=0) for each treated firm (IRACS=1) that is in the sample. Eventually, we get 1194
pairs of treated-control firms. In Panel A of Table 8, we report the mean characteristics of
the two groups of firms. We also test the characteristics difference between the treated
and control firms. We find that there is no significant difference in the characteristics of
the two groups of firms, suggesting that we are successful in pairing accessible firms with
non-accessible firms.
[Table 8 about here]
In Panel B, we present the average values of negative skewness in accessible and
non-accessible firms and the difference between them. With all paired firms, we find that
the average NCSKEW in non-accessible firms is -0.207 and is -0.272 in accessible firms.
The difference is -0.065, significant at 1%. It is slightly smaller than the coefficient on
IRACS as reported in Table 3. We also divide the full sample into two sub-samples based
on the four public information opaqueness measures. Within each sub-sample, we do a
similar matching job by pairing accessible firms with non-accessible firms. From the
table, we can see that accessible firms have lower stock price crash risk than
non-accessible firms, but the results are only significant in the groups of low analyst
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
coverage (below the median), high analyst forecast dispersion (above the median), no Big
4 auditor employment (BIG4=0), and high earnings management (above the median). The
results are consistent with our findings in Table 6, where we find that the effect of
corporate accessibility in reducing stock price crash risk concentrates in firms with
weak public information.
In Panel C, the outcome variable is CRASH. We show that, in the non-accessible
group, 12.9% of firms experience stock price crash. However, in the accessible group, the
percentage declines to 9.8%. The difference is 3%, which is significant at the 1% level. In
the subsample analysis, similar results are found. The percentage of firms that experience
stock price crash is lower in accessible firms than in non-accessible firms, but the results
are concentrated in the groups of low analyst coverage (below the median), high analyst
forecast dispersion (above the median), no use of a Big 4 auditor (BIG4=0), and high
earnings management (above the median). Overall, we find robust evidence showing that
accessibility is negatively related with stock price crash risk and the effect is most
pronounced in firms with weak public information.
3.5. More evidence on information hoarding hypothesis
So far, we have documented that corporate accessibility is negatively related with
stock price crash risk, which is consistent with the information hoarding hypothesis that
accessible firms are more transparent and accumulate less bad news information. In this
section, we attempt to examine whether accessible firms are indeed more transparent and
hoard fewer negative news items than non-accessible firms and whether accessible firms
are associated with more active private communications.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
3.5.1. Market synchronicity
The movement of individual stock prices tend to be synchronized with that of the
general market when the flow of firm-specific news is restricted (Morck et al. 2000;
Durnev et al. 2003). If the communication between firm insiders and outside stakeholders
facilitates the transmission of firm-specific news, the stock prices of accessible firms
would less co-move with the market and exhibit a higher level of idiosyncratic risk than
those of non-accessible firms. In this section, we examine the relation between corporate
accessibility and individual stock market synchronicity.
To measure the market synchronicity, we obtain the 𝑅 2 from model 1 as specified in
section 2. Following Morck et al. (2000), we define idiosyncratic risk using a logistic
transformation of 𝑅 2 , namely, 𝐼𝐷𝐼𝑂𝑆𝑌𝑁 = 𝑙𝑛(
1−𝑅 2
𝑅2
) . The market synchronicity of
individual stocks is lower when IDIOSYN is higher. We regress IDIOSYN on accessibility
measures by controlling for the same set of variables as in models 3 and 4. The results are
presented in Table 9.
[Table 9 about here]
In column 1, we find that IRACS is significantly and positively related with
IDIOSYN, suggesting that accessible firms have lower levels of market synchronicity
than do non-accessible firms. We also estimate the models by including the interaction
terms between IRACS and transparency measures. From columns 2-5, we can see that the
positive relation between accessibility and IDIOSYN is mitigated when firms are
followed by more financial analysts, have lower analyst forecast dispersion, hire Big 4
auditors, and have lower levels of earnings management. The results confirm our findings
that the effect of corporate accessibility in facilitating the transmission of firm-specific
information concentrates in firms plagued by information asymmetry.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
3.5.2. The change in crash risk when short-sales constraints are removed
China allowed margin trading and short selling from March 2010. It has a list of
designated stocks that are allowed to be sold short; other stocks cannot be shorted. Prior
studies suggest that short-sales constraints inhibits the impounding of negative
information into stock prices as pessimistic investors are forced to sit out of the stocks
(Miller 1977; Diamond & Verrecchia 1987). When the constraints are removed, stock
prices are more likely to reflect the full information possessed by investors and are valued
closer to the intrinsic value. In line with this thinking, Chang et al. (2007) find that the
stock return skewness is significantly reduced when short-sales restrictions are lifted
using a list of designated securities that can be sold short in the Hong Kong stock market.
In this section, we attempt to examine the change of stock price crash risk in the period
after the lifting of the short-sale restrictions. We expect that if accessible firms are more
transparent and hoard less negative information, their stock price risk would decline less
than that of non-accessible firms.
During our sample period from 2011 to 2013, 631 firms are newly added to the list
of stocks that are allowed to be sold short. Specifically, 153 firms are added on December
5 2011, 273 firms are added on January 31 2013, and 205 firms are added on September
16 2013. The information is presented in Panel A of Table 10. We compute the change of
stock price crash risk (the likelihood of crash) using NCSKEW (CRASH) one year after
the date that the firms are allowed to be sold short minus NCSKEW (CRASH) one year
before the date. Again, NCSKEW and CRASH are estimated based on the weekly
firm-specific abnormal returns.
[Table 10 about here]
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
In Panel B, we present the change in risk results. As expected, we find that the stock
price crash risk has declined after the restriction is removed, but the decline mainly
comes from non-accessible firms. For example, NCSKEW in non-accessible firms
(IRACS=0) declines by 0.135, but it declines only by 0.047 in accessible firms
(IRACS=1). The difference is 0.088 and is significant. Similarly, the percentage of firms
experiencing stock price crashes (CRASH) is reduced by 3.7% in non-accessible firms but
only by 0.8% in accessible firms, with a significant difference of 2.8%. We obtain similar
results when corporate accessibility is measured by TEL, EMAIL, and FORUM. The
results suggest that accessible firms are actually less likely to accumulate negative
information than non-accessible firms.
3.5.3. Private communications via corporate site visits
Private communications are difficult to observe directly. Since 2007, the Shenzhen
Stock Exchange in China requires its listed firms to disclose data on company site visits –
an important type of private communication. This provides us with a valuable chance to
test whether accessible firms have more communications via site visits and the whether
the effect of corporate visits in reducing stock price crash risk is enhanced in accessible
firms relative to non-accessible firms.
To conduct the test, we collect data on corporate site visits during the period of
2011-2013 from the WIND database. Our data set has information on private meetings
between firms and outsiders, such as the meeting date, places, and parties joining the
meetings. Details of the types of parties visiting the firms are also disclosed in the
database. These parties include financial analysts, media, mutual funds, individual
investors, bank and insurance companies, foreign institutions, assets management and
consultant companies, and others. We use the number of site visits by each type of
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
outsider visiting the firms to measure the frequency of communications between firms
and those outsiders. However, as only the Shenzhen Stock Exchange requires firms to
disclose this private meeting information, our sample is restricted to firms listed on it.
This gives us 2418 firm-year observations.
[Table 11 about here]
In Panel A of Table 11, we present the average number of site visits by each type of
outsider in accessible firms and non-accessible firms. We also test for the difference
between the two groups of firms. We find that accessible firms tend to have significantly
more site visits than non-accessible firms. For example, the frequency of visits by
financial analysts is 5.4 times per year in accessible firms (IRACS=1) and is 3.7 times per
year in non-accessible firms (IRACS=0). The difference is 1.7 and significant at the 1%
level. The visit frequency by the media is generally low but it is significantly higher in
accessible firms than in non-accessible firms. It is interesting to note that the visit
frequency by foreign institutional investors is unrelated to corporate accessibility. This
result is reasonable because the visits by foreign institutional investors are more likely to
be arranged via contacts other than the public channels captured by our accessibility
measure. In the last row, we sum the visits by all types of outsiders and find that the visit
frequency in accessible firms is higher than in non-accessible firms. Our results suggest
that accessible firms actually have more frequent communications with outsiders.
In an unreported table, we find that corporate visits are negatively related with stock
price crash risk. This effect should increase with corporate accessibility if our
accessibility measure identifies firms with better quality of communications with visitors.
To test this, we include the interaction terms between corporate accessibility measures
and the frequency of visits in the baseline models as specified in models 3 and 4. The
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
results are reported in Panels B and C of Table 11.
From the table, we find that the coefficients on IRACS*Financial analysts,
IRACS*Media, and IRACS*Individuals are significantly negative, suggesting that
corporate accessibility increases the dissemination of firm specific information through
communications with financial analysts, media, and individual investors reduce stock
price crash risk in accessible firms. We also find similar results, albeit less significant, for
site visits by mutual funds and assets management and consultant companies. However,
we do not find significant results for other types of visitors. One interesting finding is that
the coefficients on IRACS continue to be significantly negative in all specifications. This
is not surprising because corporate visits are only one type of private communication.
The results suggest that there are other channels (such as telephone interviews, email
communications, forum discussion) that increase corporate transparency in addition to
the communications via site visits with financial analysts, media, and individuals, which
is captured by our corporate accessibility measure. Overall, the results on corporate visits
suggest that corporate accessibility can be a valid signal of the frequency as well as the
quality of private communications.
3.6. Alternative explanations
We have documented that corporate accessibility reduces the accumulation of
negative firm-specific information and thus is negatively related with stock price crash
risk. Accessible firms are those that could have a better investor community. They could
have more sophisticated investors, such as long-term institutional investors. In contrast,
non-accessible firms could have more myopic investors that contribute to stock
over-valuation and frequent crashes. To deal with this concern, we examine the
investment horizons and investor characteristics of accessible firms and non-accessible
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
firms.
Following Gaspar et al. (2005) and Yan and Zhang (2009), we use the frequency of
overall portfolio rotation for institutional investors, or the churn rate, to measure an
investor‘s investment horizon. In each quarter t, the churn rate for investor j is defined as
min (𝐶𝑅𝑏𝑢𝑦 𝑗,𝑡 , 𝐶𝑅𝑠𝑒𝑙𝑙 𝑗,𝑡 ) /[∑𝑖∈𝑄[(𝑁𝑗,𝑖,𝑡 𝑃𝑖,𝑖,𝑡 − 𝑁𝑗,𝑖,𝑡−1 𝑃𝑖,𝑖,𝑡−1 )/2]] , where 𝑁𝑗,𝑖,𝑡 is the
number of shares of stock i held by investor j at the end of quarter t; 𝑃𝑖,𝑖,𝑡 is the share
price for stock i at the end of quarter t; Q is the set of companies held by investor j. We
average each investor‘s quarterly churn rate to get the annual measure. We examine the
difference of investor horizons between accessible firms and non-accessible firms. The
results are reported in Table 12.
[Table 12 about here]
From the table, we can see that the churn rate for both accessible firms and
non-accessible firms is around 0.130 no matter which accessibility measure is used. We
also define short-term institutional investors as those with the highest average churn rate
(i.e., the top tertile) and those in the bottom tercile as long-term institutional investors.
Table 12 shows that around 27% of the institutional investors that invest in
non-accessible firms are short-term investors. This number is approximately identical to
that of the accessible firms. Surprizingly, we find that the percentage of long-term
institutional investors in non-accessible firms is higher than in accessible firms. In
addition, we define short-term (long-term) institutional ownership as the ratio of the
number of shares held by short-term (long-term) institutional investors and the total
number of shares outstanding. We find that the average short-term institutional ownership
in accessible firms is significantly higher than in non-accessible firms. However, we find
that the two types of firms have no significant difference in long-term institutional
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
ownership. The results thus refute the argument that our main findings are driven by the
difference in investor types.
We also look at the investor and trading information based on the entire body of
shareholders. First, as myopic investors are more likely to be driven by market sentiment,
stocks with positive momentum are more likely to attract speculative investors, which
increase the likelihood of crash risk. Using the quarterly growth of total shareholder
numbers, we find that non-accessible firms experience a quarterly growth rate of 0.81%,
which is significantly lower than the 1.34% that accessible firms experience. The results
go against the argument that the high stock price crash risk in non-accessible firms is
driven by the growth of new investors.
In addition, theoretical models predict that overconfident investors will trade more
than rational investors (Diether et al. 2002; Hong & Stein 2003; Statman et al. 2006).
Can non-accessible firms be dominated by overconfident investors such that their stock
prices are more over-valued and thus suffer from more crash risk? We investigate share
trading activity and find that the trading turnover in accessible firms is significantly
higher than in non-accessible firms. The results thus do not support the overconfidence
explanation. Lastly, we find that accessible firms are significantly more liquid than
non-accessible firms using the Amihud (2002) illiquidity measure. We interpret the
higher level of trading turnover and liquidity in accessible firms as being due to the
higher level of transparency in these firms.
4. Conclusion
In emerging markets, such as China, the financial sophistication of institutions is
poor and the information environment is weak. In such economies, investors and
information processors find it difficult to obtain valuable information from firms‘
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
disclosures due to the deficiencies in accounting and reporting quality. As a result, the
stock prices of Chinese listed firms are prone to crash risk. In this study, we construct a
novel measure of corporate transparency by contacting listed firms using telephone,
email, and online discussion forum and assessing their responsiveness to our
communication. We find that accessible firms are associated with a lower level of stock
price crash risk than non-accessible firms.
Our analysis shows that corporate accessibility complements traditional corporate
disclosure and information intermediaries in reducing information asymmetry. We find
that the negative relation between accessibility and price crash risk is enhanced in firms
that are followed by fewer financial analysts, have higher analyst forecast dispersion, hire
non-Big 4 auditors, and engage in more earnings management.
We further show that corporate accessibility is associated with more communications
with outsiders and less accumulation of ‗bad news‘ firm-specific information.
Specifically, we find that accessible firms have more frequent corporate site visits by
individual investors, financial analysts, media, and fund managers. We also find that
accessible firms experience smaller declines in stock price crash risk than non-accessible
firms when short-sales constraints are removed. In addition, the stock prices of accessible
firms are less synchronized with those of non-accessible firms. Lastly, we find that the
investment horizon of investors between accessible firms and non-accessible firms has no
significant difference, and there is no discernible pattern that non-accessible firms are
dominated by overconfident investors.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 1
Sample and IR Survey
All non-financial firms listed on one of
China‘s two stock exchanges at the end of
June 2010. (i)
1798
Number of firms after excluding firms with
an invalid website (a. the websites are newly
published within one year. b. the web
address provided by CSMAR is invalid). (ii)
1572
Number of firms after excluding firms with
missing financial data. (iii)
1555
Number of firms equipped with IR subpages. (iv) (iv/iii)
1042 (67%)
Communication channels
Tel-phone
E-mail
Forum
Overall
The channel is provided, (v) (v/iv)
888 (85.2% )
670 (64.3%)
295 (28.3%)
971 (93.2%)
The channel is accessible, (vi) (vi/v)
173 (19.5%)
99 (14.8%)
252 (85.4%)
398 (41%)
Table 2
Summary Statistics Variables
This table provides the summary statistics of variables used in this study. All variables are defined in Appendix A.
Variables
N
Mean
Std.
Q1
Q2
Q3
NCSKEW
CRASH
4654
4654
-0.229
0.116
0.76
0.32
-0.68
0.00
-0.27
0.00
0.18
0.00
IDIOSYN
ANALYST
DISP
BIG4
DTURN
SIGMA
RET
SIZE
MB
LEV
ROA
ACCM
4654
4654
4654
4654
4654
4654
4654
4654
4654
4654
4654
4654
0.498
1.439
8.527
0.047
-0.094
0.057
-0.001
29.137
2.296
0.076
0.065
0.054
0.71
1.27
1.77
0.21
0.16
0.02
0.01
1.02
2.55
0.11
0.08
0.06
0.01
0.00
7.00
0.00
-0.18
0.05
-0.01
28.43
1.22
0.00
0.03
0.02
0.45
1.39
9.00
0.00
-0.08
0.06
0.00
28.95
1.65
0.02
0.06
0.04
0.91
2.56
10.00
0.00
-0.01
0.07
0.00
29.65
2.47
0.12
0.09
0.07
Tel_Attitude
Email_Timely
Email_Length
Forum_PostN
4654
4654
4654
4654
2.190
3.320
0.160
0.540
2.07
0.45
0.85
1.60
0.00
3.40
0.00
0.00
3.00
3.40
0.00
0.00
4.00
3.40
0.00
0.00
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 3
Corporate Accessibility and Stock Price Crash Risk
This table reports the OLS estimate results of the relation between corporate accessibility and stock price crash risk.
We measure firms' stock price crash risk using NCSKEW, which is the negative skewness of firm-specific weekly returns
over the fiscal year period. Our key independent variables are the measures of corporate accessibility, including a score
index (IRSCORE) and four dummy variables (IRACS, TEL, EMAIL, and FORUM). All variables are defined in Appendix
A. The sample consists of 4654 firm-year observations covering the period of 2011-2013 (accessibility measures are
constructed based on the survey in 2010). Industry, province, and year fixed effects are included. The t statistics based on
a robust standard error estimate clustering at the industry and province levels are reported in parentheses. Significance at
the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Dep. variable
NCSKEW
(1)
(2)
(3)
(4)
(5)
IRSCORE
-0.070***
( -5.04)
IRACS
-0.083***
( -4.00)
TEL
-0.057**
( -2.49)
EMAIL
-0.120***
( -3.54)
FORUM
-0.106***
( -4.22)
DTURN
0.046
0.045
0.043
0.046
0.047
( 0.68)
( 0.66)
( 0.63)
( 0.67)
( 0.68)
NCSKEWt-1
0.108***
0.108***
0.108***
0.107***
0.107***
( 7.03)
( 7.06)
( 7.00)
( 6.98)
( 6.99)
SIGMA
0.282
0.286
0.299
0.320
0.330
( 0.73)
( 0.74)
( 0.78)
( 0.84)
( 0.84)
RET
0.728
0.707
0.754
0.710
0.669
( 0.47)
( 0.46)
( 0.49)
( 0.46)
( 0.43)
SIZE
0.051***
0.051***
0.050***
0.052***
0.051***
( 4.04)
( 4.11)
( 4.01)
( 4.12)
( 4.10)
MB
0.051***
0.051***
0.051***
0.051***
0.052***
( 8.09)
( 8.08)
( 8.13)
( 8.14)
( 8.16)
LEV
-0.164
-0.157
-0.154
-0.160
-0.165
( -1.43)
( -1.36)
( -1.33)
( -1.39)
( -1.42)
ROA
0.423**
0.407**
0.396**
0.386**
0.401**
( 2.18)
( 2.11)
( 2.05)
( 2.00)
( 2.09)
ACCM
0.520**
0.525**
0.526**
0.533**
0.534**
( 2.20)
( 2.23)
( 2.22)
( 2.24)
( 2.26)
INTERCEPT
-1.888***
-1.907***
-1.886***
-1.939***
-1.899***
( -4.97)
( -5.05)
( -4.98)
( -5.12)
( -5.04)
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Ind, Prov
4654
7.75%
Yes
Yes
Yes
Ind, Prov
4654
7.65%
Yes
Yes
Yes
Ind, Prov
4654
7.49%
Yes
Yes
Yes
Ind, Prov
4654
7.52%
Yes
Yes
Yes
Ind, Prov
4654
7.64%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 4
Corporate Accessibility and The Likelihood of Stock Price Crash
This table reports the probit estimate results of the relation between corporate accessibility and the likelihood of stock
price crash. The dependent variable is CRASH, which is an indicator variable that takes the value one for a firm-year that
experiences one or more firm-specific weekly returns falling 3.2 standard deviations below the mean firm-specific
weekly returns over the fiscal year. Our key independent variables are the measures of corporate accessibility, including
a score index (IRSCORE) and four dummy variables (IRACS, TEL, EMAIL, and FORUM). All variables are defined in
Appendix A. The sample consists of 4654 firm-year observations covering the period of 2011-2013 (accessibility
measures are constructed based on the survey in 2010). Industry, province, and year fixed effects are included. The
chi-square statistics are reported in parentheses. Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***,
respectively.
Dep. variable
CRASH
(1)
(2)
(3)
(4)
(5)
IRSCORE
-0.114***
( 8.59)
IRACS
-0.146***
( 7.78)
TEL
-0.118**
( 3.94)
EMAIL
-0.276**
( 5.08)
FORUM
-0.151**
( 4.63)
DTURN
0.533***
0.529***
0.528***
0.538***
0.536***
( 10.90)
( 10.75)
( 10.72)
( 11.11)
( 11.04)
NCSKEWt-1
0.089**
0.089**
0.088**
0.087**
0.087**
( 6.52)
( 6.54)
( 6.39)
( 6.24)
( 6.30)
SIGMA
-12.419***
-12.435***
-12.530***
-12.421***
-12.406***
( 35.98)
( 36.11)
( 36.63)
( 36.05)
( 35.92)
RET
-1.527
-1.561
-1.333
-1.511
-1.461
( 0.16)
( 0.17)
( 0.12)
( 0.16)
( 0.15)
SIZE
-0.034
-0.034
-0.037
-0.033
-0.035
( 1.36)
( 1.30)
( 1.54)
( 1.28)
( 1.43)
MB
0.034***
0.033***
0.034***
0.034***
0.034***
( 9.37)
( 9.15)
( 9.48)
( 9.51)
( 9.89)
LEV
-0.054
-0.044
-0.038
-0.048
-0.048
( 0.04)
( 0.03)
( 0.02)
( 0.03)
( 0.03)
ROA
-0.638*
-0.649*
-0.663*
-0.694*
-0.679*
( 2.80)
( 2.90)
( 3.03)
( 3.32)
( 3.18)
ACCM
1.444***
1.456***
1.457***
1.468***
1.467***
( 10.31)
( 10.48)
( 10.50)
( 10.67)
( 10.66)
INTERCEPT
-0.133
-0.155
-0.086
-0.189
-0.117
( 0.02)
( 0.03)
( 0.01)
( 0.04)
( 0.02)
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
4654
3.99%
Yes
Yes
Yes
4654
3.96%
Yes
Yes
Yes
4654
3.85%
Yes
Yes
Yes
4654
3.89%
Yes
Yes
Yes
4654
3.87%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 5
Robustness Tests
In Panels A and B, we examine the effect of corporate accessibility by controlling for accounting conservatism,
which is Khan and Watts's (2009) firm-year conservatism measure. In Panels B and C, we present the results of exploring
the quality of corporate accessibility. We use continuous variables to measure the quality of accessibility, including the
attitude rating in the telephone interview (Tel_Attitude), the timeliness of email reply (Email_Timely), the information
content of the email reply (Email_Length), and the number of posts on the discussion forum (Forum_PostN). In Panels A
and C, the dependent variable is NCSKEW and OLS estimate is used. The t-statistics based on a robust standard error
estimate clustering at firm level are reported in parentheses.In Panels B and D, the depdent variable is CRASH and probit
model is used. The chi-square statistics are reported in parentheses. All variables are defined in Appendix A. The sample
consists of 4654 firm-year observations covering the period of 2011-2013 (accessibility measures and IR variables are
constructed based on the survey in 2010). Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***,
respectively.
Panel A: Stock price crash risk controlling for accounting conservatism
Dep. variable
NCSKEW
(1)
(2)
(3)
IRSCORE
-0.069***
( -4.92)
IRACS
-0.081***
( -3.90)
TEL
-0.057**
( -2.47)
EMAIL
(4)
(5)
-0.116***
( -3.40)
FORUM
Accounting conservatism
-0.726***
( -6.42)
-0.727***
( -6.44)
-0.730***
( -6.45)
-0.727***
( -6.42)
-0.104***
( -4.14)
-0.727***
( -6.43)
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Ind, Prov
4654
8.62%
Yes
Yes
Yes
Ind, Prov
4654
8.52%
Yes
Yes
Yes
Ind, Prov
4654
8.37%
Yes
Yes
Yes
Ind, Prov
4654
8.39%
Yes
Yes
Yes
Ind, Prov
4654
8.52%
(4)
(5)
Panel B: The likelihood of stock price crash controlling for accounting conservatism
Dep. variable
CRASH
(1)
(2)
(3)
IRSCORE
-0.110***
( 8.00)
IRACS
-0.141***
( 7.22)
TEL
-0.118**
( 3.90)
EMAIL
-0.266**
( 4.70)
FORUM
Accounting conservatism
Industry FE
Province FE
Year FE
N
Adj. R-squared
-1.176***
( 21.56)
-1.177***
( 21.61)
-1.190***
( 22.10)
-1.180***
( 21.73)
-0.153**
( 4.55)
-1.182***
( 21.77)
Yes
Yes
Yes
4654
4.59%
Yes
Yes
Yes
4654
4.57%
Yes
Yes
Yes
4654
4.48%
Yes
Yes
Yes
4654
4.51%
Yes
Yes
Yes
4654
4.49%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel C: Quality measure of corporate accessibility and stock price crash risk
Dep. variable
NCSKEW
(1)
(2)
(3)
Tel_Attitude
-0.008*
( -1.70)
Email_Timely
0.038**
( 2.37)
Email_Length
-0.017**
( -2.03)
Forum_PostN
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Ind, Prov
4654
7.44%
Yes
Yes
Yes
Ind, Prov
4654
7.45%
Yes
Yes
Yes
Ind, Prov
4654
7.44%
(4)
-0.014***
( -2.66)
Yes
Yes
Yes
Ind, Prov
4654
7.50%
Panel D: Quality measure of corporate accessibility and the likelihood of stock price crash
Dep. variable
CRASH
(1)
(2)
(3)
(4)
Tel_Attitude
-0.019*
( 3.23)
Email_Timely
0.093**
( 4.92)
Email_Length
-0.046*
( 3.69)
Forum_PostN
-0.027**
( 5.79)
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
4654
3.76%
Yes
Yes
Yes
4654
3.83%
Yes
Yes
Yes
4654
3.82%
Yes
Yes
Yes
4654
3.85%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 6
The Impact of Information Asymmetry on Stock Price Crash Risk
This table reports the OLS estimate results of the impact of information asymmetry on the relation between corporate
accessibility and stock price crash risk. We measure firms' stock price crash risk using NCSKEW, which is the negative
skewness of firm-specific weekly returns over the fiscal year period. Our key independent variables are the measures of
corporate accessibility, including a score index (IRSCORE) and four dummy variables (IRACS, TEL, EMAIL, and
FORUM). In Panel A, we measure information asymmetry using analyst coverage (ANALYST). In Panel B, we measure
information asymmetry using analyst forecast dispersion (DISP). In Panel C, we measure information asymmetry using
the employment of Big 4 auditors (BIG4). In Panel D, we measure information asymmetry using a firm‘s earnings
management (ACCM). All variables are defined in Appendix A. The sample consists of 4654 firm-year observations
covering the period of 2011-2013 (accessibility measures are constructed based on the survey in 2010). Industry,
province, and year fixed effects are included. The t statistics based on a robust standard error estimate clustering at the
industry and province levels are reported in parentheses. Significance at the 10%, 5%, and 1% level is indicated by *, **,
and ***, respectively.
Panel A: The impact of analyst coverage
Dep. variable
(1)
IRSCORE
-0.119***
( -5.32)
IRSCORE*ANALYST
0.025**
( 2.27)
IRACS
IRACS*ANALYST
(2)
NCSKEW
(3)
(4)
(5)
-0.149***
( -4.47)
0.036**
( 2.23)
TEL
-0.114***
( -3.15)
0.035**
( 2.38)
TEL*ANALYST
EMAIL
-0.180**
( -2.16)
0.033*
( 1.90)
EMAIL*ANALYST
FORUM
ANALYST
0.051***
( 3.62)
0.048***
( 3.35)
0.049***
( 3.50)
0.059***
( 4.60)
-0.211***
( -4.78)
0.051**
( 2.45)
0.054***
( 4.15)
Control
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Yes
Ind, Prov
4654
8.30%
Yes
Yes
Yes
Yes
Ind, Prov
4654
8.19%
Yes
Yes
Yes
Yes
Ind, Prov
4654
8.16%
Yes
Yes
Yes
Yes
Ind, Prov
4654
8.05%
Yes
Yes
Yes
Yes
Ind, Prov
4654
8.21%
FORUM*ANALYST
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel B: The impact of analyst forecast dispersion
Dep. variable
(1)
IRSCORE
0.114*
( 1.65)
IRSCORE*DISP
-0.022***
( -2.76)
IRACS
IRACS*DISP
(2)
NCSKEW
(3)
(4)
0.163*
( 1.66)
-0.029**
( -2.56)
TEL
0.202*
( 1.86)
-0.031**
( -2.48)
TEL*DISP
EMAIL
0.103
( 1.23)
-0.022*
( -1.81)
EMAIL*DISP
FORUM
-0.002
( -0.23)
-0.001
( -0.14)
-0.004
( -0.47)
-0.008
( -1.07)
0.194
( 1.58)
-0.036**
( -2.49)
-0.006
( -0.83)
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.93%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.81%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.64%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.57%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.80%
(2)
NCSKEW
(3)
(4)
(5)
FORUM*DISP
DISP
Control
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Panel C: The impact of Big 4 auditors
Dep. variable
IRSCORE
IRSCORE*BIG4
(1)
-0.076***
( -5.32)
0.118**
( 2.11)
IRACS
-0.090***
( -4.20)
0.133**
( 1.97)
IRACS*BIG4
TEL
-0.061***
( -2.61)
0.110*
( 1.68)
TEL*BIG4
EMAIL
-0.135***
( -3.67)
0.151**
( 2.07)
EMAIL*BIG4
FORUM
-0.096
( -1.46)
-0.092
( -1.36)
-0.060
( -1.06)
-0.051
( -0.90)
-0.117***
( -4.56)
0.183**
( 2.13)
-0.074
( -1.32)
Yes
Yes
Yes
Yes
Ind, Prov
4654
Yes
Yes
Yes
Yes
Ind, Prov
4654
Yes
Yes
Yes
Yes
Ind, Prov
4654
Yes
Yes
Yes
Yes
Ind, Prov
4654
Yes
Yes
Yes
Yes
Ind, Prov
4654
FORUM*BIG4
BIG4
Control
Industry FE
Province FE
Year FE
Cluster
N
(5)
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Adj. R-squared
7.80%
Panel D: The impact of earnings management
Dep. variable
(1)
IRSCORE
-0.018
( -0.89)
IRSCORE*ACCM
-1.047***
( -3.37)
IRACS
IRACS*ACCM
7.69%
7.51%
7.54%
7.70%
(2)
NCSKEW
(3)
(4)
(5)
-0.006
( -0.21)
-1.525***
( -3.57)
TEL
0.014
( 0.43)
-1.427***
( -2.89)
TEL*ACCM
EMAIL
-0.020
( -0.65)
-1.012**
( -2.02)
EMAIL*ACCM
FORUM
ACCM
0.940***
( 3.25)
1.023***
( 3.40)
0.835***
( 3.04)
0.533**
( 2.16)
-0.026
( -0.72)
-1.634***
( -3.21)
0.768***
( 2.96)
Control
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.96%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.88%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.65%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.62%
Yes
Yes
Yes
Yes
Ind, Prov
4654
7.80%
FORUM*ACCM
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 7
The Impact of Information Asymmetry on the Likelihood of Stock Price Crash
This table reports the probit estimate results about the impact of information asymmetry on the relation between
corporate accessibility and the likelihood of stock price crash. The dependent variable is CRASH, which is an indicator
variable that takes the value one for a firm-year that experiences one or more firm-specific weekly returns falling 3.2
standard deviations below the mean firm-specific weekly returns over the fiscal year. Our key independent variables are
the measures of corporate accessibility, including a score index (IRSCORE) and four dummy variables (IRACS, TEL,
EMAIL, and FORUM). In Panel A, we measure information asymmetry using analyst coverage (ANALYST). In Panel B,
we measure information asymmetry using analyst forecast dispersion (DISP). In Panel C, we measure information
asymmetry using the employment of Big 4 auditors (BIG4). In Panel D, we measure information asymmetry using
managerial earning management (ACCM). All variables are defined in Appendix A. The sample consists of 4654
firm-year observations covering the period of 2011-2013 (accessibility measures are constructed based on the survey in
2010). Industry, province, and year fixed effects are included. The chi-square statistics are reported in parentheses.
Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Panel A: The impact of analyst coverage
Dep. variable
IRSCORE
IRSCORE*ANALYST
(1)
-0.295***
( 19.31)
0.108***
( 12.48)
IRACS
(2)
CRASH
(3)
(4)
(5)
-0.400***
( 21.60)
0.159***
( 14.90)
IRACS*ANALYST
TEL
-0.295***
( 9.63)
0.115**
( 6.04)
TEL*ANALYST
EMAIL
-0.409**
( 5.18)
0.269**
( 5.51)
EMAIL*ANALYST
FORUM
ANALYST
-0.047
( 2.32)
-0.058*
( 3.39)
-0.036
( 1.43)
-0.010
( 0.13)
-0.472***
( 13.02)
0.195***
( 11.76)
-0.032
( 1.24)
Control
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
Yes
4654
4.34%
Yes
Yes
Yes
Yes
4654
4.38%
Yes
Yes
Yes
Yes
4654
4.03%
Yes
Yes
Yes
Yes
4654
4.01%
Yes
Yes
Yes
Yes
4654
4.16%
FORUM*ANALYST
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel B: The impact of analyst forecast dispersion
Dep. variable
(1)
IRSCORE
0.263
( 2.23)
IRSCORE*DISP
-0.045**
( 4.62)
IRACS
IRACS*DISP
(2)
CRASH
(3)
(4)
(5)
0.478*
( 3.73)
-0.074**
( 6.53)
TEL
0.228
( 0.67)
-0.061**
( 4.58)
TEL*DISP
EMAIL
0.132
( 0.05)
-0.049*
( 3.50)
EMAIL*DISP
FORUM
DISP
0.043**
( 5.71)
0.050***
( 7.15)
0.035**
( 4.01)
0.027*
( 3.01)
0.575*
( 3.22)
-0.083**
( 4.67)
0.037**
( 5.08)
Control
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
Yes
4654
4.18%
Yes
Yes
Yes
Yes
4654
4.21%
Yes
Yes
Yes
Yes
4654
4.00%
Yes
Yes
Yes
Yes
4654
3.98%
Yes
Yes
Yes
Yes
4654
4.01%
(1)
-0.140***
( 12.02)
0.446***
( 7.30)
(2)
CRASH
(3)
(4)
(5)
FORUM*DISP
Panel C: The impact of Big 4 auditors
Dep. variable
IRSCORE
IRSCORE*BIG4
IRACS
-0.178***
( 10.77)
0.499**
( 5.39)
IRACS*BIG4
TEL
-0.149**
( 5.89)
0.584**
( 5.21)
TEL*BIG4
EMAIL
-0.288**
( 4.88)
0.399*
( 3.06)
EMAIL*BIG4
FORUM
BIG4
-0.215
( 2.06)
-0.195
( 1.62)
-0.118
( 0.79)
0.014
( 0.01)
-0.152**
( 4.55)
0.561**
( 4.85)
-0.104
( 0.63)
Control
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
Yes
4654
4.19%
Yes
Yes
Yes
Yes
4654
4.11%
Yes
Yes
Yes
Yes
4654
3.99%
Yes
Yes
Yes
Yes
4654
3.93%
Yes
Yes
Yes
Yes
4654
3.95%
FORUM*BIG4
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel D: The impact of earnings management
Dep. variable
(1)
IRSCORE
-0.018
( 0.11)
IRSCORE*ACCM
-1.972**
( 6.30)
IRACS
IRACS*ACCM
(2)
CRASH
(3)
(4)
-0.033
( 0.20)
-2.205**
( 5.04)
TEL
-0.003
( 0.00)
-2.284**
( 3.99)
TEL*ACCM
EMAIL
-0.184
( 1.11)
-2.016*
( 2.82)
EMAIL*ACCM
FORUM
2.061***
( 16.36)
2.053***
( 15.60)
1.873***
( 14.49)
1.517***
( 11.14)
0.011
( 0.01)
-2.532*
( 3.28)
1.739***
( 13.49)
Yes
Yes
Yes
Yes
4654
4.17%
Yes
Yes
Yes
Yes
4654
4.10%
Yes
Yes
Yes
Yes
4654
3.97%
Yes
Yes
Yes
Yes
4654
3.91%
Yes
Yes
Yes
Yes
4654
3.92%
FORUM*ACCM
ACCM
Control
Industry FE
Province FE
Year FE
N
Adj. R-squared
(5)
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 8
Accessible Firms and Matched Non-accessible Firms
This table reports the differences of crash risk and likelihood of crash between accessible firms and matched
non-accessible firms. We first run a probit model where the dependent variable is the accessibility dummy variable,
IRACS. We control for a set of variables in the model, including DTURN, NCSKEW, SIGMA, RET, SIZE, MB, LEV, ROA,
ACCM, and industry, province, and year fixed effects. With the propensity score from the probit model, we find a best
matched control firm (IRACS=0) for each treated firm (IRACS=1). Eventually, we get 1194 pairs of treated-control firms.
In Panel A, we report the characteristics difference between treated firms and matched control firms. We then take the
difference of NCSKEW and CRASH for each pair of treated-control firms. Finally, we test whether the difference is
significantly different from zero. In panels B and C, the outcome variables are NCSKEW and CRASH, respectively. We
also divide the full sample into subsamples, low analyst coverage (below the median) and high analyst coverage (above
the median), low analyst forecast dispersion (below the median) and high analyst forecast dispersion (above the median),
not employing Big 4 auditors and employing Big 4 auditors, and low earnings management and high earnings
management. All variables are defined in Appendix A. Significance at the 10%, 5%, and 1% level is indicated by *, **,
and ***, respectively.
Panel A: Firm characteristics between accessible firms and matched control non-accessible firms
Control firms (IRACS=0)
Treated firms (IRACS=1)
DTURN
-0.092
-0.092
NCSKEW
-0.143
-0.138
SIGMA
0.056
0.056
RET
-0.001
-0.001
SIZE
29.188
29.198
MB
2.015
2.052
LEV
0.077
0.077
ROA
0.067
0.068
ACCM
0.049
0.049
Panel B: Stock price crash risk
Outcome variables
Control firms (IRACS=0)
NCSKEW
Treated firms (IRACS=1)
Dif (1-0)
0.000
0.005
-0.000
0.000
0.011
0.037
-0.001
0.001
0.001
Dif (1-0)
All firms
-0.207
-0.272
-0.065***
Analyst coverage
Low
High
-0.195
-0.165
-0.364
-0.205
-0.169***
-0.039
Analyst forecast dispersion
Low
High
-0.187
-0.268
-0.218
-0.346
-0.031
-0.078**
Big4 employment
No
Yes
-0.156
-0.245
-0.258
-0.308
-0.102***
-0.063
Earnings management
Low
High
-0.236
-0.171
-0.253
-0.297
-0.016
-0.126***
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel C: The likelihood of stock price crash
Outcome variable
Control firms (IRACS=0)
CRASH
Treated firms (IRACS=1)
Dif (1-0)
All firms
0.129
0.098
-0.030***
Analyst coverage
Low
High
0.159
0.114
0.086
0.109
-0.073***
-0.005
Analyst forecast dispersion
Low
High
0.113
0.127
0.108
0.086
-0.004
-0.041**
Big4 employment
No
Yes
0.140
0.105
0.091
0.108
-0.049***
0.003
Earnings management
Low
High
0.104
0.134
0.101
0.096
-0.003
-0.038**
Table 9
Corporate Accessibility and Market Synchronicity
This table reports the OLS estimate results of the relation between corporate accessibility and market synchronicity.
We measure firms' market synchronicity using IDIOSNY=ln((1-R2)/R2), where R2 is the R-squared taken from the
expanded market model as specified in equation (1). Our corporate accessibility measure is IRACS. All variables are
defined in Appendix A. The sample consists of 4654 firm-year observations covering the period of 2011-2013
(accessibility measures are constructed based on the survey in 2010). Industry, province, and year fixed effects are
included. The t statistics based on a robust standard error estimate clustering at the industry and province levels are
reported in parentheses. Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Dep. variable
IDIOSYN
(1)
(2)
(3)
(4)
(5)
IRACS
0.054**
0.163***
-0.192*
0.064***
0.015
( 2.41)
( 4.59)
( -1.88)
( 2.77)
( 0.50)
IRACS*ANALYST
-0.067***
( -4.03)
ANALYST
0.012*
( 1.72)
IRACS*DISP
0.030**
( 2.45)
DISP
-0.006
( -0.77)
IRACS*BIG4
-0.169*
( -1.84)
BIG4
0.114*
( 1.73)
IRACS*ACCM
0.783**
( 2.09)
ACCM
-0.822***
( -3.24)
Control
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Yes
Ind, Prov
4654
13.35%
Yes
Yes
Yes
Yes
Ind, Prov
4654
13.75%
Yes
Yes
Yes
Yes
Ind, Prov
4654
13.61%
Yes
Yes
Yes
Yes
Ind, Prov
4654
13.43%
Yes
Yes
Yes
Yes
Ind, Prov
4654
13.42%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 10
The Change of Crash Risk by Accessibility When Short-selling is Allowed
This table reports the change in crash risk by accessibility when short-selling is allowed. We calculate the change in
crash risk (the likelihood of crash) using NCSKEW (CRASH) in the post-event period (one year after the date that the
firm‘s shares are allowed to be sold short) minus NCSKEW (CRASH) in the pre-event period (one year before the date that
the firm‘s shares are allowed to be sold short). NCSKEW is the negative skewness of firm-specific weekly returns over the
fiscal year period. CRASH is an indicator variable that takes the value one for a firm-year that experiences one or more
firm-specific weekly returns falling 3.2 standard deviations below the mean firm-specific weekly returns over the fiscal
year. In Panel A, we report the date and the number of firms whose shares are allowed to be sold short. We exclude the
firms where permission for short selling is cancelled within one year. In Panel B, we present the change in crash risk and
the likelihood of crashes by accessibility, and the differences between accessible firms and non-accessible firms.
Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Panel A: The date and the number of firms whose shares can be sold short
Date
Number of firms
12/5/2011
153
1/31/2013
273
9/16/2013
205
Total
631
Panel B: The change of crash risk by accessibility
Variable
Change of NCSKEW
(1)
Change of CRASH
(2)
IRACS=0
IRACS=1
Difference (1-0)
-0.135
-0.047
0.088**
-0.037
-0.008
0.028**
TEL=0
TEL=1
Difference (1-0)
-0.133
-0.000
0.132***
-0.038
0.014
0.051***
EMAIL=0
EMAIL=1
Difference (1-0)
-0.147
-0.099
0.048*
-0.027
0.000
0.027**
FORUM=0
FORUM=1
Difference (1-0)
-0.113
-0.045
0.068**
-0.036
-0.018
0.017*
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 11
Corporate Accessibility and Site Visits by Outsiders
This table reports the number of corporate visits by outsiders in firms listed on the Shenzhen Stock Exchange. We identify 7
types of outsiders that visit and meet the company. They are financial analysts, media, mutual funds, individuals, bank and
insurance companies, foreign institutions, and assets management and consultant companies. In Panel A, we report the average
number of visits by each type of outsider and the difference between accessible and non-accessible firms. In Panels B and C, we
include the interaction terms between the accessible measures and the number of corporate visits in the baseline model. In Panel B,
the dependent variable is NCSKEW, which is the negative skewness of firm-specific weekly returns over the fiscal year period. The
t statistics are reported in parentheses. In Panel C, the dependent variable is CRASH, which is an indicator variable that takes the
value one for a firm-year that experiences one or more firm-specific weekly returns falling 3.2 standard deviations below the mean
firm-specific weekly returns over the fiscal year. The chi-square statistics are reported in parentheses. All variables are defined in
Appendix A. The sample consists of 2418 firm-year observations covering the period of 2011-2013 (accessibility measures are
constructed based on the survey in 2010). Industry, province, and year fixed effects are included. Significance at the 10%, 5%, and
1% level is indicated by *, **, and ***, respectively.
Panel A: Corporate visits by types in accessible and non-accessible firms
IRACS
Visitor types
0
1
Dif (1-0)
Financial analysts
3.637
5.366
1.728***
Media
0.064
0.109
0.045***
Mutual funds
2.263
3.339
1.076***
Individuals
0.889
1.206
0.317**
Bank & Insurance
0.189
0.341
0.152***
Foreign Inst.
0.152
0.177
0.024
Assets Mgt. & Consultant Ltd.
1.138
1.640
0.502***
0
4.003
0.067
2.527
0.996
0.213
0.160
1.241
TEL
1
5.378
0.097
3.260
1.423
0.361
0.170
1.651
Dif (1-0)
1.375***
0.030*
0.733***
0.427**
0.148***
0.010
0.410***
Total visits
8.690
12.704
4.014***
9.259
12.888
3.629***
Visitor types
Financial analysts
Media
Mutual funds
Individuals
Bank & Insurance
Foreign Inst.
Assets Mgt. & Consultant Ltd.
0
4.294
0.065
2.656
0.719
0.250
0.165
1.334
EMAIL
1
5.838
0.110
3.829
1.112
0.333
0.145
1.714
Dif (1-0)
1.544***
0.045**
1.173***
0.393**
0.083
-0.019
0.380*
0
4.001
0.063
2.508
0.927
0.233
0.155
1.261
FORUM
1
5.947
0.120
3.633
1.121
0.344
0.195
1.742
Dif (1-0)
1.946***
0.057***
1.126***
0.194
0.110**
0.040
0.481***
Total visits
9.876
13.000
3.124**
9.437
13.287
3.849***
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel B: Stock price crash risk
Dep. variable
IRACS
IRACS * Financial analysts
Financial analysts
(1)
-0.119***
( -3.12)
-0.009*
( -1.89)
0.006
( 1.55)
IRACS * Media
(2)
-0.101***
( -2.74)
(3)
-0.113***
( -3.17)
NCSKEW
(4)
-0.094***
( -2.68)
(5)
-0.126***
( -4.09)
(6)
-0.122***
( -4.09)
(7)
-0.119***
( -3.25)
-0.061**
( -2.03)
-0.049**
( -2.11)
Media
IRACS * Mutual funds
-0.008*
( -1.75)
0.006
( 1.49)
Mutual funds
IRACS * Individuals
-0.007**
( -2.45)
-0.005**
( -2.02)
Individuals
IRACS * Bank & Insurance
-0.017
( -0.49)
0.001
( 0.02)
Bank & Insurance
IRACS * Foreign Inst.
-0.004
(-0.19)
-0.017**
( -1.97)
Foreign Inst.
IRACS * Assets Mgt.
& Consultant Ltd.
-0.004*
Assets Mgt. & Consultant Ltd.
( -1.67)
-0.003
( -1.05)
Control
Industry FE
Province FE
Year FE
Cluster
N
Adj. R-squared
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.76%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.83%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.72%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.89%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.40%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.45%
Yes
Yes
Yes
Yes
Ind, Prov
2418
7.67%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Panel C: The likelihood of stock price crash
Dep. variable
(1)
IRACS
-0.189***
( 8.06)
IRACS * Financial analysts
-0.011*
( 3.39)
Financial analysts
-0.003
( 0.10)
IRACS * Media
Media
(2)
-0.171***
( 7.86)
(3)
-0.191***
( 6.94)
CRASH
(4)
-0.175***
( 7.24)
(5)
-0.200***
( 7.61)
(6)
-0.209***
( 8.88)
(7)
-0.192**
( 6.30)
-0.136**
( 5.13)
-0.074*
( 3.18)
IRACS * Mutual funds
-0.010
( 2.05)
0.013
( 1.21)
Mutual funds
IRACS * Individuals
-0.027**
( 5.03)
-0.010
( 1.47)
Individuals
IRACS * Bank & Insurance
-0.091
( 1.29)
0.062
( 0.94)
Bank & Insurance
IRACS * Foreign Inst.
-0.124
( 1.27)
0.007
( 0.07)
Foreign Inst.
IRACS * Assets Mgt.
& Consultant Ltd.
-0.023*
Assets Mgt. & Consultant Ltd.
( 3.06)
0.017
( 1.14)
Control
Industry FE
Province FE
Year FE
N
Adj. R-squared
Yes
Yes
Yes
Yes
2418
6.15%
Yes
Yes
Yes
Yes
2418
6.98%
Yes
Yes
Yes
Yes
2418
5.97%
Yes
Yes
Yes
Yes
2418
6.20%
Yes
Yes
Yes
Yes
2418
5.85%
Yes
Yes
Yes
Yes
2418
5.90%
Yes
Yes
Yes
Yes
2418
6.05%
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Table 12
Corporate Accessibility and Investor Investment Behaviors
The table reports the investor investment information between accessible firms and non-accessible firms. The churn
rate for institutional is calculated using the method in Gaspar, Massa, and Matos (2005). We rank all institutional
investors into three tertile portfolios for each quarter during the period 2011-2013. We define short-term institutional
investors as those with a churn rate in the top tertile and those in the bottom tertile as long-term institutional investors. For
each stock, we define the short-term (long-term) institutional ownership (IO) as the ratio of the number of shares held by
short-term (long-term) institutional investors and the total number of shares outstanding. We compute the percentage
short-term (long-term) institutional investors following the firms, and the average short-term (long-term) institutional
ownership in accessible firms and non-accessible firms. We also calculate the quarterly shareholder number growth rate.
Trading turnover is the daily trading volume in dollar scaled by the total market value at the end of that day. Finally, we
calculate the Amihud (2002) illiquidity measure using the absolute stock return scaled by the daily trading volume (the
ratio is multiply by a billion). We also test the difference between these two groups of firms. Significance at the 10%, 5%,
and 1% level is indicated by *, **, and ***, respectively.
Variable
IRACS=0
IRACS=1
Difference (1-0)
Churn rate
0.130
0.131
0.001
% Short-term Inst. Investors
0.271
0.272
0.001
% Long-term Inst. Investors
0.420
0.404
-0.016***
Short-term IO
0.010
0.012
0.002**
Long-term IO
0.018
0.019
0.001
The growth in shareholder numbers
Trading turnover
Amihud (2002) Illiquidity
0.811
1.208
0.938
1.342
1.284
0.817
0.532***
0.075***
-0.120***
Churn rate
% Short-term Inst. investors
% Long-term Inst. Investors
Short-term IO
Long-term IO
TEL=0
0.130
0.270
0.421
0.011
0.018
TEL=1
0.131
0.274
0.390
0.013
0.019
Difference (1-0)
0.001
0.004
-0.031***
0.002***
0.001
The growth of shareholder numbers
Trading turnover (%)
Amihud (2002) Illiquidity
0.933
1.222
0.905
1.214
1.268
0.864
0.281
0.045
-0.041
EMAIL=0
0.131
0.273
0.415
0.011
0.018
EMAIL=1
0.130
0.271
0.394
0.013
0.018
Difference (1-0)
-0.001
-0.002
-0.021***
0.002**
0.001
0.954
1.136
0.902
1.682
1.265
0.798
0.728*
0.129**
-0.104**
FORUM=0
0.130
0.272
0.412
0.011
0.019
FORUM=1
0.130
0.269
0.419
0.011
0.018
Difference (1-0)
0.000
-0.004
0.007*
-0.000
-0.000
0.860
1.215
0.911
1.728
1.265
0.815
0.868***
0.050
-0.096**
Churn rate
% Short-term Inst. investors
% Long-term Inst. Investors
Short-term IO
Long-term IO
The growth of shareholder numbers
Trading turnover
Amihud (2002) Illiquidity
Churn rate
% Short-term Inst. investors
% Long-term Inst. Investors
Short-term IO
Long-term IO
The growth of shareholder numbers
Trading turnover
Amihud (2002) Illiquidity
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Appendix A
Definitions of Variables
Variables
Definitions/Descriptions
NCSKEW
CRASH
The negative skewness of firm-specific weekly returns over the fiscal year period.
1 if a firm experiences one or more firm-specific weekly returns falling 3.2 standard
deviations below the mean firm-specific weekly returns over the fiscal year and 0
otherwise.
IRSCORE
An accessibility score index that is the number of communication channels (phone,
email, and forum) that are accessible (maximum score = 3 and minimum score is 0).
1 if at least one of the three communication channels (phone, email, and forum) is
accessible and 0 otherwise.
1 if the communication channel by phone is accessible and 0 otherwise.
1 if the communication channel by email is accessible and 0 otherwise.
1 if the communication channel by online discussion forum is accessible and 0
otherwise.
A rating (in the value of 0, 1, 2, 3, 4, 5) given by our telephone interviewers to the firms
that answer the telephone to evaluate their attitude and service quality, with 0 to be the
worse and 5 to be the best.
The logarithm of the number of days it takes from sending the email to receiving the
firm's reply. The number of days that it takes for us to receive the last email reply is 26
days. We truncate the number of days to 30 for firms who have never replied us (infinite
numbers of days it takes to reply us).
The logarithm of the number of characters in the replied email text.
The logarithm of the number of postings on the online discussion forum.
IRACS
TEL
EMAIL
FORUM
Tel_Attitude
Email_Timely
Email_Length
Forum_PostN
IDIOSYN
ANALYST
DISP
BIG4
DTURN
SIGMA
RET
SIZE
MB
LEV
ROA
ACCM
ln((1-R2)/R2), where R2 is the R-squared taken from the expanded market model as
specified in equation (1).
The log of the number of analysts following a firm.
Decile rank of analyst earnings forecast dispersion. Forecast dispersion is the standard
deviation of forecast earnings forecasted scaled by the mean of forecast earnings
forecasted. When there is no earnings forecast, it is assigned to the decile 10.
1 if a Big 4 auditor is hired and 0 otherwise.
The average monthly share turnover over the current fiscal year period minus the
average monthly share turnover over the previous fiscal year period, where monthly
share turnover is calculated as the monthly trading volume divided by the total number
of shares outstanding during the month.
The standard deviation of firm-specific weekly returns over the fiscal year period.
The mean of firm-specific weekly returns over the fiscal year period.
The log of the market value of equity.
The market value of equity divided by the book value of equity.
Total long-term debts divided by the total assets.
Income before extraordinary items divided by lagged total assets.
The absolute value of discretionary accruals, where discretionary accruals are estimated
form the modified Jones model.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Appendix B
Details of the survey and data collection procedure
The data collection involves two major processes. First, we manually collected data
from company websites. Second, we directly contacted the listed companies by using
telephone and email. The whole data collection process lasted from July 1 to September
30 in 2010.
1. Data collection from IR section
We first obtained company website addresses of all firms listed on China‘s two stock
exchange firms. We then started with a survey of the information provided via the IR
section on a firm‘s website. The survey involves collecting data in three parts, namely,
telephone and email contact information, online forum, and other characteristics of the IR
section and the website. The data collection form is provided in Appendix B.
The procedures to collect data from the IR section are specified as follows: First we
went to the main web page of the listed companies by using the website address provided
by the CSMAR. If the website is missing or invalid, we search for the company‘s home
page on Baidu.com (Chinese google). We then went to the main web page and checked
whether there was an IR section within the website. If yes, we went to the IR section and
collected the telephone and email contact information (if any). We then checked whether
there was an online discussion forum. If yes, we examined whether there were postings
by visitors and corresponding replies by the company. We also counted and recorded the
number of postings by visitors and replies by the company. We treat the online forum as
accessible if both ―Posting by visitors exists?‖ and ―Responses by the company exist?‖
are answered as yes and give the firm a score of 1 and a score of 0 if the firm is
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
non-accessible8.
In addition, we also collected information in the IR section or on other pages that are
relevant for the research, such as fax contact information, financial announcements, and
other news announcements, etc. This data collection from companies‘ websites took us
one month from July 1 to July 31 in 2010.
2. Contacting listed firms
After we completed the data collection from companies‘ websites, we directly
contacted the companies via telephone and email in the capacity of a minority
shareholder. In order to ensure the reliability of the collected data, we developed a set of
clear protocols to govern the whole data collection process.
2.1. Contacting by telephone call
We made telephone calls to each listed company by enquiring whether a company
visit can be arranged. Appendix C shows the telephone survey form that we prepared for
making the calls to the listed companies. The instrument that we used to make the calls
was Skype. All conversations were made in Chinese Putonghua and recorded using the
Goldwave software. All calls were made during general office hours (8:00am—12:00am
& 2:00pm—6:00pm) and the process lasted from August 1 2010 to September 30 in 2010
except on holidays. Diagram 1 illustrates the call process.
We scheduled two rounds in making the call (the gap between the two rounds was
generally one week). In each round, we scheduled two times to make the calls—one in
the morning, the other one in the afternoon. For example, in one morning, we made the
first call to a company, but with no answer. We then made the second call in the
8
The terms in this sentence with double quotation marks come from the IR section survey form, see the
survey forms attached in this Appendix.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
afternoon, with no answer again. We then tried a second round one week later and made
the third and the fourth trials in the morning and the afternoon, respectively. No further
calls are made and the company was recorded as non-accessible if we were not able to
contact the company after four trials.
Telephone numbers collected in the IR Section
General Schedule: 8am-12am & 2pm-6pm, Aug 1-Sep 30 2010
1st round
Call 1: Morning
2nd round
(1 week later)
Call 2: Afternoon
Call 3: Morning
Call 4: Afternoon
 No more calls will be made after four trials
Exceed 4 Calls?
Yes
No
Make the call
Special treatment list:
Call the company on
(at) the date (time)
appointed
No
Contact successfully?
Yes
Yes, without
appointment
Special arrangement?
No
Yes, with
appointment
Effective talk?
No
Non-accessible
Yes
Accessible
Diagram 1
Once we contacted the company successfully, we tried to find the person in charge
of the company (e.g., the person in charge of the investor relations department). However,
sometimes we did not find a responsible person at that moment. In some cases, the person
had to seek advice from his boss or colleagues (but they were absent). In this case, we
needed to find another time to call back the company (a special arrangement was
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
needed). We would try to make a next-call appointment with the company. If such an
appointment was made, we would put the company in the special treatment list, and call
it back on (at) the date (time) appointed. If we needed a special arrangement but the
company was unwilling to accommodate us, we were unable to go to the next step and
would record the company as non-accessible.
Our aim is not to actually visit the firm but to ascertain how the company effectively
responds to us. We consider the communication channel in terms of a telephone call as
accessible if we were able to effectively discuss with the company about the issue of a
company visit. An effective discussion can be either a case where the company
affirmatively accepted our request or a case where it rejected our request for some
acceptable reasons. One instance in the case of ―rejected our request for some acceptable
reasons‖ was that some companies replied ―we do not accept visitors these days as the
company is under financial auditing in preparation for the issue of the annual report.
However, we will consider your request after this period‖. In some other cases where it
was difficult to judge whether the respondents ―rejected our request for some acceptable
reason‖, we followed a conservative strategy and treated it as non-accessible (if it‘s
difficult to judge, then accessibility is a problem).
Overall, the telephone channel is accessible if the answer to the ―Allow a company
visit?‖ is yes, or the answer is no but an acceptable reason can be found in the ―If no,
reasons‖. It is non-accessible if 4 call trials are made but the ―Who answers the phone?‖
is empty, or the ―Is a special arrangements needed‖ is yes but the ―Appointed date or
time‖ is empty, or the ―Allow a company visit?‖ is no and the given reasons/excuses
cannot be justified in ―If no, reasons‖. Otherwise, the variable measuring the telephone
accessibility will be a missing observation9.
9
The terms in this paragraph with double quotation marks come from the telephone survey form, see
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
2.2. Contacting by email
Using the email address collected from the IR section on firms‘ websites, we sent
emails to the listed companies by asking a general question—the major locations where
the firms have their business operations. Diagram 2 illustrates the email sending process.
Email address collected in the IR Section
The first sending: 8:30am, Aug 30 2010 (Monday)
 Email service provider: Google Gmail
 Sent to all listed firms at one time with ―undisclosed
recipients‖
 The email was written in Chinese
Send the email
No, with two
emails sent
Reply?
The second sending: (2
weeks later)
Re-send the email to firms
that didn’t reply to the first
sending
No, with one
email sent
Yes
Non-accessible
Accessible
Diagram 2
The email service provider we chose was Google Gmail because it is widely
recognized and generally would not be dumped in the spam email category. The email
was written in Chinese and sent during general office hours (specifically, it was 8:30pm,
Aug 30 2010 (Monday)). It was sent to all listed companies at one time but with
―undisclosed recipients‖ function. The companies might fail to receive our email due to
some technical problems or wrongly deleted the email received. To minimize this risk, we
re-sent the email to the companies that didn‘t reply to us in the first sending two weeks
later. The email translated in English can be seen as below:
“Dear Sir or Madam
Appendix C.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
I am a minority shareholder of this company. I have a question about the
operating conditions of the company. I would like to know the major locations where
the company has its business operations, such as the city or province. I didn’t find
consistent answers based on my own searches. To get first-hand information, I am
therefore contacting the company directly. I look forward to hearing your reply soon.
Many thanks.
Yours sincerely
Mr. Zhao”
In all reply cases, our questions were answered directly and the reply appeared to
cluster at 3-4 days after the email was sent with very few responses after 10 days. We
record the communications in terms of email as accessible if we did receive a reply from
the company that contains any information that is relevant for our question in either of
the two email sendings, and count it as non-accessible otherwise.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Appendix C
Check sheet for coding survey data
IR Section Survey Form
Basic information (given)
Website
Company
IR discussion forum exists?
IR hot-line
exists?
Web
available?
Telephone and Email information in the IR section (yes: 1, no: 0)
IR
IR
IR phone
IR
section
IR phone
email
exists?
email
exists?
exists?
On-line forum information in the IR section (yes: 1, no: 0)
Number of
Postings by visitors
Responses by the company exist?
postings by
exist?
visitors
Other information in the IR in section (yes: 1, no: 0)
Financial
IR fax
IR hot-line
IR fax announcements
exists?
exist?
Other
announcements
exist?
Number of responses by the
company
Information on general web pages (not in the IR
section) (yes: 1, no: 0)
General
Financial
Other
General
phone
announcements announcements
phone
exists?
exist?
exist?
Instructions to check the IR section
1. Use Internet Explorer to open the company website. The website might not be opened due to some technical problems (e.g., the website is busy or out
of service at the time). In this case, mark the company in a different color and make more trials later on. Treat it as a failure after enough trials have been
made (at least three times on three different days).
2. Generally, an item named in "Investor Relations" can be found on the main menu of the website. Click it and go into the IR section where you can see
various IR items. In case you cannot find the IR section at first glance, be careful to search around by jumping back and forth inside the page.
3. In the IR section, check whether it provides telephone and email contact information. If yes, input 1 in the cell of "IR phone exists?" and "IR email
exists?", respectively, and input 0 otherwise. Also copy the contact information into the cells provided. Then check whether there is an online
discussion forum. If yes, see whether there are postings by visitors and corresponding replies by the company. Input 1 if yes and input 0 if no. Also
count the number of postings by visitors and responses by the company and input them in the cells.
4. Similarly, collect other information in the IR section and on other pages of the website. Input 1 in the cell when the corresponding item on the website
exists, and 0 when it does not exist. Specific information on the item, if any, is recorded in the next cell.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia, ISBN: 978-1-922069-89-4
Telephone Survey Form
Basic information (given)
Company
Industry
Telephone
number
Step 2: Confirm with the company (backup)
Special arrangement 2nd chance
Is a special
Who
arrangement
Appointed
answers the
needed? (yes:1,
date or time
phone?
no:0)
Step 1: Contact the company (mark with the date)
1st Round
2nd Round
1st call
2nd call
3rd call
4th call
Step 3: Question and talk
Allow a company
visit? (yes:1,
no:0)
If yes, any
arrangements
Step 2: Confirm with the company
Special arrangement 1st chance
Is a special
Who
arrangement
Appointed
answers
needed? (yes:1,
date or time
the phone?
no:0)
Step 4: Evaluation
1 to 5: 1 is the best, 5 is the worst
If no,
reasons
Attitude
Minutes
Overall satisfaction
Instructions to making calls to the listed companies
1. Install Skype, log in with the account provided. Install Goldwave and make voice recordings of the calls. The softwares together with a guideline (in
Chinese) can be found in the packet given to you.
2. Calls should be made during general office hours (8:00am—12:00am & 2:00pm—6:00pm) on weekdays.
3. Make the 1st call in the morning (first round). If unsuccessful, make the 2nd one in the afternoon; If unsuccessful again, mark down the date and try
the second round one week later. The process in the second round is the same as in the first round. Whenever successfully connected, go to the next step.
4. In step 2, introduce yourself (You are a minority shareholder of the company. You and 4 other minority shareholders would like to have a company
visit to the listed company). Before the request, try to find the person in charge of the operation (e.g., person in charge of the investor relations
department). Input yes in the cell of "Is special arrangement needed" in cases where the responsible person cannot be found at that moment. Try to make
a next-call appointment with the department. If such an appointment was made, put it in the special treatment list and mark down the date or time, and
call back the company on (at) the date (time) appointed.
5. On reaching the person in charge of company visits, begin the talk by asking whether it is possible to have a visit to the facilities of the company, e.g.,
the factory. If yes, mark down any arrangements. If no, mark down any reasons for it. No more calls will be made to this company.
6. As the call finishes, evaluate the talk by giving a rating on the attitude of the person answering the call and on your overall satisfaction. 1: excellent;
2: good; 3: general; 4: bad; 5: very bad.
7. Minutes should be taken as precisely as possible.
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia,
ISBN: 978-1-922069-89-4
References
Agarwal, V., Liao, A., Taffler, R., Nash, E., 2008. The Impact of Effective Investor
Relations on Market Value. SSRN eLibrary
Aharony, J., Lee, C.-W.J., Wong, T.J., 2000. Financial packaging of IPO firms in
China. Journal of Accounting Research, 103-126
Amihud, Y., 2002. Illiquidity and stock returns: Cross-section and time-series effects.
Journal of Financial Markets 5, 31-56
Bowen, R.M., Davis, A.K., Matsumoto, D.A., 2002. Do Conference Calls Affect
Analysts' Forecasts? The Accounting Review 77, 285-316
Chang, E.C., Cheng, J.W., Yu, Y., 2007. Short‐sales constraints and price discovery:
Evidence from the Hong Kong market. The Journal of Finance 62, 2097-2121
Chen, J., Hong, H., Stein, J.C., 2001. Forecasting crashes: Trading volume, past
returns, and conditional skewness in stock prices. Journal of Financial
Economics 61, 345-381
Chen, K.C., Yuan, H., 2004. Earnings management and capital resource allocation:
Evidence from China's accounting-based regulation of rights issues. The
Accounting Review 79, 645-665
Cheng, Q., Du, F., Wang, X., Wang, Y., 2013. Are investors' corporate site visits
informative. Working paper
Chong, A., Porta, R.L., Lopez-de-Silanes, F., Shleifer, A., 2014. Letter grading
government efficiency. Journal of European Economic Association 12,
277-299
DeFond, M.L., Hung, M., Li, S., Li, Y., 2014. Does Mandatory IFRS Adoption Affect
Crash Risk? The Accounting Review 90, 265-299
Diamond, D.W., Verrecchia, R.E., 1987. Constraints on short-selling and asset price
adjustment to private information. Journal of Financial Economics 18,
277-311
Diether, K.B., Malloy, C.J., Scherbina, A., 2002. Differences of Opinion and the Cross
Section of Stock Returns. The Journal of Finance 57, 2113-2141
Durnev, A., Morck, R., Yeung, B., Zarowin, P., 2003. Does greater firm-specific return
variation mean more or less informed stock pricing? Journal of Accounting
Research, 797-836
Fan, J.P.H., Wong, T.J., 2002. Corporate ownership structure and the informativeness
of accounting earnings in East Asia. Journal of accounting and economics 33,
401-425
Fan, J.P.H., Wong, T.J., 2005. Do External Auditors Perform a Corporate Governance
Role in Emerging Markets? Evidence from East Asia. Journal of Accounting
Research 43, 35-72
Frank, M.M., Lynch, L.J., Rego, S.O., 2009. Tax reporting aggressiveness and its
relation to aggressive financial reporting. The Accounting Review 84, 467-496
Gaspar, J.-M., Massa, M., Matos, P., 2005. Shareholder investment horizons and the
market for corporate control. Journal of Financial Economics 76, 135-165
Green, T.C., Jame, R.E., Markov, S., Subasi, M., 2012. Access to Management and
the Informativeness of Analyst Research. SSRN eLibrary
Hong, H., Stein, J.C., 2003. Differences of opinion, short‐sales constraints, and
market crashes. Review of financial studies 16, 487-525
Hutton, A.P., Marcus, A.J., Tehranian, H., 2009. Opaque financial reports, R 2, and
crash risk. Journal of Financial Economics 94, 67-86
54
Proceedings of 32nd International Business Research Conference
23 - 25 November, 2015, Rendezvous Hotel, Melbourne, Australia,
ISBN: 978-1-922069-89-4
Jian, M., Wong, T., 2010. Propping and tunneling through related party transactions.
Review of Accounting Studies
Jin, L., Myers, S.C., 2006. R 2 around the world: New theory and new tests. Journal
of Financial Economics 79, 257-292
Kao, J.L., Wu, D., Yang, Z., 2009. Regulations, earnings management, and post-IPO
performance: The Chinese evidence. Journal of Banking & Finance 33, 63-76
Khan, M., Watts, R.L., 2009. Estimation and empirical properties of a firm-year
measure of accounting conservatism. Journal of Accounting and Economics 48,
132-150
Kim, J.-B., Li, Y., Zhang, L., 2011. Corporate tax avoidance and stock price crash risk:
Firm-level analysis. Journal of Financial Economics 100, 639-662
Kim, J.B., Zhang, L., 2015. Accounting Conservatism and Stock Price Crash Risk:
Firm‐level Evidence. Contemporary Accounting Research
Kim, Y., Li, H., Li, S., 2014. Corporate social responsibility and stock price crash risk.
Journal of Banking & Finance 43, 1-13
Kothari, S., Ramanna, K., Skinner, D.J., 2010. Implications for GAAP from an
analysis of positive research in accounting. Journal of Accounting and
Economics 50, 246-286
Lang, M., Lins, K.V., Maffett, M., 2012. Transparency, liquidity, and valuation:
International evidence on when transparency matters most. Journal of
Accounting Research
Lang, M., Maffett, M., 2011. Transparency and liquidity uncertainty in crisis periods.
Journal of Accounting and Economics 52, 101-125
Liu, Q., Lu, Z.J., 2007. Corporate governance and earnings management in the
Chinese listed companies: A tunneling perspective. Journal of Corporate
Finance 13, 881-906
Miller, E.M., 1977. Risk, uncertainty, and divergence of opinion. The Journal of
Finance 32, 1151-1168
Morck, R., Yeung, B., Yu, W., 2000. The information content of stock markets: why
do emerging markets have synchronous stock price movements? Journal of
financial economics 58, 215-260
Piotroski, J.D., Wong, T., Zhang, T., 2011. Political incentives to suppress negative
financial information: Evidence from state-controlled Chinese firms. Working
paper, Stanford University and The Chinese University of Hong Kong
Statman, M., Thorley, S., Vorkink, K., 2006. Investor overconfidence and trading
volume. Review of Financial Studies 19, 1531-1565
Sunder, S., 2010. Riding the accounting train: from crisis to crisis in eighty years. In:
Presentation at the Conference on Financial Reporting, Auditing and
Governance, Lehigh University, Bethlehem, PA
Yan, S., 2011. Jump risk, stock returns, and slope of implied volatility smile. Journal
of Financial Economics 99, 216-233
Yan, X.S., Zhang, Z., 2009. Institutional investors and equity returns: Are short-term
institutions better informed? Review of financial Studies 22, 893-924
Yu, F., 2008. Analyst coverage and earnings management. Journal of Financial
Economics 88, 245-271
55