Analyst Private Information Acquisition and Stock Price Synchronicity
‐A Regulatory Perspective from China
Abstract:
This study investigates whether analysts’ private information acquisition influences
the extent to which firm-specific information is capitalized into stock prices, as measured by
stock price synchronicity in Chinese stock markets. We also study the moderating effect of
the restrictions on selective disclosures imposed by the CSRC Directive 40 on the
relationship between analyst private information and synchronicity.
We find that synchronicity correlates negatively with analyst private information
content, and also moves in a direction opposite to analyst private information acquisition over
our sample period. This finding supports the information role of analysts’ private information
seeking activities. We uncover that analyst private information acquisition reduces
substantially after the 2007 regulation, which partially supports the effectiveness of Directive
40 in its objective of ‘levelling the information playing field’. However, the synchronicityreducing effect of analyst private information acquisition is attenuated after Directive 40,
implying an impaired firm-specific information flow attributable to the restriction on
selective disclosures.
Keywords: Analyst private information acquisition; stock price synchronicity; selective
disclosures; regulation; China
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1. Introduction
This study aims to investigate the effect of analyst private information acquisition on
the stock price synchronicity of Chinese listed companies, and the way this relationship is
shaped by the regulatory restriction on selective disclosures imposed by the Chinese 2007
regulation on selective disclosures.
By conducting stock valuation using their specialized knowledge, and then
disseminating information via earnings forecasts and stock recommendations, analysts
provide valuable input for investors to use in estimating firms’ future earnings and in decision
making (Barron et al., 2002a). It is recognized that both publicly available and privately
acquired information are important to financial analysts (e.g., Arnold & Moizer, 1984;
Barker, 1999; Pike et al., 1993). The importance of private information acquisition is well
addressed in the literature (e.g., Barker, 1998; Holland, 2006). Although most of the extant
evidence is from developed stock markets, in line with this argument, recent Chinese studies
demonstrate that Chinese analysts exhibit better performance when they have more access to
a firm’s indirect information and conduct more company-level surveys (Hu et al., 2008).
With their access to macro-economic, firm-specific information and specialized
knowledge, securities analysts play an important role in improving market efficiency, and
more accurate analyst forecasts should reduce information asymmetry and improve the
efficiency of equity valuation (Frankel et al., 2006). We use stock price synchronicity
(hereafter synchronicity) to measure firms’ information environment because synchronicity is
a measure of the relative amount of firm-specific information impounded into stock price.
Prior literature provides evidence that firms with a better information environment have
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lower synchronicity (Jin & Myers, 2006; Morck et al., 2000; Wurgler, 2000).1 From a market
perspective, analyst activity, as a whole, can make more fundamental information capitalize
into stock prices, and thus lead to lower stock price synchronicity and higher firm specific
information content. However, using a sample of 45 emerging markets, including China,
Chan & Hameed (2006) find that more analyst coverage leads to an increase in stock price
synchronicity, indicating that analysts following emerging market stocks are primarily
involved in the production of industry-wide and/or market-wide information rather than the
costly acquisition of private information that is idiosyncratic to a firm. Piotroski & Roulstone
(2004) and Ayers & Freeman (2003) find a similar positive relation between analyst coverage
and synchronicity using US data.
One problem that plagues the above analyst-synchronicity studies is that their
measure of analyst coverage does not speak to the nature, public or private, of the
information that analysts use in their forecasts. For example, analyst reliance on public or
private information for analysis and forecasts may have a distinct effect on the capitalization
of firm-specific information into stock prices. Thus, our study advances this topic by focusing
on analyst private information acquisition. Specifically, we use analyst private information
acquisition as a direct measurement of analyst effort in seeking private information,
following Barron et al. (1998) to investigate the relation between this acquisition and firms’
stock price synchronicity. We posit that analyst private information acquisition reduces the
1
Lately, there is some doubt regarding synchronicity as a measure of firm-specific information
incorporated into share prices in the international context (Alves et al., 2009; Ashbaugh-Skaife et al.,
2005). Many of the concerns are related to other confounding factors that cannot be easily controlled
in a cross-country study such as the size of the country, investor property rights, corruption levels and
the role of the government. To check the validity of synchronicity as a proxy for firm information
environment in a single country-setting, Gul et al. (2010) conduct an Earnings Responses Coefficients
(ERC) test. Their analysis confirms that that synchronicity is a robust proxy for the usefulness a firm’s
information in the Chinese context.
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synchronic component of stock returns and thus improves the information environment for
firms in China.
The China Securities Regulatory Commission (CSRC) promulgated and put into
effect ‘The Listed Companies Information Disclosures Administrative Rules’ (hereafter
Directive 40) on 30 January 2007. The purpose of Directive 40 is to restrict listed companies’
selective disclosures of material information to financial analysts and institutional investors
before their public disclosures in order to curb insider trading, ‘level the information playing
field’, and protect individual investors. In spirit, Directive 40 is largely similar to the
Regulation Fair Disclosure (hereafter Reg FD) that was issued by the US Securities Exchange
Commission (SEC) in 2000. Due to the restriction of Directive 40 on private information
supply, it is important to inquire whether there is an information gap as a result of this
restriction, and whether the public information supply is sufficient to fill the potential
information gap resulting in unchanged firm-specific information incorporated into stock
prices post-regulation. Thus, our second research question investigates whether and how
2007 Directive 40 modifies the association between analyst private information content and
synchronicity. Additionally, we also examine whether private information acquisition and
synchronicity differs between affiliated and non-affiliated analysts, and whether this
relationship is dependent on the institutional development of the province where a listed
company resides.
The distinct features of the Chinese institutional environment make Chinese data
particularly suitable for our research questions. First, China’s widespread social connection
network eases analysts’ private information seeking. ‘Guanxi’, translated as ‘personal
connection’, facilitates private meetings and other flexible communication between analysts
and management (Jing et al., 2012). It is reported that Chinese local analysts have
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information advantages, measured as greater earnings forecast accuracy, due to their physical
proximity to the firms or their affiliated relationships (Bartholdy & Feng, 2013). Directive 40,
aiming to eliminate private information communication between firms and their analysts,
may be difficult to implement owing to these social connections and cultural expectations.
Furthermore, the investment service industry is at an early stage of development. Chinese
analysts are less competent than their counterparts in developed countries. They often rely on
private information obtained informally for stock valuation, earnings forecasts, and
recommendations. Empirical studies in China have also reported uninterrupted private
communication between firms and professional market participants post-Directive 40, and
thus the effectiveness of regulatory restriction on private information acquisition is limited.
Second, the information asymmetry level of Chinese listed companies is high. The
truthfulness of financial information is impaired by opportunistic reporting and accounting
manipulation (Chen & Yuan, 2004; Ding et al., 2007; Kimbro, 2005; Wang et al., 2008).
Also, the voluntary disclosures of firms are deficient. There exists a high degree of
information asymmetry between listed companies and investors (Piotroski & Wong, 2012). In
this context, analysts’ private information is important in meeting the information demand of
investors. How the restriction on selective disclosures impacts on analysts’ performance and
the information environment of firms will be of interest to regulators and market participants.
Our study will be informative in this regard. Lastly, Directive 40 in China presents a
laboratory setting for drawing theoretical and regulatory inferences on the efficiency of
regulatory restrictions on private information acquisition in an institutional environment that
differs considerably from that of the US. Reg FD is implemented in the US, where there is
stringent legislation and penalties against accounting and financial fraud, resulting in
comparatively high quality accounting information. Also, US firms have greater transparency
levels, as is evidenced by the greater extent of their public disclosures and their willingness to
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make voluntary disclosures. In this context, US analysts can obtain sufficient reliable and
relevant information from public sources for their analysis post-Reg FD (Kross & Suk, 2012).
In contrast, Chinese analysts rely mainly on private information-seeking and, owing to the
aforementioned reasons, may face an enlarged information gap post-Direction 40. Our
inquiry into the economic consequences of Directive 40 in relation to analysts’ private
information acquisition will shed light on the efficacy and efficiency of this regulation in a
distinct institutional environment.
We find that in general, synchronicity correlates with analyst private information
content negatively, and moves in a direction opposite to analyst private information content
during our sample period. This finding supports the informational role of analysts’ private
information-seeking activities. In addition, we document that analysts’ private information
acquisition reduces substantially after Directive 40. Furthermore, this reduction is more
pronounced for non-affiliated than for affiliated analysts, indicating a continuous availability
of other private communications between firms and their affiliated analysts. We further
reveal that the synchronicity-reducing effect of analysts’ private information acquisition is
attenuated after Directive 40 taking effect in 2007, implying impaired firm-specific
information attributable to the restriction on selective disclosures. We also test whether the
moderating effect of Directive 40 on synchronicity-analyst private information varies
between affiliated and non-affiliated analysts. We find that the impairment of firm-specific
information post-regulation is significantly lower for firms followed by affiliated analysts
possibly due to their continuous access to private information post-regulation. Lastly, our
additional analysis shows a significant modifying effect on the above relationship from the
level of financial market development in the province where a firm domiciles. Overall, the
results suggest efficacy of Directive 40, as evidenced by the decrease in analyst private
information acquisition in recent years. However, our findings also pinpoint a lack of
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efficiency of Directive 40 from the market perspective, in that synchronicity is increased
along with the decrease of analyst private information. Our findings highlight that regulation
to restrict selective disclosures that is effective in a low information asymmetry and more
transparent public disclosures (e.g., the US) context, is not necessarily applicable to countries
where the information asymmetry level is high, public disclosure level is low, and the
financial analyst industry is still in its infancy.
The next section discusses the relevant literature, describes the characteristics of the
Chinese analyst industry and followed by the development of hypotheses. Section 3 describes
the research methodology. Section 4 explains the test conducted, and reports the empirical
findings. Section 5 concludes the paper.
2. Literature survey, China analyst industry and development of hypotheses
2.1 Literature survey
The role of financial analysts is to analyze publicly available information (e.g., in
respect of the macro economy, industry and company) and collect information through
particular channels (e.g., company visits, meetings with company management). After
synthesis and analysis, analysts transfer their information output to the public. Thus, analysts
serve as an important mechanism to distribute information and improve capital market
efficiency. It is recognized that accounting information, especially annual reports and direct
contact with a company, are the most important and useful sources of information to financial
analysts, even though there is a clear shift in the relative importance of these sources over
time (e.g., Arnold & Moizer, 1984; Barker, 1999; Pike et al., 1993).
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The importance of private information acquisition is well addressed in the literature.
Barker (1998) contends that analysts and fund managers can draw substantial value from
meeting directly with companies because such meetings offer an opportunity to assess a
company’s strategies and management abilities. Thus, analysts rank them as the most
important source of information. Holland (2006) investigates how fund managers utilize
information obtained from companies directly in order to overcome problems with publicly
available information. For instance, financial reports are perceived to be too complex and
overwhelming by many users. In all, the literature suggests that information sources, both
public and private, have significant impact on the performance of analysts.
With respect to the effect of analyst activities on synchronicity, the literature provides
inconclusive findings. On the one hand, contrary to the conventional wisdom that security
analysts specialize in the production of firm-specific information, several studies find that
securities which are covered by more analysts incorporate greater (lesser) market-wide (firmspecific) information. Piotroski & Roulstone (2004) find that in the US, although the
presence of insiders and large institutional investors has the net effect of increasing the
amount of firm-specific information that is incorporated into stock prices, security analysts
decrease that amount. In other words, in the US, security analysts do not have an advantage
over insiders and institutional investors in accessing firm-specific information. Ayers &
Freeman (2003) also report that price synchronicity is associated with analyst forecasting
activities positively and inversely associated with insider trading. Similarly, Chan & Hameed
(2006) demonstrate that more analyst coverage leads to an increase in stock price
synchronicity in 45 emerging markets including China, indicating that analysts are primarily
involved in the production of industry-wide and/or market-wide information rather than in the
costly acquisition of private information that is idiosyncratic to a firm. Their argument is that
the payoff to analysts who produce firm-specific information may be too low in emerging
8
markets because it is impossible to arbitrage on them. Also, there is difficulty associated with
collecting firm-specific information in emerging markets. One important point not discussed
in Chan & Hameed (2006) is that the analysts included in their sample are retrieved from and
covered by I/B/E/S International, which collects information from analysts who agree to
provide their estimates in return for free use of IBES products. Thus, IBES’s choice of
analysts may introduce biases. For instance, analysts from major brokerage houses and
foreign analysts are the main population of analysts covered, whereas analysts of smaller
brokerage houses from developing countries and regional exchanges may be ignored. 2 Chan
& Hameed’s (2006) finding of a positive effect of (foreign) analysts on synchronicity is
important in the sense that this finding indicates foreign analysts mainly disseminate industry
and market-wide information.
On the other hand, analysts’ access to quality information and their analyzing abilities
are expected to improve firms’ information environment and result in low synchronicity.
Bushman et al. (2004) show greater idiosyncratic variation (low synchronicity) in countries
with better developed financial analysis industries. Jin & Myers (2006) study 40 stock
markets from 1990-2001 and find countries’ R²s (resulting in high synchronicity by
computation) are correlated with opaqueness positively, as measured by the analyst forecast
dispersion, Global Opacity Index, Global competitiveness report, and an index of accounting
standards. This finding indicates a negative relation between analyst forecast performance
(low forecast dispersion) and synchronicity. Kim & Shi (2008) investigate whether voluntary
IFRS adoption reduces synchronicity and also study the role of analyst following and
institutional infrastructure in shaping the relation between IFRS adoption and R². They find
2
Although IBES does not provide information on the identity of analysts and broker agencies, evidence
suggests that the majority of analysts covered by IBES are foreign analysts in the case of China. For instance,
Bae et al. (2008) report that in their sample derived from IBES, there are 74 analysts following Chinese firms
and 67 of them are foreign analysts. Thus, Chinese domestic analysts conducting stock valuation for most of Ashare firms may have been excluded in Chan & Hameed (2006) as they use IBES as their analyst data source.
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that the synchronicity-reducing effect of IFRS adoption is muted (strengthened) for firms
with high (low) analyst following, and is stronger (weaker) for firms in countries with poor
(good) institutional infrastructures. These findings indicate that analysts have already been
playing an important role in improving firms’ information environments and reducing
synchronicity prior to IFRS adoption. Thus, introducing IFRS in these firms has a minor
impact on firms with good analyst coverage. One problem that plagues the above analystsynchronicity studies is that their measure of analyst coverage does not speak to the nature,
public or private, of the information that analysts use in their forecasts. For example, an
analyst’s reliance on public or private information for analysis and forecasts may have a
distinct effect on the capitalization of firm-specific information into stock prices. Instead of
using the analyst following, our study takes a more direct approach by focusing on analyst
private information acquisition.
Due to concerns over insider trading and selective disclosures of material information
to market professionals and certain shareholders, US SEC passed Reg FD in 2000 in an effort
to level the information playing field among all investors. Even in the US, literature on the
effect of Reg FD on US corporations’ information environment is controversial. 3 Studies
report that there is substantial evidence that Reg FD is effective in levelling the market
participants’ playing field because post-Reg FD firms have reduced selective disclosures,
increased their public disclosures and thus ameliorated information asymmetry (e.g., Dong et
al., 2012; Sinha & Gadarowski, 2010). Frazzini et al. (2008) examine the effect of Reg FD on
analyst behaviours and report that analysts’ ability to exploit social ties in order to gain
informational advantages has virtually disappeared in the post-Reg FD era. Studies also find
that analysts show increased reliance on public disclosures because firms use more earnings
Fisch (2013) provides a detailed summary of the literature on the effect of the US Reg FD on firms’
information environment.
3
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guidance as a substitute for selective disclosures post-Reg FD (Charoenrook & Lewis, 2007;
Kross & Suk, 2012).
Nevertheless, critics of Reg FD show that information asymmetry persisted after Reg
FD took effect, and selective access to management continues to provide investors with
trading advantages via private meetings, etc (Solomon & Soltes, 2013). For instance, Bushee
et al. (2013) find that post-Reg FD, some investors’ trading size has increased following
private meetings with company management, and this effect increases if the management
involved is the CEO. Unusually large trading size normally indicates an investors’ material
information advantage, which raises concern about selective communication between firms
and certain types of investors. In addition, studies report that post-Reg FD, the ability of
institutional investors to identify mispriced stocks is discounted (Cook & Tang, 2010),
analysts’ forecast accuracy is reduced (Agrawal et al., 2006), and analysts’ ability to predict
earnings surprises is weakened (Palmon & Yezegel, 2011). Gintschel & Markov (2004)
examine whether Reg FD has reduced the informativeness of analysts’ information outputs.
They find that in the post-Reg FD period the absolute price impact of information
disseminated by financial analysts is reduced by 28%, and this drop in price impact varies
systematically with brokerage house and stock characteristics related to the level of selective
disclosure prior to Reg FD. In all, the countervailing evidence on the effect of the US Reg FD
on firms’ information environments makes drawing a definitive conclusion difficult.
2.2 China analyst industry and development of hypotheses
The Chinese financial analyst service industry was underdeveloped in the early years,
and only limited analysis was conducted by Chinese securities firms. Often their research
reports did not contain any quantitative forecasts and in most cases whatever material existed
was treated as internal (Li & Fleisher, 2004). During the 1990s, most formal and public
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analyses were performed by international securities firms on Chinese B-shares. Local groups
of analysts appeared over the 1990s and in 1999 a licensing system was adopted. In July
2000, as a self‐disciplinary committee, the Securities Analysts Association of China (SAAC)
was established, marking a milestone in the development of China’s securities analysis
services. The SAAC is to direct the healthy development of the securities investment
consultation industry and to help promote rational investment in China’s securities market
(Asian Securities Analysts Federation, 2004).
Recent developments in Chinese capital markets have necessitated the development of
a financial analyst industry and the importance of financial analysts is increasing
considerably. China opened its capital market gradually, resulting in a rapid increase in
institutional shareholding. In addition, the introduction of the QFII (Qualified Foreign
Institutional Investors4) and QDII (Qualified Domestic Institutional Investors5) programs has
stimulated the growth of investment analyses. In addition, after the share split-regulation’s
relaxation on the trading restriction of state owned shares in 2005, investors’ demand on
professional stock valuation services increased in order to facilitate trading. In 2005, financial
analysts got a formal name and ethics regulations were issued. There were about 20 securities
firms with more than 700 analysts, which had increased to 106 firms by 2010 (Bartholdy &
Feng, 2013). Most Chinese financial analysts work in three types of organisations - securities
firms (brokerage firms), fund management firms and consultancy firms. Making investment
recommendations is the main service provided by financial analysts irrespective of the
departments or organisations in which they work (Wang et al., 2011).
4
QFII is a program that was launched in 2002 to allow licensed foreign investors to buy and sell Chinese
currency-denominated A shares in the Shanghai and Shenzhen stock exchanges, which were previously closed
off to foreign investors in order for government to exercise tight capital controls and restrict the movement of
assets in and out of the country.
5
The QDII program started on 13 April 2006. It allows Chinese domestic financial institutional investors to
invest in foreign securities markets via certain fund management institutions, insurance companies, securities
companies and other assets management institutions which have been approved by CSRC.
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Wang & Ahammad (2012) find that Chinese financial analysts use both publicly and
privately available information to validate the truthfulness of each source. Specifically, their
interview data reveals that company visits and private meetings are regarded ‘useful’ to
‘extremely useful’ for gathering information because they provide price-sensitive information
including product cost structure and possible assets injection from the listed company’s
principle shareholders. Although public information was weighted higher than private level
information by Chinese analysts (Wang et al., 2011), it is found that a private source provides
a more efficient way to obtain ‘first hand’ information about the future development of the
company (Wang & Ahammad, 2012).
Evidence from interviews with sample analysts shows that private information
seeking is highly valued in the industry, in that analysts are required by their employers to
conduct site visits from twice quarterly to once a year, with key companies from the industry
allocated to them. One of the main objectives of the company visit is to search for price
sensitive information that is unavailable to the public at that time. Also, the ‘effectiveness’ of
the conversation largely depends on the relationship or ‘Guanxi’ between analysts and
managers. In addition, verifying the truthfulness of published financial information is another
important aim of company visits, especially when analysts are sceptical of the quality of
publicly available financial information (Wang & Ahammad, 2012). Furthermore, verifying
the real asset value is also an objective of company visits because the historical values of
non-current assets in those state ownership enterprises (SOEs), that is recorded during
privatization, do not reflect the true financial position of the company in a fast growing
economy. Therefore, analysts often make judgements about the true value of those assets
during visits (Wang & Ahammad, 2012). Lastly, other information that analysts normally
seek includes firms’ long-term investments, financing plan, management abilities, business
13
strategies, as well as their access to ‘political lending’ and government connections that are
highly valued in the Chinese business context.
After collecting information, Chinese analysts analyze data using a module that is
similar to the one being used in developed countries. This module is designed firstly to assess
the macro-economic information, then the industry-based information, and lastly the firmspecific information. Interview evidence from Wang & Ahammad (2012) suggests that
company-based analysis is based on integrated information from every aspect. They also
illustrate how private meeting information such as investment strategies and financing plans
are used for analyst stock valuation.
In line with the above interview evidence, empirical studies show Chinese analysts
tend to exhibit greater information comprehension and better job quality or performance
when they have more access to a firm’s indirect information and conduct more companylevel surveys (Hu et al., 2008). Studies also report a local analyst forecast advantage
evidenced by lower earnings forecast errors of local analysts compared with those of nonlocal analysts (e.g., Li et al., 2011).6 Bartholdy & Feng (2013) study Chinese A-share listed
companies over the period 2002-2009, and find that affiliated securities firms, defined as
securities firms acting as investment banker or underwriter, provide better earnings forecasts
than un‐affiliated firms. Also, forecast errors produced by local securities firms and star
analysts are smaller. Those findings on the analysts’ home advantage may be related to the
information advantage of private information acquisition.
6
Most of the studies on local analyst advantages are conducted by Chinese scholars and many publications are
in the Chinese language. Due to the underdevelopment of the financial analyst profession in mainland China,
early Chinese analyst studies focus mainly on H and B shares, thanks to the availability of financial analyst data,
more mature security markets, and the greater transparency of H- and B-share firms. The published evidence in
the English language about A-share markets is scant. For a review of the analyst literature on H- and B-share
markets, please refer to Bartholdy & Feng (2013).
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Although the international literature is inconclusive on the association between
synchronicity and analyst activities normally measured as analyst following or forecast
dispersion, it is reported in China that analyst activity, as a whole, can generate more
fundamental information capitalized into stock prices and, thus, lead to lower stock price
synchronicity and higher firm specific information content (Zhu et al., 2007). Given the
importance of analyst private information in the Chinese context as aforementioned, we
predict a negative effect of analyst private information acquisition on synchronicity.
H1: Analyst private information acquisition moves in a direction opposite to
synchronicity.
Directive 40 issued on 30 January 2007 introduced the discipline of ‘fair disclosure’,
along with the requirement that information disclosure should be ‘truthful, accurate,
complete, and timely’. Article 2 states that information disclosure should be ‘public and
simultaneous’. Article 4 states that ‘No person with knowledge of inside information shall,
prior to a lawful disclosure of inside information, make public or disclose such information or
conduct insider trading with such information’. Shortly after this regulation, empirical
evidence tends to draw inferences on its effectiveness. Studies published in the Chinese
language report controversial evidence on the effectiveness and efficiency of Directive 40.
On the one hand, studies report reduced selective disclosures by firms to analysts and other
institutional investors, and larger analyst forecast dispersion and errors post-regulation. This
increase in forecast errors deteriorates with the years post Directive 40. Additionally, this
increase in analyst forecast errors post-regulation is most severe for the firms with inferior
public disclosures (Liu & Peng, 2012). Thus, as a result of Directive 40, the information flow
in the market is reduced (Zhu & Wang, 2009). On the other hand, evidence has shown a
continuous supply of private information by firms to analysts and other market participants,
15
especially affiliated financial analysts. It is reported that there still exists material earnings
information leakage before pre-earnings announcements in the post-regulation era (Yang,
2010), and insider trading based on such private information still exists (Yang, 2012).
Literature on the effect of Directive 40 in English is absent, with the exception of Gong
(2012) which studies the impact of the 2007 regulations on the information environment of
financial analysts in Chinese A-share markets. He finds that the forecast error and forecast
dispersion of local analysts increased significantly in the post-regulation period, indicating a
deteriorated information environment for local analysts and an increased level of information
asymmetry between companies and local financial analysts post regulation. However, the
forecast error and forecast dispersion of foreign analysts did not show any change in the postregulation period. Based on the mixed evidence, we develop the following hypothesis in the
null form.
H2a: Analyst private information acquisition is not changed after the implementation
of Directive 40.
We investigate not only the effect of reform on analyst private information flow, but
also the economic consequence of the change in analyst private information content brought
about by Directive 40. Gintschel & Markov (2004) investigate whether Reg FD affects the
informativeness of analysts’ information output, and report that in the post-Reg FD period,
the absolute price impact of information disseminated by financial analysts is lowered by
28%. Their measure of price impact is the absolute return on or around the announcement of
analyst earnings forecasts or stock recommendations.
We argue that the effect of Chinese Directive 40 may be inefficient from a market
information perspective for the following reason. Due to the restriction on selective
disclosures, analysts may receive less private information from firms directly. Meanwhile,
16
firms’ public disclosures are insufficient and less useful because of the lack of public
disclosures, impaired truthfulness of accounting information resulting from rampant earnings
management, and related party transactions (Piotroski & Wong, 2012). In contrast, the US
Reg FD prompts analysts’ reliance on firms’ public disclosures in that US analysts rely more
on firms’ earnings announcements, management forecasts and conference calls for their
analysis post-Reg FD and, as a result, their forecast dispersion and errors decline post-Reg
FD (Kross & Suk, 2012).7 Therefore, Chinese analysts may face an even greater information
asymmetry problem after regulatory restriction on selective disclosures. Studies report an
increase in analyst forecast dispersion and errors after Directive 40 (Gong, 2012).
Additionally, this increase in analyst forecast errors post-regulation is most severe for firms
with inferior public disclosures (Liu & Peng, 2012). Therefore, we argue that this enlarged
information gap will reduce firm-specific information content in stock price leading to larger
synchronicity post-regulation.
H2b: Directive 40 mitigates the synchronicity-reducing effect of analyst private
information acquisition.
In addition, we argue that the effect of analyst forecasts on synchronicity is dependent
on the type of analysts concerned. Xu et al. (2013) compare the effect of the star analyst on
stock price synchronicity with the effect of non-star analysts. They find that star analyst
coverage actually decreases synchronicity, whereas there is a positive association between
analyst coverage and synchronicity for non-star analysts. Their findings suggest the
heterogeneity of analysts in China. The analysis further attributes star analysts’ better
performance to their firm-specific experiences as proxied by the number of years and the
number of forecasts made by the analyst concerned. We focus on affiliated analysts vs. non7
As reviewed in the literature survey section, the US evidence is also inconclusive regarding whether Reg FD
increases or decreases analyst forecast errors.
17
affiliated analysts, because the former have more direct access to information via site visits,
private meetings and other communication channels, owing to their economic relationship
with firms. In addition, recent Chinese analyst forecast literature finds that private
information communication opportunities between firms and their affiliated analysts remain
post-Directive 40 (Yang, 2010). We argue that firms followed mainly by affiliated analysts
have relatively higher private information content in their forecasts and thus, those firms
should show relatively lower stock price synchronicity post-Directive 40 in comparison to the
firms followed mainly by unaffiliated analysts. The following hypothesis is formulated
accordingly.
H3: Affiliated analysts’ private information acquisition serves to reduce firms’
synchronicity post-Directive 40.
Morck et al. (2000) contend that poor property right protection could discourage
informed risk arbitrage because expropriation risk can increase the cost of collecting firmspecific information. The reduction in informed trading in the countries with poor investor
protection can impede the capitalization of firm-specific information into stock prices
resulting in a high stock price synchronicity. Using 20 non-US countries, Jiang et al. (2013)
find that stock price informativeness (SPI) reduces with the detachment of voting rights from
cash flow rights, and this SPI-reducing effect is attenuated for firms with high analyst
following and in countries with strong country-level institutions. Kim & Shi (2012) find that
synchronicity decreases with the strength of a country’s institutional infrastructures. Hasan et
al. (2013) argue and report that synchronicity is a function of the institutional development in
China as measured by property rights, the rule of law, and political pluralism. In addition, a
stream of literature reports that legal and financial reporting environments also affect
financial analysts forecast characteristics and accuracy (Barniv et al., 2005; Chang et al.,
18
2000). Lang et al. (2004) find that analysts’ impact on stock valuation is also modified by
investor protection in a country.
Therefore, we posit that the effect of analyst private information on synchronicity is
conditional upon institutional factors. Specifically, among the institutional factors, we focus
on the strength of financial market development only, because the financial analyst industry
is an integral part of the financial market development. The Financial Development Index is a
sub-index of the Chinese Provincial Institutional Development Index constructed by Fan et
al. (2011). The testable hypothesis is formulated as follows.
H4: The synchronicity-reducing effect of analyst private information acquisition is
conditional on financial market development in the province where a firm resides.
3. Empirical methodology
3.1 Data and sample description
The financial analysts’ earnings forecasts for the A-share sample are retrieved from
the CSMAR (China Stock Market Trading Database). Specifically, the CSMAR Analysts
Forecasts database provides analysts’ forecasts solely for A-shares and is a primary source of
broker forecasts, stock recommendations, dates and names of underwriters for IPOs and
SEOs and analysts’ rankings. We also retrieve stock trading volume, daily stock returns, daily
market returns and annual financials, ownership structures, pre-announcement of earnings
and management earnings forecasts information from CSMAR. The MSCI (Morgan Stanley
Capital International) World index is obtained from DataStream. The institutional
development index is from Fan et al. (2011).
19
Our sample is based on all shares listed on the Chinese A-share market (i.e., A, AH,
AB and AHB shares). The sample period is from 2003 to 2012. In July 2000, a professional
committee called the Securities Association of China was formed, signalling the analyst
industry’s establishment and self‐discipline. However, during the period between 2000 and
2002, a limited number of firms were followed by analysts. For instance, only 46 firms were
followed by analysts in 2001, and most of them, by only one financial analyst. So, we can't
calculate the variable of private information acquisition (PRIVATE) which requires data
from multiple analysts. As a result, our sample starts from year 2003. The sample selection
procedure is provided in Table 1. Financial companies are excluded. Shares with high
delisting risk (classified as ST shares8 by the CSRC) are removed from our sample. To be
included in our sample, a firm must have been continuously trading during the period 20032012. For PRIVATE calculation, we need at least two forecasts. Therefore, 7869
observations are deleted because of an insufficient number of forecasts for PRIVATE’s
computation. The number of deleted observations for this reason is much greater in the early
years than in the latter years because there is an increasing supply of analyst services in
recent years. For R² estimation, we require firms to have at least 200 trading days. For this
reason, 1710 firm-year observations are eliminated. Owing to missing data required to
calculate the control variables, we further exclude 525 observations.
INSERT TABLE 1 ABOUT HERE
Panel B of Table 1 reports the industry distribution, showing that the largest industry
in our sample is Machinery, Equipment and Instrument (C7), and it accounts for 17.38% of
sample observations. The next two largest industries are Petroleum, Chemical, Rubber,
8
ST stands for Special Treatment, which applies to a Chinese listed firm when it reports losses for two
consecutive years. Firms with an ST symbol face some trading restrictions, including a daily price fluctuation
bracket of more than 5 percent, while the normal price fluctuation allowed for a listed company is 10 percent. If
the ST firm continues to suffer a loss in the third year, it will be signified by a “*ST” and suspended from
trading. A further loss in the following quarter will de-list the firm.
20
Plastic (C4) and Metal and Nonmetal (C6), contributing 10.28% and 9.74% of total
observations respectively. Finance Industry (I) is excluded from the sample.
3.2 Variables
All variables are explained in the Appendix. Several main variables of interest are
explained in detail as follows.
Analyst private information acquisition (PRIVATE)
To measure the average proportion of private information conveyed in analysts’
forecasts, we use the private information component (PRIVATE), common information
component (COMMON) and analyst consensus construct (CONSENSUS) developed by
Barron et al. (1998).9 COMMON and PRIVATE are estimated using observable properties of
analysts’ forecasts (i.e., the variance of analysts’ forecasts, D; the squared error in the mean
forecast, SE; and the number of forecasts, N) as follows (Barron et al., 1998; Proposition 1, p.
427):
( SE 
COMMON 
[(1 
CONSENSUS 
D
)
N
1
) D  SE ]2
N
D
, PRIVATE 
[(1 
1
) D  SE ]2
N
COMMON
COMMON  PRIVATE
An alternative measure of analysts’ investment in private information acquisition includes the analyst coverage
measured as the number of analysts following a firm, as used by Tong (2007) and Bushman et al. (2004).
9
21
Where N is the number of analysts’ forecasts. D is the sample variance of the
analysts’ individual forecasts. SE is the squared error in the mean forecast, calculated as the
squared difference between the mean forecast and actual earnings, i.e., (Actual EPS – Mean
EPS Forecast)².
Barron et al.’s (1998) measurement has been well used in financial analyst studies
(Barron et al., 2002b; Barron et al., 2001; Byard & Shaw, 2003; Venkataraman, 2001). An
increase in COMMON along with a decrease in PRIVATE is more consistent with a case
levelling the playing field than any of the other cases. An increase in PRIVATE along with a
decrease in COMMON is inconsistent with the ‘level playing field’ idea. Thus, distinguishing
these two cases may provide some important insights into researched issues (Venkataraman,
2001).
Stock price synchronicity
For the dependent variable, stock price synchronicity, measurement we estimate the
market model to decompose total return variations into two components: those related to
common (market-wide and/or industry-wide) factors and those related to firm-specific
factors. Following Gul et al. (2010), we use four different specifications because of the
Chinese market’s segmentation in issuing different types of shares. Specifically, for all three
types of share-issuing firms in our sample (i.e. firms with A-shares only, with A+B shares,
and with A+H shares), we first estimate the following market model for each fiscal year:
RETit    1MKTRETt   2 MKTRETt 1  3 INDRETt   4 INDRETt 1   it
(1)
Where, for firm i and t, RET represents the daily returns on A-shares traded on either
the Shanghai or Shenzhen exchange; MKTRET and INDRET are the value-weighted A-share
22
market return10 and industry return respectively; and ε represents unspecified random factors.
INDRET, the industry return is created using all firms within the same industry with firm i’s
daily return omitted. In Equation 1, we also include lagged industry and market returns to
tackle the concerns over potential non-synchronous trading biases that may result from the
use of daily returns for estimating the market model (Scholes & Williams, 1977; French et
al., 1987).
To address the possibility that Chinese stock returns may be correlated with world
market returns, in the following three models we also control for world market factors
measured by the MSCI (Morgan Stanley Capital International) World index.11 In addition, to
tackle Chinese segmental markets, we use equations 1a, 1b and 1c to estimate the share price
co-movement for A shares only firms, A+B share firms and A+H share firms respectively,
because returns on stocks of A+B (A+H) share firms are likely to co-move with B-share (Hshare) market factors in addition to A-share market factors in B (H) share markets. This
approach has been used by both Yu et al. (2013) and Gul et al. (2010).
RETit    1MKTRETt   2WORDRET   it
(1a)
RETit    1MKTRETt   2 MKTRET B  3WORDRET   it
(1b)
10
A-shares are listed on either the Shanghai or Shenzhen stock exchange. This A-share market return is based on
the composite (value-weighted) A-share index which reflects A-share price movements in both the Shanghai
and Shenzhen exchanges. The value-weighted A-share market return equals the change in the composite valueweighted A-share indices from day t to day t – 1 deflated by the composite value-weighted A-share index on day
t - 1. The composite A-share index data are extracted from the China Stock Market and Accounting Research
(CSMAR) database.
11
The MSCI (Morgan Stanley Capital International) World index is a world market index that is based on stock
prices of listed companies, representative of 22 stock markets in North America, Europe, and the Asia/Pacific
region, and is weighted by the market capitalization of each constituent market. The index data are extracted
from the Datastream database.
23
RETit    1MKTRETt   2 MKTRET H  3WORDRET   it
(1c)
In estimating Equation 1 and Equations 1a, 1b and 1c, we require that daily return
data be available for at least 200 trading days in each fiscal year. Stock price synchronicity is
defined as the ratio of common return variation to total return variation, which is equivalent
to R² of the market model used. To circumvent the bounded nature of R2 within [0, 1], we
use a logistic transformation of R²:
2
R
SYNCH i  log( i 2 )
1  Ri
Where, SYNCH i is annual synchronicity for firm i. Ri2 estimated from Equation (1) is
denoted as SYNCH (1), while the R²s estimated using Equations 1a, 1b and 1c are
represented as SYNCH (1a, 1b, 1c).
Institutional Development Provincial Index
Given that administrative regions or provinces in China typically exhibit large
variations in economic and institutional development, Fan et al. (2011) construct institutional
development indices for each province to capture the strength of institutions at the provincial
level within China. The larger this index is, the better the institutional development is. This
measure incorporates six elements, namely overall market-institutions development;
government intervention in markets; private enterprise development; regional protectionism;
financial market development; and legal environment. The composite index is used as a
control in our regression analysis on equations (2) and (3). For testing H4, we focus on the
financial market development sub-index, because it is more directly pertinent to financial
analyst activities.
24
4. Test and results
4.1 Descriptive statistics
The variables’ descriptive analysis is presented in Table 2. All variables are
winsorized at the bottom and top 1% points of their empirical distributions. Panel A shows
that our mean values of PRIVATE, COMMON and CONSENSUS are 95.17, 57.47 and 0.53
respectively, which are all lower than those reported in Barron et al. (2002b) with mean
values of 207.84, 118.28 and 0.63 over the period of 1986-1997 using US data. In addition,
the sample variance of the analysts’ individual forecasts (D) (mean 0.41) and the squared
error in the mean forecast (SE) (mean 1.01) are larger than their counterparts reported by
Barron et al. (2002) with mean values of 0.067 and 0.638 respectively. The mean values of
stock price synchronicity (-0.10, -0.34, -0.31 and -0.15 for SYNCH (1) and SYNCH
(1a,1b,1c) and the mean values of the R²(1) and R²(1a,1b,1c) are in accordance with
synchronicity studies using Chinese data. For example, the mean values of R² reported by
Morck et al. (2000) is 0.453, while our R² values range from 0.42 to 0.47. The mean value of
SYNCH reported by Gul et al. (2010) ranges from -0.316 to -0.232 using the sample period
1996-2003, which is comparable to our mean values of SYNCH (1), SYNCH(1a,b,c) that
range from -0.34 to -0.10. It is noteworthy that Chinese studies have consistently reported
higher R² and SYNCH than US studies. For example, using the same market model defined
by Piotroski & Roulstone (2004), we find a much higher R² and SYNCH than their reported
mean values, 0.193 for R² and -1.742 for SYNCH. This discrepancy in synchronicity and R²
measures among countries is well addressed by Morck et al. (2000) and is interpreted as a
significant difference in the information environment across countries.
Panel B presents the comparison of variables between pre- and post-regulation
periods. There is a decrease in both PRIVATE and COMMON, although the decrease in
25
COMMON is less severe than PRIVATE (from 247 to 64.06 and from 112.99 to 46.09
respectively). Thus, H2a is rejected, suggesting a sharp decline in analyst private information
content post-Directive 40. In addition, Venkataraman (2001) suggests that a decrease in
PRIVATE along with an increase in COMMON is consistent with the argument of ‘levelling
the playing field’ in any test of analyst private information precision (PRIVATE). However,
our data doesn’t show the increase in COMMON. Instead, both PRIVATE and COMMON
decline sharply, suggesting deteriorated information content for analyst forecast postDirective 40. SYNCH1 has increased significantly from -0.40 to -0.04 due to a sharp increase
in R² from the pre-regulation to the post-regulation period. The same increasing trends have
been found for A-share only firms with increased SYNCH1a and R²1a post-regulation. Of
note, this increasing trend in synchronicity has not been reported by recent Chinese
synchronicity studies (Gul et al., 2010; Hasan et al., 2013). Forecast errors (actual EPS –
forecast EPS) are more negative in recent years with a value of -2.41 compared to -0.97 preregulation, suggesting analyst forecasts are more optimistic post-regulation. The Institutional
Development Index (INSDEV) has improved significantly, which is consistent with the
reported statistics (Fan & Wang, 2001; Fan et al., 2009). The increased number of firms with
affiliated analysts (AFFLATE), the increases in management earnings forecasts (MEF) and
pre-announcement of earnings (PREANCE) are explained by the increasing analyst service
and firm-level disclosures in recent years (Piotroski & Wong, 2012). Similarly, Brown et al.
(2002), Bushee et al. (2004) and Heflin et al. (2003) report that the frequency and the price
impact of public disclosures in the form of management forecasts, public conference calls,
and pre-announcements of earnings have increased after Reg FD in the US. In general, most
of the control variables have increased post-regulation except for three ownership structure
variables, including state ownership (STATE), local government ownership (LOCGOV), and
central government ownership (CENGOV). The split share structure regulation taking effect
26
in 2005 lifts the trading restrictions imposed on listed companies’ non-tradable shares (NTS),
allowing NTS holders to sell and realize gains from stock price appreciation. With time, those
previous NTS holders trade their shares in open markets. As a result of increasing trading
activities, we observe a reduction in state ownership in recent years.
Panel C reports the correlation matrix among variables. Synchronicity is negatively
related to PRIVATE and COMMON, but positively associated with CONSENSUS. REG
shows a positive correlation with synchronicity, but a negative correlation with PRIVATE
and COMMON, suggesting greater synchronicity and lower PRIVATE and COMMON postregulation compared to pre-regulation. Most of the control variables are significantly
correlated with synchronicity except for STATE. As proxies for public disclosures,
PREANCE and MEF are both significantly and negatively correlated with synchronicity,
which is consistent with the finding that firm-level transparency facilitates the incorporation
of firm-specific information into stock prices (Ferreira & Laux, 2007).
INSERT TABLE 2 ABOUT HERE
4.2 The trend in private information acquisition and synchronicity
Over our sample period, the trends in analyst private information and synchronicity
have been reported in Panel B of Table 2. Synchronicity (SYNCH1) increases from -0.40 preregulation to -0.04 post-regulation, and the same trend is shown for SYNCH1a. In contrast,
there is a significant decreasing trend in analyst private information as evidenced by the tstatistic of -10.92.
To provide direct evidence on the association between private information acquisition
and synchronicity, we compare the mean synchronicity at different levels of private
information: below 20%, 20~30%, 30~40%, 40~50%, 50~60%, 60~70% and over 70%.
27
Figure 1 depicts how synchronicity changes with analyst private information. With the
increase in PRIVATE, SYNCH1 decreases. This relation is most obvious when PRIVATE is
beyond 20-30% of its sample value. Thus, this evidence lends support to H1.
INSERT FIGURE 1 ABOUT HERE
4.3 The effect of private information acquisition on synchronicity pre and post-Directive 40
To test the effect of private information acquisition on synchronicity in the pre and
post-regulation periods, we estimate the following regression.
SYNCH  0  1TIMEi ,t   2 PRIVATE i ,t  3 REGi ,t * PRIVATE i ,t  k  k CONTROLi ,t   i ,t ....(2)
Where, for firm i and year t, TIME is the difference between the fiscal year and 2003 used to
control for the trend in synchronicity. This approach is similar to Cohen et al. (2008), which
investigates the effect of SOX on real and accrual earnings management controlling for the
possible trends in earnings management activities. We have identified a discernible trend in
SYNCH as shown in Panel B of Table 2. Thus, we expect the coefficient on TIME to be
positive. PRIVATE represents analyst private information, and we expect its coefficient to be
negative. REG is a dummy variable which is 1 for fiscal years over 2007-2012, and 0
otherwise. So, REG is the specific event based on the year. We don’t expect REG to affect
synchronicity directly because Directive 40 is designed to restrict private information
seeking, which then affects the firm-specific information incorporated into stock price. So,
REG’s effect is indirect via its moderation on PRIVATE. In all the regression specifications,
we employed cluster analysis in the panel data setting to control for cross-sectional variance
that is not explained by the independent variables in our models (Peterson, 2009).
28
Following previous related research (Chan & Hameed, 2006; Gul et al., 2010;
Piotroski & Roulstone, 2004) and referring to Chinese unique institutional features, we
include a total of 16 control variables that may affect synchronicity. They are: annual trading
volume turnover (VOL), firm size (SIZE), leverage (LEV), market-to-book ratio (MB),
earnings volatility (STDROA), the number of firms in the industry to which a firm belongs
(INDNUM), and industry size (INDSIZE). In addition, we include analyst forecast common
information content (COMMON) and forecast consensus (CONSENSUS) in regressions to
address the argument that both analyst’s private and common information contents benefit a
firm’s information environment. State ownership (STATE) and whether a firm is controlled
by central (CENGOV) or local government (LOCGOV) are controlled for, owing to the
influence of ownership structures on synchronicity. We also use two proxies, preannouncements of earnings (PREANCE) and management earnings forecast (MEF) to control
for the effect of public disclosures on synchronicity, because increasing trends in voluntary
disclosure of other value-relevant information – for example, via conference calls or the
release of pro-forma earnings – increase the market reaction to firms’ earnings (Collins et al.,
2009; Francis et al., 2002). Year dummies are not included as TIME and REG are all
measured based on year, and the trend in synchronicity is expected to be captured by the
coefficient on TIME. Industry dummies are included to control for potential industry fixed
effects. The appendix provides the definitions of all variables included in the regressions.
INSERT TABLE 3 ABOUT HERE
Table 3 presents multivariate regression results for equation (2) and equation (3). In
this section, we firstly discuss the analysis results using equation (2), which examines the
effect of private information acquisition on synchronicity pre and post-Directive 40. Equation
(3) results are discussed in section 4.4. Regarding equation (2) results, the coefficients on
29
PRIVATE are negative and significant for both regressions using SYNCH1 and SYNCH1a as
dependent variables (coefficients are -0.0001, t-statistics are -4.17, and -3.59 respectively,
both having p < 0.00). This negative coefficient suggests that analyst private information
acquisition serves as an important mechanism to convey firm-specific information to
investors and, thus, reduces synchronicity before 2007. The moderating effects of Directive
40 are significantly positive for both estimations using SYNCH1 and SYNCH1a as
dependent variables (coefficients are 0.0002, t-statistic are 5.84, and 6.63 respectively, both
having p < 0.00). The results suggest that regulatory restriction on selective disclosures
results in an underproduction of analyst private information thus reduces firm-specific
information content in stock price, observed as a greater synchronicity post-regulation. Thus,
the finding lends support to our H2b.
As more than 90% of the total sample observations are from A-share only firms, and
we conclude that the above findings are driven mainly by the A-share market. We also
estimate equation (2) using SYNCH1b and SYNCH1c as dependent variables. However,
untabulated analysis results for these two sub-samples do not support the hypotheses. Due to
the limited observations in these two sub-samples (360 and 358 firm-year observations
respectively), we are cautious about interpreting the results.
With respect to the control variables, we find that the trend variable (TIME) is
positive (coefficient 0.04, t statistic 5.39, p < 0.00) when SYNCH1 is used as the dependent
variable, which is in accordance with the univariate analysis presented in Table 2 showing an
increasing trend in synchronicity with time. This effect is more pronounced for all shares.
Analyst common information content (COMMON) is associated with synchronicity
negatively (coefficient -0.0001, t statistics -1.89, p < 0.05), whereas their consensus forecasts
(CONSENSUS) is related to synchronicity positively (coefficient 0.09, and t statistics 4.61, p
30
< 0.00). Thus, the findings suggest that the common information content in analyst forecasts
also facilitates firm-specific information to be incorporated into the share price, but
consensus in forecasts increases the co-movement of a firm’s share price. The coefficient for
VOL is significantly negative (coefficient -0.01, t statistic 5.73, p < 0.00) for A-share only
firms. This suggests that active trading enhances the incorporation of firm-specific
information into stock prices. Firm size (SIZE) is positively associated with synchronicity
(coefficients 0.11 and 0.07, t statistics 11.47 and 6.65, p < 0.00), which is consistent with
Piotroski & Roulston (2004) and Gul et al. (2010). It is argued that large Chinese firms tend
to mirror the market to a greater extent than small firms do. In addition, large firms constitute
a major proportion of firms included in the market and industry indices, so their performance
affects market and industry indices returns to a greater extent (Gul et al., 2010). The
coefficients of MB are significantly negative, implying greater firm-specific information
content in the share price for high growth firms in that those firms may have more research
and development, and major capital investment information being incorporated into their
share prices. Both STDROA and INDNUM are significantly and negatively related to
synchronicity, suggesting that firms with larger earnings volatility participating in the
industry with more operators are more likely to have informative stock prices. Interestingly,
STATE shows a negative association with synchronicity. In contrast, LOCGOV has positive
association with synchronicity which is consistent with Gul et al. (2010) in that they also find
synchronicity is higher when the largest shareholder is government-related in China. Our
finding highlights that local government is the driving force of this synchronicity-increasing
effect of government shareholding.
Lastly, for those firms issuing only A shares (SYNCH1a as the dependent variable), it
seems their public disclosure does not play a role in reducing stock price synchronicity.
Instead, the pre-earnings announcement (PREANCE) increases the stock price co-movement
31
(coefficient 0.06, t statistic 3.28, p < 0.00). As expected, management earnings forecasts
(MEF) reduces the level of share co-movement significantly (coefficient -0.07, t statistic 5.39, p < 0.00), suggesting that managers communicate firm-specific information via their
earnings forecasts. Analyst forecast errors (ABSFE) have a significantly positive effect on
synchronicity, which is consistent with the belief that owing to the analyst efforts in
information seeking and research, the more precise an analyst forecast is, the more
information about the fundamental value of the firm will be incorporated into the price (Lee
& Liu, 2011). The signs on these control variables are highly consistent with prior literature,
and the results, by and large, resemble analyses using SYNCH1 and SYNCH1a..
4.4 The moderating effect of affiliated versus non-affiliated analysts
We also test whether the above relationships are moderated by a particular type of
analyst, affiliated analysts, owing to their close connection with firms, and the direct effect of
regulation on them. Firstly, we compare analyst private information content between
affiliated and non-affiliated analysts from the pre- to post-regulation eras in order to
understand the changes. Table 4 Panel A demonstrates these comparisons.
INSERT TABLE 4 ABOUT HERE
Based on a full sample analysis, the affiliated analysts’ forecasts have more private
information content (129.21) than the non-affiliated ones (94.99), although this difference is
not statistically significant over the sample period of 2003-2012. Non-affiliated analysts’
private information reduces significantly from the pre-regulation period (247.00) to the postregulation period (63.64) (t-statistic -10.94, p < 0.00). In contrast, although the affiliated
analysts’ private information content also declines post-regulation, the decline is not
statistically significant (t-statistic -0.58). As a result, there is a discernible difference in
32
forecasts’ private information content between affiliated (122.61) and non-affiliated analysts
(63.64) post-regulation (t-statistic 1.29, p < 0.10). In comparison, the PRIVATE of the two
types of analyst does not differ in the pre-regulation period (264.54 for affiliated analysts
versus 247.00 for non-affiliated analysts). The findings suggest that post-Directive 40, the
private information gap between affiliated and non-affiliated analysts has increased due to the
decrease in non-affiliated analysts’ private information. Thus, the results imply that Directive
40 may have forced firms to reduce the selective disclosures to general analysts but not to
their affiliated analysts. Post-regulation, affiliated analysts still have private information
acquisition channels, even if there is restriction on the selective disclosure of material
information from management to certain market participants. Recent Chinese studies
corroborate this inference (Yang, 2010).
If analysts have less private information post-regulation, their forecast accuracy will
reduce, resulting in larger forecast errors. To draw a more definite inference, we also
compare the forecast errors between two types of analysts over pre- and post-regulation
periods. The results are reported in Panel B of Table 4. We find that affiliated analysts have
lower forecast errors (0.41) than those of non-affiliated ones (2.27) over our sample period.
However, the difference in forecast errors between the two types of analyst is not significant
in the pre-regulation period (t-statistics -0.77). Post-regulation, although both types of
analysts experience larger forecast errors than before, the increase in forecast errors is more
prominent for non-affiliated analysts (t-statistic 8.09, p < 0.00). As a result, post-regulation
there is a smaller forecast error for affiliated analysts than for non-affiliated ones (2.02 versus
2.58, t-statistic -2.48, p < 0.00). This result is in accordance with the Bartholdy and Feng
(2013) finding that affiliated securities firms issue more accurate earnings forecasts than
non‐affiliated firms over the period of 2002-2009 in China. In conjunction with the evidence
reported in Panel A of Table 4, we infer that affiliated analysts have more accurate forecasts
33
especially in the post-regulatory period possibly due to their continuous access to superior
firm-specific information that may not be available to the public. Then, we argue that since
affiliated analysts have more private information than their non-affiliated counterparts, the
stock price of firms followed by affiliated analysts should have more firm-specific
information content than the firms followed by non-affiliated analysts post-regulation. Thus,
we expect a negative effect of affiliated analysts on synchronicity post-regulation resulting
from their superior access to private information and greater PRIVATE as conjectured in H3.
The following equation, as an expansion of equation (2) is used to test this proposition.
SYNCH   0  1TIMEi ,t   2 PRIVATE i ,t   3 REGi ,t * PRIVATE i ,t   4 AFFLATE i ,t * REGi ,t * PRIVATE i ,t 
  CONTROL
k
k
i ,t
  i ,t .................................................................................(3)
Where, AFFLATE is a binary variable with a value of 1 if at least one of the firm’s analysts
is affiliated. Equation (3) results are reported in Table 3. All other variables are defined as
before.
Our variable of interest is the three-way interaction, AFFLATE*REG*PRIVATE.
The coefficient on it is negative as expected for estimations using both SYNCH1 and
SYNCH1a. Although this decremental effect is only marginally significant for SYNCH1
analysis (t statistic -1.64, p < 0.10), it is more prominent for SYNCH1a analysis (t statistic 3.46, p < 0. 00). This implies that affiliated analysts play more important information roles
for firms issuing only A shares. We also estimate equation (3) for SYNCH1b, 1c. No definite
inferences can be drawn from analysis.12 All other variables have shown largely consistent
12
B and H share markets have features distinct from the A-share market. With respect to the financial
investment industry, analysts of the A-share market are fledglings, whereas analysts of B- and H-share markets
are more sophisticated and have more incentives to compete and provide quality services to investors, especially
foreign investors in these two markets (Barniv, 2009). In addition, information asymmetry in the A-share market
is significantly greater than it is in B- and H-share markets, in that A-shares tend to have low levels of public
disclosures, inferior financial reporting quality and poor corporate governance (Piotroski & Wang, 2012).
34
results with equation (2) analysis. Thus, our findings support the conjecture that affiliated
analysts’ private information acquisition tends to lower firm’s stock price synchronicity postregulation. H3 is supported. It is noted that Bartholdy and Feng (2013) fail to find the market
differential reaction to the stock recommendations made by affiliated and non-affiliated
analysts over the period of 2002-2009, although affiliated analysts show lower forecast errors
than their non-affiliated counterparts. Our finding of the significantly negative coefficient on
AFFLATE*REG*PRIVATE suggests that earnings forecasts made by affiliated analysts
accelerate the incorporation of firm-specific information into stock prices perhaps due to their
continuously access to private information from management post-regulation.
4.4
The moderating effect of financial market development on the association between
analyst private information acquisition and synchronicity
To test H4, we compare the amount of analyst private information content
(PRIVATE) between provinces with high and low financial market development using
univariate analysis, as reported in Table 5.
INSERT TABLE 5 ABOUT HERE
The simple t statistic shows the significant higher (lower) analyst private information
content in their forecasts in the provinces with a high (low) level of financial market
development (t statistic 2.88, p < 0. 00). However, the difference in SYNCH1 is not
statistically significant, although synchronicity is slightly lower in provinces with a high level
of financial market development than it is in the provinces with a low level of financial
development. Other factors influencing synchronicity need to be controlled in regression
analysis in order to draw a definite inference. We thus conduct the following test reported in
Table 6. Table 6 shows that during our full sample period, the effect of PRIVATE on
35
synchronicity is not significant. However, once the financial market development is taken
into account, the coefficient on the interactive term FINDEV*PRIVATE is negative and
significant across all four model specifications (t statistics range from -1.76 to -4.15). It
suggests that for firms in provinces with a better developed financial markets (FINDEV takes
value of 1), the analyst private information acquisition is an important factor lowering stock
price synchronicity. Thus, the information usefulness of analyst private information
acquisition seems strongest in provinces with relatively developed financial markets.
Furthermore, although we have not developed a formal hypothesis, when we add the threeway interaction, REG*FINDEV*PRIVATE into our model to test the regulation effect, the
synchronicity-reducing effect of analyst private information acquisition is mitigated
(coefficients 0.0002, t statistics 5.04 and 5.16 respectively, p < 0.00). This result suggests that
post-Directive 40, the firm-specific information content of analyst private information
acquisition is reduced (synchronicity is increased) for those firms operating in the provinces
with better developed financial markets.
INSERT TABLE 6 ABOUT HERE
4.5 Sensitivity test
To test the robustness of our results, several sensitivity tests are conducted. The
financial crisis in 2008 may affect our expected associations, so we re-estimate our
regressions controlling for a dummy variable, CRISIS, which is 1 for observations in 2008,
and 0 otherwise. The results are reported in Table 7, which shows that synchronicity is
greater during the crisis period in 2008 with a significant coefficient on CRISIS for all four
specifications. In addition, as expected, the effect of private information acquisition on
synchronicity is moderated positively by regulation, showing a significant coefficient on
REG*PRIVATE, and the negative moderating effect of affiliated analysts holds continuously,
36
as evidenced by the negative coefficient on AFFLATE*REG*PRIVATE. However, the effect
of private information on synchronicity before 2007 is not statistically significant, although
the sign is negative as expected (t statistics range from -0.54 to -0.86). Alternatively, we reestimate equations (2) and (3) after excluding observations from the 2008 fiscal year. The
results on our main variables of interest are similar to those reported in Table 3.
Other sensitivity analyses include (1) an alternative cut-off point to define affiliated
analysts used for H3 testing. AFFLATE is 1 if more than half of the analysts following the
same firms are affiliated and, 0 otherwise. The results for H3 testing using equation (3) are
qualitatively unchanged; (2) we use an alternative measurement of management forecasts
(MEF2) for regression estimations. The results are largely consistent with the results reported
in Table 3.
INSERT TABLE 7 ABOUT HERE
5. Summary and concluding remarks
Financial analysts are the main information intermediaries serving to disseminate
information and reduce information asymmetry between firms and investors. Studying the
impact of analyst activities on stock prices and returns improves our understanding of the
economic utility of analyst activities. Extant studies on the association between analyst
coverage and synchronicity usually employ analyst following as a proxy for analyst effort
and have produced mixed evidence. Our paper advances this topic by explicitly using analyst
private information acquisition to proxy for analyst effort in private information seeking. We
argue that analysts’ reliance on public or private information for analysis and forecasts may
have distinct effects on the capitalization of firm-specific information into stock prices. Thus,
using analyst following to measure the effect on synchronicity is a crude approach to drawing
37
inferences on analysts’ information advantage, and the economic consequences of analyst
information acquisition. To the best of our knowledge, our paper is the first to link the nature
of analyst information to firm’s stock price synchronicity directly, which is a major
contribution of our study.
In addition, we argue that analyst private information-seeking is exceptionally
important in Chinese security markets because Chinese listed firms generally have a low
level of, and poor quality public disclosures, resulting in high information asymmetry
between firms and market participants. Analyst private information acquisition in this context
is an important supplement to public information in overcoming information asymmetry and
conveying firm-specific information to the wider investment community. We document a
negative correlation between synchronicity and analyst private information acquisition, and
both move in opposite directions during our sample period. Regarding the restriction on
selective disclosures promulgated as Directive 40, we unveil that the CSRC Directive 40
promulgated in 2007 has reduced analyst private information content in their forecasts
significantly, resulting in an increased synchronicity in the post-regulation era from 2007 to
2012. However, this reduction in private information acquisition is more pronounced for
non-affiliated than affiliated analysts, indicating an ongoing availability of other private
communications between firms and their affiliated analysts. Furthermore, we find that the
above relations are modified by the extent of the financial market development in the
province where a firm operates. Overall, although our finding supports the effectiveness of
Directive 40 in its objective of ‘levelling the information playing field’ as is evidenced by the
reduced analyst private information content in their forecasts, we also find a lack of
efficiency of Directive 40 from a market perspective. That is, the synchronicity-reducing
effect of analyst private information acquisition is ameliorated post-Directive 40. Overall, our
findings provide new insights into the economic consequences of regulatory changes made to
38
curb insider trading and selective disclosures in an emerging market where public
information is less than optimal and the analyst industry is in its infancy.
39
Table 1
Sample selection and industry distribution
Panel A: Sample selection
Year
Listed companies in China
Less: Financial companies
Less: ST companies
Less: Delisted companies
Less: PRIVATE
Total Sub-sample 1
Less: Trading days less than
200 days
Total Sub-sample 2
Less: Control variables
Total Sample
2003
1268
9
106
60
866
227
2004
1356
9
111
51
837
348
2005
1352
9
101
36
692
514
2006
1435
15
127
29
599
665
2007
1549
27
160
18
478
866
2008
1603
27
146
18
387
1025
2009
1694
30
149
10
299
1206
2010
1919
36
165
10
229
1479
2011
2048
40
147
4
200
1657
2012
2115
41
104
5
321
1644
Total
19745
263
1518
409
7869
9686
43
59
18
137
135
90
103
227
173
101
1710
184
48
136
289
13
276
496
29
467
528
34
494
731
45
686
935
48
887
1103
60
1043
1252
67
1185
1484
66
1418
1543
63
1480
8597
525
8072
Panel B: Industry distribution of the sample observations
CSRC Industry Classification
Firm-year Obs.
Percentage Distribution
1-A Farming, Forestry, Animal Husbandry and Fishery
152
1.88%
2-B Mining and Quarrying
256
3.17%
3-C0 Food and Beverage
348
4.31%
4-C1 Textile, Clothing, Fur
268
3.32%
41
0.51%
6-C3 Papermaking, Printing
148
1.83%
7-C4 Petroleum, Chemical, Rubber, Plastic
830
10.28%
8-C5 Electronic
415
5.14%
9-C6 Metal, Nonmetal
786
9.74%
1403
17.38%
519
6.43%
84
1.04%
13-D Production & Supply Of Power, Gas & Water
334
4.14%
14-E Construction
186
2.30%
15-F Transportation, Storage
388
4.81%
16-G Information Technology Industry
520
6.44%
17-H Wholesale and Retail Trades
442
5.48%
19-J Real Estate
420
5.20%
20-K Social Services
267
3.31%
68
0.84%
5-C2 Timber, Furniture Industry
10-C7 Machinery, Equipment, Instrument
11-C8 Medicine, Biologic Products
12-C9 Other manufacturing
21-L Transmitting, Culture Industry
22-M Integrated
Total
197
2.44%
8072
100.00%
40
Table 2
Descriptive statistics
Panel A: Descriptive statistics
Variables
Analyst private
PRIVATE
information
COMMON
acquisition
D/∣actual EPS∣
SE/∣actual EPS∣
N
CONSENSUS
Stock price
SYNCH1
synchronicity
R²1
(SYNCH) and
SYNCH1a
R²
R²1a
SYNCH1b
R²1b
SYNCH1c
R²1c
Other variables
FE
INSDEV
AFELATE
VOL
SIZE
STDROA
MB
INDNUM
INDSIZE
STATE
LOCGOV
CENGOV
MEF
PREANCE
Mean
95.17
57.47
0.41
1.01
41.39
0.53
-0.10
0.47
-0.34
.42
-0.31
0.42
-0.15
0.46
-2.17
54.45
0.12
5.15
22.00
0.02
3.54
4.68
27.19
0.34
.38
0.18
0.57
0.30
Median
4.43
4.42
0.05
0.07
20
0.59
-0.11
0.47
-0.33
0.41
-0.32
0.41
-0.12
0.46
-0.40
54.25
0
4.23
21.84
0.02
3.00
4.68
27.29
0.33
0
0
1
0
Panel B: Difference in main variables’ mean values pre and post-regulation
Variable
Pre-regulation
Post-regulation
PRIVATE
247.00
64.06
COMMON
112.99
46.09
CONSENSUS
0.46
0.54
SYNCH1
-0.40
-0.04
R²1
0.41
0.49
SYNCH1a
-0.57
-0.30
R²1a
0.37
0.43
FE
-0.97
-2.41
ABSFE
1.07
2.51
INSDEV
48.57
55.66
AFFLATE
0.07
0.13
VOL
3.84
5.42
SIZE
21.86
22.03
STDROA
0.023
0.028
MB
0.002
0.003
INDNUM
102.32
143.20
INDSIZE
26.34
27.37
STATE
0.37
0.34
LOCGOV
0.48
0.35
CENGOV
0.21
0.17
Std. Dev.
363.48
246.89
1.26
3.87
54.39
0.33
0.56
0.13
0.58
.13
0.56
0.13
0.64
0.14
5.95
14.04
0.32
3.64
1.19
0.02
2.69
0.70
1.06
0.17
0.48
0.38
0.49
0.46
Differences (t-statistics)
-10.92***
-6.87***
6.96***
23.47***
23.58***
15.64***
16.35***
-11.96***
11.96***
20.56***
8.88***
19.64***
5.44***
7.68***
24.98***
20.35***
43.50***
-4.97***
-8.51***
-2.86***
41
Variable
Pre-regulation
Post-regulation
Differences (t-statistics)
MEF
0.37
0.61
16.89***
PREANCE
0.09
0.35
27.27***
Note:
*** ** *
, , represent statistical significance at a probability of <0.01, <0.05, and <0.01 respectively (two-tailed
test).
42
Panel C: Correlation matrix
SYNCH1
PRIVATE
COMMON
CONSENSUS
REG
VOL
SYNCH1
1
PRIVATE
-.041**
1
COMMON
-.030**
.217**
1
CONSENSUS
.033**
-.341**
.182**
REG
.240**
-.189**
-.102**
.088**
VOL
-.098**
.001
.045**
.031**
.164**
SIZE
.238
**
-.045
**
**
**
.032
**
-.011
LEV
**
-.066
.001
SIZE
LEV
STDROA
INDUM
STATE
CENGOV
LOCGOV
PREANCE
MEF
1
-.097
.000
1
.055
-.016
1
-.373**
1
-.046**
.455**
1
STDROA
-.048**
-.062**
-.018
.081**
.074**
.063**
-.044**
-.022*
1
INDUM
-.053**
-.088**
-.065**
.088**
.165**
.010
-.057**
-.017
.006
1
STATE
-.006
.012
-.021
-.031**
-.058**
-.072**
.210**
-.043**
.011
-.049**
1
CENGOV
.029**
.005
.001
-.049**
-.033**
-.084**
.248**
.137**
.012
.023*
.126**
LOCGOV
.093**
.071**
.042**
-.074**
-.097**
-.073**
.156**
.151**
-.035**
-.114**
.167**
-.370**
1
PREANCE
-.055**
-.088**
-.103**
.081**
.215**
.096**
-.255**
-.268**
-.039**
.116**
.086**
-.100**
-.234**
1
MEF
-.112**
-.108**
-.071**
.113**
.184**
.171**
-.197**
-.073**
.139**
.112**
.001
-.064**
-.196**
.338**
1
AFFLATE
-.020
-.006
.003
.033**
.094**
-.017
-.045**
.031**
.011
.042**
-.008
-.040**
.061**
.019
.007
AFFLATE
1
Notes: *, ** and *** represent correlation coefficient significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively.
43
1
The percentage of PRIVATE and their dividing values
0
-0.02
-0.04
<0.4548 1.0695 2.1615 4.4337 9.0903 19.01 >19.01
0-20% 20-30% 30-40% 40-50% 50-60% 60-70%
70100%
-0.06
-0.08
SYNCH1
-0.1
-0.12
-0.14
-0.16
Percentage of
PRIVATE
Dividing values
of PRIVATE
SYNCH1
0-20%
<0.4548
-0.0603
20-30%
30-40%
40-50%
50-60%
60-70%
1.0695
2.1615
4.4337
9.0903
19.01
-0.0496
-0.0811
-0.082
-0.0882
-0.1326
70-100%
>19.01
-0.1505
Figure 1. The relationship between stock price synchronicity, analyst private information acquisition
44
Table 3
The effect of private information acquisition on synchronicity pre and post-Directive 40
Equation (2)
Variables
Sign
SYNCH1
SYNCH1a
Coefficient
(t-stat)
-0.25
(-0.27)
0.04***
(5.39)
-0.0001***
(-4.17)
0.0002***
(5.84)
Coefficient
(t-stat)
-1.02
(-1.11)
0.01
(1.14)
-0.0001***
(-3.59)
0.0002***
(6.63)
Equation (3)
SYNCH1
SYNCH1a
Coefficient
Coefficient
(t-stat)
(t-stat)
Constant
-0.28
-1.10
(-0.31)
(-1.20)
TIME
+
0.04***
0.01
(5.37)
(1.11)
PRIVATE
- (H1)
-0.0001***
-0.0001***
(-4.17)
(-3.59)
REG*PRIVATE
+ (H2b)
0.0002***
0.0002***
(6.01)
(7.32)
AFFLATE*REG*PRIVATE
- (H3)
-0.0001*
-0.001***
(-1.64)
(-3.46)
COMMON
-0.0001**
-0.00004
-0.0001**
-0.00004
(-1.89)
(-1.55)
(-1.89)
(-1.55)
CONSENSUS
+
0.09***
0.15***
0.09***
0.15***
(4.61)
(6.45)
(4.60)
(6.41)
VOL
-0.0005
-0.01***
-0.0005
-0.01***
(-0.27)
(-5.73)
(-0.24)
(5.68)
SIZE
+
0.11***
0.07***
0.11***
0.07***
(11.47)
(6.65)
(11.52)
(6.80)
LEV
+
-0.16***
-0.04
-0.16***
-0.04
(-2.79)
(-0.57)
(-2.82)
(-0.65)
MB
-0.03***
-0.04***
-0.03***
-0.04***
(-6.73)
(-6.89)
(-6.73)
(-6.89)
STDROA
-0.68***
-0.69**
-0.68***
-0.69**
(-2.54)
(-2.38)
(-2.56)
(-2.42)
INDNUM
-0.002***
-0.002***
-0.002***
-0.002***
(-5.91)
(-5.37)
(-5.93)
(-5.45)
INDSIZE
-0.09**
-0.03
-0.09**
-0.03
(-2.37)
(-0.78)
(-2.34)
(-0.73)
STATE
?
-0.30***
-0.38***
-0.30***
-0.38***
(-6.58)
(-8.10)
(-6.53)
(-8.00)
CENGOV
?
0.04*
0.01
0.04*
0.01
(1.66)
(0.32)
(1.65)
(0.27)
LOCGOV
?
0.09***
0.08***
0.09***
0.08***
(4.91)
(3.96)
(4.86)
(3.86)
PREANCE
0.02
0.06***
0.02
0.06***
(1.39)
(3.28)
(1.43)
(3.40)
MEF
-0.07***
-0.01
-0.07***
-0.01
(-5.39)
(-0.97)
(-5.40)
(-1.03)
ABSFE
+
0.003***
0.004***
0.003***
0.004***
(3.09)
(4.01)
(3.09)
(4.02)
INSDEV
-0.0003
-0.00001
-0.0003
0.00004
(-0.58)
(-0.02)
(-0.55)
(0.07)
Ind. Dummies
Included
Included
Included
Included
N
8072
7354
8072
7354
Adj. R2
0.13***
0.14***
0.13***
0.14***
Note: ***, **, and * represent statistical significance at the 1%, 5% and 10% levels respectively (two-tailed
test).
See appendix for variable definitions.
SYNCH1 is measured for all A-shares including A-share firms, AB-share firms, AH-share firms; SYNCH1a is
for only A-share firms; SYNCH1b is for AB-share firms; SYNCH1c is for AH-share firms.
All variables are defined in the Appendix.
45
Table 4
The comparison in analyst private information and forecast errors between affiliated and non-affiliated analysts
and between pre- and post-regulation
Panel A: Analyst private information
PRIVATE
Full sample
periods
Pre
Post
Affiliated analysts
129.21
264.54
122.61
Difference between preand post-regulation periods
(t-statistics)
-0.58
Non-affiliated analysts
94.99
247.00
63.64
-10.94***
Difference between affiliated
and non-affiliated analysts
0.67
0.19
1.29*
N/A
Full sample
periods
Pre
Post
Affiliated analysts
0.41
0.79
2.02
Difference between preand post-regulation periods
(t-statistics)
2.13**
Non-affiliated analysts
2.27
1.08
2.58
8.09***
Difference between affiliated
and non-affiliated analysts
-2.06**
-0.77
-2.48***
N/A
Panel B: Forecast error.
ABSFE
Note: ***, **, and * represent statistical significance at the 1%, 5% and 10% levels respectively (two-tailed
test).
46
Table 5
Comparison in mean value of PRIVATE and SYNCH1 for firms between high and low FINDEV provinces
Provinces
with high
FINDEV
Provinces with
low FINDEV
Difference in PRIVATE (SYNCH1)
between high and low FINDEV provinces
(t-statistic)
PRIVATE
108.16
84.48
2.88**
SYNCH1
-0.10
-0.09
-0.46
Note: ***, **, and * represent statistical significance at the 1%, 5% and 10% levels respectively (two-tailed
test); FINDEV is defined in Appendix. It is a dummy variable presenting high or low levels of financial market
development. It is 1 if a firm is located in a province where the financial market development index is above the
sample mean value; 0 otherwise.
47
Table 6
The effect of private information acquisition on synchronicity conditional on financial market development
Without regulation effect
Variables
Sign
SYNCH1
SYNCH1a
Coefficient
(t-stat)
0.53
(0.75)
0.05***
(7.50)
0.0001
(0.72)
-0.00006**
(-2.08)
Coefficient
(t-stat)
-0.40
(-0.53)
0.01***
(2.54)
0.0001
(1.60)
-0.00005*
(-1.76)
With regulation effect
SYNCH1
SYNCH1a
Coefficient
Coefficient
(t-stat)
(t-stat)
Constant
0.53
-0.41
(0.75)
(-0.54)
TIME
+
0.04***
0.01**
(7.10)
(2.17)
PRIVATE
0.0002
0.0001*
(0.81)
(1.67)
FINDEV*PRIVATE
- (H4)
-0.0002***
-0.0002***
(-4.11)
(-4.15)
REG* FINDEV*PRIVATE
+
0.0002***
0.0002***
(5.05)
(5.16)
COMMON
-0.00004*
-0.00003
-0.0001**
-0.00004
(-1.60)
(-1.24)
(-1.93)
(-1.56)
CONSENSUS
+
0.09***
0.15***
0.09***
0.15***
(4.39)
(6.33)
(4.57)
(6.50)
VOL
0.001
-0.01***
0.0002
-0.01***
(0.59)
(-5.22)
(0.13)
(5.67)
SIZE
+
0.11***
0.07***
0.11***
0.07***
(15.27)
(7.83)
(15.25)
(7.86)
LEV
+
-0.15***
-0.03
-0.15***
-0.03
(-3.06)
(-0.56)
(-3.04)
(-0.56)
MB
-0.03***
-0.04***
-0.03***
-0.04***
(-6.81)
(-7.06)
(-6.82)
(-7.05)
STDROA
-0.64***
-0.67***
-0.64***
-0.66**
(-2.81)
(-2.58)
(-2.78)
(-2.56)
INDNUM
-0.46***
-0.47***
-0.45***
-0.47***
(-6.03)
(-5.92)
(-5.93)
(-5.81)
INDSIZE
-0.06*
0.01
-0.06*
0.01
(-1.83)
(0.28)
(-1.84)
(0.28)
STATE
?
-0.30*
-0.38***
-0.30***
-0.38***
(-8.33)
(-9.60)
(-8.36)
(-9.66)
CENGOV
?
0.05**
0.01
0.04**
0.01
(2.42)
(0.55)
(2.33)
(0.45)
LOCGOV
?
0.09***
0.08***
0.09***
0.08***
(6.48)
(4.95)
(6.33)
(4.81)
PREANCE
0.02
0.05***
0.02
0.06***
(1.53)
(3.63)
(1.48)
(3.60)
MEF
-0.07***
-0.01
-0.07***
-0.02
(-5.51)
(-1.06)
(-5.53)
(-1.09)
ABSFE
+
0.003***
0.004***
0.003***
0.004***
(2.72)
(3.64)
(2.78)
(3.71)
Ind. Dummies
Included
Included
Included
Included
N
8072
7354
8072
7354
Adj. R2
0.13***
0.14***
0.13***
0.14***
Note: ***, **, and * represent statistical significance at the 1%, 5% and 10% levels respectively (two-tailed
test).
See appendix for variable definitions.
SYNCH1 is measured for all A-shares including A-share firms, AB-share firms, AH-share firms; SYNCH1a is
for only A-share firms; SYNCH1b is for AB-share firms; SYNCH1c is for AH-share firms.
All variables are defined in the Appendix.
48
Table 7
Sensitivity test for the effect of private information acquisition on synchronicity pre and post-Directive 40
Expanded Equation (2)
Variables
Sign
SYNCH1
SYNCH1a
Coefficient
(t-stat)
-1.10
(-1.35)
0.04***
(6.66)
0.83***
(57.49)
-0.0002
(-0.86)
0.0001***
(2.57)
Coefficient
(t-stat)
-2.13***
(-2.58)
0.01*
(1.82)
0.80***
(42.69)
-0.0001
(-0.54)
0.0001***
(3.53)
Expanded Equation (3)
SYNCH1
SYNCH1a
Coefficient
Coefficient
(t-stat)
(t-stat)
Constant
-1.15
-2.22***
(-1.41)
(-2.68)
TIME
+
0.05***
0.01*
(6.63)
(1.79)
CRISIS
+
0.84***
0.81***
(57.54)
(42.97)
PRIVATE
- (H1)
-0.0001
-0.0001
(-0.86)
(-0.53)
REG*PRIVATE
+ (H2)
0.0001***
0.0001***
(2.89)
(4.38)
AFFLATE*REG*PRIVATE
- (H3)
-0.0002**
-0.0004***
(-2.37)
(-3.96)
COMMON
0.0001
-0.00007
0.0001
-0.00004
(0.07)
(0.31)
(0.08)
(-1.55)
CONSENSUS
+
0.03
0.08***
0.03
0.08***
(1.46)
(4.01)
(1.43)
(3.96)
VOL
0.01***
-0.005**
0.01***
-0.004**
(3.34)
(-2.33)
(3.38)
(-2.25)
SIZE
+
0.13***
0.09***
0.13***
0.09***
(14.39)
(9.10)
(14.47)
(9.27)
LEV
+
-0.25***
-0.13**
-0.26***
-0.14**
(-5.41)
(-2.26)
(-5.46)
(-2.36)
MB
-0.02***
-0.03***
-0.02***
-0.03***
(-5.71)
(-6.61)
(-5.71)
(-6.61)
STDROA
-0.95***
-0.98**
-0.96***
-0.99***
(-3.98)
(-3.83)
(-4.01)
(-3.88)
INDNUM
?
-0.0004
-0.0004
-0.0004
-0.0004
(-1.53)
(-1.37)
(-1.56)
(-1.45)
INDSIZE
-0.08**
-0.01
-0.08**
-0.01
(-2.40)
(-0.32)
(-2.37)
(-0.26)
STATE
?
-0.23***
-0.31***
-0.23***
-0.30***
(-5.50)
(-6.95)
(-5.43)
(-6.83)
CENGOV
?
0.02
-0.01
0.02
-0.01
(0.99)
(-0.30)
(0.97)
(-0.37)
LOCGOV
?
0.09***
0.07***
0.09***
0.07***
(4.98)
(3.79)
(4.92)
(3. 67)
PREANCE
0.01
0.05***
0.02
0.05***
(0.94)
(3.05)
(0.99)
(3.20)
MEF
-0.08***
-0.02*
-0.08***
-0.02*
(-6.28)
(-1.66)
(-6.30)
(-1.73)
ABSFE
+
0.003***
0.004***
0.003***
0.004***
(3.17)
(4.15)
(3.18)
(4.16)
INSDEV
0.00002
0.0003
-0.00004
0.0003
(0.04)
(0.49)
(-0.08)
(0.59)
Ind. Dummies
Included
Included
Included
Included
N
8072
7354
8072
7354
Adj. R2
0.33***
0.31***
0.33***
0.31***
Note: ***, **, and * represent statistical significance at the 1%, 5% and 10% levels respectively (two-tailed
test).
See appendix for variable definitions.
49
SYNCH1 is measured for all A-shares including A-share firms, AB-share firms, AH-share firms; SYNCH1a is
for only A-share firms; SYNCH1b is for AB-share firms; SYNCH1c is for AH-share firms.
FINDEV is defined in Appendix. It is a dummy variable presenting high or low levels of financial market
development. It is 1 if a firm is located in a province where the financial market development index is above the
sample mean value; 0 otherwise.
All variables are defined in the Appendix.
Appendix
Variable definitions
Variables
PRIVATE
Definition
Analyst private information acquisition (precision) measured as
D
PRIVATE 
[(1 
COMMON
1
) D  SE ]2
N
Where N is the number of analysts’ forecasts. D is the sample variance of the
analysts’ individual forecasts. SE is the squared error in the mean forecast,
calculated as the squared difference between the mean forecast and actual earnings,
i.e., (Actual EPS – Mean EPS Forecast)².
Analyst common information acquisition (precision) measured as
( SE 
COMMON 
D
)
N
1
) D  SE ]2
N
CONSENSUS Analysts’ consensus information
[(1 
COMMON
COMMON  PRIVATE
Stock price synchronicity, measured using the three regressions.
CONSENSUS 
SYNCH
RETit    1MKTRETt   2 MKTRETt 1  3 INDRETt   4 INDRETt 1   it
(1)
Where, for firm i and t, RET presents the daily returns on A-shares traded on either
the Shanghai or Shenzhen exchange; MKTRET and INDRET are the valueweighted A-share market returns and industry returns, respectively. The valueweighted A-share market return equals the change in the composite value-weighted
A-share indices from day t to day t – 1 deflated by the composite value-weighted
A-share index on day t - 1. The industry return is created using all firms within the
same industry with firm i’s daily return omitted. Industry weighted daily returns are
calculated using the weighting of each firm’s market capitalization as a percentage
of all firms’ total market capitalization in a particular industry. Lagged MKTRET
and INDRET are also included.
50
To address the possibility that Chinese stock returns may be correlated with world
market returns, in the following three models, we also control for world market
factors as measured by the MSCI (Morgan Stanley Capital International) World
index.13 In addition, to tackle Chinese segmental markets, we use Equations 1a, 1b
and 1c to estimate the share price co-movement for A shares-only firms, A+B share
firms and A+H share firms respectively, because returns on stocks of A+B (A+H)
share firms are likely to co-move with B-share (H-share) market factors in addition
to A-share market factors in B (H) share markets. This approach follows (Yu et al.,
2013) and (Gul et al., 2010).
RETit    1MKTRETt   2WORDRET   it
(1a)
RETit    1MKTRETt   2 MKTRET B  3WORDRET   it
(1b)
RETit    1MKTRETt   2 MKTRET H  3WORDRET   it
(1c)
In estimating Equation 1 and Equations 1a, 1b and 1c, we require that daily returns
data be available for at least 200 trading days in each fiscal year. Stock price
synchronicity is defined as the ratio of common return variation to total return
variation, which is equivalent to R2 of the market model used. To circumvent the
bounded nature of R2 within [0, 1], we use a logistic transformation of R²:
2
R
SYNCH  log( i 2 )
1  Ri
Where, SYNCHi is annual synchronicity for firm i.
FE
REG
AFFLATE
Control
variables
VOL
The calculation using R² estimated from Equation (1) is denoted as SYNCH (1),
while the calculation using R² estimated using Equations 1a, 1b and 1c are
represented as SYNCH (1a, 1b, 1c).
Forecast error measured as the difference between actual EPS and forecasted EPS,
deflated by actual EPS.
The Directive 40, measured as dummy variable with a value of 1 for observations
from 2008 to 2012, and a value of zero for observations in early sample years.
A dummy variable for affiliated analysts. It takes a value of 1 if more than half of
the analysts following a firm are affiliated. Affiliated analyst is defined as an
analyst from the securities firm acting as investment banker/underwriter services
for a firm. In particular, an affiliated analyst issues an earnings forecast on a stock
within five years of its IPO date or within two years of its SEO date.
Annual trading volume turnover, computed as the total number of shares traded in a
year, divided by the total number of shares outstanding at the end of that fiscal
13
The MSCI (Morgan Stanley Capital International) World index is a world market index that is based on the stock
prices of listed companies representing of 22 stock markets in North America, Europe, and the Asia/Pacific region,
and is weighted by the market capitalization of each constituent market. The index data are extracted from the
Datastream database.
51
SIZE
LEV
STDROA
MB
INDNUM
INDSIZE
STATE
LOCGOV
CENGOV
PREANCE
MEF
INSDEV
FINDEV
year.
Firm size, measured as the log of total assets at the end of a fiscal year.
Leverage measured as total liabilities divided by total assets at the end of a fiscal
year.
Earnings volatility measured as the standard deviation of a firm’s ROAs over the
preceding five-year period, including the current year.
Market to book ratio computed as the total market capitalization divided by the
total net assets at the end of fiscal year.
The number of firms in the industry to which a firm belongs.
Industry size measured as the log of the year-end total assets of all the sample firms
in the industry to which a firm belongs.
The percentage of shares held by state owner(s) at year’s beginning.
Local government shareholding measured as a dummy variable. It equals 1 if the
firm’s largest shareholder is local government-related and 0 if the firm’s largest
shareholder is non-local government-related.
Central government shareholding measured as dummy variable. It equals 1 if the
firm’s largest shareholder is central government related and 0 if the firm’s largest
shareholder is non-central government-related.
Public pre-announcements of earnings, which is a dummy variable. 1 is assigned to
a firm if a pre-announcement of earnings is made and, 0 otherwise. Preannouncement of earnings is not compulsory in China.
Management forecasts measured in two ways, MEF1 and MEF2. MEF1 is 1 if a
firm’s management has forecasted their earnings, otherwise zero; MEF2 equals 1,
if a firm discloses its management forecast, but CSRC didn't require the firm to
disclose; it equals -1 if CSRC requires a firm to disclose, but companies didn't
disclose the management forecast, 0 otherwise.14 MEF1 is used for the main
analysis as tabulated, while MEF2 is used for one sensitivity test.
Institutional development index, which is a composite index developed for each
province in China by Fan et al. (2011). It includes six components, namely (1)
overall market-institutions development; (2) government intervention in markets;
(3) private enterprise development; (4) regional protectionism; (5) financial market
development; and (6) legal environment.
Financial market development measured as a dummy variable representing high or
low levels of financial market development based on the fifth component of Fan et
al.’s (2011) index. It is 1 if a firm is located in a province where the financial
market development index is above the sample mean; and 0 otherwise.
14
CSRC requires companies to disclose management forecasts when: 1) companies turn loss into profit; 2)
companies’ net profit is loss; 3) companies’ earnings are fluctuating, that is earnings grow at more than 50%
from the previous year, or earnings decline more than 50% from the same period in the previous year. This is
the so-called ‘Semi-mandatory Management Forecast policy’.
52
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