Journal of Business Finance & Accounting, 32(1) & (2), January/March 2005, 0306-686X
Information Content and Timing of
Earnings Announcements
GONGMENG CHEN, LOUIS T. W. CHENG
AND
NING GAO*
Abstract: The China Securities Regulatory Commission requires all listed
firms to make earnings announcements by the end of April each year. This
requirement creates a unique opportunity for us to evaluate the timing of
earnings announcements in a four-month cluster. Firms, which are willing to
make early announcements, tend to surprise the market, as indicated by the
higher volume and price reactions. Later announcements are more predictable, as indicated by the lower volume and price reactions. These results
indicate that an information asymmetry exists between early and late earnings
announcements in Mainland China.
Keywords:
earnings, information asymmetry, volume, Chinese market
1. INTRODUCTION
In this study, we examine the abnormal volume and price
effects of the timings of earnings announcements in China.
The Chinese stock market recently eclipsed Hong Kong’s as
the second largest in Asia. China has become one of the top
10 trading nations, and the second largest recipient of foreign
direct investment in the world. The ratio of stock market
capitalization to GDP increased from zero in 1990 to 50% in
* The first and second authors are Associate Professors of Finance and the third author
is a Research Fellow at the School of Accounting and Finance, Hong Kong Polytechnic
University. The second author is also HSBC Fellow at the School of Business and
Economics, University of Exeter. They thank the anonymous referee for his valuable
comments. Any remaining errors are the authors’ own.
Address for correspondence: Louis T. W. Cheng, Associate Professor of Finance,
School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom,
Kowloon, Hong Kong.
e-mail: aflcheng@polyu.edu.hk
Blackwell Publishing Ltd. 2005, 9600 Garsington Road, Oxford OX4 2DQ, UK
and 350 Main Street, Malden, MA 02148, USA.
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2001. This rapid growth is mainly due to the improved allocation
of financial resources in the economy, accelerated growth in key
industries, and increased enterprise efficiency. About 60 million
Chinese citizens, or approximately 13% of households, have
securities brokerage accounts. At the end of 2002, the total
market capitalization value of the two stock exchanges, Shanghai
and Shenzhen, was almost US $539.6 billion.
China is now a member of the World Trade Organization.
Foreign companies will be able to tap into the massive accumulation of household savings and list their shares in the two stock
exchanges. Moreover, China plans to gradually open the
A-share market to foreign investors. However, the Chinese
stock market is still relatively unknown to Western investors.
In this paper, we choose a Chinese data set that enables us to
examine information asymmetry from a new perspective
through the timing of announcements. When China’s stock
exchanges were opened in 1990, little attention was paid to
the development and implementation of regulations governing
the market. Before 1993, there were almost no regulations
requiring listed firms to publish their annual reports. Therefore,
information disclosure for listed firms was minimal. This situation
remained until 1993, by which time numerous illegal dealings and
trading scandals emerged as a result of rapid expansion and
overheated market.
On April 22, 1993, the State Council promulgated the first
formal regulations on information disclosure, entitled ‘The
Provisional Regulations Governing the Issue and Trading of
Shares’ (also known as the Securities Provisional Regulations).
These regulations require the listed firms to submit their
audited annual reports to the China Securities Regulatory
Commission (CSRC) and the related stock exchanges within
120 days of the end of the previous financial year. In June
1993, the CSRC added a second important regulation, ‘The
Implementation Measures on Disclosure of Information Pursuant
to the Securities Provisional Regulations’. This regulation
requires listed firms to submit 10 copies of their audited annual
reports to the CSRC within 120 days of the end of the previous
financial year. In addition, it requires the firms to publish their
annual reports in at least one approved newspaper within 20
working days before their annual shareholder meetings.
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On February 21, 1995, the CSRC imposed another requirement on all listed firms that they publish their annual reports in
at least one CSRC-appointed newspaper within 120 days of the
end of the previous fiscal year, or by the end of April, whichever
comes first. The two conditions result in the same effect, as
December 31 is the financial year-end for all Chinese firms.
Furthermore, in 1994 China adopted a new accounting
standard, which is very similar to the international standard,
but very different from that which was traditionally used.
During the same year a new tax system came into effect, covering
value-added tax, sales tax, consumption tax, resources tax,
increment tax on land value, and business income tax.
Previous research on earnings announcements has generally
documented that volume reactions are positively related to the
informativeness of the public disclosures (measured, for
example, by the magnitude of unexpected earnings), and are
negatively related to the level of pre-disclosure information
(typically proxied by firm size). Morse (1981), Bamber (1986
and 1987), Kim and Verrecchia (1991a and 1991b), Atiase and
Bamber (1994) and Bamber, Barron and Stober (1997) find that
firms with less pre-disclosure information tend to receive stronger
volume reactions, which indicates that the earnings announcements of these firms provide more information to the market.
The timing literature has provided ample empirical evidence
for well-developed markets (Chambers and Penman, 1984;
Kross and Schroder, 1984; Sinclair and Young, 1991; Bowen,
Johnson, Shevlin, and Shores, 1992; and Begley and Fischer,
1998). The consensus is that firms announce good news earlier
than bad news. In addition, firms that earn more tend to
accelerate their announcements, while firms that earn less
delay their announcements.
While ample evidence on earnings announcements has been
presented for the US market, the availability of similar studies
for other international markets is limited. The results of the
study of Brookfield and Morris (1992) on the UK firms are
consistent with those of the US. The authors find that the
earnings announcements of the UK firms contain significant
residual information, and stock prices react rapidly to this
news. Pope and Inyangete (1992) examine the variation of
returns during earnings announcement in the UK and find
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that the return variability is substantially higher during the
actual announcement than that during the pre-announcement
period.
However, research related to earnings announcements in
emerging markets is relatively limited. In this study, we use
the stock market in China to examine how the timing of annual
earnings announcements may affect the trading volume and
abnormal returns of the firms. We first study the overall
announcement effects in terms of abnormal trading volume
and abnormal returns, and then further examine the relations
between the timing of announcements and their market reactions. We also look at the timing surprises (accelerated and
delayed announcements) and their effects on trading volume
and abnormal returns in order to evaluate the issue of information asymmetry. Finally, we use a regression analysis to control
for industry, size, foreign ownership, and degree of public
ownership, and examine the timing effect in a multivariate
framework. Our results indicate that earlier announcements
possess more information content, as measured by trading
volume and returns, than do the later announcements. Thus,
information asymmetry exists between earlier and later earnings
announcements. Our regression results also show that earlier
announcements receive greater abnormal trading volume and
price reactions. These results support our argument that the
timing of earnings announcements serves as an important
strategy for firms to reduce information asymmetry.
Our paper is structured as follows. Section 2 presents the
theoretical background and discusses the hypothesis of our
paper. Section 3 lists the data and methodology. Section 4
describes the results. Our conclusions are reported in Section 5.
2. THEORETICAL BACKGROUND AND HYPOTHESIS
(i) Literature Review
Previous studies on information asymmetry focus on the
amount of pre-announcement information and its relationship
to unexpected trading volume and returns. Bamber, Barron
and Stober (1997) suggest that trading volume is related to
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the magnitude of the disagreement among investors about a
firm’s earnings. They employ variation in earnings forecasts by
analysts to proxy disagreement.
Kim and Verrecchia (1991a) argue that price changes reflect
the average change in the aggregate market’s average beliefs,
while trading volume is the sum of all individual investors’
trades, which also depends on the prevailing information asymmetry level before disclosure. They suggest that although all
investors have equal access to public pre-disclosure information,
they acquire private pre-disclosure information with different
degrees of precision.
Atiase and Bamber (1994) and Kross et al. (1994) suggest that
trading volume is an increasing function of the degree of
divergent pre-disclosure expectations. Bamber and Cheon
(1995) argue that the reason for different reactions is that
price reactions reflect the average belief revision, while trading
volume arises when individual investors make differential belief
revisions.
Some studies argue that any information relating to earnings
around the earnings announcement period is able to initiate
share price movements. These studies (Shores, 1990; Graham
and King, 1996; Kim et al., 1996; and Abarbanell and Bushee,
1997) find that the interim information, the firm’s information
environment, the characteristics of the trading transactions
around the earnings announcement period, and the accounting-related fundamental signals such as gross margin and
inventory, are the factors affecting the magnitude of the share
price reactions to earnings announcements.
In addition, Kim and Verrecchia (1994) suggest that there may
be more information asymmetry at the time of an announcement
than in a non-announcement period. This is because earnings
announcements provide information that allows certain traders to
make judgements about a firm’s performance that are superior
to the judgements of other traders.
In a separate paper, Kim and Verrecchia (1997) introduce a
model of a rational trade with both pre-announcement and
event-period information. The former is private information
that is gathered in anticipation of a public disclosure, while the
latter is private information that is useful in conjunction with
the announcement itself.
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Relying on the analytical models of trading volume, earnings
announcement, and pre-disclosure information asymmetry,
Lobo and Tung (1997) find that the trading volume around
quarterly earnings announcements is related to the level of predisclosure information asymmetry. For firms with a high level of
pre-disclosure information asymmetry, the trading volume is
low prior to and after the announcement, but high during the
announcement.
(ii) Hypothesis
The China Securities Regulatory Commission (CSRC) requires
all firms to announce earnings in the first four months of the
year. This is different from the requirements of developed
countries. The time limitation for the announcement, from
January to April, can create time pressures and lead to different
market behaviors.
These requirements have created a unique opportunity for us
to evaluate the timing of earnings announcements under a
regulatory mandate in a four-month cluster. As a firm can
only choose a pre-determined date between January and April
to make its announcements, and is required to apply for
approval from the government, its flexibility in delaying the
announcement is limited.
Bamber (1986) employs the divergence of earnings forecasts from analysts’ forecasts as a proxy for information
asymmetry. She finds that the higher the information
asymmetry, the greater the abnormal volume reaction. In
this study, we first use unexpected earnings as a control
variable for information asymmetry. This is because no earnings forecasts are available in China. We suggest that the
timeliness in disclosing the information is also related to
information asymmetry. Therefore, our hypothesis focuses
on the relation between the timeliness of the announcement
and the abnormal market reactions in terms of both
volume and returns. A further explanation of our hypotheses
is provided below.
Null Hypothesis: Firms with earlier and later earnings
announcements should receive similar
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abnormal market reaction in terms
of abnormal trading volume and
abnormal returns.
Alternative Hypothesis: Firms with earlier earnings announcements should receive a higher abnormal market reaction in terms of
abnormal trading volume and
abnormal returns than firms with
later announcements.
The timing literature focuses on the relationship between the
timing of announcements and abnormal returns. We examine
the information content of the timing of the announcements by
measuring both the abnormal trading volume and abnormal
returns resulting from these announcements. In fact, timing of
announcements can be measured by an absolute or a relative
benchmark. Earlier announcements should generate a greater
surprise in the market because it is more difficult to predict
earlier announcements than later announcements. Chambers
and Penman (1984) argue that longer reporting lags provide
the opportunity for more of the report’s information to be
supplied by other sources, either through search activity by
investors, through other voluntary disclosures by firms, or
through predictions that are supplied in the earnings releases
of earlier reporting firms.
The market values of the tradable shares of some Chinese
listed companies are small, as is the case in many other
emerging markets. It is not unusual for Chinese companies to
have concentrated holdings (i.e. some large shareholders
control most of the tradable shares of the company). The longer
the delay in announcing earnings, the greater the magnitude of
the information leakage to the large shareholders and the
smaller the surprise are. Therefore, we hypothesize that later
reports will be associated with lower information content than
earlier reports, and that the abnormal trading volume will
be lower. Using abnormal trading volume (ATV) to measure
unexpected market reaction, we propose that the ATV from
earlier announcements is greater than the ATV from later
announcements.
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In addition, we are also interested in examining the relation
between the abnormal returns of the earnings announcements
and their timeliness. We thus empirically test whether the
abnormal return (AR) and cumulative abnormal return (CAR)
for earlier announcements are greater than the AR and CAR for
later announcements.
3. DATA AND METHODOLOGY
(i) Data
Our sample period covers eight years from 1995 to 2002. There
are two kinds of shares in circulation among public shareholders
in China: A-shares and B-shares. A-shares are common stocks,
denominated in Chinese Renminbi, and are only available to
domestic investors. B-shares are common stocks denominated
in foreign currencies and were only available to international
investors during most of our sample period. By April 1, 2001,
domestic investors are also allowed to trade B-shares. The
B-shares that are listed on the Shanghai Stock Exchange
(SHSE) are traded in US dollars, while those on the Shenzhen
Stock Exchange (SZSE) are traded in Hong Kong dollars.
There are three possible types of listing for firms in China.
First, a firm can list B-shares only. Second, a firm can choose to
list A-shares only, and this constitutes the majority of the
listings. Finally, a firm can list both A-shares and B-shares. As
there are a very limited number of earnings announcements
made by firms with B-shares only, we exclude announcements
by firms with B-shares only and focus on firms with A-shares
only or both A- and B-shares.
The announcement dates for Shenzhen are obtained from
the Securities Times, and those for Shanghai are obtained from
Shanghai Securities. Daily stock price and volume data are provided by the two stock exchanges. Accounting and other related
company data are collected from the annual reports, the two
newspapers, the yearbooks of the two exchanges, and the China
Stock Market and Accounting Research (CSMAR) database
(jointly developed by the China Accounting and Finance
Research Centre of Hong Kong Polytechnic University and
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the ShenZhen GTA Information Technology Co. Ltd.). As we
need to estimate volumes and returns, we eliminate firms with a
listing history of less than 280 working days before the
announcement and with missing data. Thus, the final sample
has 3,802 events.
(ii) Research Methodology
(a) Measurement of Market Reactions
We use a maximum event window of [20, þ7] for daily
event-study analysis, and six different event windows, [20,
2], [20, 3], [1, þ1], [2, þ2], [5, þ5] and [7, þ7], to
capture the cumulative announcement effects. In designing
the event window, two rationales are being employed. First,
normally due to potential insider trading and information
leakage, it is possible that the market reaction starts long
before the actual announcements. Consequently, we employ
[20, 2] and [20, 3] to capture the possible pre-event
reaction. Second, in the relatively efficient market, announcement effects should not exist in the long event window.
Therefore, we use four short symmetrical event windows to
capture announcement effects. They are [1, þ1], [2, þ2],
[5, þ5] and [7, þ7]. The announcement day is defined as
day 0.
The market model that we use to measure the abnormal
returns and abnormal trading volume1 is:
Rit ¼ ai þ bi Rmt þ ARit ;
ð1Þ
Vit ¼ ci þ di Vmt þ ATVit ;
ð2Þ
where Rit is the daily return of firm i, and is calculated by
dividing (Pit Pit1) by Pit1; Rmt is the value-weighted
1 Based on the recommendation of an anonymous referee, we adopt the natural log
transformation of the volume measures by Ajinkya and Jain (1989) and re-run our
results. The findings are similar, which indicates that our original empirical results are
not affected by the problem of measurement. After evaluating the advantages and
disadvantages of the transformation, and to save space, we report only the original
measures in our paper. The log results are available from the authors upon request.
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market return (Shanghai and Shenzhen are treated as
separate markets); Vit is measured by dividing the shares
traded for firm i on day t by the number of tradable shares
outstanding for firm i; Vmt is computed by dividing the total
shares traded in the SHSE (SZSE) on day t by the total
tradable shares outstanding in the SHSE (SZSE); ARit denotes
the abnormal (residual) return for firm i on day t; and ATVit
denotes the normalized abnormal (residual) trading volume
for firm i on day t.
We adopt an estimation window of 250 trading days from
day 280 to day 31. A time gap between the end of the
estimation window and the beginning of the event window
(i.e. from day 30 to day 21) is employed to avoid using
unusual price or volume data (due to information leakage)
for model estimation.
To test the daily market reaction, ARit and ATVit are
described by their corresponding t-statistics for the total sample,
and by month. We also report the cumulative abnormal return
(CAR) and the normalized cumulative abnormal trading
volumes (CATV), and their z-statistics over various event
windows.
CAR and CATV are calculated as follows:
CARð1; 2Þ ¼
2
X
ARit ;
ð3Þ
ATVit :
ð4Þ
¼1
CATVð1; 2Þ ¼
2
X
¼1
Hereafter we define CAR (1, þ1) as CAR3, CAR (2, þ2) as
CAR5, CAR (5, þ5) as CAR11, CAR (7, þ7) as CAR15, CAR
(20, 3) as CAR18, CAR (20, 2) as CAR23; CATV (1, þ1)
as CATV3, CATV (2, þ2) as CATV5, CATV (5, þ5) as
CATV11 and CATV (7, þ7) as CATV15, CATV (20, 3) as
CATV18, and CATV (20, 2) as CATV23.
Under the null hypothesis that the event has no impact on the
behavior of abnormal return and trading volume, the distribution of the cumulative abnormal return and trading volume
should be normally distributed as:
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CARð1; 2Þ Nð0; 21i ð1; 2ÞÞ;
CATVð1; 2Þ Nð0; 22i ð1; 2ÞÞ:
(b) Proxies for Information Asymmetry
Haw et al. (2000) study the Chinese stock market and find that
firms with good news publicize their annual reports earlier
than those with bad news,2 and loss-making firms are the last
to release their annual reports. They define the reporting lag as
the number of days from the fiscal year-end to the report
announcement date. However, in the Chinese lunar calendar,
there is a long Chinese New Year holiday, which begins on a
different date each year. To focus our analysis on the number of
tradable days, we define the reporting lag as the number of
working days from the fiscal year-end to the annual release
date. To compare the timings of the earnings announcements,
2 Although this paper focuses on timeliness instead of good news/bad news, to
informally examine this conjecture, we have classified our total sample into good news
(announcement with earnings per share greater than and equal to þ20%), bad news
(announcement with earnings per share less than and equal to 20%), and no news
(announcement with earnings per share between 20% and þ20%) according to the
time they are released and the statistics are shown in the following table. There is a
higher percentage of good news announcements disclosed in January and February.
Also, a majority of the announcements disclosed in April are classified as bad news.
Total Good News
Jan
96
Feb
321
Mar 1286
Apr 2099
Total 3802
51
161
487
471
1170
(%)
Bad News
(%)
No News
(%)
(53.12%)
(50.16%)
(37.87%)
(22.44%)
(30.77%)
18
69
359
1112
1558
(18.75%)
(21.50%)
(27.92%)
(52.98%)
(40.98%)
27
91
440
516
1074
(28.13.%)
(28.34%)
(34.21%)
(24.58%)
(28.25%)
We also compute the differences in the mean reporting lag, mean ATI, and mean UATI
between the GN sample and the BN sample. The mean difference values indicate that
the GN sample has the smallest mean of the three variables, and the mean differences
are significantly different from each other at the 0.01 level (both by the two-independent
sample test and Mann-Whitney test). These results indicate that the timing of the release
of annual reports is related to the type of earnings. Firms with good news tend to make
announcements earlier than those with bad news. In addition, the test of the UATI
shows that firms with good news accelerate their announcements relative to previous
years, while firms with bad news delay them.
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we build three timing variables, collectively named as TEA
(Timing of Earnings Announcement). The first TEA is a
continuous variable, Announcement Timing Index (ATI),
to proxy the reporting lag, which is defined as ATI ¼ n/N,
where n is the nth working day from January 1 on which
the earnings announcement is made. N is the total number of
working days in the period from January 1 to April 30 in the
event year.
The second TEA, the unexpected ATI (UATI), a relative
timing measure reflecting the unexpected reporting lag, is
defined as the difference between the actual and expected ATI
(the expected ATI of the current year should be the same as the
ATI of the previous year), UATI ¼ ATIt ATIt1. The final
TEA is a dummy variable, called MAD, with a value of 1 for
March and April announcements and 0 otherwise.
(c) Determinants for Abnormal Return and Abnormal Trading
Volume
To study the determinants of the trading volume and return
reactions during annual earnings announcements, we develop
two sample comparing treatments for CATV between (1) the
January and February announcement sample and the March
and April announcement sample; (2) the lowest 40% of the ATI
sample and the highest 40% of the ATI sample; and (3)
the positive UATI sample and the negative UATI sample.
Two sample t-tests and two sample Mann-Whitney tests are
employed to check if the earlier announcement sub-samples
will possess a stronger abnormal trading volume and abnormal
returns than the later announcement sub-samples. The
two-sample t-tests assume a normal distribution for the
data while the Mann-Whitney tests are a non-parametric
procedure which does not depend on normality. By using
both tests, we can make sure that our results are not distribution
dependent.
To further examine the major determinants of the trading
volume and return reactions during annual earnings announcements, we develop the following multiple regression model with
some additional control variables:
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CATVðCARÞ ¼ 0 þ 1 UEAðUEÞ½UEARWðUERWÞ;UEAGMðUEGMÞ
þ 2 SIZE þ 3 POWN þ 4 TEA½UATI; ATI; MAD
þ 5 EXCH þ
12
X
i YEARi5
i¼6
þ
17
X
j INDj12 þ 18 FOR
ð5Þ
i¼13
where CATV is the cumulative abnormal trading volume
over the four event windows; CAR is the cumulative abnormal
return over the four event windows; UE(A) denotes the (absolute value of) the unexpected earnings (both the Random Walk
Model and the Growth Model measurements are used);3 SIZE is
measured by taking the natural logarithm of the total assets in
thousand RMB (Chinese Yuan); POWN represents the percentage of public shares (in China, public shares are tradable, while
State shares and other legal shares cannot be traded); UATI is
the difference between the actual and expected ATI (i.e. the
ATI (announcement timing index) of the previous year); MAD
is a March and April Dummy that takes the value of 1 for an
announcement made in March or April and 0 otherwise; EXCH
is an exchange dummy that takes a value of 1 for the Shanghai
3 There are no publicly available earnings forecasts for listed firms in China.
Therefore, we adopt two approaches to estimate the unexpected earnings: the
Random Walk model and the Growth model.
(1) The Random Walk Model: Without additional information, the market expects a
firm to have the same earnings as the previous year. Especially for an emerging market
such as China, domestic investors do not have access to timely corporate information
and firms do not disclose much information at all. Consequently, the previous year’s
earnings can serve as a good reference for the current year’s earnings estimate.
Following Bamber (1987), we measure the unexpected earnings as follows:
UEARW ¼ jðEit Eit1 Þ=jEit1 jj
ð1Þ
where UEARW denotes the absolute unexpected earnings of the random walk model;
Eit is company i’s EPS in year t; Eit1 is company i’s EPS in year t 1. UERW is similar
but the absolute value sign for the equation is removed.
(2) The Growth Model: Investors may expect a firm to improve their annual earnings
and simply forecast the firm to have the same earnings growth rate as the previous year.
Therefore, the unexpected earnings based on the growth model are calculated as:
UEAGM ¼ jðEit git1 Eit1 Þ=jgit1 Eit1 jj
ð2Þ
where UEAGM is the absolute unexpected earnings of the growth model; git1 is
measured by dividing (Eit1 Eit2) by jEit2j; Eit2 is company i’s EPS in year t 2.
UEGM is similar but the absolute value sign for the equation is removed.
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Stock Exchange and 0 for the Shenzhen Stock Exchange;
YEARi5 is an announcement year dummy for year i, where
YEAR1 ¼ 1 for 1995 and 0 otherwise; YEAR2 ¼ 1 for 1996 and 0
otherwise; YEAR3 ¼ 1 for 1997 and 0 otherwise; YEAR4 ¼ 1 for
1998 and 0 otherwise; YEAR5 ¼ 1 for 1999 and 0 otherwise;
YEAR6 ¼ 1 for 2000 and 0 otherwise; YEAR7 ¼ 1 for 2001 and 0
otherwise; INDj12 is an industry dummy where IND1 ¼ 1 for
the finance industry and 0 otherwise; IND2 ¼ 1 for the utility
industry and 0 otherwise; IND3 ¼ 1 for the property and construction industry and 0 otherwise; IND4 ¼ 1 for conglomerates
and 0 otherwise; IND5 ¼ 1 for the commercial industry and 0
otherwise; and FOR is a foreign listing dummy that takes the
value of 1 if the company has foreign (B, H, or N where
H-shares are Chinese stocks listed on the Hong Kong Stock
Exchange, while N-shares are those listed on the New York
Stock Exchange) shares and 0 otherwise.
Based on the findings of previous studies (Bamber, 1987;
Foster et al., 1984; and Kim et al., 1997), we expect a positive
relation between UEA and abnormal volume; and between UE
and abnormal returns. On the other hand, we suggest that
there is an inverse relation between firm size and the market
reactions. The smaller the firm, the higher is the unexpected
trading volume and returns around its earnings announcements. Large firms are more likely to be closely monitored by
the government or regulatory agencies in a transitional economy like China. All of these lead to a smaller informational
difference between the investors and the company.
We also assume that POWN, the percentage of public shares, is
positively related to trading volume and returns. The major
component of the trading volume is generated by individual
investors in China, while trading by institutional investors
represents a type of passive investment. Finally, there is no
theoretical basis for us to predict the direction of the relations
between the market reactions and EXCH, YEAR, IND and FOR.
4. RESULTS
Before testing our hypothesis, we first report some basic
descriptive statistics for the continuous variables we employ in
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our analysis. Various descriptive statistics for all variables are
reported in Table 1. Panel A of Table 1 lists the monthly
frequencies of the announcements. As mentioned earlier, the
China Securities Regulatory Commission requires that all listed
firms make annual earnings announcements on or before April
30 of each year. The January and February sample (N ¼ 417) is
much smaller than that of March and April (N ¼ 3,385), and the
April sample (N ¼ 2,099) is much larger than the January
(N ¼ 96), February (N ¼ 321) and March (N ¼ 1,286) samples.
As indicated by Haw et al. (1999), many firms postpone the
announcement of their annual reports until April. Thus, since
1997, the CSRC has required the two exchanges to plan for an
even release of annual reports by limiting the maximum
number of announcements for each stock exchange to ten
per day. Consequently, firms have to release their annual
reports on pre-determined dates. However, firms can ask for a
later date if they demonstrate compliance hardship. As a result,
the percentages of April announcements after 1998 are significantly reduced. Chi-square tests for all yearly samples are
statistically significant, which indicates that a significantly
greater percentage of firms in the total sample announce their
earnings later rather than earlier. Table 1, Panel B, lists the
descriptive statistics for the five variables, namely, UERW
(the normalized unexpected earnings estimated by the Random
Walk Model); $SIZE (the total assets of the firm in thousand
RMB); POWN (public ownership of the firm expressed as a
fraction of the total number of shares); ATI (announcement
timing index), and the UATI (unexpected ATI). The median
timing index (ATI) is 0.77, which suggests that most firms
report their earnings late. These findings indicate that the
timing of announcements is not a random choice, and may
contain information that explains volume and price reactions
to announcements.
Table 2 shows the abnormal market reactions by trading
volume over various event windows. We first list the daily normalized abnormal trading volume (ATV), and its corresponding
t-statistics from day 7 to day þ7. In addition, we report the
normalized cumulative abnormal trading volume (CATV) and
its z-statistics for the [20, 2], [20, 3], [7, 7], [5, 5], [2, 2],
and [1, 1] intervals.
#
Blackwell Publishing Ltd 2005
80
CHEN, CHENG AND GAO
Table 1
Descriptive Statistics for Earnings Announcement Event During the
1995–2002 Period
Panel A: Distribution of Sample Sizes of Earnings Announcements-Monthly
Statistics
Januarya Februarya
Marchb
Aprilb
Event Year No. of Obs.
N (%)
N (%)
N (%)
N (%)
1995
1996
1997
1998
1999
2000
2001
2002
Total
265
294
350
590
350
531
663
759
3,802
6(2.26)
1(0.34)
4(1.14)
4(0.68)
8(2.28)
45(8.47)
13(1.96)
15(1.98)
96(2.52)
9 (3.40)
6 (2.04)
10 (2.86)
45 (7.63)
9 (2.57)
50 (9.42)
108(16.29)
84(11.07)
321 (8.45)
81(30.57)
169(63.77)
33(11.22)
254(86.40)
52(14.86)
284(81.14)
269(45.59)
272(46.10)
87(24.86)
246(70.29)
188(35.41)
248(46.70)
299(45.10)
243(36.65)
277(36.50)
383(50.45)
1,286(33.82) 2,099(55.21)
Panel B: Descriptive Statistics for Firm-specific Variables
No. of Obs. Mean
Median St. Dev.
Min.
UERW
$SIZE (‘000)
POWN
ATI
UATI
3,802
3,802
3,802
3,802
3,802
Max.
0.09
0.09
8.26 143.35
116.47
1,314,631 587,336 4,964,348
8,726 173,690,683
0.34
0.32
0.15
0.01
1.00
0.73
0.77
0.21
0.05
1.00
0.002
0.00
0.22
0.81
0.81
Notes:
UERW is the normalized unexpected earnings estimated by the Random Walk Model.
$SIZE is the total assets of the firm in thousands of RMB. POWN is the fraction of public
ownership. ATI is the Announcement Timing Index. (ATI ¼ n/N, where N is the total
number of working days in the period January 1 – April 30 in the corresponding year,
which varies from year to year). n is the nth working date on which the firm makes the
announcement.) ATI measures the timeliness of the earnings announcement, which
ranges from 1/N to 1. UATI is the unexpected ATI, which is the difference between
the actual and expected ATI.
A two-sample Mann-Whitney test is conducted for the combined January and
February (a) subsample versus the combined March and April (b) subsample for every
year. The Chi-square statistics indicate that the difference between the two subsamples
for each year is significant at the 0.01 level for all eight years.
The overall results from Table 2 indicate that annual earnings
announcements lead to significant abnormal volume reactions
on most of the days surrounding the announcement dates and
in all six intervals. The results provide empirical support for our
hypothesis, which argues that earlier announcements tend to
surprise the market more and hence receive greater abnormal
volume reactions. Table 2 also shows the differences in the
#
Blackwell Publishing Ltd 2005
#
Abnormal Trading Volume Around Earnings Announcement by Bi-monthly Sample
January and February (No. of Obs. ¼ 417)
Day
ATV
7
6
5
4
3
2
1
0
þ1
þ2
þ3
þ4
þ5
þ6
þ7
0.0015
0.0024
0.0024
0.0044
0.0045
0.0055
0.0092
0.0134
0.0129
0.0091
0.0055
0.0032
0.0030
0.0018
0.0020
t-value
1.64
2.39*
2.25*
3.40**
3.59**
4.41**
6.25**
7.87**
7.63**
5.62**
4.05**
2.64**
1.86
1.44
1.58
March and April (No. of Obs. ¼ 3385)
ATV
t-value
0.0007
0.0010
0.0009
0.0007
0.0011
0.0010
0.0019
0.0071
0.0071
0.0036
0.0018
0.0008
0.0006
0.0009
0.0010
1.63
2.12*
1.86
1.61
2.38*
2.05*
3.78**
12.02**
12.24**
6.95**
3.61**
1.67
1.31
1.89
2.02*
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
Blackwell Publishing Ltd 2005
Table 2
81
82
Table 2 (Continued)
January and February (No. of Obs. ¼ 417)
Interval
a
0.0841
0.0340b
0.0808c
0.0731d
0.0501e
0.0355f
Z-value
13.32**
7.57**
14.62**
14.94**
14.21**
12.56**
CATV
a
0.0380
0.0173b
0.0302c
0.0266d
0.0207e
0.0161f
Z-value
15.67**
8.98**
14.76**
14.92**
16.57**
16.19**
#
Blackwell Publishing Ltd 2005
Notes:
a
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0461 which is significant at the 0.01 level by the two-sample t-test and 0.05 level by the Mann-Whitney test.
b
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0167 and the difference is not significant by the two-sample t-test and Mann-Whitney test.
c
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0506 which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
d
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0465 which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
e
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0294 which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
f
The difference in the cumulative abnormal trading volume (CATV) between the January and February sample and the March and April sample
is 0.0194 which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
* Significant at the 0.05 level.
** Significant at the 0.01 level.
CHEN, CHENG AND GAO
[20,2]
[20,3]
[7,7]
[5,5]
[2,2]
[1,1]
CATV
March and April (No. of Obs. ¼ 3385)
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
83
mean CATVs for the two bi-monthly samples. The magnitudes
of the ATVs and CATVs for the January and February sample
are much greater than those for the March and April sample.
For instance, the ATV for day 0 of the January and February
sample is 0.0134, whereas the corresponding ATV for the
March and April sample is only 0.0071. Similarly, the CATV
of the earlier months for the [5,5] interval is 0.0731, which is
much higher than the CATV (0.0266) for the same interval of
the later bi-monthly sample.
Table 3 shows the bi-monthly results for abnormal returns
around earnings announcement. The January and February
bi-monthly sample reports positively significant abnormal
returns on day 1 and for all the six intervals ([20, 2], [20,
3], [7, 7], [5, 5], [2, 2] and [1, 1]). However, the
March and April sample contains negatively significant
abnormal returns on day 0 and for the three intervals ([5, 5],
[2, 2] and [1, 1]). Nevertheless, for all six intervals, no matter
whether the returns for the January and February sub-sample
are significant or not, all these returns are nominally higher
than those from the March and April sub-sample. In addition,
the abnormal returns from the January and February subsample are significantly greater than those from the March
and April sub-sample during [20, 2] and the four shorter
intervals.
Table 4 shows the differences in the mean CATVs for (1) the
lowest 40% of the ATI sample and the highest 40% of the ATI
sample; and (2) the positive UATI sample and the negative
UATI sample. For four of the six intervals in Panel A, the
lowest 40% of ATI samples demonstrates a significantly greater
volume reaction than those of the highest 40% of ATI samples.
Moreover, for all six intervals in Panel B we find that the
negative UATI samples demonstrate a significantly greater
volume reaction than those of the positive UATI samples.
These results strongly support our hypothesis that earlier
announcements provide more information content to the
market than later announcements do.
When the cumulative abnormal returns of different subsamples are compared (Table 5), significant differences are found.
We use two methods to categorize the subsamples, ATI and
UATI. These two methods report that there are significant
#
Blackwell Publishing Ltd 2005
84
Table 3
Abnormal Returns Around Earnings Announcement by Bi-monthly Sample
January and February (No. of Obs. ¼ 417)
March and April (No. of Obs. ¼ 3385)
Blackwell Publishing Ltd 2005
AR
t-value
AR
t-value
7
6
5
4
3
2
1
0
þ1
þ2
þ3
þ4
þ5
þ6
þ7
0.0010
0.0015
0.0016
0.0041
0.0033
0.0029
0.0092
0.0029
0.0008
0.0003
0.0017
0.0022
0.0042
0.0006
0.0005
0.84
1.32
1.30
3.01**
2.79**
2.36*
6.10**
1.62
0.58
0.25
1.48
1.79
3.83**
0.50
0.37
0.0004
0.0005
0.0007
0.0002
0.0008
0.0014
0.0002
0.0036
0.0001
0.0002
0.0003
0.0003
0.0010
0.0018
0.0006
1.11
1.29
1.69
0.54
1.99*
3.32**
0.47
5.51**
0.23
0.37
0.68
0.68
2.42*
4.72**
1.70
CHEN, CHENG AND GAO
#
Day
#
January and February (No. of Obs. ¼ 417)
Interval
[20,2]
[20,3]
[7,7]
[5,5]
[2,2]
[1,1]
CAR
Z-value
a
0.0342
0.0187b
0.0178c
0.0164d
0.0155e
0.0129f
5.47**
3.73**
3.02**
3.14**
4.66**
4.79**
March and April (No. of Obs. ¼ 3385)
CAR
Z-value
a
0.0059
0.0110b
0.0007c
0.0040d
0.0051e
0.0039f
4.20**
6.90**
0.60
1.96*
4.09**
3.58**
Notes:
a
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0283
which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
b
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0077
and the difference is not significant by the two-sample t-test and Mann-Whitney test.
c
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0185
which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
d
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0204
which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
e
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0206
which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
f
The difference in the cumulative abnormal return (CAR) between the January and February sample and the March and April sample is 0.0168
which is significant at the 0.01 level by the two-sample t-test and Mann-Whitney test.
* Significant at the 0.05 level.
** Significant at the 0.01 level.
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
Blackwell Publishing Ltd 2005
Table 3 (Continued)
85
86
Table 4
Two-sample Comparison for Cumulative Abnormal Trading Volume
Panel A: Between the Lowest 40% of the ATI Sample and Highest 40% of the ATI Sample
CATV3
CATV5
CATV11
CATV15
0.0253
0.0141
0.0112cd
0.0337
0.0182
0.0155cd
0.0478
0.0229
0.0249cd
Panel B: Between the Positive UATI Sample and Negative UATI Sample
CATV3
CATV5
CATV11
Positive UATI Sample
Negative UATI Sample
Difference in Mean CATV
0.0106
0.0290
0.0184cd
0.0132
0.0413
0.0281cd
0.0160
0.0631
0.0471cd
0.0545
0.0258
0.0287ab
CATV23
0.0265
0.0298
0.0033
0.0602
0.0479
0.0123
CATV15
CATV18
CATV23
0.0166
0.0755
0.0589cd
0.0110
0.0407
0.0297a
0.0242
0.0820
0.0578cd
#
Blackwell Publishing Ltd 2005
Notes:
CATV3 is the current year’s cumulative abnormal trading volume for a 3-day interval (1 to þ1). CATV5 is the current year’s abnormal trading
volume for a 5-day interval (2 to þ2). CATV11 is the current year’s abnormal trading volume for an 11-day interval (5 to þ5). CATV15 is the
current year’s abnormal trading volume for a 15-day interval (7 to þ7). CATV18 is the current year’s abnormal trading volume for an 18-day
interval (20 to 3). CATV23 is the current year’s abnormal trading volume for a 23-day interval (20 to þ2).
a
Two sample t-test significant at the 0.05 level.
b
Two sample Mann-Whitney test significant at the 0.05 level.
c
Two sample t-test significant at the 0.01 level.
d
Two sample Mann-Whitney test significant at the 0.01 level.
CHEN, CHENG AND GAO
Lowest 40% of ATI Sample
Highest 40% of ATI Sample
Difference in Mean CATV
CATV18
#
Two-sample Comparison for Cumulative Abnormal Return
Panel A: Between the Lowest 40% of the ATI Sample and Highest 40% of the ATI Sample
CAR3
CAR5
CAR11
CAR15
CAR18
Lowest 40% of ATI Sample
Highest 40% of ATI Sample
Difference in Mean CAR
0.0170
0.0077
0.0093cd
0.0028
0.0077
0.0105cd
0.0046
0.0117
0.0163cd
0.0085
0.0143
0.0228cd
Panel B: Between the Positive UATI Sample and Negative UATI Sample
CAR3
CAR5
CAR11
Positive UATI Sample
Negative UATI Sample
Difference in Mean CAR
0.0031
0.0019
0.0050ad
0.0038
0.0040
0.0078cd
0.0021
0.0081
0.0102cd
0.0110
0.0100
0.0210cd
CAR23
0.0218
0.0036
0.0254cd
CAR15
CAR18
CAR23
0.0007
0.0139
0.0133cd
0.0069
0.0222
0.0153cd
0.0031
0.0266
0.0235cd
Notes:
CAR3 is the current year’s cumulative abnormal return for a 3-day interval (1 to þ1). CAR5 is the current year’s abnormal return for a 5-day
interval (2 to þ2). CAR11 is the current year’s abnormal return for an 11-day interval (5 to þ5). CAR15 is the current year’s abnormal return
for a 15-day interval (7 to þ7). CAR18 is the current year’s abnormal return for an 18-day interval (20 to 3). CAR23 is the current year’s
abnormal return for a 23-day interval (20 to þ2).
a
Two sample t-test significant at the 0.05 level.
b
Two sample Mann-Whitney test significant at the 0.05 level.
c
Two sample t-test significant at the 0.01 level.
d
Two sample Mann-Whitney test significant at the 0.01 level.
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
Blackwell Publishing Ltd 2005
Table 5
87
88
CHEN, CHENG AND GAO
differences between earlier and later announcements. These
findings support our conjecture that earlier announcements
receive positive abnormal returns while later announcements
do experience negative price reactions.
Finally, to support the argument that the timing of the
earnings announcements is related to abnormal trading volume
even after controlling for other firm characteristics and market
factors, we conduct three variations of timing variable, TEA
(UATI, MAD and ATI) in the regression analysis to prove this
assertion.4
Table 6 relates CATVs across four intervals to a timing variable and other control variables. These variables include the
unexpected earnings (UEA proxied by UERW5), the natural
log for total assets (SIZE), the percentage of public ownership
(POWN), six dummy variables for the years (YEAR2, YEAR3,
YEAR4, YEAR5, YEAR6 and YEAR7), five dummy variables for
various industries (IND1, IND2, IND3, IND4 and IND5), and a
dummy variable for a foreign listing company (FOR).
The timing variable, UATI, are negatively significant in all
subsamples, which implies that the market differentiates the
earlier and later announcements by providing a greater volume
reaction to the earlier announcements. While the regression for
MAD and ATI are not reported in the table due to space
limitation, the results are very similar. Hence, the earlier the
announcement by one company relative to other companies
and the earlier the announcement of the company relative to
its time of disclosure of the previous year, the greater the
abnormal trading volume. Greater unexpected earnings
(UERW), smaller firm size (SIZE), and Shanghai stocks
(EXCH) also lead to greater volume reactions.
In addition, the year of the announcement has an interesting
effect on trading volume. More specifically, the years of
announcements are negatively related to trading volume.
Finally, the finance industry is positively related to trading
4 The results of all three timing variables, UATI, ATI and MAD are qualitatively the
same. As per request by the referee, in order to save space, only the results of UATI are
reported. The results for ATI and MAD are available upon request from the authors.
5 The results of both UERW and UEGM are almost identical. As per request by the
referee, in order to save space, the results of UEA proxied by UEGM in Tables 6 and 7
are not reported but are available upon request from the authors.
#
Blackwell Publishing Ltd 2005
Table 6
#
Intercept
UEARW
SIZE
POWN
UATI
EXCH
YEAR2
YEAR3
YEAR4
YEAR5
YEAR6
YEAR7
IND1
CATV5
CATV11
CATV15
0.1590
(4.12)**
0.0005
(2.42)*
0.0068
(3.31)**
0.0052
(0.43)
0.0282
(3.47)**
0.0082
(2.37)*
0.0410
(5.69)**
0.0117
(1.72)
0.0596
(5.17)**
0.0397
(3.52)**
0.0599
(5.59)**
0.0592
(5.73)**
0.0549
(2.84)**
0.0000
(0.01)
0.2480
(4.28)**
0.0010
(3.19)**
0.0110
(3.57)**
0.0105
(0.57)
0.0384
(3.14)**
0.0159
(3.05)**
0.0623
(5.74)**
0.0107
(1.04)
0.0941
(5.42)**
0.0604
(3.56)**
0.0893
(5.54)**
0.0877
(5.63)**
0.0753
(2.59)**
0.0019
(0.20)
0.4760
(4.40)**
0.0018
(3.25)*
0.0228
(3.96)**
0.0085
(0.25)
0.0568
(2.48)*
0.0336
(3.45)**
0.1090
(5.36)**
0.0078
(0.41)
0.1800
(5.55)**
0.1050
(3.31)**
0.1560
(5.16)**
0.1590
(5.46)**
0.1540
(2.83)**
0.0010
(0.06)
0.7070
(5.16)**
0.0023
(3.21)**
0.0341
(4.67)**
0.0362
(0.83)
0.0596
(2.06)*
0.0392
(3.19)**
0.1420
(5.53)**
0.0057
(0.24)
0.2580
(6.28)**
0.1550
(3.88)**
0.2180
(5.71)**
0.2230
(6.07)**
0.2190
(3.19)**
0.0011
(0.04)
89
IND2
CATV3
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
Blackwell Publishing Ltd 2005
Results of Regression Model for CATV
IND3
IND4
IND5
FOR
CATV3
CATV5
CATV11
CATV15
0.0010
(0.14)
0.0039
(0.79)
0.0060
(1.13)
0.0048
(0.88)
0.0550
9.8060
0.0000
0.0023
(0.20)
0.0046
(0.61)
0.0100
(1.25)
0.0048
(0.58)
0.0510
9.1480
0.0000
0.0027
(0.12)
0.0034
(0.24)
0.0175
(1.18)
0.0022
(0.14)
0.0430
7.8220
0.0000
0.0008
(0.03)
0.0002
(0.01)
0.0203
(1.08)
0.0028
(0.14)
0.0440
8.0040
0.0000
#
Blackwell Publishing Ltd 2005
Notes:
CATV3 is the current year’s cumulative abnormal trading volume for a 3-day interval (1 to þ1). CATV5 is the current year’s abnormal trading
volume for a 5-day interval (2 to þ2). CATV11 is the current year’s abnormal trading volume for an 11-day interval (5 to þ5). CATV15 is the
current year’s abnormal trading volume for a 15-day interval (7 to þ7). UEARW denotes the absolute unexpected earnings of the random walk
model. SIZE is the natural log of the total assets (in thousand RMB yuan) of the firm. POWN is the public ownership in percent. UATI is the
Unexpected Announcement Timing Index variable. To compute UATI, two years of ATI are needed and so the first year (1995) data cannot be used
in these regressions. ATI is the Announcement Timing Index. (ATI ¼ n/N, where N is the total number of working days in the period January 1 – April
30 in the corresponding year, which varies from year to year). n is the nth working date on which the firm makes the announcement.) ATI measures the
timeliness of the earnings announcement, which ranges from 1/N to 1. EXCH is the dummy variable for the two exchanges (Shanghai Exchange ¼ 1,
Shenzhen Exchange ¼ 0). YEAR2 is the announcement year dummy variable for 1996 (1996 ¼ 1, others ¼ 0). YEAR3 is the announcement year
dummy variable for 1997 (1997 ¼ 1, others ¼ 0). YEAR4 is the announcement year dummy variable for 1998 (1998 ¼ 1, others ¼ 0). YEAR5 is the
announcement year dummy variable for 1999 (1999 ¼ 1, others ¼ 0). YEAR6 is the announcement year dummy variable for 2000 (2000 ¼ 1,
others ¼ 0). YEAR7 is the announcement year dummy variable for 2001 (2001 ¼ 1, others ¼ 0). IND1 is the dummy variable for Finance industry.
IND2 is the dummy variable for the Utility industry. IND3 is the dummy variable for Property and Construction industry IND4 is the dummy
variable for Conglomerates industry. IND5 is the dummy variable for the commercial industry. FOR is a dummy variable that takes the value one (1)
if the company has foreign (B, H, or N) shares; otherwise coded zero (0).
* Significant at the 0.05 level.
** Significant at the 0.01 level.
CHEN, CHENG AND GAO
R2adj
F
p-value
90
Table 6 (Continued)
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
91
volume. The percentage of public ownership bears no relationship to trading volume. The significant F-value of the regressions and the small cross-sectional R2 (between 4.3% and 5.5%)
indicate that a significant but weak multivariate relation exists
between volume and these explanatory variables.
To strengthen our hypothesis that there should be significant
market reaction to the timing of earnings announcements, we
also regress the timing variables against abnormal returns and
the results are reported in Table 7. All of the coefficients for the
timing variable, UATI, are negative. In addition, the unreported analysis using MAD and ATI demonstrates similar
results. Therefore, the regression analysis for abnormal return
supports the results from Tables 3 and 5 that an earlier earnings
announcement brings forth a higher abnormal return and a
later announcement receives a lower abnormal return.
5. CONCLUSION
The timing literature suggests that there is information disparity
between firms and outside parties about the current value and
future prospects of the firms. Hence, we first test the informational difference by examining the impact on the trading volume
and returns caused by the earnings announcements. Using 3,802
Chinese firms’ earnings announcements, some evidence is found
to support the argument that earlier announcements have more
information content, as measured by trading volume and returns,
than do the later announcements. Thus, information asymmetry
does exist between earlier and later earnings announcements.
Judging from the results that early announcements receive
higher abnormal volume and return reactions than do later
announcements, we conclude that early announcements surprise the market by their unexpected timing. In addition, the
market is able to gradually form a more accurate expectation of
later earnings announcements.
To strengthen our argument about the relation between the
volume and price reactions and the timing of announcements,
we employ regression analyses, using three proxies of timing
variables (MAD, ATI and UATI) and other controlling
factors. Statistical results in these regression analyses support our
#
Blackwell Publishing Ltd 2005
92
Table 7
Results of Regression Model for CAR
Intercept
UERW
POWN
UATI
EXCH
YEAR2
#
YEAR3
Blackwell Publishing Ltd 2005
YEAR4
YEAR5
YEAR6
YEAR7
CAR5
CAR11
CAR15
0.0267
(1.00)
0.0000
(0.14)
0.0017
(1.20)
0.0061
(0.72)
0.0225
(3.99)**
0.0008
(0.34)
0.0007
(0.14)
0.0153
(3.21)**
0.0040
(0.50)
0.0001
(0.01)
0.0075
(1.00)
0.0014
(0.20)
0.0252
(0.75)
0.0001
(0.70)
0.0016
(0.90)
0.0080
(0.76)
0.0256
(3.64)**
0.0039
(1.30)
0.0034
(0.54)
0.0325
(5.45)**
0.0066
(0.66)
0.0007
(0.07)
0.0088
(0.94)
0.0015
(0.16)
0.0565
(1.28)
0.0000
(0.12)
0.0032
(1.36)
0.0174
(1.24)
0.0353
(3.77)**
0.0043
(1.08)
0.0049
(0.58)
0.0518
(6.56)**
0.0228
(1.72)
0.0032
(0.25)
0.0210
(1.70)
0.0115
(0.96)
0.1220
(2.53)*
0.0000
(0.16)
0.0059
(2.28)*
0.0116
(0.76)
0.0335
(3.28)**
0.0019
(0.44)
0.0192
(2.08)
0.0481
(5.57)**
0.0497
(3.41)**
0.0191
(1.34)
0.0425
(3.14)**
0.0341
(2.61)**
CHEN, CHENG AND GAO
SIZE
CAR3
#
IND1
IND3
IND4
IND5
FOR
R2adj
F
p-value
0.0035
(0.21)
0.0114
(1.99)*
0.0124
(1.87)
0.0005
(0.13)
0.0021
(0.45)
0.0090
(1.87)
0.0270
5.2570
0.0000
0.0240
(1.09)
0.0165
(2.17)*
0.0149
(1.69)
0.0019
(0.33)
0.0057
(0.94)
0.0111
(1.75)
0.0360
6.7090
0.0000
0.0327
(1.35)
0.0174
(2.10)*
0.0167
(1.74)
0.0022
(0.36)
0.0048
(0.71)
0.0181
(2.61)**
0.0300
5.6890
0.0000
93
Notes:
CAR3 is the current year’s cumulative abnormal return for a 3-day interval (1 to þ1). CAR5 is the current year’s abnormal return for a 5-day
interval (2 to þ2). CAR11 is the current year’s abnormal return for an 11-day interval (5 to þ5). CAR15 is the current year’s abnormal return
for a 15-day interval (7 to þ7). UERW denotes the unexpected earnings of the random walk model. SIZE is the natural log of the total assets (in
thousand RMB yuan) of the firm. POWN is the public ownership in percent. UATI is the Unexpected Announcement Timing Index variable. To
compute UATI, two years of ATI are needed and so the first year (1995) data cannot be used in these regressions. ATI is the Announcement
Timing Index. (ATI ¼ n/N, where N is the total number of working days in the period January 1 – April 30 in the corresponding year, which
varies from year to year). n is the nth working date on which the firm makes the announcement.) ATI measures the timeliness of the earnings
announcement, which ranges from 1/N to 1. EXCH is the dummy variable for the two exchanges (Shanghai Exchange ¼ 1, Shenzhen
Exchange ¼ 0). YEAR2 is the announcement year dummy variable for 1996 (1996 ¼ 1, others ¼ 0). YEAR3 is the announcement year dummy
variable for 1997 (1997 ¼ 1, others ¼ 0). YEAR4 is the announcement year dummy variable for 1998 (1998 ¼ 1, others ¼ 0). YEAR5 is the
announcement year dummy variable for 1999 (1999 ¼ 1, others ¼ 0). YEAR6 is the announcement year dummy variable for 2000 (2000 ¼ 1,
others ¼ 0). YEAR7 is the announcement year dummy variable for 2001 (2001 ¼ 1, others ¼ 0). IND1 is the dummy variable for Finance industry.
IND2 is the dummy variable for the Utility industry. IND3 is the dummy variable for Property and Construction industry. IND4 is the dummy
variable for Conglomerates industry. IND5 is the dummy variable for the commercial industry. FOR is a dummy variable that takes the value one
(1) if the company has foreign (B, H, or N) shares; otherwise coded zero (0).
* Significant at the 0.05 level.
** Significant at the 0.01 level.
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
Blackwell Publishing Ltd 2005
IND2
0.0080
(0.60)
0.0074
(1.60)
0.0076
(1.42)
0.0000
(0.00)
0.0026
(0.72)
0.0044
(1.16)
0.0150
3.2820
0.0000
94
CHEN, CHENG AND GAO
hypothesis that early announcements do receive greater abnormal
trading volume reactions, which suggests that the timing of earnings announcements serves as an important strategy for firms to
reduce information asymmetry. Also, the regression analysis for
abnormal return also indicates that earlier announcements receive
a higher abnormal return than later announcements. Overall
results suggest that, even controlling for the magnitude of earnings
and other firm characteristics, the timing and information content
of earnings announcements are significantly related.
In short, we conjecture that due to the fact that the China
Securities Regulatory Commission (CSRC) requires all firms to
announce earnings in the first four months of the year, the
timing of earnings announcements is constrained in Mainland
China and provides additional information for us to examine
information asymmetry. Such a constrained timing is different
from the practice of other developed countries. This time limitation for the announcements, from January to April, has created a unique market setting to test the abnormal volume and
returns to earnings announcements in China.
REFERENCES
Abarbanell, J.S. and B.J. Bushee (1997), ‘Analysis, Future Earnings and Stock
Prices’, Journal of Accounting Research, Vol. 35, No. 1, (Spring), pp. 1–24.
Ajinkya, B.B. and P.C. Jain (1989), ‘The Behavior of Daily Stock Market Trading
Volume, Journal of Accounting and Economics, Vol. 11, pp. 331–59.
Atiase, R.K. and L.S. Bamber (1994), ‘Trading Volume Reactions to
Annual Accounting Earnings Announcements – The Incremental Role
of Predisclosure Information Asymmetry’, Journal of Accounting and
Economics, Vol. 17, pp. 309–29.
Bamber, L.S (1986), ‘The Information Content of Annual Earnings Releases:
A Trading Volume Approach’, Journal of Accounting Research, Vol. 24, No.
1 (Spring), pp. 41–56.
——— (1987), ‘Unexpected Earnings, Firm Size, and Trading Volume
Around Quarterly Earnings Announcements’, The Accounting Review,
Vol. LXII, No. 3 (July), pp. 510–31.
——— and Y.S. Cheon (1995), ‘Differential Price and Volume Reactions
to Accounting Earnings Announcements’, The Accounting Review, Vol.
70 (July), pp. 417–41.
———, O.E. Barron and T.L. Stober (1997), ‘Trading Volume and Different
Aspects of Disagreement Coincident with Earnings Announcements’, The
Accounting Review, Vol. 72, No. 4 (October), pp. 575–97.
Begley, J. and P.E. Fischer (1998), ‘Is There Information in an Earnings
Announcement Delay?’ Review of Accounting Studies, Vol. 3, pp. 347–63.
#
Blackwell Publishing Ltd 2005
INFORMATION CONTENT AND EARNINGS ANNOUNCEMENTS
95
Bowen, R.M., M.F. Johnson, T. Shevlin and D. Shores (1992), ‘Determinants of
the Timing of Quarterly Earnings Announcements’, Journal of Accounting,
Auditing & Finance, Vol. 7, pp. 395–422.
Brookfield, D. and R. Morris (1992), ‘The Market Impact of UK Company
News Announcement’, Journal of Business Finance & Accounting, Vol. 19,
No. 4 (June), pp. 585–601.
Chambers, A.E. and S.H. Penman (1984), ‘Timeliness of Reporting and the
Stock Price Reaction to Earnings Announcements’, Journal of Accounting
Research, Vol. 22, pp. 21–47.
Foster, G., C. Olsen and T. Shevlin (1984), ‘Earnings Releases, Anomalies, and the
Behavior of Security Returns’, The Accounting Review, Vol. 59, pp. 574–603.
Graham, R.C. and R.D. King (1996), ‘Industry Information Transfer: The
Effect of Information Environment’, Journal of Business Finance & Accounting, Vol. 23, Nos. 9 & 10 (December), pp. 1289–306.
Haw, I.M., D.Q. Qi and W. Wu (2000), ‘Timeliness of Annual Report Releases
and Market Reaction to Earnings Announcements in an Emerging
Capital Market: The Case of China’, Journal of International Financial
Management and Accounting, pp. 108–31.
Kim, J. B., I. Krinsky and J. Lee (1997), ‘Institutional Holdings and Trading
Volume Reactions to Quarterly Earnings Announcements’, Journal of
Accounting, Auditing & Finance, Vol. 12, pp. 1–14.
———, J. Lee and T.H. Park (1996), ‘Transaction Responses to Analysts’
Earnings Forecasts, News Types and Trader Type’, Journal of Business
Finance & Accounting, Vol. 23, No. 7 (September), pp. 1043–58.
Kim,O. and R. Verrecchia (1991a), ‘Market Reaction to Anticipated
Announcements’, Journal of Financial Economics, Vol. 30, pp. 273–309.
——— ——— (1991b), ‘Trading Volume and Price Reactions to Public
Announcements’, Journal of Accounting Research, Vol. 29, pp. 302–21.
——— ——— (1994), ‘Market Liquidity and Volume Around Earnings
Announcement’, Journal of Accounting and Economics, Vol. 17, pp. 41–67.
——— ——— (1997), ‘Pre-Announcement and Event-Period Private Information’, Journal of Accounting and Economics, Vol. 24, pp. 395–419.
Kross, W. and D.A. Schroeder (1984), ‘An Empirical Investigation of the Effect
of Quarterly Earnings Announcement Timing on Stock Returns’, Journal
of Accounting Research, Vol. 22, pp. 153–76.
Lobo, G. and S. Tung (1997), ‘Relation Between Pre-disclosure Information
Asymmetry and Trading Volume Reaction Around Quarterly Earnings
Announcements’, Journal of Business Finance & Accounting, Vol. 24, No. 6
(July), pp. 851–67.
Morse, D. (1981), ‘Price and Trading Volume Reaction Surrounding Earnings
Announcements: A Closer Examination’, Journal of Accounting Research,
Vol. 19, No. 2 (Autumn), pp. 374–83.
Pope, P. and C.G. Inyangete (1992), ‘Differential Information, the Variability
of UK Stock Returns and Earnings Announcement’, Journal of Business
Finance & Accounting, Vol. 19, No. 4 (June), pp. 603–23.
Shores, D. (1990), ‘The Association Between Interim Information and Security
Returns Surrounding Earnings Announcement’, Journal of Accounting
Research, Vol. 28, No. 1 (Spring), pp. 164–81.
Sinclair, N. and J. Young (1991), ‘The Timeliness of Half Yearly Earnings
Announcements and Stock Returns’, Accounting & Finance, Vol. 31,
pp. 31–52.
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