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The Effect of Annual EarningsAnnouncements on the Chinese

Stock Markets

SHUHONG KONG* AND MAJID TAGHAVI**

Abstract

This paper examines the annual earnings announcement effect of the stock marketsin China. The investigation is based on events analysis and carried out by modeling the

daily changes of stock returns using the M-EGARCH approach, by testing the news

effects of annual earnings announcement on the conditional mean of abnormal return

and the variance of the returns. It is found that a higher than expected earnings

announcement leads to a rise in the conditional mean of stock returns on days before the

news announcement and a fall afterwards. The conditional volatility of the changes are

significantly reduced by bigger absolute values of reported earnings before the news

announcement and increased afterwards, supporting the rejection of semi-strong-form

efficiency. (JEL G10, G12, G14)

Introduction

Many studies on the semi-strong-form efficiency of stock market are focused on the

analysis of the information content of annual earnings and dividend announcements.

The purpose of these public disclosure announcements is to provide information that

meets investors needs for decision-making. According to Fama[1991], in a sub-efficient

market, the share price may fail to fully reflect all relevant information, and abnormal

returns may be obtained by taking advantage of public information because there is a

significant time lag between announcement and full incorporation of the information.

Previous empirical studies on annual earning announcements in the Chinese stock

market largely concentrate on the drift effect on the overall market attributed to

information disclosure [Zhao,1998; Chen and Liu,1999; Chen and Yang,1999; Chen andChen, 2002], without considering the precise quantitative relationship between yield

change and earnings change given new information disclosure. We investigate these

effects using the M-EGARCH model. We intend to discover the precise quantitative

relationship between the earnings and the yield shift. Specifically, the daily changes of

stock prices are modeled by M-EGARCH to ascertain the existence and the nature of the

annual earning announcement effects on the conditional mean and variance of the

changes. In the European and the U.S. markets, most such news announcements affect

stock prices on the days of the information disclosure and the effect would not continue

after the day of disclosure. This means that the European and American markets are

basically semi-strong-form efficient stock markets. In contrast, we will show that theChinese markets are not as efficient as the western markets.

International Advances in Economic Research (2006) 12:318Y326 * IAES 2006

DOI:10.1007/s11294-006-9020-8

* University of International Business and Economics Beijing, China and ** University of

NorthumbriaNewcastle upon Tyne NE3 5AJ, UK.

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This paper will find evidence of the sub-efficient character of Chinese markets by

means of an EGARCH model. The EGARCH model is based on exponential generalised

autoregressive conditional heteroskedastic model, which was first put forward by Nelson

[1991]. This model is more apt to explain the asymmetric changes of the conditionaldeviation, and the extent of yield change due to information disclosure. The estimated

coefficients of the model would provide us with answers to our questions. This type of

modeling is important in our research because of the following reasons.

First, the model used here is based on the conditional heteroskedasticity property of

the daily changes, so that the results of empirical study are more coincident with actual

condition. Many researchers of Chinese stock markets using other models have often

ignored the adverse consequences of heteroskedasticity.

Second, the model will estimate the exact value of the mean of daily changes of stock

prices in the events window, and the elasticity relating to EPS (earning per share) over

time. This empirical result describes more exactly the effect of annual earnings

announcement. Other studies, on the other hand, only distinguish the difference betweenthe good and the bad news.

Finally, and more importantly, using the model we can examine and evaluate the

effects of announcement news on the conditional volatility of the abnormal return

changes in the events window. The exact value of the variance in response to the news,

higher volatility due to good news or bad news and the asymmetric reactions towards good

and bad news are also offered by the model.

The rest of the paper is organized as follows. The next section offers a literature review

of the empirical investigations in this area. The following section discusses the nature of

the data and the modeling issues. Then, the paper presents the empirical results. Finally,

the last section concludes the paper.

Literature Review

The theory of rational-informed efficient financial markets has been extensively

tested for nearly aquarter of a century. Although there has recently been an increase in

empirical research regarding emerging/developing financial markets, a glance through

the literature reveals that a significant amount of work needs to be conducted in this

area. It has been noted by Balaban and Kunter [1996] that research on developing

economies financial markets may offer valuable opportunities for diversification beyond

national markets. Amongst many [Balaban,1995; Cornelius,1991,1993; Muradoglu and

Metin,1995], Keane [1993] has presented a detailed analysis and empirical investigationof the Efficient Market Hypothesis (EMH) in developing markets. In particular, as China

becomes more integrated into global financial markets, any examination of and analysis

based on Chinese stock markets should be of significant interest to academics, policy

makers, and investors.

Many empirical studies on the European and American capital markets tend to focus

on the reaction speed of the market to important events or other new information.

Particular use has been made of event studies, which examine the extent to which

returns are abnormal in the time period surrounding a public announcement. Numerous

studies have analysed price reactions following specific information disclosures, such as

stock splits, new exchange listings, or earning announcements. Pettengill and Jordan

[1990] found overreaction and, hence, abnormal returns for European and American

markets. In contrast, Zarowin [1989] discovered size anomalies, but found no evidence

for overreaction to news announcements. Amihud et al. [1999] found evidence for the

size of shareholders effect on stock price. Bernard and Thomas [1990] went on to point

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out that new information exerted its full influence on the stock price within 1 h after

disclosure. There appears, therefore, to be very little evidence for the European and the

American markets not being semi-strong-form efficient.

In China, however, event studies based on information disclosure only commenced inthe late 1990s. Zhao [1998] looked at the information content from earnings information

disclosure in the Shanghai Stock Market by means of accumulated abnormal returns. He

found evidence of both overreaction and under-reaction. Chen and Liu [1999] examined

the impact of earnings information disclosure in both the Shanghai and Shenzhen

Markets using the OLS. They concluded that earnings announcements are rich enough

in information content to exert a significant influence on the market. However, there are

few studies on the precise effect of information disclosure on changes in yield. Moreover,

the previous empirical studies disregard the non-constancy of the dispersal about the

mean of yield and stock price. Real markets often disobey the assumption of constant

deviation due to influence of various factors. It is necessary to introduce GARCH or

EGARCH models to describe this varying deviation.There are many other studies where the time varying nature of daily return dispersal

about the mean is modeled using GARCH models. For example, Bollerslev, et al. [1992]

and Ritchken and Trevor [1999] applied the GARCH and EGARCH models to dispersals

about the mean for daily return changes with asymmetric changes of the conditional

deviation. However, no research in this regard has ever been conducted for the Chinese

markets. This paper will use the EGARCH model to investigate the daily change of stock

prices on Chinese markets and discover the precise quantitative relationship between

yield change and earnings change given new information disclosure.

Data and Models

In a semi-strong-form efficient market, the yield should adjust instantaneously to

unanticipated information. However, new information can also increase or reduce un-

certainty in the market. Therefore, it would be expected that volatility (and, hence, the

appropriate risk adjusted return) would also change. There are three probable cases.

First, announcements have no significant effect on either the mean or the conditional

volatility of the abnormal return changes on the days of their announcements. Second,

announcements significantly affect the value of the abnormal return and the adjustment

process takes a significant amount of time. Third, announcements significantly affect the

conditional volatility.

The first case indicates that information disclosure has no influence on the market.One could infer from this that the information has already been fully impounded into the

price, and that there is a strong-form efficiency. The second case indicates temporary

deviation from semi-strong efficiency if adjustment is not instantaneous. The third case

indicates that risk/uncertainty associated with a stock may be modified by the in-

formation disclosure.

This paper aims to discover which of the above three cases is most likely to occur in

the Chinese stock markets. By establishing the EGARCH model for the data of daily

change of stock prices on the events window and testing for the overall significance of the

model, we will be able to find answers to our questions.

The data includes annual earnings announcement in Shanghai and Shenzhen Stock

Markets, and the daily stock price returns for each stock where there has been an

announcement. The sample data fulfill two prerequisites. First, they encompass an

annual earnings announcement within the period under analysis. Second, the requisite

stock market prices are available a year before the annual announcement. In the year

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chosen there were 1,224 listed companies altogether, 698 in Shanghai and 526 in

Shenzhen. There were also 3,672 observations on the earnings and losses announcement

days and 23,680 observations on daily stock price returns in the period around the

announcement day.The normal period chosen for studies on the European and American modern capital

market is often as long as 10 years, so that the influence of unusual or extraordinary

factors on earnings information may be reduced or eliminated. Since the Chinese

markets are still at the early stage of development, it is difficult to find a sample with a

stable group of member firms. Moreover, the very limited experience of trading on the

Chinese stock market means that it is much more difficult to explore what really

constitutes the regular and normal behaviour from which anomalies can be subtracted.

Hence we take the latest year available instead.

The time window wrapped around each annual earnings information disclosure is

j10 days to +10 days, for various earnings announcements, taken place in the year 2001

in both Shanghai and Shenzhen stock markets. The purpose of the exercise is to analysethe impact of any change in EPS on returns by comparison with the previous year.

Daily returns are defined as follows:

Rt lnPtlnPt1 100 1

where Pt is the closing prices of stocks on day t.Abnormal return is defined as follows:

ARjt RjtbRRj 2where ARjj is the flow of abnormal return of j, andbRRj is the expected return (normalreturn). The expected return is calculated over all n sample data points, while theabnormal return is calculated over the event period.

bRRj 1n

Xnt1

Rjt 3

Chen and Chen [2002] show that expression (3) of abnormal return is superior

compared to other complex models, because it ignores the disturbance noise to the

expected return. It could more effectively examine the stock prices reaction on various

events, especially in the case of such events which have weaker influences on stock

prices.The EGARCH model is an appropriate technique for any differentiated deviation

modeling, hence, to explain asymmetric change in the conditional deviation. EGARCH

has already been extensively applied in financial capital sequence analysis. Given the

potential for an increase in deviation, investors would require additional risk premium as

compensation, indicating that the return should be modeled as a function of deviation.

Following a test for serial correlation, the M-EGARCH model chosen here would be of

order (1,1) as follows:

ARt 0 1NEWYt hffiffiffiffiffi

htp

"t 4

"t ffiffiffiffiffi

htp

vt

ln ht 0hln ht11 NEWYtj j g NEWYtf g 5

8>>>>>:

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The variable ARt is the abnormal return on day t; t = j10, . . . ,+10. The variable

NEWYt is Y

Y, where Y is the stock price on day j10, and DYis the increase margin of

EPS compared to the previous year. In this model, ht is the conditional deviation of

return, and vt is a white noise.Equation (4) represents the conditioned mean of abnormal return, while equation (5)determines the effect of new information on the deviation of the return. The testing of

the three cases, mentioned earlier, is carried out by examining the sign and the

significance of the coefficients of the news variables in the conditional mean and variance

equations. The news variables in the conditional mean equation would pick up the news

effects on the mean of the return changes on the days of their announcement. If the

announcements exhibit to have impact on the volatility of the changes, their non-linear

influence would still be represented in the residuals of the conditional mean equation,

after their linear influence have been removed. The coefficients of the news variables in

the conditional mean equation convey information regarding the effects of the

announcement on the price changes, which depend on the equilibrium relationshipbetween the announced economic variable and stocks prices.

As explained earlier, the focus of this paper is on the news effects on the conditional

mean and volatility of the stocks prices changes. Estimated coefficients of the news

variables will reveal which of the three cases is supported. If there are no news effects on

either the conditional mean or variance of the changes, the news coefficients will not be

significantly different from zero and, hence, case (1) is supported. Case (2) is relevant if

the news variables are significant only in the conditional mean equation. If the

TABLE 1

Abnormal Return in the Shanghai Stock Market

t Mean Median Max Min Std. Dev. Skewness Kurtosis N

j10 0.333* 0.217 8.042 j5.126 2.176 0.513 4.270 698

j9 0.170 0.267 7.411 j8.842 2.681 j0.315 4.313 698

j8 0.357* 0.685 6.165 j9.896 2.839 j0.545 4.008 698

j7 0.071 0.203 7.601 j6.359 2.499 0.063 3.164 698

j6 0.103 0 9.320 j5.333 2.676 0.636 3.929 698

j5 0.226 0.105 8.292 j6.724 2.652 0.444 3.964 698

j4 0.523* 0.133 9.579 j3.659 2.424 0.883 4.403 698

j3 0.105 0.365 6.276 j7.045 2.338 j0.375 3.675 698

j2 0.414* 0.403 7.696

j4.669 2.228 0.495 3.526 698

j1 0.540* 0.870 6.390 j5.078 2.465 j0.102 2.626 698

0 j0.261 j0.781 6.460 j5.923 2.701 0.289 2.760 698

1 j0.235 j0.309 5.451 j4.535 1.905 0.343 3.412 698

2 j0.030 0 4.791 j4.890 1.906 0.042 3.151 698

3 j0.274 j0.687 5.870 j4.806 2.289 0.611 3.126 698

4 j0.112 j0.216 4.143 j4.076 1.703 0.253 2.958 698

5 0.281 0.175 5.121 j5.087 2.066 0.056 2.702 698

6 0.037 j0.139 5.674 j3.917 1.960 0.550 3.485 698

7 0.034 j0.075 6.551 j6.216 2.166 0.002 4.211 698

8 j

0.372* j

0.477 3.714 j

5.432 1.818 j

0.212 3.083 6989 0.179 j0.179 5.955 j3.533 2.002 0.534 3.180 698

10 0.128 0.044 8.909 j6.06 2.275 0.281 5.144 698

*Significance at the 5 percent level.

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announcements significantly affect the stocks prices and raise (lower) the volatility of the

daily changes, the coefficients in the deviation equation will be positive (negative) and

significant, then case (3) is to be supported.

Empirical Results and Analysis

We have chosen2,448 observations for EPS: a set of 1,224 for each of the years 2000

and 2001. Of these, 698 cases relate to Shanghai and 526 to Shenzhen. The distributional

information for the 1,224 stocks is given in Tables 1and2.

Table1 shows that there are five mean daily returns in the Shanghai market relating

to the pre-announcement being significantly and positively signed, whilst onlyone such

case can be found being significant and negatively signed after the announcement. In

Table 2, we found five cases of significantly positive estimates relating to pre-

announcement and two negative cases to post announcement. There is also one case of

significant positive post-announcement in the Shenzhen market. However, these basicstatistical inferences are largely consistent with our basic hypothesis.

The estimated findings for the M-EGARCH model [equations (4) and (5)] are given in

Tables3and4. Table3shows that in the Shanghai market the mean ofa1is positive and

significant (at the 5 percent level) five days before the announcement, while it is positive

and significant (at the 10 percent level) two days after the announcement. Thus, a 1

percent increase in EPS should on average lead to a 0.1102 percent increase in abnormal

TABLE 2Abnormal Return in the Shenzhen Stock Market

T Mean Median Max Min Std. Dev. Skewness Kurtosis N

j10 0.243 0.203 8.542 j7.600 2.928 0.003 4.119 526

j9 0.461* 0.359 9.564 j8.106 3.066 0.560 4.122 526

j8 0.292 0.279 8.156 j5.129 2.541 0.458 4.067 526

j7 0.386* 0.170 8.058 j7.933 2.735 0.080 3.693 526

j6 0.420* 0.256 5.988 j6.266 2.551 0.005 2.648 526

j5 j0.230 j0.135 9.573 j5.711 2.349 0.398 5.261 526

j4 0.545* 0.378 7.923 j6.589 2.338 0.262 3.925 526

j

3 0.292 0.264 8.464 j

8.324 2.431 j

0.047 5.359 526j2 j0.038 0.104 4.871 j5.135 2.278 j0.018 2.654 526

j1 0.439* 0.142 8.358 j3.326 1.866 0.861 5.221 526

0 0.442* 0.064 8.369 j4.953 2.801 0.535 3.187 526

1 j0.054 0 5.283 j3.439 1.998 0.488 2.718 526

2 0.231 0.189 9.592 j3.564 2.036 1.003 6.337 526

3 j0.053 j0.120 7.080 j5.145 1.983 0.462 4.116 526

4 j0.143 j0.149 9.531 j5.147 2.038 1.184 8.416 526

5 0.134 0.279 5.446 j6.772 2.049 j0.404 3.845 526

6 j0.389* j0.196 3.279 j5.291 1.829 0.214 2.740 526

7 j0.015 0 7.031 j6.760 2.208 j0.056 4.078 526

8 j

0.381* j

0.325 6.436 j

6.519 2.483 0.729 5.292 5269 0.350* 0.135 6.259 j5.512 2.035 0.056 3.372 526

10 j0.037 j0.168 7.648 j3.695 1.944 0.905 4.645 526

*Significance at the 5 percent level.

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return five days before the announcement, followed by a further 0.0797 percent increase

two days afterwards. There is no evidence here for retrenchment. From Table 4 it is

evident that in the Shenzhen market the mean ofa1is positive and significant (at the 5

percent level) four days and one day before the announcement, while it is negative and

significant (at the 10 percent level) five days after the announcement. Thus, a 1 percent

increase on EPS should on average lead to 0.235 percent and 0.123 percent increases,

respectively, on abnormal return four days and one day before announcement and onaverage 0.1 percent decrease after announcement.

The results are indicative of strong impact of NEWYt on the conditional volatility of

the abnormal return via parameter b1. In the Shanghai market, the estimates of b1suggest that volatility is increased by new information three and four days after the

announcement. In the Shenzhen market, by contrast, volatility is reduced three days

before the announcement and increased six days after announcement.

The parameter ah is not significantly different from zero for all periods in both

markets, which indicates that the conditional deviation does not significantly influence

the return. This may give rise to the extent of inappropriateness measure of risk, and,

hence, suggesting that a Beta based measure of risk would be more appropriate thanindividual volatility. The parameter bh is found not to be significant, indicating that

there is no persistence or autocorrelation in conditional deviation. The parameter g is

significant in only one case out of four, indicating a rather weak effect of news on

conditional returns.

TABLE 3

Coefficients in M-EGARCH Model in Shanghai Stock Market

T a0 a1 ah b0 b1 bh g

j10 0.2627

j0.0526 0.0013

j9 0.1348 j0.0265 j0.0006 0.7326 j0.0978 j0.0609 0.0544

j8 0.3667 0.0074 0.0052 0.7826 0.0195 j0.1002 j0.0282

j7 0.1143 0.0328 0.0013 0.1154 0.0805 j0.1942 j0.0496

j6 0.0590 j0.0331 j0.0028 0.2371 0.0447 j0.2502 j0.0118

j5 0.3724 0.1102* 0.0063 0.4840 j0.0300 j0.2458 0.0866

j4 0.6903 0.0503 0.0049 0.2178 0.0827 0.0016 0.0423

j3 0.1687 0.0215 j0.0074 0.0244 0.0786 j0.0104 0.0350

j2 0.5208 0.0053 j0.0018 j0.0831 j0.0177 0.1740 j0.0223

j1 0.6458 0.0045 0.0056 0.4949 0.0716 j0.0475 0.0594

0 j0.3297 j0.0503 0.0031 1.1655 j0.0716 0.0026 j0.0290

1 j0.2133 0.0147 0.0072 j0.1843 0.0261 j0.0901 j0.0607

2 0.0710 0.0797* j0.0058 j0.3350 0.0109 j0.1899 0.0038

3 j0.2214 0.0400 0.0076 0.0374 0.1098* j0.0992 0.0252

4 j0.1090 j0.0020 j0.0016 j0.6297 0.1229* j0.0193 0.0385

5 0.3220 0.0310 j0.0027 0.1234 0.0622 j0.1826 0.0544

6 0.0236 j0.0100 0.0028 j0.5515 0.0862 0.1006 j0.0138

7 j0.0411 j0.0567 0.0083 j0.2135 0.0103 0.0552 j0.0263

8 j0.3293 0.0325 0.0055 0.0085 j0.0160 j0.0019 0.0335

9 0.0930 j0.0647 j0.0091 j0.2739 0.0524 j0.0914 j0.0290

10 0.1882 0.0453 j0.0078 0.1673 0.0185 j0.1406 j0.0249

*Significance at 10 percent level, and **significance at 5 percent level.

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Conclusions

In testing the features of the semi-strong-form efficiency in the Chinese stock

markets in terms of the return of annual earnings announcement, an M-EGARCH model

has been applied. It has been demonstrated that the changes of the news variable

significantly influence the mean of abnormal return in that the mean of abnormal return

markedly increases four days before announcement, while decreases four days after

announcement. This indicates that there is advanced overreaction in both the Shanghai

and Shenzhen markets towards the annual earnings announcement by four to six daysbefore announcement, while exhibiting a remarkable rectification of four to six days after

announcement to readjust the overreaction. The overreaction can be attributed to

banking behaviour and advanced information disclosure. A significant number of

bankers, being closely associated or even colluding with listed companies, take advantage

of their privilege to dominate the market price, contributing to marked information

asymmetry while clusters of followers and speculators propel the overreaction and

generate the abnormal returns for the bankers. In this sense, the Shenzhen and

Shanghai markets fail to represent the semi-strong-form efficiency.

Moreover, we have tested the influence of the annual earnings announcement on the

conditional deviation of the fluctuation of the abnormal return, with the conclusion thatit decreases the deviation three to four days before announcement, while increases three

to six days afterwards with relatively small advanced overreaction, but with large

rectification. There are also asymmetric reactions towards good and bad news in

Shenzhen market with more severe fluctuation caused by good news. On the basis of

TABLE 4

Coefficients in M-EGARCH Model in Shenzhen Stock Market

T a0 a1 ah b0 b1 bh g

j10 0.5015

j0.0385 0.0033

j9 0.3762 j0.1687 j0.0097 0.2082 0.1462 j0.1162 0.0226

j8 0.3466 j0.0417 j0.0039 0.2791 0.0058 0.0307 0.0321

j7 0.4400 0.0490 j0.0059 0.5910 j0.0294 j0.0969 0.0416

j6 0.3785 j0.0381 0.0038 0.7028 j0.1377 j0.0214 j0.0378

j5 j0.1430 0.0800 0.0037 0.1086 j0.0571 j0.0110 j0.0600

j4 0.9023 0.2350** 0.0011 j0.1155 j0.0274 j0.0138 0.0183

j3 0.6080 0.1060 0.0066 j0.0505 j0.1788* j0.1332 j0.0816

j2 0.0693 0.0981 j0.0082 0.0937 0.0496 j0.0167 0.0774

j1 0.5734 0.1227** 0.0025 j0.1677 j0.0226 0.1305 0.0706

0 0.5132 0.0650 0.0003 0.8681 j0.0140 j0.0328 0.1117

1 j0.0605 j0.0057 0.0085 0.2665 j0.0428 j0.0347 0.0309

2 0.2245 j0.0060 0.0109 j0.0152 j0.0498 0.1853 0.0074

3 j0.0742 j0.0195 j0.0029 j0.0336 j0.0176 0.1187 0.0583

4 j0.2275 j0.0770 0.0053 j0.3308 j0.0945 j0.0370 j0.1488**

5 0.0200 j0.1040* j0.0149 j0.0550 j0.0408 0.0464 j0.0243

6 0.3452 j0.0407 0.0025 j0.4524 0.1675* j0.0608 0.0217

7 0.0220 0.0345 0.0080 j0.0281 0.0891 j0.1034 0.0167

8 0.4270 0.0416 0.0067 0.1985 0.0123 j0.1682 0.0327

9 0.3866 0.0332 0.0019 0.3855 j0.0633 0.0117 0.0808

10 j0.0211 0.0149 0.0022 j0.2608 0.1060 0.0392 0.0428

*Significance at 10 percent level, and **significance at 5 percent level.

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our empirical investigation, it can be said that this paper supports the conclusion that

the Chinese stock markets fail to represent a semi-strong-form efficient towards annual

earning announcement.

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