Does Public Information Arrival Matter in Emerging Markets?:
Evidence from Stock Exchanges in China
by
Ali M. Kutan*
and
Shangkuan Yuan
Department of Economics and Finance
Southern Illinois University Edwardsville
Edwardsville, IL 62026-1102
ABSTRACT
Recent empirical studies have heightened the debate about the importance of public information
arrival in asset pricing. Although this issue has been studied extensively for industrial markets, it
has not received much attention in the literature on emerging markets. Public information may
play even a larger role in the latter countries due to a higher level of government intervention in
these markets. Using daily data from January 1998 to January 2001, we propose to examine the
impact of public information arrival on stock returns in emerging Chinese A and B markets.
Public information is measured by the announcements of macroeconomic data. As a control
variable, significant political news is also included in estimations. Trade news is found as
having the most significant impact on stock market returns in China, suggesting that public
information plays an important role in asset price movements. Trade announcements have larger
impact on the B market than two A markets studied. In addition, news about entry to the World
Trade Organization (WTO) and other political news are found to be other important determinants
of share price movements in China. Implications of the empirical findings for policymakers and
investors are discussed.
______________________________________________________________________________
* Corresponding author. E-mail: akutan@siue.edu, Phone: (618) 650-3473, Fax (618) 650-3047
We would like to thank Pug Edmonds, Garret Jones, and Jacky So for their useful comments on an earlier version of
this paper. The usual disclaimer applies.
1
I. Introduction
Information arrival is a building block of many theoretical models of asset price
determination. The mixture of distributions model (MODM) and the recent microstructure
theories rely on public information arrival to explain movements in asset returns.1 Although the
empirical evidence on linking public information to asset market behavior is still accumulating,
the main focus has been on industrial countries.2 There is an ongoing debate about the
importance of the public information in industrial financial markets. Ito et al. (1998), Melvin
and Yin (2000), and Edmonds and Kutan (2002) have recently heightened the debate on the role
of public information versus private one in asset markets. Ito et al. (1998) provided evidence of
private information in the Tokyo foreign exchange market. Melvin and Yin (2000) and
Edmonds and Kutan (2002) reported significant public announcement effects in the yen/dollar
market and the Japanese stock market, respectively, however. We contribute to this growing
literature by investigating the role of public information arrival in an emerging market like
China.
Since its establishment in 1990, the stock market in China has been a subject
of many influential investigations, but there are no studies linking public information arrival to
stock market activity.3 Using daily data from January 1998 to January 2001, we examine how
1
MODM models are associated with Clark (1973) and Tauchen and Pitts (1983), while microstructure theories are
reviewed in O’Hara (1995).
2
See Melvin and Yin (2000) and the studies cited therein for a review of recent work. Earlier studies can be found in
Bery and Howe (1994) and Mitchell and Harold (1994).
3
Poon and Fung (2000) examine the information flow between China-backed securities, namely H shares, Red
Chips, and Shanghai and Shenzhen listed common shares. Huang, Yang, and Hu (2000) explore the causality and
cointegration relationships among the stock markets of the United States, Japan and China. Studies by Darat and
Zhong (2000) and Mookerjee and Yu (2000) test for the efficiency of the Chinese stock market.
2
public information arrival affects stock market returns in China.4 To our knowledge, this is the
initial study in the literature on China.
Our focus in this paper is on Shanghai A and B, and Shenzhen A markets.5 Public
information arrival is measured by the announcements of key macroeconomic data. During our
sample period, there were significant number of political news, including the conflict between
the Mainland China and Taiwan and World Trade Organization news regarding China’s
membership. Such news might have affected the stock market activity and therefore is included
in this study as well.
The rest of the paper is organized as follows. Section II provides a brief description of
securities markets in China. In Section III, we present our empirical model and empirical results
are reported in Section IV. We discuss some limitations of our study in Section V. Finally,
Section VI concludes the paper with a summary of the findings and their implications for
investors and policy makers.
II. A Brief Description of Chinese Securities Market
China has a nationwide equity market with two stock exchanges located in Shanghai and
Shenzhen. The trading system reaches all large and medium-sized cities with 2,412 retail
branches all over the nation. By the end of 1998, the Shanghai and Shenzhen stock exchanges
had a total market capitalization of RMB 1,950.5 billion, equivalent to 24.46% of China’s GDP.
Shares in China are divided into two broad categories: untradable and tradable. By the
end of 1998, the total untradable equity of the listed companies was 166.648 billion shares,
65.89% of the total equity of the listed companies, allocated as follows: (1) shares owned by
4
Given the lack of higher frequency data, we are constrained to use daily data. This should not bias our results
greatly, however, as the news releases take longer to reach across investors due to relatively less advanced
information infrastructure of China.
3
government: 86.551 billion; (2) shares owned by legal persons: 71.617 billion; (3) shares owned
by employees and others: 8.317 billion. By the end of 1998, investors had opened 39.107 million
investment accounts, of which 155,800 were in the name of institutional investors and
38,951,200 in the name of individual investors (China Securities Regulatory Committee).
In 1991, the Shanghai Stock Exchange and Shenzhen Stock Exchange began to offer B
shares, providing foreign investors with a legal chance to invest in China’s equity markets. B
shares are offered and traded on these exchanges, which designate domestic or overseas
securities dealers especially licensed brokers to accept foreign investors’ consignment for
trading. By the end of 1998, 106 companies had issued B shares with a total of 9.589 billion
shares issued and a total of US $4.745 billion capital raised (China Securities Regulatory
Committee). By the end of January 2001, there were 584 firms listed in the Shanghai Stock
Exchange and 514 firms in the Shenzhen Stock Exchange. The combined capitalization of the
two markets reached $500 billion, which accounted for 50% of China’s GDP in 2000. The total
number of investors in the two markets now exceeds 50 million people.
Until most recently, B shares have been issued to and traded by foreign investors only.
Recently, a new policy was passed to open B shares to domestic investors with foreign currency
holdings. This policy change reflects the openness and the ongoing globalization efforts of the
country. Our empirical results for B shares may be useful for domestic investors trade in A
market in that it allows a comparison of what types of news driving both markets. It is possible
that different news affect the markets.
5
We do not study Shenzhen B market due to lack of reliable trading data. This market is also very small compared
to the other markets.
4
III. Methodological Issues
Recently, Melvin and Yin (2000) utilized a GARCH(1,1) model via the mixture of
distributions framework to examine the effect of public information arrival on the foreign
exchange returns. According to this model, new information to the market brings about
equilibrium changes in asset prices. In recent years, GARCH models have been frequently
employed in empirical work to provide time-series estimates of asset prices and volatility (see
Bollerslev, et al. (1992)). Lamoureux and Lastrapes (1990) and Laux and Ng (1993) demonstrate
that the mixture of distributions model offers a good economic reason for GARCH modeling of
asset returns. We will, accordingly, use a GARCH model(1,1) to estimate the public information
arrival on the daily return of the Shanghai and Shenzhen markets. Consider the following
autoregressive GARCH (p,q) model:
n
n
n
i 1
j 1
k 1
rt = c +   i * Newsipos   j * News neg
 * HSEt    k *rt k +  t
j
(1)
 t ~ N (0,  t2 )
(2)
p
q
i 1
j 1
 t2  w   i *  t2i    j *  t2 j   * volume(t  1)
(3)
where:
rt = returns to individual A and B shares (log differenced share price index, multiplied by 100),
pos
Newsi
= positive news variable, including lead and lags
neg
News j = negative news variable, including lead and lags,
HSE:
Hong Kong Hang Seng Index returns
 t2 = conditional variance of the returns, and
5
volumet = log(volumet / volumet-1)*100 6.
Public macroeconomic announcements are divided into three categories: economic, trade,
and policy news. These announcements are released irregularly and hence they are treated as
"surprises" or exogenous variables in this paper. Each category is separated into both positive
and negative news. Exception is policy news. By their nature, they include only positive news as
they capture the announcements of economic reforms designed to spur economic growth.
Economic news cover releases of key macroeconomic data, such as the Consumer Price
Index (CPI), which has recently replaced the formerly used retail price index, and the growth
rates of industry investment and real GDP. We treat a decrease (increase) in investment and/or
GDP growth rate as negative (positive) news. During our sample period (1998-2001), the price
level was stable; therefore, inflation has not been a major concern for investors. Rather, investors
have tended to view a rising price level as a positive sign, indicating stronger demand.
Trade news is based on export figures released by the Bureau of Statistics. Given the
significance of exports for the Chinese economy, we assume that investors are more concerned
about exports than imports. Therefore, exports announcements are used alone as trade news.
Since investors generally expect a positive export growth rate, an export figure larger than
previous period’s is treated as positive news.
Economic policy news includes announcements about monetary or fiscal policy programs
within the framework of the five-year economic growth plan, such as releases of reform news
and monetary/fiscal policy announcements directed towards improvement of major industries or
overall economic growth. Therefore, all policy news announcements are treated as positive news.
As control variables, this study also includes significant political news in estimations.
Political news includes announcements made by high-level government officials, concerning war
6
Volume enters the conditional variance equation with a lag to prevent any endogeneity bias.
6
threats, external relationship with other countries, and other important political events deemed to
be important to the stock market. News that signaled significant setback between the Mainland
China and Taiwan and other important trading partners, and other news signaling political
instability are considered as negative political news, while those announcements encouraging
better foreign economic relationships, peace, and political stability are viewed as positive
political news.
News about China’s entry to WTO is included as a separate variable, because this was a
significant news event during our sample period and local and international press covered it
extensively. WTO news includes the outcome of bilateral or multilateral negotiations regarding
entry, such as comments made by top government officials and/or statements by the members of
the negotiating team. Any news indicating a higher chance of entry and/or significant progress
during negotiations is treated as positive news and vice versa.
Following earlier studies, we construct our news variables using a set of (0,1) dummy
variables. Each dummy takes a value of 1 when there is positive (negative) news and zero
otherwise. This allows us to better handle the case where good and bad news for the same
category simultaneously occurs.
We expect the Chinese stock market to be highly correlated with the Hong Kong market,
because there are about 50 leading Chinese companies that are also listed in the Hong Kong
stock market. Moreover, the two economies are closely connected through trade and investment.
Therefore, the Hang Seng index, which is the Hong Kong stock exchange index, is also included
as an explanatory variable in our estimations.7 The empirical models are estimated with one-day
7
We also experimented with adding a U.S. stock market index (S&P500) to the model, but it was insignificant in
estimated models. Therefore, it was not included in final estimations. This finding is consistent with the belief that
Chinese markets are relatively closed and not exposed to financial shocks from the rest of the world.
7
lag and lead variables. The former may capture investors' expectations about news while the
later measures the persistent effects of the news on returns the next day. 8
IV. Empirical Results
Data
Our dependent variable is the daily return in each individual stock market, measured by
the log difference of the daily (closing) price indexes, multiplied by 100. Volume data refers to
the number of shares traded. Our sample period runs from Jan 5, 1998 to Jan 19, 2001, with 737
observations. Data are taken from the websites of the stock exchanges of Shanghai and
Shenzhen as listed in the references.
To construct news, we mainly rely on the People’s Daily as our news source. It is the
most authoritative paper in China with the highest circulation rate, reporting political, economic
and financial news. In our estimations, we use the actual announcements dates as the event date.
Those events that happened during a weekend, which were few and included mainly economic
news, are included in Monday.
Descriptive Statistics
Table 1 reports the descriptive statistics for the returns. Mean returns are about the same
for all markets except that the Shenzhen A market has lower returns than the rest. Shanghai and
Shenzhen A shares have a much lower standard deviation than the Shanghai B and Hang Seng
markets. There is significant evidence of the non-normality of the returns as confirmed by
Jarque-Bera tests, which is significant at the 1% level. To account for this problem, our
empirical estimations utilize the Bollerslev-Woodrige (1992) standard errors.
8
One-period lead and lag selection was based on the Akaike criterion.
8
Empirical Findings: Impact of Public Information and Political News on Stock Returns
Table 2 reports the lead-lag results for the impact of public information for on stock
returns all three markets.9 In all markets, the returns in the Hong Kong stock market have a
significant and positive effect on all Chinese stock market returns. Although the two A share
markets react to the movements in the Hong Kong stock index by about the same amount, the
reaction of the Shanghai B market is about 5 times larger than the A markets. Foreign investors
in market B appear to be much more sensitive to developments in the Hong Kong markets than
domestic investors.
Positive and negative economic news has no impact on stock returns in all three markets.
This finding may be explained by the relative stability of the Chinese economy during our
sample period. Looking at trade news, we observe that positive trade news does not affect any of
the market returns, suggesting that investors in general are optimistic and therefore do not react
to such news. However, negative trade news has a negative and significant impact on all returns.
Negative news lowers the returns by 1.08% and 1.67% in the B market on the day of the
announcement and the next day. The A market returns also decline on the next day of the
announcement but by a much smaller amount. It seems that investors in B market seem more
sensitive to trade news than domestic investors. In addition, the impact of negative news
continues to persist the next day, which could be due to an under-reaction to negative news on
the day of the announcement, so that there is a correction in the market on the following day.
9
Prior to determining our appropriate model, we first have checked the stationarity of our variables using the
Augmented Dickey-Fuller and Phillips-Perron tests. The results indicated that the level of the series is nonstationary
and the first differenced series (returns) are stationary. In the case of significant autocorrelation in returns, we added
proper number of lags of the dependent variable to account for autocorrelation in the returns. These terms are not
reported in Table 2 for space considerations. All estimations below are based on the Bollerslev-Wooldridge (1992)
robust standard error procedure to take into account the nonnormality of the residuals as reported in Table 1.
9
Economic policy news has no impact on returns in all three markets. This is not a
surprising result, because the government generally follows its previously announced 5-year
economic plan closely without much surprise.
Political news appears to affect both Shanghai B and Shezhen A markets. Investors tend
to react to political news the day before the announcement. This may reflect investors’
expectations about the news or suggests some insider information. The day before the
announcement of positive political news, both markets react to the news, but the reaction is in the
opposite direction. The returns in Shanghai B market go up by about 0.76% but there is a
decline of about 0.45% in the Shenzhen A market. This different outcome could be due to
several factors. First, local investors in the A market may not find the announced political news
as positive as expected. Second, foreign investors in market B would view the released
information more favorable than those in A market. Finally, there might be an asymmetry of
information in the market between domestic and foreign investors. 10
Turning to the effect of negative political news, the only market that reacts to such news
is the B market. Note that the reaction comes after the day of announcement. This may be due
to the difference in the degree of information in the markets.11 Investors may receive better
information following the announcement and thus able to react to it accordingly. Overall, the B
market appears to be more sensitive to political news than the A markets. For foreign investors
may not be informed as well as local investors, or they might be more sensitive to political issues
than their local counterparts as “outsiders”.
10
Chakravarty et. al (1998) find evidence of informational asymmetry between A and B markets.
Kaye and Cheng (1992) and Sze (1993) argue that it is more difficult for foreign investors to acquire and assess
information about local firms because of language barriers, different accounting standards, and lack of reliable
information about local economy and companies.
11
10
WTO news has a significant impact on all market returns. Both A markets react
favorably to positive WTO news at the day of the announcement, while the thinner B market
reacts the following day. This suggests that the B markets may follow the information flow in
the thicker A market. 12 Note that the impact of positive WTO news is about the same on all
market returns, ranging about 0.24% to 0.35%. Negative WTO news influences both A markets
only and the day after the announcement. The effect is about a 0.83% and 0.63% decline in
Shanghai and Shenzhen markets, respectively. One explanation for why negative WTO news
does not affect the B market but the A markets is that investors in the former market may not be
as concerned about the negative WTO news as those in local markets. Another reason is that
foreign investors in market B might be more informed about global news, such as WTO news,
than domestic investors.
Looking at the results for the variance equation, ARCH and GARCH coefficients are
both significant and the sum is less than 1, indicating no unit root in the conditional variance.13
The past volume term is significant only for the B market with the expected sign. The diagnostic
tests indicate no serial correlation and further dependency in the squared residuals at the 1%
significance level. 14 Thus, our model provides a good fit for the behavior of the returns.
V. Limitations
Our study is subject to some limitations. First, the classification of news into positive
and negative news and into different categories is obviously subjective. The same kind of news
may be either good or bad news depending on current economic conditions, or how investors
12
Since trading volume and liquidity of A shares are much larger than B shares, this finding is consistent with
financial theory. For evidence on the lead-lag relationship between the A and B markets, see Chan et al. (1998).
13
We do not include “news” variance in the variance equation because of high co-linearity between trading volume
and the public announcements, suggesting no asymmetric shocks to the volatility in the Chinese stock
14
We also estimated asymmetric versions of the GARCH models, including TARCH and EGARCH specifications.
In all cases, the results were not qualitatively different and the asymmetric terms were not significant. Yeh and Lee
(2000) also found that GARCH (1,1) specification is by far the most appropriate model for stock returns in China.
11
perceive this information at a particular time. A rise in the price level would be good news when
an economy is suffering from deflation but bad news if it is experiencing inflation. We did
encounter some cases like this where we had to make some judgment based on the overall
political and economic climate at that time in the markets. We cannot be certain, however, if this
judgment really represents the investor consensus. In classifying the news, we have followed the
general practice in previous literature. Thus our results are subject to the same criticism as
earlier studies.
Second, we have used the People’s Daily as our source for news. Investors would use
other sources, such as the Internet, TV, radio, and financial newspapers. While in most cases the
source of the news should not matter, investors may receive news at different times, when they
use sources other than People’s Daily. We believe that the lead-lag procedure employed in this
paper alleviates this problem significantly.
VI. Conclusions
We have examined the effect of public information arrival on daily returns in Shanghai
and Shenzhen markets. The empirical evidence has indicated that the most significant public
information arrival is trade news (exports announcements) affecting stock market returns in
China. Foreign investors in market B seem to be more sensitive to trade news arrival than
domestic investors in market B. The relative stability of the economy during our sample period
may explain the insignificance of economic news. Finally, the insignificance of policy news can
be explained by the fact that the government follows the pre-announced five-year economic plan
without much surprise.
12
The significance of trade news in affecting stock returns here suggests that private
information does not mostly determine asset price movements in China with public information
arrival mainly irrelevant. Our finding is consistent with Melvin and Yin (2000) who have
provided evidence on the impact of public information on the Japanese Yen and German Mark
price of the U.S. dollar. As they argue, private information must clearly play a significant role,
but public information is relevant as well.
Political news, which can be considered a proxy for political risk, has a significant impact
on the B market. Negative news lowers the returns while positive news raises the returns. Given
the recent attempts to merge both A and B markets, this finding suggests that policymakers need
to take steps to reduce news signaling political instability in order to encourage foreign investors
to invest in China. Besides political news, WTO news also has a significant impact on all market
returns. Good (bad) news tends to raise (lower) the returns.
These results suggest that political news may play an important role in emerging markets
and affect the market activity even more significantly than macroeconomic news. It is therefore
important that empirical modeling of emerging markets should take this evidence into
consideration and attempt to include some measures of political news in estimations.
13
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15
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16
Table 1: Descriptive statistics- Market returns
Mean
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability
Observations
Shanghai A
0.0715
8.6653
-8.7277
1.4886
0.0753
8.4099
Shanghai B
0.0608
9.4108
-9.9236
2.8162
0.3363
4.7766
Shenzhen A
0.0128
8.9478
-7.8704
1.6796
0.4535
7.2495
Hang Seng
0.0592
19.7983
-9.0979
2.2790
0.9537
11.7848
898.2064
0
110.6698
0
579.0181
0
2478.1890
0
736
736
736
736
17
Table 2: GARCH (1,1) estimates
Coefficients
Shanghai A
Shanghai B
Mean Equation
Constant
0.0365
-0.0442
H. Kong returns
0.0533**
0.2778**
ECON_POS(-1) -0.0859
-0.0826
ECON_POS
-0.0245
-0.0401
ECON_POS(1)
-0.0231
-0.0872
ECON_NEG(-1) -0.2253
1.1455
ECON_NEG
-0.0521
1.1348*
ECON_NEG(1)
0.3046
-0.6576
TRADE_POS(-1) -0.1552
0.5093
TRADE_POS
0.1746
0.4723
TRADE_POS(1) 0.0821
-0.1723
TRADE_NEG(-1) 0.5019
-0.1992
TRADE_NEG
-0.3708
-1.0832*
TRADE_NEG(1) -0.3827*
-1.6769**
POLICY_POS(-1) 0.2130
-0.3114
POLICY_POS
-0.1022
-0.1142
POLICY_POS(1) 0.0929
0.1692
POL_POS(-1)
-0.3216
0.7558*
POL_POS
0.1272
0.0228
POL_POS(1)
-0.0441
-0.3560
POL_NEG(-1)
-0.1152
0.1406
POL_NEG
0.3630
-0.2406
POL_NEG(1)
-0.5173
-1.2635*
WTO_POS(-1)
0.1329
-0.0426
WTO_POS
0.3013**
0.2336
WTO_POS(1)
0.1266
1.1744**
WTO_NEG(-1)
-1.0092
-0.0419
WTO_NEG
-0.2556
0.5289
WTO_NEG(1)
-0.827**
0.2080
Shenzhen A
-0.0807
0.0569**
-0.0325
-0.0234
0.0040
-0.1573
0.2322
0.1031
0.1766
0.2921
0.1115
0.5901
-0.3004
-0.5304*
0.3100
-0.0878
0.1941
-0.4473
0.1186
0.1295
0.2139
0.2090
-0.6965
0.0193
0.3469**
0.1515
-0.3937
0.2442
-0.6296**
Variance Equation
Constant
ARCH(1)
GARCH(1)
VOLUME(-1)
0.0828**
0.1582**
0.8081**
-0.0040
0.3932**
0.0979**
0.8463**
0.0327**
0.0535*
0.1064**
0.8733**
-0.0009
Q (10)
Q²(10)
11.534(0.317)
2.528(0.990)
15.318(0.121)
8.542(0.576)
9.439(0.491)
4.639(0.914)
* and ** denote significance level at 1 and 5 percent level, respectively.
P-values are in the parenthesis.
18