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Classifying Chinese Bull and Bear Markets: Indices and Individual Stocks
Wei Chia, Robert Brooks
b,1, Banita Bissoondoyal-Bheenick
c, Xueli Tang
d
a Department of Accounting and Finance, Monash University
b Department of Business Statistics and Econometrics, Monash University
c Department of Accounting and Finance, Monash University
d School of Accounting, Economics and Finance, Deakin University
Abstract: Using a Markov Regime Switching model, this paper discusses Chinese bull and
bear markets. A focus of the analysis is to compare classifications based on indices and
individual stocks. By comparing the intervals of bull and bear markets between stocks and
indices, this paper finds a high degree of overlapping of bear cycles between stocks and
indices. This helps to explain the long duration of Chinese bear market cycles. We also find a
high level of overlapping between the bear market and a fraction of stock with increasing
stock prices. This leads to the conclusion that the stock performance and trading behaviour
are widely diversified. Furthermore, we find that the same industry may have different
overlapping intervals of bull or bear cycles in the Shanghai and Shenzhen stock markets.
Firms with different sizes could have different overlapping intervals with bull or bear cycles.
JEL classification: C13; C32; G32;
Keywords: Markov Switching; Overlapping Intervals; Bull and Bear Markets; Emerging
Market.
1 Corresponding author. Tel.:+613 9903 1423.
E-mail addresses:kelly.chi@monash.edu, robert.brooks@monash.edu,
banita.bissoondoyal-bheenick@monash.edu, xueli.tang@deakin.edu.au
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1. Introduction
Discussion of bull and bear market cycles attracts much attention in the literature, e.g., Pagan
and Sossounov (2003), Yan, et. al (2007), Rutledge, Zhang and Karim (2008), Zhou, et al
(2009), de Bondt, Peltonen and Santabarbara (2011), because cycles of bull and bear markets
not only reflects the economic development and investors‟ confidence but has a significant
impact on the whole economy and social welfare. This is important for all countries around
the world in particular for developing countries which have emerging financial markets and
are more vulnerable to global economic fluctuations. As found in Chen, Chong and Li (2011),
the Chinese stock market has experienced a long period bear cycle from early 2000 until
2006 and then it fluctuated greatly until 2010. However, the cyclical behaviour of stock
markets during this period is less well-established. We may ask why the Chinese stock
market experienced a long duration of bear market, and what industries would contribute to
this cyclical behaviour, and whether firm size can determine the relationship between the firm
stock cycles on the market cycles. By comparing the intervals of bull and bear markets
between stocks and indices, this paper will provide more explanation to the cycles of Chinese
stock markets, and will contribute to the literature regarding the development of emerging
markets.
Given that the Chinese stock market is one of the largest emerging markets in the world and
some of its unique characteristics, such as economic, institutional and microstructural
features, plus the existence of A- and B-share markets, provides a rich environment for the
investigation of bull and bear cycles in an emerging market. The Chinese stock market is
composed of two markets according to the traders, specifically, A- and B-share markets. Prior
to 2001, domestic investors could only trade in the A-share markets in RMB while foreign
investors could only trade in the B-share markets in U.S. dollars in Shanghai or in Hong
Kong dollars in the Shenzhen stock market. After 2001, the Chinese government allowed
domestic investors to freely trade B-shares. However, due to foreign currency control,
Chinese citizens cannot freely use RMB to buy foreign currencies and purchase B-shares.
Therefore, there still remains a certain degree of segmentation between A- and B-share
markets. After 2002, Qualified Foreign Institution Investment (QFII) are allowed to invest in
local currency and use specific accounts to invest in the A-share stock markets. As Chinese
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GDP has become the world number two in 2010 and stock market capitalization has
overtaken Japan as the world‟s second-largest stock market, any change in the Chinese
economy or stock market could potentially influence the world economy significantly.
In this paper, a Markov-switching model will be applied to investigate bull and bear markets.
Hamilton (1989) first introduces the Markov-switching model to replicate the recessions and
expansions of the U.S. economy as measured by the NBER. Subsequently, based on
Hamilton (1989), there are a number of articles to investigate bull and bear markets, such as
Durland and McCurdy (1994) and Maheu and McCurdy (2000).2 In this paper, rather than
focusing on macroeconomic shocks or policy issues, we explore the bull and bear cycles from
a new perspective, by studying the overlapping intervals of bull and bear cycles between
stock and index data.
This paper aims to identify and to account for cyclical regimes in the Shanghai and Shenzhen
stock markets. Specifically, the contribution of this study is three-fold. First, we find that the
overlapping of bear cycles between stocks and the index is quite high. It helps explain the
long period of bear cycle in the Chinese stock market. Second, by grouping the stocks
according to 13 industry categories and firm size, we find that stock performance is widely
diversified across the market. Firms with different size could have different overlapping
intervals with bull or bear cycles. Third, we find that in the Shanghai and Shenzhen stock
markets, even the same industry may have different overlapping intervals of bull or bear
cycles. This implies that a certain shock to one industry could have different impacts on these
two markets even though these two markets have strong correlations with each other.
There is some literature discussing Chinese bull and bear markets. For example, following
Pagan and Sossounov (2003), Girardin and Liu (2003) use a Markov-switching model to
investigate movements in capital gains and losses on the Chinese stock market from 1995
through 2002. Based on the index of the Shanghai A-share market, at a weekly frequency,
they found that in overall, the Chinese stock market is like a „Casino‟ because, most of the
2 Ryden, Terasvirta, and Asbrink (1998) have shown that the Markov-switching model is well suited to
explaining the temporal and distributional properties of stock returns as the information set to the
econometrician and agents are not necessarily assumed to coincide.
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time, an investor with a weekly horizon finds herself in the bear market and makes capital
losses but also makes substantial capital gains in very short periods of „luck‟ to compensate
her for the losses. Instead, using monthly stock index data from 1991 through 2006, Yan, et.
al (2007) identify and describe cyclical regimes in the Shanghai and Shenzhen stock markets
based on the algorithm developed by Bry and Boschan (1971). They identify bull and bear
market regime-turning points using five-month average returns and show non-identical cycles
for these two markets. In addition, they find that the return differences between bull and bear
market regimes decrease recently reflecting a maturing of the Chinese market.
Using monthly data from April 1999 to September 2009, de Bondt, Peltonen and
Santabarbara (2011) examined Shanghai A-share price misalignments in bull and bear
markets. They found that it can be reasonably well explained by some fundamentals, such as
corporate earnings and the risk-free interest rate. In addition, they found that stock prices in
booms and busts can be significantly influenced by some policy actions from the Chinese
authorities, either in the form of low deposit rates, loose liquidity conditions or stock market
liberalizations. This implies that bull or bear markets are not only closely related with
economic fundamentals but also with a wide spectrum of policy instruments.
Further, Yao and Luo (2009) argued that due to some government policies, such as
privatisation and strong state support for the state-owned commercial banks, investors can be
over-optimistic about the Chinese future economic performance. Moreover, besides the
change in interest rates, trade balances, exchange rates, employment and inflation, which
could affect share prices, the poor psychological factors, such as greed, envy and speculation,
could also help explain the Chinese stock market bubble and burst during 2005 and 2008.
There is some literature arguing that there exits bubbles in the Chinese stock market in bull
phases as the average daily return jumps become much higher than previous periods. For
example, using the Shanghai Composite Index obtained from the TX Investment Consulting
Co., Ltd. from January 3, 2004 to December 31, 2007, Nishimura and Men (2010) find that
the average daily return from December 2006 to October 2007 is much higher than that
during January 2004 to November 2006. This result shows that the Chinese stock market
entered a speculative bubble period after the second half of 2006. By comparing the abnormal
market returns of the Shanghai and Shenzhen A- and B-share markets, Lehkonen (2010) finds
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that the weekly data demonstrate bubbles but monthly data does not show bubbles for all of
the Mainland Chinese stock markets. This implies that the duration dependence, a
characteristic of the hazard function for duration times, is sensitive to the use of weekly
versus monthly data and should be taken into account for bubble analysis. This also indirectly
shows that there are no differences in terms of bubble existence between Chinese A- and B-
share stock markets even though the A-shares are dominated by individuals and B-shares by
more sophisticated institutional investors.
Rutledge, Zhang and Karim (2008) examine the relationship between firm size and excess
stock returns in the Chinese stock exchange (Shanghai and Shenzhen) bull and bear market
phases from 1998 to 2003. They found that small firms had greater positive excess returns
during the bull market period but greater negative returns or no significant difference in
returns (using float market value) during the bear market period. In contrast, large firms show
greater or similar portfolio returns as compared to small firms during the bearish time period.
This finding reflects that small stocks react stronger than large stocks to economic conditions
and events.
There are a number of articles discussing bull and bear markets for other emerging markets.
For example, Assoe (1998) investigates regime-switching behaviour of nine emerging
markets3 as these markets experienced significant changes in government policies and capital
market reforms from December 1975 to December 1997. He finds, based on Markov-
switching models, that these emerging market returns and volatilities change significantly
over time in response to government policies and capital market reforms. This implies that
booms and busts in emerging stock markets could be influenced by events such as monetary
shocks and productivity switches, as these events could have an impact on traders‟
confidence. Similarly, following Bry and Boschan‟s (1971) nonparametric approach, Biscarri
and Gracia (2004) identify the bull and bear phases of Spanish stock market and discuss its
characteristics. They find that the process of financial development, such as capital market
opening, financial liberalization or integration processes, affect the Spanish stock market
3 The nine emerging markets are Argentina, Brazil, Chile, Greece, India, Korea, Mexico, Thailand, and
Zimbabwe. The data is from the International Finance Corporation‟s Emerging Markets Data Base.
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behaviour significantly. As the financial liberalization process proceeded, the Spanish stock
market became more concordant with other developed markets.
The remainder of the paper is organized as follows. Section 2 describes the data and
methodology. Section 3 presents empirical results and discusses their implications, and
Section 4 concludes.
2. Data and Methodology
2.1 Data
The monthly returns for Shanghai and Shenzhen Stock Exchanges are calculated based on the
average daily return. The daily price is from the Thomson Reuters Tick History Server via the
Securities Industry Research Centre of Asia-Pacific (SIRCA). The dataset consists of all
ordinary common stocks listed on the Shanghai and Shenzhen Stock Exchanges from 1
January 2002 through 31 December 2010.4 Stock market capitalization is from Datastream
and industry category is from the Shanghai and Shenzhen Stock Exchange website. In both
the Shanghai and Shenzhen Stock Exchanges, there are two trading sessions, one is in the
morning from 09:30am to 11:30am, and another one is in the afternoon from 13:00pm to
15:00pm. There is a one-and-a-half-hour lunch break.
To deal with missing observations in time series data, the Spline Interpolation method is
applied as follows:5
))(()32())(2()132( 12
23
1
23
21
23
1
23
ttttttt DDDDDDD ,
where tD is the missing observation at time t that needs to be filled in, is the relative
position of the missing observation divided by the total number of missing observations in the
4 The stocks listed less than five months are not included as we consider five lags in the Markov Switching
model.
5 This method has been used extensively in the literature, e.g., Norbert (1949); Friedman (1962); Damsleth
(1980) and Pavlov and Vladimir (2004).
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series. The 1tD and 2tD are the next two non-missing observations. The 1tD and 2tD are
the previous two non-missing observations.
A-shares (B-shares) are identified by a stock code commencing with “600” (“900”) in the
Shanghai stock market, and in Shenzhen stock market, A-shares (B-shares) are identified by a
stock code commencing with “000” or “001” (“200”). The summary characteristics of the
Chinese stock market are presented in Table I.
<Insert Table I here>
Clearly, Table I shows that the number of B-shares and their market capitalization are much
smaller than A-shares because the number of domestic traders is much more than foreign
traders. The market capitalization increases steadily in both A- and B-share markets except
for a drop in 2008 due to the subprime crisis. The number of companies listed in the
Shenzhen Stock Exchange was lower than in the Shanghai Stock Exchange before 2009 but
greater after as the Shenzhen Stock Exchange introduced Chinext, a second board market, in
which small high-technology companies with high growth rates are listed.
<Insert Figure 1 here>
Figure 1 plots the raw data series of Shanghai and Shenzhen A-, B- and composite index
from 2002 to 2010. The data in Figure 1 shows that from 2002 to mid 2006, the Chinese
stock market was in a bearish period. After that, the Chinese stock market went up and
experienced a bullish period. The Shanghai A-index and Shanghai Composite index, and
Shenzhen A-index and Shenzhen Composite index move closely while the Shanghai B-index
and Shenzhen B-index are much lower than A- or Composite index. This is because most of
the composite index is drawn from the A-index while the B-index is just composed by a small
number of stocks as shown in Table I.
<Insert Table II here>
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Table II shows that the B-index has higher mean returns and kurtosis than the A- and
Composite index. The standard deviation of the Shanghai B-index is higher than the Shanghai
A- and Composite index while the Shenzhen B-index has a lower standard deviation than the
Shenzhen A- and Composite index. This means that the Shanghai B-index is more volatile
and has thinner tails while the Shenzhen B-index is less volatile but has fatter tails than other
indices.
<Insert Table III here>
Table III shows the unconditional cross-correlations for all of the indices. It can be seen that
the highest degree of correlation for Shanghai and Shenzhen Stock Exchanges is between
Shanghai A and Shanghai Composite, and Shenzhen A and Shenzhen Composite. This is
because most of the composite index is composed of A-shares. The correlation between
Shanghai A and Shenzhen A is 0.921 and between Shanghai-B and Shenzhen-B is 0.869. The
correlations among other indices are also quite high. This means that these indices would
have similar bull and bear cycles. These results are consistent with Lin, Menkveld and Yang
(2009) in which they find increased correlation between Shanghai and Shenzhen Stock
markets.
2.2 Research Methodology
Following Hamilton (1989) and subsequently Hamilton and Gang (1996), the Markov regime
switching model of stock returns is as follows
t
j
i
titititt vSSrSr
1
,
where the unobserved state is governed by a state variable tS ( tS =1 or tS =2) that accords
with the regime either labelled bull or bear market, j is the number of lags, tS and tS
are the conditional mean and variance, tv ~ i.i.d (0,1).
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By maximizing the log likelihood of the observed data,6
we estimate the transition
probabilities. Let 1,1P denote the probability of being in a bull market next period if in a bull
market this period, 2,1P the probability of moving out of a bull market, 2,2P the probability of
staying in a bear market and 1,2P the probability of moving out of a bear market. Thus, the
probability transition matrix can be written as follows,
2,21,2
2,11,1
PP
PPP .
The conditional mean and variance tS and tS can be either state dependent or not.
This implies that if the mean return switches while the variance does not, we have tS and
. It is denoted as S=[1,0]. Similarly, we can have S=[0,1] or S=[1,1] for the case with
variance switching or both the mean and variance are switching with state.
If there are three states, i.e., tS can be 1, 2 or 3, then the probability transition matrix will be
in a 33 format. In this paper, we use the Bayesian Information Criterion (BIC) to determine
the optimum number of regimes, and to compare different competing Markov switching
models with the mean of return switching or the variance of error term switching or both.
BIC is designed to penalize models with more parameters. Thus, lower BIC implies better fit.
After choosing the number of regimes and mean or variance switching, we follow Ang and
Bekaert (2002) to use the Regime Classification Measure (RCM) to assess whether the
regime switching model can be used to discriminate regimes well or not. The explicit formula
for the 2-state RCM is following Chan et al (2011) is:
,11
4001
T
t
TtTt jSPjSPT
RCM
6 The log likelihood function can be found from Hamilton (1989, page 370-371) or Hamilton and Gang (1996,
page 580).
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where Tt jSP denotes the smoothed regime probability conditioned on the full
information set T . The statistic is normalized to be between 0 and 100 by the constant term
400. If the regime switching model is able to discriminate between regimes, the statistic
should be below 50.
3. Empirical Results
The smoothed probability determines if and when the regime switches. Following Hamilton
(1989), we define the bull and bear market as below:7
Definition 1: A bull market is said to occur in month t+1 if the market was in an expansion in
month t and P( 1tS =1) > 0.5.
Definition 2: A bear market is said to occur in month t+1 if the market was in a recession in
month t and P( 1tS =1) 0.5.
3.1 Selection of Regime Switching Model
In order to determine the number of regimes, we estimate BIC values for two- and three-state
univariate Markov Switching models for the monthly returns of Shanghai and Shenzhen A-,
B- and composite index (stock). Then, we estimate the RCM to check whether the regime
switching models is appropriate. In addition, by the high negative values of skewness of
Shanghai A- and composite index, and Shenzhen A-, B- and composite index from Table II,
we choose Student‟s t-distribution, which is like the normal distribution but has fatter tails,
for tv . In contrast, the skewness of Shanghai B-index is close to zero. Thus, we choose
normal distribution for its tv .
<Insert Table IV here>
7 In contrast, Pagan and Sossounov (2003) and Yan, et. al (2007) take the absolute return as an important
indicator such that the market is identified as bull or bear market if the stock market price is higher or lower than
five-month average returns.
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Table IV shows that by the value of BIC, 2-state regime switching model with the mean
return switching, i.e., S=[1,0], is the most appropriate model for estimating Shanghai and
Shenzhen index.8 This implies that the Chinese stock market has two regimes, bull and bear
regimes, with the mean returns switching while the variance is constant across regimes.
Correspondingly, the RCM values are all less than 50. It implies that this 2-state regime
switching model can be used to discriminate between regimes. This regime classification is
identical to Yan, et al (2007) which is based on the algorithm developed by Bry and Boschan
(1971).
3.2 Maximum Likelihood Estimates and Smoothed Probability
After choosing the number of regimes and switching variables, in this subsection, we present
the Maximum likelihood estimates of the parameters for the Shanghai and Shenzhen A-, B-
and composite index.
<Insert Table V here>
The first and second columns of Table V show the mean return for the six indices. It can be
seen that: 1) the mean returns are positive in bull markets but negative in bear markets.
Moreover, the absolute values of the mean returns in bull markets are higher than the bear
markets; 2) the mean return for the A-index is higher than the mean return for the B-index in
both Shanghai and Shenzhen markets. This result is comparable to Chen, Chong and Li (2011)
and Yan, et al (2007) in which they also found that the difference of return between the bull
and bear markets could be more than double and highly significant compared to those of
traditional markets, e.g., Schaller and Van Norden (2002) and Gonzalez et al (2005); 3) most
of the mean returns are significant; 4) the mean return of the A-index is equal to the mean
return of composite index. This is because most of composite index overlaps the A-index as
compared to the B-index.
8 Note that the Shanghai B-index has the lowest BIC for 2-state regime model with S=[0,1]. To be consistent
with other indices, however, we chose S=[1,0], instead of S=[0,1], for the Shanghai B-index.
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The third and fourth columns of Table V show the probability of staying in a bull or bear
market from this period to the next period. It can be noted that if the market is in a bear
market, it is more likely the market will stay in a bear market next period compared to the
case in which a bull market stays in a bull market from one period to the next period, i.e.,
2,21,1 pp .
The fifth column of Table V shows the standard deviation of the market. It can be seen that: 1)
the variance of the A-index is equal to the variance of composite index; 2) The variance of
Shanghai B index is higher than the variance of the Shanghai A index. In contrast, the
variance of the Shenzhen B index is lower than the variance of Shenzhen A index. This is
consistent with Table II.
The sixth and seventh columns of Table V show the duration of regime 1 and 2, i.e., bull and
bear markets. It can be seen that the bear market has a longer duration than the bull market.
This has been indicated by 2,21,1 pp . This result is consistent with Girardin and Liu (2003)
and Chen, Chong and Li (2011) in which they find that the mean duration of the contraction
period is greater than the expansion period for the Chinese market.
Figure 2 shows the smoothed regime probability conditioned on the full information set T ,
Tt jSP , for the markets of Shanghai and Shenzhen A-, B- and composite index from
Jan 2002 to Dec 2010. It highlights that, except for a short bull period from the end of 2003
to the March of 2004 for the Shanghai A- and composite index, these two markets were in
bear markets from 2002 to the end of 2005, and then stayed in the bull markets until the
September of 2007, and went out from bear markets from Nov 2008. This result is highly
consistent with Yan, et al (2007, Table 1) in which they show the bull and bear cycles of the
Shanghai and Shenzhen Stock Exchanges from 1991 to 2006 based on the algorithm
developed by Bry and Boschan (1971).9 Furthermore, the big jump of the stock market in
2006 implicitly supports the conclusion made in Nishimura and Men (2010) that the Chinese
stock market would enter a speculative bubble period after the second half of 2006.
9 The Shenzhen B index shows a slightly different path due to its low trading and small number of stocks listed.
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The A- and composite index are very consistent with each other relative to the B-index. This
is because A-index and composite index, which is mostly composed of A-index, are
determined by A-shares which are traded by domestic traders. In contrast, the B-index is
determined by B-shares which are traded by foreign traders. To explain why the B index has
different bull and bear cycles from the A and composite index, we need to know what factors
could affect them. We know that the B index is determined by the B share trading while the
A index is determined by A share trading and the composite index is determined by both A
and B share trading. The difference between A- and B-share trading has been discussed in the
literature, such as Chan, Menkveld and Yang (2007, 2008) and Ahlgren, Sjo and Zhang
(2009). By studying A- and B-share trading, and comparing the price discovery role of the
two segmented markets, they find that there exists information asymmetry between A-share
traders and B-share traders. The domestic investors either have more private information or
trade on the information faster than do foreign investors. Due to information asymmetry,
therefore, they may have different trading behaviours even though they trade the share which
is issued by the same company but traded in different markets.
From mid 2005 after a few favourable policies announced, such as the Lawsuit System of
Shareholder Representatives, Non-tradable Share Reform, and adopting a certain degree of
flexible exchange rate, etc, the Chinese stock market soared. In 2008, however, due to the
subprime crisis, Chinese stock market suffered a sharp down turn as shown in Figure 2. To
overcome the crisis, the Chinese Ministry of Finance and Administration of Taxation decided
to adjust the stamp duty rate of the stock market from April 24, from 0.3% to 0.1%. This
favourable policy stimulates both the Shanghai and Shenzhen stock markets. It can be seen
from Figure 2 that both the Shanghai and Shenzhen index went up from late 2008. This result
is consistent with Lin, Menkveld and Yang (2009) and de Bondt, Peltonen and Santabarbara
(2011) in which they find that Chinese bull or bear markets can be influenced by a wide
spectrum of policy instruments. Similarly, Assoe (1998) by investigating other emerging
markets, finds that government policies and capital market reforms can significantly affect
booms and busts in the stock markets.
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In summary, our results clearly show two regimes during this period, which is consistent with
our choice of 2-state regime switching model. In the next subsection, based on the 2-state
regime model, we will discuss the cyclical behaviour of the bull and bear market.
3.3 Overlapping of Bull and Bear Cycles between Stocks and Index
In this subsection, we discuss the overlapping intervals of bull and bear cycles between the
index and individual stocks to assess consistency between index and its corresponding stocks.
This will explain the properties of bull and bear cycles, and the diversification of stock
performance in the market and their reactions to macroeconomic shocks.10
<Insert Table VI here>
The first column of Table VI, “Bull – Bull”, shows the percentage of stocks‟ bull cycles that
overlap the market‟s bull cycles. For example, the first result in the first column, 18.89%,
means that 18.89% of Shanghai A-share bull cycles overlap with the Shanghai A index bull
cycles.
Comparing the results of “Bear – Bear” with “Bull – Bull”, it can be noted that the
overlapping of bear markets between stocks and the market index is much higher than the
overlapping of bull markets except for the overlapping between the Shanghai B-index and B-
shares which have close values. This implies that in the bear market cycles, a large fraction of
shares are in the bearish cycles concurrently. These results are consistent with Table V which
shows that the duration of the bear market is longer than the duration of the bull market, and
2,21,1 pp . This result can be explained by the herding effect as discussed in Tan, et al (2008)
in which they find stronger herding effects of A-share investors in the Shanghai market. In
contrast, in the bull market cycles, a smaller fraction of shares concurrently have price
increases. The Shanghai B-index and B-share have a higher value of “Bull – Bull” because as
shown in Table V the Shanghai B-index has a longer duration of bull cycles. 10
For example, Zhang et al (2011) find that monetary policy could affect the volatility of Chinese stock market
significantly based on a Markov regime switching GARCH model.
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From the high values of “Bear – Bull”, we can conclude that the performance of each stock is
widely diversified across the market especially when the market is in a bearish cycle. This
diversification is more obvious in the emerging markets due to low levels of management
skills and investment strategies of firms or higher heterogeneous behaviours of traders as
discussed in Yao and Luo (2009). This can also indirectly help understand why the Chinese
stock market experienced a long period of bear market and shorter period of bull market even
though the values of “Bull – Bear” are low.
Among the eight combinations, we can see that the overlapping between the B-index and B-
share, in particular the “Shanghai B index – B share”, have the highest values of “Bull –
Bull”. This can be explained by the finding from Chiang, et al (2008) in which they find that
B-shares react quickly to good news. Even though domestic investors were allowed to trade
B shares after 2001, there is still some degree of segmentation between the A- and B-share
markets in China. This is consistent with Ahlgren, Sjo and Zhang (2009) in which they find
that firms’ A and B shares are cointegrated, but not for all firms.
<Insert Table VII.I here>
Table VII.I shows the percentage of the overlapping between A-shares and Shanghai and
Shenzhen A-index, by industry and firm size, from January 2002 through December 2010. It
can be seen that “Bear – Bear” has very high percentage in all the industries except industry
L (Communication and cultural industries), which is just 0.65% of the total firms in Shenzhen
market. A high percentage from “Bear – Bear” implies that there would be strong herding
effects among investors in the bearish period as discussed in Tan, et al (2008). This result is
consistent with Welch (2000) in which he finds that when the stock market has fallen due to
bad news, traders would revise their forecasts and follow the prevailing consensus. Almost
50% of industry I, i.e., Finance and Insurance, stocks overlap its bear cycles with the
Shanghai A index. In contrast, 30.51% of industry I stocks overlaps its bear cycles with the
index in the Shenzhen market. This big difference implies that the rise or fall of stock price in
a certain industry could have different effects on these two markets even though these two
markets have strong correlations as shown in Table III. This can be due to the composition of
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stocks between these two markets, as shown in Table VII.I that industry I is just 1.32% of the
total firms in Shanghai while 0.86% of the total firms in Shenzhen market. This difference
also reflects the non-identical property of cycle between these two markets as found in Yan,
et. al (2007).
Industries K (Social services), L (communication and cultural industries) and M (General
field) in the Shanghai stock market, and industries C (manufacturing), G
(telecommunications) and L (communication and cultural industries) in the Shenzhen stock
market, have a very high result of “Bear – Bull” overlapping. This can be explained as when
the market is in a bear cycle, stocks in certain industries go up. This could be due to risk
avoidance from other industries or the diversification of firm performance. Especially,
industries C and G compose a large fraction of the Shenzhen stock market. The high
inconsistency between stock performance and the market leads to a short duration in both bull
and bear markets as compared to the Shanghai stock market, as shown in Table V.
Industry C, i.e., manufacturing, is the largest industry. It comprises of more than 50% of the
whole market. The results for this industry in Table VII.I are consistent with Table VI. This
implies that any policy which affects the manufacturing industry would affect the whole
market directly and significantly. Next to the manufacturing industry, industries G
(telecommunications), H (wholesale and retail trade), and J (real Estate) are relatively large
compared to the rest. Policies relevant to these industries also have a significant impact on the
market. In contrast, industry L, communication and cultural industries, is just composed of
0.65% of the Shenzhen stock market. It would not affect the market much even thought its
“Bear – Bear” result is far from other industries.
The results of “Bull – Bull” overlapping decrease with firm size while the results of “Bear –
Bear” overlapping increase with firm size in both the Shanghai and Shenzhen stock markets.
This implies that larger firm stocks tend to have different bull cycles but consistent bear
cycles with the market. Large firms would have greater impact on the whole market. This
also explains why the bear cycles of the market are longer than the bull cycles.
<Insert Table VII.II here>
17
Table VII.II shows the overlapping of bull and bear cycles between B-shares and Shanghai
and Shenzhen B-index from January 2002 through December 2010. It can be seen that there
are some industries missing in either Shanghai or Shenzhen B-share markets because of a
small number of firms listed in these two markets and also some requirements for listing B-
shares in the market.11
The results in Table VII.II are different from Table VII.I. 12
For
example, industry H, i.e., wholesale and retail trade, stocks have high overlapping of “Bull –
Bull” cycles in the Shanghai stock market but high overlapping in “Bear – Bear” cycles in the
Shenzhen stock market. The bull or bear cycles of industry D, i.e., Electricity & Coal &
Water Supplies, have few overlapping intervals in the Shanghai stock market but many
overlapping intervals in the Shenzhen market. The bull or bear cycle overlapping differs
significantly according to the firm size especially in the Shanghai stock market. For example,
the results of “Bull – Bull” overlapping and “Bear – Bear” overlapping for large-size firm are
22.12% and 23.27%, respectively. They increase to 33.42% and 30.41% respectively for
small-size firms. This means that the smaller the firm size is, the more consistent the firm‟s
stock can be with the market index. However, this result is not as strong in the Shenzhen
stock market. All of these differences reflect the heterogeneity between these two markets as
argued in Wang and Firth (2004) in which they find the Chinese A-stock market is more
comparable to a developed market than an emerging market while Chinese B-shares are
better categorized as being emerging than developed.
<Insert Table VII.III here>
Table VII.III gives similar results as Table V.I because most of the composite index is
composed of A-shares.
11
See the following website for the detailed requirements for issuing B shares:
http://www.sipf.com.cn/en/chinassecuritiesmarketoverview/introtobshare/index.shtml
12Some extreme results in Table VII.II could be due to the low number of firms of each industry listed in the B-
share market. See the total number of stocks listed in B-share market from Table I.
18
<Insert Table VII.IV here>
Table VII.IV shows the overlapping intervals of bull and bear cycles between the Shanghai
and Shenzhen composite index and B-share stocks from January 2002 through December
2010. Comparing Table VII.IV with Table VII.II, it can be seen that the overlapping intervals
of bear cycles between stocks and the index is quite high while the overlapping intervals of
bull cycles is relatively low. This is consistent with the results in Table VII.I. In addition, it
can be noted that both the “Bull – Bull” and “Bear – Bear” overlapping decrease significantly
with the firm size as shown in Table VII.II.
Overall, from Tables VII.I-VII.IV, we can see that result of “Bear – Bear” overlapping and
“Bear – Bull” overlapping are high across different industries. That could help us
understanding why China has a long duration of bear market. The results of “Bull – Bull”
overlapping decrease while the results of “Bear – Bear” overlapping increase with the firm
size in both the Shanghai and Shenzhen stock markets. This implies that larger firm stocks
tend to have different bull cycles but consistent bear cycles with the market. Large firms
would have greater impact on the whole market. This result indirectly supports the
observation of the longer duration of bear markets. In addition, among 13 industries, the
Manufacturing industry composes more than 50% of the total listed firms. Next to it are
Telecommunications, Wholesale and Retail Trade and Real Estate. This implies that any
policy relevant to these industries could have a significant impact on the market.
4. Conclusion
In this paper, following Hamilton (1989) and Hamilton and Gang (1996), we use a Markov
regime switching model to analyse the bull and bear cycles in the Chinese stock market. We
find the 2-state regime Markov switching model with the mean return switching and variance
constant gives us a good fit to the data. We then find that the Chinese stock market
experienced a long period of a bear cycle. By grouping the stocks according to 13 industry
categories and firm size to explain the bull and bear cycles, we find that: 1) the overlapping
19
of bear cycles between stocks and the index is quite high. This implies that there would be
strong herding effects among investors in the bearish period as discussed in Welch (2000)
and Tan, et al (2008). In addition, the high value of “Bear – Bull” overlapping leads to the
conclusion that stock performance is widely diversified across the market. This feature is
consistent with the other emerging markets in which management skills and investment
strategies of firms are relatively low and trader behaviours are highly heterogeneous. This can
also indirectly help in understanding why Chinese stock market experienced a long period of
a bear market cycle; 2) in the Shanghai and Shenzhen stock markets, the same industry may
have different overlapping intervals of bull or bear cycles. This implies that a certain shock to
one industry could have different impacts on these two markets even though these two
markets have strong correlations with each other; 3) firms with different size could have
different overlapping intervals with bull or bear cycles. Specifically, we find that firms with
larger size have more overlappings with bear market cycles. It means that bear market would
have greater impact on large firms relative to small firms.
Our result, through discussing overlapping intervals of bull and bear cycles between stock
and index, provides implications for portfolio diversification and hedging. For example, in a
bearish period, there exists certain industries which may have an increasing stock price, such
as social services, communication and cultural industries in the Shanghai stock market or
manufacturing, telecommunications and communication and cultural industries in the
Shenzhen stock market. In a bullish period, investors may find small size firms to earn high
returns. Overall, our finding may help investor to hedge the risk or diversify their portfolio in
bull and bear markets.
Acknowledgement: We use Perlin, M. (2010) MS Regress - The MATLAB Package for
Markov Regime Switching Models. It is available at SSRN:
http://ssrn.com/abstract=1714016. We also thank Professor Perlin for his useful suggestions
regarding the Matlab code.
20
Reference
Ahlgren N., B. Sjo and J. Zhang (2009). “Panel cointegration of Chinese A and B shares.”
Applied Financial Economics, 19, 1859–1871.
Ang, A., Bekaert, G. (2002). “Regime switches in interest rate.” Journal of Business and
Economic Statistics 20, 163-182.
Assoe, K. G. (1998). “Regime-switching in emerging stock market returns.” Multinational
Finance Journal 2, 101-132.
Biscarri, J. G. and F. Perez de Gracia (2004). “Stock market cycles and stock market
development in Spain.” Spanish Economic Review 6, 127-151.
Bry, G. and C. Boschan (1971). “Cyclical Analysis of Time Series: Selected Procedures and
Computer Programs.” National Bureau of Economic Research, New York.
Chan, K., A. Menkveld, and Z. Yang (2007). “The informativeness of domestic and foreign
investors‟ stock trades: Evidence from the perfectly segmented Chinese market.” Journal of
Financial Markets, 10, 391-415.
(2008). “Information Asymmetry and Asset Prices: Evidence from the China Foreign
Share Discount.” Journal of Finance, 63, 159-196.
Chan, K. F., S. Treepongkaruna, R. Brooks and S. Gray (2011). “Asset market linkages:
Evidence from financial, commodity and real estate assets.” Journal of Banking and Finance.
Chen, H., T. Chong and Z. Li (2011). “Are Chinese Stock Market Cycles Duration
Independent?” Financial Review, 46, 151–164.
Chiang, T. C., Nelling, E. and L. Tan (2008). “The speed of adjustment to information:
Evidence from the Chinese stock market.” International Review of Economics and Finance
17, 216-229.
21
Damsleth, E. (1980). “Interpolating missing values in a time series.” Scandinavian Journal of
Statistics, Theory and Application 7, 33–39.
de Bondt, G. J., Peltonen, T. A. and D. Santabarbara (2011). “Booms and busts in China‟s
stock market: estimates based on fundamentals.” Applied Financial Economics 21, 287-300.
Durland, J. M., and T. H. McCurdy (1994). “Duration-Dependent Transitions in a Markov
Model of U.S. GNP Growth.” Journal of Business & Economic Statistics 12, 279-288.
Friedman, M. (1962). “The interpolation of time series by related series.” Journal of
American Statistical Association 57, 729–757.
Girardin, E. and Z. Liu (2003). “The Chinese Stock Market: A Casino with „Buffer Zones‟?”
Journal of Chinese Economic and Business Studies 1, 57-70.
Gonzalez, L., Powell, J.G., Shi, J. and A.,Wilson (2005). “Two centuries of bull and bear
market cycles.” International Review of Economics and Finance 14, 469–486.
Hamilton, J.D.A. (1989). “A New Approach to the Economic Analysis of Nonstationary
Time Series and the Business Cycle.” Econometrica, 57, 357–384.
Hamilton, J.D. and L.Gang (1996). “Stock market volatility and the business cycle.” Journal
of Applied Econometrics 11, 573-593.
Lehkonen, H (2010). “Bubbles in China.” International Review of Financial Analysis 19,
113-117.
Lin, K., A. J. Menkveld and Z. Yang (2009). “Chinese and world equity markets: A review of
the volatilities and correlations in the first fifteen years.” China Economic Review 20, 29-45.
Maheu, J. M. and McCurdy, T. H (2000). “Volatility dynamics under duration-dependent
mixing,” Journal of Empirical Finance 7, 345-372.
22
Nishimura, Y. and M. Men (2010). “The paradox of China‟s international stock market co-
movement Evidence from volatility spillover effects between China and G5 stock markets.”
Journal of Chinese Economic and Foreign Trade Studies 3, 235-253.
Norbert, W. (1949). “Extrapolation, Interpolation, and Smoothing of Stationary Time Series.”
Cambridge: The M.I.T Press.
Pagan, A.R. and K.A. Sossounov (2003): “A Simple Framework for Analyzing Bull and Bear
Markets.” Journal of Applied Econometrics 18, 23-46.
Pavlov and Vladimir (2004). “Missing data and interpolation in dynamic term structure
models.” In S. Hurn and R. Becker (Eds.), Contemporary Issues in Economics and
Econometrics: Theory and Application, 162–175. Cheltenham, United Kingdom: Edward
Elgar Publishing.
Perlin, M. (2010). MS Regress - The MATLAB Package for Markov Regime Switching
Models. It is available at SSRN: http://ssrn.com/abstract=1714016.
Ryden, T., Terasvirta, T. and S. Asbrink (1998). “Stylized facts of daily returns series and the
hidden Markov model.” Journal of Applied Econometrics 13, 217–244.
Rutledge, R., Zhang, W., Z. and K. Karim (2008). “Is There a Size Effect in the Pricing of
Stocks in the Chinese Stock Markets? The Case of Bull Versus Bear Markets.” Asia-Pacific
Financial Markets, 15:117–133.
Schaller, H., Van Norden, S., (2002). “Fads or bubbles?” Empirical Economics, 27, 335–362.
Tan, L., Chiang, T.C., Mason, J. and E. Nelling (2008). “Herding behavior in Chinese stock
markets: an examination of A and B shares.” Pacific-Basin Finance Journal 16, 61-77.
23
Wang, S. S. and M. Firth (2004). “Do bears and bulls swim across oceans? Market
information transmission between greater China and the rest of the world.” Journal of
International Financial Markets, Institutions and Money 14, 235-254.
Welch, I. (2000). “Herding among security analysts.” Journal of Financial Economics, 58,
369-396.
Yan, W., Powell, J. G., Shi, J. and W. Xu (2007). “Chinese stock market cyclical regimes:
1991–2006.” Economics Letters, 97, 235–239.
Yao, S. and D. Luo (2009). “The Economic Psychology of Stock Market Bubbles in China.”
World Economy, 32, 667-691.
Zhang, C., D. Zhang and J. Breece (2011). “Financial Crisis, Monetary Policy, and Stock
Market Volatility in China.” Annals of Economics and Finance, 12-2, 371-388.
Zhou, W. C., Xu, H.C., Cai, Z.Y., Wei, J.R., Zhu, X.Y., Wang, W., Zhao, L. and J.P. Huang
(2009). “Peculiar statistical properties of Chinese stock indices in bull and bear market
phases.” Physica A 388, 891-899.
24
Table I: Summary of Chinese Stock Market.
2002 2003 2004 2005 2006 2007 2008 2009 2010
No. of Companies listed
Shanghai Stock Exchange 715 780 837 834 842 860 864 870 894
Shenzhen Stock Exchange 508 505 536 544 579 670 740 830 1169
No. of Share listed
Shanghai Stock Exchange 759 824 881 878 886 904 908 914 938
A share 705 770 827 824 832 850 854 860 884
B share 54 54 54 54 54 54 54 54 54
Shenzhen Stock Exchange 551 548 578 586 621 712 782 872 1211
A share 494 491 522 531 566 657 727 818 1157
B share 57 57 56 55 55 55 55 54 54
Marekt Capitaliztion (100 m)
Shanghai Stock Exchange 25363.72 29804.92 26014.34 23096.13 71612.38 269838.9 97251.91 184655.2 179007.2
A share 24921.42 29400.65 25714.07 22856.07 71117.95 268497.3 96875.31 183799.9 17800.02
B share 442.3 404.27 300.27 240.06 494.43 1341.6 376.59 855.35 1007.22
Shenzhen Stock Exchange 12965.41 12652.79 11041.23 9334.15 17791.52 57302.02 24114.53 59283.89 86415.35
A share 12605.14 12119.83 10595.28 8954.48 16996.01 56090.47 23691.24 58327.14 85220.52
B share 360.27 532.96 445.95 379.67 795.51 1211.55 423.29 956.75 1194.83
Table II: The Mean, Standard Deviation, Skewness, and Kurtosis for All of the Returns for
the Entire Sample Period. The monthly return of the index is the average of daily return, tr ,
which is defined as 1lnln*100 ttt ppr , where tp denotes the daily share or index price.
The sample period runs from Jan 2002 to Dec 2010.
Mean Standard Deviation Skewness Kurtosis
SHH A index 0.00031 0.00452 -0.62109 0.95614
SHH B index 0.00036 0.00589 -0.17254 2.54899
SHH Composite index 0.00030 0.00453 -0.62210 0.94604
SHZ A index 0.00054 0.00496 -0.51363 0.63253
SHZ B index 0.00060 0.00485 -0.58742 1.35348
SHZ Composite index 0.00054 0.00493 -0.52595 0.62382
25
Table III: Unconditional Cross-correlations For All of the Returns.
Shanghai A Shanghai B Shanghai Composite Shenzhen A Shenzhen B
Shanghai A
Shanghai B 0.788
Shanghai Composite 1.000 0.792
Shenzhen A 0.921 0.847 0.922
Shenzhen B 0.796 0.869 0.799 0.792
Shenzhen Composite 0.923 0.853 0.925 1.000 0.804
Table IV: Panel A reports Bayesian Information Criteria (BIC) values for two- and three-
state univariate Markov Switching models for the monthly returns of the Chinese Shanghai
and Shenzhen A-, B- and composite index (stock). Panel B reports the RCM statistics for the
2-state regime model as determined by the lowest BIC value reported in Panel A. S=[1 1],
S=[1,0] and S=[0,1] mean that both the mean return and variance of the error term are
switching or just the mean return switching or just the variance switching.
2-state
regime
3-state
regime
2-state
regime
3-state
regime
2-state
regime
3-state
regime
Shanghai A index -824.04 -798.17 -827.83 -811.09 -823.14 -790.86
Shanghai B index -778.20 -742.45 -769.54 -750.02 -782.68 -742.98
Shanghai composite -824.13 -806.16 -827.94 -820.17 -827.86 -794.03
Shenzhen A index -804.79 -790.97 -809.01 -797.54 -806.52 -784.29
Shenzhen B index -815.98 -795.57 -817.99 -801.35 -808.45 -785.90
Shenzhen composite -804.54 -789.51 -808.32 -788.26 -804.79 -774.68
Shanghai A index 30.54 14.50 31.92 14.44 25.13 24.27
Shanghai B index 30.46 4.05 36.85 0.49 29.83 24.48
Shanghai composite 30.14 15.03 31.46 1.23 24.88 21.56
Shenzhen A index 32.57 10.07 33.69 14.76 23.53 53.72
Shenzhen B index 22.98 16.59 36.02 45.09 18.96 77.82
Shenzhen composite 33.30 11.23 35.64 48.14 24.47 26.09
S=[1,1] S=[1,0] S=[0,1]
Panel B: RCM values
Panel A: BIC values
26
Table V: Maximum Likelihood Estimates of Parameters and Duration of Regime 1 and 2
Based on Data for Shanghai and Shenzhen A-, B- and composite Index from January 2002
through December 2010. *, ** and *** denote significance at 10%, 5% and 1% levels,
respectively.
1 2 p1,1 p2,2
Duration of Regime 1
(months)
Duration of Regime 2
(months)
Shanghai A index 0.0033*** -0.0008** 0.9*** 0.94*** 0.000008*** 10.24 17.61
Shanghai B index 0.0031*** -0.0016** 0.94*** 0.95*** 0.000028*** 17.94 19.17
Shanghai composite 0.0033*** -0.0008** 0.9*** 0.94*** 0.000008*** 10.34 17.95
Shenzhen A index 0.0041*** -0.0008* 0.89*** 0.94*** 0.000011*** 8.81 17.01
Shenzhen B index 0.004*** -0.0002 0.86*** 0.94*** 0.000008*** 6.97 15.87
Shenzhen composite 0.0041*** -0.0008* 0.88*** 0.94*** 0.000011*** 8.50 15.71
Table VI: The Percentage of the Overlapping Interval of Bull and Bear Cycles in Shanghai
and Shenzhen Stock Exchange from January 2002 through December 2010. “Shanghai A
index – A share” means the match of bull and bear cycles between A-share and Shanghai A
index. This shows the percentage of the bull or bear cycles of each A-share can match or
mismatch with the Shanghai A-index. The same explanation applies to other combinations,
such as “Shanghai B index – B share”, etc. “Bull – Bull” means the case when both the index
and stock are in the bull markets. Similarly, “Bear – Bull” means the case when the index is
in the bear market while the stock is in the bull market.
Bull - Bull Bear - Bear Bear - Bull Bull - Bear
Shanghai A index - A share 18.89% 31.04% 32.95% 17.12%
Shanghai B index - B share 26.83% 25.66% 17.89% 29.62%
Shanghai composite index - A share 18.53% 31.35% 33.31% 16.81%
Shanghai composite index - B share 12.52% 40.43% 32.20% 14.85%
Shenzhen A index - A share 18.77% 31.03% 34.48% 15.73%
Shenzhen B index - B share 18.35% 39.07% 27.22% 15.36%
Shenzhen composite index - A share 18.77% 31.03% 34.48% 15.73%
Shenzhen composite index - B share 18.12% 38.48% 27.45% 15.95%
27
Table VII: The Percentage of the Overlapping Interval of Bull and Bear Cycles in Shanghai
and Shenzhen Stock Exchange, by Industry and firm size, from January 2002 through
December 2010. It contains four sub-Tables, from Table VII.I – VII.IV. It shows that for each
industry, from Category A to M, the percentage of A and B-share‟s bull or bear cycles
overlapping with the Shanghai and Shenzhen A-, B- and Composite index. Each industry,
categorized from A to M, are: A: Agriculture; B: Mining; C: Manufacturing; D: Electricity &
Coal & Water Supplies; E: Construction; F: Transportation & Logistic; G:
Telecommunications; H: Wholesale and Retail Trade; I: Finance and Insurance; J: Real
Estate; K: Social Services; L: Communication and cultural industries; M: General Field.
“Bull – Bull” means the case when both the index and each stock are in the bull markets.
Similarly, “Bear – Bull” means the case when the index is in the bear market while the stock
is in the bull market. “% of the Total Firms” shows the fraction of the firm in each industry to
the whole market. Small-Size firm, Medium-Size firm and Large-Size firm are categorized
according to the market capitalization, bottom 30%, middle 40%, and top 30%.
28
Table VII.I
Shanghai A index -
A share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
Shenzhen A index -
A share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
A 20.89% 26.24% 36.83% 16.03% 2.51% A 17.92% 38.29% 27.89% 15.90% 1.61%
B 16.12% 36.57% 24.79% 22.53% 2.28% B 15.07% 44.58% 26.37% 13.97% 1.94%
C 18.98% 31.84% 32.20% 16.98% 53.53% C 19.63% 29.61% 35.39% 15.36% 63.66%
D 19.42% 33.11% 30.56% 16.91% 4.91% D 16.96% 31.54% 34.15% 17.35% 2.69%
E 18.21% 35.60% 27.56% 18.62% 2.40% E 18.98% 42.88% 22.54% 15.59% 1.61%
F 18.46% 29.19% 35.22% 17.13% 4.91% F 18.49% 33.11% 33.33% 15.07% 1.94%
G 22.20% 28.70% 34.81% 14.28% 5.15% G 17.65% 29.75% 37.73% 14.87% 7.74%
H 16.19% 33.00% 30.34% 20.47% 7.43% H 17.18% 30.82% 34.94% 17.06% 5.48%
I 13.55% 47.66% 15.89% 22.91% 1.32% I 19.40% 30.51% 35.80% 14.29% 0.86%
J 19.24% 28.43% 35.79% 16.54% 7.78% J 15.85% 35.57% 30.88% 17.70% 6.02%
K 20.56% 19.84% 44.97% 14.63% 1.92% K 17.90% 34.28% 30.25% 17.56% 3.44%
L 20.20% 27.44% 39.23% 13.13% 1.32% L 29.08% 9.80% 55.23% 5.88% 0.65%
M 19.23% 22.99% 42.72% 15.06% 4.55% M 17.61% 32.35% 33.24% 16.80% 2.37%
Small-Size Firm 19.41% 29.09% 35.74% 15.76% 30.02% Small-Size Firm 19.09% 30.28% 35.37% 15.26% 29.50%
Medium-Size Firm 19.29% 30.76% 33.20% 16.75% 40.07% Medium-Size Firm 18.80% 30.84% 34.68% 15.69% 40.27%
Large-Size Firm 17.94% 33.19% 30.04% 18.82% 29.91% Large-Size Firm 18.39% 32.08% 33.26% 16.27% 30.24%
29
Table VII.II
Shanghai B index -
B share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
Shenzhen B index -
B share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
A A 54.84% 16.13% 29.03% 0.00% 1.79%
B 9.26% 53.70% 0.00% 37.04% 1.85% B 0.00% 29.03% 16.13% 54.84% 1.79%
C 27.44% 22.86% 24.12% 25.57% 51.85% C 18.31% 41.34% 25.82% 14.53% 64.29%
D 2.13% 13.83% 5.32% 78.72% 3.70% D 30.65% 11.29% 33.87% 24.19% 3.57%
E 0.71% 41.43% 4.29% 53.57% 3.70% E
F 23.04% 10.29% 26.96% 39.71% 7.41% F 26.32% 36.84% 24.81% 12.03% 7.14%
G 54.17% 9.38% 9.38% 27.08% 5.56% G 3.70% 0.93% 73.15% 22.22% 1.79%
H 60.94% 12.50% 6.25% 20.31% 3.70% H 27.07% 46.29% 19.21% 7.42% 8.93%
I I
J 39.10% 38.14% 4.81% 17.95% 11.11% J 8.17% 33.66% 31.37% 26.80% 8.93%
K 23.18% 35.47% 5.31% 36.03% 11.11% K 22.22% 54.63% 19.44% 3.70% 1.79%
L L
M M
Small-Size Firm 33.42% 30.41% 16.07% 20.09% 29.63% Small-Size Firm 18.69% 39.70% 24.01% 17.60% 28.55%
Medium-Size Firm 25.35% 23.72% 16.82% 34.11% 40.72% Medium-Size Firm 20.32% 41.05% 24.82% 13.82% 41.09%
Large-Size Firm 22.12% 23.27% 20.88% 33.72% 29.65% Large-Size Firm 15.67% 36.21% 32.19% 15.93% 30.36%
30
Table VII.III
Shanghai
composite index -
A share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
Shenzhen
composite index -
A share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
A 20.60% 26.34% 37.12% 15.94% 2.51% A 17.92% 38.29% 27.89% 15.90% 1.61%
B 15.93% 36.85% 24.98% 22.24% 2.28% B 15.07% 44.58% 26.37% 13.97% 1.94%
C 18.62% 32.15% 32.56% 16.67% 53.53% C 19.63% 29.61% 35.39% 15.36% 63.66%
D 19.13% 33.44% 30.85% 16.57% 4.91% D 16.96% 31.54% 34.15% 17.35% 2.69%
E 17.97% 35.93% 27.81% 18.29% 2.40% E 18.98% 42.88% 22.54% 15.59% 1.61%
F 18.03% 29.52% 35.65% 16.80% 4.91% F 18.49% 33.11% 33.33% 15.07% 1.94%
G 21.88% 29.09% 35.13% 13.89% 5.15% G 17.65% 29.75% 37.73% 14.87% 7.74%
H 15.91% 33.36% 30.62% 20.11% 7.43% H 17.18% 30.82% 34.94% 17.06% 5.48%
I 13.18% 48.15% 16.26% 22.41% 1.32% I 19.40% 30.51% 35.80% 14.29% 0.86%
J 18.82% 28.69% 36.21% 16.28% 7.78% J 15.85% 35.57% 30.88% 17.70% 6.02%
K 20.02% 20.02% 45.51% 14.45% 1.92% K 17.90% 34.28% 30.25% 17.56% 3.44%
L 19.53% 27.61% 39.90% 12.96% 1.32% L 29.08% 9.80% 55.23% 5.88% 0.65%
M 18.65% 23.17% 43.30% 14.88% 4.55% M 17.61% 32.35% 33.24% 16.80% 2.37%
Small-Size Firm 19.00% 29.42% 36.16% 15.43% 30.02% Small-Size Firm 19.09% 35.37% 30.28% 15.26% 29.50%
Medium-Size Firm 18.93% 31.03% 33.56% 16.48% 40.07% Medium-Size Firm 18.80% 34.68% 30.84% 15.69% 40.27%
Large-Size Firm 17.60% 33.52% 30.38% 18.49% 29.91% Large-Size Firm 18.39% 33.26% 32.08% 16.27% 30.24%
31
Table VII.IV
Shanghai
composite index -
B share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
Shenzhen
composite index -
B share Bull - Bull Bear - Bear Bear - Bull Bull - Bear % of the Total Firms
A A 41.94% 16.13% 41.94% 0.00% 1.79%
B 9.26% 72.22% 0.00% 18.52% 1.85% B 0.00% 41.94% 16.13% 41.94% 1.79%
C 14.54% 35.58% 37.03% 12.86% 51.85% C 18.06% 39.68% 26.07% 16.19% 64.29%
D 0.00% 53.19% 7.45% 39.36% 3.70% D 24.19% 17.74% 40.32% 17.74% 3.57%
E 0.71% 69.29% 4.29% 25.71% 3.70% E
F 5.88% 30.88% 44.12% 19.12% 7.41% F 20.30% 39.85% 30.83% 9.02% 7.14%
G 14.58% 29.17% 48.96% 7.29% 5.56% G 12.96% 4.63% 63.89% 18.52% 1.79%
H 17.19% 28.13% 50.00% 4.69% 3.70% H 26.20% 48.03% 20.09% 5.68% 8.93%
I I
J 14.10% 44.23% 29.81% 11.86% 11.11% J 7.19% 32.68% 32.35% 27.78% 8.93%
K 10.34% 52.51% 18.16% 18.99% 11.11% K 30.56% 57.41% 11.11% 0.93% 1.79%
L L
M M
Small-Size Firm 17.63% 41.55% 31.87% 8.95% 29.63% Small-Size Firm 20.19% 44.20% 22.51% 13.10% 28.55%
Medium-Size Firm 10.85% 42.02% 31.32% 15.81% 40.72% Medium-Size Firm 19.45% 38.70% 25.69% 16.16% 41.09%
Large-Size Firm 9.47% 37.52% 33.54% 19.47% 29.65% Large-Size Firm 15.17% 34.70% 32.69% 17.44% 30.36%
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Figure 1: Shanghai and Shenzhen A-, B- and Composite Index from 2002 to 2010.
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Figure 2: Smoothed Probability of Regime 1 for Univariate Markov Switching Model. This Figure shows the smoothed regime probability
conditioned on the full information set T , Tt jSP , for the bull markets of Shanghai and Shenzhen A and B index from Jan 2002 to Dec
2010.