HOW DOES CIRCUIT BREAKER CAUSE EFFECTS EVIDENCE FROM...
Transcript of HOW DOES CIRCUIT BREAKER CAUSE EFFECTS EVIDENCE FROM...
HOW DOES CIRCUIT BREAKER CAUSE EFFECTS
TO CHINESE STOCK MARKET?
EVIDENCE FROM CSI 300 INDEX DATA
BY
PAO PUI MAN
STUDENT NO. 13210203
A PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE
REQUITREMENTS FOR THE DEGREE OF
BACHELOR OF SOCIAL SCIENCES (HONORS) DEGREE IN CHINA STUDIES
ECONOMICS CONCENTRATION
ECONOMICS CONCENTRATION
HONG KONG BAPTIST UNIVERSITY
APRIL 2017
2
Page of Acceptance
HONG KONG BAPTIST UNIVERSITY
April 2017
We hereby recommend that the Project by Miss. Pao Pui Man entitled “How does
circuit breaker cause effects to Chinese stock market? Evidence from CSI 300 Index
data” be accepted in partial fulfillment of the requirements for the Bachelor of Social
Sciences (Honours) Degree in China Studies in Economics.
____________________________ ____________________________
Dr. Luk Sheung Kan Dr. ____________________________
Project Supervisor Second Examiner
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Acknowledgement
I would like to thank my supervisor Dr. Luk Sheung Kan for suggesting the research
topic and guiding me through the entire study. This paper is hardly finished without his
generous support and valuable advice.
_______________________
Student‟s signature
China Studies Degree Course
(Economics Concentration)
Hong Kong Baptist University
Date: ____________________
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Table of Contents
Abstract .......................................................................................................................... 5
1. Introduction and Background ................................................................................ 6
1.1 What is circuit breaker? .................................................................................................. 6
1.2 Reason to study circuit breaker ....................................................................................... 7
2. Review on Chinese stock market ......................................................................... 10
2.1 Historical development of Chinese stock market .......................................................... 10
2.2 Current Chinese stock market ........................................................................................ 11
2.3 What is CSI 300 Index? ................................................................................................ 13
2.4 Circuit breaker in Chinese stock market ....................................................................... 14
3. Review on other countries‟ circuit breaker .......................................................... 18
3.1 US stock market ............................................................................................................ 18
3.2 Taiwan stock market...................................................................................................... 19
3.3 India stock market ......................................................................................................... 20
4. Literature Review................................................................................................. 22
5. Data ...................................................................................................................... 26
6. Methodology and Empirical Results .................................................................... 27
6.1 Regression on Last Price ............................................................................................... 27
6.2 Regression on Trading Volume ..................................................................................... 33
6.3 Regression on Volatility ................................................................................................ 40
7. Economic Interpretation....................................................................................... 45
8. Limitation ............................................................................................................. 47
9. Conclusion and Suggestion .................................................................................. 48
9.1 Review on Chinese stock market and suggestion ......................................................... 48
9.2 Review on circuit breaker mechanism and suggestion ................................................. 49
Appendix ...................................................................................................................... 52
References .................................................................................................................... 59
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Abstract
Using the time-series data of CSI 300 Index from May 2005 to November 2016,
this paper analyzes how Chinese stock market responses to the introduction of circuit
breaker mechanism. Regression models are set up with different methods, and the
empirical results reveal the relationship between the last price, volume, volatility of
CSI 300 Index and circuit breaker respectively. Using the result to further project the
nature of Chinese stock market and how this administrative measure affects Chinese
stock market‟s development.
Reform and opening-up in 1980s marked the start of economic reform in China.
Since then, Chinese stock market has been one of the most important channels for
business to raise funds. This study is important because the investigation of this recent
administrative measure provides a framework on how influential circuit breaker
brings. The result allows Chinese government officials to rethink how intervention
can affect the fluctuation of Chinese stock market; and investors to be aware of the
nature of Chinese stock market, in order to facilitate healthy investment.
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1. Introduction and Background
1.1 What is circuit breaker?
Circuit breaker refers to the mechanism that when price fluctuation reaches a certain
value stipulated by the Exchange, the trading of stocks will be suspended for a period of
time, or trades will only be conducted within the stipulated threshold.
Circuit breaker was first introduced in the US due to the 1987 US Stock Market Crash.
A huge drop, a decrease of 28.6%, of the Standard & Poor's 500 (S&P 500 Index) was
recorded on 19th October, 1987. Circuit breaker mechanism was launched in the New
York Stock Exchange (NYSE) and Chicago Mercantile Exchange (CME) in 1988 after
the crash. Later, circuit breaker mechanism was introduced to stock markets in Europe
and other Asian countries.
Different countries which implement circuit breaker mechanism have different
reasons. In which Chinese government launched circuit breaker for the following two
major reasons. The first one is to allow investors to have more time to reconsider and
evaluate market information, so as to prevent drastic rise or decline in stock prices
caused by public panic. The second reason is to prevent short-term dramatic price
fluctuation due to market illiquidity.
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1.2 Reason to study circuit breaker
The major reason to study this administrative measure, implementation of circuit
breaker, is because of the growing importance of Chinese stock market domestically
and internationally. In local perspective, stock market serves as an important platform
for companies to raise equity from a large pool of investors and as a market for
investors to later sell their shares thereby increasing economic growth and improving
living standard. In international aspect, different stock markets are now having greater
influences on one another under globalization.
China, as a fast-growing developing country, is experiencing a stage of sprouting
number of non-state owned firms and companies. According to Wall Street Journal,
China‟s private sector includes 40 million companies and accounts for 80% of the
country‟s jobs and more than 50% of the economic output. (Wei, 2011) Private
enterprises‟ development is very important for the whole economy‟s development and
sustainability.
There are three major sources for private-owned enterprises (POEs) to raise capital,
which are bank financing, shadow banking and public financing. First, bank financing,
in general, favors the larger state-owned companies (SOEs). They are given priority in
the approval process. SOEs have been a tool for central government to carry out
macro-economic adjustment in practical level and they act as pioneers to new
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administrative measures. Therefore, SOEs, comparatively, receive bank loans approval
without much obstacle. On the other hand, Chinese government places stricter loan
quotas and approval criteria in the case of private enterprises as there are more
uncertainties on POEs. Moreover, it is undoubtable that „guanxi‟, or relationship, plays
a crucial role in successful loan grant from banks. For example, tapping into the right
network for fund-raising, having exclusive channels of deal sourcing, having
relationships with the government for receiving approvals in many instances etc., can
affect the future of one company. (Yong, 2012) Therefore, apart from stricter
regulations of bank loans, POEs without network have great obstacles to grant loans
from banks.
Second, SOEs backed by Chinese government, who easily gained 60% of bank loans,
started to further offer loans to POEs to generate income. (Sheng, 2016) The term
„shadow banking‟ was generated. In supply side, SOEs have the resources to provide
further loans to other parties as mentioned. In demand side, POEs have limited access
to bank loans and are willing to risk to borrow money from unofficial channels for
business development. Thus, they only borrow at higher interest rates – often from the
shadow banking sector – to finance their investments and their cash flow needs. The
emergence of shadow banking is one of the sources for POEs to capture capital but the
potential problem causes danger of unsustainability. While such source of capital
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raising is not under government‟s regulation, borrowing with abnormal high interest
rate does not guarantee a safe fund raising source for stable business development.
Third, public financing includes capital collection from friends and listing the
companies to collect capital in stock market. While stock market is one of the few
channels for private enterprises to collect capital publicly compared to wide sources of
capital collection of SOEs, it is essential to investigate the issue to ensure Chinese stock
market is healthy and stable for private enterprises to collect capital.
Circuit breaker does not only contain financial power but also brings different effects
to traders. Apart from restricting what traders do when it is triggered, it also has
psychological power. It can change how people think about the stock market. The sign
can be interpreted with a positive or negative mind that affects the trend of stock market.
It also contains political power as the central government can decide the upper and
lower limit and which market will adopt it. (Shanghai Stock Exchange, 2015)
The topic of circuit breaker is important not only because it brings lots of effects on
stock market. It should be investigated because stock market is closely related to other
sectors and economics issues in China. For example, insurance and banking sector
invest in stock market to earn profit for further investment. It potentially brings
„butterfly effect‟ once the stock market is adversely affected by circuit breaker.
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2. Review on Chinese stock market
2.1 Historical development of Chinese stock market
Evolution of Chinese stock market started in 1980s when Chinese bonds were first
issued to individuals by Chinese government. In the period of reform and opening-up,
which started in 1978, over hundreds of companies issued corporate bonds and stocks.
(Shanghai Stock Exchange, 2015) However, inefficient management of SOEs due to
low effectiveness of execution of laws and corruption led to heavy debts of SOEs.
Central government could not tolerate heavy loss and shareholding system started to
develop to reform the financial system.
Companies started to issue corporate securities to employees, which were not
transferable but with a guaranteed rate of interest. Starting from January 1987, two
SOEs were allowed to issue shares to the public. Besides the state-controlled shares,
local governments, governmental departments, enterprises and institutions were
allowed to own its equities, but not individuals. These equities cannot be traded. Such
low stock liquidity made it very difficult for shareholding companies to market their
initial offerings.
Due to the necessity to transfer and trade the stocks, a stock exchange market was
gradually formed in 1985. On 26th November 1990, the Shanghai Securities Exchange
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(SSE), China‟s first recognized stock exchange, officially opened and listed 8 shares.
Later, the Shenzhen Stock Exchange (SZSE) began its trading of its first 5 listed
companies on 11st April 1991. (Shanghai Stock Exchange, 2015) Starting from then,
central government gradually allowed more companies to list their stocks to raise
funds and more individual investors started to buy and sell stocks which facilitate
economic development.
Under communism, the principle of stability is very important. The stock market was
not a platform for capital marketization but a tool to regulate capital flow. Stock market
allows more frequent and larger volume of capital flow but the implementation of
circuit breaker is a sign that Chinese government still closely monitors the stock
market.
2.2 Current Chinese stock market
2.2.1 Shanghai Stock Exchange (SSE)
Shanghai Stock Exchange (SSE) issues two types of stocks, namely „A Share‟ and „B
Share‟. „A Share‟ is priced in yuan and „B Share‟ is priced in US dollars. Local investors
mainly trade „A Share‟. Foreign investors were only allowed to sell and buy „B Share‟
before 2012. Starting from 2012, foreign investors were allowed (with limitations) to
trade in „A Share‟ under the Qualified Foreign Institutional Investor (QFII) program.
Foreign investments in China are restricted due to foreign exchange control. The quota,
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products, accounts, and fund conversions are strictly monitored and regulated. As of
February 2016, a total of 279 foreign institutional investors have been approved to buy
and sell „A shares‟ under the QFII program with a quota of US$80.795 billion only.
(SSE, 2015)
2.2.2 Shenzhen Stock Exchange (SZSE)
There are also „A Share‟ and „B Share‟ in Shenzhen Stock Exchange (SZSE). For „A
Share‟, companies can list on Main Board, Small and Medium Enterprises board (SME
Board) or CHiNext. Smaller private firms who aim at attracting smaller amounts of
capital financing mainly list their stocks in SZSE. The exchange achieves this through
the SME Board, which focuses on supporting small and medium enterprises with
well-defined core business, growth potential and hi-tech contents; and the ChiNext,
which focuses on development of innovative enterprises and other growing start-ups.
This helps to stimulate and facilitate innovation within the Chinese economy by
facilitating the growth of new, high-potential enterprises in these fields.
In order to attract more investment, the regulations of SME board and ChiNext are
less straight compared to the listing on the Main Board. Therefore, the capital raised
tends to be significantly smaller than those from the Main Board. Nevertheless, small
private enterprises that are looking for financing their early-stage growth will tend to
list in SZSE. (SZSE, 2015)
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2.2.3 Operation Hour and other arrangement of Chinese stock market
For both Exchange markets, they have the same operation hour. As of April 2017, the
morning session begins with centralized competitive pricing from 09:15 to 09:25, and
continues with consecutive bidding from 09:30 to 11:30. This is followed by the
afternoon consecutive bidding session, which starts from 13:00 to 15:00.
Before the implementation of circuit breaker, there is general daily price limit. Both
stock markets impose the daily price limit on trading of individual stocks, with a daily
price up or down with limit of 10%. A daily price up or down of limit of 5% for stocks
under special treatment (ST shares or *ST shares) is imposed. The price limit is
calculated as follows:
Price limit = previous closing price × (1 price up or down limit percentage)
2.3 What is CSI 300 Index?
The index is compiled by the China Securities Index Company, Ltd. The short form
of China Securities Index, which is CSI 300 Index, consists of 300 stocks with the
largest market capitalization and liquidity of listed A Share companies in China.1
Launched on 8th April 2005, the index aims to measure the fluctuation and
performance of all the A Shares traded on the Shanghai and Shenzhen stock exchanges.
1 Refer to Appendix 2
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(Bloomberg, 2016) CSI 300 Index is designed as performance benchmarks and as basis
for derivatives innovation and indexing.
CSI 300 Index includes all the A Shares listed at the two exchanges satisfying two
conditions. The first consideration is listing time. The listing time of a Non-ChiNext
Stocks should be more than 3 months unless the daily average market value of a stock
since its initial listing is ranked top 30 in all the A Shares. For ChiNext stocks, the
listing time of a stock should be more than 3 years. Second, the stocks must be
non-special treatment stocks (ST stocks) or non-suspension stocks from listing.
This cross-market index is more representative than single-market index.
Cross-market index better represents the market, as it can reflect the general
fluctuations of the A Shares market more comprehensively.
2.4 Circuit breaker in Chinese stock market
Chinese stock market is dominated by small and medium retail investors and has
relatively high market volatility. Since June 2015, abnormal fluctuations occurred in
Chinese stock market. The dramatic rises and falls of the stock price alarms central
government. With the guidance of China Securities Regulatory Commission (CSRC),
SZSE, SSE and China Financial Futures Exchange (CFFEX) introduced the circuit
breaker mechanism to protect investors‟ interests and promote the long-term sound
development of China‟s capital market.
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2.4.1 Mechanism of circuit breaker
The mechanism in China is comprised of price limits and trading halts. The value of
threshold, acted as price limits, is determined based on analysis of historical data. When
the CSI 300 Index increases or decreases by 5% compared to its close on the previous
trading day, the trading of A Shares will be halted for 15 minutes. It aims at meeting
both needs of providing a cooling-off period and of maintaining normal trading. A call
auction will take place before the resumption of continuous auction session. If the
circuit breaker does not last 15 minutes when the morning session ends, the remaining
time of trading halts will continue in the afternoon session. After the 15-minute
suspension, SZSE and SSE will continue auction trading following the call auction
order matching.
If, during the continuous auction session, any increase or decrease of the CSI 300
Index on the same trading day reaches 7% compared to its close on the previous trading
day, the trading of A Shares will be suspended for the rest of the trading day. Such
measure is to prevent against major abnormities. A 7% rise or fall in CSI 300 Index
price often implies drastic market volatility and extreme systematic risk. Therefore, the
market needs a longer cooling-off period to avert market panic from inducing more
severe market fluctuations. It means the two-way circuit breaker can only be reached at
most once in intraday trading.
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Another condition for the suspension of market until it closes is when the 5%
threshold is triggered at or after 14:45. Chinese stock market often experience dramatic
volatility during the closing session. The arrangement of halting trading till market
close for triggering the 5% threshold at or after 14:45 will help prevent unusual market
movement risks during the closing session.
Suspension of A Shares trading means all stock and stock related products are
delayed. Investors can only apply non-trading businesses such as new shares issuance,
rights issue, and voting.
2.4.2 Timeline of implementation of circuit breaker
The consultation on drafting the circuit breaker started in September 2015. On 4th
December 2015, CFFEX announced that circuit breaker to be in force starting from 1st
January 2016. (CFFEX, 2016) Since 1st January 2016 (Friday) was New Year public
holiday, the stock market was closed. (SZSE, 2015) When the Chinese Stock market
operates at 09:15 on the first day of trading day in 2016, which is 4th January 2016
(Monday), circuit breaker started to operate.
On 4th January 2016, circuit breaker was triggered for the first time, 5% decrease
started from 13:13 and the stock market was halted until 13:28. After a while, 7%
decrease started at 13:34 and market was closed for the whole day. Figure 1 shows the
last price and trading volume on 4th January 2016.
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Figure 1 CSI 300 Index on 4th January 2016
Further on 7th January 2016 (Thursday), circuit breaker was triggered for the second
time. 5% drop happened at 09:45 and ended at 09:57. From Figure 2, it is observed that
further drop of 2% led to ceased of trading immediately.
The same day after the second time of trigger with 7% threshold, the authority
announced that circuit breaker would be suspended since 8th January 2016 (Friday).
The execution of circuit breaker in Chinese stock market only lasted for 4 trading days
when the mechanism was removed from Chinese stock market.
Figure 2 CSI Index on 7th January 2016
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3. Review on other countries’ circuit breaker
3.1 US stock market
Circuit breaker was first used in the US. There are three thresholds. If the market
drives the Standard & Poor 500 Index (S&P 500 Index) down 7% from previous day‟s
close, the market will be halted for 15 minutes. Further drop to 13% will cause a
suspension of another 15 minutes. If a drop of 20% happnes, the trading session will be
terminated. This intervention in price determination prevents fluctuations from creating
excessively high prices or a price collapse which happened in the market crash of
October 1987. (D. Rutherford, 2013)
On 19th October 1987, the US equity market suffered its largest single-day
percentage decline in history. The S&P 500 Index fell by 57.86 points, a decline of
20.46%. The Dow Jones Industrial average suffered a similar decline, falling by 508
points, 22.6% of its value. The Nasdaq Stock Market (NASDAQ) fell by 46 points,
11.35% of its value. (McKeon, R. & Netter, J., 2009)
Noted that the downturn did not happen suddenly, the down side started on 6th
October 1987 and it fell continuously over the next two trading weeks. Starting from
14th October 1987, the Dow Jones Index fell by 95 points, 58 points and 108 points on
successive day and the historical drop happened. (Bank of England, 1988) The effect
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did not only happen in the US but also affect stock markets in other countries.
There were different versions for the cause of the event. One is the efficient market,
that the market reacted to some fundamental news and led market participants to
revalue stocks down by more than 20% in one day. Second one is that liquidity declined
significantly, which made the stock price dropped. Third one is some irrational trading
on some panicking news that significantly depressed prices. (McKeon & Netter, 2009)
The above incident was a turning point for the US government to set a more
comprehensive institution for stock market to function properly and started to use
circuit breaker to avoid excessive price fluctuation and prevent future crashes. Until
now, the stock market in the US is still adopting the circuit breaker mechanism.
3.2 Taiwan stock market
Circuit breaker mechanism was imposed in the Taiwan Stock Exchange (TSE) since
the Exchange‟s debut in the 1950s. It served as stabilizing the equity market. There
were a few revisions for the setting of price limit. The threshold started at a level of 5%
from January 1979 to October 1987. Since then, there were three revisions in less than
three years. The first change was reduced to 3% on 27th October 1987. The second
change is a raise to 5% on 14th November 1988, and the third change was an increase
further to 7% on 11st October 1989.
The circuit breaker allows each stock listed in the TSE to fluctuate on any given
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trading day within the pre-specified percentage level above or below the previous day‟s
closing price for that stock. Instead of declaring a market halt, trading (if any) continues
at the ceiling or floor price until the demand and supply conditions are reversed, or until
the closing of the trading day. (Chen, 1993)
3.3 India stock market
The National Stock Exchange of India (NSE) implemented index-based market-wide
circuit breakers with effect from July 2002. There are three stages of the index
movement, either way movement at 10%, 15% and 20% level based on the previous
day's closing level. The circuit breaker brings about a coordinated suspension in all
equity and equity derivative markets nationwide. The market-wide circuit breaker is
triggered by movement of either the Bombay Stock Exchange (BSE Sensex) or the
National Stock Exchange of India's benchmark stock market index (Nifty 50),
whichever is breached earlier.
A rise or fall of 10% triggers a market suspension for 45 minutes if the movement
takes place before 13:00. If the movement happens between 13:00 and 14:30, the
suspension will last for 15 minutes. In case the movement takes place on or after 14:30,
there will be no market suspension.
A rise or fall of 15% will cause suspension for 105 minutes when the movement takes
place before 13:00. If the 15% fluctuation started between 13:00 and 14:00, the market
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will be stopped for 45 minutes. If the movement happens on or after 14:00, the market
will be closed for the rest of the day. For the last level, fluctuation of 20% level at any
time during market hour will lead to an immediate close of the market for the day. (NSE,
2014)
Below is the summary table of the three stock markets with circuit breaker.
Country Benchmark Threshold Duration
the US S&P 500 Index
-7% 15 min
-13% 15 min
-20% Market closed
Taiwan TSE 7%
Trading continues
at the ceiling or floor price
India BSE Sensex or
Nifty 50
10%
45 min before 13:00
15 min between 13:00 – 14:30
No trading halts after 14:30
15%
105 min before 13:00
45 min between 13:00 – 14:00
Market closed after 14:00
20% Market closed
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4. Literature Review
Price limits, trading halts and circuit breaker are widely used in stock exchanges all
over the world. Moser (1990) stated that there are three types of circuit breaker. The
first, the order-imbalance circuit breaker happened when orders to buy or sell threaten
the viability of the specialist. The second type, volume-induced circuit breaker is
triggered when volume effect pushes trading costs to uneconomic levels. The third type,
price-limit circuit breakers closes markets when a given price level is reached. In this
section, we will focus on the type that Chinese stock market implemented, which is the
third type, following by different perspective of viewing the pros and cons of
implementation of circuit breaker.
Circuit breaker was first introduced in „Report of the Presidential Task Force on
Market Mechanism‟. (Brady, 1988) Circuit breaker mechanisms involve trading halts
and price limits. The circuit breaker launched in China is market-wide circuit breaker
that halts trading on the entire market when CSI 300 Index breaches a pre-specified
level.
In the report, it listed out the advantages and disadvantage of circuit breaker
respectively. The major benefit is to allow everyone to digest sudden information and to
cushion violent movements in the market. The possible disadvantage is that it may
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hinder trading and hedging strategies. For example, trading halts may lock investors
from exiting the market. Greenwald and Stein (1988) pointed out that circuit breaker
may disturb market stability if the mechanism is not well designed.
Here are some findings and argument of price limits and trading halts from different
scholars to better understand the potential outcome of circuit breaker in China.
Brady (1988), who supported the mechanism, suggested that price-limit circuit
breaker limits credit risks and loss of by providing a cooling-off period for investors to
digest market information and to decide new investment strategy. Another benefit is
that news can be publicized in time and act as a pause to prevent public panic from
sudden news. This is also the goal that the US decided to launch the first-ever circuit
breaker after the 1987 Stock Market Crash.
However, some argue that even though price limits can stop the price of a share from
free falling on the trading day when a shock hits, the price will continue to move toward
equilibrium as new limits are established in subsequent trading periods. Chen (1993)
proved with the data from TSE that price limits did not provide a cool-off effect. He
compared stock volatility over three different price limit regimes and performed
bivariate regressions. Except for the case of the tightening of price limits in October
1987, price limits prolonged price reaction as implied in longer serial correlations.
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Other scholars concluded price limits, which is included in circuit breaker
mechanism, face four potential problems. They are volatility spillover, delayed price
discovery, trading interference and magnet effect. It is stated that volatility does not
return to normal levels as quickly as the stocks did not reach price limits (volatility
spillover hypothesis) (Kim, 2001; Kim & Rhee, 1997); price continuations occur more
frequently than for stocks that did not reach limits (delayed price discovery hypothesis)
(Veld-Merkoulova, 2003); trading activity increases on the day after the price limit day,
while all other stock sub-groups experience drastic trading activity declines (trading
interference hypothesis) (Wong, Chang & Tu, 2009); and the price accelerates toward
the limits as it gets closer to the limits. (Cho, 2003)
After introducing price limits, trading halt which is also included in circuit breaker
is discussed. Trading halts represent a system of temporary (usually not exceeding 1-
2 hours) breaks in the trading process, established by the exchange and triggered by
the movements of asset prices or stock indices outside the pre-specified boundaries.
After the market is reopened, new boundaries are established. (Lee, Ready & Seguin,
1994)
To sum up, Subrahmanyam‟s work can provide a framework on investigating the data
of CSI 300 Index in next section. Subrahmanyam (1994) proposed “ex ante” effect. It is
a one period, one-market model with discretionary liquidity traders who have the
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flexibility to choose the time when to execute their trades. It showed that if the price is
close to the limit, price variability and market liquidity might increase because
discretionary traders have an incentive to trade earlier, rather than split their trades
across time and run the risk of being unable to trade. It provides one possibility on the
movement of volume and volatility change of CSI 300 Index in the following
regression.
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5. Data
Data is exported from Bloomberg Terminal. It is a computer system provided by the
financial data vendor Bloomberg L.P. that enables professionals in finance and other
industries to access the Bloomberg Professional service through which users can
monitor and analyze real-time financial market data and place trades on the electronic
trading platform.
Last price, trading volume and high & low price of CSI 300 Index are collected
from 1st May 2005 to 8th November 2016. There is a total number of 2800 trading
days and 2800 observations are obtained for regression.
Last price and trading volume obtained from Bloomberg can be directly used for
analysis. For volatility, there are two ways to calculate. The first one is to calculate the
difference of high price and low price and the second one is to use the mean as
volatility. While using the mean as volatility will cause overlapping in time series, this
paper will use the difference between high and low price as volatility. The calculation
of volatility is as the following:
Volatility = High price – low price (1)
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6. Methodology and Empirical Results
6.1 Regression on Last Price
6.1.1.1 Regression on Last Price with first difference
Last price of CSI 300 Index from May 2005 to Nov 2016 is a time-series data, which
is a set of observation of the same variable at discrete and equally spaced time intervals.
From Figure 3, it is observed that the data is non-stationary series. In order to study
the data of last price (Pt) with regression, the data set is taken first difference to make
the series stationary first:
∆Pt = Pt – Pt-1 (2.1)
0
1000
2000
3000
4000
5000
6000
1/5
/200
5
1/1
0/2
005
1/3
/200
6
1/8
/200
6
1/1
/200
7
1/6
/200
7
1/1
1/2
007
1/4
/200
8
1/9
/200
8
1/2
/200
9
1/7
/200
9
1/1
2/2
009
1/5
/201
0
1/1
0/2
010
1/3
/201
1
1/8
/201
1
1/1
/201
2
1/6
/201
2
1/1
1/2
012
1/4
/201
3
1/9
/201
3
1/2
/201
4
1/7
/201
4
1/1
2/2
014
1/5
/201
5
1/1
0/2
015
1/3
/201
6
1/8
/201
6
Las
t P
rice
(R
MB
)
Figure 3 Last Price of CSI 300 from May 2005 - Nov 2016
Note: Red box marked dates after 1st Jane 2016
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Figure 4 shows a set of stationery data which is suitable for regression. While the
major finding of this paper is to study how circuit breaker causes effects, Chow test is
used in all three sets of data. Dummy variable (Dt) is introduced in the equation as the
existence of circuit breaker.
Chow test is used to find out if circuit breaker is acted as structural break to the
Chinese stock market. It is a time series analysis to test whether the coefficients in two
linear regressions on different data sets are equal. If circuit breaker is statistically
significant, the summation of coefficient of ∆Pt and ∆Pt-1 should be different from the
coefficient of original linear regression of the whole set of data without Dt.
According to Chow (1960), the procedure is divided into two sub-periods by first
estimate the parameters for each sub-period, and test the equality of the two sets of
parameters using F test. Noted that the original Chow test is that breakdate must be
known. Either to pick an arbitrary candidate breakdate or to pick a breakdate based on
some known feature of the data. Such condition is solved by Andrews and Fair (1998).
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6
Las
t P
Ric
e (R
MB
)
Figure 4 Change in Last Price of CSI 300 from May 2005 - Nov 2016
Note: Red box marked dates after 1st Jane 2016
29
In this case, all regressions take the first implantation date of circuit breaker, 4th
January 2016, as the breakdate unless other specified.
Under such condition, 206 out of 2800 data are added Dt. The equation is taking the
following form with ∆Pt is the last price with first difference and the dependent variable
∆Pt and ∆Pt-1 is the auto-correlation of ∆Pt:
∆Pt = α + β1 ∆Pt-1 + β2 (∆Pt-1*Dt) + ε (2.2)
Before 1st Jan 2016: ∆Pt = α + β1 ∆Pt-1 + ε
After 1st Jan 2016: ∆Pt = α + β1 ∆Pt-1 + β2 (∆Pt-1*Dt) + ε
∆Pt = α + (β1 + β2) ∆Pt-1 + ε
6.1.1.2 Empirical Result of Last Price with first difference
Table 1.1 Result of Last Price with first difference (Equation 2.2)
Number of Observation: 2800
Dependent Variable: ∆Pt
Period with Dt: 4th Jan 2016 – 8th Nov 2016
Adjusted R-square: 0.0034
Independent Variables Coefficient P-value
Intercept -0.80 0.48
∆Pt-1 0.054#
0.0055
∆Pt-1*Dt1 -0.22# 0.011
Note: # represents P-value is statistically significant at 5% significance level.
There are two interesting findings in the result. The first one is the statistically
significant at 5% significance level of ∆Pt-1 and ∆Pt-1*Dt. Another interesting result is
30
the negative value in ∆Pt-1*Dt. With the summation of β1 and β2, it has the value of
-0.166. It means the increase in last price of today may result in decrease of last price of
the following day.
The statistically significance of ∆Pt-1 and ∆Pt-1*Dt is abnormal in a sense that it
violates the „Efficiency market hypothesis‟. (Fama, 1970) It is believed that stock
markets are very efficient in reflecting information about individual stocks and the
whole stock market. When information exists, news spread quickly and is incorporated
into prices of stock without delay. Therefore, neither technical analysis, which is the
study of past stock price in attempt to predict future prices, nor fundamental analysis,
which is the analysis of financial information such as company earnings and assets
values to select 'undervalued' stocks, would enable an investor to achieve greater
returns. (Malkiel, 2003)
However, change in last price of CSI 300 Index shows there is positive relationship
between the price of today and the day before. The violation of „Efficiency market
hypothesis‟ can be partly explained by unique characteristics of Chinese stock market.
Major investors are domestic individual investors, who lack significant knowledge and
experience in investments when compared to institutional investors. Herding behavior,
which is a behavioral tendency for an investor to follow the actions of others, exists in
higher probability. (Tan, Chiang, Mason & Nelling, 2008)
31
6.1.2.1 Regression on Last Price with detrend
As the result of last price with first difference shows the violation of „Efficiency
market hypothesis‟, another method is used to check whether the result is abnormal. As
mentioned earlier, that last price of CSI 300 Index is time-series data. Apart from using
first difference to make it stationary, another method is to detrend the data. There are
two steps to reach the result and the result will be compared with the data set with first
difference. The first equation, to detrend the data, is used to obtain the intercept and
coefficient of time (T) in order to generate the third equation for regression. The first
and second equation are taking the following form:
Pt = α + β3*T + ε (3.1)
Pt_detrend = Pt – α – β3*T (3.2)
Figure 5 shows the data with detrend and is ready for regression using Chow test. The
equation for regression with detrend data is taking the following form:
Pt_detrend = α + β4 Pt-1_detrend + β5 (Pt-1_detrend*Dt) + ε (3.3)
-2000
0
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6
Las
t P
rice
(R
MB
)
Figure 5 Last Price of CSI 300 from May 2005 - Nov 2016 (Detrend)
Last Price Last Price_detrend
Note: Red box marked dates after 1st Jane 2016
32
6.1.2.2 Empirical Result of Last Price with detrend
Note: # represents P-value is statistically significant at 5% significance level.
Similar to the result of last price with first difference, both coefficient of Pt-1_detrend
and Pt-1_detrend *Dt are statistically significant at 5% significance level. While similar
abnormal result, violation of „Efficiency market hypothesis‟, is also concluded after the
regression, this unique feature of Chinese stock market is worth considering when
designing different mechanism to maintain a stable Chinese stock market.
Table 1.2 Result of Last Price with detrend (Equation 3.3)
Number of observation: 2800
Dependent Variable: Pt_detrend
Period with Dt : 4th Jan 2016 – 8th Nov 2016
Adjusted R-square: 1.00
Independent Variables Coefficient P-value
Intercept -0.51 0.68
Pt-1_detrend 1.00#
0
Pt-1_detrend *Dt -0.086# 0.0078
33
6.2 Regression on Trading Volume
6.2.1.1 Regression on Trading Volume with first difference
From Figure 6, it is observed that the data is non-stationary series. The time series
data with first difference is used to facilitate regression to test weather volume is
significantly affected by circuit breaker. The equation to take first difference is taking
the following form:
∆Vt = Vt – Vt-1 (4.1)
0
1E+10
2E+10
3E+10
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6Tra
din
g V
oum
e (
RM
B)
Figure 6 Trading Volume of CSI 300 from May 2005 - Nov 2016
-2.2E+10
-1.7E+10
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6
Tra
din
g V
olu
me
(RM
B)
Figure 7 Change in Trading Volume of CSI 300 from May 2005 - Nov 2016
Note: Red box marked dates after 1st Jane 2016
Note: Red box marked dates after 1st Jane 2016
34
Figure 7 shows the data of trading volume is stationery and appropriate for
regression. Similar steps are done with last price with first difference. 206 out of 2800
data are added Dt for Chow test. The equation is taking the following form with ∆Vt is
the trading volume with first difference as the dependent variable ∆Vt and ∆Vt-1 is the
auto-correlation of ∆Vt:
∆Vt = α + β6 ∆Vt-1 + β7 (∆Vt-1*Dt) + ε (4.2)
Before 1st Jan 2016: ∆Vt = α + β6 ∆Vt-1 + ε
After 1st Jan 2016: ∆Vt = α + β6 ∆Vt-1 + β7 (∆Vt-1*Dt) + ε
∆Vt = α + (β6 + β7 ) ∆Vt-1 + ε
6.2.1.2 Empirical Result of Trading Volume with first difference
Table 2.1 Result of Trading Volume with first difference (Equation 4.2)
Number of observation: 2800
Dependent Variable: ∆Vt
Period with Dt: 4th Jan 2016 – 8th Nov 2016
Adjusted R-square: 0.046
Independent Variables Coefficient P-value
Intercept 4400000 0.92
∆Vt-1 -0.21#
0
∆Vt-1*Dt -0.074 0.20
Note: # represents P-value is statistically significant at 5% significance level.
The large value of intercept, which is not statistically significant at 5% significance
level, is due to huge number of daily trading volume, which is over 1 billion RMB, of
35
CSI 300 Index.
Only the result of the autocorrelation of ∆Vt, which is ∆Vt-1, is statistically
significant in 5% significance level, which is -0.074. It means that an increase in
change of trading volume of today will lead to a decrease in change of trading volume
of the following day. However, adding dummy variable, which is the incident of circuit
breaker, does not make an effect in the change of trading volume. Therefore, it leads to
another way to investigate in the data set and analyze the result.
6.2.2.1 Regression on Trading Volume with ARCH model
The unstatisfying result from Table 2.1 draws a conclusion that using first difference
merely is insufficient to analyze the data of trading volume. Therefore, auto-regressive
conditional heteroscedastic model (ARCH model) is used to better analyze the data of
trading volume.
ARCH model was first introduced by economist Robert F. Engle in 1982, for which
he won the 2003 Nobel Memorial Prize in Economic Sciences. ARCH model allows
the variance of a regression to change over time and it is used to test variance in a time
series. It can be used to describe a volatile variance in which there is short period of
increased variation. (Engle, 1983)
Under normal condition, shape of residuals is in bell shape, which is normal
distribution. However, from Figure 8, the abnormal of distribution of residuals is
36
observed. It is also observed, from Figure 8, that not individual residuals are abnormal
but clusters of them are.
The model explicitly recognizes the difference between the conditional and
unconditional variance; the conditional variance may depend upon random variables in
the conditioning set such as the past disturbances, while the unconditional variance
would often be a constant as traditionally assumption. (Engle, 1983)
In practical, square of the whole formula is to avoid negative variance. The basic
version of the least squares model assumes that, the expected value of all error terms
when squared, is the same at any given point. This assumption is called homoskedasticity.
Data in which the variances of the error terms are not equal, in which the error terms may
reasonably be expected to be larger for some points or ranges of the data than for others,
are said to suffer from heteroskedasticity. (Engle, 2001)
ARCH model assumes that the variance of the current error term is related to the size
of the previous periods' error terms, giving rise to volatility clustering. The statement
-2E+10
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6
Res
idual
of
Vo
lum
e (R
MB
)
Figure 8 Residual of Trading Volume of CSI 300 from May 2005 - Nov 2016
Note: Red box marked dates after 1st Jane 2016
37
from McNees, stated, „The inherent uncertainty or randomness associated with
different forecast periods seems to vary widely over time.‟ and „Large and small errors
tend to cluster together (in contiguous time periods).‟ (NcNees, 1979) He suggested that
data in time series can be affected by previous data and this is the reason for using
ARCH model to analyze the fluctuation of trading volume.
Here residuals are investigated. Assume that the forecast of today's value based upon
past information, it means Vt depends upon the value of Vt-1. the approach of
heteroscedasticity is to introduce an exogenous variable, that is the incident of circuit
breaker.
The first equation to find the intercept α and coefficient β8, so as to compute
residuals (Rt) is taking the following form:
Vt = α + β8 Vt-1 + ε (5.1)
Rt = Vt - α - β8 Vt-1 (5.2)
After generating a new set of data with the residuals of trading volume using
equation 5.2, equation 5.3 is taking the following form with Rt2, the residual of
trading volume, as independent variable and the dependent variable Rt-12 and dummy
variable Dt, while taking square to avoid negative variance:
Rt2 = α + Dt
2 + β9 Rt-1
2 + ε (5.3)
38
6.2.2.2 Empirical Result of Trading Volume with ARCH model
Table 2.2 Result of Trading Volume with ARCH model (Equation 5.3)
Number of observation: 2800
Dependent Variable: Rt2
Period with Dt: 4th Jan 2016 – 8th Nov 2016
Adjusted R-square: 0.12
Independent Variables Coefficient P-value
Intercept 3.75E+18# 0
Dt2 3.32E+18 0.067
Rt-12 0.36
# 0
Note: # represents P-value is statistically significant at 5% significance level.
The large value of intercept and Dt2
are due to huge number of daily trading volume
of CSI 300 Index, which is over 1 billion RMB, and the square of the residuals to
avoid negative variance.
With ARCH model, the intercept and change in residual of trading volume are
statistically significant in 5% significance level, with the coefficient as 3.75E+18 and
0.36 respectively. The result shows that circuit breaker causes a positive change on
trading volume.
However, the insignificance of dummy variable is unexpected. The major reason
requires further statistical investigation but two incidents that happened in 2015 can be
taken as reference to explain the result. From Figure 8, the variance of residual before
1st January 2016 (Especially between May and September 2015) has been moving
39
frequently, even more volatile than the period after 1st January 2016. The first incident
is the free fall of A-share market in China from June to August in 2015. When the
market regulator tightened leverage norms, it wiped out $5 trillion RMB of market
value and prompted the regulator to launch an unprecedented rescue. That is the
possible reason why Chinese government decided to launch circuit breaker mechanism
after that.
The second incident which brings to the result is Communist Party of China (CPC)
Fifth Plenary Session. (Song Wei, 2015) It was held from 26 to 29 October 2015 and
economic development was focused in response to slowdown of development during
the year before. The party announced several important administrative decisions such
as raising quality and efficiency of energy output. (Zhang Hui, 2015) The stock market
responds to the news and cause fluctuation.
The above mentioned incidents are possible explanation that even the change in
residuals of trading volume of CSI 300 is observable from Figure 8, it is statistically
insignificant.
40
6.3 Regression on Volatility
6.3.1.1 Regression on Volatility with ARCH model
Referring to Figure 9, the abnormal distribution of volatility is similar to Figure 8
with clusters of volatility in the data set of trading volume. With the big variance
change, ARCH model is used to compute the residual. With steps are similar to the
computation of trading volume, regression is done on the residuals to find out how
volatility (vt) responds to the circuit breaker.
The first equation which finds the intercept α and coefficient β10 is used to compute
residuals (rt) is taking the following form:
vt = α + β10 vt-1 + ε (6.1)
rt = vt – α – β10 vt-1 (6.2)
0
50
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6
Vo
lati
lity
(R
MB
)
Figure 9 Volatility of CSI 300 from May 2005 - Nov 2016
Note: Red box marked dates after 1st Jane 2016
41
After generating a new set of data with the residual of volatility using equation 6.2,
it is observed from Figure 10 that the residual of volatility is suitable for regression
using Chow test. Equation 6.3 is taking the following form with rt2 is the residual of
volatility as dependent variable and the independent variable rt-12, while taking square
to avoid negative variance:
rt2 = α + Dt
2 + β11 rt-1
2 +β12 (rt-1*Dt)
2 + ε (6.3)
For this set of data, dummy variable is put in three different periods to find out how
different dates have different effects on residual of volatility. The first one is from 4th
Dec 2015 to 7th Jan 2016, which starts from announcement date of circuit breaker to
the last day of circuit breaker; following the period between 4th Jan 2016 and 7th Jan
2016, which is the period of execution date; and the last one is between 4th Jan 2016
and 8th Nov 2016, which the dummy variable is also added to the dates after circuit
breaker was stopped on 7th Jan 2016.
Of the three periods, the regression of period 1 and 3 are the same, but period 2 is
-190
-140
-90
-40
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/201
6
Res
idual
(R
MB
)
Figure 10 Residual of Volatility of CSI 300 from May 2005- Nov 2016
Note: Red box marked dates after 1st Jane 2016
42
different. Independent variable, (rt-1*Dt)2, is excluded in period 2 since only 4 data are
added dummy variable and it is insignificant.
6.3.1.2 Empirical Results of Volatility with ARCH model
Regression Result with dummy variable in period 1:
Table 3.1 Result of Volatility with ARCH model (Period 1) (Equation 6.3)
Number of observation: 2800
Dependent Variable: r2
Period with Dt: 4 Dec 2015 – 7 Jan 2016
(Announcement date to last day of circuit breaker)
Adjusted R-square: 0.035
Independent Variables Coefficient P-value
Intercept 1200# 0
Dt2 3000
# 0.0038
rt-12 0.19
# 0
(rt-1*Dt)2 -0.23
# 0.046
Note: # represents P-value is statistically significant at 5% significance level.
Intercept and all other independent variables are statistically significant at 5%
significance level. The positive coefficient of dummy variable, which is 3000, means a
great effect of circuit breaker to volatility of CSI 300 Index starting from announcement
date to the last day of execution of circuit breaker.
However, the summation of β11 (0.19) and β12 (-0.23), which is -0.04, shows a
negative relationship between the effect of circuit breaker and the volatility. It means
volatility is proven to be related to circuit breaker, but negative relationship is recorded.
43
Regression Result with dummy variable in period 2:
Table 3.2 Result of Volatility with ARCH model (Period 2) (Equation 6.3)
Number of observation: 2800
Dependent Variable: r2
Period with Dt: 4 Jan 2016 – 7 Jan 2016
(4 days which circuit breaker was implemented)
Adjusted R-square: 0.042
Independent Variables Coefficient P-value
Intercept 1200# 0
Dt2 13000
# 0
rt-12 0.17
# 0
Note: # represents P-value is statistically significant at 5% significance level.
As aforementioned, independent variable, rt-1*Dt, is excluded in this period since
only 4 data are added dummy variable and the data point is too small to be significant.
Intercept, dummy variable and autocorrelation of residual of volatility are highly
statistically significant with P-value near to 0. The correlation is more significant than
the result in Table 3.1. The positive coefficient of dummy variable, which is 13000, is
over 4 times than dummy variable in period 1. It means that circuit breaker makes most
of its effect on the 4 days of execution date rather than the inclusion of announcement
date. The value of rt-12 is 0.17 and the positive value shows the change in high price and
low price of stock price of CSI 300 is significant in the presence of circuit breaker from
4th Jan 2016 to 7th Jan 2016.
44
Regression Result with dummy variable in period 3:
Table 3.3 Result of Volatility with ARCH model (Period 3) (Equation 6.3)
Number of observation: 2800
Dependent Variable: r2
Period with Dt: 4th Jan 2016 – 8th Nov 2016
(First implementation date to last date of the whole data set)
Adjusted R-square: 0.032
Independent Variables Coefficient P-value
Intercept 1300# 0
Dt2 -180 0.61
rt-12 0.18
# 0
(rt-1*Dt)2 -0.077 0.39
Note: # represents P-value is statistically significant at 5% significance level.
Independent variable which includes dummy variable, that is Dt2 and (rt-1*Dt)
2, are
both statistically insignificant in 5% significance level with the P-value are 0.61 and
0.39. With only the coefficient of rt-12
is significant but the coefficient is insignificant
when Dt is added, it means that circuit breaker only takes its effect short-termly but
does not affect the long term volatility of CSI 300 Index.
45
7. Economic Interpretation
To summarize the empirical result from section 6, there are three major findings
about Chinese stock market stated below.
The first one is the violation of „Efficiency market hypothesis‟ in Chinese stock
market from the regression result of last price.2 One possible explanation is that major
traders in Chinese stock market are individual investors, rather than institutional
investors. Herding behavior, which is a behavioral tendency for an investor to follow
the actions of others, exists in higher probability and this affects the response to
implementation of circuit breaker mechanism.
The second finding is the unexpected insignificance of including dummy variable to
trading volume of CSI 300 Index.3 Although a large change of trading volume is
observed from Figure 7 and 8, the regression result is statistically insignificant. The two
possible reasons are stated. The first one is the free fall of A Share market started in
June 2015, which fluctuation is even larger compared to the launch of circuit breaker
mechanism. The second possible incident which brings to the result is Communist
Party of China (CPC) Fifth Plenary Session, that announced several important
administrative decisions. The above mentioned incidents are possible explanation that
2 Refer to section 6.1
3 Refer to section 6.2
46
even the change in residuals of trading volume of CSI 300 Index is observable from
Figure 8, it is statistically insignificant.
The last finding is the circuit breaker mechanism does not have long term effect on
Chinese stock market. In short term, volatility of CSI 300 Index responses significantly
to implementation of CSI 300 Index. In long term, when dummy variable is added to
the dates after circuit breaker was stopped, it is statistically insignificant.4 One possible
reason is that Chinese traders are familiar with the role of Chinese government in the
stock market. China, that is ruled by communist party, has a government who actively
intervenes in different areas by issuing different administrative measures. To the traders,
the implementation of circuit breaker mechanism is just one of the many other
measures and they are only responsive to it when it is in force. When the mechanism is
stopped, they go back to their normal trading pattern. Another possible reason is that
the implementation date is too short, with only 4 days, that traders could not picture the
long term effect that circuit breaker would bring.
4 Refer to section 6.3
47
8. Limitation
There is one major limitation of the study that may affect the empirical result. The
implementation date of circuit breaker is relatively short. Only 24 data of CSI 300
Index carry the mechanism, which is from the announcement date to the last day of
implementation of circuit breaker. While only 4 data of CSI 300 Index carry the effect
of implementation of circuit breaker, this setting is not sufficient to analyze the long
term effect of the mechanism to Chinese stock market.
48
9. Conclusion and Suggestion
In this paper, with the use of Chow test, regressions are taken with three sets of data
of CSI 300 Index. The three sets of data generate three major findings. The analysis of
last price demonstrates the unique environment of Chinese stock market, which is the
violation of „Efficiency market hypothesis‟; that of trading volume shows the huge
fluctuation of trading volume before circuit breaker mechanism, which tells the reason
why Chinese government launched circuit breaker; that of volatility shows that only
short term effect of circuit breaker take force in Chinese stock market.
9.1 Review on Chinese stock market and suggestion
While using the investigation of circuit breaker mechanism with CSI 300 Index as
an example to examine Chinese stock market allows me to have to broader
understanding the feature of Chinese stock market. First, the evidence of different
nature of Chinese stock market, compared to western stock markets, is showed in the
first and third finding. It means that a different approach should be adopted.
One typical nature of Chinese stock market is that it is a just tool for Chinese
government to develop the economy, but not a free market for free transaction. Under
such setting, together with most individual investors who may have herding behavior,
administrative measures are normal but responsive.
49
With this conclusion, it is important for Chinese government to conduct thorough
research before launching any other administrative measures. One limitation that
Chinese government faces when issuing new regulations or policies is the little public
opinion due to the political culture. This leads to shortening time for public to digest
the news and be prepared to it, compared to a political system with public
consultation. Therefore, Chinese government should provide enough time for the
public to digest all administrative measures before implementation. One simple
method is to announce the measure early and explain the mechanism to the public in
an easy way.
From the perspective of individual traders, it is important to be aware of the
herding behavior of Chinese stock market. Such culture is unhealthy not only to the
whole stock market, but also harms own wealth. One possible solution is to be more
educated with the news of Chinese stock market so that one does not need to be
blindly follow the majority action. This should be achieved with the help of higher
transparency of news and information of the market.
9.2 Review on circuit breaker mechanism and suggestion
Circuit breaker, as one of the administrative measures from Chinese government to
stabilize the stock market, was triggered twice in just four days and ended less than a
week. This unsuccessful measure brings the question whether administrative measures
50
should be used to stabilize the market since, in practical, it may trigger a larger
fluctuation in stock market. If it is used, whether it is effective or it will cause a more
serious herding behavior.
It is difficult to trace the causal relationship between the fluctuation of Chinese
stock market and the existence of administrative measures. In addition, the stock
market is structurally different from western stock markets. Therefore, it is difficult to
conclude whether Chinese government should use administrative measures, just like
other western ones. However, with the herding culture, it is essential to first allow
larger range of fluctuation of price limits if the circuit breaker will be relaunched. In
other words, the 5% and 7% threshold is too tight for such market nature.
With the fact that many mature stock markets have circuit breaker mechanism
included to maintain the sustainability of the stock market, it means circuit breaker
works well in stock market under certain condition. However, from section 2.2.3,
Chinese government has already imposed price limit of ±10% to both Exchanges. It
is questionable on the necessity to impose another similar mechanism with smaller
price limit. Nonetheless, in foreseeable future, China can modify and relaunch the
market-wide circuit breaker mechanism so that it can work according to the aim,
which is to give more time for investors to digest news and avoid irrational trading.
51
Compare to other countries‟ example from section 3 with the summary table, a
larger range of threshold can be adopted. Also, to consider the situation of herding
behavior, more time for individual traders to digest market information is needed.
Therefore, China can take reference to the case of India stock market and consider the
more active trading before the market hour closed. Then, different time period of
suspension can be adopted in morning and afternoon session to allow circuit breaker
to function as a cool-off period.
In conclusion, nature of Chinese stock market is revealed from the study of circuit
breaker mechanism. The result provides a framework to further understand the
relationship between the implementation of administrative measures and development
of a stable stock market in the future.
52
Appendix
1. List of Illustration
1.1 List of Figures
Figure 1: CSI 300 Index on 4th January 2016 -----------------------------------------------17
Figure 2: CSI 300 Index on 7th January 2016 -----------------------------------------------17
Figure 3: Last Price of CSI 300 from May 2005 – Nov 2016 -----------------------------27
Figure 4: Change in Last Price of CSI 300 from May 2005 – Nov 2016 -----------------28
Figure 5: Last Price of CSI 300 from May 2005 – Nov 2016 (Detrend) -----------------31
Figure 6: Trading Volume of CSI 300 from May 2005 – Nov 2016 ----------------------33
Figure 7: Change in Trading Volume of CSI 300 from May 2005 – Nov 2016 ---------33
Figure 8: Residual of Trading Volume of CSI 300 from May 2005 – Nov 2016 --------36
Figure 9: Volatility of CSI 300 from May 2005 – Nov 2016 ------------------------------40
Figure 10: Residual of Volatility of CSI 300 from May 2005 – Nov 2016 --------------40
1.2 List of Tables
Table 1.1: Result of Last Price with first difference ----------------------------------------29
Table 1.2 Result of Last Price with detrend -------------------------------------------------32
Table 2.1 Result of Trading Volume with first difference ---------------------------------34
Table 2.2 Result of Trading Volume with ARCH model-----------------------------------38
Table 3.1 Result of Volatility with ARCH model (Period 1) ------------------------------42
Table 3.2 Result of Volatility with ARCH model (Period 2) ------------------------------43
Table 3.3 Result of Volatility with ARCH model (Period 3) ------------------------------44
53
2. List of Listed companies in CSI 300 Index
Code Constituent Name Exchange Code Constituent Name Exchange
000001 Ping An Bank Shenzhen 600150 China CSSC Holdings Shanghai
000002 China Vanke Shenzhen 600153 Xiamen C&D Inc Shanghai
000009 China Baoan Group Shenzhen 600157 Wintime Energy Shanghai
000027 Shenzhen Energy Group Shenzhen 600170 Shanghai Construction Shanghai
000039 China International Marine
Containers (Group) Shenzhen 600177 Youngor Group Shanghai
000046 Oceanwide Hoodings Shenzhen 600188 Yanzhou Coal Mining Shanghai
000060 Shenzhen Zhongjin Lingnan
Nonfemet Shenzhen 600196
Shanghai Fosun Pharmaceutical
(Group) Shanghai
000061 Shenzhen Agricultural Products Shenzhen 600208 Xinhu Zhongbao Shanghai
000063 ZTE Corporation Shenzhen 600221 Hainan Airlines Shanghai
000069 Shenzhen Overseas Chinese
Town Shenzhen 600252
Guangxi Wuzhou Zhongheng
Group Shanghai
000100 TCL Corporation Shenzhen 600256 Guanghui Energy Shanghai
000156 Wasu Media Holding Shenzhen 600271 Aisino Shanghai
000157 Zoomlion Heavy Industry
Science & Technology Shenzhen 600276 Jiangsu Hengrui Medicine Shanghai
000166 Shenwan Hongyuan Group , Shenzhen 600309 Wanhua Chemical Group Shanghai
000333 Midea Group Shenzhen 600317 Yingkou Port Liability Shanghai
000338 Wei Chai Power Shenzhen 600332 Guangzhou Baiyunshan
pharmaceutical holdings Shanghai
000402 Financial Street Holding Shenzhen 600340 China Fortune Land
Development Shanghai
000413 Dongxu Optoelectronic
Technology. Shenzhen 600352 Zhejiang Longsheng Group Shanghai
000415 Bohai Financial Investment
Holding Shenzhen 600362 Jiangxi Copper Shanghai
000423 Shandong Dong-Ee Jiao Shenzhen 600369 Southwest Securities Shanghai
000425 XCMG Construction Machinery Shenzhen 600372 China Avionics Systems Shanghai
000503 Searainbow Holding Shenzhen 600373 Chinese Universe Publishing
And Media Shanghai
000538 Yunnan Baiyao Group Shenzhen 600376 Beijing Capital Development Shanghai
000540 Zhongtian Urban Development
Group Shenzhen 600383 Gemdale Shanghai
54
000559 Wanxiang Qianchao Shenzhen 600398 Heilan Home Shanghai
000568 Luzhou Lao Jiao Shenzhen 600406 NARI Technology Shanghai
000623 Jilin Aodong Pharmaceutical
Group Shenzhen 600415
Zhejiang China Commodities
City Group Shanghai
000625 Chongqing Changan Automobile Shenzhen 600446 Shenzhen Kingdom Technology Shanghai
000630 Tongling Nonferrous Metals
Group Shenzhen 600485
Beijing Xinwei Telecom
Technology Group Shanghai
000651 Gree Electric Appliances,Inc. of
Zhuhai Shenzhen 600489 Zhongjin Gold Shanghai
000686 Northeast Securities Shenzhen 600518 Kangmei Pharmaceutical Shanghai
000709 Hesteel Shenzhen 600519 Kweichow Moutai Shanghai
000712 Guangdong Golden Dragon
Development Shenzhen 600535 Tasly Pharmaceutical Group Shanghai
000725 BOE Technology Group Shenzhen 600547 Shandong Gold-Mining Shanghai
000728 Guoyuan Securities Shenzhen 600570 Hundsun Technologies Shanghai
000729 Beijing Yanjing Brewery Shenzhen 600578 Beijing Jingneng Power Shanghai
000738 AVIC Aero-Engine Controls Shenzhen 600582 Tiandi Science & Technology Shanghai
000750 Sealand Securities Shenzhen 600583 Offshore Oil Engineering Shanghai
000768 Avic Aircraft Shenzhen 600585 Anhui Conch Cement Shanghai
000776 GF Securities Shenzhen 600588 Yonyou Network Technology Shanghai
000778 Xinxing Ductile Iron Pipes Shenzhen 600600 Tsingtao Brewery Shanghai
000783 Changjiang Securities Shenzhen 600606 Greenland Holdings Corporation Shanghai
000792 Qinghai Salt Lake Industry Shenzhen 600637 Shanghai Oriental Pearl Media Shanghai
000793 Huawen Media Investment Shenzhen 600642 Shenergy Shanghai
000800 FAW Car Shenzhen 600648 Shanghai Waigaoqiao Free Trade
Zone Group Shanghai
000825 Shanxi Taigang Stainless Steel Shenzhen 600649 Shanghai Chengtou Holding Shanghai
000826 Tus-Sound Environmental
Resources Shenzhen 600660 Fuyao Glass Industry Group Shanghai
000839 CITIC Guoan Information
Industry Shenzhen 600663
Shanghai Lujiazui Finance and
Trade Zone Development Shanghai
000858 Wuliangye Yibin Shenzhen 600666 Aurora Optoelectronics Shanghai
000876 NEW HOPE LIUHE Shenzhen 600674 Sichuan Chuantou Energy Shanghai
000883 Hubei Energy Group Shenzhen 600685 Cssc Offshore & Marine
Engineering (Group) Shanghai
000895 Henan Shuanghui Investment &
Development Shenzhen 600688 Sinopec Shanghai Petrochemical Shanghai
55
000898 Angang Steel Shenzhen 600690 Qingdao Haier Shanghai
000917 Hunan TV & Broadcast
Intermediary Shenzhen 600703 Sanan Optoelectronics Shanghai
000963 Huadong Medicine Shenzhen 600704 Zhejiang Material Industrial
Zhongda Yuantong Group Shanghai
000977 Inspur Electronic Information
Industry Shenzhen 600705 Avic Capital Shanghai
000999 China Resources Sanjiu Medical
& Pharmaceutical Shenzhen 600718 Neusoft Shanghai
001979 China Merchants Shekou
Industrial Zone Holdings Shenzhen 600737 Cofco Tunhe Shanghai
002007 Hualan Biological Engineering Shenzhen 600739 Liaoning Cheng Da Shanghai
002008 Han's Laser Technology Industry
Group Shenzhen 600741 HUAYU Automotive Systems Shanghai
002024 Suning Commerce Group Shenzhen 600783 Luxin Venture Capital Group Shanghai
002027 Focus Media Information
Technology Shenzhen 600795 GD Power Development Shanghai
002065 DHC Software Shenzhen 600804 Dr. Peng Telecom&Media Group Shanghai
002081 Suzhou Gold Mantis
Construction Decoration Shenzhen 600816 Anxin Trust Shanghai
002129 Tianjin Zhonghuan
Semiconductor Shenzhen 600820 Shanghai Tunnel Engineering Shanghai
002142 Bank of Ningbo Shenzhen 600827 Shanghai Bailian Group Shanghai
002146 Risesun Real Estate
Development Shenzhen 600837 Haitong Securities Shanghai
002152 GRG Banking Equipment Shenzhen 600839 Sichuan Changhong Electric Shanghai
002153 Beijing Shiji Information
Technology Shenzhen 600863
Inner Mongolia Mengdian
Huaneng Thermal Power Shanghai
002183 Eternal Asia Supply Chain
Management Shenzhen 600867
Tonghua Dongbao
Pharmaceutical Shanghai
002195 Shanghai 2345 Network Holding
Group Shenzhen 600871 Sinopec Oilfield Service Shanghai
002202 Xinjiang Goldwind Science &
Technology Shenzhen 600873 Meihua Holdings Group Shanghai
002230 Iflytek Shenzhen 600875 Dongfang Electric Shanghai
002236 Zhejiang Dahua Technology Shenzhen 600886 SDIC Power Holdings Shanghai
002241 GoerTek Shenzhen 600887 Inner Mongolia Yili Industrial
Group Shanghai
56
002252 Shanghai RAAS Blood Products Shenzhen 600893 Avic Aviation Engine Shanghai
002292 Alpha Group Shenzhen 600895 Shanghai Zhangjiang Hi-tech
Park Development Shanghai
002294 Shenzhen Salubris
Pharmaceuticals Shenzhen 600900 China Yangtze Power Shanghai
002304 Jiangsu Yanghe Brewery
Joint-Stock Shenzhen 600958 Orient Securities Shanghai
002385 Beijing Dabeinong Technology
Group Shenzhen 600959
Jiangsu Broadcasting Cable
Information Network Shanghai
002399 Shenzhen Hepalink
Pharmaceutical Shenzhen 600998 Jointown Pharmaceutical Group Shanghai
002415 Hangzhou Hikvision Digital
Technology Shenzhen 600999 China Merchants Securities Shanghai
002422 Sichuan Kelun Pharmaceutical Shenzhen 601006 Daqin Railway Shanghai
002424 Guizhou Bailing Group
Pharmaceutical Shenzhen 601009 Bank of Nanjing Shanghai
002450 Kangde Xin Composite Material Shenzhen 601016 CECEP Wind-Power Shanghai
002456 Shenzhen O-film Tech Shenzhen 601018 Ningbo Port Shanghai
002465 Guangzhou Haige
Communications Group Shenzhen 601021 Spring Airlines Shanghai
002470 Kingenta Ecological Engineering
Group Shenzhen 601088 China Shenhua Energy Shanghai
002475 Luxshare Precision Industry Shenzhen 601098 China South Publishing & Media
Group Shanghai
002500 Shanxi Securities Shenzhen 601099 The Pacific Securities Shanghai
002568 Shanghai Bairun Investment
Holding Group Shenzhen 601106 China First Heavy Industries Shanghai
002594 Byd Shenzhen 601111 Air China Shanghai
002673 Western Securities Shenzhen 601117 China National Chemical
Engineering Shanghai
002736 Guosen Securities Shenzhen 601118 China Hainan Rubber Industry
Group Shanghai
002739 Wanda Cinema Line Shenzhen 601166 Industrial Bank Shanghai
300002 Beijing Ultrapower Software Shenzhen 601169 Bank of Beijing Shanghai
300003 Lepu Medical Technology
(Beijing) Shenzhen 601179 China XD Electric Shanghai
300015 Aier Eye Hospital Group Shenzhen 601186 China Railway Construction Shanghai
300017 Wangsu Science and Technology Shenzhen 601198 Dongxing Securities Shanghai
57
300024 Siasun Robot & Automation Shenzhen 601211 Guotai Junan Securities Shanghai
300027 Huayi Brothers Media Shenzhen 601216 Inner Mongolia Junzheng Energy
& Chemical Group Shanghai
300058 BlueFocus Communication
Group Shenzhen 601225 Shaanxi Coal Industry Shanghai
300059 East Money Information Shenzhen 601258 Pangda Automobile Trade Shanghai
300070 Beijing Originwater Technology Shenzhen 601288 Agricultural Bank of China Shanghai
300085 Shenzhen Infogem Technologies Shenzhen 601318 Ping An Insurance (Group)
Company of China Shanghai
300104 Leshi Internet Information &
Technology Corp Beijing Shenzhen 601328 Bank of Communications Shanghai
300124 Shenzhen Inovance Technology Shenzhen 601333 Guangshen Railway Shanghai
300133 Zhejiang Huace Film & TV Shenzhen 601336 New China Life Insurance Shanghai
300144 Songcheng Performance
Development. Shenzhen 601377 Industrial Securities Shanghai
300146 By-Health Shenzhen 601390 China Railway Shanghai
300168 Wonders Information Shenzhen 601398 Industrial and Commercial Bank
of China Shanghai
300251 Beijing Enlight Media Shenzhen 601555 Soochow Securities Shanghai
300315 Ourpalm Shenzhen 601600 Aluminum Corporation of China Shanghai
600000 Shanghai Pudong Development
Bank Shanghai 601601 China Pacific Insurance (Group) Shanghai
600005 Wuhan Iron and Steel Shanghai 601607 Shanghai Pharmaceuticals
Holding Shanghai
600008 Beijing Capital Shanghai 601608 Citic Heavy Industries Shanghai
600009 Shanghai International Airport Shanghai 601618 Metallurgical Corporation of
China Shanghai
600010 Inner Mongolia Baotou Steel
Union Shanghai 601628 China Life Insurance Shanghai
600011 Huaneng Power International Shanghai 601633 Great Wall Motor Shanghai
600015 Hua Xia Bank Shanghai 601668 China State Construction
Engineering Shanghai
600016 China Minsheng Banking Shanghai 601669 Power Construction Corporation
of China Shanghai
600018 Shanghai International Port
(Group) Shanghai 601688 Huatai Securities Shanghai
600019 Baoshan Iron &Steel Shanghai 601718 Jihua Group Shanghai
600021 Shanghai Electric Power Shanghai 601727 Shanghai Electric Group Shanghai
58
600022 Shandong Iron And Steel Shanghai 601766 Crrc Shanghai
600023 Zhejiang Zheneng Electric
Power Shanghai 601788 Everbright Securities Shanghai
600027 Huadian Power International Shanghai 601800 China Communications
Construction Shanghai
600028 China Petroleum & Chemical Shanghai 601808 China Oilfield Services Shanghai
600029 China Southern Airlines Shanghai 601818 China Everbright Bank Shanghai
600030 CITIC Securities Shanghai 601857 PetroChina Shanghai
600031 Sany Heavy Industry Shanghai 601866 Cosco Shipping Development Shanghai
600036 China Merchants Bank Shanghai 601872 China Merchants Energy
Shipping Shanghai
600037 Beijing Gehua CATV Network Shanghai 601888 China International Travel
Service Shanghai
600038 AVIC Helicopter Shanghai 601898 China Coal Energy Shanghai
600048 Poly Real Estate Group Shanghai 601899 Zijin Mining Group Shanghai
600050 China United Network
Communications Shanghai 601901 Founder Securities Shanghai
600060 Hisense Electric Shanghai 601919 COSCO SHIPPING Holdings Shanghai
600061 SDIC Essence Shanghai 601928 Jiangsu Phoenix Publishing &
Media Shanghai
600066 Zhengzhou Yutong Bus Shanghai 601933 Yonghui Superstores Shanghai
600068 China Gezhouba Group Shanghai 601939 China Construction Bank Shanghai
600074 Jiangsu Protruly Vision
Technology Group Shanghai 601958 Jinduicheng Molybdenum Shanghai
600085 Beijing Tongrentang Shanghai 601985 China National Nuclear Power. Shanghai
600089 Tbea Shanghai 601988 Bank of China Shanghai
600098 Guangzhou Development Group Shanghai 601989 China Shipbuilding Industry Shanghai
600100 Tsinghua Tongfang Shanghai 601991 Datang International Power
Generation Shanghai
600104 SAIC Motor Shanghai 601992 Bbmg Shanghai
600109 Sinolink Securities Shanghai 601998 China Citic Bank Shanghai
600111 China Northern Rare Earth
(Group) High-Tech Shanghai 603000 People.cn Shanghai
600115 China Eastern Airlines Shanghai 603885 Juneyao Airlines Shanghai
600118 China Spacesat Shanghai 603993 China Molybdenum Shanghai
59
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