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

3

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: ____________________

4

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

5

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.

6

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

8

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

9

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.

10

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

11

(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,

12

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)

13

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

14

(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.

15

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.

16

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.

17

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

18

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

19

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

20

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

21

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

22

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

23

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.

24

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

25

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.

26

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)

27

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

28

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).

-400

-300

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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


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

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1/1

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01

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


32

6.1.2.2 Empirical Result of Last Price with detrend


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



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

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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

-1.2E+10

-7E+09

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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



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)


Dependent Variable: ∆Vt




Intercept 4400000 0.92

∆Vt-1 -0.21#

0

∆Vt-1*Dt -0.074 0.20


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

-1.5E+10

-1E+10

-5E+09

0

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Res

idual

of

Vo

lum

e (R

MB

)

Figure 8 Residual of Trading Volume of CSI 300 from May 2005 - Nov 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)


Dependent Variable: Rt2




Intercept 3.75E+18# 0

Dt2 3.32E+18 0.067

Rt-12 0.36

# 0


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

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6

Vo

lati

lity

(R

MB

)

Figure 9 Volatility of CSI 300 from May 2005 - Nov 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

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6

Res

idual

(R

MB

)

Figure 10 Residual of Volatility of CSI 300 from May 2005- Nov 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)


Dependent Variable: r2

Period with Dt: 4 Dec 2015 – 7 Jan 2016

(Announcement date to last day of circuit breaker)



Intercept 1200# 0

Dt2 3000

# 0.0038

rt-12 0.19

# 0

(rt-1*Dt)2 -0.23

# 0.046


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





Period with Dt: 4 Jan 2016 – 7 Jan 2016

(4 days which circuit breaker was implemented)



Intercept 1200# 0

Dt2 13000

# 0

rt-12 0.17

# 0


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






(First implementation date to last date of the whole data set)



Intercept 1300# 0

Dt2 -180 0.61

rt-12 0.18

# 0

(rt-1*Dt)2 -0.077 0.39


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


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.


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



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


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


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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