ban tom tat
TRANSCRIPT
PREFACE
A market is considered efficient when the stock price fully reflects the
economic news and information. Thus, if no new information has been published,
the changes in stock prices will be relatively small. In other words, there rarely
exits a substantial increase or decrease. In the Vietnam, however, the market is
quite different. We found many trading sessions in which the VN-Index
significantly increased although there is no good information about the whole
economy as well as the business situation of enterprises was announced. In
addition, there are also many trading versions that VN-Index dropped up, even
though none of negative information has been found. Such fluctuation and signs in
stock market allow some financial analysis and investors to pay more attention to
the concept “herding behavior” to explain investor psychology. Thus, what is
herding behavior? Whether or not herding exits in Vietnamese stock market? If
yes, how much it give impact to Vietnamese stock market? And is there any
correlation between stock exchanges in Vietnam? In this research, we conduct a
research to find down the answer for these controversial issues.
We named a topic “Herding Behaviour in Vietnam’s stock market” for such
purpose. This research consists 05 main parts including:
Chapter 1 : Introduction
Chapter 2 : Literature Review
Chapter 3 : Methodology
Chapter 4 : Data analysis and Findings
Chapter 5 : Recommendation
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CHAPTER 1: INTRODUCTION
1. Background of Stock Market in Vietnam
1.1. Overview of stock market in Vietnam
With rapid growth and development in more than a decade since its birth, the stock market in Vietnam has become a potential channel of investment for financial institutions, credit funds and individual investors. More importantly, stock market has made a significant contribution to the industrialization and modernization of the country
In reality, driven by greed and fear and mislead by extremes of emotion and
the impulse of the crowd, investors passively form irrational expectation for
the companies’ future performance and the overall economy. As a
consequence, stock prices overestimate or underestimate their fundamental
values.
The behavior of an investor to imitate the actions of others or to follow the
movements of market, instead of following his own information and
strategy, is usually regarded as “herding”. Possibly herding is among the
most mentioned but least understood terms in the financial lexicon.
This paper examines whether herding behavior exists in HOSE and HNX
Exchange markets. By applying the methodology proposed by Chang,
Cheng, and Khorana (2000) to examine Vietnamese stock data, we provide
evidence showing that there is herding behavior in HOSE exchange market.
However, no supportive evidence for herding behavior is found in HNX
market. There is no concrete evidence illustrating the correlation between
investment decision between HOSE and HNX markets.
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1.2. Purpose of the research
This paper provides a thorough investigation of herd behavior and then
comes up with the correct answer for such issue.
We confirm the results of previous studies regarding the existence of herding
and also propose a new measure of herding based on a run test. Once
herding has been shown to be significant in our data, we firmly believe that
the herding does exist in Vietnam stock market.
Finally, we indicate the connection between herd behavior and changes in
stock’s return in a stock market to illustrate that the herding behaviors are
consistent with the changes in surveyed stock’s return.
1.3. The significance of research
Behavioral Finance, a field of finance that proposes psychology-based
theories to explain stock market anomalies, has given learners a better
understanding about the determining factors that result in the particular
behavior and performance of institutions and individuals from around the
globe, enhancing the understand the psychology and the emotions underlying
the decisions behind creating the goal.
Behavioral finance study comes up with Herd Behavior - the tendency for
individuals to mimic the actions (rational or irrational) of a larger group.
Over the past decade, financial economists have become increasingly
passionate in herd behavior in stock markets.
Because a strong herd mentality can even affect financial decision-making
process, understanding herb behavior is utmost important in judging the
efficiency of the stock market, in particular, and the whole economy, in
general.
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In this paper, we provide readers with evidences to examine the existence of
herd behavior in Vietnam stock market, to measure how much it impact the
investor’s decision making process and to document the correlation between
stock exchanges in Vietnam, if existing.
1.4. Research questions
This research focuses to provide the response for the following questions:
1. Whether or not herding behavior exists in HNX and HO exchange floors
since 2008 until now?
2. Does herding behavior exist in these trading floors when the market goes
down and goes up?
3. If yes, in which situation does herding behavior appear to be stronger, up or
down?
4. Is there any correlation between the investor in HNX and HO in term of
investing imitation and herding behavior?
1.5. The limitation
In this paper, we focus on herding behavior and research information in
Vietnamese stock market with listed stocks conducted in both HNX and
HOSE trading floors in the period from 2008 to 2010.
Thank to the research of Ms. Tran Thi Hai Ly about the herding behavior
that was published in Finance and development magazine on June, 2010, the
existing herding behavior in HOSE stock market was captured in the period
from January 1, 2002 to December 31, 2008. However, the research of Ms
Ly did not mention the situation in HNX stock Exchange since its operation
on March 8, 2005.
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We, therefore, continue and consolidate this research by collecting,
analyzing and processing the figure and data in both two trading floors until
now. In the scope of this research, we plan to use data of all stocks in these
stock exchanges
CHAPTER 2: LITERATURE REVIEW
In fact, the existence of herd behavior among particular participants in markets has
been analyzed empirically in a number of studies. In this part, we will briefly look
at and examine some of the methods that have been employed.
Several measures have been developed to investigate herd behavior in financial
markets, including:
Lakonishok, Shleifer, and Vishny (1992) (LSV) based their criterion on the
trades conducted by a subset of market participants over a period of time.
Wermers (1995) proposed a portfolio-change measure (PCM) which is
designed to capture both the direction and intensity of trading by investors.
Christie and Huang (CH) (1995) investigates the magnitude of cross-sectional
dispersion (or volatility) of individual stock returns during large price changes.
Chang, Cheng and Khorana (2000) have recently suggested a variant of the
CH method, showing that under CAPM assumptions, rational asset pricing
models suggest that the equity return dispersion, measured by the cross-
sectional absolute deviation of returns, should be a linear function of market
returns.
Nofsinger and Sias analysis adopt a different approach to examine the
relative importance of herding by institutional and individual investors.
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In the scope of this research, we focus on two main measures and its application in
both Vietnam and foreign context. These two measures chosen is LSV method and
CH one thank to their significant roles and applications.
1.1. LVS measure of herding
Lakonishok, Schleifer, and Vishny (1992) (hereafter called LSV) define
herding as “the average tendency of a group of fund managers to buy and
sell particular stocks simultaneously relative to what would be expected if
managers traded independently” (Bikhchandani and Sharma 2001).
The LSV measure is based on trades conducted by a subset of market
participants over a period of time. This subset usually consists of a
homogenous group of fund managers whose behavior is of interest
(Bikhchandani and Sharma 2001).
In LSV’s paper, they denote B(i,t) [S(i,t] as the number of investors in this
subset who buy [sell] stock I in quarter t and H(i,t) as the measure of herding
in the stock I for quarter t. The measure of herding used by LSV is defined
as follows: H(i,t) = p(i,t)- p(t) – AF (i,t)
According to Bikhchandani and Sharma (2001), the LSV (1992) measure of
herding behavior is deficient in two aspects:
Firstly, this measure only uses the number of investors on the two sides
of the market (extreme market conditions), without taking the amount
of stock they buy/sell into account, to assess the extent of herding in a
particular stock.
Secondly, it is impossible to identify inter-temporal trading patterns
using the LSV measure. To specify, the LSV measure could be used to
test whether herding in a particular stock persists over time, that is to
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evaluate whether E [H(I,t) H (I, t-k)]= E [H(I,t)], but it cannot inform us if it
is the same fund that continue to herd.
In case of Vietnam stock market, a number of market participants are hardly
to be measured correctly since an individual investor can illegally open more
than one account in a security company. About 85% investors in stock
market are individual investors while the quantity of stock they trade in the
market, according to the second drawback of LSV, cannot be measured.
Until now, the application of LSV measure in Vietnam stock market to find
out herding behavior is not employed yet.
1.2. Modification of the LSV measure of herding
Wermers (1995) develops a new measure of herding that captures both the
direction and intensity of trading by investors. This new measure which is
called a portfolio-change measure (PCM) of correlated trading, overcomes
the first drawback listed above of LSV measure. Intuitively, “herding is
measured by the extent to which portfolio weights assigned to the various
stocks by different money managers move in the same direction” according
to Wermers (1995).
The PCM measure has three main drawbacks which have been summarized
in Bikhchandani and Sharma (2001) paper:
First of all, according to PCM measure, the buy or sell decision by the
amount traded should be weighted, but doing this introduces another
bias since larger fund managers tend to get a higher weight.
Second, Wermer’s statistic which looks at changes in fractional weights
of stocks in portfolios may yield spurious herding as weights of stocks
that increase (decrease) in price tend to go up, even without any buying
(selling). Taking the average of beginning and end-quarter prices to
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determine portfolio weights may correct for it as Wermers claims. It,
however, depends on exactly how it is done.
Finally, the justification of using net asset values as weights in
constructing the PCM measure is not clear (Bikhchandani and Sharma
2001).
2. The CH measure of herding
Christie and Huang (1995) (hereafter called CH) investigates the magnitude
of cross-sectional dispersion (or volatility) of individual stock returns during
large price changes. If the dispersion is small during the large price changes
then they suggest that there is evidence of herding.
Christie and Huang (1995) propose that the market impact of herding can be
measured by considering the dispersion or the cross-sectional standard
deviation (CSSD) of returns. In CH paper, they mention that traditional
asset-pricing theory predicts as a results of varying stock sensitivities to
market returns, the dispersion of return increases with the aggregate market
return
The rationale behind the use of this dispersion measure is that if the herding
occurs in the whole market, returns on individual stocks would be more than
usually clustered around the market return as investors suppress their private
opinion in favor of the market consensus (Henkers et al 2003).
Since dispersion measures the average proximity of individual returns to the
mean, when all market returns move in perfect unison with the market,
dispersion is zero. When individual returns differ from the market return, the
level of dispersion increases.
By using daily and monthly returns on U.S equities, CH finds a higher level
of dispersion around the market return during large price movements,
evidence against herding (Henkers et al 2003).
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As Richards (1999) points out that the CH test looks for particular form of
herding and only in the asset-specific component of returns. It does not
allow for other forms of herding that may show up in the common
component of returns. Therefore, although CH test can be regarded as a very
accurate estimation of particular form of herding, the absence of evidence
against this form of herding should not be construed as showing that other
types of herding do not exist (Henkers et al 2003).
2.2. CCK- the modification of CH measure of herding
Chang, Cheng and Khorana (2000) (hereafter called CCK) propose a
modification to the model presented by CH. This model uses the cross-
sectional absolute standard deviation (hereafter CSAD) of returns as a
measure of dispersion to detect the existence of herding.
Their model suggests that if market participants herd around indicators, a
non-linear relationship will result between the absolute standard deviation of
returns and the average market return during periods of large price
movements.
CCK develop a more sensitive means of detecting herding by including an
additional regression parameter to capture a potential non-linear relationship
between security return dispersions and the market return. This alleviates the
limitation inherent in the Christie and Huang approach, which require a
greater magnitude of non-linearity in the return dispersion and mean return
relationship to identify herding (Henkers et al 2003).
Application of CCK in foreign countries
CCK uses monthly data of individual returns to analyze and find out that
under CAPM assumption, rational asset pricing models suggest that the
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equity returns dispersion, measured by the cross-sectional absolute deviation
of returns, should be a liner function of market returns
They find a significant non-linear relationship between equity return
dispersion and the underlying market price movement of the South Korean
and Taiwanese markets, providing evidence of herding within these
emerging markets. However, they do not find evidence to support the
presence of herding in the developed markets of the U.S, Hong Kong and
Japan (Henkers et al 2003).
Application of CCK in Vietnam
Research about Herbing behavior in Vietnam stock market
- The research of M.A Tran Thi Hai Ly about the herding behavior was
published in Finance and development magazine on June, 2010. Ms Ly use
CCK measure to research the herding behavior in Vietnam and successfully
conclude the existing herding behavior in HoChiMinh stock exchange in the
period from January 1, 2002 to December 31, 2008.
- M.A Tran Thi Hai Ly used the point that if market participants herd around
indicators, a non-linear relationship will result between the absolute standard
deviation of returns and the average market return during periods of large
price movements.
- With these results, the author suggest that strong herd behavior exists in
Vietnamese market, and that herd behavior tend to be stronger in cases
where the market is flourished and boomed than that in downturn side.
Study about Psychology in securities investment in HoChiMinh City
This research is to understand the impact of psychological factors in the
securities investment in Vietnam and based on this analysis, the authors propose
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solutions to improve professional relationships with investors in the securities
company in HCM City.
Subjects studied: 100 investors in the securities trading floor
Scope of study: In Ho Chi Minh city
Experimental method of investigation comprises two parts:
* Qualitative research: the research team attempted to find information and
opinions from the expert consultation including the consultant and the dream world
of securities certificates unhappy investors, organizing the focus groups to retrieve
information.
* Quantitative research: the team conducted a survey with 100
questionnaires to 100 investors in the securities trading floor.
This research concluded that communication is increasing its importance
because it is an effective method influencing social and emotional psychology and
that when financial markets grows up, investors and financial analysis gain focus
on the impact of information on the investor’s psychology.
The measure developed by Chang, Cheng and Khorana (2000) neither
consider the time-varying properties of beta in the CAPM nor herding
towards other factors which might be important in the interpretation of asset
returns. (Henkers et al 2003).
CHAPTER 3: METHODOLOGY
1. Source of information
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Data used in this analysis (hereafter called DATA) are freely imported from
website: http://youthdragoncapital.com, the official website of the Youth
Dragon Capital Investment Fund.
DATA consists of prices and trading volumes of all stocks listed on the
Vietnam’s stock market (including HNX- the Hanoi Stock Exchange and
HSX- Hochiminh Stock Exchange) from their first trading day up to April 8,
2011; and the daily results of indices.
We do not use information on UPCOM trading floor due to its new
establishment and ineffective operation.
Including in the data is information about open price; close price, the highest
price and lowest price of stock each day (see the data table for more
information). However, for the purpose of calculating the daily return of
stocks, we only use the close price of each trading day.
Downloaded data are in the form of text.file, we import them into EXCEL
and do most of the data processing on EXCEL. Besides this powerful tool,
we also take advantage of two famous statistic and econometric softwares
which are MEGASTAT and EVIEW in our analysis.
Selected data includes information of 350 stocks in HNX and 265 stocks in
HSX (after excluding non-qualified stocks, using the sampling method
below).
However, in some stocks, due to the statistician’s carelessness, he/she just
either left the blank cells or filled in that with a random number, seriously
affecting the return calculation. Due to our group’s experiment, there are
about 131 stocks in HNX and 58 stocks in HSX had that error.
There are two ways to deal with the problem without affecting the final
result:
First, we can delete these stocks from the data.
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Second, we can fix the data by filling in the blank cells/ refill the cells
containing wrong number with the price of the previous day.
Due to the time limitation (the mistakes have just been discovered), we
choose the simple option which is to remove the stocks out of the data.
Therefore, the remaining number of stocks on each trading floor is 219 in
HNX and 207 in HSX, still adequate to produce a reliable result.
2. Sampling method
The stocks will be reselected basing on the following criteria:
Number of observations must be more than 30. In the other words, there
must be more than 30 trading days (to ensure the statistic meaning).
Following that, nine stocks in HSX have been excluded, and more than 20
stocks in HNX have been put out of range.
Stock price information must be in full. Because there are some stocks
which are not have all prices listed.
Then, sample table were constructed and there are 5 tables used in this research.
EXCEL file: HNX-FINAL and HSX-FINAL, which contains all data
processing steps. Data in here are from the first trading day of all stocks
File FINAL DATA_HNX and FINAL DATA_HSX, which are used
directly in EVIEW software. Data of HNX were from August 9, 2006 to
January 28, 2011, that of HSX were from April 24, 2006 to January 28, 2011.
However, when running the regression on EVIEW, we only use 520 observations
on HSX, meaning from January 1, 2009 to January 28, 2011 because the previous
research has already worked with data until the year end 2008.
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File CROSS DATA which contains information of Index return on HNX,
HSX, and CSAD_HNX and CSAD_HSX from January 2, 2007 to January
28, 2011.
3. Data processing
3.1. Model
The model used in this paper is a modified version of CCK’s model- the
model developed by Chang, Cheng, and Khorana in 2000.
In the CCK’s model, we have
, the Cross Sectional Absolute Deviation,
represents the return dispersion between return on stock and return on market.
β1: Coefficient of the abs(rm) which is the absolute value of market return.
β2: Coefficient of the (rm)2 which is the square value of market return
ri: Return on stock i
rm: Return on the market, which is the daily return of HNX index and HSX index.
: The stochastic error.
In our research with regard to herding behavior, we will look at β2 only. As
Chang, Cheng, and Khorana (2010) explained in their research, when there
is a big market movement, such as market return goes up (down), if herding
behavior exists among investors who tend to herd around market return, the
return dispersion measured as CSAD will decrease or increase at a
decreasing rate. Hence, in here, the restriction is that negative β2 implies
herding behavior.
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4.2. Processing data
With the data provided, we will need to do the following steps to transform
them into the final data:
Step 1: Calculate daily return of each stock and the indices, using the simple
Holding Period Return (HPR) method: rt = (Pt- Pt-1)/Pt-1
Reason: It is the most common and simple method used in calculating
return.
Step 2: Construct a table of all stock returns (TABLE enclosed)
Step 3: From that table, we calculate the absolute value of deviation between return
on stock i and return on the market in which it is listed (which is HNX
return and HSX return correspondingly). abs(ri- rm)
Step 4: Calculating the average value of these absolute values, we will obtain
CSAD.
Step 5: The final data that we will use in our research consists of Return on the
Index (RHNX and RHSX) and CSAD (ABSRETURN).
(Please read file HSX_FINAL and HNX_FINAL for more information)
CHAPTER 4: DATA ANALYSIS AND FINDINGS Herding behavior happens in each market separately. In this part, we will
use the model of CCK as explained previously and data in files FINAL
DATA_HNX and FINAL DATA_HSX
Cross herding between two markets. In this part, we will use another model
(to be introduced) and the data in the file CROSS DATA.
PART I: HERDING BEHAVIOR IN EACH MARKET
A. Hochiminh Stock Market
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There is herding behavior in Hochiminh stock market period from January 1,
2009 to January 28, 2011.
The Hypothesis that we will test in this part regarding to herding behavior
includes:
H0: The overall model is significant
H1: The model is not significant
and
H0: β2 ≥ 0, the herding behavior does not exist in this market.
H1: β2 <0, the herding behavior exists in this market
After we estimate the equation, we come up with the result below:
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Dependent Variable: CSAD_HSXMethod: Least SquaresDate: 05/11/11 Time: 11:22Sample: 1 520Included observations: 520
Variable Coefficient Std. Error t-Statistic Prob. C 0.017391 0.000523 33.24383 0.0000
ABS(RHSX) 0.557847 0.069690 8.004648 0.0000RHSX^2 -13.32119 1.708924 -7.795076 0.0000R-squared 0.110855 Prob(F-statistic) 0.000000
F-statistic 32.22887Table 4.2: HSX Estimation Result
Regression Model:
From the table, we can see that F-statistic is quite high, forcing the
probability to nearly 0, less than 1%. So, we will reject H0 in the first
hypothesis. It means that overall estimators are statistically significant from
0 or the model is statistically significant at 1% level of significance.
With the second hypothesis, we will look at the coefficient of RHSX^2. The
estimation shows that β2=-13.32, strongly negative and t-statistic=-7.79.In
addition, t-statistic < tα = -2.32 with α=0.01, So, we will reject H0.
In addition, the coefficient β1 that is positive but very small, is also
significant. So, it means that CSAD does not depend linearly on
ABS(RHSX), instead, it is related to RHSX^2 following a non-
linear quadratic function.
General conclusion is that there is evidence for the existence of herding
behavior on HSX stock market in the studied period.
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3. Herding exists both when the market goes up and goes down at the same
level.
Similarly doing as for the whole market, in here, we run the same regression
model, but restrict that RHSX will be negative and positive and we get the
following results for the case the market goes up and down correspondingly.
Two regression models:
CSAD_HSX=0.178 + 0.569*ABS(RHSX) – 13.798*RHSX^2
Dependent Variable: CSAD_HSXMethod: Least SquaresDate: 05/11/11 Time: 11:25Sample(adjusted): 3 520 IF RHSX<0Included observations: 237 after adjusting endpoints
Table 4.3: Estimation Result when the market goes down
CSAD_HSX= 0.17 + 0.544*ABS(RHSX)- 12.885* RHSX^2
Table 4.4: Estimation Result when the market goes up
Variable Coefficient Std. Error t-Statistic Prob. C 0.017822 0.000828 21.53027 0.0000
ABS(RHSX) 0.569758 0.113588 5.016019 0.0000RHSX^2 -13.79831 2.896964 -4.763024 0.0000
R-squared 0.097092 Prob(F-statistic) 0.000006
Dependent Variable: CSAD_HSXMethod: Least SquaresDate: 05/11/11 Time: 11:27Sample(adjusted): 1 517 IF RHSX>0Included observations: 282 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob. C 0.017074 0.000677 25.20864 0.0000
ABS(RHSX) 0.543976 0.088614 6.138723 0.0000RHSX^2 -12.88487 2.106745 -6.116010 0.0000
R-squared 0.121363 Prob(F-statistic) 0.00000
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From the table, we can somehow predict that herding behavior does exist in
both cases and it would be stronger when the market goes down. The
detailed analysts will be as following:
In the sample including observations of negative RHSX, we will test the
following hypothesis:
H0: β2rhsx<0 ≥0, there is no herding behavior when the market goes down.
H1: β2rhsx<0 <0, there is herding behavior when the market goes down
In the regression model, β2rhsx<0 is strongly negative (-13.798) and t-statistic=
- 4.76< t α=0.01= -2.32. Hence, we reject H0 at 1% significance level.
Moreover, β1rhsx<0 is positive, but very small. So, there is evidence that
herding exists when the market goes down.
In the sample including observations with RHSX positive, we will test the
following hypothesis:
H0: β2rhsx>0≥ 0, there is no herding behavior when the market goes up.
H1: β2rhsx>0<0, there is herding behavior when the market goes up.
From the equation, we can see that β2rhsx<0 is strongly negative (-12.885) and
t-statistic= -6.16 < t α=0.01=-2.32. Hence, again, we reject H0 at 1%
significance level. Thus, evidence shows that when the market goes up,
herding does exist.
And the last hypothesis to test whether the herding levels are equal in those
market movements.
H0: β1rhsx<0 = β1rhsx>0, β2rhsx<0 = β2rhsx>0
H1: β2rhsx<0 ≠ β2rhsx>0, β1rhsx<0 ≠ β1rhsx>0
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To test for this hypothesis, we use the F-test.
F=
k: number of parameters in the model, k=3. N=520.
RSSR is the Residual Sum of Square of the model of the whole HSX, RSSR=
0.017417
RSS1 is the Residual Sum of Square of the model of HSX when RHSX<0,
RSS1= 0.008783
RSS2 is the Residual Sum of Square of the model of HSX when RHSX>0,
RSS2= 0.008541
So, F-stat= 0.92 < F0.01,3,520
Thus, do not reject H0 at 1% significance level.
Hence, there is no evidence that the herding level is different when market is
up or down.
A. Hanoi Stock Exchange
1.1. There is no evidence for herding behavior in HNX Stock Exchange for
the period from August 9, 2006 to January 28, 2011.
To remind you the regression used to test for herding behavior:
Similarly doing, we import the data on the file FINAL DATA_HNX, into EVIEW
4 and use the whole data, corresponding to the period from August 2006 to Jan
2011. The Hypothesis that we will test in this part regarding to herding behavior
includes:
H0: β1=β2=0, The overall model insignificant
H1: The model is significant
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and
H0: β2 ≥ 0, the herding behavior does not exist in this market.
H1: β2 <0, the herding behavior exists in this market
After we estimate the equation, we come up with the result below:
Table 4.5: Estimation Result for HNX
Regression Model:
CSAD= 0.030077 + 0.170693* ABS(RHNX) + 2.7826* RHNX^2
For the first hypothesis, we will test it using the F-test. In here, the F statistic
has probability= 0.000 < 0.01 or 1%, hence we reject H0 of the first hypothesis.
So, there is evidence that the overall model is significant at 1% level of
significance.
Dependent Variable: CSAD
Method: Least Squares
Date: 05/11/11 Time: 11:37
Sample: 1 1118
Included observations: 1118
Variable Coefficient Std. Error t-Statistic Prob.
C 0.030077 0.000527 57.09002 0.0000
ABS(RHNX) 0.170693 0.041506 4.112507 0.0000
RHNX^2 2.782607 0.587668 4.735002 0.0000
R-squared 0.311097 Prob(F-statistic) 0.0000
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However, for the second hypothesis, the t-test is applied. However, t-statistic
is highly positive (4.375), so for certain, H0 is not rejected at any level of
significance (1%, 5%, 10%), since we expect it to be negative, at least.
So, we can conclude that there is no evidence for herding behavior in Hanoi
Stock Exchange.
1.2. There is no evidence for herding in HNX Exchange in both cases: market
up and market down
Similarly doing as for the whole HNX market, in here, we run the same
regression model, but restrict that RHNX will be negative and positive and we get
the following results for the case the market goes up and down correspondingly.
Dependent Variable: CSAD
Method: Least Squares
Date: 05/11/11 Time: 11:39
Sample: 1 1118 IF RHNX>0
Included observations: 529
Variable Coefficient Std. Error t-Statistic Prob.
C 0.030381 0.000803 37.83968 0.0000
ABS(RHNX) 0.191283 0.063350 3.019464 0.0027
RHNX^2 2.218753 0.912302 2.432038 0.0153
R-squared 0.286378 Prob(F-statistic) 0.00000
Table 4.6: Estimation Result when the market goes up
The regression model for the case of market going up:
CSAD= 0.030381 + 0.191283* ABS(RHNX) + 2.218753* RHNX^2
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Dependent Variable: CASD
Method: Least Squares
Date: 05/11/11 Time: 11:40
Sample(adjusted): 4 1117 IF RHNX<0
Included observations: 587 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C 0.029861 0.000698 42.77935 0.0000
ABS(RHNX) 0.151897 0.054925 2.765516 0.0059
RHNX^2 3.267128 0.763103 4.281374 0.0000
R-squared 0.335073 Prob(F-statistic) 0.00000
Table 4.7 Estimation Result when the market goes down
The regression model for the case of market going down:
CSAD= 0.029861 + 0.151897* ABS(RHNX) + 3.267128* RHNX^2
Look at the results above, we can conclude that there is no evidence of
herding behavior in both cases since t- statistic in both cases are positive.
PART II: CROSS HERDING
1. Data summary
In this part, we use different part of the data, which is contained in the file CROSS
DATA.
Here is the summary of the statistics for this time period (the second sample)
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-.06
-.04
-.02
.00
.02
.04
.06
-.2 -.1 .0 .1 .2
RHNX
RHSX
RHNX RHSX CASD_HNX CASD_HSX
Mean -0.000445 -0.000197 0.035988 0.022834
Median -0.001647 0.000130 0.034025 0.020883
Maximum 0.100740 0.047564 0.103079 0.106621
Minimum -0.120692 -0.046884 0.018636 0.001627
Std. Dev. 0.026108 0.019274 0.010695 0.010158
Sum -0.449887 -0.198899 36.42031 23.10846
Observations 1012 1012 1012 1012
Table 4.8: Summary statistics of the second period
Please note that the total number of observation in consideration is now changed to
1012 in both markets.
2. Prediction and Model Building
On the right hand side is the graph
between the return of HSX index and
return of HNX index. At the first glance,
we can conclude that there is strong
linear relationship between return of
those two markets.
The result is emphasized by the correlation table here. Accordingly, the correlation
between return of two markets is very high, at 0.85 or 85%.
Table 4.9: Correlation of two markets
RHNX RHSXRHNX 1.000000 0.852802RHSX 0.852802 1.000000
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The return of the two markets are closely related, we suspect that probably,
there is chance that investors in HNX stock market made their investment
decisions based on HSX investors’ decision. Hence, we go a step further to
examine this. If the evidence appears to conform to this argument, we say
that investors on HNX herd around HSX Index and vice versa.
To test for this hypothesis, we add an additional factor of cross-market
return squared into the model as follows:
1.
2.
The equation 1 is used to test whether HSX investors herd on HNX market;
whereas, the equation 2 is used to test whether HNX investors herd on HSX
market.
If HSX investors herd around HNX market or vice versa, we will expect that
both and in the two model were have negative signs, and is
statistically significant.
3. Testing & Results
Importing data from CROSS DATA file into EVIEW, and run the regression
equation, we got the following results
Dependent Variable: CSAD_HSX
Method: Least Squares
Date: 05/11/11 Time: 12:02
Sample: 1 1012
Included observations: 1012
Variable Coefficient Std. Error t-Statistic Prob.
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C 0.018843 0.000691 27.26338 0.0000
ABS(RHSX) 0.362765 0.085204 4.257628 0.0000
RHSX^2 -3.474573 2.131059 -1.630444 0.1033
RHNX^2 -0.241265 0.345227 -0.698858 0.4848
R-squared 0.063709 Prob(F-statistic) 0.000000
The first regression model:
The overall model is statistically significant at 1% because F-statistic has
probability of nearly 0.
H0: β3 ≥ 0, no herding behavior of HSX on HNX
H1: β3 < 0, herding behavior of HSX on HNX
The key thing we need to look at is the sign of β3 and its significance. Here,
β3=-0.2412, negative. However, t-statistic is -0.6988 > t0.01= -2.32. Just we do not reject H0. So, there is no evidence of herding behavior of HSX on HNX market.
Dependent Variable: CASD_HNXMethod: Least SquaresDate: 05/11/11 Time: 11:59Sample: 1 1012Included observations: 1012
Variable Coefficient Std. Error t-Statistic Prob. C 0.032179 0.000555 57.96233 0.0000
ABS(RHNX) 0.062620 0.046069 1.359264 0.1744RHNX^2 3.796057 0.587049 6.466334 0.0000RHSX^2 0.025917 0.831713 0.031161 0.9751
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R-squared 0.295514 Prob(F-statistic) 0.000000
The second regression model:
H0: β3 ≥ 0, no herding behavior of HNX on HSX
H1: β3 < 0, herding behavior of HNX on HSX
In here, the model is significant, too. However, β3 is positive and
insignificant due to t-statistic > t0.01 =-2.32. So, we cannot reject H0 and
conclude that there is no evidence for cross herding between these markets.
CHAPTER 5: CONCLUSION AND RECOMMENDATION
The significant fluctuation and chaos in stock market is increasing the
importance of studying about herding behavior in Vietnam’s stock market.
To examine the herding behavior, we use the indirect approach developed by
Chang, Cheng, and Khorana through the use of the return dispersion of each
stock versus the market. And after analyzing, a number of findings have
been released.
As expected, in HSX, Hochiminh stock market, there is strong herding
behavior in the period 2009-2011. And interestingly, herding behavior exists
both when the market goes up and when it goes down at nearly same level.
However, when we do the testing in Hanoi Stock exchange, we found out an
unexpected result. As many researchers have mentioned, it is likely that
herding will exist in emerging stock markets like China and India. However
when we do the analysis in Vietnam market, surprisingly there is no
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evidence for herding in one trading floor- HNX. No matter the market goes
up or down, no evidence is found.
Also, we also want to conduct a further test to see if investors in HSX
market will trade basing on the up and down movements of HNX market
and vice versa. Hence, a cross herding testing is done and the result is that
people do not cross-herd, meaning, they do not make investment decisions
follow the movements/trends of the other market.
Specifically, results indicate that investors herding behavior existing in
Vietnam Stock market and imply that investors have got illegible investment
style. This phenomenon is not good for improvement of investors’
confidence and establishment of rational investment, and also has bad
impact to development of stock market. Therefore, we should adopt some
process to boost Vietnam’s capital market development:
Enhance the informational mechanism and make sure investors can
get more information and detect false information. If firm, broker
dealers or accountant firms create fake information, once they are
found, government will ascertain where the responsibility lies.
Create more training and education for investors, establish the concept of
rational investment gradually and increase the value of making stock
investment. Government should adopt more good- performance
companies to come into the market.
Strengthen the control on the market and establish the healthy auditing
system in accountant firm by self- discipline and ethics of employees.
Improve the regulation or the policy that affect to financial environment,
which avoid some cheating of broker or investors in recent time in Vietnam
stock market.
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