BAN TOM TAT

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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http://youthdragoncapital.com/

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


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


ABS(RHSX) 0.543976 0.088614 6.138723 0.0000RHSX^2 -12.88487 2.106745 -6.116010 0.0000


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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


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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


Date: 05/11/11 Time: 11:39

Sample: 1 1118 IF RHNX>0



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


Table 4.6: Estimation Result when the market goes up

The regression model for the case of market going up:


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Dependent Variable: CASD


Date: 05/11/11 Time: 11:40

Sample(adjusted): 4 1117 IF RHNX<0

Included observations: 587 after adjusting endpoints


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


Table 4.7 Estimation Result when the market goes down

The regression model for the case of market going down:


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


Date: 05/11/11 Time: 12:02

Sample: 1 1012



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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


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


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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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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