Herding behavior in vietnamese stock market, empirical evidence from quantile regression analysis

UNIVERSITY OF ECONOMICS HO CHI MINH CITYInternational School of Business ---Phan Dang Bao Anh HERDING BEHAVIOR IN VIETNAMESE STOCK MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION AN

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

-Phan Dang Bao Anh

HERDING BEHAVIOR IN VIETNAMESE STOCK

MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION ANALYSIS

MASTER OF BUSINESS (Honours)

Ho Chi Minh City – Year 2015

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

-Phan Dang Bao Anh

HERDING BEHAVIOR IN VIETNAMESE STOCK

MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION ANALYSIS

ID: 22130006

MASTER OF BUSINESS (Honours)

SUPERVISOR: A.Pro.Dr VO XUAN VINH

Ho Chi Minh City – Year 2015

Firstly, I would like to express my gratefulness to my supervisor A.Prof Dr.Vo Xuan Vinh for his professional guidance, intensive support, valuable suggestions, instructions and continuous encouragement during the time of research and writing this thesis

I would like to express my deepest appreciation to ISB Research Committee for their valuable time as their insightful comments and meaningful suggestions were contributed significantly for my completion of this research

My sincere thanks also go to all of all of my lecturers at InternationalBusiness School- University of Economics Ho Chi City for their teaching andguidance during my Master course

Last but not least, I would like to thanks my family, whom were always

supporting me and encouraging me with their best wishes

TABLE OF CONTENT

CHAPTER 1: INTRODUCTION 1

1.1 Research background 2

1.2 Research gap 4

1.3.Research objectives 5

1.4 Research methodology and scope 6

1.5 Research structure 7

CHAPTER 2: LITERATURE REVIEW 8

2.1 Theoretical literature review 8

2.2 Empirical literature review 11

2.3 Measuring herding in financial markets 19

2.4 Hypothesis development 25

CHAPTER 3: RESEARCH METHODOLOGY 27

3.1 Data collection and sample description 27

3.2 Regression model for testing the hypotheses 28

3.2.1 Regression model for testing the presence of herding bahavior in Vietnamese stock market: 28

3.2.2 Regression model for estimation the degree of herd in rising and falling market: 29

3.3 Regression methodology 30

3.3.1 Research process 31

3.3.2 Quantile regression analysis 31

CHAPTER 4: EMPIRICAL RESULT 34

4.1 Decriptive statistics 34

4.2 Correlation analysis among variables 35

4.3 Regression result 36

4.3.1 Evidence on herd presence in Vietnamese stock market 36

4.3.2 Herding behavior in up and down markets 38

4.4 Regression result from Quantile regression analysis 39

CHAPTER 5: CONCLUSION AND IMPLICATIONS 45

5.1 Conclusion 45

5.2 Implications of herding behavior in Vietnamese stock market 46

5.3 Limitations and further research direction 48

REFERENCES 50

APPENDICES 55

LIST OF TABLES

Table 1.1: A summary of empirical evidence on herding behavior 15

Table 3.1: Summary of data observations used in the study 27

Table 4.1: Descriptive statistics for daily market return and cross-sectional absolute deviation (CSAD) for the Vietnamese stock market from 1/2005 to 4/2015 34

Table 4.2 Correlation among main variables 35

Table 4.3: Regression result of herding behavior in Vietnamese stock market 36

Table 4.4: Regression results of herding behavior in rising and declining market 38

Table 4.5: Analysis of herding behavior in Vietnamese stock market by quantile regression 40

Table 4.6: A summary of research results 43

ABSTRACT

This study examines the herding behavior of investors in Vietnamese stock market using data sample of 299 companies listed on Ho Chi Minh City Stock

Exchange Using a least square method, the author finds evidence of herding presence

in rising and falling market when considering over the period of 2005 – 4/2015 as well

as in the periods of pre-crisis and post-crisis By applying quantile regression analysis

to estimate the herding equation, the author find supporting evidence of herding

during the period studied as well as when splitting the market into two sub-periods; however, the level of this trend is somewhat different conditional on quantile region

Key words: herding behavior, Vietnamese stock market, quantile regression,

asymmetry.

2008, which originated from US and then expanded globally As soon as the crisis began,many economists and financial forecasters were no longer able to analyze the bankruptcy

of a variety of enterprises or banks in an intensive way

The Vietnamese stock market is not an exception From its foundation in 2000, the Vietnamese stock market experiences “hot” growth and drastical fluctuation

without stability causing virtual stock matter The value of VN-Index in 2000 of 100 points increases to 571 points after just one year and a half which astonishes

economic experts; however, this increment does not last long and rush to fall under

140 points in 2003, 150-200 points in 2004 The peak of growing phase is in the period of 2006-2007 as Vietnamese stock market has the highest growth of 1100 points (approximately 145%) in Asia – Pacific region, even exceeding the Shanghai stock market growth of 135% Particularly, the VN-Index reaches to the record of 1170.67 points on March 12th, 2007 – the highest level in the world This event makesstock experts and market managers difficult to understand, thereby bring out the fear

of bubble formation in the stock market

After a long time of increasing prices, the Vietnamese stock market has signal

to considerably decrease with the lowest record of 236 points on February 24th, 2009 The happening in the market during this period is very complicated to anticipate Onceagain, economics experts doubt the precision of the efficient market theory A paradox

is present that when the stock price is driven further from the fundamental value of 30% investors still trade constantly; whereas, when the stock prices decrease at an attractive level in declining market investors massively sell stocks instead of buying

Is it true that the Vietnamese stock market operation does not abide by any rules or there are phenomena dominating the market which cause an unusual fluctuation? Failure of the economists as well as their theories leads to a list of different questions

in different context: Are people rational? Or are they influenced by emotion such as fear, greed which caused wrong decision?

Then, a new branch of financial research appears beside traditional financial framework which helps economic experts and finance researchers partial explain unusual fluctuation Behavioral finance is a new strand of finance which investigates the behavior of investors in financial market; in other word, it is a combination

between psychology and finance It considers psychological factors as essential input

to financial analysis Behavioral finance can elucidate several financial reactions thatcontrast with standard financial theory and can thus make a contribution to avoidance

of mistakes as well as advancing investment strategies (Fromlet, 2001)

Previous researchers put sustained effort to understand investors’ behaviour in the market as well as its impact on stock price These investment behaviors are

influenced by some factors such as investors’ insight, criterion to measure investment efficiency or market instability… In terms of psychology, investors are assumed to be rational and always strive to optimize their actions but the fact that the rationality appears to be inhibited by numerous cognitive biases, such as overconfidence, over-optimism, herding, representativeness … and so on In this research, the author focuses

on the investment behavior of market participants regarding to their tendency to followthe actions of others, which engages in herd behavior

Herding behavior is defined as the trend of investors to imitate the actions of others (Luu, 2013) This tendency is considered an inherent psychology of investors but it becomes stronger as they have to make decision in a market condition with highuncertainty and low transparency Over last decades, research regarding this topic receives an attention from scientists and empirical researchers A numerous theories are developed and empirical investigations are conducted to examine the presence andreasons of this phenomenon in financial market Researchers in this field believe that the presence of herding behavior has impact on results derived from asset pricing model because it influences stock price fluctuation, thus influencing risk and return ofstocks (Tan et al, 2008) Similar to speculation, herding behavior may be rational or irrational If market participants follow market consensus, the fluctuation is more and more serious that can leads to instability in financial system, particularly in the period

of global crisis In addition, herding behavior lasting so long can drive the stock

prices further fundamental value which causes destabilization If investors are

dominated by sentiment such as greedy or fear of loss, they can trade in a “frenzied” way; as a results, economic bubbles are created and may collapse the stock market Insum, herding behavior can lead to bad consequences of reducing the efficiency of market, even result in the market instability and financial collapse

Basing on these arguments, doing research about herding behavior can help investors have an objective overview and be prudent when making investment

decision Therefore, the author decides to do a research of “Herding behavior in

Vietnamese stock market: an empirical evidence from Quantile regression analysis” The study applies research model proposed by Chang, Cheng and Khorona (2000) and modified by Chiang et al (2010) to investigate the presence of herd in Vietnamesestock market

1.2 Research gap

Several empirical studies have examined and detected the herding behavior

in many region throughout the world, form developed to emerging countries For

example: Chang et al (2000) find evidence of herding in Japan and South Korea but no detection of herd in US and Hong Kong, Fu and Lin (2010) detect the presence of herd inChinese stock market which was in accordance with the results from Zhou (2007) and Zhao (2011), Caparrelli, D’Arcangelis and Cassuto (2010) find the evidence of herd in Italian stock market and Caporale, Economou and Phillipas (2008) prove the existence of this trend in Athen, Greece… In Vietnam, there are a little

research test for the existence of this tendency applying different model for detection.Nevertheless, previous research concentrates on using Ordinary Least Square (OLS) method to regress their model, which consists of a few drawbacks that may lead to wrong results Tran and Truong (2011) overcome this situation by using a more

powerful approach called GARCH; however, this method is still not optimal and complicated in processing data

The new point of this study is the choice of methodology Instead of standard method of OLS and dummy variable models which are pronounced in earlier literaturereview, this research utilizes the model of Chang et al (2000) and applies quantile regression analysis in empirical investigation Quantile regression is considered a valid alternative to the estimation of herding model such as Christie and Huang (1995)and Chang et al (2000) (Jani, 2008)

In addition, preceding research investigated herding during very old period, usually from the formation of stock market in Vietnam to five years later At that time, the number of securities was extremely small, even there were only 5, 10 and 20 stocks listed in the Vietnamese stock market in 2000, 2001 and 2002, respectively Thedata set of this research is collected from 2005, when Vietnamese stock market marked

an impressive growth, until now, in which the market is gradually matured The

purpose of gathering the data set over the new period is to indicate a precise result with current condition of Vietnamese stock market

1.3.Research objectives

The substantial objective of this research is to test for the existence of herdingbehavior in Vietnamese stock market through examining the relationship between thelevel of equity return dispersion which measured by CSAD and the overall market return

The study proceeds to test for the asymmetric effect in case herd exists throughinvestigating whether to have differences in degree of herding behavior under variousmarket conditions

1.4 Research methodology and scope

With the research objectives are set earlier, the thesis uses indirect approach by examining the relationship between market return and stock return dispersion in order

to determine the existence as well as the level of herding behavior in Vietnamese stock market This research methodology is proposed by Chang et al (2000) and then modified by Chiang et al (2010), which is applied commonly in research regarding herd later

The thesis conducts research based on data sample of 2568 daily observations

of 299 companies listed on Ho Chi Minh City Stock Exchange (HSX) over the period

of 2005-4/2015 Besides the traditional OLS method is used to test for the

hypotheses, quantile regression analysis is also used as a new point in choosing

research methodology to test for the regression model The selection of quantile

regression has more advantages compared to OLS

Ordinary least square approach is highly effective for intensive comprehension about the central tendencies in a data set But OLS analysis will be less useful for

understanding about the points which lie closer to the upper or lower tail of the

distribution within a population (Gowlland, Xiao & Zeng, 2009) Meanwhile, Gowlland

et al (2009) assert that quantile regression allows researchers to describe more accuratelybehavior of the points lying towards the tails of the distribution Moreover, this method makes contribution to alleviating some of the statistical

problem of traditional OLS method such as outlier sensitivity, non-normal distribution,errors in variables and so on (Zhou & Anderson, 2011)

1.5 Research structure

This research includes five chapters in total The first chapter presents the

background of the research, researcher’s motivation and objectives doing this research as well as general methodology and scope Chapter two provides relevant academic literature

on herding which is separated into theoretical and empirical studies The next chapter presents detailed methodology in terms of the model used and the quantile regression method as well as the data collection The results of data analysis will be discussed in chapter four which contains the results from using traditional Ordinary Least Squares (OLS) approach and from applying the new method Finally, chapter five will summarize the main findings of research, drawing out some implications and the limitations as well

as further research direction are also mentioned

CHAPTER 2: LITERATURE REVIEW

In this chapter, the theoretical framework and empirical investigation will be discussed more clearly Theoretical framework indicates general information about herd such as definition of this trend in financial market, particularly in stock market Empirical investigation shows the detection of herding in numerous countries as well as asymmetric effect consideration Moreover, measurement of herding

behavior in financial market is also presented In addition, the hypothesis is

developed based on some argument

2.1 Theoretical literature review

Generally speaking, in economics and finance, herd or herding behavior

means the process participants are imitating other actions or base their decision on others’ actions (Spyrou, 2013) Nofsinger and Sias (1999) define herd as a tendency where investors in the market trade in the same direction during the same time Averyand Zemsky (1998) suggest that investors who ignore their initial assessment and trade by following the trend in the previous trade Many other definitions regarding herd are proposed by other researchers For example, it is a mutual imitation (Welch, 2000), a behavior converge to the consensus (Hirshleifer and Teoh, 2003) or a form

of correlated behavior (Hwang and Salmon, 2004) Luu (2013) suggests that herd effect in financial market is identified as a phenomenon in which investors tend to mimic others’ behavior

In the security market, investors herd by making their investment decisions based

on the masses’ decisions of buying or selling stocks If rational investors usually follow their own belief that makes the market efficient, herding; in contrast, causes the

inefficiency of the market In general, herding investors tend to act the same ways as the groups who have little inadequate information about the surrounding circumstances but behave in the same direction to get more secure (Caparrelli et al, 2010) Detecting the existence of herd is considered carefully by practitioners because of the fact that investors tend to ignore their own private information and usually follow majority of

Trueman (1994) indicates that analyst abilities affect analyst compensation Theexpert tends to forecast earnings closer to prior earnings previously announced by other analysts in an effort to mimic higher ability and achieve higher compensation Graham (1990) suggests that analysts more likely to herd are the one who

characterized by high reputation and low ability Specifically, high reputation analysts have greater motives to follow market consensus in order to protect their reputation while low ability analysts are confused when there is strong public information

inconsistent with their private information and they know if they make forecasts different from the consensus, they are more likely to be fired

Another reason driving herding behavior is information-based issues An

informational cascade takes place when individuals feel optimal to follow actions of individuals before them without considering their own information (Bikhchandani et

al, 1992) For example, for investors who are newly enter the market at the later stage,

it may be an optimal decision to imitate trading behavior of previous investors since they surmise that previous investors possess more accurate information A decision model analyzed by Banerjee (1992) shows that decision makers tend to look at the

investment Investors under the changed situations may want to open a bank account

to earn interest instead of holding a larger percentage of stocks in their portfolio This

is not herding in accordance with the definition above because investors are not

making their decision after observing others Instead, they are reacting to commonly known public information, which is the increment of interest rates

Bikhchandani and Sharma (2000) suggest that “spurious herding” may lead to

an efficient outcome while “intentional herding” may not, even causes fragile market, excess volatility and systematic risk Spyrou (2013) argues that herding behavior spreading among institutional and individual investors is often cited as the main

reason behind periods of extreme volatility and market instability Herd drives stock price away the fundamental value; as a consequences, herd lasting in the long-term can destabilize the market even lead to collapse Bikhchandani and Sharma (2001) indicate that when there are many investors imitating action of others who receive mis-information, which is very popular in any emerging stock markets (i.e Vietnam), herding behavior is formed Once there is any investor realizing that information is wrong, the imitation causes them to react back; as a result, this leads to extreme

volatility and pushes the market into crisis Research results found in Nakagawa, Oiwa

and Takeda (2010) assert that relatively strong herding behavior in financial institution

in 1980 may result in the formation and collapse of asset-price bubble

In summary, there are many reasons leading to herding behavior As above discussion, market participants may infer information from the actions of previous participants, fund managers may herd to protect their reputation, analysts herd for the reason related to remuneration or investors are simply non-rational and herding

behavior arises from the consequences of psychological or social conventions It is argued that herding may result in efficient outcome (spurious herding); however, someeconomists suggest that it may destabilize prices and give rise to bubble-like

phenomena in financial markets (intentional herding)

2.2 Empirical literature review

Empirical studies have focused on testing the existence of herding behavior aswell as its effect on financial market Since 1990s, many researchers have made

intensive efforts to detect this trend in terms of various market participants within different markets of the world, such as individual or institutional investors in both emerging and developed countries Nevertheless, the evidence is found contrary

One branch of the empirical literature shows weak or no evidence of herd in stock market Lakonishok, Shleifer and Vishny (1991) use a quarterly portfolio of 769equity pension funds between 1985 and 1989 as a new data set to evaluate how their trading affects stock prices The results indicate that no herding behavior is found in smaller stocks as well as no cross-sectional relationship between changes in pension funds’ holding of a stock and its abnormal return within the US

Christie and Huang (1995) propose a model to test the herd by using

cross-sectional standard deviation (CSSD) with a set of daily and monthly data from July 1962

to December 1988 The findings show that no evidence regarding the presence of herding behavior in US markets In the same approach, Chang et al (2000) extend the model to a new and more powerful level in which CSSD is replaced by cross-sectional absolute deviation (CSAD) at time t to detect the herd They analyze US, Hong Kong,

Japan, South Korea and Taiwan and find no evidence of herding in US and HongKong, Japanese investors herd partially and especially notice the existence ofthis trend in two Asian countries (South Korea and Taiwan)

Fu and Lin (2010) examine herding behavior in Chinese stock market by

applying both Christie and Huang’s model and Chang et al’ and determine that herding

is not found in this country However, asymmetric effect exists which reveals that herding is greater in downward market than in the upward

Houda and Mohamed (2013) investigate how returns behavior involved the movement of the MSCI world index; in other words, herding during market upturns and downturns in Africa, Asia, Europe and America stock market by using another new approach in the spirit of CH and CCK model called EGARCH(1, 1) The

results show that herd is significantly higher during rising market than declining market and this asymmetry is confirmed by ARCH model

On the other hand, another branch shows the evidence of herding presence Nofsinger and Sias (1999) find the relationship between institutional investors and herding formation in US stock market using Sias model with monthly data from

1977 to 1996 Similarly, Wermers (1999) also find the empirical evidence of herd in

US mutual fund industry over the period of 1975 to 1994 Hwang and Salmon (2001)investigate herding in US, UK and South Korea and show an interesting results that advanced markets such as US and UK reveal less herd behavior than emerging

markets such as the South Korea

Caparrelli et al (2010) examine the herd existence in Italian stock market and found a non-linear relationship between dispersion and returns The results support Christie and Huang’s conclusions that herding is present in extreme market

condition in this country

Zhou (2007) indicates the presence of herding in both A shares and B shares inChinese stock market applying Sias model With the same results, Tan et al (2008) andZhao (2011) find the existence of herding in both Shanghai and Shenzhen stock

in Italy and Greece during this period of time, this conclusion suites the results from examination of Caparrelli et al (2010) and Caporale et al (2008) for the Italian and Greek stock market, respectively In addition, asymmetric herding behavior is also investigated in terms of market returns, trading volume and return volatility Several outcome are found but noticeably, there is no evidence of herding in the Spanish stockmarket During the global financial crisis of 2008, herding is found only in

Portuguese stock market; investors of three rest Mediterranean countries seem to havebeen rational

A recent research of Choi and Skiba (2015) applying Sias’s model extended literature review about herding measure on international markets which includes 41 countries Using a set of quarterly institutional holdings data during the span of the last quarter of 1999 to the first quarter of 2010, the study finds statistically significant herding propensities in 41 target countries that have significant presence of

institutional investors

Tran and Truong (2011) examine the presence of herding behavior in

Vietnamese stock market as well as its asymmetric effect conditional on the direction

of market movements Using the data of daily price series of all securities in Ho ChiMinh City Stock Trading Center during the period of 2002 to 2007, applying

GARCH(1,1) model to reduce drawbacks in OLS method, the findings found the

existence of herding in this emerging market; however; there is no evidence for

asymmetric effect in this case In conformity with these above authors, Kallinterakis(2007) investigates herding in Vietnamese stock market applying Hwang and Salmon(2004) model using market direction and market volatility as control variables Then,

in declining market than in rising market Besides, Nguyen (2013) proceeds to divide the analyzed period into four sub-stages as well as categorize stocks into two types of large market capitalization stocks and small market capitalization stocks to test for the

existence and the level of this tendency His final conclusion is that herding behavior is present in all sub-periods with different levels depending on the development

characteristic of each phase and there is no distinction in herding degree in terms of large

or small market capitalization

(2010)

Houda and Africa, Asia,

the US (28countries)

Panel B: Studies showing the evidence of herding behavior

Sias (1999)

(2010)

Zhou (2007) China

(2008)

Panel C: Studies showing the evidence of herding behavior in Vietnamese stock market

2.3 Measuring herding in financial markets

Generally speaking, empirical methodologies to measure herding behavior can

be classified into two main categories: studies that rely on micro-data or proprietary data and investigate whether specific investor types herd and the other are studies that rely on aggregate price and market data and investigate whether herd presents or not based on the market consensus Lakonishok et al (1992) and Sias (2004) are the representatives of two of the most commonly used measures of the former which be presented firstly; then this study proceeds with a discussion of two of the most

commonly used the latter (Christie and Huang, 1995; Chang et al, 2000)

A common herding measure of institutional investors is proposed by

Lakonishok et al, (1992; hereafter LSV) The notion behind their metric is that herdingbehavior is concluded to exist at the level of individual stocks if money managers have a tendency to disproportionately buy (sell) an individual stock (i.e end up on the same side of the market) They compute herd as the fraction of net buyers (money managers who increase their holding in a stock during a given quarter) to the total money managers who trade that stock excluding an adjustment factor that declines as the number of money managers active in that stock rises The LSV herding measure (H) is calculated as:

H(i) = | ( ) ( )

+ ( ) − ( )| − ( ) (1)

In the above specification, B(i) is the number of money managers who are net buyers, S(i) is the number of money managers who are net sellers (who decrease theirholding), p(t) is the expected proportion of money managers buying in that quarter relative to the number active, AF(i) (is called the adjustment factor) is the expected value of |B/(B+S) – p| under the null hypothesis of no herding LSV point out that, for any stock, the more declining the AF is, the more rising the number of money managers active in that stock is

LSV conclude that if herd exists, there should be significant cross-sectional variation in this measure On the other hand, if no herd exists, the expected value

of this metric should not vary from period to period

In the same spirit, Sias (2004) argues that the proportion of institutional

investors buying this quarter and last quarter will covary with each other, if

institutional investors herd or follow their own trades into and out of the same

securities; thus, herd can be evaluated by estimating the cross-sectional correlation between demand for an asset by institutional investors last quarter and demand for

an asset by institutional investors this quarter The calculation begins with an

estimation of every institutional investors’ position in every asset as a ratio of asset’sshares outstanding at both the beginning and the end of each quarter If an

institutional investors increases (decreases) ownership in the stock, this investor is defined as a buyer (seller) For each stock quarter, the portions of investors that are buyers are estimating This ratio is estimated as in the following equation:

Raw∆ , = , (2)

, + ,

Where BI is the number of institutional buying asset k during quarter t, SI is the

number of institutional selling asset k during quarter t Sias then standardizes

the fraction of institutional investors buying asset k in quarter t in order to allow

aggregation over time and comparison for different market capitalizations and

investors types as follows:

quarter trades, then the fraction of institutions buying in the current quarter will be positively correlated with the fraction of institutions buying in the previous quarter” (Sias, 2004, p 172) The key difference between the LSV and the Sias measure is thatwhile the former test indirectly for the cross-sectional temporal dependence within periods, the latter is a direct test of whether institutional investors follow each other’strades during the following periods (Spyrou, 2013)

Other different approaches which are the most commonly used are proposed byChristie and Huang (1995) (hereafter CH) and Chang et al (2000) (hereafter CCK) using stock return as a data set to identify herding behavior CH suggest that overall market conditions are the element which major of market participants rely on before making any investment decision According to rational asset pricing model, these two authors insist that return dispersion will rise with the absolute value of market return throughout conventional phases, because individual investors use their own various private information as a basis for trading in stock market Nevertheless, during periods

of tremendous market movements, individuals have a tendency to ignore their own beliefs and incline to form investment decision based on majority in the market

Under these conditions, individual stock returns lean toward bunching around the overall market return Therefore, CH suggest that herding will be more dominant during periods of market stress, which is defined in terms of extreme returns The following equation is used in their herding test:

(5)where

is the return dispersion at time t

= 1, if the return on the market for time period t lies in the extreme lower tail of thereturns distribution, and zero otherwise

= 1, if the return on the market for time period t lies in the extreme upper tail of the returns distribution, and zero otherwise

In order to quantify the return dispersion, CH proposed the use of

cross sectional standard deviation (CSSD) method, which is defined as:

√ ∑ ( , − , ) 2

= =1 ( −1) (6)

where N is the number of firms in the portfolio, , is the observed stock return of firm i at time t and ,

is the cross – sectional average stock of N returns in the aggregate market portfolio at time t According

to the model, if herd exists, return dispersion will be smaller during period of market stress because investors will make similar decision Thus, statistically significantly negative values of and in Eq.

(5) would indicate the presence of herding Hwang and Salmon (2004) who employ the cross-sectional dispersion of asset sensitivity to various fundamental factors

rather than asset returns propose a comparable methodology This measure can

discriminate between price adjustments to information about fundamentals from herding behavior due to shifts in market sentiment

However, Christie and Huang (1995) method exists a few drawbacks Firstly,the definition of extreme return is not objective CH’s study used a value of 1% and 5% as the cut - off points to define the extreme market movements As such, to be considered as an extreme movement, an observation of market return must lie in thefirst and fifth lower or upper percentile of the market return distribution (Garg & Gulati, 2013) In reality, the feature of return distribution may change over time anddepend on investors’ opinion, even differ from each of them Secondly, CH model recognizes herding under the condition of extreme return only However, herding behavior may be present at the entire return distribution and become more dominantduring period of market stress (Chiang et al, 2010)

An alternative model to Christie and Huang model to test for herding was

proposed by Chang et al (2000) These two authors claimed that CH approach requires defining what is meant by market stress in order to find evidence of herding, which is a strict test; therefore, they construct a methodology in spirit to the CH

approach They argue that if investors have a tendency to follow aggregate market activity during periods of large price swing, the linear and increasing relation betweendispersion and market return will no longer hold; instead of that, it becomes non-linearly increasing or even decreasing They develop a non-linear regression

specification to estimate the relation between the cross-sectional absolute deviation ofstock returns and the market return Beginning with the conditional version of CAPMmodel to define the expected cross-sectional absolute deviation of stock return

(ECSAD) in period t, they rewrite the equation as follows:

=

In (7), Rm is the return on the market portfolio, 0 is return on the zero-portfolio, is the systematic risk

of an equally weighted market portfolio, and is the systematic risk on the asset i CCK then demonstrate that the increasing and linear relation between dispersion and time-varying market expected return is:

( )

Based on (8) and (9), they propose a test of herding behavior that also requires

a parameter to address any possible non-linear relation between asset return dispersionand market return CCK use the cross-sectional absolute deviation at time t (CSADt) toproxy for the unobservable ECSADt For easily understanding, the herding equation can be substituted as followed:

is a cross – sectional absolute deviation and constructed as a method

to measure return dispersion, which is calculated by the following formula:

where , is the return of market portfolio and , is the return of stock i at time t It should be noted that both | , | and its quadratic form , 2 are appeared as

independent variables in Eq (10) Chang et al (2000) assert that under normal

conditions, the relationship between return dispersion and market volatility, as

determined by the rational pricing model, is linear As such, an increase in the absolutevalue of the market return will lead to a rise in the dispersion of individual investor return However, the notion behind this approach is that during periods of relatively large price movement, if participants incline to make decision based on aggregate market behavior, such increasingly linear relationship no longer holds; instead, is morelikely to be non-linear increasing or even decreasing The point should be noted here

is that CSAD is not the metric used to test for the existence of herd, the fact that herd

is identified through the relationship between CSAD and the market return

CCK proceed to run these following regressions in order to examine

whether the degree of herding behavior is asymmetric in rising and falling markets:

The first category of herding measure which is based on trading pattern of a group

of investors is later applied by many researchers but nearly conduct in countries with developed stock market However, gathering data of institutional investors in immature and developing Vietnamese stock market is more difficult than in other developed

markets; thus, this method is hardly applied Meanwhile, the second category measures herd indirectly depending on return dispersion of individual stocks compared with

market return, which these two data are easily to collect because they

are always available Besides, CH method existing some drawbacks makes the

application of this model to Vietnamese stock market inappropriate The relatively short history of this market is one of difficulties in collecting data; thus, in identifyingthe occurrence of extreme return In addition, stock market in Vietnam is evaluated asdrastically fluctuated; therefore, using the same level of dispersion as other countries can lead to inaccurate regression results For that reason, the study applies CCK’s model to analyze level of return dispersion as an approach to measure herding

behavior in Vietnamese stock market

2.4 Hypothesis development

In recent years, Vietnamese economy has experienced notable economic growthand gradually integrated with the global market but its financial market has been still small and in process of development Being established in 2000 with only two stocks, the Vietnamese stock market has matured to consist of 192 listed companies in 2006 (Tran and Truong, 2011) and till now it includes 308 stock stickers In addition to its limited size and number of participants, the Vietnamese stock market is governed by incompletely improved regulatory environment which is likely to cause a lack of transparency This could result in a greater likelihood of making investment decision based on market consensus among investors

Moreover, relatively high degree of government intervention in equity marketoperation with the imposition of many restrictions through monetary policy of centralbank as an instrument may limit investment opportunities of market participants

Meanwhile, there are few alternatives for investors as the bond and the real estate markets are underdeveloped As a result, investors are more likely to speculate on Vietnamese stock market which may lead to a larger price movement This potentiallyresults in a tendency of herding behavior

Given the structure of Vietnamese stock market, investors are more likely toimitate others’ action to feel more secure This phenomenon is also similar to the tendency of investors in many other emerging markets which are confirmed the

in Vietnamese stock market Therefore, the following hypothesis is tested:

Hypothesis 1: Herding behavior exits in the Vietnamese stock market.

Apparently, direction of the market seems to impact on investor behavior

Several studies have found that there are some changes in the level of herding under different market circumstances, conditional on the market is rising or declining (Tran

& Truong, 2011) In the study context, it is interesting to examine whether herding behavior presents an asymmetric effect on days when the market is up vis-à-vis days when the market is down Therefore, it is assumed that there is distinction in degree

of herding bahavior in case of market ups and downs This argument leads to the second hypothesis as follow:

Hypothesis 2: There is difference in herding level between rising market and falling market.

stickers were eliminated from the entire sample because of the lack of data caused bynewly listed on the Exchange or intermittent data caused by company operation

After the data collection, collected sample included 299 stocks equivalent to 299 firms which provided 2568 daily observations over period studied

In addition, the author also divided the entire sample into two separated periods: before and after the global financial crisis Specifically, the period of 2005-

sub-2007 was pre-crisis which provided 749 observations and the period of 2008-2015 wasthe interval of during and after global financial crisis (hereafter post-crisis) which provided 1819 daily observations

The reason for the division into two sub-periods helped to provide more

accurate insight regarding the nature of herding bahavior in Vietnamese stock marketbefore and after influenced by the global financial crisis which began from the sub-prime crisis in the US

Table 3.1: Summary of data observations used in the study

(Source: Synthesized by the author)

3.2 Regression model for testing the hypotheses

3.2.1 Regression model for testing the presence of herding bahavior in

Vietnamese stock market:

As mentioned above, the author used the model in spirit of Chang et al

(2000) to test for the existence of herd in Vietnamese stock market In this part,

CCK approach is presented briefly regarding the detection of herding over the entiredistribution of market returns The herding equation is set as followed:

= 0 + 1 | , | + 2 , 2+

If 2 <0, herd exists in Vietnamese stock market.

In normal market condition, investors make their investing decision independently

so the correlation between each individual stock return is low As a result, an increase in market return can lead to an increase in stock return dispersion because each asset has its own sensitivity to market return; as such, CSAD and Rm,t go with the same direction Therefore, if 2 is greater than 0 and significantly statistical, no evidence of herd found in Vietnamese stock market

In opposition, investors tend to react similarly during periods of large price swing.This action results in an increase in correlation and a decrease in dispersion between individual stocks It means the linear relationship between CASD and Rm,t is no longer correct; instead, it is a non-linear relation (proportionately increase and then decrease) Hence, a significantly statistical and negative 2 proves the existence of herd

The study proceeded to the next step of variables construction

Regression model included independent and dependent variables which were

described the calculation as follows:

Independent variables: As mentioned earlier, this research used indirect

approach depending on return dispersion in relation with market return to detect herd;

therefore, the independent variable in this research was market return From the

original data were closed price of VN-Index, the author conducted the calculation ofdaily market return as the following formula:

, = 100 (ln( ) − ln( −1 )) (12)

where: Pt is closed price of VN-Index at time t and Pt-1 is closed price of VN-Index

at time t-1

Dependent variable: the dependent variable in this research was return

dispersion measured by cross-sectional absolute deviation, which was expressed as:

1

= ∑|

, − , | =1

where N is the number of firms in the portfolio, , is the return of market portfolio at time t and , is the return of stock i at time t.

Similarly, Ri,t in the formula was calculated as follow:

, = 100 (ln( ) − ln( −1))

where Pt is closed price of stock i at time t and Pt-1 is closed price of stock i at time t-1

Note that closed price of stock was the fixed price after adjustment of dividendpayment such as cash dividend, stock dividend and bonus stock dividend This

adjustment helped to estimate more precisely the return that investors received as well as reflected exact nature of volatility

3.2.2 Regression model for estimation the degree of herd in rising and falling market:

In this part, through testing of herd under different market conditions, the

research investigated whether to have difference in level of this phenomenon in up anddown market or not Regression model modified by Chiang et al (2010) was used for this estimation Instead of dividing the sample into two parts, Chiang et al (2010)

added dummy variable to the model to test for the asymmetric effect The model was

as follow:

= 0 + 1 (1 − ) , + 2 , + 3 (1 − ) , 2 + 4 , 2 + (14)

where D is a dummy variable and D = 1 if , <0, D = 0 otherwise.

By using dummy variable, model coefficients included two separate parts In detail, 1 and 3 express the relationship between CSAD and

R m,t on days when the market is up or D = 0 Whereas, D = 1 or when the market is down, this relationship is expressed by 2 and 4.

Furthermore, 3 and 4 express the non-linear relationship between CSAD and , in market ups and market downs, respectively The research conducted considering the sign of each coefficient to prove the presence of herd; moreover, the magnitude of 3 and 4 was also taken into account If 3 < 4, CSAD in

up market is smaller than in down market As such, with the same level of volatility in market return, the return dispersion will strongly decrease on days in down market vis-à-vis days in up market.

In conclusion, the author tested hypothesis from Chiang et al (2010) model: 3 < 0 and 4 < 0 to find evidence of herd in Vietnamese stock market and considered accepting or rejecting hypothesis of 3 = 4 to investigate the asymmetry in different market conditions.

3.3 Regression methodology

OLS and Quantile Regresson (QREG) were two approaches chosen for model estimation For the former, this was a very popular method used in econometrics so the author presented the research process conducted in this study when using OLS instead of introducing clearly about this method For the latter, the author would present basic

theory, equation foundation and regression model used to test for the hypothesis

3.3.1 Research process

Step 1: Descriptive statistics was used to describe basic characteristics of dataset Some features such as mean, min, max, standard deviation,… were described preliminarily The purpose of this step not also built a foundation for the subsequent quantitative analysis but also provided an overview of the observed data series

Step 2: Test for correlation between all variables was conducted This step showed the basic overview regarding the correlative relationship between

independent and dependent variables

Step 3: Testing hypothesis by investigating the relationship between

independent and dependent variables applying CCK’s model for the presence of herd and modified model by Chiang et al (2010) for herding in different market condition

If herd was detected, Wald test was performed to examine the similarity in the level ofherd in up and down market

Step 4: Hypothesis test used Quantile Regression as a new measure to detectherding behavior in different percentiles

3.3.2 Quantile regression analysis

To test for the hypotheses, quantile regression was an analysis used beside OLSmethod because of its usefulness in measuring the dispersion, particularly in non-stable environment; thus, could lead to more efficient estimation since it was enable

us to perform the regression over the entire distribution of dependent variable (Zhou

& Anderson, 2011)

Briefly defined, “quantile regression is a statistical procedure designed to

estimate conditional quantile functions” (Chiang et al, 2010 as cited in Koenker, 2005;Alexander, 2008, p.119) The linear conditional quantile function is as follow:

QYi(τIX = x) = xi′γ (15)

where Yi is dependent variable, Xi is a vector of independent variables and γ is a vector of coefficients

interrelationship between dependent and independent variables can be estimated at any specific quantile because it does not have limitation at median level (Chiang et al, 2010) Hence, the correlation between CSADt and R2m,t is tested easily.

Quantile regression for measuring dependent variable CSADt and a set

of independent variables Xt, for τ quantiles are formularized as:

( |X t ) = 0, + 1, (1 − D)R m,t + 2, R m,t + 3, (1 − D)R 2

m,t + 4, R 2 m,t +

ε τ,t

Xt: a vector of the right-hand-side variables of Eq

D: a dummy variable by setting D = 1 if R m,t <0 and D = 0 otherwise.

This formula above is useful to test for the herding behavior in case of rising and falling market condition In this situation, the coefficient 4 is expected to significantly negative for expression of asymmetric effect as in hypotheses 2.

OLS is a regression method based on the mean of the conditional distribution of stock return dispersion; thus, it is difficult to discriminate between different quantile and even cause the ignorance of herding that exists only in certain quantiles Whereas,

quantile regression provides a more complete view of how this trend performs over

different quantiles In addition, Barnes and Hughes (2002) argue that quantile regression can alleviate some of the statistical problem of the standard OLS method, such as non – normal distribution, outlier sensitivity, errors in variables and omitted variable bias Manyfinancial datasets contain one or more of these challenges so quantile regression may be useful for employing such data (Gowlland et al, 2009) It is concluded that quantile

regression builds on more efficiency for estimators than OLS

(Source: Results analyzed by Eviews 8)

Table 4.1 provides descriptive statistics on daily market returns and the returndispersion measured by CSAD for entire period studied as well as two sub-periods

of before and after global financial crisis

For the stage before financial crisis, value of return dispersion measured by CSAD fluctuates between 0.00% and 6.139% with magnitude of volatility of

0.8088% represented by standard deviation (SD) Meanwhile, for the period of the beginning and after global financial crisis (2008-4/2015), maximum and minimum values of CSAD are 4.118% and 0.324%, respectively and fluctuation level decreases

to 0.4777% Magnitude of volatility of return dispersion tends to be downtrend

suggesting that investors in post-crisis period have a tendency to follow market

consensus greater than period of pre-crisis

The research also reports univariate statistics on the daily market return The average value of VN-Index is approximately -0.0181% over entire period studied Its