1

Herd Behavior in emerging equity markets: Evidence from Vietnam

Xuan Vinh Vo

School of Banking, University of Economics, Ho Chi Minh City

and CFVG Ho Chi Minh City, Vietnam,

279 Nguyen Tri Phuong Street, District 10, Ho Chi Minh City, Vietnam.

Email address: vxvinh@yahoo.com

Dang Bao Anh Phan

Faculty of Tax and Customs, University of Finance and Marketing, Ho Chi Minh City,

Vietnam,

2/4 Tran Xuan Soan Street, District 7, Ho Chi Minh City, Vietnam.

Email address: baoanhphan.ueh@gmail.com

Abstract

This study examines the herd behavior in Vietnam stock market using a sample of 299

companies listed on the Ho Chi Minh City Stock Exchange covering the time period 2005-2015.

We find evidence of herding in both rising and falling market employing the common least

squares estimation. Further analysis by applying quantile regression method, we also confirm

evidence of herding during the whole period. The results are robust when we split the data into

two sub-periods.

Key words: herd behavior, Vietnam stock market, quantile regression, asymmetry.

2

Herd Behavior in emerging equity markets: Evidence from Vietnam

1. Introduction

Herding is an important phenomenon in finance and it has been the topic for a huge amount of

work. This herd behavior has been documented in many published papers (Venezia, Nashikkar,

& Shapira, 2011; Wagner, 2003; Welch, 2000; Wermer, 1999). Most of the current papers

investigate herding in the context of advanced markets. However, there seems to be a light

volume of work in the context of emerging economies where stock markets are still at an early

stage of development.

The main objective of this paper is to examine the existence of herding in Vietnam stock

market. Particularly, we analyze the relationship between the level of equity return dispersions,

which is measured by the cross-sectional absolute deviation (CSAD) and the overall market

return. In addition, we consider the asymmetric effect of herding. In other words, we investigate

the existence of the difference in the herding degree under various market conditions.

Similar to other emerging markets, Vietnam stock market has been characterized by significant

volatility and remarkable fluctuations. Particularly, the VN-Index increases to 571 points in

2001 from 100 points in 2000. However, the index falls to 140 points in 2003, and fluctuates

around the range from 150 to 200 points in 2004. The peak period is during 2006-2007 when

the index reaches the peak of 1170 points. This makes Vietnam stock market become the

highest gain in Asia Pacific region before financial crisis but the global financial crisis seems

to wipe out many previous gain. The VN-Index considerably decreases to the lowest record of

236 points in 2009.

Previous authors produce an enormous amount of work to promote a better understanding of

herd behavior in stock markets. Further, many papers analyze the impact of herding on stock

prices. These investment behaviors are influenced by factors such as investors’ insight,

criterion to measure investment efficiency or market instability. Following the trend, this paper

provides further insight into the tendency of market participants to follow the actions of others

in an emerging market. In other words, this paper examines herd behavior in the context of

Vietnam stock market.

3

Herding is defined as the behavior of investors to imitate the actions of others (Tan, Chiang,

Mason, & Nelling, 2008). This tendency is considered as an inherent psychology of investors

but it becomes stronger as they have to make decisions in a market condition with high

uncertainty and low transparency. A numerous theories are developed and empirical

investigations are conducted to examine the presence and reasons of this phenomenon in

financial market. Previous authors also document that the presence of herding has a strong

impact on stock prices and this behavior significantly affects risk and return of stocks (Tan et

al., 2008). If market participants follow market consensus, the problem is more serious in many

perspectives. For example, herding leads to instability in the financial system, particularly in

the period of global crisis as documented in many previous studies. In addition, herding can

drive stock prices further from fundamental values causing excessive destabilization in the

stock market. Further, herding reduces market efficiency and even leads to financial collapse.

This paper advances previous studies by employing a more robust estimation analysis. Instead

of using the prevalent methods in the current literature, we utilize the quantile regression

approach outlined by Chang, Cheng, and Khorona (2000). This estimation approach is

considered as a valid alternative in the analysis of herding model in previous herding studies

(for example, see Christie and Huang (1995) and Chang et al. (2000)).

The remainder of the paper is organized as follows. The next section presents a review of

literature. The third section describes data and research methodology. The fourth section

presents the results and discussion of results. The final section summarizes the core findings

and concludes the paper.

2. Literature Review

There is a huge volume of previous papers examining the presence of herd behavior in the

context of both developed and developing countries. Theoretically, many studies focus on

concepts and classifications of herding. Other papers analyze what drives herding and its

impact on financial system (Bikhchandani & Sharma, 2001; Hsieh, 2013; Scharfstein & Stein,

1990; Spyrou, 2013). On the empirical side, most of previous work focus on examining the

existence of herding in financial markets. Especially, many authors investigate herding of

different group of investors (ie. individual and institutional investors) in different markets (Y.-

C. Chiang, 2008; Nicole Choi & Sias, 2009; Gavriilidis, Kallinterakis, & Ferreira, 2013; Hsieh,

4

2013; Hung, Lu, & Lee, 2010; Kremer & Nautz, 2013; Lakshman, Basu, & Vaidyanathan,

2013; Wermers, 1999; Zheng, Li, & Zhu, 2015).

However, empirical studies produce inconclusive results in the current literature. Particularly,

many papers show weak or no evidence of herding in stock markets. For example, Lakonishok,

Shleifer, and Vishny (1992) use a quarterly portfolio of 769 equity pension funds between 1985

and 1989 to evaluate how their trading affects stock prices. The findings indicate that no herd

behavior is found in smaller stocks. Further, there is no cross-sectional relationship between

changes in pension funds’ holding of a stock and its abnormal return in the US stock market.

In an influential study, Christie and Huang (1995) propose a model to examine the existence

of herding. The findings show that there is no herding in the US market. In the same approach,

Chang et al. (2000) extend the analysis using an alternative model. This study reports no

evidence of herding in the US and Hong Kong stock markets while investors herd partially in

Japan equity market. Another notice drawn from the findings is the existence of this

phenomenon in two Asian countries (ie. South Korea and Taiwan).

In addition, many previous papers provide evidence supporting the presence of herd behavior

in stock markets. For example, Nofsinger and Sias (1999) document a strong relationship

between institutional investors and herding formation in the US stock market. Similarly,

Wermer (1999) reports the empirical evidence of herding in the US mutual fund industry over

the period of 1975 to 1994. From the international perspective, Hwang and Salmon (2001)

investigate the presence of herding in the US, the UK and South Korea. This study also shows

that herd behavior tends to be stronger in emerging markets than in advanced markets.

Caparrelli, D’Arcangelis, and Cassuto (2010) examine the herding existence in Italian stock

market and present a non-linear relationship between return dispersions and market returns.

The results support the common outcome that herding exists in extreme market conditions.

Economou, Kostakis, and Philippas (2011) investigate herd behavior in four Mediterranean

stock markets. The findings indicate that there is evidence for the presence of this phenomenon

in Italy and Greece during investigation period. This conclusion is in line with the results of

Caparrelli et al. (2010) and Caporale, Fotini, and Nikolaos (2008) for Italian and Greek stock

market. In addition, asymmetric herd behavior is also examined with respect to market returns,

trading volume and return volatility. Noticeably, there is no evidence of herding in the Spanish

5

stock market. During the global financial crisis of 2008, herding is reported only in Portuguese

stock market while investors in the three other Mediterranean countries seem to be rational.

Jeon and Moffett (2010) examine the herd behavior in an emerging market and this paper

reports a significant herding impact on stock returns. Chiang et al (2010) find the existence of

herding in both Shanghai and Shenzhen stock exchanges using a modified model in the spirit

of Chang et al (2000). Sharma, Narayan, and Thuraisamy (2015) focus on investigating the

presence of herd behavior in Chinese stock market and find supporting evidence. More

importantly, this paper also documents asymmetric effect with a greater magnitude of herding

in up markets than in down markets.

Recently, Choi and Skiba (2015) use a set of quarterly institutional holdings data. The study

finds statistically significant herding propensities in 41 countries that have significant presence

of institutional investors. Bernales, Verousis, and Voukelatos (forthcoming) suggest that

herding is more profound during periods of high market volatility risk.

3. Data and methodology

3.1. Data

We collect the data of daily closing prices of all stocks listed on the Ho Chi Minh City Stock

Exchange (HSX) and the VN-Index (as a proxy for market returns) over the period from 2005

to 2015. The final data set includes 299 firms yielding 2568 daily observations. In addition, we

divide our sample into two sub-periods covering pre and post global financial crisis.

Specifically, the pre-crisis period (from 2005 to 2007) includes 749 observations and the post-

crisis period (from 2008 to 2015) includes 1819 daily observations.

3.2. Methodology

We run the following equation which is proposed by Chang et al (2000) in order to investigate

the presence of herding in Vietnam stock market:

𝐶𝑆𝐴𝐷𝑡 = 𝛾0 + 𝛾1|𝑅𝑚,𝑡| + 𝛾2𝑅𝑚,𝑡2 + 𝜀𝑡 (1)

where 𝐶𝑆𝐴𝐷𝑡 is a cross – sectional absolute deviation. It is constructed to measure return

dispersions, which is calculated as follows:

𝐶𝑆𝐴𝐷𝑡 = 1

𝑁∑ |𝑅𝑖,𝑡 − 𝑅𝑚,𝑡|𝑁

𝑖=1 (2)

6

where 𝑅𝑚,𝑡 is the market return and 𝑅𝑖,𝑡 is the return of stock i at time t. The return of individual

stock at time t is calculated as 𝑅𝑖,𝑡 = 100 𝑥 (ln(𝑃𝑡) − ln(𝑃𝑡−1)), where Pt and Pt-1 is the closing

price at time t and t-1, respectively.

Chang et al. (2000) assert that under normal conditions, the relationship between return

dispersions and market volatility, as determined by the rational asset pricing model, is linear.

Therefore, an increase in the absolute value of the market returns will lead to a rise in the

dispersions of individual investor returns. However, the notion behind this approach is that

during periods of relatively large price movement, if market participants incline to make

decisions based on aggregate market behavior, such increasingly linear relationship no longer

holds; instead, it is more likely to be non-linear increasing or even decreasing. Thus, a negative

and statistically significant of coefficient 𝛾2 in equation (1) will indicate the presence of this

phenomenon in Vietnam stock market. We use OLS approach as the estimation to run the above

regression model.

It is noticed that the herding coefficient of 𝛾2 in equation (1) is not under consideration of

asymmetric effect arising from up and down markets. We further examine whether the degree

of herd behavior is asymmetric in rising and falling markets. The following model which is

modified by Chiang et al (2010) in spirit of Chang et al (2000) is applied:

𝐶𝑆𝐴𝐷𝑡 = 𝛾0 + 𝛾1(1 − 𝐷)𝑅𝑚,𝑡 + 𝛾2𝐷𝑅𝑚,𝑡 + 𝛾3(1 − 𝐷)𝑅𝑚,𝑡2 + 𝛾4𝐷𝑅𝑚,𝑡

2 + 𝜀𝑡 (3)

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

The coefficients of 𝛾3 and 𝛾4 express the non-linear relationship between CSADt and 𝑅𝑚,𝑡 in

up and down markets, respectively. If 𝛾3 < 𝛾4, CSADt in up market is smaller than in down

market. As such, with the same level of volatility in market returns, the return dispersions will

strongly decrease on days of down market vis-à-vis days of up market.

Quantile regression analysis (QREG) is an alternative estimator used besides OLS approach to

investigate the asymmetry in rising and falling market. This additional analysis is used in order

to provide a more robust result. This quantile regression method has advantages over others in

measuring the dispersions, particularly in a non-stable environment. Moreover, it enables us to

perform the regression over the entire distribution of dependent variable to produce a more

reliable result (Zhou & Anderson, 2011).

7

In the spirit of test equation (3), quantile regression for measuring dependent variable CSADt

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

𝑄𝑟 (𝜏|Xt) = 𝛾0,𝜏 + 𝛾1,𝜏 (1 − D)Rm,t + 𝛾2,𝜏 𝐷. Rm,t + 𝛾3,𝜏(1 − D)Rm,t 2 + 𝛾4,𝜏𝐷. Rm,t

2 + ετ,t

where Xt is a vector of the right-hand-side variables; D is a dummy variable in which D = 1 if

Rm,t < 0 and D = 0 otherwise.

4. Empirical results

4.1. Descriptive statistics

Table 1: Descriptive statistics for daily market returns and cross-sectional absolute deviation (CSAD)

of returns for Vietnam stock market from 2005 to 2015.

Variables Mean Max Min Standard

Deviation

The whole period

Rm,t 0.0060 7.7414 -6.0512 1.4595

CSADt 1.7596 6.1391 0.0000 0.6182

Pre-crisis period

Rm,t 0.1424 7.7414 -4.9714 1.7147

CSADt 1.4907 6.1391 0.0000 0.8089

Post-crisis period

Rm,t -0.0056 4.6529 -6.0512 1.4349

CSADt 1.8704 4.1179 0.3241 0.4777

Before financial crisis, value of return dispersions measured by CSAD fluctuates between

0.00% and 6.139% with magnitude of volatility (standard deviation) of 0.8088%. After global

financial crisis, maximum and minimum values of CSAD are 4.118% and 0.324%, respectively

and fluctuation level reaches 0.4777%. The decrease in volatility magnitude of return

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

consensus greater than in pre-crisis period.

The paper also reports univariate statistics on the daily market returns. The average value of

the VN-Index returns is about -0.0181% over the entire period. The maximum and minimum

values are 7.741% and -6.051%, respectively. The daily market returns in Vietnam exhibit a

magnitude of volatility with standard deviation of 1.4595% per day, which is considered a

relatively high fluctuation compared to that of other countries’ stock markets in the world. This

8

point is completely consistent with high gain and drastic fluctuation in Vietnam stock market.

However, it is interesting to observe that the variation of the market returns is comparatively

greater in pre-crisis period (1.7147%) than that in post-crisis period (1.4349%).

In general, the preliminary descriptive statistics indicate that Vietnam stock market is

characterized by high magnitude of volatility in daily returns. Further, the decrease in stock

return dispersions might be considered as an evidence for the existence of herd behavior in this

market over the investigated period.

4.2. Regression results

Table 2 below shows the estimation results to analyze the existence of herding in Vietnam

stock market. We estimate the model for the full sample and different sub-period samples.

Table 2: Regression result of herding in Vietnam stock market

Variables The whole period Pre-crisis period Post-crisis period

Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic

𝜸𝟎 1.482*** 66.659 0.9837*** 21.918 1.7216*** 80.858

𝜸𝟏 0.468*** 15.379 0.675*** 11.202 0.349*** 11.812

𝜸𝟐 -0.104*** -14.109 -0.098*** -6.902 -0.104*** -14.260

R-squared 0.084 0.2226 0.1107

Adjusted R-

squared

0.0839 0.2205 0.1098

F-statistic 118.5819 106.7831 113.0672

Prob (F-statistic) 0.0000 0.0000 0.0000

Note: This table reports the estimation results of the equation 𝑪𝑺𝑨𝑫𝒕 = 𝜸𝟎 + 𝜸𝟏|𝑹𝒎,𝒕| + 𝜸𝟐𝑹𝒎,𝒕𝟐 + 𝜺𝒕, in which

𝐶𝑆𝐴𝐷𝑡 is the equally weighted cross-sectional absolute deviation of returns, Rm,t is the market return at time t.

The whole sample period is from 2/1/2005 to 22/4/2015. *** denotes statistical significance at the 1% level.

Our results indicate a negative and significant value on the coefficient 𝛾2 which shows the

evidence of herding. As such, the relationship between CSAD and Rm,t is quadratic non-linear.

In other words, there is evidence supporting the existence of herding in Vietnam stock market

during the whole period. Clearly, investors tend to follow market fluctuation and ignore their

own private information when the stock market fluctuates strongly. This makes the disparity

between stock returns and market returns decrease, or unsystematical risk of individual stock

nearly changes into market risk and dominates systematical risk. In summary, stock market

participants have a tendency to observe and follow market consensus during volatile period

since they believe other investors have more precise signals.

9

Further analyzing the data by separating the sample into two distinctive sub-periods of pre-

crisis and post-crisis, we find both estimated coefficients 𝛾2 are negative and statistically

significant at the 1% level. This result displays herding existence in Vietnam stock market both

before and after global financial crisis. The incomplete and ineffective institutional

environment of an emerging market makes the market more vulnerable to adverse effects,

which in turn influences the information transparency; thus, leading to herding. This

phenomenon drives stock prices further from their fundamental values which causes many bad

consequences in financial system.

In post-crisis period, result indicates that herding is evidenced during this time. Moreover,

absolute value of the coefficient of Rm,t2 in post-crisis period is greater than that in pre-crisis

period which means herd behavior manifests more strongly in post-crisis period. This might be

explained by the fact that stock market volatility is very unusual in this period and investors

with risk-averse sentiment hold declining-price stocks and hope the price will rise in the future,

while others expect to earn partial amount to cover previous loss. Therefore, individual

investors tend to ignore their possessive information and consider other market participants’

actions instead. They believe it is more important to follow investment decisions of other

investors, particularly institutional investors.

In conclusion, we find evidence of a statistically significant negative relationship between

return dispersions (measured by cross-sectional absolute deviation) and market returns. In other

words, our results indicate the existence of herding in Vietnam stock market for the whole

period.

Table 3 reports the results in rising and declining market where OLS is employed. Both

coefficients of 𝛾3 and 𝛾4 are statistically significant and negative, which prove the existence of

herding throughout the entire period in both rising and falling markets. In order to examine

whether asymmetric effect exists in up and down markets, a Wald test is applied to investigate

the equality of slopes. The results indicate there is a distinction in herding level between days

when the market is up and days when the market is down. Specifically, for the pre-crisis period,

the difference 𝛾3 − 𝛾4 = 0.1383 indicates herding in downside market is considerably stronger

than that in upside market. Conversely, for the post-crisis period, 𝛾3 − 𝛾4 = -0.0269 suggests

that herd behavior is more popular in rising market than in declining market.

Table 3: Regression results of herding in rising and declining market by OLS approach.

Variables The whole period Pre-crisis period Post-crisis period

10

Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics

𝜸𝟎 1.4792*** 66.650 0.9395*** 21.005 1.7165*** 80.691

𝜸𝟏 0.4141*** 12.078 0.5935*** 9.348 0.3781*** 10.739

𝜸𝟐 -0.5379*** -14.639 -0.9874*** -11.599 -0.3460*** -10.454

𝜸𝟑 -0.0853*** -9.511 -0.0631*** -4.133 -0.1227*** -12.494

𝜸𝟒 -0.1271*** -13.187 -0.2014*** -8.737 -0.0958*** -11.229

R-squared 0.0895 0.2549 0.1189

Adjusted

R2

0.0881 0.2509 0.1170

F-statistic 62.9711 63.6220 61.2454

Prob 0.0000 0.0000 0.0000

Wald coefficient test

H0: 𝛾3 − 𝛾4 = 0 H0: 𝛾3 − 𝛾4 = 0 H0: 𝛾3 − 𝛾4 = 0

F-statistic 170.1855 126.8234 52.9643

p-value 0.0000 0.0000 0.0000

𝜸𝟑 − 𝜸𝟒 0.0418 0.1383 -0.0269

Note: This table reports results of the following equation: 𝑪𝑺𝑨𝑫𝒕 = 𝜸𝟎 + 𝜸𝟏(𝟏 − 𝑫)𝑹𝒎,𝒕 + 𝜸𝟐𝑫𝑹𝒎,𝒕 +

𝜸𝟑(𝟏 − 𝑫)𝑹𝒎,𝒕𝟐 + 𝜸𝟒𝑫𝑹𝒎,𝒕

𝟐 + 𝜺𝒕 where D = 1 if Rm,t < 0, Rm,t is the market returns, CSADt is the equally

weighted cross-sectional absolute deviation of returns. The whole sample period is from 2/1/2005 to 22/4/2015.

*** denotes statistical significance at the 1% level.

Table 4 presents the results in each period using the quantile regression method. In panel A,

the estimates suggest that both coefficients 𝛾3 and 𝛾4 are negative and statistically significant

in most of quantiles (𝜏 = 10%, 25%, 50% and 75%). In the highest quantile (𝜏 = 90%), we

observe that the herding coefficient is positive on days when the market is up but on days when

the market is down. Overall, our finding indicates that herding exists. This finding is relatively

consistent with the analysis using OLS estimation method.

We observe that there are some differences in the results when we split sample into sub-periods.

Specifically, beyond the median level for the time before financial crisis (panel B), herding

coefficient is negative and statistically significant in case of declining market in the 75th

quantile (𝜏 = 75%) only and no evidence found in other quantiles in both market conditions.

On the other hand, looking at the panel C, herding coefficients are negative in all quantiles with

very high significance level, except that 𝛾4 in the low quantile (𝜏 = 25%) on the market down

days is statistically significant but positive, which means no presence of herding found in this

case.

11

In general, the insignificance of this phenomenon for cases in upper quantiles reflects the fact

that herding activity is less likely to occur for the return dispersions at the upper tail of the

distribution. It also reveals that investors display more homogeneous trading behavior,

particularly on days when the market is declined.

We further examine the equality of slopes by employing the Wald test. The last column in table

4 shows the estimated statistics and indicates that the null hypothesis (𝛾3 − 𝛾4 = 0) is uniformly

rejected at all quantile distributions throughout the period studied. In other words, there is a

difference in the herding level in rising market and falling market. However, when

investigating each sub-period, the test results are somewhat different. Namely, in the upper

quantile (𝜏 = 90%) at pre-crisis stage and in the lower quantile (𝜏 = 10%) at post-crisis stage,

F-statistic value is not statistically significant. However, this does not affect much the result of

the whole period.

We also compare the quantile regression results with those derived from the OLS approach.

Generally, both of the test results are similar when examining over entire sample period.

Nevertheless, there are some differences in the results from two methods when we consider

each sub-period separately. Using OLS approach, we find evidence of distinction in herding

degree between rising and falling market in both pre-crisis and post-crisis period over the entire

distribution. When applying quantile regression approach; however, we discover there is no

difference in herding level, even the presence of herd behavior in some upper tails and lower

tails of return dispersions curve depending on each separated period. The reason is OLS

approach focuses on mean as a measure of location, while the quantile regression analysis

allows the author to compute a family of regression curves, each corresponding to a different

quantile of the conditional distribution of the dependent variables (Chiang et al, 2010). Further,

quantile regression provides a much more overall picture of the conditional distribution

between return dispersionss and independent variables.

12

Table 4: Analysis of herding in rising and falling market by quantile regression estimator.

Wald test

Variables 𝛾0 𝛾1 𝛾2 𝛾3 𝛾4 R2 F-statistic

Panel A: The whole period

Quantile (𝝉 =

𝟏𝟎%)

0.4343***

(15.53)

0.9918***

(22.5464)

-0.8676***

(-14.0651)

-0.2165***

(-20.8949)

-0.1768***

(-13.3933)

0.1419 119.776***

Quantile (𝝉 =

𝟐𝟓%)

1.0465***

(20.07)

0.7769***

(14.2379)

-0.75447***

(-12.9177)

-0.1946***

(-17.7744)

-0.1776***

(-15.3503)

0.1036 119.54***

Quantile (𝝉 =

𝟓𝟎%)

1.4771***

(65.00)

0.5754***

(13.1415)

-0.6024***

(-16.6710)

-0.1542***

(-10.7475)

-0.1518***

(-14.7756)

0.0856 193.89***

Quantile (𝝉 =

𝟕𝟓%)

1.7873***

(63.60)

0.4556***

(7.0005)

-0.5183***

(-9.8965)

-0.1069***

(-4.3048)

-0.1279***

(-7.6685)

0.0514 76.49***

Quantile (𝝉 =

𝟗𝟎%)

2.2073***

(40.53)

0.1695

(1.4639)

-0.3753***

(-3.2861)

0.0012

(0.0313)

-0.0773**

(-1.9497)

0.0337 13.36***

Panel B: Pre-crisis period

Quantile (𝝉 =

𝟏𝟎%)

0.2717***

(10.44)

0.9610***

(14.6701)

-0.9160***

(-16.8769)

-0.1996***

(-11.2899)

-0.1810***

(-15.0789)

0.3003 199.21***

Quantile (𝝉 =

𝟐𝟓%)

0.3763***

(11.62)

1.0378***

(12.6786)

-1.0178***

(-15.2940)

-0.2102***

(-8.9594)

-0.2017***

(-13.2370)

0.2815 173.14***

Quantile (𝝉 =

𝟓𝟎%)

0.6096***

(12.76)

1.0940***

(8.2119)

-1.1193***

(-11.3814)

-.02112***

(-4.7470)

-0.2189***

(-8.4219)

0.2171 100.71***

13

Quantile (𝝉 =

𝟕𝟓%)

1.1753***

(10.51)

0.8392***

(3.5206)

-1.1527***

(-7.0115)

-0.1152

(-1.5397)

-0.2534***

(-6.8950)

0.1309 54.61***

Quantile (𝝉 =

𝟗𝟎%)

1.9023***

(10.37)

0.3427**

(2.3154)

-1.1286

(-1.1274)

0.0264

(1.5779)

-0.2328

(-0.4333)

0.0964 1.36

Panel C: Post-crisis period

Quantile (𝝉 =

𝟏𝟎%)

1.3808***

(55.15)

0.2844***

(4.9813)

-0.2087***

(-2.7442)

-0.1038***

(-9.0034)

-0.0758***

(-4.7820)

0.1453 2.20

Quantile (𝝉 =

𝟐𝟓%)

1.4407***

(81.95)

0.4555***

(16.5153)

-0.4560***

(-13.0181)

-0.1421***

(-23.6701)

0.1279***

(-15.9817)

0.1318 91.97***

Quantile (𝝉 =

𝟓𝟎%)

1.6104***

(90.97)

0.5021***

(18.4118)

-0.5146***

(-17.0062)

-0.1558***

(-23.6212)

-0.1422***

(-17.1981)

0.1042 159.28***

Quantile (𝝉 =

𝟕𝟓%)

1.8505***

(74.42)

0.4598***

(10.5505)

-0.4438***

(-10.0693)

-0.1371***

(-10.8205)

-0.1119***

(-8.0684)

0.0581 56.76***

Quantile (𝝉 =

𝟗𝟎%)

2.2306***

(47.20)

0.2734**

(2.0849)

-0.2793***

(-5.1724)

-0.0804*

(-1.7358)

-0.0543***

(-6.0147)

0.0249 16.43***

Note: This table reports the regression results in the Vietnam stock market by different CSAD quantile groups. The estimated equation is given by: 𝑸𝒓 (𝝉|𝐗𝐭) = 𝜸𝟎,𝝉 +

𝜸𝟏,𝝉 (𝟏 − 𝐃)𝐑𝐦,𝐭 + 𝜸𝟐,𝝉 𝑫. 𝐑𝐦,𝐭 + 𝜸𝟑,𝝉(𝟏 − 𝐃)𝐑𝐦,𝐭 𝟐 + 𝜸𝟒,𝝉𝑫. 𝐑𝐦,𝐭

𝟐 + 𝛆𝛕,𝐭 where Rm,t is market returns at time t, CSADt is equally weighted cross-sectional absolute deviation

of returns, which is the dependent variable. Xt represents a vector of independent variables. D is a dummy variable by setting D = 1 if Rm,t < 0 and D = 0 otherwise. 𝛾𝑘,𝜏 is the

kth coefficient conditional on the 𝜏th quantile distribution of the above equation. The whole sample period is from 2/1/2005 to 22/4/2015. ***, ** and * denote statistical

significance at 1%, 5% and 10% level, respectively.

14

5. CONCLUSION AND IMPLICATIONS

This paper reports the presence of herd behavior in Vietnam stock market by investigating the

non-linear relationship between stock return dispersions and market returns. We use the model

proposed by Chang et al (2000). Our data sample includes all stocks listed on the Ho Chi Minh

City stock exchange (HSX) over the period from 2005 to 2015. Because previous studies show

that herding research is sensitive to different quantiles in distribution of dependent variable

(CSAD), we use the quantile regression analysis as an alternative method to examine the level

of herding in different market conditions. This estimator also provides a more complete picture

of the conditional distribution between return dispersions and independent variables.

Our results provide empirical evidence of herd behavior in Vietnam stock market over the

sample period. We also examine the nature of this phenomenon by dividing the sample data

into two sub-periods (pre-crisis and post-crisis). The finding suggests that herding is higher in

post-crisis period than in pre-crisis period.

The study also finds evidence indicating that herding level is different in different market

conditions. More specifically, regression results suggest that there is a distinction in herding

degree in up and down markets. The level of herd behavior is stronger in declining market over

the whole sample and pre-crisis period. For the post-crisis period, herding phenomenon

develops in opposite direction (ie. it is stronger in up market). In addition, results derived from

quantile regression suggests some differences in herding level on days when the market is

rising vis-à-vis on days when the market is falling for each separated period corresponding to

specific quantiles.

References

Bernales, A., Verousis, T., & Voukelatos, N. (forthcoming). Do investors follow the herd in option markets? Journal of Banking & Finance. doi: http://dx.doi.org/10.1016/j.jbankfin.2016.02.002

Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial market. IMF Staff Papers, 47(3), 279-310.

Caparrelli, F., D’Arcangelis, A. M., & Cassuto, A. (2010). Herding in the Italian stock market: A case of behavioral finance. Journal of Behavior Finance, 5(4), 222-230.

Caporale, G. M., Fotini, E., & Nikolaos, P. (2008). Herding behavior in extreme market conditions: The case of the Athens stock exchange. Economics Bulletin, 7(17), 1-13.

Chang, E. C., Cheng, J. W., & Khorona, A. (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking and Finance, 24(10), 1651-1779.

Chiang, T. C., Li, J., & Tan, L. (2010). Empirical investigation of herding behavior in Chinese stock markets: Evidence from quantile regression analysis. Global Finance Journal, 21(1), 111-124. doi: http://dx.doi.org/10.1016/j.gfj.2010.03.005

15

Chiang, Y.-C. (2008). Foreign Investor Herding on the Taiwanese Stock Market. International Journal of Management, 25(3), 546-550,593.

Choi, N., & Sias, R. W. (2009). Institutional industry herding. Journal of Financial Economics, 94(3), 469-491. doi: http://dx.doi.org/10.1016/j.jfineco.2008.12.009

Choi, N., & Skiba, H. (2015). Institutional herding in international markets. Journal of Banking and Finance, 55, 246-259.

Christie, W., & Huang, R. D. (1995). Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal, 51(4), 31-37.

Economou, F., Kostakis, A., & Philippas, N. (2011). Cross-country effects in herding behaviour: Evidence from four south European markets. Journal of International Financial Markets, Institutions and Money, 21(3), 443-460. doi: http://dx.doi.org/10.1016/j.intfin.2011.01.005

Gavriilidis, K., Kallinterakis, V., & Ferreira, M. P. L. (2013). Institutional industry herding: Intentional or spurious? Journal of International Financial Markets, Institutions and Money, 26(0), 192-214. doi: http://dx.doi.org/10.1016/j.intfin.2013.05.008

Hsieh, S.-F. (2013). Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market. International Review of Financial Analysis, 29(0), 175-188. doi: http://dx.doi.org/10.1016/j.irfa.2013.01.003

Hung, W., Lu, C.-C., & Lee, C. F. (2010). Mutual fund herding its impact on stock returns: Evidence from the Taiwan stock market. Pacific-Basin Finance Journal, 18(5), 477-493. doi: http://dx.doi.org/10.1016/j.pacfin.2010.06.001

Hwang, S., & Salmon, M. (2001). A new measure of herding and empirical evidence. Business school UK.

Jeon, J. Q., & Moffett, C. M. (2010). Herding by foreign investors and emerging market equity returns: Evidence from Korea. International Review of Economics & Finance, 19(4), 698-710. doi: http://dx.doi.org/10.1016/j.iref.2010.03.001

Kremer, S., & Nautz, D. (2013). Causes and consequences of short-term institutional herding. Journal of Banking & Finance, 37(5), 1676-1686. doi: http://dx.doi.org/10.1016/j.jbankfin.2012.12.006

Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32(1), 23-43.

Lakshman, M., Basu, S., & Vaidyanathan, R. (2013). Market-wide herding and the impact of institutional investors in the Indian capital market. Journal of Emerging Market Finance, 12(2), 197-237.

Nofsinger, J. R., & Sias, R. W. (1999). Herding and feedback trading by institutional and individual investors. The Journal of Finance, 54(6), 2263-2295.

Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. The American Economic Review, 80(3), 465-479.

Sharma, S. S., Narayan, P., & Thuraisamy, K. (2015). Time-varying herding behavior, global financial crisis, and the Chinese stock market. Review of Pacific Basin Financial Markets and Policies, 18(02), 1550009.

Spyrou, S. (2013). Herding in financial markets: A review of the literature. Review of Behavioral Finance, 5(2), 175-194.

Tan, L., Chiang, T. C., Mason, J. R., & Nelling, E. (2008). Herding behavior in Chinese stock markets: An examination of A and B shares. Pacific-Basin Finance Journal, 16(1–2), 61-77. doi: http://dx.doi.org/10.1016/j.pacfin.2007.04.004

Venezia, I., Nashikkar, A., & Shapira, Z. (2011). Firm specific and macro herding by professional and amateur investors and their effects on market volatility. Journal of Banking & Finance, 35(7), 1599-1609. doi: http://dx.doi.org/10.1016/j.jbankfin.2010.11.015

Wagner, F. (2003). Volatility cluster and herding. Physica A: Statistical Mechanics and its Applications, 322(0), 607-619. doi: http://dx.doi.org/10.1016/S0378-4371(02)01810-1

16

Welch, I. (2000). Herding among security analysts. Journal of Financial Economics, 58(3), 369-396. doi: http://dx.doi.org/10.1016/S0304-405X(00)00076-3

Wermer, R. (1999). Mutual fund herding and the impact on stock prices. The Journal of Finance, 54(2), 581-622.

Wermers, R. (1999). Mutual fund herding and the impact on stock prices. The Journal of Finance, 54(2), 581-622.

Zheng, D., Li, H., & Zhu, X. (2015). Herding behavior in institutional investors: Evidence from China’s stock market. Journal of Multinational Financial Management, 32, 59-76.

Zhou, J., & Anderson, R. I. (2011). An empirical investigation of herding behavior in the US REIT market. Journal of Real Estate, Finance and Economics, 47, 83-108.