Futher evidence on the herd behaviour in the stock market in viet nam

Email address: baoanhphan.ueh@gmail.com Abstract This paper examines the presence of herd behavior in Vietnam stock market using a sample of 299 companies listed on the Ho Chi Minh Cit

Further evidence on the herd behavior in Vietnam stock market

Xuan Vinh Vo School of Banking, University of Economics, Ho Chi Minh City 59C Nguyen Dinh Chieu Street, District 3, Ho Chi Minh City, Vietnam

and CFVG Ho Chi Minh City, Vietnam,

91 Ba Thang Hai Street, District 10, Ho Chi Minh City, Vietnam

Email address: vinhvx@ueh.edu.vn

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 paper examines the presence of 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 The study employs the herding measures proposed by Christie and Huang (1995) and Chang, Cheng and Khorona (2000) We provide a comprehensive analysis using daily, weekly and monthly frequency The results indicate the evidence of herding over the whole period studied Moreover, the results are robust when we split the data into three sub-periods including pre-crisis, during crisis and post-crisis Asymmetric effect is also evidenced under various market conditions and trading volume

Keywords: asymmetry, herd behavior, trading volume, Vietnam stock market

JEL: G02, G10, G12, G15

This paper sheds further light on the presence of herd behavior and the impact of global financial crisis on this phenomenon in Vietnam stock market We also examine its asymmetric effect in terms of different market conditions, various extreme market movements and trading volume The data sample includes daily, weekly and monthly closing prices of 299 firms listed

on the Ho Chi Minh Stock Exchange covering the period from 2005 to 2015 We employ the commonly applied cross-sectional standard deviation (CSSD) method proposed by Christie & Huang (1995) and cross-sectional absolute deviation (CSAD) method developed by Chang et

al (2000) to investigate herding in this study

Our paper is motivated by a number of reasons The first motivation arises from the inconclusive conclusion in existing literature about herding in emerging market in general and limited study regarding this topic in Vietnam stock market in particular Therefore, further

investigation is needed to provide a better understanding of the complete picture of herd behavior in stock markets

The second motivation is stem from the context of an emerging market There is a huge amount

of work focusing on the presence of herd behavior in advanced countries However, herding tends to be more pronounced in emerging markets where information asymmetry is stronger Moreover, stock markets of some emerging countries are gradually developed and make a contribution to the global financial markets The increasing importance of emerging markets is one of the motivations for further investigation about herd behavior in this context

The third motivation is from the characteristics of Vietnam stock market For more than decades from its establishment, Vietnam stock market has undergone a lot of ups and downs but information transparency has always been an issue A series of violation in information disclosure, illegal transactions, and price manipulation along with the shortage of legislation framework, management of government and operation of auditing enterprise result in the lack

of transparency in this equity market Bikhchandani et al (1992) claim that non-transparency

is one of the key reasons leading to herding Moreover, Vietnam stock market recently has gained more attention from foreign investors (Vo 2015) With those features of Vietnam stock market, it is important and interesting to investigate the existence and prevalence of herd behavior in this emerging equity market

A growing amount of literature has attempted to investigate the presence of herding using indirect measures by comparing return dispersion of individual stocks to market returns Recently, Vo & Phan (2016) examine the presence of herd behavior in Vietnam stock market utilizing quantile regression analysis The authors employ model outlined by Chang et al

(2000) with daily data set The findings indicate that herd behavior is evident in case of market stress and no evidence is found in the higher quantile of return dispersion distribution Moreover, the asymmetric effect of herding exists in Vietnam equity market with the prevalence of this phenomenon in down market than in up market

In Vietnam context, this paper extends Vo & Phan (2016) by investigating the presence of herding and its asymmetry in different market conditions in Vietnam stock market We employ cross-sectional standard deviation (CSSD) method proposed by Christie & Huang (1995) together with CSAD method to achieve main objectives However, this paper differs Vo & Phan (2016) in a number of perspectives Firstly, we examine the asymmetric effect of herding during extremely upward and downward market movements Previous research on the topic in the context of Vietnam stock market is limited so our research extension helps to provide a better understanding regarding herding in different extreme market conditions Secondly, we examine the presence of this phenomenon during global financial crisis as a separate period to clarify its nature Global financial crisis is considered a phase of high uncertainty which is more likely to have significant impact on the existence of herd behavior However, there is conflicting evidence of herding in this time period in Vietnam equity market; even there is not

a clear division among stages of before, during and after crisis Therefore, global financial crisis period in 2008 is detected separately to investigate its impact on herding

This paper makes several contributions to the current literature Firstly, to the best of our knowledge, we are among the first to provide the evidence of this phenomenon in Vietnam using daily, weekly and monthly data set ranging from 2005 to 2015 to investigate both middle-term and long-term perspective Most of previous studies focus on analyzing herd behavior in short-term based on daily observations Secondly, we associate level of herd behavior with

trading volume in order to find out the relationship between return dispersion and market consensus when the market is in high and low volume states This investigation has not been done before for Vietnam stock market

The remainder of the paper is structured as follows Section 2 presents a review of literature Section 3 describes data and research methodology Section 4 reports the empirical results and section 5 concludes the paper

2 Literature Review

There is a huge volume of recent work in finance literature attempting to investigate herd behavior Theoretically, many studies focus on concepts and classifications of herding (Bikhchandani & Sharma 2001; Spyrou 2013) Other papers analyze what drives herding and its impact on financial system Some argue that this phenomenon drives the prices further from the fundamental values and causes destabilization (Bikhchandani & Sharma 2001; Hsieh 2013; Scharfstein & Stein 1990; Spyrou 2013) Others argue that herding actually makes the market more efficient because prices are adjusted faster to new information (Hirshleifer et al 1994; Hirshleifer & Teoh 2003)

On the empirical side, many previous papers examine the presence of herding from international perspectives Particularly, a number of studies investigate this phenomenon in a multi-market setting (Blasco & Ferreruela 2008; Borensztein & Gelos 2003; Chang et al 2000; Chiang & Zheng 2010; Choi & Skiba 2015; Hwang & Salmon 2001) Chang et al (2000) extend an influential analysis by Christie & Huang (1995) by employing market index returns

of different countries to explore herding propensities This study reports no evidence of herding

in the US and most other developed countries but strong evidence in two Asian emerging

markets (ie South Korea and Taiwan) Similarly to Chang et al (2000)’s results, Hwang & Salmon (2001) investigate the presence of herding in the US, the UK and South Korea and suggest that herd behavior tends to be stronger in emerging markets than in advanced markets

In contrast, Chiang & Zheng (2010) find evidence of this phenomenon in some developed stock markets when employing market index data to compute herding propensities in country-specific level Blasco & Ferreruela (2008) investigate herding in seven countries using cross-sectional standard deviation (CSSD) measure The authors find evidence of herd behavior in only Spain among the sample countries Borensztein & Gelos (2003) study herding in mutual funds of 400 emerging markets and show significant evidence in different market conditions from tranquil to crisis periods Recently, Choi & Skiba (2015) use a set of quarterly institutional holdings data of “target countries” including 41 countries in the sample and document the existence of wide-spread herding propensities

On the other hand, a huge volume of previous works focus on examining the existence of herding in a single stock market setting Table 1 shows some of empirical evidence on herding

in this perspective The results are different from countries to countries In general, herding is not only observed in the advanced market but also widely found in Asian and other European stock markets

Several papers in financial literature also examine herding in stock markets at the firms’ stock level (Choi & Skiba 2015) A few studies have also investigated this phenomenon in other assets For example, Gleason et al (2003) extend previous herding studies on common stocks

to examine herding in contracts traded in European futures markets They employ the CSSD method by Christie & Huang (1995) to examine the presence of herding in 13 commodities futures contracts on three European exchanges (FOX, MATIF, and ATA) They conclude that

herding is not evident in this future markets Oehler & Chao (2000) and Galariotis et al (2015) focus on bond markets In particular, Oehler & Chao (2000) find strong evidence of herding in German bond market using the sample of 57 German mutual funds However, the authors show that herding level is weaker in bond market than in stock market Galariotis et al (2015) utilize the commonly applied CSAD method to examine the return clustering in European bond market The results report no evidence of investors herding neither before nor after Europe crisis Zhou & Anderson (2011) investigate the market-wide herd behavior in the US real estate market Using quantile regression analysis, the authors find that investors tend to herd under turbulent market conditions In addition, the findings also support the existence of asymmetric effect which indicates the prevalence of herding in declining market than in rising market

Table 1: A summary of empirical evidence on herding in single market setting

The US Lakonishok et al (1991) Quarterly data from 1985 to 1989 LSV The study finds no evidence of substantial herding by US

pension fund managers, except in small stocks

Nofsinger & Sias (1999) Monthly data from 1977 to 1996 NS The results report the presence of herding in both US

institutional investors and individual investors However, institutional herding impacts prices more than herding by individual investors

Wermer (1999) Quarterly data from 1975 to 1994 LSV The authors find little herding by mutual funds in the

average stock but much higher levels in trades of small stocks in the US

Jiao & Ze (2014) Quarterly data from 2000 to 2007 LSV The findings indicate evidence of US mutual fund herding

and the associated price destabilization effects Moreover, the results reveal that mutual funds herd into or out of stocks following the herd of hedge funds, not vice-versa The UK Wylie (2005) Semi-annual data from 1986 to 1993 LSV Significant amount of fund manager herding is found in the

largest and smallest UK stocks after adjusting for the positive bias in the LSV herding measure

Italy Caparrelli et al (2010) Daily data from 1988 to 2011 CH, CCK and

HS

Herd behavior is evident during extreme market conditions

in terms of both sustained growth rate and high stock levels according to CH model

Greece Caporale et al (2008) Daily, weekly and monthly data from

1998 to 2007

CH and CCK Herding exists in the Athens stock market

Germany Walter & Weber (2006) Data from 1998 to 2002 LSV The results provide evidence of herding by German mutual

fund managers Moreover, the authors find that a significant portion of herding detected in the German market is associated with spurious herding

Spain Gavriilidis et al (2013) Quarterly data from 1995 to 2008 Sias model The results provide evidence that Spanish institutional

industry herds intentionally for most sectors which are underperformed; thus, generating high volatility and volume

Japan Kim & Nofsinger (2005) Monthly data from 1975 to 2001 NS The authors find the presence of herding in Japan with a

large impact on price movements In addition, the effects and behavior of institutional herding depend on the economic condition and the regulatory environment Korea Choe et al (1999) Daily data from 1996 to 1997 LSV Herding is found by foreign investors in Korea before

economic crisis However, herding falls during the crisis and no destabilization impacts on Korea stock market over the entire sample

Jeon & Moffett (2010) Monthly data from 1992 to 2003 Sias model The study finds evidence of strong impact of foreign

investors herding on stock returns Moreover, the results also indicate the opposite direction in buying and selling shares between foreign and domestic investors in the herding years

China Tan et al (2008) Daily data from 1994 to 2003 CCK and

modified CCK

The results reveal the presence of herding within Chinese stock market as well as asymmetric effect in terms of stock returns, trading volume and volatility

Chiang et al (2010) Daily data from 1996 to 2007 CCK and

modified CCK

Herding is found in within both the Shanghai and Shenzhen A-share markets but no evidence is reported within both B- shares when using OLS method Moreover, A-share investors display herding in both up and down market By applying Quantile Regression Analysis, the authors find supporting evidence of herding in both Exchange conditional on the return dispersion in the lower quantile region

Fu & Lin (2010) Monthly data from 2006 to 2009 CH and CCK Herding is not found in Chinese stock market but the

asymmetric effect is found with the prevalence of herding

in up market than in down market

India Lakshman et al (2013) Daily data from 1996 to 2008 HS The study shows that herding is observed in India stock

market but not very severe

Vietnam Vo & Phan (2016) Daily data from 2005 to 2015 CCK Herding is reported in Vietnam stock market, particularly

in the median and lower quantile of return dispersion distribution The results also reveal that herding is more pronounced in down market than in up market

Note: LSV, CH, CCK, HS and NS refers to Lakonishok-Schleifer-Vishny Model; Christie and Huang Model; Chang, Cheng and Khorana Model, Hwang and Salmon Model and Nofsinger and Sias model

3 Data and methodology

3.1 Data

We use daily, weekly and monthly frequency in order to provide a more comprehensive analysis into the herding behavior in Vietnam stock market Moreover, daily closing prices of all stocks listed on the Ho Chi Minh Stock Exchange (HSX) are collected in order to investigate herding in the short run since herd behavior is assumed as a very short-lived phenomenon (Christie & Huang 1995) However, Christie & Huang (1995) assert that daily data limit the manifestation of herding during periods of market stress Weekly and monthly stock prices of the same companies are also collected in consideration this phenomenon in longer time span All daily, weekly and monthly data sets cover the period from 2005 to 2015 The VN-Index data are collected and used as a proxy for the market returns Trading volume is also gathered

to attain the objectives in our paper

The final data set includes 299 firms yielding 2568, 515 and 122 daily, weekly and monthly observations, respectively In addition, we divide our sample into three sub-periods covering pre, during and post global financial crisis Specifically, the pre-crisis period (April 2005 - December 2007) includes 749 daily observations, the number of observations in global financial meltdown (January 2008 – December 2008) is 246 and the post-crisis period (January

2009 - April 2015) includes 1573 daily observations

3.2 Methodology

We employ equation similar to the method proposed by Christie & Huang (1995) to examine the presence of herding in Vietnam stock market:

𝑅𝐷𝑡 = 𝛼 + 𝛽1𝐷𝑡𝐿+ 𝛽2𝐷𝑡𝑈 + 𝜀𝑡 (1) where

𝑅𝐷𝑡 is the return dispersion at time t

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

𝐷𝑡𝑈 = 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, Christie & Huang (1995) propose the use of cross-

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

𝐶𝑆𝑆𝐷𝑡 = √∑𝑁𝑖=1(𝑅𝑖,𝑡 − 𝑅 𝑚,𝑡 )2

where N is the number of firms in the portfolio, 𝑅𝑖,𝑡 is the observed stock return of firm i at time t and 𝑅𝑚,𝑡 is the market return at time t If herding exists, return dispersion will be smaller during period of market stress because investors will make similar decision Thus, statistically significantly negative values of 𝛽1 and 𝛽2 in Eq (1) indicate the presence of herding Our study use the 1%, 5% and 10% cut-off points to define the extreme market movements As such, an observation of daily, weekly and monthly market return which lies on the first, fifth and tenth lower or upper percentile of its distribution is considered as an extreme market movement

However, there are a number of inherent drawbacks in the estimation method of Christie & Huang (1995) Firstly, the definition of extreme return is arbitrary The authors use the value

of 1%, 5% and 10% as the cut-off points to define the extreme market movements In reality, the feature of return distribution may change over time and depend on investors’ opinion, even differ from each of them Secondly, the model suggested by Christie & Huang (1995) recognizes herding under the condition of extreme return only However, herd behavior may

be present at the entire return distribution and become more dominant during period of market stresses (Chiang et al 2010)

In order to confirm the evidence of herding in Vietnam stock market, we proceed to estimate the following equation which is proposed by Chang et al (2000) This model also follows the approach of Christie & Huang (1995):

𝐶𝑆𝐴𝐷𝑡 = 𝛾0+ 𝛾1|𝑅𝑚,𝑡| + 𝛾2𝑅𝑚,𝑡2 + 𝜀𝑡 (3) where 𝐶𝑆𝐴𝐷𝑡 is a cross – sectional absolute deviation It is constructed to measure return dispersions, which is calculated as follows:

price at time t and t-1, respectively

Chang et al (2000) assert that the relationship between return dispersions and market volatility

is linear under normal conditions according to rational asset pricing model Therefore, an increase in the absolute value of the market returns will lead to a rise in the dispersions of individual investor returns However, Chang et al (2000) notice that during periods of relatively large price movement, if market participants tend to make decisions based on aggregate market behavior, the linear relationship becomes non-linear increasing or even decreasing In order to find out the non-linearity, the quadratic term of market return variable

R 2 m,t is included in the regression model Thus, a negative and statistically significant of coefficient 𝛾2 in equation (3) will indicate the presence of this phenomenon in Vietnam stock market

It is important to note that the herding coefficient of 𝛾2 in equation (3) does not account for the asymmetric effect arising from up and down markets We further examine whether the degree

of herd behavior is asymmetric in rising and falling markets by utilizing the following models:

𝐶𝑆𝐴𝐷𝑡𝑈𝑃 = 𝛼 + 𝛾1𝑈𝑃|𝑅𝑚,𝑡𝑈𝑃| + 𝛾2𝑈𝑃(𝑅𝑚,𝑡𝑈𝑃)2+ 𝜀𝑡 , R m,t > 0 (5) 𝐶𝑆𝐴𝐷𝑡𝐷𝑂𝑊𝑁 = 𝛼 + 𝛾1𝐷𝑂𝑊𝑁|𝑅𝑚,𝑡𝐷𝑂𝑊𝑁| + 𝛾2𝐷𝑂𝑊𝑁(𝑅𝑚,𝑡𝐷𝑂𝑊𝑁)2+ 𝜀𝑡 , R m,t < 0 (6) Where 𝑅𝑚,𝑡𝑈𝑃 is the market return at time t when the market rises, (𝑅𝑚,𝑡𝑈𝑃)2 is the quadratic term

of the previous one, 𝐶𝑆𝐴𝐷𝑡𝑈𝑃 is the CSAD at time t corresponding to 𝑅𝑚,𝑡𝑈𝑃 Symbols are similarly used in case of the declining market The market is considered to be rising when its return is greater than zero, otherwise it is regarded as falling

In addition, herding is assumed to be more likely to pronounced in extremely high or low market stress due to psychological reasons (Demirer & Kutan 2006) In order to investigate the potential asymmetric effect during time of extreme market conditions, we employ the following models:

𝐶𝑆𝐴𝐷𝑡𝑈𝑃 = 𝛼 + 𝛾1𝑈𝑃|𝑅𝑚,𝑡𝑈𝑃| ∗ 𝐷𝑡𝑈+ 𝛾2𝑈𝑃𝑅𝑚,𝑡2 𝑈𝑃∗ 𝐷𝑡𝑈+ 𝜀𝑡 , 𝑅𝑚,𝑡 > 0 (7)

𝐶𝑆𝐴𝐷𝑡𝐷𝑂𝑊𝑁 = 𝛼 + 𝛾1𝐷𝑂𝑊𝑁|𝑅𝑚,𝑡𝐷𝑂𝑊𝑁| ∗ 𝐷𝑡𝐿 + 𝛾2𝐷𝑂𝑊𝑁𝑅𝑚,𝑡2 𝐷𝑂𝑊𝑁 ∗ 𝐷𝑡𝐿+ 𝜀𝑡 , 𝑅𝑚,𝑡 < 0 (8) where 𝐷𝑡𝑈 = 1 if the market return on day t lies in the extreme upper tail of the distribution, otherwise 𝐷𝑡𝑈 = 0, and 𝐷𝑡𝐿 = 1 if the market return on day t lies on the extreme lower tail of the distribution, otherwise 𝐷𝑡𝐿 = 0 We determine the 1%, 5% and 10% criterion as upper and lower tails distribution

A number of previous authors have associated the level of herd behavior with trading volume

In order to examine the herding during days with high and low trading volume, the empirical specifications below are used:

𝐶𝑆𝐴𝐷𝑡𝑉−ℎ𝑖𝑔ℎ𝑡 = 𝛼 + 𝛾1𝑉−ℎ𝑖𝑔ℎ𝑡|𝑅𝑚,𝑡𝑉−ℎ𝑖𝑔ℎ𝑡| + 𝛾2𝑉−ℎ𝑖𝑔ℎ𝑡(𝑅𝑚,𝑡𝑉−ℎ𝑖𝑔ℎ𝑡)2+ 𝜀𝑡 (9) 𝐶𝑆𝐴𝐷𝑡𝑉−𝑙𝑜𝑤 = 𝛼 + 𝛾1𝑉−𝑙𝑜𝑤|𝑅𝑚,𝑡𝑉−𝑙𝑜𝑤| + 𝛾2𝑉−𝑙𝑜𝑤(𝑅𝑚,𝑡𝑉−𝑙𝑜𝑤)2+ 𝜀𝑡 (10)

Where 𝑅𝑚,𝑡𝑉−ℎ𝑖𝑔ℎ𝑡, 𝑅𝑚,𝑡𝑉−𝑙𝑜𝑤 are the market returns at time t when the trading volume is high and low, respectively Trading volume is regarded high if on day t it is greater than the previous 30-day moving average If trading volume is less than the previous 30-day moving average, it

in Vietnam stock market exhibits a high magnitude of volatility with a daily standard deviation

of 1.59% Weekly returns and monthly returns are lower, which are 0.04% and 0.1%, respectively

Table 2: Descriptive statistics for daily, weekly and monthly data from 2005 to 2015