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 20052015 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 Electronic copy available at: https://ssrn.com/abstract=2896779 Further evidence on the herd behavior in Vietnam stock market Introduction Herding is defined as a convergence of behavior which means investors follow the action of others from security to security and from market to market (Choi & Skiba 2015) The importance of examining the existence of herding arises from its impact on stock market in particular and on financial system in general As investors make investment based on collective decision, the stock prices are driven away from their underlying fundamentals The divergence between stock prices and their intrinsic values can result in opportunity to reap profit from arbitrage If herd behavior lasts longer and the stock prices fail to adjust towards their fundamental values, it may lead to the great instability and inefficiency, even the collapse of financial system 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 Electronic copy available at: https://ssrn.com/abstract=2896779 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 middleterm 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 presents a review of literature Section describes data and research methodology Section reports the empirical results and section concludes the paper 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 countryspecific level Blasco & Ferreruela (2008) investigate herding in seven countries using crosssectional 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 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 Countries The US Author(s) Lakonishok et al (1991) Period Quarterly data from 1985 to 1989 Model(s) Main findings 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 Herd behavior is evident during extreme market conditions HS 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 The results reveal the presence of herding within Chinese modified CCK 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 Herding is found in within both the Shanghai and Shenzhen modified CCK A-share markets but no evidence is reported within both Bshares 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 10 Observation 2568 2568 2569 513 513 514 122 122 123 This table also provides a preliminary description of the CSSD and CSAD series Previous authors indicate that when all the stock returns cluster around the market returns, return dispersions measured by CSSD and CSAD are bound to be low to zero (Chang et al 2000; Christie & Huang 1995) When individual returns deviate further from the market returns, the level of both increases The daily average values of CSSD and CSAD are 2.40% and 1.82%, respectively, which indicates the high deviation of stock returns from market consensus The study also finds that the average CSSD and CSAD based on weekly and monthly data are lower than the daily one which implies the declining of dispersion over time In other words, individual returns have a tendency to move towards the market returns in the long term 4.2 Regression results We begin with the investigation of the existence of herd behavior in Vietnam stock market by applying dummy variables regression model proposed by Christie & Huang (1995) Table reports the estimated results The coefficients of dummy variables 𝛽1 and 𝛽2 measure herding in extremely downward and upward market movement, respectively As the definition of extreme market condition is arbitrary, we choose 1%, 5% and 10% criteria to restrict 𝐷𝑡𝐿 and 𝐷𝑡𝑈 to lower and upper tail of market return distribution The negative and significant 𝛽1 and 𝛽2 coefficient under 1% criterion indicates the presence of herding in 1% lower and upper tail of market return distribution As the market encounters the extremely large price swings, individual investors have a tendency to cluster around the market consensus The positive and insignificant 𝛽1 in most criteria of three intervals report no evidence in support for herd behavior in lower tail of market distribution In case of extreme 16 upward market movement, the positive and significant 𝛽2 coefficients based on weekly and monthly data indicate that stock dispersions tend to increase rather than decrease The results 17 Table 3: Regression results for the whole sample using CSSD measure Variables Daily Weekly Monthly 1% 5% 10% 1% 5% 10% 1% 5% 10% 2.4100*** 2.3989*** 2.3689*** 0.0554*** 0.0543*** 0.0527*** 0.1135*** 0.1108*** 0.1089*** 155.8422 147.2863 137.5783 61.9564 59.3919 55.5841 31.0974 29.6791 27.2669 -0.5989*** -0.0458 0.2078*** 0.0039 0.0043 0.0073*** 0.0187 0.0153 0.0141 -3.9099 -0.6467 4.0252 0.6369 1.0845 2.5965 0.6611 1.0109 1.2204 -0.8778*** -0.0298 0.0535 0.0454*** 0.0283*** 0.0245*** 0.1052*** 0.0669*** 0.0479*** -5.7406 0.0708 1.0353 5.5295 7.1487 8.6897 3.7237 4.4194 4.1439 R-squared 0.0183 0.002 0.0064 0.0568 0.0919 0.1329 0.1068 0.1444 0.1295 F-statistics 23.8936 0.2837 8.2758 15.3705 25.7998 39.0786 7.1124 10.0419 8.8534 Probability 0.0000 0.7530 0.0003 0.0000 0.0000 0.0000 0.0012 0.0001 0.0003 𝛂 𝜷𝟏 𝜷𝟐 Note: This table reports the estimation results of the equation 𝑅𝐷𝑡 = 𝛼 + 𝛽1 𝐷𝑡𝐿 + 𝛽2 𝐷𝑡𝑈 + 𝜀𝑡 in which 𝑅𝐷𝑡 is return dispersion measured by cross-sectional standard deviation (CSSD) method, 𝐷𝑡𝐿 and 𝐷𝑡𝑈 are dummy variables at time t taking on the value unity when the market return at time t lies in the extreme lower and upper tail of the returns distribution, respectively; and zero otherwise (***) denotes significance at 1% level 18 are consistent with prediction of asset pricing model and contradictory with the definition of herd behavior which requires the decrease in CSSD measure We further apply Chang et al (2000) model to investigate the presence of herding in entire stock market in order to overcome the drawbacks of CSSD method in choosing criteria as the definition of extremely large price swings Table provides the regression results of empirical specification given by Chang et al (2000) Equation (3) estimates separately for daily, weekly and monthly data over the whole period Significant and positive coefficients of absolute market returns (𝛾1 ) indicate a linearity between dependent and independent variables which is consistent with the rational asset pricing model Our results reveal the quadratic non-linear relationship between return dispersion and market returns through the negative and significant coefficients of square market return (𝛾2 ) which show the existence of herding It is noticed that herding coefficients are statistically significant and negative with both daily and weekly data sets We therefore conclude that herd behavior is found in Vietnam stock market in short and middle terms and no evidence supports the existence of this phenomenon in the longer term This finding is consistent with the notion of Christie & Huang (1995) that herding is a shortlived phenomenon Table 4: Regression results for the whole sample using CSAD measure Variables Daily Weekly Monthly Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics 𝜸𝒐 1.5641*** 69.0021 0.0313*** 27.3257 0.0722*** 15.2802 𝜸𝟏 0.4509*** 14.5440 0.0401*** 7.4899 0.1258 1.2399 𝜸𝟐 -0.1045*** -13.8831 -1.1755*** -2.8479 0.3735 0.8750 R-squared 0.0764 0.2482 19 0.2344 0.0756 0.2453 0.2215 F-statistics 106.0543 84.1891 18.2164 Probability 0.0000 0.0000 0.0000 Adjusted R-squared Note: This table reports the estimation results of the equation 𝐶𝑆𝐴𝐷𝑡 = 𝛾0 + 𝛾1 |𝑅𝑚,𝑡 | + 𝛾2 𝑅𝑚,𝑡 + 𝜀𝑡 in which 𝐶𝑆𝐴𝐷𝑡 is the equally-weighted cross-sectional absolute deviation of returns, Rm,t is the market returns at time t (***) denotes significance at 1% level We then examine whether asymmetric effect in various market condition exists in Vietnam stock market by using CSAD measure Table presents the estimation results based on daily, weekly and monthly data in rising and falling market conditions Statistically significant (at 1% level) and negative herding coefficients (𝛾2 ) for daily and weekly data in declining market imply the presence of herd behavior on days when the market is down Result is similar for daily data series when examining this phenomenon in rising market However, there is distinction between up and down markets for weekly observations A negative but insignificant coefficient (𝛾2 ) in rising market implies herding does not exist in this time interval Positive coefficients (𝛾2 ) for monthly data sets mean no herding presence reported even in case of up or down markets This finding confirms our above conclusion that there is no evidence supporting the existence of herd behavior based on monthly data In other words, market participants seem to make investment decision rationally in the long term Table 5: Regression results in different market conditions Variables Daily Coefficient t-statistics Weekly Coefficient t-statistics Monthly Coefficient t-statistics Panel A: Up market 𝜸𝑼𝑷 𝟎 1.6061*** 51.2332 0.0313*** 17.8882 0.0683*** 8.8355 𝜸𝑼𝑷 𝟏 0.3502*** 8.3808 0.4315*** 5.1249 0.1706 1.1194 20 𝜸𝑼𝑷 𝟐 -0.0773*** -7.6486 -0.6845 -1.0274 0.5241 0.9707 R-squared 0.0506 0.3428 0.3818 Adjusted 0.0491 0.3375 0.3597 F-statistics 35.1960 68.3212 17.2945 Probability 0.0000 0.0000 0.0000 R-squared Panel B: Down market 𝜸𝑫𝑶𝑾𝑵 𝟎 1.5152*** 46.3774 0.0319*** 23.1594 0.0740*** 14.0802 𝜸𝑫𝑶𝑾𝑵 𝟏 0.5723*** 12.4396 0.3256*** 5.1693 0.1433 1.1551 𝜸𝑫𝑶𝑾𝑵 𝟐 -0.1369*** -12.2056 -1.4049*** -2.9791 -0.2279 -0.4774 R-squared 0.1123 0.1548 0.0715 Adjusted 0.1108 0.1479 0.0406 F-statistics 78.4149 22.4326 2.3112 Probability 0.0000 0.0000 0.0000 R-squared 𝑈𝑃 | Note: This table reports the estimation results of the equation 𝐶𝑆𝐴𝐷𝑡𝑈𝑃 = 𝛼 + 𝛾1𝑈𝑃 |𝑅𝑚,𝑡 + 𝛾2𝑈𝑃 𝑅𝑚,𝑡 𝐷𝑂𝑊𝑁 | 𝜀𝑡 , 𝑅𝑚,𝑡 > and 𝐶𝑆𝐴𝐷𝑡𝐷𝑂𝑊𝑁 = 𝛼 + 𝛾1𝐷𝑂𝑊𝑁 |𝑅𝑚,𝑡 + 𝛾2𝐷𝑂𝑊𝑁 𝑅𝑚,𝑡 𝐷𝑂𝑊𝑁 𝑈𝑃 + + 𝜀𝑡 , 𝑅𝑚,𝑡 < in which 𝐶𝑆𝐴𝐷𝑡 is the equally-weighted cross-sectional absolute deviation of returns, Rm,t is the market returns at time t (***) denotes significance at 1% level In addition, we also compare the herding level on days when the market is up vis-à-vis days when the market is down The comparison is conducted based on daily data The disparity of herding coefficients under various market conditions indicates herding in downside market is stronger than that in upside market When the market is up, market participants strongly invest but they tend to imitate the action of others to get secure This action is partially caused by the limitation in information transparency in Vietnamese institutional background In case of declining market, the non21 transparency together with quality of information published by listed companies also contribute to raising investors’ risk- aversion In consequences, individual investors have a tendency to ignore their own possessive information and make decision based on market consensus We further employ add dummy variables representing upper and lower tails of market return distribution to investigate asymmetry under turbulent market movements Table presents empirical results for herding presence during various extreme market conditions Estimations are run separately for each time interval with 1%, 5% and 10% criteria as extreme market movement definition However, monthly regression results for some criteria are not available due to limitation in data collection The results indicate that daily data under 5% and 10% criteria in extremely downside market generate significant and negative herding coefficients It implies that return dispersion tends to decrease during market stress which shows the existence of herd behavior In this time interval, unsystematic risk of individual stocks turns into systematic risk of entire market which make diversification strategy to mitigate risk less effective Moreover, most of individual investors in Vietnam are less experienced with the lack of analysis engine and limited evaluation ability Investors’ capability and the stronger decrease of market return may be the main reasons to reduce investors’ confidence and raise risk-averse sentiment; thus, investors have a tendency to “flight to safety” during bad time periods (Tan et al 2008) We further employ Chang et al (2000) model to investigate the possible asymmetric effects of herding with respect to trading volume Table reports the results of the asymmetric volume herding regressions The herding coefficients are both negative and significant The results indicate that herd behavior exists during high and low trading volume days The market with high trading volume is associated with high liquidity and more publicly available information As a result, herding is enhanced in liquid markets since investors have more available 22 Table 6: Regression results during extreme market movements Variables Daily 1% 5% Weekly 10% 1% 5% Monthly 10% 1% 5% 10% Panel A: Extremely upside market 𝛄𝐨 1.8289*** 1.8439*** 1.8309*** 0.0429*** 0.0414*** 0.0406*** - 0.0855*** 0.0834*** 112.946 109.722 103.8381 37.6616 38.6672 37.4198 - 18.4039 17.9309 -1.4819*** -0.5880*** -0.0277 -0.9810 0.5093** 0.2203* - -0.7176 -0.3321 -10.290 -6.6596 -0.4363 -0.6527 2.4995 1.7119 - -1.5486 -1.1773 0.2605*** 0.1131*** -0.0004 7.4950 -1.7109 0.3379 - 3.2221** 2.0676** 9.6859 5.6541 -0.0244 0.7879 -1.1181 0.3195 - 2.1159 2.0389 R-squared 0.0765 0.0451 0.0028 0.0346 0.1843 0.2082 - 0.2332 0.2721 Adjusted R- 0.0741 0.0437 0.0013 0.0273 0.1781 0.2022 - 0.2058 0.2461 F-statistics 53.9498 31.2322 1.8524 4.7002 29.6011 34.4448 - 8.5164 10.4664 Probability 0.0000 0.0000 0.1572 0.0099 0.0000 0.0000 - 0.0006 0.0001 0.0382*** - - - 𝛄𝟏 𝛄𝟐 squared Panel B: Extremely downside market 𝜸𝒐 1.8329*** 1.8512*** 1.8397*** 0.0389*** 23 0.0386*** 100.6252 100.4654 97.3740 46.1245 45.1406 43.9294 - - - -0.9745** 0.1865 0.4993*** 0.8488 0.0711 0.1255 - - - -2.3127 1.0418 5.5152 0.4959 0.5610 0.1340 - - - 0.1644* -0.0719* -0.14*** -4.5724 0.0248 -0.3061 - - - 1.9244 -1.7637 -6.2518 -0.4582 0.0277 -0.4747 - - - R-squared 0.0194 0.0373 0.0409 0.0093 0.0249 0.045 - - - Adjusted R- 0.0178 0.0358 0.0394 0.0012 0.0169 0.037 - - - F-statistics 12.2393 24.0371 26.4709 1.1541 3.1267 5.7771 - - - Probability 0.0000 0.0000 0.0000 0.3171 0.0456 0.0035 - - - 𝜸𝟏 𝜸𝟐 squared Note: This table reports the estimation results of the equation 𝐶𝑆𝐴𝐷𝑡𝑈𝑃 = 𝛾2𝐷𝑂𝑊𝑁 𝑅𝑚,𝑡 𝐷𝑂𝑊𝑁 𝑈𝑃 | 𝛼 + 𝛾1𝑈𝑃 |𝑅𝑚,𝑡 ∗ 𝐷𝑡𝑈 + 𝛾2𝑈𝑃 𝑅𝑚,𝑡 𝑈𝑃 𝑈 ∗ 𝐷𝑡 + 𝜀𝑡 , 𝑅𝑚,𝑡 > and 𝐷𝑂𝑊𝑁 | 𝐶𝑆𝐴𝐷𝑡𝐷𝑂𝑊𝑁 = 𝛼 + 𝛾1𝐷𝑂𝑊𝑁 |𝑅𝑚,𝑡 ∗ 𝐷𝑡𝐿 + ∗ 𝐷𝑡𝐿 + 𝜀𝑡 , 𝑅𝑚,𝑡 < in which 𝐶𝑆𝐴𝐷𝑡 is the equally-weighted cross-sectional absolute deviation of returns, Rm,t is the market returns at time t (***)(**)(*) denote significance at 1%, 5% and 10% level, respectively 24 information so they can earn return faster (Gregoriou & Ioannidis 2006) In the context of Vietnam stock market, the difference between estimated coefficients (𝜸𝟐) suggest that herding in the low volume state is more pronounced than that in the high volume state Juvaira & Hassan (2015) argue that trading volume could be low during large price swings This reflects the fact in Vietnam as the stock price volatility is very high over the analyzed period In addition, Vietnam stock market is characterized by weak form market efficiency, which respond more slowly to information together with the lack of available information These are the main triggers of herding propensity during low trading days In conclusion, this finding supports the fact that herding is related to trading volume level in Vietnam stock market Table 7: Regression results during high and low trading volume days Variables High trading volume Low trading volume Coefficient t-statistics Coefficient t-statistics 𝜸𝒐 1.7683*** 49.9290 1.4075*** 50.4079 𝜸𝟏 0.2987*** 6.7289 0.5768*** 13.8629 𝜸𝟐 -0.0639*** -6.0439 -0.1469*** -14.3491 R-squared 0.0361 0.1337 Adjusted R- 0.0345 0.1325 F-statistics 22.8556 103.5965 Probability 0.0000 0.0000 squared 𝑉−ℎ𝑖𝑔ℎ |∗ Note: This table reports the estimation results of the equation 𝐶𝑆𝐴𝐷𝑡𝑉−ℎ𝑖𝑔ℎ𝑡 = 𝛼 + 𝛾1𝑉−ℎ𝑖𝑔ℎ |𝑅𝑚,𝑡 𝑉−ℎ𝑖𝑔ℎ𝑡 + 𝛾2 𝑅𝑚,𝑡 𝑉−ℎ𝑖𝑔ℎ𝑡 + 𝜀𝑡 , 𝑅𝑚,𝑡 > and 𝑉−𝑙𝑜𝑤 | 𝐶𝑆𝐴𝐷𝑡𝑉−𝑙𝑜𝑤 = 𝛼 + 𝛾1𝑉−𝑙𝑜𝑤 |𝑅𝑚,𝑡 + 𝛾2𝑉−𝑙𝑜𝑤 𝑅𝑚,𝑡 𝑉−𝑙𝑜𝑤 + 𝜀𝑡 , 𝑅𝑚,𝑡 < in which 𝐶𝑆𝐴𝐷𝑡 is the equally-weighted cross-sectional absolute deviation of returns, Rm,t is the market returns at time t (***) denotes significance at 1% level 25 We finally divide the sample into three sub-periods based on daily data in which global financial crisis (year 2008) is examined separately to investigate its impact on herding Table provides the results The estimated coefficient ( 𝜸𝟐) is found to be insignificant, suggesting that no evidence supports the decrease of return dispersion over the global financial meltdown Interestingly, the results indicate that investors not exhibit herd behavior during crisis period while this phenomenon exists both before and after crisis This finding supports the argument of rational asset pricing model theory which supposes that dispersion increases during periods of market stress since the sensitivity of individual stock to market movements is different Table 8: Regression results in different time periods Variables Pre-crisis During crisis Post-crisis Coefficient t-statistics Coefficient t-statistics Coefficient t-statistics 𝛄𝐨 1.0007*** 22.8251 1.9402*** 12.6789 1.8134*** 102.9703 𝛄𝟏 0.6652*** 11.2963 -0.0629 -0.4073 0.3924*** 15.0075 𝛄𝟐 -0.0971*** -7.0269 -0.0271 -0.8662 -0.1107*** -15.999 R-squared 0.2228 0.0893 0.1402 Adjusted 0.2207 0.0818 0.1391 F-statistics 106.9193 11.9096 128.0113 Probability 0.0000 0.0000 0.0000 R-squared Note: This table reports the estimation results of the equation 𝐶𝑆𝐴𝐷𝑡 = 𝛾0 + 𝛾1 |𝑅𝑚,𝑡 | + 𝛾2 𝑅𝑚,𝑡 + 𝜀𝑡 in which 𝐶𝑆𝐴𝐷𝑡 is the equally-weighted cross-sectional absolute deviation of returns, Rm,t is the market returns at time t (***) denotes significance at 1% level We continuously compare the magnitude of γ2 coefficients to figure out the herding level 𝒑𝒐𝒔𝒕 between pre-crisis and post-crisis The difference of 𝜸𝒑𝒓𝒆 - 𝜸𝟐 = 0.0136 indicates the 𝟐 prevalence of herding in post-crisis than in pre-crisis It is easily understandable that numerous 26 investors entered the market during post-crisis period with the expectation of stock market recovery However, the market still contains risks and unusual volatility after the global meltdown, which makes risk-averse investors tend to ignore their private information and follow the action of others In addition, the limitation of legislation framework and state governance also result in the non-transparency This has direct impact on the trust and confidence of investors when making decisions; consequently, leading to the spread of herd behavior Conclusions This paper examines the presence of herd behavior in Vietnam stock market by using two approaches The former relies on CSSD proposed by Christie & Huang (1995) and the latter builds on CSAD developed by Chang et al (2000) The study analyzes herd phenomenon in Vietnam over the period from January 2005 to April 2015 Empirical results indicate that herding exists in Vietnam equity market Both results drawn from two approaches confirm the short-lived feature of herding as no evidence is reported using monthly data set Moreover, herding is found to be more pronounced in down market than in up market due to the tendency of “flight to safety” during bad time The findings reveal that herd behavior is evident during high and low trading volume days; however, it is stronger in low volume state The results also provide evidence to support the prevalence of this phenomenon in extremely downside market movement Moreover, we present an interesting finding that herding does not exhibit during global meltdown However, we observe that herding is stronger in post-crisis than in pre-crisis This paper provides some implications for individual investors, firm managers and policy makers Firstly, herding is an inherent sentiment which cannot eliminate completely Therefore, investors need to build up knowledge and information analysis capability to make reasonable 27 investment decisions Secondly, managers of firms listed on the stock exchange must increase the quality and transparency 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