Herd behavior in Vietnam's stock market: Impacts of COVID-19

Phan Hong Mai et al. Article Herd behavior in Vietnam's stock market: Impacts of COVID-19 Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Phan Hong Mai et al. (2023) : Herd behavior in Vietnam's stock market: Impacts of COVID-19, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-25, https://doi.org/10.1080/23322039.2023.2266616 This Version is available at: https://hdl.handle.net/10419/304227 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Herd behavior in Vietnam’s stock market: Impacts of COVID-19 Hong Mai Phan, Thi Nhu Quynh Le, Vu Duc Hieu Dam, Manh Son Tran, Thi Hoai Linh Truong & Quoc Anh Le To cite this article: Hong Mai Phan, Thi Nhu Quynh Le, Vu Duc Hieu Dam, Manh Son Tran, Thi Hoai Linh Truong & Quoc Anh Le (2023) Herd behavior in Vietnam’s stock market: Impacts of COVID-19, Cogent Economics & Finance, 11:2, 2266616, DOI: 10.1080/23322039.2023.2266616 To link to this article: https://doi.org/10.1080/23322039.2023.2266616 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 11 Oct 2023. Submit your article to this journal Article views: 1150 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Herd behavior in Vietnam’s stock market: Impacts of COVID-19 Hong Mai Phan 1 *, Thi Nhu Quynh Le 2 , Vu Duc Hieu Dam 3 , Manh Son Tran 4 , Thi Hoai Linh Truong 1 and Quoc Anh Le 1 Abstract: This paper investigates herd behavior in frontier Vietnamese stock markets under the impacts of COVID-19. Using models with two measures of return dispersions, we find that herd behavior does not exist in the three stock markets in extreme movements but in normal market conditions. Herding is more severe in two stock exchanges, HoSE and HNX, than in the OTC market UpCOM. Intentional herding is the main form and has been more intense in HoSE and HNX since the COVID-19 outbreak, while it is mainly significant in UpCOM in the pre-pandemic period. There is strong evidence of significant intentional herding on days of high volatility in UpCOM and HNX for all the timeframes, while considerable spurious herding on days with low volatility is found in UpCOM and HNX for all the examined periods except for the pandemic one. The evidence that herding was more pronounced during high volatility days in HoSE was relatively weak overall. Finally, pandemic uncertainty or government responses do not affect heightening or mitigating herd behavior, respectively. Subjects: Social Influence; Intergroup Behavior; Investment & Securities Keywords: herd behavior; COVID-19; Vietnam ABOUT THE AUTHORS Hong Mai Phan held a PhD in Finance and Banking from National Economics University in 2012. Phan has been affiliated with National Economics University as a Lecturer and Researcher for 17 years. Her research interests lay in banking, private sector development, corporate finance, and emerging markets issues. Thi Nhu Quynh Le is a quantitative analysis specialist at Vietcombank, Vietnam. Her research interests lay in risk management, corporate finance, and digital finance. Vu Duc Hieu Dam is currently employed as a R&D Associate at the Trading Department of the Mercantile Exchange of Vietnam. He earned his BA in Finance and Banking from the National Economics University in 2022. His research interests include investment decisions, information, and efficiency in financial markets. Manh Son Tran is currently studying at Knox College, IL, USA. His work focuses on applied mathematics and data science-related fields such as probability theory, numerical optimization, mathematical statistics, and deep learning. Thi Hoai Linh Truong is a PhD in Banking and Finance at National Economics University. Truong is currently a Lecturer and Researcher at National Economics University. Her research areas focus on banking, micro-finance, and emerging market issues. Quoc Anh Le is a Lecturer and Researcher at the School of Banking and Finance, National Economics University. His main research areas are corporate finance and macro-finance. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 1 of 25 Received: 15 February 2023 Accepted: 29 September 2023 *Corresponding author: Hong Mai Phan, School of Banking and Finance, National Economics University, Hanoi, Vietnam E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, UK Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 1. Introduction The COVID-19 pandemic triggered an emergency globally, harmed the global economy, and made the future unpredictable (Carlsson-Szlezak et al., 2020). First detected in China in December 2019, the coronavirus rapidly spread worldwide, posing threats to life and normality as we know it. No one could be sure about how long the pandemic would be; whether the government measures would be temporary or permanent; how low the consumer spending would be, and consequently, the revenue of firms would even fall (Altig et al., 2020; Moran et al., 2022). To global financial markets, COVID-19 was a real “black swan” as stock markets worldwide experienced an unfolding journey with the ups and downs of share prices (Pochea, 2021), resulting in rising uncertainty among investors. There is mounting evidence that the COVID-19 pandemic also deeply affected investor sentiment, causing them to panic and grow pessimistic about their investments (Dash & Maitra, 2022). This situation created ideal conditions for herd behavior—the tendency for investors to ignore their information and mimic others’ investment decisions (Bikhchandani & Sharma, 2000). However, herding would occur not without its consequences: as fundamental information is suppressed, the asset prices are driven away from their intrinsic value, resulting in subsequent market crashes and price bubbles (Bikhchandani et al., 1992; Hott, 2009). The studies on developed and emerging stock markets around the world have found compelling evidence of causality between herd behavior and its COVID-19-related stimulators, such as government responses or public sentiment (Aslam et al., 2021; Bogdan et al., 2022; Bouri et al., 2020; Yuan, 2021). Despite growing literature and increasing interest in frontier markets thanks to their superior performance in the past and their diversification benefits (Spiru & Qin, 2016), the impacts of the pandemic on herding in those markets are left relatively understudied. The study aims to bridge the gap in the literature by examining herd behavior in a frontier market – Vietnam – during the period of the pandemic. Generally, Vietnamese stock market is characterized by a number of common features of a typical frontier market 1 : being located in a developing country with solid macroeconomic foundation and potential economic growth; experienced significantly high rate of return for years; and has been suffering from lack of transparency as well as inefficient information flows. 2 In addition, the market also has its unique characteristics, for instance, superior performance compared to other Asian frontier markets during the pandemic 3 as well as the low valuation due to the current decline in the market. As a result, this study would not only try to fill in the literature gap about herding in frontier markets during the crisis, but would also seek to provide useful insights into the current situation of the market. The purpose of this study is to investigate herd behavior in the frontier Vietnamese stock market, which, because of its attractiveness, has recently gained increasing prominence and drawn more and more attention from investors, both foreign and domestic. Thanks to its solid foundations, the country’s economy had constantly grown several times faster than the world average for years right before 2019 4 and remained resilient through one of the worst crises in history, the COVID-19 pandemic, as a few countries had a positive rate of growth during the period of turbulence. 5 The country also experienced a strong rebound, with a growth rate of 8% in the first year of the endemic period—the highest for the last three decades. 6 While the growth rate is expected to slow to 6.3% in 2023 due to the pessimistic outlook of the global economy, 7 the recent stock market decline has reduced valuations to extraordinarily appealing levels with a forward P/E ratio of 8.1 compared to the above-10 values of other emerging and frontier markets in the world. 8 Furthermore, the market would also benefit from other macroeconomic factors, such as the depreciation of the US dollar relative to the domestic currency and China’s opening after lockdown, which would help increase capital flows to Vietnam’s stock market as well as the economy as a whole. Such a period of low valuation, with the support of macroeconomic conditions, opens up both advantageous and successful investment opportunities in the Vietnamese stock market, besides substantial diversification benefits for investors whose portfolios consist mainly of assets from developed markets. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 2 of 25 In spite of its increasing importance and appeal over time, there are still significant obstacles to the development of Vietnam’s stock market, for instance, information flow and transparency, 9 which has been referred as one of the main causes of herding (Bikhchandani et al., 1992). Indeed, herding in the Vietnamese stock market was well documented prior to the outbreak of COVID-19 (Vo & Phan, 2017). In addition, exogenous shocks are also proved to trigger herding among investors (Chiang & Zheng, 2010; Vo & Phan, 2017), and the COVID-19 pandemic is nothing more than an unexpected global one. As a result, there is a strong possibility that herding may occur among investors throughout this period of time, which is not without the cost. When private information is suppressed, the market may move toward inefficiency as the market price does not accurately reflect all the relevant fundamental information (Banerjee, 1992; Bikhchandani et al., 1992). In combination with uncertainty about the accuracy of information, herding may cause mispricing in asset prices, resulting in subsequent bubbles and crashes. This study focuses on four main research questions. First, do herding exist in three Vietnamese stock markets pre-, during, and post-COVID-19? Based on the pioneering work of Christie and Huang (1995) and Chang et al. (2000), we investigate the occurrence of herding in three periods of three markets of different characteristics. We further explore whether herding, if it exists, is driven by common fundamental factors (spurious herding) or by the intent to imitate others' decisions among investors (intentional herding) based on the work of Galariotis et al. (2015). Second, how does herding change under different levels of volatility? Gleason et al. (2004) hypothesize that with the increase in volatility, the tendency to imitate would increase because investors feel more comfortable following the market consensus to achieve the average return of the market. Third, does a high level of pandemic uncertainty in Vietnam stimulate herding among investors, either intentional or spurious? We use the search volume-based measure of pandemic uncertainty to capture COVID-19 uncertainty (Chundakkadan & Nedumparambil, 2022). Finally, motivated by the work of Kizys et al. (2020), the last question is whether the strictness of government response to COVID-19 mitigates herding. This question will be addressed by investigating whether the Oxford Government Response Stringency Index has any impact on the relation between the stock return dispersions and the squared term of market returns. The remainder of the paper is organized as follows. Section 2 briefly presents the literature on herding and the tests for its presence. Sections 3 and 4 address the data and research methodology, while the findings and discussions are presented in Section 5. Section 6 provides a summary and discusses the implications of the findings. 2. Literature review 2.1. Herd behavior in financial markets In finance and economics, the term “herding” or “herd behavior” refers to the act of economic agents imitating one another’s actions and/or basing their judgments on those of others (Spyrou, 2013). In a market setting, early studies often described herding as the effort by investors to suppress their own beliefs and copy the behavior of others (Avery & Zemsky, 1998; Bikhchandani & Sharma, 2000; Christie & Huang, 1995), causing them to trade in a collective manner (Nofsinger & Sias, 1999). Namely intentional herding, it could be the result of information cascades (Banerjee, 1992; Bikhchandani et al., 1992) or reputational reasons (Scharfstein & Stein, 1990). Intentional herding should also be distinguished from another situation of spurious herding, where many investors make the same trading decisions simultaneously due to the change in fundamental factors (Bikhchandani & Sharma, 2000). Spurious herding may also occur among traders sharing some common characteristics, such as market experience, information set, and the way they process the information (Teh & De Bondt, 1997), their preferences to a specific kind of stocks (Falkenstein, 1996), or the regulations that they comply with (Voronkova & Bohl, 2005). Lastly, spurious herding could also result from style investing, in which traders buy the recent winners and dump those recent losers (De Long et al., 1990). Thus, similar decisions were made Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 3 of 25 not because investors follow the previous actions intentionally, which is not herding according to the definition of herding discussed above. Spurious herding is a favorable and efficient outcome since the change in asset prices simply reflects the movement of fundamentals, while intentional herding does not necessarily have those features (Bikhchandani & Sharma, 2000) and may lead to market inefficiency (Banerjee, 1992). Both spurious and intentional herding hence will be examined later in this study. 2.2. Herd behavior in worldwide financial markets during COVID-19 In the context of rare and unique events such as COVID-19, a growing literature has been spent on herding in the financial markets worldwide, especially in developed markets. Espinosa-Méndez et al. (2020) examine herding on six European primary stock markets between January 2000 and June 2020 using Chang et al. (2000) model. The result of the non-linearity test in different market conditions indicates that, generally, the COVID-19 pandemic amplified herding among investors. Fang et al. (2021) conduct research in six Eastern European countries and find that during the pandemic, herding existed in almost five out of six countries except for Poland, and the degree of herding intensified over the pandemic. Bogdan et al. (2022) compare herding in 15 European stock markets during COVID-19 based on Chang et al. (2000) with static and rolling window methods. They find that herding existed during COVID-19 and was most pronounced in emerging markets, followed by frontier and developed markets. They then suggest lower liquidity and volatility in frontier markets that make herding to be less intensified than in emerging counterparts. Pochea (2021) investigates herding toward the market consensus in European and US stock markets. He finds that the uncertainty triggered by the outbreak of COVID-19 amplified the observed herd behavior, and this behavior was driven by non-fundamental information. Strong evidence that high sentiments characterized herding and that ECB’s non-standard monetary policy announcement induced spurious and intentional herding is also found, while the Fed’s releases did not result in herding. Aslam et al. (2021) analyze quarterly changes in herd behavior by quantifying the self-similarity intensity of six stock markets in Europe (UK, France, and Spain) and Asia (China, India, and Japan). Using a multifractal detrended fluctuation analysis (MFDFA) on intraday trade prices with a 15-min frequency from Jan-2020 to Dec-2020, they demonstrate that herd behavior in European markets was more evident than in Asian markets and was highly affected by COVID-19 waves. Several studies have been conducted on other financial markets. Luu and Luong (2020) apply CSAD and state space models to identify herd behavior across different industries during the pandemic (H1N1 and COVID-19) in Vietnam and Taiwan stock markets. Their results reveal that Taiwan stock market, an emerging market, was less sensitive to changes in the pandemic conditions than Vietnam market, which is a frontier market. They argue that the pandemic created anxiety from a health perspective and caused psychological instability for investors when investing in the market. Espinosa- Méndez and Arias (2021) report that the pandemic increased herding by utilizing the method of Chang et al. (2000) on a sample consisting of 90 listed firms from June 2008 to June 2020 in Australia. The effect of COVID-19 manifested in sessions with negative returns, higher volatility, and lower trading volume. Yuan (2021) examines the herding effect in the Chinese A-share mainboard market using market and industry-level data and find that herding formation existed in the Chinese A-share market during the pre-pandemic period and became pronounced during the COVID-19. Herding was more evident in the down phase than the up phase, while shares of firms operating in transportation, leasing, business, and culture products experienced the most intensified herding effects during COVID-19 among those examined. Ghorbel et al. (2022) present a study analyzing herd behavior in developed and BRICS stock market indices using a modified CSAD and the wavelet coherence (WC) analysis. They show that herding was present during all four waves of COVID-19 and was boosted by transaction volume and the number of deaths. Applying WC analysis, they report the presence of herding between China and developed and emerging stock markets, especially during the first wave of crisis, and between Indian and stock markets during the third wave. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 4 of 25 3. Scope of study and data 3.1. Scope of study The Vietnamese securities market was officially established in 2000. Two securities trading centers were founded in Ho Chi Minh in 2000 and Hanoi in 2005, later named the Ho Chi Minh Stock Exchange (HoSE) in 2007 and the Hanoi Stock Exchange (HNX) in 2009, respectively. A market for securities of unlisted public companies (UpCOM) was opened in HNX in June 2009. Table 1 shows a comparison of stock markets in Vietnam as of 31 October 2022. A company must conduct an approved public offer of the shares to qualify for listing in either exchange, HoSE or HNX. Both exchanges also apply various listing criteria, including minimum capital requirements, required periods of profitable operation prior to listing, a minimum number of shareholders, and commitments by managers. In general, HoSE requirements are stricter than those of HNX. Both exchanges apply trading rules and restrictions, including trading price bands and minimizing price fluctuations. UpCOM is dealing with “over the counter” shares of unlisted public companies with looser regulations. This study investigates herd behavior in all three markets. 3.2. Data The study uses stocks’ daily trading and book value data between 1 January 2018 and 31 October 2022 in three markets, including HoSE, HNX, and UpCOM. The daily trading data comprise the daily closing prices and trading volumes of all the stocks listed on three markets and corresponding market index data. All the trading data was gathered from FiinPro’s database, which provides the most comprehensive data about the Vietnamese financial market. Over the period, several stocks were prevented from trading or delisted, while some stocks were traded for the first time. As of 31 October 2022, the number of stocks made up main indices in each of the three stock markets is 323 in HoSE, 342 in HNX, and 798 in UpCOM. Due to the low attrition rate for each stock market, the sample of stocks is the list as of 31 October 2022. In addition, the study used data on the Vietnamese government’s responses to COVID-19 in the Government Response Trackers. The Stringency Index is a composite measure based on nine response indicators, including school closures, workplace closures, cancellation of public events, Table 1. Comparison of stock markets in Vietnam as of 31 October 2022 Stock markets Ho Chi Minh Stock Exchange (HoSE) Hanoi Stock Exchange (HNX) (for listed companies) UpCom (for unlisted public companies) Stock index VNI HNX UPCOM No. of stocks 323 342 798 Face value 10,000 VND NA NA Daily price change limit ±7% of the previous day’s close ±20% on 1 st day of listing ±10% of the previous day’s close ±30% on 1st day of listing ±15% of the previous day’s VWAP ±40% on 1st day of listing Minimum price fluctuation ●Price ≤10,000 VND: 10 VND ●10,000 VND < Price <49,950 VND: 50 VND ●Price ≥50,000 VND: 100VND 100 VND 100 VND Board lot 100 shares 100 shares 100 shares Market capitalization 3,407,421 billion VND 215,726 billion VND 896,866 billion VND Source: Viet Capital Securities (2022). 20 Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 5 of 25 restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movement, and international travel controls. 10 The index is the daily mean score of nine metrics, each taking a value between 0 and 100 (the strictest). The research also uses Google Trends to measure pandemic uncertainty. The analysis has been conducted for the complete sample and its subsamples corresponding to pre-COVID-19, during COVID-19, and the endemic period. The pre-pandemic sample is from January 2018 to 23 January 2020, as the first case of COVID-19 was confirmed in Vietnam. The COVID-19 period continued until the government considered COVID-19 as endemic on 5 March 2022 and the endemic sample includes trading day from 5 March 2022. 4. Research methodology 4.1. Detection of herding in Vietnam’s stock market The first step in the methodology is testing for the presence of herding in different stock markets in Vietnam in three phases: pre-, during COVID-19 and endemic period. Two well-known models are used to detect herding in this research with some modifications: (i) cross-sectional standard deviation (CSSD) by Christie and Huang (1995); (ii) cross-sectional absolute deviation return (CSAD) by Chang et al. (2000). Christie and Huang (1995) model would first be implemented to search for evidence of herding during market stress periods with sharp movement in market price as follows: The measure of return dispersion is calculated below: where Ri;t is the return of stock index i at time t, Rm;t is the return of the market capitalizationweighted index for market at time t, and N is the total number of stocks. DL t¼1 if the market return on day t lies in the extreme lower tail (1% and 5%) of the distribution, and equal to zero otherwise. DU t¼1 if the market return on day t lies in the extreme upper tail (1% and 5%) of the distribution, and equal to zero otherwise. However, the model of Christie and Huang (1995) has some inherent weaknesses related to how they link the presence of herding with the tail of the return distribution and ignore herding during normal market conditions, which can be overcome by using the method proposed by Chang et al. (2000). Chang et al. (2000) state that if herding exists, the returns of individual stocks would move closer to that of the overall market; thus, the dispersion may “increase at a decreasing rate” or even fall in case the herding is serious. The relationship is represented by the following specification: The return dispersion is measured by the cross-sectional absolute deviation below: Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 6 of 25 Accordingly, a statistically significant and negative value of γ2 indicates the existence of herding, while a significantly positive one implies anti-herding. With such a form of function, the larger absolute value of return stands for the case that the market price moves more considerably, the return dispersion would fall more significantly, and herding would become more prevalent. 4.2. Spurious herding and intentional herding We then go further to see if herding among investors in Vietnam's stock market was spurious or intentional. Based on the work of Galariotis et al. (2015), we decompose the total CSAD into two components: (i) deviations due to reaction to common fundamental factors (spurious herding) and (ii) deviations due to non-fundamental information (intentional herding). First, the following regression is estimated: In which Rf;t is the risk-free rate, Rm;tRf;t  �is the equity market premium (the excess return of the market portfolio), HMLt is the high-minus-low factor (the value premium), SMBt is the small minus big factor (size premium), and MOMt is the momentum factor. The residual from (3) is considered the cross-sectional deviations after removing the effect of fundamental information. In other words, it is a measure of crowding due to non-fundamental information, proxying for intentional herding. We denote the term CSADnonfundamental: The deviation due to investors’ reaction to changes in fundamental information is CSADfundamental, a proxy for spurious herding: To test for the existence of spurious and intentional herding, we estimate the regression (2) but with CSADfundamental;t and CSADnonfundamental;t as dependent variables: A negative and statistically significant value of γ2 in equation (4) implies spurious herding exists, while that in equation (5) provides evidence for the presence of intentional counterparts. Regressions (2), (4), and (5) are also estimated for three markets for the whole sample period and each sub-period. 4.3. Herd behavior under different market conditions Next, this paper investigates the relationship between volatility and herding in the markets in different COVID-19 periods. Two measures of historical daily volatility proposed by Parkinson (1980) and Garman and Klass (1980) are used. First, Parkinson’s measure incorporates the maximum and the minimum daily prices to reflect the movement of extreme price intraday variations: Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 7 of 25 Table 4. Estimates of herd behavior by non-linearity test between total CSAD and market returns HoSE HNX UpCOM α Rm j j R2 mα Rm j j R2 mα Rm j j R2 m WHOLE 0.0148*** 0.4783*** −4.2630*** 0.0171*** 0.6962*** −2.3320*** 0.0171*** 0.8240*** −1.2083* (0.0002) (0.0356) (1.0278) (0.0002) (0.0225) (0.5387) (0.0001) (0.0223) (0.4377) PRE 0.0140*** 0.4107*** −0.2052 0.0157*** 0.7219*** −1.3967*** 0.0160*** 0.9195*** −5.4418*** (0.0002) (0.0349) (1.2165) (0.0001) (0.0219) (0.5302) (0.0002) (0.0404) (1.1795) COVID 0.0159*** 0.4272*** −4.0429*** 0.0193*** 0.5962*** −1.1733** 0.0179*** 0.7620*** −0.1420 (0.0003) (0.0514) (1.3745) (0.0003) (0.0394) (0.9651) (0.0003) (0.0348) (0.6862) POST 0.0150*** 0.5369*** −6.1934*** 0.0168*** 0.6722*** −2.5602*** 0.0189*** 0.7598*** −0.6382 (0.0006) (0.0778) (1.6519) (0.0004) (0.0521) (1.0229) (0.0004) (0.0568) (1.2968) ***, **, * denote significance at 1%, 5%, and 10% level, respectively. Robust standard errors are reported in parentheses. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 14 of 25 Table 5. Estimates of herd behavior by non-linearity test between decomposed components of CSAD and market returns HoSE HNX UpCOM α Rm j j R2 mα Rm j j R2 mα Rm j j R2 m Panel A: Fundamental CSAD WHOLE 0.0179*** 0.0160 1.1036*** 0.0230*** 0.0454** 1.4301** 0.0217*** 0.0215 4.0395*** (0.0001) (0.0161) (0.3859) (0.0001) (0.0210) (0.4933) (0.0001) (0.0358) (1.1654) PRE 0.0170*** −0.0278** 1.4667*** 0.0210*** −0.0106 0.8586** 0.0203*** −0.0456** 3.1754*** (0.0000) (0.0101) (0.3206) (0.0001) (0.0156) (0.4897) (0.0000) (0.0204) (1.1658) COVID 0.0188*** 0.0105 0.8387*** 0.0259*** −0.0363** 1.7596*** 0.0233*** −0.0726*** 4.0829*** (0.0001) (0.0196) (0.3902) (0.0001) (0.0149) (0.3171) (0.0001) (0.0307) (0.5321) POST 0.0185*** 0.0678 0.5355 0.0247*** −0.0985* 5.5177*** 0.0237*** 0.0231 7.9036*** (0.0003) (0.0616) (1.5909) (0.0004) (0.0626) (1.0269) (0.0003) (0.0958) (2.1628) `Panel B: Non-fundamental CSAD WHOLE −0.0031*** 0.4599*** −5.3255*** −0.0060*** 0.6550*** −3.8345*** −0.0046*** 0.8047*** −5.3003*** (0.0002) (0.0331) (0.9268) (0.0002) (0.0277) (0.7475) (0.0002) (0.0387) (1.2818) PRE −0.0031*** 0.4416*** −1.7314** −0.0053*** 0.7290*** −2.1903** −0.0043*** 0.9671*** −8.7380*** (0.0002) (0.0385) (1.3133) (0.0002) (0.0303) (0.8909) (0.0002) (0.0463) (1.7945) COVID −0.0029*** 0.4134*** −4.8192*** −0.0068*** 0.6465*** −3.1482*** −0.0054*** 0.8429*** −4.3659*** (0.0003) (0.0496) (1.2810) (0.0003) (0.0403) (0.9963) (0.0003) (0.0421) (0.7612) POST −0.0034*** 0.4449*** −6.2647** −0.0079*** 0.7468*** −7.6830*** −0.0048*** 0.7442*** −8.6531** (0.0006) (0.0681) (1.4215) (0.0005) (0.0731) (1.2616) (0.0005) (0.0939) (1.9363) ***, **, * denote significance at 1%, 5%, and 10% level, respectively. Robust standard errors are reported in parentheses. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 15 of 25 Figure 3. Relationship between market return and fundamental CSAD in different periods. Note: Figure 3 displays CSAD fundamental and market return (Rm) in the three markets HoSE, HNX, and UPCOM. The sample periods are 1 January 2018– 23 January 2020 (PRE), 24 January 2020–5 March 2022 (COVID) and 6 March 2022– 31 October 2022 (POST). Figure 4. Relationship between market return and nonfundamental CSAD in different periods. Note: Figure 4 displays CSAD non-fundamental and maket return (Rm) in the three markets HoSE, HNX, and UPCOM. The sample periods are 1 January 2018– 23 January 2020 (PRE), 24 January 2020–5 March 2022 (COVID) and 6 March 2022– 31 October 2022 (POST). Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 16 of 25 Table 6. Estimates of herd behavior on days of high volatility by non-linearity test between total CSAD and market returns HoSE HNX UpCOM γ3γ4γ3γ4γ3γ4 Panel A: Parkinson’s volatility measure WHOLE Coefficient −3.220*** −2.759*** −1.826*** −0.272 −1.125* −3.572 (0.529) (1.045) (0.438) (1.653) (0.630) (2.827) Wald statistic 3.2184* 1.1940 0.1668 PRE Coefficient −0.162 1.895 −1.563** −0.234 −6.401*** −5.283 (1.017) (1.952) (0.608) (1.793) (1.685) (3.240) Wald statistic 2.2105 0.4694 0.0239 COVID Coefficient −2.828*** −2.332 −0.724 5.311** −0.216 4.868 (0.807) (1.475) (0.668) (2.437) (0.870) (5.916) Wald statistic 1.6467 5.4119** 1.5337 POST Coefficient −5.248*** −0.779 −1.314 −2.874 2.477 −9.368 (1.911) (4.999) (1.126) (3.975) (2.525) (6.929) Wald statistic 0.3615 0.0527 1.5432 Panel B: Garman & Klass’s volatility measure WHOLE Coefficient −4.031*** −1.934*** −2.087*** −2.103** −1.282** −3.387 (0.566) (0.741) (0.453) (0.786) (0.643) (2.439) Wald statistic 0.7524 0.5599 0.3591 PRE Coefficient −0.569 3.060 −1.577** −1.513 −7.226*** −4.743 (1.071) (1.991) (0.625) (1.524) (1.754) (3.163) Wald statistic 4.6259** 0.0000 0.3061 COVID Coefficient −3.722*** −0.597 −1.086 2.186 −0.240 0.108 (0.834) (1.157) (0.719) (1.725) (9.871) (4.127) Wald statistic 0.2427 5.7211** 0.0806 POST Coefficient -9.574*** −1.439 −2.514* −0.519 1.311 −4.084 (2.384) (2.202) (1.284) (1.385) (2.736) (6.657) Wald statistic 8.2569*** 2.1574 0.1963 This table reports the estimated coefficients γ3 and γ4 for Eq. (6) with total CSAD as dependent variable and the Wald statistics corresponding to the test for the null hypothesis γ3γ4¼0. Robust standard errors are reported in parentheses. ***, **, * denote significance at 1%, 5%, and 10% level, respectively. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 17 of 25 Table 7. Estimates of herd behavior on days of high volatility by non-linearity test between fundamental CSAD and market returns HoSE HNX UpCOM γ3γ4γ3γ4γ3γ4 Panel A: Parkinson’s volatility measure WHOLE Coefficient 1.184** 1.104** 1.070** −4.286** 2.366*** −9.595*** (0.499) (0.417) (0.499) (1.840) (0.803) (3.100) Wald statistic 0.0243 12.3918*** 24.3602*** PRE Coefficient 1.560*** 0.018 0.820** −1.940** 3.759*** −2.367** (0.264) (1.618) (0.480) (0.647) (0.506) (0.505) Wald statistic 8.4616*** 8.7699*** 44.7465*** COVID Coefficient 0.655* 1.222** 1.510*** 1.399 3.042*** −1.937 (0.513) (0.477) (0.317) (2.150) (0.514) (7.102) Wald statistic 1.2724 0.0084 2.0730 POST Coefficient −0.787 0.629 4.139*** −6.078 7.174** −18.02** (1.851) (3.536) (1.163) (4.504) (3.121) (4.648) Wald statistic 0.1455 6.3403** 19.1720*** Panel B: Garman & Klass’s volatility measure WHOLE Coefficient 0.624* 1.594*** 0.991** 2.925*** 1.918*** −5.165** (0.501) (0.516) (0.495) (1.000) (0.761) (5.317) Wald statistic 3.8172* 5.3351*** 11.2270*** PRE Coefficient 1.324*** −1.716*** 0.667** −1.951** 3.250*** −2.988*** (0.278) (0.572) (0.485) (0.551) (0.494) (0.503) Wald statistic 37.5601*** 11.1840*** 50.4590*** COVID Coefficient 0.502 1.087*** 1.444*** 3.773*** 2.830*** 4.740* (0.576) (0.427) (0.300) (0.937) (0.557) (6.602) Wald statistic 1.4235 10.5138*** 0.5899 POST Coefficient −3.231** 4.798*** 4.197*** 7.852*** 4.470* −19.40*** (2.350) (1.070) (1.165) (1.337) (2.573) (3.893) Wald statistic 16.4205*** 4.5747** 19.0635*** This table reports the estimated coefficients γ3 and γ4 for Eq. (6) with fundamental CSAD as dependent variable and the Wald statistics corresponding to the test for the null hypothesis γ3γ4¼0. Robust standard errors are reported in parentheses. ***, **, * denote significance at 1%, 5%, and 10% level, respectively. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 18 of 25 Table 8. Estimates of herd behavior on days of high volatility by non-linearity test between non-fundamental CSAD and market returns HoSE HNX UpCOM γ3γ4γ3γ4γ3γ4 Panel A: Parkinson’s volatility measure WHOLE Coefficient −4.913*** −6.554*** −3.433*** 3.478* −3.660*** 7.132** (0.659) (1.763) (0.786) (1.619) (0.892) (3.127) Wald statistic 3.1924* 14.4814*** 9.8974*** PRE Coefficient −2.304*** 1.461 −2.228*** 1.807 −10.38*** −3.187 (1.120) (1.523) (0.874) (2.312) (1.392) (2.499) Wald statistic 4.1616** 3.8740** 3.9420*** COVID Coefficient −3.988*** −6.042*** −3.092*** 2.119 −3.403*** 8.398 (0.861) (1.738) (0.949) (2.337) (0.853) (7.976) Wald statistic 2.6781 4.1266** 3.0993* POST Coefficient −5.167** −2.772 −6.058*** 3.379 −5.421* 10.33 (1.613) (4.830) (1.451) (4.292) (2.930) (7.109) Wald statistic 0.1671 3.3335* 3.2996* Panel B: Garman & Klass’s volatility measure WHOLE Coefficient −5.487*** −5.642*** −3.649*** −5.014*** −3.307*** 2.020 (0.696) (1.730) (0.843) (1.472) (0.855) (4.841) Wald statistic 0.0297 1.8575 3.1585* PRE Coefficient −2.213** 4.223** −2.126** 0.536 −10.59*** −2.438 (1.127) (1.997) (0.897) (1.502) (1.487) (2.662) Wald statistic 12.2081*** 2.3179 5.1573** COVID Coefficient −5.038*** −4.774*** −3.402*** −2.618* −3.112*** −4.183 (0.960) (2.225) (0.959) (2.328) (0.930) (6.842) Wald statistic 0.0442 0.2602 0.0491 (Continued) Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 19 of 25 Table 8. (Continued) HoSE HNX UpCOM POST Coefficient −7.038*** −5.686** −7.118*** −8.576*** −4.265 16.50* (2.140) (1.836) (1.655) (1.787) (2.917) (6.231) Wald statistic 0.1731 0.4313 5.9386** This table reports the estimated coefficients γ3 and γ4 for Eq. (6) with non-fundamental CSAD as dependent variable and the Wald statistics corresponding to the test for the null hypothesis γ3γ4¼0. Robust standard errors are reported in parentheses. ***, **, * denote significance at 1%, 5%, and 10% level, respectively Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 20 of 25 Table 9. Estimates of the impact of pandemic uncertainty and government response strictness on herd behavior HoSE HNX UpCOM α γ1γ2γ3α γ1γ2γ3α γ1γ2γ3 Panel A: Pandemic uncertainty Total 0.016*** 0.422*** −3.278 −0.190 0.019*** 0.589*** −0.593 −0.131 0.018*** 0.790*** −6.438 1.575 Fundamental 0.019*** 0.028 0.282 0.033 0.026*** −0.030** 0.948 0.121 0.024*** −0.066** 1.052 0.685 Non-fundamental −0.003*** 0.395*** −3.560* −0.224 −0.007*** 0.619*** −1.541 −0.252 −0.006*** 0.856*** −7.490 0.891 Panel B: Government responses strictness Total 0.016*** 0.426*** −4.302*** 0.004 0.019*** 0.593*** −1.047 −0.001 0.018*** 0.773*** −0.580 0.006 Fundamental 0.019*** 0.029* 0.875 −0.008 0.026*** −0.034** 2.823*** −0.020 0.024*** −0.071** 5.175** −0.023 Non-fundamental −0.003*** 0.396*** −5.177*** 0.012 −0.007*** 0.627*** −3.870* 0.019 −0.006*** 0.844*** −5.755* 0.029 ***, **, * denote significance at 1%, 5%, and 10% level, respectively. Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 21 of 25 not be extreme movement of asset prices in those trading days, resulting in a medium or low level of volatility. In contrast, as intentional herding occurs, a large number of investors would try to mimic others in buying or selling off some specific stocks, causing the prices of those assets to move significantly from the current price in a relatively short period of time and resulting in a higher level of volatility. Those results are also similar to the findings of some previous studies, for example, those of Fang et al. (2021) and Ferreruela and Mallor (2021). Ferreruela and Mallor (2021) suggested that this phenomenon could result from a cognitive bias called loss aversion, in which investors tend to avoid losses over seeking equivalent profit. In the context of the pandemic, as most people suffered from a drop in their income due to the disruption in economic activities, this kind of bias might have become stronger among investors, especially those who were new to the market for an alternative source of income. As a result, they took the market volatility as a signal, herding in or out on every single occasion they noticed the market volatility behaves in an unusual way. 5.4. Herding under different levels of pandemic uncertainty and strictness of government response Table 9, panel A, shows estimates for Equation (7) during the pandemic. The γ3 coefficient is expected to be statistically significant if herding was affected by the pandemic uncertainty. However, all the estimated values of γ3 are insignificant in three markets for three measures of dispersions. Therefore, evidence on the relationship between pandemic uncertainty and herd behavior is not found in Vietnam’s stock markets, although several studies have confirmed their impact on stock market returns (Chundakkadan & Nedumparambil, 2022; Costola et al., 2021). Table 9, panel B, shows estimates for Equation (8) during the pandemic. Similar to the expectation with Equation (7), the γ3 coefficient is also expected to be statistically significant to show that the strictness in response of government may have some effects on herding; however, all of the estimated values are insignificant in every scenario. Hence, unlike Kizys et al. (2020) and Pochea (2021), with the sample in Vietnam, we find neither the government’s strict response to COVID-19 nor the uncertainty triggered by the pandemic had affected the intensity of herding. A possible explanation for those findings is that most of the investors in Vietnam’s market did not pay enough attention to or did not even consider both of those mentioned factors, as sources of fundamental regarding their investments. 6. Conclusions This study investigates herd behavior in frontier Vietnamese stock markets, including the OTC (UpCOM) and two listed stock exchanges (HNX and HoSE), under the impacts of COVID-19. Firstly, we find that herding does not exist in the three stock markets in extreme movements but in normal market conditions, which suggests the divergence in investors’ decisions in large jumps of the markets. Secondly, Intentional herding was dominant in the three markets for all periods under study, and herding is more severe in two stock exchanges, HoSE and HNX, than in the OTC market UpCOM. Thirdly, we find strong evidence of significant intentional herding on days of high volatility in UpCOM and HNX for all the timeframes, while considerable spurious herding on days with low volatility is found in UpCOM and HNX for all the examined periods except for the pandemic one. The evidence that herding was more pronounced during high volatility days in HoSE was relatively weak overall. Finally, we find no effect of pandemic uncertainty or government responses to the pandemic on heightening or mitigating herd behavior, respectively. Besides its contribution to the current understanding of herd behavior during and post-pandemic and the drivers behind this tendency, this study may have several practical implications. Vietnamese regulators should promote listed companies’ information disclosure by stipulating clearly and specifically the requirements for information disclosure in legal documents. Penalties for information disclosure violations must be raised. And it is necessary to issue additional Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 22 of 25 sanctions such as banning transactions and restricting activities in the security sector. The authorities also have to closely monitor activities in the market as well as cash flow trends, thereby proactively warning investors in a timely manner and implementing reasonable measures in case there would be the presence of a “bubble” in the market. In addition, the new investors need to acquire more knowledge about the market and skills for investing, so they can confidently make their decisions based on their own analysis instead of that of someone else. Finally, this study also suggests several directions for future research based on its limitations due to its nature: (i) we only applied models that are said to be “static” for the detection of herding. As a result, the dynamic aspects of herding, such as structural breaks and regime changes, could not be captured in the study. It will be better to adopt more other herding detection methods such as Hwang and Salmon (2001)’s model on the measurement of herding magnitude or Balcilar et al. (2012) Markov-switching model. (ii) The subsample for the endemic period only consists of trading data for around 8 months. Therefore, expanding the sample will be a potential method to fully capture the characteristics of this period. (iii) while testing for the impact of pandemic uncertainty and government response, we found that both factors did not affect the magnitude of herding; however, the explanation for those findings is left relatively open. Further research should be conducted to bridge those gaps. Acknowledgments We are grateful to the Editorial Office and the anonymous referee for helpful comments and suggestions. This research is funded by the National Economics University, Hanoi, Vietnam [Grant number 1204/QÐ-ÐHKTQD]. Funding The work was supported by the National Economics University [1204/QÐ-ÐHKTQD]; National Economics University [1204/QĐ-ĐHKTQD]. Author details Hong Mai Phan 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-6280-6873 Thi Nhu Quynh Le 2 Vu Duc Hieu Dam 3 ORCID ID: http://orcid.org/0009-0003-4816-3087 Manh Son Tran 4 ORCID ID: http://orcid.org/0000-0002-3792-2100 Thi Hoai Linh Truong 1 Quoc Anh Le 1 1 School of Banking and Finance, National Economics University, Hanoi, Vietnam. 2 Quant Department, Joint Stock Commercial Bank for Foreign Trade of Vietnam, Hanoi, Vietnam. 3 Trading Department, Mercantile Exchange of Vietnam, Hanoi, Vietnam. 4 Math Department, Knox College, Galesburg, IL, USA. Citation information Cite this article as: Herd behavior in Vietnam’s stock market: Impacts of COVID-19, Hong Mai Phan, Thi Nhu Quynh Le, Vu Duc Hieu Dam, Manh Son Tran, Thi Hoai Linh Truong & Quoc Anh Le, Cogent Economics & Finance (2023), 11: 2266616. Notes 1. Frontier Market (wallstreetmojo.com). 2. A New Frontier in Vietnam (ukinvestormagazine.co.uk). 3. MSCI Frontier Markets Asia Index (msci.com). 4. Vietnam’s Economy Expanded by 6.8 Percent in 2019 but Reforms are Needed to Unleash the Potential of Capital Markets (worldbank.org). 5. Vietnam: Successfully Navigating the Pandemic (imf.org). 6. Vietnam 2022 GDP Growth Quickens to 8.02%, Fastest since 1997 (reuters.com). 7. Taking Stock: Vietnam Economic Update, March 2023 (worldbank.org). 8. Asia Frontier Capital (AFC) Vietnam Fund Market— November 2022 Update (asiafrontiercapital.com). 9. Why is it Risky to Invest in Vietnam’s Stock Market (febis.org). 10. Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser (2020)—“COVID-19 – Stringency Index”. Published online at OurWorldInData.org. Retrieved from: “https://ourworldindata.org/covid-stringency-index” [Online Resource] 11. The Number of Trading Accounts of Domestic and Foreign Investors (vsd.vn). 12. Stock Market Faces Bumpy Ride in Second Half of 2022 (vietnamnews.vn). 13. Hot online groups to invest in securities be careful to be “twisted around” (vietnam.postsen.com). 14. Vietnam Stock markets 2020: a rollercoaster year that ends happily (e.vnexpress.net). 15. Vietnam’s stock market sets historic record in 2021 (vietnamnet.vn). 16. More than half of listed companies comply with information disclosure norms (bizhub.vn). 17. Vietnam Ranks 5th in Economic Openness in Asia: Fitch (e.vnexpress.net). 18. Market Capitalization of Stock Market until 3.2023 (ssc.gov.vn). 19. Average Trading Value 3.2023 (ssc.gov.vn). 20. https://www.vcsc.com.vn/en/new-investor-guide Disclosure statement No potential conflict of interest was reported by the author(s). References Altig, D., Baker, S., Barrero, J. M., Bloom, N., Bunn, P., Chen, S., Davis, S. J., Leather, J., Meyer, B., Mihaylov, E., Mizen, P., Parker, N., Renault, T., Smietanka, P., & Thwaites, G. (2020). Economic uncertainty before and during the COVID-19 pandemic. Journal of Public Economics, 191, 104274. https://doi.org/10.1016/j. jpubeco.2020.104274 Andrikopoulos, P., Niklewski, J., & Rodgers, T. (2016). Chapter 9 - the portfolio diversification benefits of frontier markets: An investigation into regional Phan et al., Cogent Economics & Finance (2023), 11: 2266616 https://doi.org/10.1080/23322039.2023.2266616 Page 23 of 25