INTRODUCTION
Problem statement
The stock market serves as a vital indicator of a country's economic health, influencing not only foreign exchange and gold markets but also the credit and options markets When the stock market is robust, investors often convert foreign currency and gold into cash for stock investments, leading to a depreciation of these assets Conversely, in a weak stock market, governments may tighten cash flow for stocks, and banks may reduce their lending for stock investments Additionally, a strong stock market fosters growth in various options, as investors anticipate higher profits.
In Vietnam, the stock market serves as a crucial indicator of the economy's fluctuations For example, announcements regarding bad debts or rising inflation often lead to a significant drop in the VN-index Conversely, when the government implements supportive economic policies, the VN-index tends to rise This dynamic illustrates the bi-directional relationship between economic information and stock market performance, reflecting both growth and recession phases in the economy.
In mean-variance analysis, investors focus on expected stock returns and return volatility, as these factors indicate the risks associated with their portfolios Additionally, trading volume plays a crucial role in reflecting market information; when investors anticipate higher returns, trading volume increases, while expectations of lower returns lead to decreased trading activity Therefore, fluctuations in trading volume can serve as indicators of changes in stock returns.
The relationships among stock returns, trading volume, and return volatility are crucial in empirical research, with numerous studies focusing on these dynamics Karpoff (1987) identifies a positive asymmetric relationship between trading volume and price changes in the equity market, a finding supported by Epps (1975) and further developed by Jennings, Starks, and Fellingham (1981) These models emphasize the role of information flow Additionally, Granger et al (1964) and Granger (1968) analyze data from the New York Stock Exchange, revealing that price changes follow a random walk, indicating that past stock price trends do not predict future movements Moreover, research by Mehrabanpoor, Valizadeh Bahador, and Jandaghi (2005) confirms a positive correlation between market turnover and indices on the Tehran Stock Exchange.
In addition, Michael Long (2007) finds a significantly positive interaction between absolute value of call price changes and trading volume in the option markets.
The relationship between stock return volatility and trading volume has been extensively studied, beginning with Engle's Autoregressive Conditional Heteroskedasticity (ARCH) Model (1982), which suggests that stock returns follow a mixture of distributions Bollerslev's Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model (1986) further considers trading volume as a proxy for information flow, supported by subsequent research from Lamoureux and Lastrapes (1990), Brailsford (1996), and others While Brailsford (1996) posits a positive relationship between return volatility and volume, contrasting views emerge from Fujihara and Mougoue (1997), who find no causal link, and Mestel, Gurgul, and Majdosz (2003), who argue that the relationship is too weak to predict one another Okan, Olgun, and Takmaz (2009) conclude that trading volume negatively impacts return volatility using GARCH, EGARCH, and VAR models In the context of the Vietnamese market, Truong Dong Loc (2009) identifies a unilateral causality from the HNX-index to trading volume, which is further explored by Truong Dong Loc and Dang Thi Thuy Duong (2011), who find that while the index influences net foreign volume, the reverse does not hold true.
This thesis addresses the limited research on the relationship between stock returns and trading volume during recent crises, specifically considering the impact of foreign trading volume across various industries using the GARCH (1, 1) model It investigates the correlation between trading volume and stock returns, as well as the relationship between returns and volatility for companies listed on the Ho Chi Minh City Stock Exchange The study aims to provide insights into how trading volume influences stock returns and return volatility.
Research questions
This paper aims to clarify the relationships between trading volume, stock returns, and return volatility by analyzing intra-day stock price data The primary objective is to address key research questions regarding these financial dynamics.
(i) Is there the relationship between trading volume and return volatility? and
(ii) Does trading volume cause stock returns?
Research objectives
To reach above aims, my objectives of the paper are:
(1) To examine the relationship between trading volume and return volatility and,
(2) To understand the impact of trading volume on stock return through GARCH (1,1) model.
This study analyzes data from eight publicly listed companies on the Ho Chi Minh City Stock Exchange (HOSE) to assess the impact of the recession caused by the US sub-prime mortgage crisis.
This paper is structured as follows: Section Two provides a concise literature review of empirical studies, while Section Three describes the Vietnamese stock market and highlights the unique characteristics of the data, including statistics on selected stocks in the HOSE Section Four outlines the methodology and discusses the empirical results, culminating in the Conclusion.
LITERATURE REVIEW
The Mixture of Distributions Hypothesis
The Mixture of Distributions Hypothesis (MDH), proposed by Clark in 1973, suggests that price changes and trading volume are influenced by the same information flow, leading to a correlation between volume and volatility In the MDH model, trading volume serves as a proxy for the rate of information flow, impacting stock returns based on new information This theory posits that both returns and trading volume respond simultaneously to the arrival of new information, indicating a positive correlation between stock prices and trading volume, as the variance of stock prices during transactions is dependent on trading volume.
The MDH, developed by Epps and Epps (1976), Tauchen and Pitts (1983), Lamoureux and Lastrapes (1990), and Andersen (1996), explores the relationship between price changes and trading volume Epps and Epps (1976) propose that price changes can follow a mixture of distributions, with trading volume serving as a mixing variable Andersen (1996) enhances this hypothesis by introducing trading volume as uncorrelated with information flow, suggesting that volume results from noise or liquidity rather than information arrival In contrast to Tauchen and Pitts (1983), who assume an independent and identically distributed (i.i.d.) information arrival process, Andersen (1996) indicates that the rate of information arrival is serially correlated, with lagged volumes and volatilities positively correlating with current volumes and volatilities.
2 i.i.d means independent and identically distributed
Chen and Tse (1993), Omran and McKenzie (2000), Zarraga (2003), Pyun et al (2000), and Bohl and Henke (2003) find supportive evidence from Japanese, UK, Spanish, Korean, and Polish stock markets, respectively.
Huson Joher Ali Ahmed, Ali Hassan, and Annuar M.D Nasir (2005) investigate the volatility of the Kuala Lumpur Stock Exchange using the Mixture of Distribution Hypothesis Their study employs the GARCH (1,1) model, revealing that this model effectively analyzes return volatility Additionally, they incorporate trading volume as an explanatory variable, concluding that the persistence of volatility remains unchanged with the inclusion of volume.
The Market Dynamics Hypothesis (MDH) faces criticism for its failure to account for volatility in trading volume, which limits its ability to demonstrate volatility persistence when volume is included as an explanatory variable Research by Fong (2003) and Xu et al (2006) indicates that the MDH does not adequately address the serial dependence between return volatility and trading volume Additionally, a study by Nowbutsing and Naregadu (2009), which analyzed thirty-six stocks and the Stock Exchange of Mauritius (SEM) Index using the ARCH-in-mean model, revealed a weakly positive relationship between trading volume and volatility Consequently, this research does not support the MDH or the Strong Information Asymmetry Hypothesis (SIAH) in the context of the SEM.
In their 2009 study, Brajesh Kumar, Priyanka Singh, and Ajay Pandey analyze data from 50 Indian stocks to explore the connection between price and trading volume Utilizing the GARCH model, they assess the relationship between conditional volatility and trading volume, revealing a positive asymmetric relationship between volume and unconditional volatility However, their findings regarding the Market Dynamics Hypothesis (MDH) are inconclusive, as they neither fully reject nor unconditionally support the hypothesis.
The Sequential Information Arrival Hypothesis
The Sequential Information Arrival Hypothesis (SIAH), introduced by Copeland in 1976 and further developed by Jennings et al in 1981, posits that new information is disseminated to informed and uninformed traders in a sequential manner rather than simultaneously This allows informed investors to act on information first, enabling them to manage their portfolios effectively The SIAH establishes a positive bi-directional causality between the absolute value of returns and trading volume, as past absolute returns can predict current trading volume and vice versa Subsequent studies by Harris (1987) and Smirlock and Starks (1988) using NYSE data confirm the positive relationship between changes in trading volume and stock prices, further supporting the SIAH framework.
(1994) contend that a flow of sequential information enables lagged trading volume to predict current absolute returns (price changes) and enables lagged absolute returns to predict current trading volume.
The MDH and SIAH both explore the connection between trading volume and price changes, but they differ in their approaches The MDH suggests a simultaneous relationship, indicating that price changes and trading volume occur at the same time In contrast, the SIAH introduces a sequential dynamic relationship, where past price changes can predict current trading volume and the reverse is also true (Darrat et al., 2003).
4 The Generalized Autoregressive Conditional Heteroskedasticity
Recent studies under the MDH theory have proposed various models to explore the relationship between price and volatility Engle (1982) introduced the Autoregressive Conditional Heteroskedasticity (ARCH) Model, which is based on the premise that stock return time series originate from a mixture of distributions rather than a single distribution This model is particularly suited for financial data, especially time series data, where the arrival of information serves as a stochastic mixing variable, resulting in daily returns that reflect a mixture of distributions.
In 1986, Bollerslev introduced the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, which, alongside the Autoregressive Conditional Heteroskedasticity (ARCH) model, effectively addresses the issue of heteroskedasticity in time series data These models are instrumental in measuring portfolio volatility and analyzing asset pricing Notably, the GARCH model utilizes trading volume as an indicator of market information flow, enabling a comprehensive analysis of market volatility.
The model developed by Lamoureux and Lastrapes (1990), Brailsford (1996), and Mestel, Gurgul, and Majdosz (2003) focuses on limiting the conditional variance of time series by utilizing past squared residuals Lamoureux and Lastrapes (1990) specifically examine the relationship between daily trading volume and volume-return volatility for active US stocks Their findings indicate that daily price variance is heteroskedastic and positively correlated with the arrival of information Additionally, they observe that incorporating trading volume into the conditional variance equation reduces the ARCH effect or volatility persistence, while the impact of lagged volume in the variance equation is largely insignificant.
Najand and Yung (1991) use treasure bond futures to analyze as Lamoureux and Lastrapes
(1990) conduct They report that the lagged volume explains volatility better than
Timothy J Brailsford (1996) explores the positive relationship between price changes and trading volume using three measures of volume, focusing on the top eight stocks in the Australian Stock Market by market capitalization By applying the GARCH model's conditional variance equation, he investigates how volume correlates with stock market volatility, revealing that the slope for negative returns is less steep than for positive returns, thus highlighting an asymmetric relationship Additionally, when trading volume is treated as an exogenous variable, the GARCH coefficients show reduced significance and magnitude This study serves as a foundational analysis of the volume-price change relationship, paving the way for further research in the field.
Research by Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003) indicates a weak contemporaneous relationship between stock returns and trading volume, suggesting that they do not effectively predict one another In contrast, there is a strong correlation between return volatility and trading volume, with volatility serving as a predictor of volume in certain instances.
Safi Ullah Khan and Faisal Rizwan (2008) investigate the connection between trading volumes and return volatility using the GARCH (1,1) model, analyzing data from the Karachi Stock Exchange (KSE-100 index) Their findings reveal a positive contemporaneous relationship between volume and volatility, even after accounting for heteroscedasticity.
Sarika Mahajan and Balwinder Singh (2009) analyzed daily data from the Sensitive Index (SENDEX) of the Bombay Stock Exchange, covering the period from October 1996 to March 2006, to investigate the interconnections between stock return, trading volume, and market volatility.
The empirical findings reveal a significant positive relationship between trading volume and return volatility, supported by both the Market Dynamics Hypothesis (MDH) and the Stock Information Acquisition Hypothesis (SIAH) Utilizing the GARCH (1,1) model, the study demonstrates a reduction in variance persistence when trading volume is incorporated as a measure of information flow within the conditional volatility equation.
In their 2010 study, Tarika Singh and Seema Mehta explore the connection between trading volume and stock return volatility in Asian markets Utilizing the GARCH (1,1) model, they analyze volatility and forecast individual stock returns Their findings reveal that all parameters in the return volatility equation are statistically significant, with a clear indication of recession effects illustrated through their data.
Pratap Chandra Pati and Prabina Rajib (2010) conducted a study using the National Stock Exchange S&P CRISIL NSE Index Nifty Index futures, applying GARCH and ARMA-EGARCH models to examine volatility persistence Their research revealed that incorporating trading volume into the GARCH model reduces volatility persistence, although the GARCH effect remains present.
Chen et al (2001) present findings that contrast with those of Lamoureux and Lastrapes (1990), demonstrating that incorporating contemporaneous trading volume into the GARCH model does not eliminate volatility persistence.
Numerous empirical studies have explored the relationship between trading volume, stock returns, and price changes, revealing both positive and negative correlations in both developed and emerging markets However, limited research has focused on the effects of foreign buy and sell volumes, as well as pre- and post-recession impacts, particularly within the context of the GARCH (1,1) model on the Ho Chi Minh Stock Exchange Consequently, this study aims to further investigate the connections between trading volume and returns, as well as the interplay between volatility and volume, utilizing data from eight listed companies on the Ho Chi Minh stock market.
Generalized The Autoregressive Conditional Heteroskedasticity
Numerous studies on the Vietnamese stock market have been conducted by both local and international researchers Since the implementation of the "Doi Moi" policy, Vietnam has embraced economic reforms aimed at global integration and achieving substantial economic growth As a result, the country's GDP growth rate has averaged around 7 percent annually over the past decade, highlighting the significant role of financial development in driving this economic progress.
As a result, Vietnamese stock market is born to meet requirement of the economy (Vuong Thanh Long, 2008).
The stock market in Ho Chi Minh City was established in July 2000, initially featuring just two listed stocks During its first two years, trading took place only on select days.
In 2010, the Ho Chi Minh Stock Exchange (HOSE) featured 247 listed companies with a market capitalization of approximately $28.28 billion In contrast, the Hanoi Securities Trading Center (HASTC), established by Decision No 127/1998/QĐ-TTg on July 11, 1998, was smaller in scale The Hanoi Stock Exchange (HNX) was created in 2009 through Decision No 01/2009/QĐ-TTg on January 2, 2009, as part of the restructuring of the HASTC by the Prime Minister of Vietnam.
From 2000 to 2003, the stock market experienced significant fluctuations, starting at 571 points in June 2001 and dropping to 139 points by April 2003 In contrast, between 2004 and 2005, the VN index showed a positive trend, rising from 213 points to 307 points.
Between 2006 and 2009, the VN Index achieved a remarkable peak of 1,167 points in February 2007, marking an unforgettable moment for investors However, this upward trend was followed by a sharp decline in the index shortly after.
VIETNAMESE STOCK MARKET AND LISTED COMPANIES ON
Vietnamese stock market
Numerous studies have been conducted on the Vietnamese stock market by both domestic and international researchers, particularly following the country's economic reforms initiated by the "Doi Moi" policy Vietnam has made significant policy adjustments to enhance global integration and achieve substantial economic growth, resulting in an impressive GDP growth rate of approximately 7 percent annually over the past decade This consistent economic growth is closely linked to the advancements in the financial sector.
As a result, Vietnamese stock market is born to meet requirement of the economy (Vuong Thanh Long, 2008).
The stock market in Vietnam was established in Ho Chi Minh City in July 2000, initially featuring just two listed stocks During its first two years, trading took place only on select days.
In 2010, the Ho Chi Minh Stock Exchange (HOSE) featured 247 listed companies with a market capitalization of approximately $28.28 billion In contrast, the Hanoi Securities Trading Center (HASTC), established by Decision No 127/1998/QĐ-TTg on July 11, 1998, was smaller than HOSE The Hanoi Stock Exchange (HNX) was later established in 2009 through Decision No 01/2009/QĐ-TTg on January 2, 2009, as part of the restructuring of the HASTC by the Prime Minister of Vietnam.
From 2000 to 2003, the stock market experienced significant fluctuations, starting at 571 points in June 2001 and dropping to 139 points by April 2003 However, between 2004 and 2005, the VN Index rebounded, rising from 213 to 307 points.
Between 2006 and 2009, the VN Index peaked at an impressive 1,167 points in February 2007, marking a significant moment for investors However, this high was followed by a sharp decline in the index later that month.
2009 at 235 points and moves up to 480 on December 31, 2010 (Nguyen Thi Kim Yen, 2011).
The stock market has significantly expanded, with the number of listed companies growing from 164 on the HOSE and 154 on the HNX in 2008 to a total of 627 by 2010, which includes companies from both exchanges and fund management firms (Nguyen Thi Kim Yen, 2011).
Some summarized numbers of the year 2010 as follow:
Average daily trading value (USD million) 80
For the Ho Chi Minh City Stock Exchange (HOSE), the Prime Minister signs Decision
On May 11, 2007, Decision No 559/2007/QD-TTg transformed the Ho Chi Minh Securities Trading Center into the Ho Chi Minh Stock Exchange (HOSE) As of December 31, 2011, stocks listed on HOSE must adhere to specific criteria to ensure compliance and maintain market integrity.
- The listing companies are joint stock companies with paid-up capital at the time of registration for listing at least VND80 billion at book value.
- Business operation in two years before the year of listing has to be profitable and has no accumulated losses up to the year of registration for listing.
No Stock Name of Company Sector Chartered capital (mil) Date of listing Market capitalization (17 Jul 12)
Portfolio weight VN30- index weight
- There are no overdue debts which has not been reserved compliance with regulations; public all debts toward the company by members of the Board of Directors,
Board of Supervisors, Manager or General Manager, Deputy Manager or Deputy General
Manager, Chief Accountant and Major shareholders.
To ensure a diverse ownership structure, at least 20% of voting shares must be held by a minimum of 100 shareholders who are neither professional investors nor major shareholders, unless the shares are part of a transition from state-owned enterprises to joint stock companies.
- Officer-shareholders have to commit to hold 100 percent of their shares in 6 months from the date of listing and 50 percent of them for the following 6 months.
This study analyzes data from listed companies on the Ho Chi Minh City Stock Exchange (HOSE) to explore the relationships influenced by HOSE's stringent requirements compared to other exchanges The research focuses on the period from 2007 to 2011 to assess the economic impact on the stock market.
Data
Database includes daily price changes and trading volume of eight stocks listed on the Ho
Chi Minh City Stock Exchange.
Table 1: Description of stocks (on 17 th Jul 2012)
No Stock Name of Company Sector Chartered capital (mil) Date of listing Market capitalization (17 Jul 12)
(mil) Portfolio weight VN30- index weight
5 VIC Vingroup J.S.C Real estates 7,004,621 07 Sep 2007 55,336,633 37.3% 16.2%
6 FPT FPT Corporation IT and
I prioritize information from listed companies due to their transparency and reliability for investors I focus on eight specific stocks that represent key industries in the economy, selected based on high market capitalization, listing history, firm size, liquidity, and their significant influence on the VN-Index for analysis.
The VN30-index, established by the Ho Chi Minh City Stock Exchange, features 30 top stocks that significantly influence the VN-index, accounting for approximately 80% of total market capitalization and 60% of total traded value From these stocks, I selected 17 that meet specific criteria as of July 17, 2012, and categorized them into eight industries: processing and manufacturing, mining, finance, consumer goods, real estate and construction, telecommunications and information, transport and warehouse, and electricity Ultimately, I identified eight representative stocks from each industry based on their high market capitalization and substantial impact on the VN-index Notably, I chose SSI for its ability to reflect overall market trends, despite having a lower market capitalization than STB (Saigon Thuong Tin Commercial J.S.C).
DPM (Petro Vietnam Fertilizer and Chemicals Company) is established in 28 March
Established in 2003 with a chartered capital of 3,800 billion, DPM specializes in fertilizer production, boasting an annual capacity of 740,000 tons The company produces ammonia at a daily capacity of 1,350 tons and urea at 2,200 tons per day, while also trading liquid ammonia with an annual capacity of 96,000 tons DPM meets 40% of the national fertilizer demand and holds a 50% market share in the fertilizer sector across the southern and central southern regions of the country.
Established in 1994, PVD (Petro Vietnam Drilling and Well Services Corporation) specializes in contract drilling, well maintenance, and a range of services for petroleum production companies Its offerings include geotechnical services, logging, oil field mapping, oil spill control, and the leasing of drilling equipment and oil rigs As a subsidiary of the Vietnam National Oil and Gas Group (Petro Vietnam), PVD operates through three joint ventures in oil exploration and production, alongside six subsidiaries overseeing its operations.
Established in 1999, Saigon Securities Inc (SSI) is a leading securities firm in Vietnam, offering a range of services including brokerage, portfolio management, and corporate advisory With a 17% market share, SSI stands out as one of the largest brokerage firms in the country The company boasts a significant foreign client base, comprising over 100 institutions and 2,500 individual accounts, which together account for 30% of the market.
VNM (Vietnam Dairy Products J.S.C) was established in 1976 Its products include:
VNM offers a diverse range of dairy products, including milk, powdered milk, solid milk, yogurt, ice cream, fruit juice, and coffee With a strong distribution network of over 1,000 agencies nationwide, VNM successfully exports its products to major markets such as the US, Germany, Canada, and China The company operates more than 70 stations dedicated to transporting fresh milk materials, achieving a daily harvest of over 260 tons, which accounts for 80% of the country's fresh milk supply Additionally, VNM invests in the construction of 60 processing plants for fresh milk.
Established in 2002, Vincom Corporation (VIC) specializes in the development and management of real estate projects, focusing on commercial and entertainment spaces The company has successfully launched several significant projects across the nation, including the Vincom Twin Tower, a mixed-use complex featuring a trade center, office spaces, and luxury buildings, as well as the Vincom Hai Phong Plaza, which incorporates a comprehensive trade complex.
FPT (FPT Corporation) was established in 1988 FPT is a multinational company, mainly operates in information technology, telecommunication, distribution, real estates, education, and financial activities FPT has 15 subsidiaries and affiliates, 53 branches, 396
FPT boasts a comprehensive network of 560 cell phone distributors across the country and collaborates with 60 renowned partners, including industry leaders such as IBM, Lenovo, Microsoft, HP, Nokia, Toshiba, Oracle, Samsung, Motorola, Veritas, Apple, and Intel.
Established in 1990, GMD (General Forwarding & Agency Corporation) is a state-owned company that offers comprehensive forwarding and logistics services across the country and its surrounding regions Leveraging its extensive scale, strong partnerships, and experienced, skilled workforce, GMD is well-equipped to meet diverse logistical needs.
VSH (Vinh Son – Song Hinh Hydropower Joint Stock Company) is established in 1991 and equitized in 2005 It is the first company on hydropower listed on HOSE The core
21 business fields are producing and trading electricity, managing and maintaining services, advising and supervising VSH also invests in hydropower projects with capacity of 330 MW.
This study analyzes data collected from January 1, 2007, to December 31, 2011, dividing the period into two intervals: pre-recession and post-recession, specifically before and after December 2008 This division is crucial for examining the distinct effects of macroeconomic and external factors on the stock market during and after the recession.
We use the daily closing prices to estimate daily returns And the percentage of stock return is identified as:
Which Pt and Pt-1 are daily price of stock on two continuous day t-1 and t.
This article examines the influence of trading volume and foreign trading activity on stock returns and volatility in the Vietnamese stock market It highlights the significant impact that foreign trading has on stock prices and overall market activity Additionally, the analysis encompasses both foreign buy volume and foreign sell volume to provide a comprehensive understanding of their effects on the market.
In my analysis, I encountered instances of null or invalid inputs in the columns representing foreign buy and sell volume, as foreign investors may not engage in transactions on certain days To ensure the data remains meaningful for model analysis, I treat these cases of "zero volume" as "0.001 volume."
Finally, I will use Eviews and Stata software to analyze the relationship between stock return, trading volume, and volatility.
Summary statistics
I start to examine the relationship among stock return, trading volume and volatility within initial analysis of statistical description of time series.
Stocks Variables Mean Std Dev Skewness Kurtosis Jarque -
Stocks Variables Mean Std Dev Skewness Kurtosis Jarque -
Source: Author’s own calculation based on dataset
The descriptive statistics for eight stocks on the HOSE from 2007 to 2011 reveal key metrics such as Mean, Standard Deviations, Skewness, Kurtosis, and Jarque-Bera for daily returns, trading volumes, foreign buy volumes, and foreign sell volumes Most stocks exhibit negative average returns, with the exceptions of VIC at 0.04 percent and VNM at 0.017 percent, while SSI records the lowest return among all stocks analyzed.
The analysis of trading volume percentages reveals that SSI leads with a mean of 13.722, followed closely by DPM at 13.031 In contrast, VIC and VNM exhibit the lowest trading volumes, recorded at 11.439 and 11.487, respectively Stocks such as GMD, PVD, VSH, and FPT occupy a mid-range position with trading volumes of 11.785, 11.932, 12.147, and 12.307 Notably, foreign investors demonstrate a stronger purchasing power for DPM.
(11.296) and FPT (11.035), but lower power of GMD (6.932) and VIC (6.889).
Among the eight stocks analyzed, SSI and VIC exhibit the highest volatility in returns, with standard deviations of 0.034 and 0.033, respectively In contrast, DPM shows the least volatility, with a standard deviation of 0.025 The standard deviations for the other stocks remain relatively stable, ranging from 0.027 to 0.030.
Most stock returns exhibit negative skewness, indicating high risk, except for DPM, which has a positive skewness of 0.1013 This negative skewness results in asymmetric and non-normal returns, primarily due to the presence of risk-averse investors (Moolman, 2004) Additionally, most stock returns are leptokurtic, with excess kurtosis greater than three, suggesting increased risk A more leptokurtic distribution correlates with a positive relationship between trading volume and return volatility, as noted by Tauchen and Pitts (1983), Karpoff (1987), and Gallant et al (1992) Conversely, DPM exhibits negative excess kurtosis, indicating lower risk compared to other stocks Consequently, SSI and VIC are identified as the most hazardous stocks, while DPM stands out as the safest option with its negative excess kurtosis.
The analysis of nearly all stock data series reveals non-normality, as indicated by the Jarque-Bera test, which shows JB > χ² critical 4, leading to the rejection of the null hypothesis of normal distribution for each stock This finding is crucial, as it suggests a violation of the weak-form efficiency condition in financial markets, as outlined by Fama.
(1965), Stevenson and Bear (1970), Reddy (1997), and Kamath (1998)) There is only case that stock DPM is normal distribution at one percent significant level.
3 Excess kurtosis: a probability (return distribution) has a kurtosis parameter larger than parameter with normal distribution around 3.
4 χ 2 critical of degree of freedom of 2 at 1% significant level is 9.21, 5% level is 5.99 and 10% level is 4.6.
RE TURN LNVOL LNFBVOL LNFSVOL
Overall, from above statistics, VIC and SSI are the most dangerous for investors whereas DPM is the most safety stock for investment.
Graphical analysis
Source: Author’s graphs based on dataset
As seen from graphs, I can infer that data series for eight listed stocks are fully stationary This will be also affirmed by some following tests in the next sessions.
ECONOMETRIC MODELS AND DISCUSSION
Test for stationarity in stock return and trading volume
Granger (1974) highlights that estimating relationships among non-stationary variables can yield misleading results due to the challenge of distinguishing between temporary and permanent relationships in non-stationary time series To mitigate spurious correlations, I will assess the stationarity of stock returns and trading volume percentages Additionally, Su (2003) and Chen, Firth, and Rui (2001) note the presence of time trends in raw trading volume within the Chinese Stock Market, prompting me to incorporate a time trend into the Augmented Dickey-Fuller equation To evaluate stationarity, I will utilize the Augmented Dickey-Fuller (ADF) test as follows: n Δxt = ρ0 + ρxt-1 + Σi=1 δi Δxt-i + εt.
And Philips – Perron (1988) (PP) test: xt = α0 + αxt-1 + ut
Where x stands for stock return and trading volume percent, and ρ0, ρ, and δ are model parameters, εt represents white noise error term, respectively.
ADF test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
ADF test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
Source: Author’s own calculation based on dataset
The ADF statistics for DPM returns indicate a value of 0.000, which is below one percent for both the level and first difference tests, regardless of whether a trend is included This result leads to the rejection of the null hypothesis that the returns are non-stationary, suggesting that the DPM return series is stationary overall This pattern is consistent across the other stock returns analyzed, as all eight stocks exhibit stationarity, making them suitable for statistical analysis.
ADF test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
PP test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
PP test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
Source: Author’s own calculation based on dataset
Table 4 indicates that DPM stock has a PP statistic of less than one percent across all cases, leading us to reject the null hypothesis of unit root in returns Consequently, DPM's returns are stationary This analysis applies similarly to the other stocks, allowing us to conclude that all stock returns are stationary.
In general, from two tests above, we can conclude that all stock returns in the paper are stationary absolutely which are necessary for below regressions.
Trading volume and return volatility
In this paper, I employ the GARCH model to evaluate and forecast the stock returns on the Vietnamese Stock Exchange.
PP test Level and Intercept 1st difference and Intercept
No trend With trend No trend With trend
The GARCH model, developed by Bollerslev in 1986, stands out as the most effective model for addressing excess kurtosis in stock returns Subsequent research by Lamoureux and Lastrapes (1990), Brailsford (1996), and Mestel, Gurgul, and Majdosz further supports its efficacy in financial analysis.
The GARCH (1,1) model, initially developed in 2003, has been widely applied to financial time series data Over the past two decades, its use has expanded beyond just analyzing magnitude to include various forms of returns (Engle, 2001).
In this study, I utilize the GARCH (1,1) model, as outlined by Lamoureux and Lastrapes (1990), to analyze the impact of trading volume on mean returns and conditional return volatility The modified GARCH (1,1) equation is expressed as follows: rt = α0 + αrrt-1 + αVollnVolt + αFBVollnFBVolt + αFSVollnFSVolt + αdd + εt.
t = β0 + βε ε t 1 + βσ t 1 + βVollnVolt + βFBVollnFBVolt + βFSVollnFSVolt + βdd + et
(a): the mean stock return equation
(b): the conditional return volatility equation
Where rt is daily return of stock; rt-1 is conditional return on past information;
2 is the conditional variance (volatility) of εt at day t; εt called the standard residual is a sequence of independent and identically distributed random variables (iid) with mean zero and variance 1; t
The trading volume percentage on day t is represented by lnVolt, while lnFBVolt indicates the percentage of foreign investors' purchasing volume and lnFSVolt reflects the percentage of their selling volume on the same day A dummy variable, d, is utilized where d equals 0 for the period from January 1, 2007, to December 12, 2008, and d equals 1 from January 1, 2009, to December 12, 2011 The model incorporates white noise (et) and a constant (β0) that is greater than zero Additionally, the coefficients βε and βσ, which are both non-negative, measure the current volatility's dependence on past squared residuals and past volatility, respectively.
The equation (βε + βσ) < 1 indicates the persistence of conditional volatility in financial markets The coefficients αVol, αFBVol, αFSVol, and αd represent the influence of trading volume percent, foreign buy volume percent, foreign sell volume percent, and a dummy variable on the average return In contrast, the coefficients βVol, βFBVol, βFSVol, and βd measure the effects of these same variables on conditional volatility, highlighting their significance in understanding market dynamics.
Table 5: GARCH (1,1) model without LnVol
DPM PVD SSI VNM VIC FPT GMD VSH
DPM PVD SSI VNM VIC FPT GMD VSH
Source: Author’s own calculation based on dataset Note: (*), (**), (***) indicate 10%, 5%, and 1%, respectively
Excluding the trading volume percentage variable significantly increases volatility persistence across most stocks, with SSI being the only exception, where it decreases from 44% to 32% Notably, FPT and VIC exhibit persistence greater than one, indicating explosive variance and undermining the assumption of stationarity, which leads to model instability Additionally, the persistence levels of VSH and PVD approach unity, suggesting that volatility shocks maintain a high degree of persistence Therefore, the percentage of daily trading volume is a crucial explanatory variable in this analysis.
I discuss more the results of ARCH and GARCH effects in Table 5 The table reveals that
The study finds that the ARCH effect (β ε) is significant at the 1 percent level, while over 80 percent of the GARCH effect (β σ) is also significant at the same level; however, the SSI shows no GARCH effect Notably, the GARCH effect disappears when the trading volume percentage is excluded, although both the ARCH and GARCH effects are significant when considering trading volume These findings contradict the results of Lamoureux and Lastrapes (1990a) regarding the US stock market.
Furthermore, I compare the results of GARCH (1,1) model including percentage of volume (in Table 6) to model excluding percentage of volume (in Table 5) by the likelihood ratio (LR) test.
The LR test is calculated as follows:
Where the probability distribution of the test statistic is approximately a chi-squared distribution with degrees of freedom of 1 (q=1)
LU: The log likelihood of the unrestricted model (with percentage of trading volume)
LR: The log likelihood of the restricted model (without percentage of trading volume) q = 1
After calculation, I get LRs of six stocks (excluding FPT and VIC) as follows:
Table 6: Likelihood ratios of stocks
Stocks DPM PVD SSI VNM GMD VSH
Source: Author’s own calculation based on dataset
The analysis reveals that the likelihood ratios (LRs) for six stocks exceed the critical value of χ²₁ (3.841), leading to the selection of an unrestricted model that incorporates the percentage of trading volume Additionally, excluding this percentage significantly reduces the GARCH effect on the Stock Sensitivity Index (SSI) Furthermore, the persistence of stocks in the model without trading volume fails to meet the stationary assumption observed in the model that includes it These factors justify the decision to use the model with trading volume percentage for further analysis.
Table 7: GARCH (1,1) model with LnVol
DPM PVD SSI VNM VIC FPT GMD VSH
DPM PVD SSI VNM VIC FPT GMD VSH
Source: Author’s own calculation based on dataset Note: (*), (**), (***) indicate 10%, 5%, and 1%, respectively
The Mixture of Distribution Hypothesis suggests that the GARCH effect is elucidated when the β Vol is significantly positive, while the sum of (β ε + β σ) is less than the persistence magnitude of the model, excluding the impact of trading volume percentage.
Table 6 reveals that the ARCH and GARCH effects for each stock are significant at the one percent level, with the exception of SSI, which is significant at the five percent level The significant ARCH term indicates that current return volatility is influenced by lagged error terms, while the significant GARCH term suggests that conditional variance is affected by past variance, highlighting the meaningful impact of historical information shocks on current return volatility The analysis shows that the Vietnamese stock market exhibits weak form inefficiency, with most stocks displaying ARCH effects between 20 and 30 percent; however, VIC stands out with a notably high ARCH effect of 77 percent The GARCH effects are particularly pronounced for GMD, FPT, and VSH, with DPM at 61 percent and FPT and VSH exceeding 59 percent VNM and DPM have GARCH parameters around 40 percent, while the remaining stocks show GARCH effects ranging from 10 to 20 percent, with VIC, PVD, and SSI at 21 percent, 16 percent, and 13 percent, respectively Overall, both ARCH and GARCH coefficients are significant across all stocks.
Conditional volatility for all stocks, represented by the sum (β ε + β σ ), is less than unity, indicating stationarity and suggesting that volatility shocks impact returns This sum also reflects volatility clustering, which contributes to asymmetry and inefficiency in emerging markets High persistence in volatility means that returns regress to the mean slowly, while low persistence indicates a rapid return to the mean Notably, the conditional variance of VIC exhibits very high volatility persistence, exceeding 90%, and approaches unity, signifying persistent volatility shocks In contrast, the persistence levels for PVD and SSI are considerably lower, falling below the threshold.
50 percent) The volatility persistence of the rest of stocks is moderate from 70 to 80 percent.
In the mean equation, all stock constants are negative and statistically significant at the one percent level, indicating a general decline in returns Conversely, the coefficient for lagged returns is consistently positive and significant at the same level Notably, the lag return coefficients for VIC (0.24), SSI (0.23), and GMD (0.21) exert a more substantial influence on returns compared to FPT (0.15), VSH (0.12), VNM (0.12), and DPM (0.11) PVD shows a minimal impact on returns with a coefficient of 0.09, highlighting that while VIC and SSI significantly affect current returns through lagged returns, PVD demonstrates the opposite effect.
Over 80 percent of stocks show a significant positive relationship between trading volume and returns, indicating that higher trading volumes are associated with increased returns However, this relationship does not hold for SSI, where the parameter is insignificant Overall, while trading volume percentage has a slight impact on average returns, its effect is limited.
Volatility clustering indicates that significant price movements in stocks are often succeeded by further substantial changes, while minor fluctuations are likely to be followed by additional small shifts Notably, trading volumes typically range from 0.001 to 0.003 across most stocks, and the impact of the percentage of volume from the SSI on returns is minimal.
In many instances, the percentage of foreign buy volume does not significantly affect returns due to minor factors However, the VIC's coefficient shows a significant positive correlation at the one percent level, albeit with a minimal impact of only 0.00047.