INTRODUCTION
Problem statement
The stock market serves as a key indicator of a country's economic health, influencing not only foreign exchange and gold markets but also the credit and options markets A robust stock market often prompts investors to 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 restrict cash flow to stocks, and banks may reduce lending for stock purchases Additionally, a strong stock market fosters growth in various types of options as investors anticipate higher profits.
In Vietnam, the stock market serves as a crucial indicator of the evolving economy, with the VN-Index reflecting significant changes For example, announcements of rising bad debts or inflation typically lead to a sharp decline in the VN-Index, while government support policies can result in an increase This fluctuation in the VN-Index effectively signals economic growth or recession, demonstrating the bi-directional relationship between information dissemination and stock market performance.
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 stock returns, trading activity increases, leading to higher trading volumes Conversely, expectations of lower returns result in decreased trading activity Therefore, fluctuations in trading volume can serve as important signals for market sentiment.
2 the fluctuations of stock returns
The relationships among stock returns, trading volume, and return volatility have become crucial in empirical research, with numerous studies exploring these dynamics Karpoff (1987) identifies a positive asymmetric relationship between trading volume and price changes in the equity market, supported by Epps (1975) and Jennings et al (1981), who also propose models that highlight this asymmetric relationship as related to information flow Granger et al (1964) and Granger (1968) analyze data from the New York Stock Exchange, concluding that price changes exhibit a random walk, indicating that past stock price trends do not predict future movements Additionally, Mehrabanpoor et al (2005) find 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 volume and return volatility is grounded in the Autoregressive Conditional Heteroskedasticity (ARCH) Model introduced by Engle (1982), which posits that stock returns are derived from a mixture of distributions Building on this, Bollerslev (1986) developed the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model, treating trading volume as an indicator of information flow Subsequent research by Lamoureux and Lastrapes (1990), Brailsford (1996), and others further refined this model Brailsford (1996) specifically emphasizes the significant connection between stock return volatility and trading volume.
The GARCH model indicates a positive relationship between stock return and trading volume; however, some researchers present contrasting views A Fujihara and Mbodja Mougoue (1997) found no causal relationship between return and volume, while Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003) argued that the connection is too weak for reliable forecasting Conversely, Berna Okan, Onur Olgun, and Sefa Takmaz (2009) demonstrated that trading volume negatively affects return volatility using GARCH, EGARCH, and VAR models In the context of the Vietnamese market, Truong Dong Loc (2009) examined the unilateral causality effect of the HNX-index on trading volume, and Truong Dong Loc and Dang Thi Thuy Duong (2011) later found that the index influences net foreign trading volume, while the reverse relationship does not hold.
This thesis addresses a gap in existing research by analyzing the relationship between stock returns and trading volume during the recent crisis, specifically focusing on foreign trading volume across various industries using the GARCH (1, 1) model It investigates how trading volume impacts stock returns and return volatility for companies listed on the Ho Chi Minh City Stock Exchange.
Research questions
This study 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 how these factors interact within the financial markets.
(i) Is there the relationship between trading volume and return volatility? and
(ii) Does trading volume cause stock returns? h
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.
The paper is structured as follows: Section Two offers a concise literature review of empirical studies, while Section Three describes the Vietnamese stock market and highlights its unique data characteristics, including statistics on selected stocks in the HOSE Section Four discusses the methodology and analyzes the empirical results, culminating in the Conclusion.
LITERATURE REVIEW
The Efficient Market Hypothesis
The Efficient Market Hypothesis (EMH), introduced by Fama in 1970, is a fundamental theory in financial data analysis widely accepted by modern economists It posits that financial markets are highly efficient in reflecting all available information regarding individual stocks and the overall stock market According to the EMH, stock prices incorporate all relevant information, representing investors' collective expectations about the future Consequently, the theory suggests that current stock prices cannot be used to predict future price movements, as information is quickly and effectively integrated into stock prices at any given time.
Keith and Dirk (2005) propose three versions of the Efficient Market Hypothesis (EMH): weak, semi-strong, and strong forms In weak-form efficiency, stock prices solely reflect all publicly available historical information, making past stock prices the most accessible data in the stock markets.
In the semi-strong form of market efficiency, stock prices instantly incorporate all available public information, including historical prices and pertinent data such as financial statements, dividend announcements, profit reports, stock splits, and mergers and acquisitions.
Macroeconomic factors such as inflation, interest rates, and exchange rates significantly influence financial markets, and these markets, in turn, affect the broader economy According to the semi-strong form of market efficiency, current market prices reflect all publicly available information, making them the most accurate predictors of fair stock prices This theory asserts that no investor can achieve excess returns by trading on information already understood by the market Notably, the semi-strong form is considered more robust than the weak form of market efficiency.
In the strong form of market efficiency, stock prices rapidly incorporate all available public and private information, making it impossible for investors to consistently identify undervalued stocks or for insiders to gain an advantage Consequently, the current market price serves as the most accurate predictor of a fair stock value.
Numerous studies support the concept of market efficiency, including research by Thomas E Copeland and Daniel Friedman (1987), who analyzed price behavior, trading volume, and portfolio composition in response to information on the NASDAQ Their comparison of price, volume, and share allocations across three market equilibrium models—telepathic rational expectations, ordinary rational expectations, and private information—revealed that stronger market forecasts predict equilibrium prices more accurately than weaker ones.
Maloney and Mulherin (2003) investigate the relationship between stock returns and trading volume during the Challenger space shuttle crash, focusing on four firms Their findings reveal that price movements occur even during trading interruptions aimed at addressing issues or in periods of low trading profits However, they are unable to identify a definitive mechanism through which informed traders can influence these price movements.
While Fama (1970) and above researchers dominates over the EMH, there are some h
Several controversial studies challenge the concept of market efficiency Grossman and Stiglitz (1980) argue that informational efficiency is unattainable due to the high costs of acquiring information, which prevents stock prices from fully reflecting available data LeRoy and Porter (1981) highlight excessive volatility as evidence against market efficiency, while Marsh and Merton (1986) also reject the Efficient Market Hypothesis (EMH) Summers (1986) examines statistical tests and finds that speculation undermines rational valuation and market efficiency In 1990, Laffont and Maskin dispute the EMH in the context of imperfect competition, and Jegadeesh presents strong evidence against the random walk hypothesis, indicating predictability in stock returns More recently, Lee et al (2010) assert that the stock market lacks efficiency concerning the stationarity of stock prices.
Karpoff (1987) conducts a comprehensive survey of previous studies to explore the relationship between price changes and trading volume across various markets He categorizes his research into two main areas: the correlation between absolute price changes and trading volume, and the relationship between price changes and trading volume His findings reveal a positive correlation between trading volume and price changes in the equity market Additionally, he discusses the potential for an asymmetric relationship between price changes and volume, highlighting that many researchers often rely on linear models to analyze this dynamic.
1 Random Walk Hypothesis: is consistent with the Efficient Market Hypothesis h
8 necessary to explain the Mixture of Distribution Hypothesis (Clark, 1973) and the Sequential Information Arrival Hypothesis (Copeland, 1976).
The Mixture of Distributions Hypothesis
The Mixture of Distributions Hypothesis (MDH), proposed by Clark in 1973, posits that price changes and trading volume are influenced by the same flow of information, leading to a correlation between volume and volatility In this model, trading volume serves as a proxy for the rate of information flow, which is essential for evaluating stock returns based on new information The MDH suggests that returns and volume adjust simultaneously in response to new information, indicating a positive correlation between stock prices and trading volume, as the variance in stock price during a transaction is dependent on its 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 logarithm changes can follow a mixture of distributions, with trading volume acting as a mixing variable Andersen (1996) enhances this hypothesis by suggesting that trading volume is uncorrelated with information flow and results from noise or liquidity trading, contrasting with information-driven trading While Tauchen and Pitts (1983) assume an independent and identically distributed (i.i.d.) information arrival process, Andersen (1996) indicates that information arrival rates are serially correlated, revealing positive correlations between lagged volumes, volatilities, and current market conditions.
2 i.i.d means independent and identically distributed h
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) examine the volatility of the Kuala Lumpur Stock Exchange using the Mixture of Distribution Hypothesis Their study utilizes the GARCH (1,1) model, demonstrating 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 of return volatility and volume Additionally, a study by Nowbutsing and Naregadu (2009), which analyzed thirty-six stocks and the Stock Exchange of Mauritius Index using the ARCH-in-mean model, revealed a weakly positive relationship between trading volume and volatility, ultimately challenging the validity of both the MDH and the Sequential Information Arrival Hypothesis (SIAH) in the context of the SEM.
In their 2009 study, Brajesh Kumar, Priyanka Singh, and Ajay Pandey analyze the relationship between price and trading volume using data from 50 Indian stocks They employ the GARCH model to explore the connection between conditional volatility and trading volume Their results reveal a positive asymmetric relationship between trading volume and unconditional volatility However, the findings regarding the Market Dynamics Hypothesis (MDH) are inconclusive, as the results 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 traders in a sequential manner rather than simultaneously This means that informed investors who access information first can strategically manage their portfolios, leading to a positive bi-directional relationship between the absolute value of returns and trading volume Subsequent studies, including those by Harris in 1987 and Smirlock and Starks in 1988, have confirmed the positive interaction between volume changes and stock prices, supporting the SIAH's implications for market behavior.
(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 Market Dynamics Hypothesis (MDH) and the Sequential Information Arrival Hypothesis (SIAH) both explore the connection between trading volume and price changes While the MDH suggests that price changes and trading volume are related in real-time, the SIAH introduces a sequential dynamic, indicating that past price changes can predict current trading volume and that trading volume can also influence future price changes (Darrat et al., 2003).
The Generalized Autoregressive Conditional Heteroskedasticity
Under the MDH theory, many recent studies propose several models in the relationship h
Engle (1982) developed the Autoregressive Conditional Heteroskedasticity (ARCH) Model, which posits that stock return time series arise from a mixture of distributions rather than a single distribution, facilitating the calculation of expected returns This model is particularly suitable for financial data, especially time series data, as it treats the arrival of information as a stochastic mixing variable, with daily returns representing a mixture of distributions.
Bollerslev (1986) introduced the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, which, along with the ARCH model, effectively addresses the heteroskedasticity present in time series data These models are essential for measuring portfolio volatility and analyzing asset prices The GARCH model specifically utilizes trading volume as a proxy for market information flow to assess market volatility.
The model developed by Lamoureux and Lastrapes (1990), Brailsford (1996), and Mestel, Gurgul, and Majdosz (2003) emphasizes the importance of limiting the conditional variance of time series by utilizing past squared residuals Specifically, Lamoureux and Lastrapes (1990) analyze daily trading volume as a key explanatory variable in understanding the volume-return volatility relationship for active US stocks Their findings indicate that daily price variance is heteroskedastic and positively correlated with information arrival Additionally, they reveal that incorporating trading volume into the conditional variance equation reduces the ARCH effect, or volatility persistence, while the effect of lagged volume is generally insignificant across most cases.
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 h
Timothy J Brailsford (1996) highlights a positive relationship between price changes and trading volume through three measures of volume, focusing on the top eight stocks in the Australian Stock Market by market capitalization Utilizing the GARCH model's conditional variance equation, he investigates the link between volume and stock market volatility, revealing that the slope for negative returns is lower than that for positive returns, indicating an asymmetric relationship Additionally, when trading volume is treated as an exogenous variable, the significance and magnitude of the GARCH coefficients decrease This paper serves as a foundational analysis of the relationship between volume and price changes, paving the way for further research in this area.
Research by Roland Mestel, Henryk Gurgul, and Pawel Majdosz (2003) indicates that the relationship between stock returns and trading volume is weak, suggesting that they do not effectively predict one another In contrast, there is a strong correlation between return volatility and trading volume, indicating that volatility can serve as a predictor of trading volume in certain instances.
Safi Ullah Khan and Faisal Rizwan (2008) investigate the link between trading volumes and return volatility utilizing the GARCH (1,1) model Their analysis of the Karachi Stock Exchange (KSE-100 index) reveals a significant positive 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 interplay between return, trading volume, and market volatility.
The empirical findings reveal a significant positive interaction between trading volume and return volatility, supported by both the Market Dynamics Hypothesis (MDH) and the Stock Information Asymmetry Hypothesis (SIAH) Utilizing a GARCH (1,1) model, the study indicates a reduction in variance persistence when trading volume is incorporated as a measure of information flow in the conditional volatility equation.
Tarika Singh and Seema Mehta (2010) explore the connection between trading volume and stock return volatility in Asian markets, utilizing the GARCH (1,1) model for their analysis Their findings indicate that all parameters in the return volatility equation are statistically significant, highlighting a clear impact of recession on stock returns as evidenced by their figures.
In their 2010 study, Pratap Chandra Pati and Prabina Rajib analyze the persistence of volatility in the National Stock Exchange S&P CRISIL NSE Index Nifty Index futures using GARCH and ARMA-EGARCH models Their findings reveal that incorporating trading volume into the GARCH model reduces the persistence of volatility, although the GARCH effect does not completely vanish.
Chen et al (2001) present findings that contrast with those of Lamoureux and Lastrapes (1990), revealing that volatility persistence remains unaffected even when contemporaneous trading volume is incorporated into the GARCH model.
Numerous empirical studies have examined the relationship between trading volume, stock returns, and price changes, revealing both positive and negative correlations in both developed and emerging markets However, there is a limited amount of research focusing on the effects of foreign buy and sell volume, as well as the pre- and post-recession impacts, specifically using the GARCH (1,1) model on the Ho Chi Minh Stock Exchange Consequently, further investigation into the relationship between volume and returns is warranted.
14 volatility and volume on the data series of eight listed companies on the Ho Chi Minh stock market h
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 Since the implementation of the "Doi Moi" policy, Vietnam has embraced economic reforms to enhance global integration and achieve significant economic growth As a result, the country's GDP growth rate has averaged approximately 7 percent annually over the past decade, underscoring the crucial role of financial development in driving 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, starting with just two listed stocks In its initial two years, trading took place only on select days.
As of 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, is smaller than HOSE The Hanoi Stock Exchange (HNX) was formed in 2009 through Decision No 01/2009/QĐ-TTg on January 2, 2009, by the Prime Minister of Vietnam, as part of the restructuring of the HASTC.
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, increasing from 213 to 307 points.
Between 2006 and 2009, the VN Index achieved a significant milestone, peaking at 1,167 points in February 2007, a memorable moment for investors However, the index experienced a sharp decline shortly thereafter.
2009 at 235 points and moves up to 480 on December 31, 2010 (Nguyen Thi Kim Yen,
The stock market has experienced significant growth, expanding from 164 companies listed on the HOSE and 154 on the HNX in 2008 to a total of 627 listed entities by 2010, which includes companies from both exchanges as well as fund management firms (Nguyen Thi Kim Yen, 2011).
Some summarized numbers of the year 2010 as follow:
Market capitalization (USD billion) 35 Average daily trading value (USD million) 80
On May 11, 2007, the Prime Minister signed Decision No 559/2007/QD-TTg, transforming 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 h
All debts owed to the company by members of the Board of Directors, Board of Supervisors, General Manager, Deputy Manager, Chief Accountant, and major shareholders are fully disclosed, ensuring compliance with regulations There are no overdue debts that have not been reserved.
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 context involves state-owned enterprises transitioning into 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 the relationships among listed companies on the Ho Chi Minh City Stock Exchange (HOSE), which has stricter requirements compared to other exchanges Focusing on the period from 2007 to 2011, the research evaluates how economic factors influence 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
Portfolio weight VN30- index weight
No Stock Name of Company Sector Chartered capital (mil)
Date of listing Market capitalization
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
Hinh Hydropower Joint Stock Company
I prioritize information from listed companies due to their provision of transparent and reliable data for investors I have selected eight stocks that represent specific industries within the economy, focusing on criteria such as high market capitalization, listing duration, firm size, liquidity, and their influence on the VN-index for analysis.
The VN30-index, curated by the Ho Chi Minh City Stock Exchange, features 30 top stocks that significantly influence the VN-index, representing approximately 80% of total market capitalization and 60% of total traded value, while ensuring high liquidity As of July 17, 2012, I identified 17 stocks from this selection, categorizing them into eight distinct industries: processing and manufacturing, mining, finance, consumer goods, real estate and construction, telecommunications and information, transport and warehousing, and electricity Ultimately, I selected eight key stocks, each representing one of the industries, based on their market capitalization and impact on the VN-index Additionally, I chose SSI for its ability to reflect overall market trends, despite its lower market capitalization.
19 capitalization than STB (Saigon Thuong Tin Commercial J.S.C) Details of corporations are below
DPM (Petro Vietnam Fertilizer and Chemicals Company) is established in 28 March
Founded in 2003 with a chartered capital of 3,800 billion, DPM specializes in fertilizer production, boasting an impressive capacity of 740,000 tons annually The company also produces ammonia at a daily capacity of 1,350 tons and urea at 2,200 tons per day, alongside trading liquid ammonia with an annual capacity of 96,000 tons DPM meets 40% of the country's fertilizer needs and holds a dominant 50% market share in the fertilizer sector across the southern and central southern regions.
PVD (Petro Vietnam Drilling and Well Services Corporation) is established in 1994
The company specializes in contract drilling, well maintenance, and conditioning services for petroleum production and energy service companies It also provides geotechnical and logging services, oil field mapping, and oil spill control, while leasing drilling equipment and oil rigs As a subsidiary of Petro Vietnam, the company operates through three oil exploration and production joint ventures, supported by six subsidiaries managing its diverse operations.
Founded 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 significant 17% market share, SSI stands out as one of the largest companies in the sector The firm boasts the largest 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: h
VNM offers a diverse range of dairy products, including milk, powdered milk, solid milk, yogurt, ice cream, fruit juice, and coffee With a robust distribution network of over 1,000 agencies nationwide, VNM also exports its products to countries such as the US, Germany, Canada, and China The company operates more than 70 stations for transporting fresh milk materials and processes over 260 tons of fresh milk daily, accounting for 80% of the country's fresh milk supply Additionally, VNM is investing in the construction of 60 fresh milk processing facilities.
Founded 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 executed numerous large-scale projects nationwide, including the Vincom Twin Towers, a complex featuring a trade center, office spaces, and luxury buildings, as well as the Vincom Hai Phong Plaza, which encompasses 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 vast network of 560 cell phone distributors across the country and collaborates with 60 renowned partners, including industry giants like 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 neighboring regions Leveraging its expansive scale, strong partnerships, and experienced, skilled workforce, GMD effectively meets the diverse needs of its clients in the logistics sector.
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 h
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
This study analyzes data collected from January 1, 2007, to December 31, 2011, dividing the timeframe into two distinct intervals: pre- and post-December 2008 This division allows for a clear examination of the differences in stock market behavior before and after the recession, highlighting the significant impact of macroeconomic and external factors during this period.
We use the daily closing prices to estimate daily returns And the percentage of stock return is identified as:
Rt = (Pt - Pt-1/ Pt-1)*100 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 of foreign trading on stock prices, trading volumes, and overall market dynamics Additionally, the analysis includes a discussion on both foreign buy volume and foreign sell volume to provide a comprehensive view of their effects on the market.
In my dataset, columns representing foreign buy and sell volumes may contain null or invalid inputs due to the absence of transactions by foreign investors on certain days To ensure that the data remains meaningful for model analysis, I treat instances 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 h
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 of daily returns, trading volumes, and foreign buy and sell volumes Most stocks exhibit negative average returns, with VIC and VNM being exceptions at 0.04% and 0.017%, respectively, 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 show the lowest mean trading volumes at 11.439 and 11.487, respectively Stocks such as GMD, PVD, VSH, and FPT fall in the mid-range with means of 11.785, 11.932, 12.147, and 12.307 Notably, foreign investors demonstrate a stronger purchasing power in 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 Conversely, DPM shows the least volatility, with a standard deviation of 0.025 The standard deviations of the other stocks remain relatively stable, ranging from 0.027 to 0.030.
Most stocks exhibit negative skewness, indicating higher risk, with the exception of DPM, which shows a positive skewness of 0.1013 This negative skewness results in asymmetric and non-normal returns, largely influenced by risk-averse investors (Moolman, 2004) Additionally, the majority of stock returns display leptokurtic characteristics, with excess kurtosis greater than three, suggesting increased risk The pronounced peak in the distribution can lead to a positive correlation between trading volume and return volatility, as noted by Tauchen and Pitts (1983), Karpoff (1987), and Gallant et al (1992) In contrast, DPM's negative excess kurtosis indicates 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 due to its negative excess kurtosis.
The data series for nearly all stocks exhibit 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 highlights a fundamental requirement for weak-form market efficiency, as proposed 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 h
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 h
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 difficulty in distinguishing between temporary and permanent relationships in non-stationary time series To mitigate spurious correlations, I aim to test the stationarity of stock returns and trading volume percentages Additionally, Su (2003) and Chen, Firth, and Rui (2001) discuss time trends in raw trading volume within the Chinese Stock Market, prompting me to incorporate a time trend in the Augmented Dickey-Fuller equation For the stationarity test, I utilize the Augmented Dickey-Fuller (ADF) test as proposed by Dickey and Fuller in 1979.
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 h
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
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 return, as shown in Table 3, are below one percent (0.000) for both the level and first difference tests, with or without a trend This indicates that the null hypothesis of non-stationarity (unit root) is rejected, confirming that the DPM return series is stationary overall This finding is consistent across the other stock returns analyzed, as all eight stocks exhibit stationary returns, making them suitable for statistical analysis.
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
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
According to Table 4, DPM stock exhibits a PP statistic of less than one percent across all cases, leading us to reject the null hypothesis that its returns have a unit root Consequently, we conclude that DPM's returns are stationary This analysis can be similarly applied to the other stocks, confirming 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 h
The GARCH model, introduced 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 has further validated its success and application in financial analysis.
The GARCH (1,1) model, developed in 2003, is widely utilized for analyzing financial time series data Over the past two decades, this model has evolved to address not only the magnitude of returns but also their variability (Engle, 2001).
This section employs the GARCH (1,1) model, based on the methodology of Lamoureux and Lastrapes (1990), to analyze the impact of trading volume on mean returns and conditional return volatility The modified GARCH (1,1) model is specified as follows: rt = α0 + αrrt-1 + αVollnVolt + αFBVollnFBVolt + αFSVollnFSVolt + αdd + εt.
t = β0 + βε ε 2 t 1 + βσ t 2 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;
t 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; h
The variables lnVolt, lnFBVolt, and lnFSVolt represent the percentages of trading volume, foreign investors' purchasing volume, and foreign investors' selling volume on day t, respectively A dummy variable, d, is defined as 0 for the period from January 1, 2007, to December 12, 2008, and as 1 from January 1, 2009, to December 12, 2011 The model includes white noise et, a positive constant β0, and coefficients βε and βσ, which indicate the dependence of current volatility on past squared residuals and past volatility, respectively, with both coefficients being non-negative.
In the context of financial analysis, the equation (βε + βσ) < 1 indicates the persistence of conditional volatility Key coefficients include αVol, αFBVol, αFSVol, and αd, which represent the impacts of trading volume percentage, foreign buy volume percentage, foreign sell volume percentage, and a dummy variable on average returns Similarly, βVol, βFBVol, βFSVol, and βd denote the effects of these variables on conditional volatility, highlighting their significant roles 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 h
Excluding the variable percentage of trading volume significantly increases volatility persistence for most stocks, with SSI being the exception, where it decreases from 44% to 32% Notably, FPT and VIC exhibit persistence greater than one, indicating explosive variance and violating the assumption of stationarity, leading to an unstable model Additionally, VSH and PVD show persistence levels approaching unity, suggesting that volatility shocks maintain a high degree of persistence Therefore, it is evident that the percentage of daily trading volume serves as a crucial explanatory variable.
I discuss more the results of ARCH and GARCH effects in Table 5 The table reveals that
The ARCH effect (β ε ) is highly significant at the 1 percent level, while over 80 percent of the GARCH effect (β σ ) also shows significance at the same level Notably, the SSI exhibits no GARCH effect, indicating that the GARCH effect disappears when the trading volume percent is excluded However, when considering trading volume percent, both the ARCH and GARCH effects are nearly significant These findings contradict the conclusions 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 Excluding this percentage significantly reduces the GARCH effect of the stock index (SSI) Additionally, the persistence observed in the model without trading volume fails to meet the stationary assumption present in the model that includes trading volume These factors justify the choice of 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 h
The Mixture of Distribution Hypothesis suggests that the GARCH effect is elucidated when β Vol is significantly positive, and the sum of (β ε + β σ) is less than the persistence magnitude of the model, excluding the impact of trading volume.
The analysis of ARCH and GARCH effects in the Vietnamese stock market reveals significant findings, with most stocks showing ARCH percentages between 20% and 30%, while VIC stands out at 77% The GARCH effects are notably high for GMD, FPT, and VSH, with DPM reaching 61% and both FPT and VSH exceeding 59% VNM and DPM exhibit GARCH parameters around 40%, while other stocks demonstrate GARCH effects ranging from 10% to 20%, specifically 21% for VIC, 16% for PVD, and 13% for SSI The significant ARCH term indicates that current return volatility is influenced by past errors, while the GARCH term shows that lagged variance significantly impacts conditional variance, highlighting the inefficiency of the Vietnamese stock market in weak form Overall, both ARCH and GARCH coefficients are significant for all analyzed stocks.
For all stocks, the conditional volatility expresses persistence calculated by (β ε + β σ ) is lower than unity; therefore, meets the presumption of stationarity and implies volatility h
The combined effects of (β ε + β σ) reveal significant shock impacts on returns, highlighting the phenomenon of volatility clustering that contributes to both asymmetry and inefficiency in emerging markets High persistence in volatility suggests a gradual decline in return fluctuations, while low persistence indicates a swift return to the mean Notably, the volatility persistence of the conditional variance of VIC exceeds 90 percent, approaching unity, which signifies that volatility shocks are long-lasting In contrast, the persistence levels of PVD and SSI are notably low, underscoring their rapid regression tendencies.
50 percent) The volatility persistence of the rest of stocks is moderate from 70 to 80 percent
The analysis reveals that all stock constants in the mean equation are negative and significant at the one percent level, indicating a general decline in returns In contrast, the coefficient of lagged returns is consistently positive and statistically significant at the same level Notably, VIC (0.24), SSI (0.23), and GMD (0.21) exhibit a stronger influence on returns compared to FPT (0.15), VSH (0.12), VNM (0.12), and DPM (0.11), while PVD shows a minimal impact with a coefficient of 0.09 Thus, VIC and SSI significantly affect current returns through lagged returns, whereas PVD demonstrates an opposite effect.
Over 80 percent of stocks exhibit a significant positive relationship between trading volume and returns, indicating that higher trading volume often correlates with increased returns However, this relationship does not hold for SSI, where the parameter lacks significance It's important to note that while trading volume percentage has a slight impact on average returns, its overall effect remains limited.
5 Volatility clustering: large changes tend to be followed by large changes and small changes tend to be followed by small changes h
44 volume are from 0.001 to 0.003 for almost stocks; especially the percentage of volume of SSI does not affect return
In many instances, the percentage of foreign buy volume has little effect on returns due to minimal influencing factors However, the VIC coefficient demonstrates a significant positive correlation at the one percent level, albeit with a minor impact of just 0.00047.
The analysis of foreign sell volume percentage reveals that DPM, FPT, and GMD show no significant relationship with stock returns In contrast, the remaining stocks exhibit a significant negative correlation at the one percent level, with the exception of SSI, which demonstrates a positive effect at the same level However, the overall impact of foreign sell volume percentage on stock returns remains minimal.