INTRODUCTION
Introduction
Stock prices serve as key indicators for investors deciding whether to invest in a particular share The primary goal of stock market investing is to maximize returns while minimizing risk Stock price volatility represents systemic risk that affects investors holding common shares Since investors are inherently risk-averse, the level of volatility is crucial as it measures their exposure to potential losses Consequently, stock price volatility consistently draws investors’ focused attention.
Furthermore, stock market growth plays an important role in predicating future economic growth in situations where the stock markets are active
Economies lacking well-functioning stock markets face three key imperfections: limited risk diversification opportunities for investors and entrepreneurs; challenges for firms in optimizing their financing structures; and a lack of vital information about the prospects of publicly traded companies, which hampers investment promotion and reduces overall market efficiency (Demirguc-Kunt & Levine, 1996).
The stock market plays a crucial role in driving economic growth, making any fluctuations or changes highly significant Monitoring stock market trends is essential for understanding broader economic health, as stock performance influences investor confidence and overall financial stability Staying informed about stock market developments can help investors and policymakers make better decisions to promote sustained economic development.
As an emerging market, Vietnam's stock market has the potential to reduce firms' cost of equity capital, enable individuals to better assess and hedge risks, and attract both foreign and domestic investment to support national development However, recent market trends reflect economic instability, exemplified by the Vietnamese Stock Index (VN-INDEX)'s significant fluctuations between 2008 and 2012 Notably, the VN-INDEX soared to 921.10 on January 2, 2008, before plummeting to a low of 235.50 on February 24, 2009, within just over a year, illustrating the market's volatility By the end of 2012, the VN-INDEX closed at 413.70 points, highlighting ongoing instability in Vietnam's economy.
As a result, stock price volatility in Vietnamese recent years becomes a topic attracting not only investors but also firm’s managers and the government
Understanding the factors that influence stock price volatility is crucial for investors, managers, and policymakers alike This study investigates how firm-specific characteristics—such as dividend yield, payout ratio, leverage ratio, asset growth rate, and firm size—impact stock price fluctuations within the Vietnamese market The findings offer valuable insights for investors in portfolio construction, assist managers in effective firm management, and guide lawmakers in formulating regulations that promote market stability and development.
This chapter is organized to guide readers through the research framework, beginning with section 1.2, which provides the research background to understand the context and significance Section 1.3 outlines the research objectives and key research questions, clarifying the study’s focus and goals The scope of the research is detailed in section 1.4, defining the boundaries and limitations of the study Section 1.5 highlights the thesis contributions, emphasizing its original insights and practical implications Finally, section 1.6 describes the overall structure of the thesis, offering a roadmap for readers to navigate the document effectively.
Research background
Stock price volatility and its determinants have been a controversial topic in both theoretical and empirical research for decades Evidence suggests that changes in fundamental variables jointly influence share price fluctuations in both developed and developing markets However, the specific fundamental factors may differ across markets It is widely recognized that a set of fundamental variables identified by existing literature are relevant as key drivers of share price movements in the short and long term.
Actually, there is a huge amount of researches has been conducted to analyze the relation between stock price volatility and firm characteristics
Various empirical studies have investigated developed markets, producing notable findings For example, Baskin (1989) identified a significant relationship between dividend yield and stock price volatility in the United States, while Fama and French (1992) contributed to understanding market behaviors in the same context Pastor and Veronesi (2003) also provided insights into U.S stock market dynamics In Australia, Allen and Rachim (1996) examined whether dividend yield impacts stock volatility but found no supporting evidence Similarly, Hussainey, Mgbame, and Chijoke-Mgbame (2011) presented evidence from the United Kingdom, highlighting regional differences in market behavior Overall, these studies underscore the varying influence of dividend policies and market factors across different developed markets.
4 but found another interesting results related to payout ratio
Vietnam's stock market, despite being established later than many developed nations, has experienced significant growth over the years The Ho Chi Minh Stock Exchange (HOSE) was launched in 2000 with just two listed companies, but increased foreign investment and the privatization of state-owned enterprises spurred a rapid expansion in listings By the end of 2012, approximately 315 companies were listed on HOSE, reflecting the market's remarkable development.
399 other firms on Hanoi stock exchange (HNX) At the end of Jan 2013, there were two more firms jointed in HNX
Most previous research on the determinants of stock return volatility has primarily focused on well-developed markets, such as the United States, Australia, and the UK, with studies by Baskin (1989), Gallant et al (1992), Pastor and Veronesi (2003), Allen and Rachim (1996), and Hussainey et al (2010) providing valuable insights In contrast, developing markets have received less scholarly attention, highlighting a significant gap in the existing literature.
Several studies have examined the determinants of stock price volatility across various markets For instance, (2013) analyzed factors influencing stock price fluctuations in Jordan Similarly, Irfan and Nishat (2003), along with Khan (2011), explored the impact of dividend policy on stock prices for companies listed on the Karachi Stock Exchange in Pakistan Hashemijoo, Ardekani, and Younesi (2012) investigated how firm attributes affect share price volatility in the Malaysian Stock Market However, limited research has addressed stock price volatility and fundamental factors within the Vietnamese market, which highlights the need for further study in this context.
This study investigates how firm characteristics influence stock price volatility in Vietnamese companies, aiming to identify key determinants affecting market stability Focusing on the Vietnam Stock Market, a developing Asian capital market, the research uses recent data and selected variables to analyze the impact of firm-specific factors on stock price fluctuations The findings will provide valuable insights into the relationship between company traits and market volatility, supporting better investment decision-making and market regulation in Vietnam.
Research objectives and research questions
This thesis aims to analyze stock price behavior from a comprehensive perspective and explore the relationship between stock price volatility and firm characteristics It investigates how stock prices fluctuate in relation to key company attributes, providing insights into market dynamics Additionally, the study examines annual changes in stock price movements to identify potential year-to-year differences The findings contribute to a deeper understanding of stock market behavior and the factors influencing price volatility over time.
This research will provide answer to the following question:
Do firm’s characteristics affect firm’s stock price volatility in Vietnam stock market?
Research scope
Psychological factors significantly influence price changes and market volatility, including investor overreactions to earnings reports, dividends, and other news Additionally, waves of social optimism or pessimism, along with prevailing fashions and fads, play a crucial role in driving market fluctuations (Shiller, 1987).
According to the efficient market hypothesis, both good and bad public news quickly influence and alter a company's share price This reflects the idea that current stock prices fully incorporate all available information, making it challenging to predict price movements based on patterns Understanding this concept is essential for investors aiming to make informed decisions in the stock market.
Stock returns tend to be more variable over weekends, holidays, and different calendar periods, as fundamental values do not evolve systematically during these times (Thaler, 1987) Research by Roll (1988) indicates that relying solely on systematic economic factors to predict individual stock return variations is challenging due to the influence of additional factors beyond fundamentals (Cutler et al., 1989) This study focuses on key firm-specific fundamental factors—such as dividend yield, payout ratio, leverage ratio, asset growth rate, and firm size—to analyze their impact on stock price volatility of companies listed on HOSE, excluding those trading on UPCOM and HNX stock exchanges.
Thesis contributions
This pioneering research examines the unique characteristics of stock price volatility in the Vietnam stock market, providing valuable insights into market dynamics The study's main contribution to financial literature is its comprehensive empirical analysis of stock price movements and their relationship with firm characteristics over an extended period By constructing detailed stock price data and analyzing firm attributes listed on the Ho Chi Minh City Stock Exchange, this research offers a nuanced understanding of volatility patterns in the Vietnamese market.
This thesis confirms the irrelevance dividend theory proposed by Modigliani and Miller (1958), validating their hypothesis that capital structure does not affect firm value under certain conditions It also verifies the findings of Baskin (1989), reinforcing the consistency of their results across different contexts Additionally, the study supports the conclusions of Allen and Rachim (1996), highlighting the ongoing relevance of their research in understanding corporate financial strategies.
7 identifying the relation between stock price volatility and firm characteristics
Furthermore, this research provides a useful caution for the investors in terms of real relation between stock price volatility and firm’s characteristics.
Structure of the thesis
This thesis is structured into five chapters, starting with an introductory Chapter 1 that outlines the research background, objectives, questions, scope, and key contributions.
Chapter 2 is the literature review In this part, it presents theoretical aspects of stock price volatility focusing on impacts from fundamental Based on theories, initial research model and hypotheses used for the research are formed
Chapter 3 outlines the research methodology, detailing the research process and data collection procedures The data collection component includes three key elements: the data sample, data size, and the methods used for data collection This section provides a comprehensive overview of how the research data is gathered to ensure accuracy and reliability.
Then, Chapter 4, data analysis will reports the analysis results of data collection
The concluding chapter summarizes the key findings of the research, highlighting their implications for management It provides practical recommendations based on the results discussed in Chapter Four to guide future decision-making Additionally, the chapter addresses the study’s limitations and offers suggestions for further research to enhance understanding and address existing gaps.
LITERATURE REVIEW
Introduction
This section offers the theoretical foundation for the upcoming model and reviews relevant literature on stock price volatility The focus is primarily on fundamental analyses that examine the long-term relationship between stock price movements and firm characteristics.
For information purpose, reviews of value relevance study for short-term relation between stock price behaviors with event announcements are also briefed
This section reviews existing research on the relationship between stock price volatility and dividend policy, highlighting studies that incorporate various control variables It summarizes key fundamental factors identified in prior literature, providing a foundation for understanding how dividend policies influence stock volatility in different contexts.
This chapter is structured to comprehensively review the literature on stock price volatility Section 2.2 examines the existing research related to stock price volatility and highlights key fundamental factors influencing it Section 2.3 explores the relationship between stock price volatility and dividend policy, summarizing prior findings Section 2.4 discusses how firm age impacts stock price volatility, based on relevant studies In Section 2.5, the focus shifts to the connection between trading liquidity and stock volatility Section 2.6 reviews other firm characteristics that affect stock price volatility, providing a holistic understanding Section 2.7 details the development of research hypotheses derived from the literature, guiding the subsequent analysis Finally, Section 2.8 summarizes the chapter, synthesizing key insights for further investigation.
Stock price volatility
Stock price volatility measures how much a security's price fluctuates over a specific time period, with higher volatility indicating increased risk of significant gains or losses Volatile stocks are harder to predict, making future share price movements uncertain Many investors favor stocks with more stable and predictable earnings, as they tend to carry lower risks and offer greater financial safety (Profilet and Bacon, 2013).
Stock price is the most accessible indicator for investors when deciding whether to invest in a particular share Numerous factors influence stock prices, which are analyzed from various perspectives Researchers like Rappoport (1986) and Downs (1991) have identified that stock price changes are closely linked to fundamental variables such as payout ratio, dividend yield, capital structure, earnings, company size, and growth prospects, all of which are vital for share valuation.
Ball and Brown (1968) pioneered the study of the relationship between stock prices and information disclosed in financial statements, highlighting the importance of financial disclosure in market valuation Empirical research on value relevance is grounded in equity valuation models, which provide a theoretical framework for understanding how financial information impacts firm valuation Ohlson (1995) established that a company's value can be expressed as a linear function of book value, earnings, and other relevant information, emphasizing the significance of financial metrics in determining stock prices.
Understanding the connection between fundamental factors and share price movements is crucial for investors, as no single element can solely determine market trends Analyzing key financial indicators, such as earnings, revenue, and economic conditions, helps predict potential stock price changes Keeping abreast of the latest market data and staying informed about corporate performance enhances investment decision-making A comprehensive approach, considering both fundamental analysis and market dynamics, is essential for achieving successful long-term investment outcomes.
While extensive research has investigated the relationship over short time horizons, few studies have modeled this over longer periods Most short-term studies rely on cross-sectional tests and event-based methodologies, often focusing on only one or two fundamental factors despite a broad range of potential variables Although price revisions at the time of disclosures demonstrate significant announcement effects over short periods, it is equally important to examine these effects over an extended timeline using multi-year data to gain a comprehensive understanding of the relationship's long-term dynamics.
Stock price volatility and dividend policy
Internal factors, particularly dividend policy—including dividend yield and payout ratio—are central to financial research and continue to be key topics of debate in the field Notable studies, such as those by Modigliani and Miller (1958), have extensively analyzed how these factors influence shareholder value and corporate financial strategies.
Research by Miller and Rock (1985), John and Williams (1987), Baskin (1989), Fama and French (1992), Allen and Rachim (1996), and Irfan and Nishat (2003) highlights varying perspectives on the relationship between dividend policy and stock prices These studies demonstrate that while some researchers believe dividend policy significantly impacts stock valuation, others suggest the connection is more nuanced or minimal, reflecting ongoing debates in financial literature Understanding these differing viewpoints is crucial for comprehending how dividend decisions influence investor perceptions and market dynamics.
The relationship between dividend payouts and stock price volatility remains an ongoing topic of discussion and investigation since it was first introduced by Modigliani and Miller in 1958 According to their seminal work, firm value is unaffected by dividend policy, suggesting that dividend decisions do not influence stock price volatility This debate continues to be relevant today, as researchers explore the dynamics between dividend strategies and market fluctuations to better understand their impact on firm valuation and investor behavior.
Miller and Rock (1985), John and Williams (1987) noted that the belief in the correlation between a firm’s earnings and its value holds true only when shareholders possess symmetric information about the company's financial position However, managers often withhold negative information and only disclose it when regulatory or financial constraints compel them to do so, resulting in asymmetric information that can impact investor perceptions and decision-making.
Gordon (1963) argues that stock prices are influenced by dividend payouts He reports that firm with large dividends faces less risk in terms of stock price volatility
Friend and Puckett (1964) initiated the work on relation between dividend and stock price volatility They found a positive relation among dividend and stock prices
Jenson (1986) identified a positive relationship between dividend payouts and stock price reactions, suggesting that dividends can enhance investor confidence He argued that dividend payments reduce a company's cost of capital and boost cash flows, making the firm more attractive to investors Additionally, paying cash dividends limits managerial access to idle funds, thereby discouraging investment in projects with low or negative net present value (NPV).
In the United States, Baskin (1989) identified a significant negative relationship between dividends and stock price volatility He proposed four primary models explaining this connection: the duration effect, rate of return effect, arbitrage effect, and informational effect Additionally, he recommended incorporating specific control variables when testing these relationships to ensure accurate analysis.
12 significance of the relation between dividend yield and price volatility: operating earnings, firm size, level of debt financing, payout ratio, and asset growth rate
Our findings indicate that dividend yield and payout ratio are negatively correlated with stock price volatility, suggesting that higher dividends and payouts can stabilize stock prices Conversely, firm size, asset growth rate, and firm leverage positively influence stock price volatility, though their impact differs slightly from their effects on stock returns rather than stock prices.
Fama and French (1992) inferred that dividend and cash flow variables such as earning, investment and industrial production may serve as indicator of stock returns
Allen and Rachim (1996) found no evidence that dividend yield influences stock price volatility in Australia However, their study revealed a significant positive correlation between stock price volatility and earning volatility, as well as leverage Conversely, they identified a significant negative relationship between stock price volatility and payout ratio Additionally, their results indicate that larger companies tend to have lower stock price volatility, possibly due to higher liabilities.
Irfan and Nishat (2003) found that in emerging markets like Pakistan, both the dividend payout ratio and dividend yield have a significant negative impact on stock price volatility Their results are largely consistent with Baskin (1989), highlighting that higher dividend payouts and yields tend to reduce stock price fluctuations Additionally, they observed a positive correlation between debt levels and stock price volatility, although the effect of debt is less pronounced compared to that of dividend yield.
Following Irfan and Nishat (2003), several studies in Pakistan have explored the relationship between dividend policy and stock price volatility These research efforts aim to better understand how dividend decisions impact market stability and investor behavior within the Pakistani financial sector.
Research by (2010) indicates a strong positive correlation between stock price volatility and dividend yield, while showing a high negative correlation between stock price volatility and asset growth Nazir et al (2010) found that both dividend yield and payout ratio significantly impact share price volatility, with the effect of dividend yield increasing over the study period Conversely, the payout ratio exhibits a significant influence only at a lower level of statistical significance.
Rashid and Rehman (2008) found a positive but insignificant relation among stock price volatility and dividend yield in the stock market of Dhaka.
Stock price volatility and firm age
Pastor and Veronesi (2003) identified a negative correlation between firm age and stock volatility, with median return volatility decreasing from 14% per month for 1-year-old firms to 11% per month for 10-year-old firms Their model suggests that younger companies tend to experience higher volatility, especially those with more volatile and uncertain profitability and those that do not pay dividends This research highlights how firm maturity and profitability stability influence stock risk, providing insights for investors assessing firm stability and volatility.
Stock price volatility and trading liquidity
Numerous studies have demonstrated significant relationships between trading volume, stock price movements, and liquidity, highlighting that trading volume serves as a key source of risk driven by informational flows For instance, Saatccioglu and Starks (1998) found that trading volume predicts stock price changes in four out of six emerging markets Additionally, Jones et al (1994) identified a positive relationship between volatility and trading volume, a pattern confirmed by many researchers, underscoring the interplay between market activity and price dynamics.
14 between volatility and the number of transactions Gallant, et al., (1992) investigated the price and volume co-movement using daily data from 1928 to
1987 for New York Stock Exchange and find positive correlation between conditional volatility and volume
Research by Song et al (2005) indicates that the number of trades plays a dominant role in the volatility-volume relationship in the Shanghai Stock Exchange, highlighting its influence on market fluctuations Additionally, multiple studies reveal that trading volume exhibits the strongest positive correlation with stock price changes, making it a key predictor of increasing price volatility in both emerging and developing markets (Sabri, 2004).
Other firm’s characteristics and stock price volatility
Ariff et al (1994) examined the joint linear effect of six variables on share price volatility across Japanese, Malaysian, and Singaporean markets using long-term firm data They found that these six variables are generally significantly related to share price volatility in all three markets, although some variables were only significant in certain markets In the analytically advanced Japanese market, fundamental factors explain approximately 40% of the variation in share price volatility However, in the less developed markets of Malaysia and Singapore, a larger portion of price fluctuations remains unexplained by these fundamental variables, indicating that other factors influence share price volatility in emerging markets.
Irfan and Nishat (2003) analyzed the long-term impact of multiple factors on share prices on the Karachi Stock Exchange, identifying key determinants such as payout ratio, company size, leverage, and dividend yield Their study emphasizes the significant joint effects of these factors across different periods, including pre-reform, post-reform, and the overall timeframe This comprehensive analysis provides valuable insights into the factors influencing stock prices in Pakistan's financial markets over time.
Despite extensive research on stock price fluctuations worldwide, there is a notable scarcity of studies focusing on firm attributes and stock price volatility in developing countries, particularly Vietnam The lack of empirical evidence on the volatility of Vietnam's stock market highlights a significant research gap and motivates further investigation This study aims to fill this gap by exploring how firm characteristics influence stock price volatility in the Vietnamese stock exchange, thereby enriching existing literature on emerging markets and investment stability.
This thesis contributes to the existing literature by addressing previous research gaps concerning the relationship between stock price volatility and firm characteristics among companies listed on the Vietnam stock market.
Developing empirical research hypotheses
Several empirical hypotheses have been identified in existing literature to explain the factors influencing stock price volatility In this study, the author focuses on five hypotheses related to specific firm characteristics—namely dividend yield, dividend payout ratio, and other relevant financial metrics—to examine their impact on stock price volatility in the Vietnamese stock market.
16 ratio, firm leverage, asset growth rate, and firm size These hypotheses and their relation to stock price volatility have been summarized as follow:
Table 2.1:Expected relation to stock price volatility
No Hypotheses Variables Expected relation to
1 H1: Stock price volatility is negatively influenced by dividend yield
2 H2: Stock price volatility is negatively influenced by dividend payout
3 H3: Stock price volatility is positively influenced by firm leverage
4 H4: Stock price volatility is positively influenced by asset growth rate
5 H5: Stock price volatility is negatively influenced by firm size
Source: Summarized by the author
This study's hypotheses are grounded in previous research (Miller and Modigliani, 1958; Asquith et al., 1983; Baskin, 1989; Allen and Rachim, 1996; Ariff and Khan, 2000; Cooper, Gulen, and Schill, 2008), highlighting the importance of existing literature in understanding financial theories The literature review emphasizes the significant contributions of these studies to our understanding of corporate finance and capital structure Incorporating insights from these prior works provides a solid foundation for analyzing current hypotheses and their implications Overall, referencing established research enhances the credibility and relevance of this investigation within the broader scholarly context.
This study enables researchers to compare stock price volatility characteristics in Vietnam with both developed markets like the United States, UK, and Australia, and emerging markets such as Pakistan, Jordan, and Malaysia The development of these hypotheses is detailed in sections 2.7.1 to 2.7.5, providing a comprehensive framework for understanding market differences and similarities.
2.7.1 Dividend yield and dividend payout ratio
Recent research reviews indicate two main perspectives on the impact of dividend policy on stock price volatility Some studies suggest that dividend policy significantly influences stock price movements, while others argue that it has minimal effect Understanding these conflicting viewpoints is essential for investors and financial analysts when assessing dividend strategies and their potential effects on market stability.
Several researchers support the dividend irrelevance theory, asserting that dividend policy does not impact shareholder wealth or returns Miller and Modigliani (1958) argued that dividend policy is irrelevant to shareholders, emphasizing that it does not influence stock prices or investor wealth Similarly, Black and Sholes (1974) employed the capital asset pricing model to examine the relationship between dividend yield and expected return, finding no significant correlation Their results indicated that different dividend policies do not lead to variations in stock prices, providing strong evidence in favor of the dividend irrelevance hypothesis.
In the other hand, the second group of researchers consists of those who agree that there is the impact of dividend policy on the stock price volatilities
Gordon (1963) proposed that a firm's dividend policy significantly influences its market value Proper dividend policies can attract investors and signal financial health, thereby impacting stock prices Implementing an effective dividend strategy is crucial for maximizing shareholder wealth and ensuring the company's reputation in the financial markets.
Investors tend to prefer dividends in hand over potential future capital gains due to the uncertain market outlook, highlighting a direct relationship between dividend policy and a company's market share value When market conditions are unpredictable, a stable dividend payout can enhance investor confidence and positively influence stock valuation Therefore, a clear and consistent dividend policy can serve as a strategic tool to boost a company's market value by meeting investors’ preferences for immediate returns amid market uncertainty.
Dividend announcements serve as key signals of a company's anticipated profitability (Asquith & Mullins, 1983) A study by Travlos, Trigeorgis, and Vafeas (2001) analyzed how stock prices reacted to dividend and cash dividend increase announcements on the Cyprus Stock Exchange between 1985 and 1995 Their findings provided strong evidence that both cash dividend announcements and increases positively impacted stock prices, highlighting the market's responsiveness to dividend policy changes.
Baskin (1989), aligning with Asquith et al (1983), suggests that fluctuations in the discount rate have a limited impact on high dividend yield stocks due to their role as signals of near-term cash flows Companies with high dividend yields are expected to experience less share price volatility, making their stock prices more stable in response to interest rate changes.
H1: Stock price volatility is negatively influenced by dividend yield with all other factors remaining constant
According to Baskin (1989), dividend yield is a key factor influencing stock price volatility He found a significant negative relationship between dividend yield and stock price fluctuations, indicating that higher dividend yields tend to reduce volatility in stock prices This relationship highlights the importance of dividend strategies for investors aiming to mitigate risk in the stock market.
H2: Stock price volatility is negatively influenced by dividend payout ratio, with all other factors remaining constant
This hypothesis is derived from the hypothesis of Allen and Rachim
(1996) which indicates a significant negative relation price volatility and payout ratio The dividend payout policy also expected to be negatively related to investment opportunities
The capital structure of a firm significantly influences its share prices, as higher leverage increases financial risk A high-risk company with substantial debt must achieve higher returns to meet investor expectations, aligning with insights from Hamada (1972) and Sharpe (1964) This interplay between debt levels and required returns is crucial for understanding a firm's valuation.
A higher level of debt in a firm's capital structure typically leads to greater volatility in its share price Consequently, changes in a company's capital structure are directly linked to fluctuations in its share price, indicating that increased leverage tends to result in more significant share price movements This relationship underscores the importance of balancing debt levels to manage stock price volatility effectively.
In theoretical finance, leverage is viewed as a primary source of risk, with higher leverage increasing the risk for equity holders As risk-averse investors face more uncertain cash flows in highly leveraged firms, they demand higher returns to compensate for the increased risk (Penman, Richardson, and Tuna, 2007).
Modigliani and Miller (1958) argued that in competitive capital markets, a firm's value is independent of its financial structure However, this principle holds true only when markets are perfect; in the presence of market imperfections such as transaction costs and taxes, the firm's capital structure can impact its overall value.
Capital structure plays a crucial role in influencing share prices, especially in the presence of informational asymmetry and agency costs An increase in leverage raises the risk associated with a company's stock, prompting equity shareholders to demand higher returns Consequently, the return on equity capital typically rises as leverage increases, making leverage a key determinant of stock performance and investor returns.
Chapter summary
This chapter reviews the theoretical background and prior research on stock price volatility and its relationship with firm characteristics such as dividend yield, payout ratio, leverage, asset growth rate, and firm size These insights inform the development of hypotheses related to how these factors influence stock price fluctuations The hypotheses are grounded in established literature, including foundational studies by Miller and Modigliani (1958), Asquith et al (1983), Baskin (1989), and Allen and Rachim (1996).
Ariff and Khan ,2000; Cooper, Gulen, and Schill,2008;)
Chapter 3 comprehensively outlines the data sources, characteristics, and sampling methods used to test the research hypotheses It details the model specifications, estimation techniques, and robustness tests implemented to validate the findings This methodological framework ensures the accuracy and reliability of the results, providing a solid foundation for the study's conclusions.
DATA AND METHODOLOGY
Introduction
This chapter outlines the data sources and the economic and empirical techniques used to test the hypotheses developed in Chapter 2 Section 3.2 provides a detailed description of the primary data sources, while Section 3.3 explains the selected variables used in the analysis The model specification, estimation methods, and robustness tests are thoroughly described and documented in Section 3.4.
This research used panel data collected from audited consolidated financial statements of listed companies in the Ho Chi Minh City Stock Exchange (HOSE) over the period from 2008 – 2012
This study analyzes daily closing share prices of listed companies on the Ho Chi Minh City Stock Exchange from January 1, 2008, to December 31, 2012 The share prices are adjusted for dividends and stock splits to ensure that the returns accurately reflect true investor gains.
The sample companies are subjected to the following selection criteria:
By the end of 2007, companies are required to be listed on HOSE Additionally, they must have five consecutive full-year audited financial statements and annual reports available from 2008 to 2012 Ensuring timely compliance with these listing and reporting requirements is essential for maintaining transparency and investor trust.
(2) the companies are non-financial companies;
(3) the companies were not de-listed from HOSE over the period from
(4) the firm’s fiscal year-end is 31 st December;
(5) the firm’s stock is consistently traded from 2008 to 2012;
This study's initial dataset included companies listed on HOSE as of December 31, 2007 However, certain observations were excluded from the final sample to ensure consistency Financial firms such as banks (STB), security companies (SSI), and investment funds (MAFPF1, PRUBF1, VFMVF1) were omitted because they are subject to different regulatory frameworks and unique financial statement formats Additionally, de-listed firms like Bach Tuyet Cotton Corporation (BBT) were also removed to maintain data integrity and comparability across the sample.
Therefore, the final list of sample consisted of 110 companies matching all the criteria of selection (see appendix A)
3.3.1 Dependent variable -Price volatility (PV)
This article is based on Parkinson's (1980) method for estimating the variance of stock returns using extreme value analysis Specifically, it focuses on calculating the annual range of stock prices each year to improve the accuracy of volatility measurement By analyzing the highest and lowest stock prices within the year, this approach provides a more efficient estimate of return variability than traditional methods Applying Parkinson's model enhances risk assessment and forecasting in financial markets, offering valuable insights for investors and analysts.
The Parkinson (1980) method improves volatility estimation by calculating the difference between the maximum and minimum stock prices, known as 27, dividing this range by the average of the high and low prices, and then squaring the result This approach offers a significant advancement over traditional methods that rely solely on closing and opening prices, providing more accurate and reliable measures of stock volatility.
This measure effectively captures annual changes in share prices, providing a clear overview of each firm's stock performance The data is sourced from HOSE's price timeline, ensuring accuracy by using prices adjusted for dividends and stock splits Such adjustments are essential for precise analysis of stock returns over the year.
This research focuses on key firm-specific attributes that are most relevant to Vietnamese investors, such as return on assets (ROA), return on equity (ROE), assets growth rate (AGR), current ratio (CURR), leverage ratio (LEVR), dividends yield (DY), and firm size While previous studies have considered numerous variables like earnings volatility (EV), payout ratio (POR), firm age, and liquidity (TOVR), this study emphasizes these particular characteristics as independent variables, aligning with investor priorities in the Vietnamese stock market.
(i) Dividend yield (DY): is the value of all cash dividends paid to common stockholders divided by the market value of the firm at year-end
This is derived from the dividend timeline on HOSE
The payout ratio (POR) measures the proportion of a company's earnings paid out as cash dividends each year It is calculated by dividing total cash dividends paid by the company's total earnings for that period This ratio provides insight into a company's dividend policy and financial strategy, reflecting how much profit is returned to shareholders versus retained for growth Analyzing the payout ratio over multiple years helps investors assess the company's consistency in dividend payments and financial stability.
The leverage ratio (LEVR) is a key indicator of long-term financial distress, assessing a company's financial stability It is calculated by dividing total liabilities by total assets at year-end, based on consolidated audited financial statements Monitoring the leverage ratio helps investors and stakeholders evaluate a company's leverage level and overall financial health.
Asset growth (AGR) is measured using the natural logarithm of the ratio between total assets at the end of the financial year and total assets at the beginning of the same year This metric is derived from consolidated audited financial statements, providing a precise indicator of an entity’s asset expansion over time.
Firm size (SIZE), measured by the book value of total assets at year-end, is a key variable in our analysis To improve the robustness of regressions, we utilize the natural logarithm of total assets, calculated from consolidated audited financial statements This approach ensures an accurate representation of company size while adhering to SEO best practices by including relevant financial terminology.
This section is to explain the econometric and empirical techniques used in this research
The researcher chose to utilize panel data in agreement with the literature, which recommends it as the most appropriate method for the focus of the study
According to Hsiao (2006), panel data provides significant advantages for analyzing historical company series, including accurate estimation of model parameters, effective tools to address model misspecifications and omitted variables, and easier computation and interpretation of results These benefits make panel data a valuable resource for comprehensive and reliable financial and economic analysis.
The regression analysis was impacted by several issues that affected the accuracy of the results These problems highlighted the need for careful data handling and model refinement Addressing these challenges is essential to improve the reliability of future regressions and ensure more precise outcomes.
29 the study performed a gradual breakdown and make additional analysis as follows
This method attempts to find the collective impact of all factors on price volatility by doing regression all the independent factors against the dependent variable
To begin with the single equations which treat all variables as exogenous The model is specified as follows:
PV=β 0 +β 1 DY+β 2 POR+β 3 AGR+β 4 LERV+β 5 SIZE+Ɛ Where
PVi,t denotes the stock price volatility of firm i at time t;
Dividend Yield (DY) and dividend payout ratio (POR) are key financial metrics that influence firm valuation and investor appeal Asset growth Rate (AGR) reflects a company's expansion capability, while firm leverage (LEVR) indicates its financial risk profile Firm size (SIZE) affects strategic decisions and market perception In regression models, the intercept (β0) represents the baseline level, and coefficients (β1 to β5) measure the impact of each variable—DY, POR, AGR, LEVR, and SIZE—on financial outcomes The error term (Ɛ) captures unexplained variability, highlighting the importance of these variables in predicting firm performance.
Variables
3.3.1 Dependent variable -Price volatility (PV)
This analysis is based on Parkinson's (1980) method for extreme value estimation of return variance It focuses on measuring the annual range of stock prices to assess market volatility, providing insights into the variability of stock returns over time.
The Parkinson (1980) method for estimating stock volatility calculates the difference between the maximum and minimum prices, known as 27, and divides this value by the average of the high and low prices This ratio is then squared to provide a more accurate measure of market variability Compared to traditional methods that rely solely on closing and opening prices, Parkinson's approach offers a significantly more precise estimate of stock price fluctuations, making it a preferred choice for volatility analysis.
This measure effectively captures annual changes in share prices, providing valuable insights into stock performance It is derived from the price timeline of each firm listed on HOSE, ensuring accuracy by adjusting for dividends and stock splits By analyzing these adjusted prices, investors can better understand true value fluctuations over time, making this variable essential for comprehensive financial analysis and investment decision-making.
Many firm-specific attributes such as earnings volatility (EV), return on assets (ROA), return on equity (ROE), assets growth rate (AGR), current ratio (CURR), leverage ratio (LEVR), dividends yield (DY), payout ratio (POR), firm size, firm age, and liquidity (TOVR) have been widely used in empirical analyses in previous research However, in the Vietnamese stock market, investors tend to focus primarily on certain key characteristics Therefore, this study selected these specific attributes as independent variables for examination.
(i) Dividend yield (DY): is the value of all cash dividends paid to common stockholders divided by the market value of the firm at year-end
This is derived from the dividend timeline on HOSE
The payout ratio (POR) measures the proportion of earnings distributed as cash dividends, calculated by dividing total cash dividends paid by total earnings each year This ratio provides insight into a company's dividend policy and financial stability, indicating how much profit is returned to shareholders versus retained for growth Monitoring the payout ratio helps investors assess the company's ability to sustain dividends and its overall profitability.
The leverage ratio (LEVR) is a key indicator of long-term financial distress, calculated by dividing total liabilities by total assets at year-end based on consolidated audited financial statements.
Asset growth (AGR) measures the increase in a company's total assets over a financial year and is calculated using the natural logarithm of the ratio between total assets at year-end and beginning of the year This metric is derived from consolidated audited financial statements, providing a comprehensive view of the company's asset development.
Firm size (SIZE) is measured by the book value of total assets at year-end, providing an indicator of the company's scale In our analysis, we utilize the natural logarithm of total assets, derived from consolidated audited financial statements, to accurately assess the impact of firm size on financial performance This approach ensures consistency and precision in measuring firm size for regression models.
Methodology
This section is to explain the econometric and empirical techniques used in this research
The researcher chose to utilize panel data in agreement with the literature, which recommends it as the most appropriate method for the focus of the study
Hsiao (2006) highlights the advantages of using panel data for analyzing historical series of companies, including accurate estimation of model parameters, tools to address model misspecifications and omitted variables, and simplified computation and interpretation of results.
The regression analysis was impacted by several issues that affected the accuracy of the results These challenges highlight the need for careful data handling and methodological adjustments to improve the reliability of future outcomes Addressing these problems is essential for ensuring more precise and valid regression analysis results.
29 the study performed a gradual breakdown and make additional analysis as follows
This method attempts to find the collective impact of all factors on price volatility by doing regression all the independent factors against the dependent variable
To begin with the single equations which treat all variables as exogenous The model is specified as follows:
PV=β 0 +β 1 DY+β 2 POR+β 3 AGR+β 4 LERV+β 5 SIZE+Ɛ Where
PVi,t denotes the stock price volatility of firm i at time t;
Dividend yield (DY) and dividend payout ratio (POR) are key financial metrics used to assess a firm's dividend policy, with DY indicating the return on investment based on dividends relative to stock price, and POR reflecting the proportion of earnings paid out as dividends Asset growth rate (AGR) measures a company's expansion and is crucial for understanding its developmental trajectory Firm leverage (LEVR) indicates the level of debt used to finance assets, impacting financial stability and risk Firm size (SIZE) influences overall firm performance and investor perceptions In our model, β0 represents the intercept, while β1 through β5 denote the coefficients for DY, POR, AGR, LEVR, and SIZE respectively, with ϵ capturing the error term These variables collectively help analyze the determinants of firm performance and dividend policy decisions in the context of financial analysis and investment strategies.
This model uses variables nearly identical to those in the previous analysis Given that the results are highly sensitive to the chosen estimation method, multiple specifications are employed to address potential issues associated with panel data, ensuring more robust and reliable findings.
3.4.2 Ordinary Least Square (OLS) regression
This basic model specification assumes constant intercepts and coefficients across both time and individuals, neglecting the unique structure of panel data that includes both cross-sectional and time dimensions By ignoring these characteristics, the approach simplifies analysis to Ordinary Least Squares (OLS) regression, which may lead to biased or inconsistent estimates when applied to panel datasets For robust analysis, it’s important to consider methods that account for the panel data’s inherent structure, such as fixed effects or random effects models.
Chapter summary
This chapter outlines the data and sample used, describes the methodology for testing the hypotheses developed earlier, and aims to answer the research questions identified in the introductory chapter The subsequent chapter presents the empirical results, including descriptive statistics, correlation analysis, and hypothesis testing.
EMPIRICAL RESULTS
Introduction
This chapter presents empirical findings and a comprehensive analysis of the results, beginning with essential assumption tests outlined in Chapter 3 The section includes detailed hypothesis testing, assessing the robustness of the findings, and interpreting the relationship between firm characteristics and stock price volatility These insights are crucial for understanding how business-specific factors influence stock price fluctuations in the market.
This chapter is organized into three key sections Section 4.2 presents descriptive statistics and correlation analysis to provide an overview of the data and examine relationships between variables Section 4.3 details the multiple linear regression analysis, including hypothesis testing and the interpretation of results to identify significant predictors Finally, Section 4.4 offers a comprehensive summary of the chapter's main findings and insights.
Descriptive Statistics and Correlation Analysis
The Table 4.1 represents the statistical description of the variables used in this research It indicates the mean and standard deviation of variables used in this study
Size 13.762582 1.1648027 110 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Table 4.1 indicates that the size variable has the highest mean value at 13.762582, making it the most prominent factor among the variables analyzed In contrast, dividend yield has the lowest mean at 0.099745, suggesting it contributes less significantly to the overall data set Additionally, the payout ratio exhibits the highest standard deviation at 11.3541653, indicating greater variability, while dividend yield shows the lowest standard deviation, reflecting more consistent data.
Table 4.2 represents the correlation amongst variables for the whole date set, in which indicates the relation between Stock Price Volatility (PV) and other explanatory variables as follow:
PV and Dividend Yield (DY) are negatively correlated with the value of -0.213 at a 5% significance level, supporting Hypothesis H1 and aligning with Baskin (1989)’s findings However, this result contradicts the findings of Allen & Rachim (1996), indicating differing perspectives in existing research.
PV and Dividend Pay Out Ratio (POR) are negatively correlated with a coefficient of -0.117, which is statistically significant at the 5% level This finding aligns with the original hypothesis, confirming that higher PV values are associated with lower dividend payout ratios Additionally, these results are consistent with Baskin (1989) and Allen & Rachim (1996), supporting previous research on the inverse relationship between PV and dividend payout behavior.
PV and firm leverage (LEVR) are negatively correlated with a coefficient of -0.185, indicating that as leverage increases, the firm's value tends to decrease This finding contrasts with earlier studies by Hamada (1972) and Sharpe (1964), which reported a positive relationship between leverage and firm value.
PV has positive correlations with both AGR and firm Size with value of 339 and 399 at the significant level of 1%
PV DY POR LERV AGR Size
* Correlation is significant at the 0.05 level (2-tailed)
The correlation observed in the study is statistically significant at the 0.01 level (2-tailed), indicating a strong relationship between the variables analyzed This finding underscores the importance of understanding these correlations for better insights into the research topic For further details or to access the full thesis, please contact via email at gmail.com.
Multiple Linear Regressions Analysis
To ensure the validity of the regression analysis, multicollinearity among independent variables was tested using tolerance and Variance Inflation Factor (VIF) values A Tolerance value below 0.1 or a VIF above 10 indicates the presence of multicollinearity As shown in Table 4.3 - Coefficients, all independent variables had Tolerance values greater than 0.1 and VIF values less than 10, confirming that there is no significant multicollinearity among the variables.
The Ordinary Least Squares Regression Model results, presented in Table 4.3, highlight the significance of individual variables included in the study Among the five independent factors, three—LEVR, AGR, and SIZE—are statistically significant with p-values of 000, 001, and 003, respectively, all below the 05 significance threshold This indicates that these factors are influential in affecting the dependent variable in the model.
The independent factors, DY and POR, show some correlation with the dependent variable; however, their significance levels of 218 and 598, respectively, exceed the 05 threshold, indicating that they do not significantly impact the dependent variable As a result, these variables should be excluded from the model to improve its accuracy and reliability.
The original model, which included five independent factors, should be revised to include only three key variables when analyzing the relationship between stock price volatility and firm characteristics of listed companies on HOSE This adjustment ensures a more accurate and focused assessment of the factors influencing stock price fluctuations Simplifying the model enhances its explanatory power and aligns with SEO best practices by emphasizing critical keywords such as "stock price volatility," "firm characteristics," and "HOSE-listed companies."
B Std Error Beta Tolerance VIF
The Model Summary Table reveals a coefficient R-value of 0.574 and an R-square of 0.329, indicating a moderate to strong correlation between the dependent variable, PV, and the five independent factors Additionally, the adjusted R-square of 0.297 suggests that approximately 29.7% of the variation in stock price volatility can be explained by the firm’s characteristics through this linear regression model.
The stock price of 36 is influenced by five independent factors—DY, POR, LERV, AGR, and SIZE—which explain 29.7% of its variance This finding aligns with previous research on developing markets, where Pakistan shows a 25.9% influence (Irfan et al., 2002), and Malaysia demonstrates a 27.2% influence (Hashemijoo et al., 2012) Additionally, the regression model is statistically significant, with an F-test and a Sig value of 000, indicating it is suitable for analyzing the collected data and can be reliably used for further insights.
Std Error of the Estimate
1 574 a 329 297 0089 329 10.206 5 104 000 2.316 a Predictors: (Constant), Size, POR, LERV, DY, AGR b Dependent Variable: PV
Following the descriptive statistical analysis, correlation testing, and validity assessments to refine the research model, the next step involved hypothesis testing through regression analysis This approach was used to examine the impact of various firm characteristics on stock price volatility, providing insights into how specific variables influence market stability.
Stock price volatility tends to decrease as dividend yield (DY) increases, assuming all other factors remain constant Higher dividend yields are associated with lower fluctuations in stock prices, providing investors with a more stable investment option This inverse relationship highlights the importance of dividend policies in managing stock price stability within financial markets.
The standardized regression coefficient (beta) for dividend yield (DY) on stock price volatility (PV) is -0.108, with a t-value of -1.240 and a significance level of 0.218, which exceeds the 0.05 threshold This indicates that, at a 5% significance level, there is no statistically significant impact of dividend yield on stock price volatility, suggesting that dividend yield does not explain fluctuations in stock prices These findings contradict earlier studies by Baskin (1989) and Irfan and Nishat (2003), which found a strong negative relationship between dividend yield and stock price volatility, and are also inconsistent with Asghar et al (2010), who reported a positive correlation Previously, many studies suggested dividend yield significantly influences stock price volatility, but this research supports the conclusions of Modigliani and Miller (1958), stating that a firm's value is unaffected by dividend policy, and stock price volatility depends primarily on earning ability Furthermore, John and Williams (1987) and Miller and Rock (1985) argued that Modigliani and Miller’s proposition holds true only under conditions of symmetric information, which is often not the case due to managerial practices of withholding negative financial information until disclosure becomes unavoidable.
Stock price volatility is negatively impacted by the dividend payout ratio (POR), indicating that higher dividend payouts tend to reduce stock price fluctuations when all other factors are held constant This relationship suggests that consistent dividend payments can enhance investor confidence and stabilize stock prices Optimizing the dividend payout ratio is crucial for companies aiming to minimize stock price volatility and attract long-term investors.
The standardized regression coefficient (beta) for the payout ratio (POR) on stock price volatility (PV) is -0.043, with a t-value of -0.530 and a significance level (Sig) of 0.548, which is greater than 0.05 This indicates that, at the 5% significance level, there is no statistically significant impact of payout ratio on stock price volatility in the Vietnamese stock market These findings support the dividend irrelevance theory of Modigliani and Miller (1958) and align with previous research by Allen and Rachim (1996), which found a negative but insignificant correlation between stock price volatility and payout ratio Overall, the payout ratio does not appear to explain variations in stock price volatility.
H3: Stock price volatility is negatively influenced by firm leverage
(LERV) with all other factors remaining constant
The standardized regression coefficient for firm leverage (LERV) on stock price volatility (PV) is -0.379, with a t-value of -4.333 and a significance level of 0.000, indicating a statistically significant negative relationship at the 5% level This suggests that higher firm leverage is associated with increased stock price volatility, confirming previous research by Sharp (1964) and Hamada (1972) that firms with higher debt levels tend to experience greater fluctuations in their share prices The findings highlight the critical impact of capital structure on stock price stability, demonstrating that increased leverage contributes to more volatile stock prices.
Research indicates a direct link between firm leverage and stock price volatility, with highly leveraged firms facing increased default risk and higher expected costs (Chen and Zhang, 2010) This suggests that a significant rise in a company's leverage is likely to lead to a decline in its stock price, especially within the Vietnamese stock market.
The research model is strongly supported by the H3 hypothesis, indicating that firm leverage has a significant negative relationship with stock price volatility This suggests that higher leverage levels are associated with reduced stock price volatility, highlighting that firm leverage is a key factor in explaining fluctuations in stock prices.
H4: Stock price volatility is positively influenced by asset growth rate with all other factors remaining constant
The standardized regression coefficient (beta) for asset growth rate (AGR) on stock price volatility (PV) is 0.329, with a t-value of 3.399, exceeding the critical value of 2, and a significance level (Sig) of 0.001, below the 0.05 threshold This indicates there is statistically significant evidence at the 5% level to conclude a strong positive effect of asset growth rate on stock price volatility This finding contrasts with previous studies by Baskin (1989) and Irfan and Nishat (2003), but aligns with research by Asghar et al (2010), Nazir et al (2010), and Cooper, Glulen, and Schill (2008), who found that asset growth rate is a robust predictor of future stock returns Consequently, a firm's annual asset growth rate is both economically and statistically significant in predicting the cross-section of stock returns.
Chapter Summary
Based on the regression analysis and hypothesis testing presented in sections 4.2 and 4.3, three out of the five original hypotheses are supported as significant to the research model, while two are rejected These findings indicate that firm characteristics do indeed influence stock price volatility in the Vietnam stock market The revised hypotheses are summarized in Table 4.5.
The research question: Do firm’s characteristics affect firm’s stock price volatility in Vietnam stock market?
H1 Stock price volatility is negatively influenced by dividend yield Rejected H2 Stock price volatility is negatively influenced by dividend payout ratio Rejected
Firm leverage has a significant negative impact on stock price volatility, indicating that higher leverage may stabilize stock prices Conversely, a higher asset growth rate positively influences stock price volatility, suggesting that rapid asset growth can lead to increased stock price fluctuations Additionally, larger firm size tends to decrease stock price volatility, implying that bigger companies may experience more stable stock performance These findings highlight the complex relationship between financial leverage, asset growth, firm size, and stock price stability.
CONCLUSION
Reviews of findings
This thesis aims to analyze the long-term relationship between stock prices and key firm characteristics such as dividend yield, dividend payout ratio, asset growth rate, firm leverage, and firm size in the Vietnam stock market The study examines a sample of 110 listed companies on the Ho Chi Minh City Stock Exchange over a five-year period from 2008 to 2012 The research provides insights into how these firm-specific factors influence stock price behavior in Vietnam's capital market, offering valuable implications for investors and policymakers.
2012 A number of different econometric techniques of panel data analysis, including OLS multiple regression are conducted in this paper in order to generate more accurate estimation
By using a rich dataset on stock price volatility and firm characteristics, the author characterizes stock price behavior in Vietnamese firms in great details
This study highlights the combined influence of multiple factors on long-term stock price movements It finds that stock price volatility decreases with higher firm leverage, while it increases with asset growth rate and firm size These findings support previous research by Baskin (1989) Additionally, dividend yield and dividend payout ratio also play significant roles in affecting stock prices, emphasizing the importance of dividend policies in stock valuation.
Research indicates that 43 dividend-related findings have an insignificant impact on stock prices, aligning with earlier studies by Allen and Rachim (1996) and Conroy et al (2000) However, this contrasts with Baskin (1989), who suggested that dividends do not significantly influence share prices These results highlight the ongoing debate regarding the relationship between dividends and stock valuation in financial markets.
Contributions
Although there are several limitations to this dissertation, the empirical findings can be viewed in the light of the contributions to the theoretical models
This study addresses a gap in finance literature by examining the factors influencing stock price volatility, with a focus on the Vietnamese stock market Unlike previous research that primarily explores the relationship between dividend policy and stock volatility, this thesis investigates how firm characteristics impact stock price fluctuations It is the first study in Vietnam to highlight the connection between firm characteristics and stock price volatility, providing valuable empirical evidence The findings also support the irrelevance dividend theory proposed by Modigliani and Miller, confirming its applicability in the Vietnamese context.
This study verifies the hypotheses proposed by Baskin (1989) and supports the findings of Allen and Rachim (1996) regarding the relationship between stock price volatility and firm characteristics Additionally, it employs multiple robustness tests to validate the regression results, addressing gaps in prior research related to panel data analysis of stock price volatility.
Implications
These findings offer several useful insights for academics, firm managers, investors, and policymakers
This study reveals that, unlike in many global stock markets, dividend yield and dividend payout ratio do not significantly impact stock price volatility in the Vietnamese stock market These findings suggest that Vietnamese stock prices are not influenced by company dividend policies, indicating that investors tend to behave as speculators rather than long-term investors Consequently, the Vietnamese stock market and its investors exhibit distinct characteristics, highlighting the need for future research to consider speculative investor behavior as a key factor in analyzing stock price volatility in Vietnam.
This thesis reveals that key firm characteristics significantly influence stock price volatility Managers should leverage these attributes strategically to enhance their firm's market value For instance, appropriate use of firm leverage as a financing tool can reduce financial risk and improve operational efficiency, leading to an increase in stock prices Understanding and effectively managing these factors are essential for firm managers aiming to optimize stock performance and overall firm value.
Investors in the Vietnamese stock market should focus on selecting stocks from firms with specific characteristics that align with their expected returns This strategic approach can help maximize investment performance and manage risk effectively Understanding these key firm attributes is essential for making informed investment decisions in Vietnam's dynamic stock market.
Policymakers should implement regulations to reduce speculation and promote long-term investment in the Vietnamese stock market The findings indicate that the market is currently more speculative, which can hinder stable development By encouraging sustainable investment practices, Vietnam's stock market can achieve more stable and resilient growth Effective regulatory measures are essential to mitigate excessive speculation and foster a healthier financial environment for investors.
Limitations and recommendations for future researches
This thesis has a number of limitations Firstly, due to the constraints of time and data availability, the data utilized in this dissertation solely consist of
This study analyzes 110 companies listed on HOSE over five years, excluding financial firms and firms listed on HNX While ideally, quarterly data would enhance the analysis, differences between internal financial statements and audited reports pose challenges Due to recent regulations, only annual audited reports are available, leading to potential limitations in capturing more nuanced financial changes Relying solely on annual data may affect the accuracy and generalizability of the findings To improve research accuracy and deepen insights, future studies should incorporate quarterly data, providing a more comprehensive understanding of company performance.
This thesis does not consider the impact of firm management and investors' characteristics on stock price volatility Incorporating these factors could provide a more comprehensive understanding of the variables influencing stock market fluctuations, offering valuable insights for investors and policymakers Future research should explore how management practices and investor demographics affect stock stability, enhancing the robustness of stock price analysis.
46 both management and investors are concerned about the volatility of stock price
Various types of investors, including local and foreign, as well as individual and institutional investors, have distinct investment decision-making processes that can differently influence stock price volatility Understanding these differences is crucial for analyzing market behavior Future research should focus on examining these factors to better comprehend their impact on stock market dynamics.
In the Vietnamese stock market, government regulation significantly limits the amplitude of stock price fluctuations, reducing the impact of other factors on stock price volatility However, this study has not examined the influence of government control on stock price determinants, which could be a crucial factor affecting market behavior.
This thesis has certain limitations that should be addressed in future research to enhance its validity and comprehensiveness Recognizing these constraints provides valuable insights for subsequent studies to explore new perspectives and methodologies Further investigations are recommended to overcome these limitations and to deepen the understanding of the subject matter.
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APPENDIX A: LIST OF SAMPLE COMPANIES
No TICKER COMPANY NAME LISTED
1 ABT Bentre Aquaproduct Import &Export JSC 25/12/2006 www.aquatexbentre.com
2 ACL Cuulong Fish Joint Stock Company 05/09/2007 www.clfish.com
3 AGF An Giang Fisheries Import and Export JSC 02/05/2002 www.agifish.com.vn
4 ALP Alphanam Joint Stock Company 18/12/2007 www.alphanam.com.vn
5 ANV Nam Viet Corporation 07/12/2007 www.navicorp.com.vn/
6 BBC Bibica Corporation 19/12/2001 www.bibica.com.vn
7 BHS Bien Hoa Sugar Joint Stock Co 20/12/2006 www.bhs.vn
8 BMC Binh Dinh Minerals Joint Stock Co 28/12/2006 www.bimico.binhdinh.com.vn
9 BMP Binh Minh Plastics Joint-stock Co 11/07/2006 www.binhminhplastic.com
10 BT6 Beton 6 Corporation 18/04/2002 www.concrete620.com
11 CDC Chuong Duong Joint Stock Company 01/11/2007 www.acic.com.vn
12 CII Ho Chi Minh City Infrastructure Investment
13 CLC Cat Loi Joint Stock Company 16/11/2006 www.catloi.com.vn
14 COM Materials - Petroleum Joint Stock Co 07/08/2006 www.comeco.vn/
15 CYC Chang Yih Ceramic Joint Stock Co 31/07/2006 www.changyih-ceramic.com
16 DCT Dongnai Roofsheet & Construction Material
17 DHA Hoa An Joint stock company 14/04/2004 www.hoaan.com.vn
18 DHG Hau Giang Pharmaceutical JSC 21/12/2006 www.dhgpharma.com.vn
19 DIC DIC Investment and Trading JSC 28/12/2006 www.dic-intraco.vn
20 DMC Domesco Medical Import - Export JSC 25/12/2006 www.domesco.com
21 DPM Petrovietnam Fertilizer and Chemical Co 05/11/2007 www.damphumy.vn
22 DPR Dong Phu Rubber Joint Stock Co 30/11/2007 www.doruco.com.vn
23 DRC Da Nang Rubber Joint Stock Co 29/12/2006 www.drc.com.vn
24 DTT Do Thanh Technology Corporation 22/12/2006 www.dothanhtech.com
25 FMC Sao Ta Foods Joint Stock Co 07/12/2006 www.fimexvn.com
26 FPT FPT Corporation 13/12/2006 www.fpt.com.vn
27 GIL Binh Thanh Import - Export Product and
28 GMC SaiGon Garment Manufacturing Trade JSC 22/12/2006 www.garmexsaigon-gmc.com
29 GMD Gemadept Corporation 22/04/2002 www.gemadept.com.vn
30 GTA Thuan An Wood Processing JSC 23/07/2007 www.tac.com.vn
31 HAI H.A.I Join Stock Company 27/12/2006 http://www.congtyhai.com.vn
32 HAP HAPACO Joint Stock Company 04/08/2000 www.hapaco.vn
34 HAX Hang Xanh Motors Service JSC 26/12/2006 www.haxaco.com.vn
35 HBC Hoa Binh Construction & Real Estate Co 27/12/2006 www.hoabinhcorporation.com
36 HDC Ba Ria – Vung Tau House Development
HMC Ho Chi Minh City Metal Corporation, established on December 21, 2006, is a leading company in the metal industry in Vietnam For more information, visit their official website at www.metalhcm.com.vn They offer comprehensive metal products and services, catering to various industrial needs The company is committed to quality and innovation, making them a trusted partner in the metal sector.
No TICKER COMPANY NAME LISTED
38 HPG Hoa Phat Group Joint Stock Co 15/11/2007 www.hoaphat.com.vn
39 HRC Hoa Binh Rubber Joint Stock Co 26/12/2006 www.horuco.com.vn
40 HSI General Materials Biochemistry Fertilizer
41 HT1 Ha Tien 1 Cement Joint Stock Co 13/11/2007 www.hatien1.com.vn
42 HTV Ha Tien Transport Joint Stock Co 05/01/2006
43 ICF Investment Commerce Fisheries Co 18/12/2006 www.incomfish.com
44 IMP Imexpharm Pharmaceutical JSC 04/12/2006 www.imexpharm.com
45 ITA Tan Tao Investment Industry Corp 15/11/2006 www.tantaocity.com
46 KBC KinhBac City Developement Share Holding
47 KDC Kinh Do Corporation 12/12/2005 www.kinhdofood.com
48 KHA Khanh Hoi Import Export JSC 19/08/2002 www.khahomex.com.vn
49 KHP Khanh Hoa Power Joint Stock Co 02/11/2005
50 L10 LILAMA 10 Joint Stock Company 25/12/2007 www.lilama10.com.vn
51 LAF LongAn Food Processing Export JSC 15/12/2000
52 LBM Lam Dong Building Material JSC 20/12/2006 http://www.lbm-vn.vn
53 LGC LuGia Mechanical Electric JSC 27/12/2006 www.lugiaco.com.vn
54 MCP My Chau Printing & Packaging Holdings Co 28/12/2006 www.mychau.com.vn
55 MHC Hanoi Maritime Holding Company 21/03/2005 www.marinahanoi.com
56 MPC Minh Phu Seafood Joint Stock Co 27/12/2006 www.minhphu.com
57 NAV Nam Viet Joint Stock Company 22/12/2006 www.navifico-corp.com
58 NSC National Seed JSC 21/12/2006 www.vinaseed.com.vn
59 NTL Tu Liem Urban Development JSC 21/12/2007 www.lideco.vn
60 PAC Dry Cell and Storage Battery JSC 12/12/2006 www.pinaco.com
61 PAN Pan Pacific Corporation 22/12/2006 www.panpacific.vn
62 PET Petrovietnam General Services JSC 12/09/2007 www.petrosetco.com.vn
63 PGC Petrolimex Gas Joint Stock Company 24/11/2006 www.pgas.com.vn
64 PJT Petrolimex Joint Stock Tanker Co 28/12/2006
65 PNC Phuong Nam Cultural Joint Stock Co 11/07/2005 www.phuongnamvh.com
66 PPC Pha Lai Thermal Power Joint Stock Co 19/05/2006 www.ppc.evn.vn
67 PTC Post and Telecommunications Investment and Construction JSC
68 PVD PetroVietNam Drilling and Well Services
69 PVT Petrovietnam Transportation Corporation 10/12/2007 www.pvtrans.com
70 RAL Rang Dong Light Sources and Vacuum
71 REE Refrigeration Electrical Engineering Co 28/07/2000 www.reecorp.com
72 RIC Royal International Corporation 31/07/2007 www.royal-gaming.com
73 SAM SACOM Development And Investment Co 28/07/2000 www.sacom.com.vn
74 SAV Savimex Corporation 09/05/2002 www.savimex.com
75 SC5 Construction Joint Stock Company No 5 18/10/2007 www.sc5.vn
76 SCD Chuong Duong Beverages Company 25/12/2006 www.chuongduong.com.vn
77 SFC Saigon Fuel Company, established on September 21, 2004, is a leading fuel provider in Vietnam Their official website, www.sfcvn.com.vn, offers comprehensive information about their products and services The company is committed to delivering high-quality fuel solutions and has a strong reputation in the industry They also provide educational resources and support for future professionals, highlighting their dedication to innovation and community development.
No TICKER COMPANY NAME LISTED
78 SFI Sea And Air Freight International 29/12/2006 www.safi.com.vn
79 SJD Can Don Hydro Power Joint Stocks Co 25/12/2006 www.candon.com.vn
80 SJS Song Da Urban & Industrial Zone
81 SMC SMC Trading- Investment Joint Stock Co 30/10/2006 www.smc.vn
82 SSC Southern Seed Joint-stock Company 01/03/2005 www.ssc.com.vn
83 ST8 Sieu Thanh Joint Stock Company 18/12/2007 www.sieuthanhricoh.com.vn
84 TAC Tuong An Vegetable Oil Joint Stock Co 26/12/2006 www.tuongan.com.vn
85 TBC Thac Ba Hydropower Joint-Stock Co 29/08/2006 www.thacba.evn.com.vn
86 TCM Thanh Cong Textile Garment Investment
87 TCR TAICERA Enterprise Co., Ltd 29/12/2006 www.taicera.com
88 TDH Thu Duc Housing Development Corp 14/12/2006 www.thuduchouse.com
89 TMS Transforwarding Warehousing JSC 04/08/2000 www.transimexsaigon.com
90 TNA Thien Nam Trading & Import-Export Corp 20/07/2005 www.tenimex-tna.com.vn
91 TNC Thong Nhat Rubber Joint Stock Co 22/08/2007 www.trcbrvt.com
92 TPC Tan Dai Hung Plastic Joint Stock Co 28/11/2007 www.tandaihungplastic.com
93 TRC Tay Ninh Rubber Joint Stock Company 24/07/2007 www.taniruco.com
94 TS4 Seafood Joint Stock Company No 4 08/08/2002 www.seapriexcono4.com
95 TSC Techno – Agricultural Supplying JSC 04/10/2007 www.tsccantho.com.vn
96 TTP Tan Tien Plastic Packaging JSC 05/12/2006 www.tapack.com
97 TYA Taya (VIET NAM) Electric Wire and Cable
98 UIC IDICO Urban and House Development
12/11/2007 www.idico-udico.com.vn
99 VFC Vinafco Joint Stock Corporation 24/07/2006 www.vinafco.com.vn
100 VHC Vinh Hoan Corporation 24/12/2007 www.vinhhoan.com.vn
101 VIC VINCOM Joint Stock Company 19/09/2007 www.vincom.com.vn
102 VID Vien Dong Investment Development
103 VIP Viet Nam Petroleum Transport JSC 21/12/2006 www.vipco.com.vn/
104 VIS Vietnam - Itaty Steel JSC 25/12/2006 www.vis.com.vn
105 VNE Vietnam Electricity Construction JSC 09/08/2007 www.vneco.vn
106 VNM Vietnam Dairy Products JSC 19/01/2006 www.vinamilk.com.vn
107 VPK Vegetable Oil Packing JSC 21/12/2006
108 VSH Vinh Son - Song Hinh Hydropower JSC 02/11/2005 www.vshpc.evn.com.vn
109 VTB Viettronics TanBinh Joint Stock Co 27/12/2006 www.vtb.com.vn
VTO Vietnam Tanker Joint Stock Company was established on September 10, 2007, and is a leading provider in the tanker industry in Vietnam Visit their official website at www.viettanker.com.vn for more information about their services and operations The company focuses on delivering high-quality tanker solutions and has a strong reputation for reliability and customer satisfaction For inquiries or professional collaborations, contact them via email at vbhtj.mk@gmail.com.
Std Error of the Estimate
R Square Change F Change df1 df2 Sig F Change
1 574 a 329 297 0089 329 10.206 5 104 000 2.316 a Predictors: (Constant), Size, POR, LERV, DY, AGR b Dependent Variable: PV
B Std Error Beta Zero-order Partial Part Tolerance VIF
Size 003 001 299 3.013 003 399 283 242 654 1.528 a Dependent Variable: PV tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg