INTRODUCTION
Introduction
Stock prices serve as crucial indicators for investors when deciding whether to invest in specific shares, with the primary goal being to maximize returns while minimizing risk The inherent volatility of stock prices represents the systemic risk that investors face when holding ordinary shares Given their natural aversion to risk, investors closely monitor this volatility as it reflects the level of risk they are exposed to (Hussainey, 2011) Consequently, stock price fluctuations command significant attention from investors.
Active stock market growth is crucial for predicting future economic expansion In economies lacking efficient stock markets, three key imperfections arise: limited risk diversification opportunities for investors and entrepreneurs, difficulties for firms in optimizing their financing structures, and a lack of critical information regarding the prospects of traded companies These deficiencies ultimately hinder investment promotion and efficiency.
The Vietnamese Stock Market plays a crucial role in driving economic growth, drawing significant public attention to its fluctuations As an emerging market, it is anticipated to reduce firms' equity capital costs, enhance risk pricing and hedging for individuals, attract foreign portfolio investment, and boost domestic resource mobilization, ultimately expanding investment resources for national development However, the market's volatility reflects underlying economic instability Between 2008 and 2012, the VN-INDEX experienced dramatic changes, peaking at 921.10 on January 2, 2008, before plummeting to a low of 235.50 by February 24, 2009 By the end of 2012, the VN-INDEX closed at 413.70 points, illustrating the market's significant fluctuations during this period.
In recent years, stock price volatility in Vietnam has garnered significant attention from investors, company managers, and government officials Understanding the factors influencing this volatility is crucial This thesis explores the impact of various firm characteristics—such as dividend yield, payout ratio, leverage ratio, asset growth rate, and firm size—on stock price fluctuations in Vietnam The study's findings offer valuable insights for investors in constructing their portfolios, assist managers in effective firm management, and provide lawmakers with guidance for developing regulations aimed at stabilizing and advancing the Vietnamese Stock Market.
This chapter is organized into several key sections: Section 1.2 outlines the research background, while Section 1.3 details the research objectives and questions Section 1.4 defines the scope of the research, Section 1.5 highlights the contributions of the thesis, and Section 1.6 describes the overall structure of the thesis.
Research background
For decades, the volatility of stock prices and its influencing factors have sparked considerable debate in both theoretical and empirical research Studies indicate that changes in fundamental variables significantly impact share price fluctuations across both developed and developing markets, although the specific fundamental factors may differ between these markets It is generally accepted that a range of fundamental variables identified in the literature plays a crucial role in affecting share price changes over both the short and long term.
Numerous studies have explored the relationship between stock price volatility and firm characteristics across various developed markets Notable research includes Baskin (1989), Fama and French (1992), and Pastor and Veronesi (2003) in the United States, Allen and Rachim (1996) in Australia, and Hussainey, Mgbame, and Chijoke-Mgbame (2011) in the United Kingdom Baskin (1989) identified a significant correlation between dividend yield and stock price volatility, while Allen's findings highlighted the importance of the payout ratio in this context.
Vietnam's stock market, established in 2000 with just two listed companies in Ho Chi Minh City, has experienced remarkable growth, fueled by rising foreign interest and the privatization of state-owned enterprises By the end of 2012, the Ho Chi Minh Stock Exchange (HOSE) boasted approximately 315 listed firms, reflecting the rapid expansion of the market.
399 other firms on Hanoi stock exchange (HNX) At the end of Jan 2013, there were two more firms jointed in HNX
Previous research on stock return volatility has predominantly concentrated on developed markets, with notable studies including Baskin (1989), Gallant et al (1992), and Pastor and Veronesi (2003) focusing on the United States, as well as Allen and Rachim (1996) examining Australia and Hussainey et al (2010) providing insights from the London Stock Exchange in the UK In contrast, there has been significantly less attention paid to the dynamics of developing markets.
In recent years, various studies have explored the determinants of stock price volatility across different markets, including Jordan, Pakistan, and Malaysia Notably, Irfan and Nishat (2003) and Khan (2011) focused on the impact of dividend policy on stock prices in Pakistan, while Hashemijoo, Ardekani, and Younesi (2012) examined firm attributes in Malaysia However, limited research has addressed stock price volatility and fundamental factors in Vietnam This gap motivates the current study, which aims to investigate how firm characteristics influence stock price volatility in the Vietnamese context, particularly within the developing Asian capital market Utilizing the latest data and selected variables, this research will provide insights into the determinants of stock price volatility in Vietnam.
Research objectives and research questions
This thesis aims to comprehensively analyze stock price behavior and explore the relationship between stock price volatility and firm characteristics Additionally, the research investigates annual variations in stock price movements to determine if there are significant differences year over year.
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 volatility, including investor overreactions to earnings and news, as well as social trends of optimism or pessimism (Shiller, 1987) The efficient market hypothesis suggests that new information impacts share prices; however, patterns in stock returns during weekends and holidays do not consistently reflect fundamental values (Thaler, 1987) Roll (1988) emphasizes that relying solely on systematic economic influences to predict individual stock returns is challenging, as other non-fundamental factors also affect stock price movements (Cutler et al., 1989) This research focuses on fundamental characteristics of firms, such as dividend yield, payout ratio, leverage ratio, asset growth rate, and firm size, to analyze their effects on stock price volatility for companies listed on the Ho Chi Minh Stock Exchange (HOSE), excluding those on the Unlisted Public Company Market (UPCOM) and Hanoi Stock Exchange (HNX).
Thesis contributions
This thesis represents the first comprehensive investigation into stock price volatility in the Vietnam stock market, contributing significantly to financial literature through extensive empirical analysis of stock price movements in relation to firm characteristics over an extended period By constructing stock price data and detailing the characteristics of listed firms on the Ho Chi Minh City Stock Exchange, the research confirms the irrelevance dividend theory proposed by Modigliani and Miller (1958), validates Baskin's (1989) hypotheses, and supports Allen and Rachim's (1996) findings regarding the connection between stock price volatility and firm characteristics Additionally, this study serves as a valuable caution for investors regarding the true relationship between stock price volatility and firm characteristics.
Structure of the thesis
This thesis is structured into five chapters, beginning with Chapter 1, which outlines the key elements of the study, including the research background, objectives, questions, scope, and 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 methods It encompasses three key elements: the data sample, data size, and the methods utilized in this study.
Then, Chapter 4, data analysis will reports the analysis results of data collection
The concluding chapter summarizes the key findings of the research, highlights the managerial implications and recommendations derived from the results presented in chapter four, and addresses the limitations of the study while offering suggestions for future research endeavors.
LITERATURE REVIEW
Introduction
This section lays the theoretical groundwork for the forthcoming model and examines existing literature on stock price volatility Primarily, it focuses on fundamental analyses that explore the long-term relationship between stock price movements and firm characteristics Additionally, it briefly addresses value relevance studies that analyze the short-term effects of event announcements on stock prices Furthermore, as previous research often investigates the connection between stock price volatility and dividend policy while incorporating other controlling variables, this section reviews these literature strands and highlights essential fundamental factors for later discussion.
This chapter is structured as follows: Section 2.2 reviews literature on stock price volatility and examines key factors influencing it Section 2.3 explores the connection between stock price volatility and dividend policy Section 2.4 analyzes how firm age relates to stock price volatility In Section 2.5, the relationship between stock price volatility and trading liquidity is discussed Section 2.6 investigates additional firm characteristics that impact stock price volatility Section 2.7 outlines the development of research hypotheses, while Section 2.8 concludes with a summary of the chapter.
Stock price volatility
Stock price volatility refers to the fluctuation rate of a security's price within a specific timeframe, indicating that higher volatility equates to increased risk of significant gains or losses Volatile stocks pose challenges in predicting future share prices, leading many investors to favor stocks with more stable earnings and lower associated risks (Profilet and Bacon, 2013).
Stock prices serve as a crucial indicator for investors when deciding whether to invest in a particular share Various factors, both internal and external, influence these prices, prompting extensive research into their relationships Notably, Rappoport (1986) and Downs (1991) identified that changes in share prices are linked to fundamental variables essential for share valuation, including payout ratio, dividend yield, capital structure, earnings, firm size, and growth potential.
Ball and Brown (1968) were pioneers in establishing the connection between stock prices and information revealed in financial statements This empirical research on value relevance is grounded in equity valuation models Ohlson (1995) further illustrates that a firm's value can be represented as a linear function of its book value, earnings, and other pertinent information.
Research has extensively examined the relationship between fundamental factors and share price changes over short timeframes, yet there is a scarcity of studies that model this connection over longer periods Most short-term studies utilize cross-sectional tests and event-based methodologies, but comprehensive models that analyze this relationship over time are limited Typically, these studies focus on just one or two fundamental factors, despite the existence of a broader range Additionally, while immediate price adjustments during the announcement of price-relevant disclosures are significant, it is crucial to evaluate their effects over extended periods, utilizing data spanning several years for a more thorough analysis.
Stock price volatility and dividend policy
Dividend policy, particularly dividend yield and payout ratio, is a crucial internal factor extensively examined in finance, with various studies highlighting its significance (Modigliani and Miller, 1958; Miller and Rock, 1985; John and Williams, 1987; Baskin, 1989; Fama and French, 1992; Allen and Rachim, 1996; Irfan and Nishat, 2003) Researchers have differing perspectives on the relationship between dividend policy and stock prices, indicating a complex and contested area of study.
The relationship between dividend payouts and stock price volatility, first explored by Modigliani and Miller in 1958, remains a topic of ongoing debate They posited that a firm's value is unaffected by its dividend policy, asserting that stock price volatility is determined solely by a company's earning potential However, subsequent studies by Miller and Rock (1985) and John and Williams (1987) suggest that this holds true only when shareholders possess symmetric information regarding the firm's financial health In practice, managers often disclose positive information while withholding negative details until compelled by regulations or financial obligations, complicating the understanding of this relationship.
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) highlights a positive correlation between dividend payouts and stock price reactions, suggesting that distributing dividends lowers funding costs and enhances a firm's cash flow By paying cash dividends to shareholders, companies minimize idle funds that managers might otherwise invest in projects with low or negative net present value (NPV).
Baskin (1989) highlighted a significant negative relationship between dividends and stock price volatility in the United States, proposing four models: duration effect, rate of return effect, arbitrage effect, and informational effect He recommended control variables such as operating earnings, firm size, debt financing level, payout ratio, and asset growth rate to assess the significance of the dividend yield and price volatility relationship His research revealed that dividend yield and payout ratio are negatively correlated with stock price volatility, while firm size, asset growth rate, and firm leverage positively influence stock price volatility, differing slightly from their impact on stock returns.
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 affects stock price volatility in Australia Instead, they identified a significant positive correlation between stock price volatility and both earnings volatility and leverage Additionally, their findings revealed a significant negative relationship between stock price volatility and payout ratio Furthermore, the study indicated a negative correlation between company size and stock price volatility, suggesting that larger companies tend to have higher liabilities.
A study by Irfan and Nishat (2003) in Pakistan revealed that both dividend payout ratio and dividend yield significantly negatively impact stock price volatility Their findings align with those of Baskin (1989), who noted a positive correlation between debt and price volatility; however, the effect of debt is less pronounced than that of dividend yield.
Following Irfan and Nishat (2003), a number of studies were conducted in Pakistan regarding to dividend policy and stock price volatility Asghar et al.,
In a study conducted by Nazir et al (2010), it was found that stock price volatility has a strong positive correlation with dividend yield, while exhibiting a significant negative correlation with asset growth Additionally, the research highlighted that both dividend yield and payout ratio significantly influence share price volatility Notably, the impact of dividend yield on stock price volatility intensifies over time, whereas the payout ratio shows a significant effect primarily at lower levels of 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
A study by Pastor and Veronesi (2003) identified a negative correlation between stock volatility and firm age, revealing that median return volatility for U.S stocks decreases from 14% per month in 1-year-old firms to 11% per month in 10-year-old firms Their model suggests that firms exhibiting more volatile or uncertain profitability, as well as those that do not distribute dividends, are likely to experience higher stock volatility.
Stock price volatility and trading liquidity
Numerous studies highlight a significant relationship between trading volume, stock price movements, and liquidity, indicating that trading volume can introduce risk through information flow For instance, Saatccioglu and Starks (1998) discovered that volume often precedes stock price changes in four out of six emerging markets Additionally, Jones et al (1994) revealed a positive correlation between volatility and transaction volume, while Gallant et al (1992) examined the co-movement of price and volume using daily data dating back to 1928.
1987 for New York Stock Exchange and find positive correlation between conditional volatility and volume
A study by Song et al (2005) investigated the impact of trade frequency, trade size, and share volume on the volatility-volume relationship in the Shanghai Stock Exchange, concluding that the number of trades is the primary driver of this relationship Furthermore, additional research indicates that stock trading volume exhibits the strongest positive correlation with price fluctuations in emerging markets, making it a key predictor of increased price volatility in both emerging and developing stock markets (Sabri, 2004).
Other firm’s characteristics and stock price volatility
Ariff et al (1994) analyzed the joint linear effects of six variables on share price volatility across Japan, Malaysia, and Singapore, using data from firms over a span of 16 years Their findings indicate that while these variables generally relate to share price volatility in all three markets, some were not significant in specific contexts In the more analytically intensive Japanese market, fundamental factors account for 40% of the variation in share price volatility Conversely, in the developing markets of Malaysia and Singapore, a larger proportion of price variation remains unexplained by these six firm-specific variables.
Irfan and Nishat (2003) conducted a study on the Karachi Stock Exchange, revealing the long-term joint effects of multiple factors on share prices Key factors identified include payout ratio, size, leverage, and dividend yield The research encompasses an analysis of pre-reform, post-reform, and overall periods, highlighting the significance of these elements in influencing market performance.
Despite extensive research on stock price fluctuations globally, there is a notable scarcity of studies focusing on firm attributes and their impact on stock price volatility in developing countries, particularly in Vietnam The existing literature lacks empirical evidence regarding stock price volatility in the Vietnamese stock exchange, highlighting the significance of this study This research aims to fill the gap by exploring how firm characteristics influence stock price volatility, thereby contributing valuable insights to the existing body of knowledge.
This thesis aims to enhance the existing literature by addressing gaps in previous research regarding the relationship between stock price volatility and firm characteristics among companies listed on the Vietnam stock market.
Developing empirical research hypotheses
This paper explores five empirical hypotheses related to firm characteristics that influence stock price volatility in the Vietnamese stock market The key factors examined include dividend yield, dividend payout ratio, firm leverage, asset growth rate, and firm size The study aims to establish the relationship between these characteristics and stock price fluctuations, providing a comprehensive summary of their impact on volatility.
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
The hypotheses outlined in this study are grounded in prior research (Miller and Modigliani, 1958; Asquith et al., 1983; Baskin, 1989; Allen and Rachim, 1996; Ariff and Khan, 2000; Cooper, Gulen, and Schill, 2008), enabling comparisons of stock price volatility characteristics between Vietnam and various markets, including developed economies like the United States, UK, and Australia, as well as emerging markets such as Pakistan, Jordan, and Malaysia The detailed development of these hypotheses is presented in sections 2.7.1 to 2.7.5.
2.7.1 Dividend yield and dividend payout ratio
Previous research findings indicate two opposing viewpoints regarding the influence of dividend policy on stock price volatility.
The first group of researchers, including Miller and Modigliani (1958), advocates for the dividend irrelevance theory, asserting that dividend policy does not influence shareholder wealth or returns Black and Scholes (1974) further examined this concept using the capital asset pricing model, finding no significant link between dividend yield and expected return Their results supported the notion that varying dividend policies do not result in different stock prices, reinforcing the dividend irrelevance hypothesis.
A second group of researchers concurs that dividend policy has a significant impact on stock price volatilities According to Gordon (1963), a firm's dividend policy influences its market value, as investors often prioritize dividend payments over potential future capital gains due to market uncertainty This suggests a direct relationship between dividend policy and the market value of shares.
Dividend announcements serve as indicators of a firm's future profitability (Asquith & Mullins, 1983) Research by Travlos, Trigeorgis, and Vafeas (2001) analyzed the impact of stock and cash dividend announcements on stock prices in the Cyprus Stock Exchange from 1985 to 1995 Their findings provided substantial evidence that stock prices respond significantly to both cash dividend announcements and increases.
Baskin (1989) supports the view of Asquith et al (1983) by suggesting that fluctuations in discount rates have a diminished effect on high dividend yield stocks This is because high dividend yields often indicate stronger near-term cash flows, leading to less volatility in the share prices of these firms.
H1: Stock price volatility is negatively influenced by dividend yield with all other factors remaining constant
According to Baskin(1989), dividend yield is the most important factors affecting stock price volatility He argues that there is a significant negative relation between dividend yield and stock price volatility
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 high-risk companies, particularly those with debt, are required to deliver returns that align with investor expectations (Hamada, 1972; Sharpe, 1964).
Higher levels of debt in a firm are associated with increased fluctuations in its share price, indicating that changes in capital structure are directly linked to share price volatility.
Theoretical finance identifies leverage as a significant source of risk, suggesting that higher leverage levels in a firm correspond to increased risk exposure for equity holders As a result, risk-averse equity holders, who are exposed to more uncertain cash flows, demand a higher rate of return on their investment to compensate for the elevated risk.
Modigliani and Miller (1958) argued that in competitive capital markets, a firm's value remains unaffected by its financial structure However, in the presence of market imperfections—such as transaction costs, taxes, informational asymmetry, and agency costs—capital structure plays a significant role in influencing share prices Consequently, the return on equity capital tends to rise with increased leverage, as higher debt levels elevate the risk associated with the stock, prompting equity shareholders to seek greater returns.
High leverage in firms can lead to increased stock price volatility due to operational risks, as it raises the likelihood of default and its associated costs (Chen and Zhang, 2010) When default risk is factored into pricing, a notable rise in leverage is expected to result in higher anticipated future returns.
H3: Stock price volatility is positively influenced by firm leverage with all other factors remaining constant
This thesis investigates the relationship between firm leverage and stock price volatility, highlighting its significance in the global capital market While previous empirical studies, such as Baskin (1989), primarily focused on U.S firms, there is a pressing need to examine this relationship in different contexts, particularly in developing countries like Vietnam This research aims to fill that gap, providing valuable insights into the asset pricing implications of leverage in a new environment.
The capital market plays a crucial role in efficiently pricing real investments, enabling companies to acquire and dispose of assets as dictated by economic efficiency However, research indicates a significant bias in how the market capitalizes corporate asset investments and disinvestments Previous studies reveal that corporate actions aimed at asset expansion, such as acquisitions and public offerings, are often followed by periods of abnormally low returns Conversely, actions related to asset contraction, including spinoffs and share repurchases, tend to result in periods of abnormally high returns.
In addition, the changes in asset growth of firms are significant to shares price while earnings appear to be universally a relevant factor (Ariff, et al.,1994)
A consensus exists that key variables identified by various theories play a significant role in influencing share price fluctuations both in the short term and long term (Ariff and Khan, 2000).
Chapter summary
This chapter explores the theoretical framework and existing literature on stock price volatility, focusing on its relationship with firm characteristics such as dividend yield, payout ratio, firm leverage, asset growth rate, and firm size The hypotheses formulated for this research are grounded in prior studies, including works by Miller and Modigliani (1958), Asquith et al (1983), Baskin (1989), Allen and Rachim (1996), Ariff and Khan (2000), and Cooper, Gulen, and Schill (2008).
Chapter 3 outlines the data and methodology employed to test the research hypotheses, detailing the sources and characteristics of the data, the sampling selection process, model specifications, estimation methods, and robustness tests that validate the primary findings.
DATA AND METHODOLOGY
Introduction
This chapter outlines the data and empirical techniques used to test the hypotheses from Chapter 2 It begins with a detailed description of the primary data sources in Section 3.2, followed by an explanation of the selected variables in Section 3.3 Section 3.4 details the model specification, estimation methods, and robustness tests employed in the analysis Finally, Section 3.5 provides a summary of the chapter.
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 To ensure accurate return measurements, the share prices have been adjusted for dividends and stock splits The selected companies meet specific criteria for inclusion in the sample.
(1) the companies must be listed on HOSE by the end of 2007 and 5 full- year audited financial statements and annual reports from 2008 to 2012 are available;
(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;
The initial dataset for this study included companies listed on the HOSE as of December 31, 2007 However, certain firms were excluded from the final sample due to their unique characteristics Financial firms, such as banks (e.g., STB), securities companies (e.g., SSI), and investment funds (e.g., MAFPF1, PRUBF1, VFMVF1), were omitted because they operate under a distinct regulatory framework and have different financial statement formats Additionally, de-listed firms, including Bach Tuyet Cotton Corporation (BBT), were also removed from consideration Consequently, the final sample comprised 110 companies that met all selection criteria (see Appendix A).
3.3.1 Dependent variable -Price volatility (PV)
Parkinson's (1980) method for extreme value estimation of variance in stock returns significantly improves upon traditional techniques that rely solely on opening and closing prices This approach calculates the annual range of stock prices by taking the difference between the maximum and minimum values, dividing it by the average of the high and low prices, and then squaring the result It effectively captures annual fluctuations in share prices, utilizing data adjusted for dividends and stock splits from the price timeline of each firm listed on HOSE.
In the Vietnamese stock market, investors primarily focus on specific firm characteristics that influence their decisions This study examines key firm-specific attributes, including earnings volatility (EV), return on assets (ROA), return on equity (ROE), asset growth rate (AGR), current ratio (CURR), leverage ratio (LEVR), dividend yield (DY), pay-out ratio (POR), firm size, firm age, and liquidity (TOVR) These attributes were selected as independent variables for empirical analysis, reflecting the preferences and priorities of investors in this market.
Dividend yield (DY) is calculated by dividing the total cash dividends paid to common stockholders by the firm's market value at the end of the year, based on data from the dividend timeline on HOSE.
The payout ratio (POR) represents the proportion of total earnings that is distributed as cash dividends to shareholders Calculated annually, this ratio is based on the dividend timeline available on the Ho Chi Minh Stock Exchange (HOSE).
The leverage ratio (LEVR) is an important indicator of long-term financial distress, calculated by dividing total liabilities by total assets at the end of the year This ratio is derived from the consolidated audited financial statements, providing a clear view of a company's financial health.
Asset growth (AGR) is determined by taking the natural logarithm of the ratio of total assets at the end of a financial year to those at the beginning of the same year, utilizing data from the consolidated audited financial statements.
Firm size, measured by the book value of total assets at year-end, is an important factor in our analysis For the regression models, we utilize the natural logarithm of total assets, which is derived from the consolidated audited financial statements.
This section is to explain the econometric and empirical techniques used in this research
The researcher opted for panel data, aligning with existing literature that identifies it as the most suitable method for the study's focus According to Hsiao (2006), panel data provides several advantages 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.
Taking into account some problems, which affected results of regression, 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
PVi,t denotes the stock price volatility of firm i at time t;
POR: is dividend payout ratio
AGR: is Asset growth Rate
SIZE:is firm size β 0: is the intercept β 1, β 2 , β 3 , β 4 , β 5: are coefficients or population slopes Ɛ is the error term
This model utilizes variables that closely resemble those used in prior analyses Given the high sensitivity of results to the estimation method, this study implements multiple specifications to address the challenges associated with panel data.
3.4.2 Ordinary Least Square (OLS) regression
The basic specification of the model overlooks the unique structure of panel data, which includes both cross-sectional and time dimensions A straightforward method is to assume that the intercept and all coefficients remain constant across time and individuals; however, this approach neglects the inherent characteristics of panel data and relies on OLS regression for coefficient estimation.
This chapter outlines the data and sample used, as well as the methodology employed to test the hypotheses formulated in Chapter 2, addressing the research question presented in Chapter 1 The following chapter will present the empirical results, including descriptive statistics, correlation analysis, and the outcomes of the hypothesis tests.
Variables
3.3.1 Dependent variable -Price volatility (PV)
The method developed by Parkinson (1980) for extreme value estimation of variance in stock returns significantly improves upon traditional estimation methods that rely solely on opening and closing prices By calculating the annual range of stock prices—defined as the difference between the maximum and minimum values—and normalizing it by the average of the high and low prices before squaring the result, this approach effectively captures annual fluctuations in share prices The data utilized for this analysis is sourced from the price timelines of firms listed on HOSE, adjusted for dividends and stock splits.
In the Vietnamese stock market, investors primarily focus on specific firm characteristics, which include earnings volatility (EV), return on assets (ROA), return on equity (ROE), assets growth rate (AGR), current ratio (CURR), leverage ratio (LEVR), dividends yield (DY), pay-out ratio (POR), firm size, firm age, and liquidity (TOVR) This study selects these attributes as independent variables for its empirical analysis, reflecting the preferences and interests of local investors.
Dividend yield (DY) is calculated by dividing the total cash dividends paid to common stockholders by the firm's market value at the end of the year, based on the dividend timeline provided by HOSE.
The payout ratio (POR) represents the proportion of cash dividends distributed to shareholders in relation to the total earnings of a company Calculated annually, this ratio is based on the dividend timeline available on the Ho Chi Minh Stock Exchange (HOSE).
The leverage ratio (LEVR) is a key indicator of long-term financial distress, representing the proportion of total liabilities to total assets at the end of the fiscal year This ratio is derived from the consolidated audited financial statements, providing insight into a company's financial health.
Asset growth (AGR) is determined by taking the natural logarithm of the ratio of total assets at the end of a financial year to total assets at the beginning of that same year, using consolidated audited financial statements for accurate calculations.
Firm size, represented by the book value of total assets at year-end, is a critical variable in our analysis For regression purposes, we utilize the natural logarithm of total assets, which is derived from the consolidated audited financial statements.
Methodology
This section is to explain the econometric and empirical techniques used in this research
The researcher selected panel data as the optimal method for this study, aligning with existing literature According to Hsiao (2006), panel data provides several advantages for analyzing historical series of companies, including accurate estimation of model parameters, the ability to address model misspecifications and omitted variables, and simplified computation and interpretation of results.
Taking into account some problems, which affected results of regression, 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
PVi,t denotes the stock price volatility of firm i at time t;
POR: is dividend payout ratio
AGR: is Asset growth Rate
SIZE:is firm size β 0: is the intercept β 1, β 2 , β 3 , β 4 , β 5: are coefficients or population slopes Ɛ is the error term
This model utilizes variables that closely resemble those used in prior analyses Given the high sensitivity of results to the estimation method, this study implements multiple specifications to address the challenges associated with panel data.
3.4.2 Ordinary Least Square (OLS) regression
The basic specification of the model overlooks the unique structure of panel data, which includes both cross-sectional and time dimensions A common method is to assume that the intercept and all coefficients remain constant across different individuals and time periods However, this approach fails to account for the inherent characteristics of panel data, leading to coefficient estimates derived through OLS regression.
Chapter summary
This chapter outlines the data and sample used, as well as the methodology employed to test the hypotheses established in Chapter 2, addressing the research question from Chapter 1 The following chapter will present the empirical findings, including descriptive statistics, correlation analysis, and hypothesis testing.
EMPIRICAL RESULTS
Introduction
This chapter provides a detailed analysis of the empirical findings, starting with the essential steps for testing the assumptions outlined in Chapter 3 It includes a thorough examination of the hypotheses, evaluates the robustness of the results, and interprets the data to illustrate the relationship between firm characteristics and stock price volatility.
This chapter includes several key sections: Section 4.2 focuses on descriptive statistics and correlation analysis, while Section 4.3 presents the results of multiple linear regression analysis, including hypothesis testing and findings Finally, Section 4.4 provides a summary of the chapter.
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
Table 4.1 reveals that size possesses the highest mean value at 13.762582, while dividend yield records the lowest mean at 0.099745 Additionally, the payout ratio exhibits the greatest standard deviation at 11.3541653, contrasting with dividend yield, which has the lowest standard deviation among the variables.
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:
The study reveals a negative correlation of -0.213 between Price-to-Value (PV) and Dividend Yield (DY) at a significant level of 5%, supporting the research model's first hypothesis (H1) This finding aligns with Baskin's (1989) results but contradicts the conclusions drawn by Allen and Rachim (1996).
The relationship between the Price-to-Value (PV) ratio and the Dividend Payout Ratio (POR) is negatively correlated, with a value of -0.117, significant at the 5% level This finding aligns with the research expectations and is consistent with the results of Baskin (1989) and Allen & Rachim (1996).
PV and Firm Leverage (LEVR) have negative correlation with the value of -.185 This result is opposite to Hamada (1972) and Sharpe (1964) who had found positive relation between them
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).
Multiple Linear Regressions Analysis
To assess multicollinearity among independent variables, it is essential to analyze the Tolerance and Variance Inflation Factor (VIF) values A Tolerance value below 0.1 or a VIF value exceeding 10 indicates the presence of multicollinearity However, as shown in Table 4.3-Coefficients, all independent variables exhibit Tolerance values greater than 0.1 and VIF values below 10 Therefore, it can be concluded that there is no significant correlation among the independent variables, confirming the absence of multicollinearity.
The Ordinary Least Squares Regression Model results, as shown in Table 4.3, highlight the significance of individual variables included in the study Among the five independent factors analyzed, three—LEVR, AGR, and SIZE—demonstrated significant levels of 000, 001, and 003, respectively, all of which are below the 05 threshold This indicates that these factors significantly influence the dependent variable.
The independent factors DY and POR show a correlation with the dependent variable; however, their significance levels of 218 and 598 exceed the 05 threshold, indicating they do not significantly impact the dependent variable Consequently, these variables should be excluded from the model.
The original model, which included five independent factors, needs to be revised to incorporate three independent variables when analyzing the relationship between stock price volatility and the characteristics of firms listed on HOSE.
B Std Error Beta Tolerance VIF
The Model Summary Table (Table 4.4) reveals a coefficient R-value of 574 and an R-square of 329, indicating a strong correlation between the dependent variable, stock price volatility (PV), and the five independent factors The adjusted R-square of 297 suggests that the linear regression model accounts for 29.7% of the variance in stock price changes due to these factors, which include DY, POR, LERV, AGR, and SIZE This finding aligns with similar studies in developing markets, such as Pakistan (25.9%) and Malaysia (27.2%) Additionally, the F-test shows a significance level of 000, confirming the regression model's suitability for the collected data.
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 and correlation testing among variables, the next step involved hypothesis testing through regression analysis to examine the influence of various firm characteristics on stock price volatility.
H1: Stock price volatility is negatively influenced by dividend yield (DY), with all other factors remaining constant
The standardized regression coefficient for dividend yield (DY) on stock price volatility (PV) is -0.108, with a t-value of -1.240 and a significance value of 0.218, indicating that at a 5% significance level, there is no statistical evidence to support an impact of dividend yield on stock price volatility This suggests that stock price volatility is not influenced by a firm's dividend yield, contradicting earlier studies by Baskin (1989) and Irfan and Nishat (2003), which found a strong negative association between the two Additionally, the results oppose Asghar et al (2010), who reported a strong positive correlation between price volatility and dividend yield While previous research often indicates that dividend yield significantly affects stock price volatility, this thesis aligns with Modigliani and Miller's (1958) assertion that firm value is unrelated to dividend policy, with stock price volatility primarily determined by earning ability Furthermore, John and Williams (1987) and Miller and Rock (1985) note that Modigliani and Miller's conclusions hold true only when shareholders possess symmetric information about the company's financial status, as managers may withhold negative information until compelled by regulations.
H2: Stock price volatility is negatively influenced by dividend payout ratio (POR), with all other factors remaining constant
The standardized regression coefficient for the payout ratio (POR) on stock price volatility (PV) is -0.043, with a t-value of -0.530 and a significance value of 0.548, indicating no significant impact of the payout ratio on stock price volatility in the Vietnamese stock market at a 5% significance level This finding supports the dividend irrelevance theory proposed by Modigliani and Miller (1958) and aligns with the results of Allen and Rachim (1996), which suggest a negative correlation between stock price volatility and payout ratio Ultimately, the negative coefficient for the payout ratio indicates that it does not significantly explain stock price volatility for firms.
H3: Stock price volatility is negatively influenced by firm leverage
(LERV) with all other factors remaining constant
The standardized regression coefficient beta of firm leverage (LERV) on stock price volatility (PV) is -0.379, with a t-value of -4.333 and a significance value of 0.000, indicating strong statistical evidence at the 5% level of a significant negative impact of firm leverage on stock price volatility This relationship suggests that higher leverage in a firm correlates with increased stock price volatility, supporting earlier findings by Sharp (1964) and Hamada (1972) that firms with greater debt experience larger fluctuations in share prices Additionally, the results imply a direct link between capital structure changes and share price volatility, with high leverage increasing the likelihood of default and associated costs (Chen and Zhang, 2010) Consequently, a significant rise in firm leverage is likely to result in a decrease in stock prices within the Vietnamese stock market.
The research model strongly supports the H3 hypothesis, indicating that firm leverage has a negative relationship with stock price volatility This suggests that stock price volatility can be significantly explained by the level of firm leverage.
H4: Stock price volatility is positively influenced by asset growth rate with all other factors remaining constant
The standardized regression coefficient for asset growth rate (AGR) on stock price volatility (PV) is 0.329, with a t-value of 3.399 and a significance value of 0.001, indicating a strong positive effect at the 5% significance level This finding contrasts with earlier studies by Baskin (1989) and Irfan and Nishat (2003), but aligns with the results of Asghar et al (2010) and Nazir et al (2010) Additionally, Cooper, Glulen, and Schill (2008) identified asset growth rate as a strong predictor of future stock returns, highlighting its importance as a statistically and economically significant factor in determining stock return variations.
The research model supports the H4 hypothesis, indicating a positive relationship between asset growth rate and stock price volatility This suggests that variations in stock prices can be attributed to the asset growth rate.
H5: Stock price volatility is negatively influenced by firm size (Size), with all other factors remaining constant
The standardized regression coefficient (beta) for firm size (SIZE) on stock price volatility (PV) is 0.299, with a t-value of 3.013, which exceeds 2, and a significance value of 0.003, indicating strong statistical evidence at the 5 percent significance level of a significant positive effect of firm size on stock price volatility This positive relationship aligns with findings from Baskin (1989) and Irfan and Nishat (2003), while the negative sign supports the underlying hypothesis.
Allen and Rachim (1996) support hypothesis 5 with a positive coefficient of return on assets, indicating that stock prices of small firms tend to be more volatile than those of large firms due to lower diversification However, this trend may be influenced by the unique characteristics of developing markets like Pakistan, Malaysia, and Vietnam, where larger companies experience greater share price volatility.
Therefore, the H5 is supported for the research model and firm size is positively related to stock price volatility It means that stock price volatility is explained by firm size.
Chapter Summary
The regression analysis and hypothesis testing conducted in sections 4.2 and 4.3 reveal that three out of the five original hypotheses are significant to the research model, while two are rejected Consequently, the research question, "Do firm characteristics affect stock price volatility in the Vietnam stock market?" is answered affirmatively A summary of the revised hypotheses can be found in Table 4.5 below.
The research question: Do firm’s characteristics affect firm’s stock price volatility in Vietnam stock market?
Stock price volatility is negatively impacted by factors such as dividend payout ratio and firm size, while it is positively influenced by the asset growth rate Additionally, firm leverage has a negative effect on stock price volatility These findings highlight the complex relationships between these financial metrics and stock price fluctuations.
CONCLUSION
Contributions
This dissertation, while acknowledging its limitations, offers valuable empirical findings that enhance theoretical models in finance Existing literature primarily investigates the relationship between dividend policy and stock price volatility; however, this thesis expands the scope by examining the influence of firm characteristics on stock price volatility within the Vietnamese stock market As the first study to establish this connection in Vietnam, it confirms the irrelevance dividend theory proposed by Modigliani and Miller, thereby contributing significantly to the understanding of stock price dynamics in the region.
This study builds on Baskin's (1989) hypotheses and supports the findings of Allen and Rachim (1996) regarding the relationship between stock price volatility and firm characteristics Additionally, it incorporates multiple robustness tests to confirm the regression results, addressing shortcomings in prior research on panel data analysis of stock price volatility.
Implications
These findings offer several useful insights for academics, firm managers, investors, and policymakers
This thesis highlights important implications for academics, revealing that in the Vietnamese stock market, dividend yield and dividend payout ratio do not significantly influence stock price volatility, unlike in most other markets This indicates that Vietnamese stock prices are largely unaffected by companies' dividend policies, suggesting that investors in this market are more likely to be speculators rather than long-term investors Consequently, these findings underscore the unique characteristics of the Vietnamese stock market and its investors, pointing to the need for future research to consider speculative behavior as a key factor in understanding stock price volatility in Vietnam.
This thesis reveals that key firm characteristics significantly influence stock price volatility, highlighting the importance for managers to leverage these attributes to enhance their firm's stock price and overall market value For instance, by strategically utilizing firm leverage as an optimal financing tool, managers can minimize financial risk and boost operational efficiency, ultimately leading to an increase in stock prices.
Investors in the Vietnamese stock market should focus on firms with specific characteristics that align with their expected returns, as indicated by the findings of this thesis.
The findings of this thesis indicate that the Vietnamese stock market is characterized by a high level of speculation To foster a more stable market, policymakers should implement regulations aimed at reducing speculative trading while promoting long-term investment strategies By doing so, the Vietnamese stock market can achieve more sustainable development.
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 those on HNX Ideally, quarterly data would enhance the analysis; however, significant discrepancies exist between internal financial statements and audited reports Due to recent regulations mandating only annual audited reports, this study relies on annual data for improved accuracy Nonetheless, the annual frequency may limit the results' generalizability To strengthen future research, obtaining quarterly data is recommended.
This thesis does not explore the impact of firm management and investor characteristics on stock price volatility, despite the fact that both management and investors are significantly affected by fluctuations in stock prices.
Different types of investors, including local and foreign as well as individual and institutional investors, make distinct investment decisions that can influence stock price volatility in various ways Consequently, it is essential for future research to explore these factors further.
The Vietnamese government regulates stock price fluctuations, which minimizes the impact of external factors on stock price volatility This thesis did not consider this regulatory aspect when analyzing the determinants affecting stock prices in the Vietnamese market These limitations highlight opportunities for future research.
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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
37 HMC HoChiMinh City Metal Corporation 21/12/2006 www.metalhcm.com.vn
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
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
110 VTO Vietnam Tanker Joint Stock Company 09/10/2007 www.viettanker.com.vn
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