However, because the Vietnamese stock market is still categorized to be weak form efficient, as such, the stock price and stock index of both stock exchanges, especially VN-Index of HOSE
INTRODUCTION
Chapter Introduction
This introductory chapter establishes the framework for the empirical thesis, examining Vietnam's financial landscape and the evolution of the Ho Chi Minh Stock Exchange (HOSE) It emphasizes the significant influence of macroeconomic factors on the VN-Index, the benchmark stock index of HOSE The chapter identifies the primary research problem and justifies the need for this study, aiming to offer valuable insights for local investors and policymakers Additionally, it outlines the main research aim and specific objectives that the study intends to achieve.
Contextual Background
The stock exchange serves as a marketplace for trading various financial instruments, including stocks and bonds, among issuers, buyers, and sellers Traditionally viewed as a key investment avenue, the stock exchange presents significant risks, prompting investors to seek methods for accurately predicting share prices This prediction often relies on various indicators, particularly macroeconomic information (Triyono and Robiyanto, 2017).
“Doi Moi” or reform policy in the 1986 has opened the new door to foster the growth of Vietnam’s economy This openness policy, by focusing on trade and
The market-oriented economic model has been the key driver of Vietnam's economy for decades Alongside rapid economic growth and an open policy approach, the Vietnamese government introduced the Ho Chi Minh initiative to further enhance development.
Securities Trading Center (renamed as Ho Chi Minh Stock Exchange – HOSE) and the Hanoi Securities Trading Center (renamed as Hanoi Stock Exchange –
HNX) in early 2000s This opened a new era that has witnessed many changes in the financial trading and equity market in Vietnam In fact, as can be seen in
Since 2008, Vietnam's stock market capitalization has experienced rapid growth, rising from $12.37 billion to $149.82 billion by 2019 This remarkable increase positioned Vietnam as the 26th largest stock market globally in terms of market capitalization.
Global Economy As a consequence of this impressive performance, VN-Index, which is the stock index of HOSE, has also remarkably grown in such an adolescent market
Figure 1.Stock Market Capitalization in Vietnam between 2008 and 2019
The establishment of the HOSE and HNX stock exchanges in Vietnam has created new investment opportunities for local investors and financial experts, particularly during the country's economic boom (Vo, 2017) However, as a developing market, Vietnam's stock market experiences significant volatility and inefficiency, characterized by weak form market efficiency where stock prices are not immediately influenced by market information Changes in macroeconomic conditions can greatly impact stock prices, which are primarily driven by historical transaction data rather than public information (Mobarek and Fiorante, 2014) This high level of risk and fluctuation in the Vietnamese stock market creates uncertainty for local investors, leading to an increased demand for reliable information, particularly historical data related to stock prices, to make informed investment decisions and mitigate potential losses.
In a highly efficient market, macroeconomic policies can be implemented without significantly affecting stock prices, as these prices fully reflect all relevant market information Conversely, in a weak market efficiency context, such as in Vietnam, the impact of macroeconomic policies on the stock market is more pronounced.
10 changes can generate enormous effect in the stock price (Mobarek and Fiorante,
Emerging and developing markets, such as Vietnam, exhibit a heightened sensitivity of stock prices to macroeconomic fluctuations compared to developed markets (Victor and Kuwornu, 2011; Zakaria and Shamsuddin, 2012) During the 2000s, Vietnam faced significant economic volatility characterized by high interest and inflation rates, which substantially impacted stock prices and the VN-Index Notably, in February 2009, the VN-Index plummeted to an all-time low of 235.5 points, primarily due to the repercussions of the 2008 financial crisis on the Vietnamese economy (Vo and Ellis, 2018).
The Vietnam stock market exhibits weak form efficiency, characterized by high volatility and stock prices predominantly influenced by historical data This presents challenges for investors attempting to predict future prices for trading decisions Therefore, it is crucial for both investors and policymakers in Vietnam to comprehend the effects of past macroeconomic factors, such as interest and inflation rates, on stock prices and indices Understanding this "historical" macroeconomic data can serve as a more reliable predictor of future stock market trends compared to technical analysis tools commonly used in more efficient markets Consequently, there is an urgent need for empirical research to deepen this understanding.
Research on the relationship between macroeconomic factors and stock indices has predominantly focused on developed markets, leaving a significant gap in understanding this dynamic in developing countries like Vietnam Due to institutional differences, findings from developed markets may not be applicable to emerging markets, highlighting the need for tailored research The influence of macroeconomic forces on Vietnam's stock market, particularly the VN-Index, remains underexplored, necessitating urgent empirical studies to equip local investors with relevant insights The COVID-19 pandemic has further emphasized the importance of understanding these influences, as local investors face heightened volatility and uncertainty in the stock market Gaining clarity on how macroeconomic factors affect stock prices is crucial for making informed investment decisions during these challenging times.
Research Aim and Research Objectives
This empirical research aims to explore the relationship between macroeconomic conditions and the Vietnam stock index (VN-Index), which is a capitalization-weighted index of all companies listed on the Ho Chi Minh City Stock Exchange, Vietnam's largest stock market To achieve this primary goal, the study outlines specific objectives focused on understanding this linkage.
To review a number of macroeconomic factors that could determine stock market index
To identify significant macroeconomic factors that have affected the Vietnam stock index (i.e VN-Index) during the period from 2011 to 2019
This article aims to offer valuable insights for investors looking to navigate the local financial market and for policymakers seeking to foster a more sustainable and stable domestic stock market, particularly in the context of the ongoing COVID pandemic.
Brief of Method
To achieve the stated aims and objectives, multiple regression analysis is employed to explore the relationships between various macroeconomic factors—such as GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI)—and the VN-Index This multiple regression model extends the ordinary least squares (OLS) regression by incorporating multiple explanatory variables, making it a widely utilized method in economic analysis.
This study employs econometric analysis to explore the linear relationships between multiple independent variables and a dependent variable, specifically focusing on the correlation between macroeconomic factors and the VN-Index in Vietnam The VN-Index, representing the stock index of the Ho Chi Minh Stock Exchange, serves as the dependent variable, while five macroeconomic factors act as predictors in the regression model Data for this analysis is sourced from publicly available online resources, including the FX Empire dataset, Investing.com, and the General Statistics Office of Vietnam The dataset comprises 36 observations, reflecting quarterly data from 2011 to 2019, which forms the basis for this longitudinal empirical study.
Thesis Structure
This thesis report is structured into four chapters, beginning with Chapter 2, which presents the theoretical background of the research topic It includes an overview of the VN-Index's history and a critical review of macroeconomic factors related to stock indices, establishing a solid theoretical foundation for the variables utilized in the study Additionally, this chapter will develop hypotheses that connect the primary macroeconomic factors to the VN-Index.
Chapter 3 outlines the research methodology, detailing the secondary data sources and variables utilized in the multiple model, alongside the analytical approach for data analysis It also justifies the selection of ordinary least squares modeling Chapter 4 presents and interprets the results of the secondary data analysis, highlighting regression outcomes that provide statistical evidence for the correlational relationships between the predicted macroeconomic factors and the VN-Index, while discussing new findings in relation to existing literature Finally, Chapter 5 concludes the thesis by summarizing key findings and their implications for theory and practice, as well as addressing limitations that future research could improve upon.
Chapter Summary
This first chapter has successfully formulated the overall tone for this empirical study by describing the context of the stock exchange market in Vietnam, namely
The Ho Chi Minh Stock Exchange (HOSE) exhibits a strong reliance on macroeconomic factors, as evidenced by the VN-Index, which serves as its stock market index This dependence underscores a key issue that this empirical study aims to investigate The research has established a clear primary aim and specific objectives, which will guide the progression of the study in the subsequent chapters.
15 ensuing chapter is going to cover the theoretical background and develop the hypotheses
LITERATURE REVIEW
Chapter Introduction
This chapter explores the theoretical foundations relevant to the empirical research topic, beginning with a definition of the stock market based on existing finance literature to aid general understanding It then introduces the stock index, its calculation, and reviews the development of the HOSE and its primary index, the VN-Index A systematic review of previous studies examines the influence of various macroeconomic factors on stock prices, establishing a theoretical basis for hypotheses linking five key macro factors—GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI)—to the VN-Index The chapter concludes by presenting the research model developed for this study.
Overview of Stock Market
The stock market serves as a fundamental component of the financial marketplace, facilitating the trading of medium and long-term securities It functions as a hub for gathering essential financial information that guides investors in making informed buying or selling decisions regarding the exchange of ownership in securities, including stocks and bonds From a behavioral standpoint, individuals with surplus funds are typically inclined to invest, seeking to enhance their earnings through stock market participation.
The stock market offers investors the chance to profit from securities transactions while serving as a platform for governments to raise capital by issuing bonds This process enables the government to gather funds from the public to invest in various projects (Fontana and Scheicher, 2016).
Stocks listed on the stock exchange must adhere to various legal requirements specific to their home market The stock price serves as a crucial financial metric for assessing a company's profitability and overall market performance (Fontana and Scheicher, 2016) Consequently, fluctuations in stock price provide significant insights into the company's future business cycle.
Brief of Stock Index and VN-Index History
The stock market index serves as a valuable tool for evaluating market performance, enabling investors to compare historical and current price levels It is typically calculated using a weighted average market capitalization method, where the current market price of selected stocks is multiplied by their outstanding shares to determine an average weight Market capitalization refers to the total value of a company's shares, calculated by multiplying the stock price by the number of shares outstanding, while each stock's weight is derived from its market capitalization relative to the total market capitalization.
This paper focuses on the VN-Index within the context of the Vietnamese stock market, which was established following the government's decision in July 1998 to open the first two securities trading centers in Ho Chi Minh City and Hanoi The Ho Chi Minh Securities Trading Center was renamed the Ho Chi Minh Stock Exchange (HOSE), serving as a centralized market, while the Hanoi Securities Trading Center became the Hanoi Stock Exchange (HNX) Initially, the number of listed companies was limited, but it has grown significantly, with HOSE now hosting larger firms and HNX primarily catering to small and medium-sized enterprises Companies must meet specific criteria to be listed, such as having a registered capital exceeding 120 billion VND (approximately $6 million) for HOSE, which is associated with the Initial Public Offering (IPO) process By the end of 2017, HOSE featured 366 stocks, along with 3 fund certificates and 45 bonds, with the VN-Index serving as the primary index for analysis in this study.
To be listed on the stock exchange, a company must meet specific legislative requirements, including having a minimum charter capital of 30 billion VND and operating as a shareholding enterprise for over one year Additionally, the company must demonstrate a Return on Equity (ROE) of above 5% in the preceding year (Hanoi Stock Exchange website, n.d.).
HOSE, as of 2017, HNX had totally 384 stocks (HOSE annual report, 2017) However, as this current paper is concentrated on VN-Index, HOSE will be the main focus in this study
The stock and security market in Vietnam plays a vital role in capital mobilization and funding, with two main exchanges facilitating investment activities for local firms and enabling the government to issue bonds This market significantly contributes to economic growth and the restructuring of the banking and financial sectors, enhancing fairness and transparency to attract foreign investments Listed commercial banks on these exchanges have successfully raised substantial capital through equity offerings, further promoting a transparent shareholding structure Additionally, the major stock markets have supported the equitization of state-owned enterprises (SOEs), aligning with the Vietnamese government's macro policy to reduce SOEs and foster the growth of private firms from 2001 to 2015.
Figure 2 VN-Index Historical Data
From 2000 to 2020, the VN-Index on the Ho Chi Minh Stock Exchange experienced significant fluctuations, notably declining in 2009 due to the global financial crisis but showing recovery starting in 2011 Between 2015 and 2017, there was a consistent increase in trading volume and market capitalization in Vietnam's securities market, with the total market capitalization reaching VND 3,515 trillion in 2017, representing 74.6% of the country's GDP This growth positions the Vietnamese stock market as one of the fastest-growing financial markets globally.
Table 1 Trading Volume in Vietnam Stock Markets from 2015 to 2017
The Vietnam stock markets exhibit significant volatility, particularly at the outset, driven by fluctuations in macroeconomic factors and the impacts of global recession.
The 2008 financial crisis highlighted significant limitations in the Vietnamese stock market, where investment options are restricted to stocks, government bonds, and a few investment funds, leading to a lack of diversification in investment portfolios Furthermore, the market suffers from a shortage of professional credit rating agencies and long-term investors, such as investment and retirement funds, which undermines overall market stability (Vo, 2016) Additionally, weak market discipline and inadequate enforcement of information disclosure obligations among participants reflect the low efficiency of the Vietnamese stock market (Vo, 2016).
2.4 Past Research about Impact of Macroeconomics on Stock Index
Stock prices have long been a central concern in financial markets, prompting extensive research into their determinants According to Al-Tamimi, Alwan, and Abdel Rahman (2011), these determinants can be categorized into internal and external factors While some researchers concentrate on organizational influences, others explore broader market conditions that impact stock valuations.
Recent studies indicate that while some investors focus on earnings to gauge stock price changes, others are increasingly considering external factors like inflation rates to better understand market dynamics (Al-Tamimi, Alwan, and Abdel Rahman, 2011).
Macroeconomic variables significantly influence stock prices, which in turn determine stock market indices The effects of monetary and fiscal policies are crucial, as government financial strategies impact macroeconomic factors and subsequently affect economic activities, including stock exchanges (Atiq, Rafiq, and Roohullah, 2010) This relationship has prompted extensive research into the connection between these macroeconomic determinants and stock prices Various studies identify key macroeconomic factors theorized to affect stock prices, such as foreign direct investment (FDI), gross domestic product (GDP), M2 money supply, inflation rate, interest rate, and consumer price index (CPI).
Numerous studies have explored the connection between money supply (M2) and stock prices, suggesting that changes in money supply can significantly influence stock price fluctuations Specifically, variations in the growth rate of money supply may serve as a leading indicator of stock price volatility While the deflated stock prices may respond to increased M2, this effect could take several months to materialize Additionally, government spending tends to stimulate the economy, which can further enhance share prices Overall, the relationship between money supply and stock prices is complex and multifaceted.
In the Malaysian stock market, M2 has a positive short-term impact on stock prices, while its long-term effects are negative Given that stock price fluctuations influence the economy, the government can utilize various monetary and fiscal policies to impact stock prices and, consequently, the stock index.
Research shows that stock prices typically increase when interest rates decrease, as lower rates reduce borrowing costs for companies, enabling them to finance projects and operations more affordably (Ferrer, Bolós, and Benítez, 2016).
Lower borrowing costs can lead to increased earnings as perceived stock values rise, establishing an inverse relationship between interest rates and stock prices—higher interest rates typically result in lower stock prices (Andrews, 2004) Researchers have also examined the impact of inflation on stock prices, noting that rising inflation creates uncertainty and diminishes investor confidence, making stocks less attractive during inflationary periods (Cleary, 2001) Consequently, higher inflation drives up interest rates, which further depresses stock prices, resulting in poor market performance (Andrews, 2004) This relationship is also supported by empirical evidence from the Chinese stock exchange market (Zhu, 1999) Additionally, Atiq, Rafiq, and Roohullah (2010) highlight that rising inflation increases costs, further complicating the investment landscape.
24 living increased and the propensity for saving increased as well, this leads to the reduction in investments in the stocks trading
The relationship between stock prices, inflation, and interest rates is complex and not always negative; changes in inflation can affect stock cash flows, but their impact on interest rates remains uncertain (Reilly and Brown, 2003) Additionally, the source of inflation can lead to positive effects on stock returns (Ewing, Forbes, and Payne, 2003).
Hypotheses Development: Impact of Macroeconomics on VN-Index
The literature review identifies five key macroeconomic factors—GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI)—that may predict stock prices and the stock market index Previous studies conducted in emerging markets like the UAE, Malaysia, and Ghana provide a strong theoretical foundation for this research, which focuses on the Vietnamese stock market This systematic review establishes the theoretical basis for exploring similar relationships within Vietnam, specifically examining how these five macroeconomic variables influence the VN-Index, the stock index of the Ho Chi Minh Stock Exchange, the largest in the country.
27 following hypotheses are then postulated in order to propose the relationships between these macro factors and VN-Index in the particular milieu of stock market in Vietnam:
Hypothesis 1: There is a relationship between gross domestic product growth rate and stock index
Hypothesis 2: There is a relationship between consumer price index and stock index
Hypothesis 3: There is a relationship between inflation rate and stock index
Hypothesis 4: There is a relationship between interest rate and stock index
Hypothesis 5: There is a relationship between foreign direct investment and stock index.
Conceptual Model
This research study utilizes a conceptual framework adapted from Tvaronavičiene and Michailova (2006), focusing on five macroeconomic factors: foreign direct investment (FDI), gross domestic product (GDP), M2 money supply, inflation rate, and interest rate, which serve as independent variables The VN-Index is identified as the dependent variable in this model This framework provides a foundational basis for gathering secondary data and conducting multiple regression analysis.
Source: Tvaronavičiene and Michailova (2006)
Chapter Summary
The second chapter focuses on establishing the theoretical framework for this empirical research paper by defining key financial concepts such as the stock market and stock index, thereby enhancing readers' understanding It includes a thorough review of the relationships between macroeconomic forces and stock prices and indices, drawing on extensive prior research across various contexts This systematic review is essential for laying the groundwork for the five main hypotheses proposed in this study, which link five critical macroeconomic factors.
This article examines the relationships between GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI) in relation to the VN-Index It concludes with a literature review that presents a conceptual model integrating these key factors and hypotheses The upcoming chapter will outline the methodology employed for this study.
RESEARCH METHODOLOGY
Chapter Introduction
This chapter outlines the research methodology, beginning with the justification for employing a quantitative method and a deductive reasoning approach for this empirical study It details the collection and utilization of secondary data sources pertinent to the research topic The chapter also defines the key variables, which include five independent variables: GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI), alongside the main dependent variable, the VN-Index, as illustrated in Figure 3 Additionally, the selection of a multiple regression model as an extension of the ordinary least squares (OLS) method is justified, and the analytical approach is thoroughly explained.
Method and Reasoning Approach
In social sciences, two primary research methods are utilized: qualitative and quantitative The qualitative method is exploratory, focusing on gathering unstructured, non-numerical data to understand the research problem (Babbie, 2012) Conversely, the quantitative method is descriptive, emphasizing the analysis of numerical data to outline key characteristics of the research issue (Babbie, 2012) Understanding these methods is essential for effective research design.
This study aims to quantify the correlations between five key macroeconomic factors and the VN-Index in Vietnam's stock market over the past decade The necessity for numerical data excludes qualitative methods, making quantitative methods the most suitable choice for collecting the required data to achieve the research objectives.
An essential component of the methodology is the reasoning approach that distinguishes between induction and deduction (Babbie, 2012) Induction emphasizes the development of new theories by gathering non-numerical data, which helps in formulating propositions that serve as the groundwork for constructing a new theoretical framework (Babbie).
This study does not aim to create new theories but instead relies on established macroeconomic and stock market theories, making an inductive approach unsuitable As Babbie (2012) notes, deduction is focused on evaluating existing theories by proposing and testing hypotheses, which aligns perfectly with the study's objective The research will test the correlational relationships between various macroeconomic factors and the VN-Index by developing hypotheses that support a deductive reasoning approach, as outlined in Chapter 2.
Secondary Data Source
This study focuses on finance-related research utilizing secondary data sources, specifically concerning the stock market and macroeconomic information in Vietnam Due to budget constraints, only publicly accessible data was collected from reliable online platforms, including the General Statistics Office of Vietnam, Investing.com, and FX Empire These platforms source their data from reputable institutions like the World Bank (WB) and the International Monetary Fund (IMF), ensuring the reliability of the information The research is limited to a nine-year period from 2011 to 2019, as earlier data is not available from these free sources.
This empirical study, based on secondary data, emphasizes the importance of ethics in research, particularly in social sciences (Babbie, 2012) It adheres to the principle of integrity by ensuring that all data is collected, analyzed, and reported ethically, without any falsification or fabrication.
The datafile has been securely stored on the principal investigator's personal computer to prevent unauthorized data leakage This study adheres to essential ethical guidelines for conducting finance-based empirical research.
Definition of Variables & Ordinary Least Squares
To examine the relationships between five macroeconomic variables and the VN-Index, a multiple regression analysis will be employed This method is suitable for assessing the correlations between multiple independent variables—namely GDP growth rate, CPI, interest rate, inflation rate, and FDI—and the dependent variable, VN-Index The selection of these independent variables is justified by existing literature that highlights their influence on stock market indices, particularly in developing economies, as discussed in Chapter 2, along with the availability of relevant secondary data Notably, the lack of quarterly data on Vietnam's money supply (M2) prevents its inclusion in the model, despite its potential significance as indicated in previous studies.
34 contexts In sum, the following equation presents the multiple regression model that includes those listed variables:
Regression model: VN-Index = β0 + β1*GDP + β2*CPI + β3*R + β4*r + β5*FDI β0: intercept
GDP: quarterly gross domestic product growth rate
CPI: consumer price index at the end of the quarter
R: quarterly inflation rate r: quarterly interest rate
FDI: accumulated amount of foreign direct investment (FDI) per quarter
The multiple regression model extends ordinary least squares (OLS) by incorporating multiple explanatory variables, making it a crucial statistical technique in econometrics, economics, and finance Specifically, multiple linear regression (MLR) is employed to estimate the linear relationships between various independent variables and a single dependent variable This approach is particularly suitable for identifying significant macroeconomic factors influencing the VN-Index from 2011 to 2019.
Regarding the drawbacks of OLS method, Field (2009) states that since ordinary least squares (OLS) is categorized into linear regression modelling, the
The implementation of Ordinary Least Squares (OLS) requires adherence to several key assumptions for effective linear regression analysis, including normality, multicollinearity, autocorrelation, and homogeneity of variance The subsequent section will provide a detailed examination of how these assumptions will be tested in this study.
Analytical Procedure
The secondary data collected for this study were stored in an Excel file and analyzed using SPSS, a widely used statistical tool in social sciences The dataset spans from January 1, 2011, to December 31, 2019, comprising 36 observations derived from four quarters over nine years This sample size is adequate for Ordinary Least Squares (OLS) regression analysis, as it exceeds the recommended minimum of 30, according to Hair et al (2018) Additionally, a 95% confidence level (p-value of 05) is adopted as the statistical standard for hypothesis testing, with hypotheses supported if the p-value is below 05 and rejected if above.
The data analytical process involves four key steps: first, collecting and storing data in an Excel file; second, inputting and cleaning the data using SPSS software; third, analyzing the data through various methods; and finally, interpreting and presenting the results.
In the analysis process, a total of 36 analyses will be conducted, focusing on reporting and interpreting the data outputs, with a sample datasheet provided in Appendix A Step 3 involves various tests to validate the three key assumptions of the multiple regression model: the skewness and kurtosis tests to assess normal distribution, the Pearson correlation matrix and variance inflation factor (VIF) to identify multicollinearity issues, the Breusch-Pagan and Koenker tests to evaluate homogeneity of variance, and the Durbin-Watson test to check for autocorrelation.
Chapter Summary
This chapter outlines the methodology for this empirical study, justifying the use of a deductive reasoning approach and a quantitative method to achieve the research objectives The quantitative method is essential for collecting numerical data to analyze the relationships between five macroeconomic factors—GDP growth rate, consumer price index (CPI), inflation rate, interest rate, and foreign direct investment (FDI)—and the VN-Index It details the acquisition of secondary data and the formulation of a multiple regression model, which extends ordinary least squares (OLS) and serves as the main empirical model for the research The rationale behind choosing the multiple regression model is thoroughly discussed, and the chapter concludes with a brief overview of the analytical procedure that will be elaborated in the subsequent chapter.
DATA ANALYSIS
Chapter Introduction
This chapter focuses on the results of the secondary data analysis, beginning with a detailed description of the data collection process The core section presents statistical outputs generated by SPSS, which are essential for testing the hypotheses formulated in Chapter 2 These outputs offer empirical evidence on descriptive statistics, collinearity, heteroscedasticity, normality, and crucially, regression results that quantify the correlations among variables The chapter concludes by discussing new findings in the context of existing literature in finance and economics research.
Data Collection
This research utilizes secondary data sourced from various publicly available platforms, specifically obtaining VN-Index data from Investing.com, which offers comprehensive historical data.
Since its inception in 2000, the VN-Index has been analyzed using quarterly data sourced from the General Statistics Office of Vietnam, specifically comparing the Consumer Price Index (CPI) with the same quarter of the previous year For instance, the CPI for Q1 2011 is based on the CPI from Q1 2010 Additionally, quarterly GDP growth rates, interest rates, inflation rates, and Foreign Direct Investment (FDI) inflows are obtained from FX Empire, a reputable financial portal that provides extensive macroeconomic data on Vietnam The GDP growth rate reflects quarterly percentage changes, while the inflation rate is recorded in March, June, September, and December The interest rate is determined by the State Bank of Vietnam during these same months, and FDI inflow is calculated as the total investment over three consecutive months within each quarter This analysis covers a nine-year period from January 1, 2011, to December 31, 2019, resulting in a dataset comprising 36 quarterly observations.
This study utilizes a dataset of 36 observations, which is derived from publicly available Internet-based secondary data sources As noted by Hair et al (2018), a minimum sample size of 30 is required for regression-based OLS analysis, making the actual sample size of 36 sufficient for robust data analysis.
Data Analysis
Table 2 presents the descriptive statistics of the secondary data collected for this study, detailing the minimum, maximum, mean (M), and standard deviation (SD) Due to the varying scales of measurement for each variable, direct comparisons of minimum and maximum values are challenging However, the coefficient of variation (CV), defined as the ratio of SD to M, serves as a useful metric for identifying outliers The consistently low CV values, all below 1, indicate low variance within the dataset and suggest that the collected data is free from outliers (Pallant, 2013).
Table 2 Descriptive Statistics of Data
One of the fundamental assumptions for conducting multiple regression analysis is the absence of multicollinearity This statistical issue occurs when two or more independent variables in the regression model are highly correlated, potentially jeopardizing the accuracy of coefficient estimations that rely on the assumption of a linear relationship among the predictors.
The Pearson correlational matrix presented in Table 3 is essential for assessing collinearity among variables in the dataset, with an absolute correlation coefficient above 0.8 indicating potential multicollinearity (Pallant, 2013) The analysis reveals that, aside from the correlations involving CPI, none of the other correlation coefficients exceed this threshold To further evaluate collinearity, the variance inflation factor (VIF) is also examined, as shown in the final column of Table 7, which confirms that all VIF values remain below the recommended cut-off of 10, as suggested by Hair et al.
(2009), except for CPI that is just slightly above this cut-off (its VIF of 10.7) This provides statistical evidence for basically disregarding the issue of multicollinearity in our regression model
GDP CPI Inflation rate Interest rate FDI
Source: SPSS outputs The problem of heteroscedasticity (or absence of homoscedasticity) is also carefully checked with both Breusch-Pagan test and Koenker test, utilizing the
The analysis utilizes robust standard errors of HC4, as outlined by Hayes and Cai (2007) Appendix B demonstrates that both the Breusch-Pagan and Koenker tests yield non-significant results (p < 05), confirming that the regression model is free from heteroscedasticity Furthermore, the normality assumption is validated through skewness and kurtosis testing, with results ranging from -3 to +3, which indicates that the data is normally distributed (Field, 2009), as shown in Table 4 below.
Table 4 Skewness and Kurtosis Normality Test
Autocorrelation refers to the correlation of a variable's values across different observations over time, posing significant challenges for longitudinal data analysis (Pallant, 2013) In our regression model, the presence of autocorrelation in the residuals may indicate potential issues with model specification, necessitating a thorough examination to ensure its accuracy.
The Durbin-Watson test, as shown in Table 5, is the standard method for evaluating autocorrelation at lag 1 in regression analysis residuals Our multiple regression analysis yielded a Durbin-Watson value of 1.393, which is near the ideal value of 2, indicating the absence of autocorrelation Values approaching 0 suggest positive autocorrelation, while those near 4 indicate negative autocorrelation (Field, 2009) Therefore, we can confidently conclude that autocorrelation is not present in our multiple regression model.
Table 5 Durbin-Watson Test for Autocorrelation
In the absence of multicollinearity, heteroscedasticity, and autocorrelation, and with a normal distribution, the regression outputs in Table 6 can be easily presented and interpreted While the Pearson correlation matrix in Table 4 illustrates the sign and magnitude of correlations between pairs of variables, it does not capture the simultaneous relationships among all five independent variables and the dependent variable Consequently, a multiple regression analysis is conducted to obtain the coefficients for these variables concurrently, as shown in Table 6, which details the correlations of the five independent factors with the VN-Index.
Table 6 Multiple Regression Model Outputs
Hypothesis 1 is supported, indicating a significant positive relationship between GDP growth rate and the VN-Index, with a correlation coefficient of β = 439 at a 95% confidence level (p < 05).
Hypothesis 2, which suggested a relationship between the consumer price index (CPI) and the stock index, was not supported due to a statistically non-significant correlation at the 95% confidence interval (p > 05) Consequently, the CPI is deemed a non-significant factor that does not relate to the VN-Index.
Hypothesis 3, which suggested a link between the inflation rate and the stock index, is not supported due to the lack of statistically significant correlation at the 95% confidence level (p > 05) Consequently, the inflation rate is deemed a non-significant factor with no relevance to the VN-Index.
Hypothesis 4, which assumed that there is a relationship between interest rate and stock index, is supported because the correlation is statistically significant at
95% level of confidence and the standardized coefficient is negative (β = -.715, p
< 0.05) Hence, interest rate has a significantly negative relationship with VN- Index
Hypothesis 5 is supported, indicating a significant relationship between foreign direct investment (FDI) and the stock index, with a positive correlation observed at a 95% confidence level (β = 294, p < 05) This demonstrates that FDI positively influences the VN-Index.
Model fit is a crucial statistical indicator in regression analysis, reflecting how well the data aligns with the model (Field, 2009) As shown in Table 7, the coefficient of determination (R²) for our multiple regression analysis indicates a strong fit The adjusted R² value of 0.664 is particularly significant, as it accounts for both sample size and the number of predictors, making it a more reliable measure (Berenson, Levine, and Krehbiel, 2012) This suggests that approximately 66.4% of the variance in the VN-Index can be attributed to five key macroeconomic factors, highlighting a substantial explanation of variability in the index.
Discussion
The relationship between macroeconomic conditions and the VN-Index in Vietnam shows a strong correlation, confirming findings from previous research Among the five macroeconomic factors analyzed, GDP growth and foreign direct investment (FDI) positively influence the VN-Index, while interest rates have a negative effect Surprisingly, the Consumer Price Index (CPI) and inflation rate do not significantly impact the VN-Index, aligning with Triyono and Robiyanto's (2017) study on Indonesia's Jakarta Composite Stock Price Index This suggests that Vietnam's stock market remains largely unaffected by fluctuations in consumer pricing and inflation levels, as noted by Huang and Liu (2005).
Chapter Summary
Chapter four focuses on the analysis and interpretation of empirical data results It begins with a detailed description of the data collection process, followed by an overview of SPSS statistical outputs, which include descriptive statistics, normality, collinearity, and heteroscedasticity These outputs provide empirical evidence for testing five hypotheses in the study The chapter concludes with a critical discussion that compares the new findings to existing research The next chapter will present conclusions and implications for both theory and practice.
CONCLUSION
Chapter Introduction
This chapter concludes the thesis by summarizing the key findings from the empirical data regression analysis presented earlier It emphasizes the significant theoretical implications that this research contributes to the existing literature in finance and economics These findings serve as a benchmark for future studies in the field.
47 recommendations to both investors and policy makers in Vietnam, especially during this sensitive time caused by the COVID-19 pandemic Eventually, some limitations are acknowledged and discussed for future endeavours.
Key Findings and Theoretical Contributions
The previous chapter revealed intriguing empirical findings, indicating that inflation rate and Consumer Price Index (CPI) are not related to the VN-Index, which contradicts earlier studies suggesting a negative correlation between these factors and stock indices This lack of connection can be rationalized through macroeconomic perspectives, as CPI and inflation are closely linked The CPI measures the average price changes for a basket of goods and services, and a decrease in CPI typically signals reduced inflation Consequently, lower CPI and inflation rates can lead to decreased consumer spending and increased savings, theoretically benefiting public firms through higher earnings and stock prices However, this theoretical framework does not hold true within the context of Vietnam.
This study confirms the negative impact of interest rates on the VN-Index, aligning with numerous prior research papers that highlight this adverse relationship in various contexts.
Research indicates that the GDP growth rate and foreign direct investment (FDI) inflows significantly influence the VN-Index, reinforcing findings from previous studies that highlight GDP and FDI as key macroeconomic factors driving stock prices.
In the Vietnamese stock market, the GDP growth rate and foreign direct investment (FDI) are crucial factors that significantly influence the VN-Index, alongside interest rates.
This paper significantly contributes to the existing scholarship in economics and finance by addressing the inconsistent findings of previous studies on the impact of macroeconomics on the VN-Index in Vietnam By utilizing a comprehensive dataset spanning from 2011 to 2019, this research provides a more accurate analysis of the relationship between macroeconomic factors and stock index performance over an extended period Additionally, it offers a fresh perspective on stock index performance in an Asian developing market, highlighting the distinct macroeconomic differences that challenge the applicability of findings from Western and developed markets.
49 of macroeconomics on stock index in an Asian emerging market would provide up-to-date knowledge and novel rigor to the literature scholarship in stock index.
Managerial Implications
Investors and policymakers in Vietnam should closely monitor interest rate changes from the State Bank of Vietnam, as these rates significantly impact stock prices and the VN-Index Additionally, national GDP performance and FDI inflows are crucial indicators for investors Due to the current economic downturn from the COVID-19 pandemic, both GDP and FDI have declined compared to previous years Therefore, investors should exercise caution during this volatile period, as the stock market may experience significant fluctuations influenced by the global financial landscape.
To stabilize the VN-Index, policymakers must carefully evaluate both monetary and fiscal policies, particularly during the pandemic's challenges It is essential to tighten stock market regulations to protect local exchanges while creating favorable conditions to encourage investment, as investors tend to favor savings during crises Additionally, implementing macro-policies that promote local economic growth, especially by providing incentives for service sectors like tourism, travel, and hospitality, as well as supporting exports, is crucial for local enterprises.
To enhance GDP growth, Vietnam must capitalize on its export activities, as GDP is influenced by consumption, government spending, investment, and net exports With a significant outflow of foreign direct investment (FDI) from China, Vietnam is positioned as a promising alternative destination The Vietnamese government should implement attractive incentives to draw multinational corporations (MNCs) from the US, South Korea, and Japan, especially given the competitive landscape posed by India Additionally, Vietnam's stock market is affected by global trends, necessitating local regulators to monitor major Asian stock exchanges like Hong Kong, Tokyo, and Shanghai to make timely adjustments in response to regional market fluctuations.
Limitations and Suggestions for Future Improvements
This paper acknowledges several limitations that should be addressed in future research Firstly, as a secondary research study, the findings are heavily reliant on the quality of the secondary sources used, which may lead to inconsistencies in the dataset due to the varied origins of the macroeconomic data To enhance reliability, future researchers should aim for a more consistent integration of data from multiple secondary sources Secondly, while the HOSE stock market has been operational since 2000, the data utilized only spans from 2000 to 2019 A more extensive dataset covering the full two decades could yield more accurate and insightful results, but the limited availability of comprehensive data presents a challenge.
51 free and public secondary data, this paper could only use the data from 2011 to
The 2019 study, based on 36 observations, may limit the statistical power of the regression analysis Future research should consider expanding the dataset by collecting data over an extended period The findings are specifically relevant to companies listed on the VN-Index in HOSE and may not apply to those on the HNX or other emerging markets due to institutional differences This suggests a potential avenue for future studies to compare the effects of macroeconomic factors on both the VN-Index and HNX-Index within Vietnam.
Chapter Summary
This final chapter presents the conclusions of the empirical study, summarizing key findings from Chapter 4 that underscore theoretical implications and offer valuable recommendations for local investors and policymakers in Vietnam, particularly in light of the ongoing COVID-19 pandemic Additionally, it discusses limitations of the research, highlighting areas for future improvement.
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