VIETNAM NATIONAL UNIVERSITY UNIVERSITY OE ECONOMICS AND BUSINESSFACULTY OF FINANCE AND BANKING Graduation Thesis THE RELATIONSHIP BETWEEN ECONOMIC POLICY UNCERTAINTY AND VIETNAMESE STOCK
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- Time scope: Effect of Economic Policy Uncertainty on the stock market between the
Ist quarter of 2002 to the first quarter of 2023
- Space scope: Studying the influence of economic uncertainty policy on the Ho Chi
Minh City Stock Exchange (HoSE).
- (1) How does Economic Policy Uncertainty affect the Vietnamese stock market and its dynamics?
- (2) What is the difference between the impact of domestic and international EPUs on
- (3) What macroeconomic factors affect the relationship between Economic Policy
Uncertainty and the stock market?
Through the study, the author is expected to make the following contributions:
First, overview, systematize the theory on the relationship between Economic Policy Uncertainty and the stock market.
The study analyzes the relationship between Economic Policy Uncertainty and the stock market using panel data models such as POLS, FEM, and REM Furthermore, it assesses how the Economic Policy Uncertainty index influences the profitability ratio of the stock market.
In particular, the study tests that the EPU index has a negative relationship with the profitability of the Vietnamese stock market.
The study presents key recommendations for the government, investors, and securities companies aimed at enhancing investor knowledge and improving market transparency.
For the purpose of the study, this thesis was formed in the following way, which includes 5 chapters.
The structure of the rest of the thesis will be as follows In Chapter 2 discuss related literature and presents the theoretical framework of EPU and Stock market The author
9 illustrates the research data and models specification Chapter 3 Chapter 4 presents empirical results Chapter 5 is the conclusion.
LITERATURE REVIEW AND THEORETICAL BASIS ON THE
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“* Economic Policy Uncertainty impact on stock market
Li et al (2015) investigate the impact of Economic Policy Uncertainty (EPU) on stock market volatility, revealing that increased EPU correlates with heightened market volatility Their out-of-sample analysis indicates that adding EPU as a predictor enhances the accuracy of existing volatility prediction models, significantly boosting their predictive capabilities.
Jun et al (2015) investigate the relationship between Economic Policy Uncertainty and stock prices using time and frequency domain analysis Their wavelet analysis reveals a predominantly negative correlation that varies over time, indicating cycles of low to high frequency Additionally, the timing of these frequency changes aligns when U.S policy uncertainty interacts with the policy uncertainty of other nations.
The study by Saud et al (2019) reviews the literature on the effects of Economic Policy Uncertainty (EPU) on corporations and the global economy, emphasizing the necessity of measuring and monitoring uncertainty due to its significant influence on financial decisions It highlights the increasing reliance on the Baker et al (2016) EPU index as a crucial metric for assessing uncertainty and explores its effects on financial markets, corporate behavior, and risk management at both macro and micro levels The findings indicate that policy uncertainty notably impacts corporate financial strategies and consumer spending, with companies adopting a more cautious approach during periods of heightened uncertainty, which in turn slows down investment in production and employment Additionally, the local ramifications of EPU extend beyond national borders, affecting economies worldwide.
A study by Phan et al (2018) analyzes data from 16 countries to determine if Economic Policy Uncertainty (EPU) can predict surplus stock returns The findings reveal that the effectiveness of EPU in forecasting stock returns varies by country and industry, highlighting its significance in certain sectors over others This suggests that earnings forecasts are influenced by both geographical and sectoral factors, indicating that EPU plays a more crucial role in some contexts than in others.
The article demonstrates that the predictive power of the Economic Policy Uncertainty (EPU) stems from both cash flow and discount rate channels, with stronger support for the discount rate channel It investigates the impact of positive and negative EPU shocks on excess stock returns, revealing evidence of asymmetric predictability Furthermore, the study considers a mean-variance investor, illustrating that such an investor gains positive utility from projections derived from the EPU-based model The findings are validated through various durability tests, reinforcing the article's conclusions.
A study by Umer et al (2019) investigates the impact of Economic Policy Uncertainty (EPU) on the performance of non-financial companies in the United States The research utilizes four performance metrics: Return on Assets, Return on Equity, Net Profit Margin, and Tobin's Q, revealing a significant and negative correlation between EPU and company performance across all measures To address endogeneity issues, the authors employ Systematic-GMM estimation, as preliminary findings indicate the presence of variable variance and autocorrelation in Ordinary Least Squares (OLS) and fixed effects estimates.
The study by Xu et al (2021) analyzes the predictive capabilities of the China Economic Policy Uncertainty Index (EPU) created by Davis et al (2019) regarding profit forecasts in the Chinese stock market Utilizing both univariate and two-variable predictive regression models, the research demonstrates that the monthly EPU significantly negatively influences stock returns for the following month Furthermore, the EPU outperforms existing EPU measures and various macroeconomic indicators in out-of-sample predictions Notably, the study reveals that the EPU's predictive power diminishes rapidly during periods of heightened uncertainty associated with special events.
The study by Chen et al (2018) examines how China's Economic Policy Uncertainty (EPU) influences expected returns in the Chinese stock market Utilizing a news-based measure of EPU, the research reveals that increased uncertainty negatively predicts future stock market returns across different time horizons This significant negative correlation persists even when accounting for various economic and market uncertainties or through out-of-sample testing The findings align with behavioral asset pricing models, suggesting that heightened uncertainty exacerbates behavioral trends and leads to speculative mispricing, particularly in short selling.
Kaan et al (2019) conducted a comprehensive study examining the influence of macroeconomic factors, German government bond yields, sentiment, and other leading indicators on the DAX30 index from 1991 to 2018 Analyzing a dataset comprising 24 factors over nearly 27 years, the research revealed significant evidence that the Composite Leading Indicator (OECD) and the ifo Export played crucial roles across various subsamples.
The Expectations Index, ifo Export Climate Index, exports, Consumer Price Index (CPI), and 3-year German government bond yields exhibit delayed effects on stock returns Additionally, the influence of the monetary aggregate M2 constituents on stock returns shifted direction between the crisis and post-crisis periods Overall, the findings indicate that during the crisis, a greater number of economic indicators significantly affected stock returns compared to the pre- and post-crisis periods, suggesting a macro-driven market dominates in the post-crisis phase.
Chang et al (2019) aim to investigate the short-run and long-run effects of macroeconomic variables, including industrial production, foreign direct investment (FDI), trade balance, exchange rate, interest rate, and consumer price index, on the stock prices of the KSE-100 index Additionally, the study explores how these relationships may be altered by the occurrence of a financial crisis.
Bhuiyan et al (2019) explore the impact of macroeconomic variables on various sectors of the stock market in the US and Canada, utilizing monthly data from 2000 to 2018 Their cointegration analysis reveals a stable long-term relationship between industrial production, money supply, long-term interest rates, and sector indices in the US, while such a relationship is absent in Canada Interestingly, US money supply and interest rates can still explain movements in the Canadian stock market These findings provide valuable insights for private investors, pension funds, and governments, as long-term investment decisions are often influenced by these macroeconomic factors.
Kofi et al (2019) investigate the relationship between stock prices and market variables (MVs) across different sectors, utilizing Random Forest (RF) with an enhanced leave-one-out cross-validation method and Long Short-Term Memory Recurrent Neural Network to forecast stock prices 30 days ahead Their empirical analysis on the Ghana Stock Exchange (GSE) demonstrates superior prediction accuracy and lower mean absolute error compared to traditional time-series methods The findings suggest that the proposed model effectively identifies and extracts MVs influencing various sector stocks, enabling accurate predictions of future stock prices.
“* Uncertainty in the market impact on Vietnam's economy
A study by Ly et al (2020) investigates how China's Economic Policy Uncertainty influences corporate cash holdings in six Southeast Asian countries, highlighting the growing interconnectedness of the Chinese economy with its neighbors The research, based on company-level data from 2010 to 2018, reveals that heightened uncertainty in China's economic policies leads to reduced cash holdings among Southeast Asian firms Furthermore, the findings indicate that Economic Policy Uncertainty mitigates the adverse effects of investment on cash reserves These results remain robust across various measures of China's Economic Policy Uncertainty and different estimation methods.
Nguyen et al (2023) investigated how Economic Policy Uncertainty affects stock volatility forecasts using the GARCH-MIDAS model This model effectively integrates low-frequency economic policy uncertainty with high-frequency securities returns to predict stock index volatility Their findings indicate that both the magnitude and variance of the economic policy uncertainty index are valuable for forecasting the Vietnam stock index's volatility Moreover, the results demonstrate that incorporating economic policy uncertainty into the GARCH-MIDAS model significantly enhances the predictability of stock index movements.
Overview of Economic Policy UnC€TfAITVY . s5 s3 vs seeeesresree 16 2.3.2 Theory regarding the effect of Economic Policy Uncertainty on the stock market TOCUINS eee eee ẻ
2.3.1.1 The concept of uncertainty in economic policy
Economic Policy Uncertainty (EPU) arises from various factors, including inflation uncertainty, negative economic growth, financial crises, significant loan reductions, pandemics, rising unemployment, foreign exchange volatility, and sudden shifts in monetary policy It is characterized by unpredictable changes in monetary, fiscal, and regulatory policies, primarily stemming from concerns about potential future policy alterations EPU encapsulates the unpredictable impacts that new policies may have on the economy and the private sector.
In recent years, global political and economic instability has intensified due to several major challenges The "Arab Spring" sparked significant political upheaval in the Middle East, while Donald Trump's presidency introduced calls for substantial shifts in the global order Additionally, the UK's decision to leave the European Union, known as "Brexit," has cast doubt on the future of the Euro and European economic policies As these developments unfold, they contribute to a growing sense of uncertainty and instability worldwide.
Since the publication of John Kenneth Galbraith's book “The Age of Uncertainty” in
Since 1977, the media and academia have emphasized uncertainty as a critical issue in finance, yet a unified definition remains elusive Despite its recognized significance, the impact of uncertainty on corporations has only recently begun to be explored Factors contributing to this uncertainty include geopolitical events, industry-specific developments, and firm-specific news, such as unclear sales forecasts or rumors regarding the departure of a capable CEO.
Economic uncertainty refers to the unpredictability stemming from changes in fiscal, regulatory, and monetary policies, which significantly contribute to market volatility It encompasses unexpected shifts that impact the economic ecosystem, influencing how corporations respond to alterations in government policies.
Policy uncertainty refers to the economic risks stemming from unclear future government policies and regulations, leading to delayed spending and investment by businesses and individuals Following the 2008 global financial crisis, research by Baker et al (2016) highlights that uncertainty surrounding government policies peaked, causing businesses and households to hesitate in their investment and consumption decisions This ambiguity in future regulatory frameworks, including spending, taxes, monetary policies, and healthcare, has notably hindered recovery from the recession Factors influencing uncertainty vary in their effects; some, like currency fluctuations and changes in management, impact both short and long-term uncertainty, while others, such as oil price variations, primarily affect the short term Understanding the time horizon of these determinants is crucial for measuring the uncertainties they create.
Uncertainty encompasses the ambiguity and unpredictability surrounding future business prospects, influenced by the judgments of various stakeholders, including customers, corporate executives, and policymakers It also refers to unpredictable changes that impact the economic system, leading to shifts in government policies This volatility stems from unforeseen economic, political, and monetary factors, highlighting the complex nature of economic policy uncertainty (Saud et al., 2020).
Increasing uncertainty significantly impacts investment and economic development, leading to long-term consequences (Sahinoz et al., 2018) Therefore, it is crucial to develop a metric that accurately represents the level of national uncertainty and its tangible effects on the overall economy.
2.3.1.2 Measuring uncertainty in economic policy
Various factors significantly influence economic policy uncertainty Long-term issues like currency fluctuations and changes in top management contribute to ongoing uncertainty, while factors such as oil price changes primarily create short-term impacts.
Understanding the impact of uncertainty determinants hinges on distinguishing between short-term and long-term effects It is essential to develop methods for measuring Economic Policy Uncertainty arising from diverse factors.
The standard deviation of stock prices and returns is a long-established measure of market volatility The Market Volatility Index (VIX), developed by the Chicago Board of Options Exchange, has served for years as a proxy for corporate uncertainties in the stock market However, as a market metric, the VIX only reflects market uncertainty and is influenced by market liquidity and depth, making it most effective in mature markets and industries, with limited applicability in emerging economies.
Recent research highlights the significance of political uncertainty, particularly during election years Julio and Yook (2012) suggest using a dummy variable to account for this uncertainty, while Jaramillo et al (2007) and Jurado et al (2015) employ econometric techniques to quantify it, linking macroeconomic instability to election-related uncertainty Jurado et al introduce a macroeconomic instability index, which incorporates various economic and financial indicators Although these measurements effectively underscore the diverse factors influencing future uncertainty, they often focus on limited aspects, making them less applicable across different contexts This limitation has led researchers to seek more comprehensive measures that encompass the multitude of elements contributing to overall uncertainty.
Baker et al (2016) were inspired by previous studies to create a comprehensive Economic Policy Uncertainty (EPU) index that encompasses various influential factors This EPU index effectively measures uncertainty derived from news, policy, market, and economic indicators The authors formulated the index by averaging three key components: the volume of newspaper coverage on policy-related economic uncertainty, the number of imminent expirations in federal tax code provisions, and the level of disagreement among economic forecasters They emphasize that the prominence of news coverage in reputable journals concerning economic uncertainty is particularly significant.
The EPU index is primarily shaped by 18 policy factors, prompting Baker et al (2016) to refine the index to focus exclusively on news content This refined approach involves measuring the presence of key terms such as “economic,” “economy,” and “uncertainty” in newspaper articles.
“uncertain” along with “regulation” and “legislation” and one or more of the following terms:
“congress” ,” “legislation”, “white house”, “regulation”, “federal reserve” or “deficit”.
The Economic Policy Uncertainty (EPU) index, derived from various indicators including media references to policy uncertainty, effectively reflects periods of significant uncertainty such as wars, debt ceiling debates, and financial crises like the Eurozone crisis and TARP legislation This index shows a strong correlation with the VIX, enhancing its relevance in economic research Its availability has ignited scholarly interest and facilitated a range of research inquiries, leading to its widespread acceptance in the academic community Building on the work of Baker et al., Davis (2016) developed a global EPU index by employing a weighted average of data from major economies, asserting that these nations provide a comprehensive view of global economic uncertainty.
The Economic Policy Uncertainty (EPU) significantly influences both micro- and macro-economic policies, particularly through uncertainties surrounding monetary and fiscal policies that impact the overall economy In financial markets, such uncertainties can affect exchange rates and trading strategies, potentially leading to high returns during periods of instability Furthermore, EPU can diminish the effectiveness of monetary policy changes and plays a crucial role in forecasting economic growth and future recessions The degree of uncertainty affects employment based on industry exposure to economic policies and international trade, highlighting the need to consider labor relocation trends and aggregate output in predicting macro-economic indicators Additionally, uncertainty regarding future corporate earnings holds substantial predictive power for GDP, as well as influences production, employment, and banking policies.
Other theories concern the relationship of Economic Policy Uncertainty and the
The Efficient Market Theory holds significant theoretical and practical importance in the financial industry, with economist Paul Samuelson noting its critical role by stating that it represents half of the "crown jewels" of financial economics.
The efficient markets hypothesis (EMH), was first put forward by Eugene Fama
The article from 1970 significantly contributes to the understanding of the Efficient Market Theory by emphasizing the concept of "efficiency" as the swift assimilation of information rather than the optimal use of resources It highlights that information, particularly news that can influence prices, is inherently unpredictable, setting the foundation for subsequent research in this area.
Efficient capital markets can be defined in various ways, with Fama (1970) describing them as markets where prices consistently reflect all available information Similarly, Malkiel (1992) asserts that a capital market is efficient if it accurately incorporates all relevant information into stock prices Generally, a market is considered efficient when security prices remain unaffected by the disclosure of certain information to participants.
The study of stock market efficiency has gained popularity since the 1960s, originating from the research of French mathematician Louis Bachelier This concept explores whether stocks and commodities exhibit random fluctuations, a question that has intrigued researchers since Karl Pearson's work in 1905.
The concept of the random walk, also known as the Drunkard's walk, was introduced in 1900, but initial efforts by Bachelier and Pearson were largely overlooked until the 1930s, when significant analyses and studies on Efficient Market Theory began to emerge.
Kendall (1953), who first used the term random walk in financial theory, observed 22
UK stock indexes and US commodity prices exhibit regular price cycles, often resembling a random walk where daily fluctuations can vary unpredictably Prices may rise or fall independently of previous trends, highlighting the inherent volatility in market movements.
Roberts (1959) finds similar results for the US indices He confirmed that changes in the Dow Jones are random Osborne (1959) demonstrated that US stocks move randomly like particles.
In his 1965 doctoral thesis presented at the University of Chicago Management Conference, Fama introduced the random walk theory, positing it as an accurate reflection of market behavior He contrasted this theory with traditional methods of stock price prediction, such as fundamental and technical analysis, which he deemed overly complex for non-mathematicians Fama argued that technical analysis relies on the premise that historical price patterns repeat, but he maintained that past price changes are independent, making extraordinary returns unattainable through this approach Meanwhile, fundamental analysis is based on the notion that a security's intrinsic value may differ from its market price, allowing analysts to forecast future prices by evaluating factors like management quality, industry conditions, and economic variables.
In his landmark 1970 paper, Fama introduced the concept of efficient markets, defining an efficient market as one where prices consistently reflect all available information This article provides a thorough review of Fama's theory, emphasizing its significance in understanding market behavior.
In the subsequent years, numerous studies emerged to examine and validate the claims surrounding Efficient Market Theory, focusing on its practical implications The efficiency of stock markets remains a contentious topic across various countries.
Efficient markets include many different hypotheses, depending on the extent to which information is reflected in security prices.
The weak form efficiency hypothesis posits that a security's price incorporates all historical information, including past stock prices and trading volumes, making it impossible to predict future prices based solely on this data It suggests that current market prices reflect all past earnings and market information, leading to the conclusion that historical returns are independent of future rates of return Empirical evidence supports the notion that markets often exhibit weak efficiency, with subsequent price changes appearing random and showing little correlation between today's stock price and the next day's price Consequently, past prices and trading volumes do not aid in forecasting future price changes, rendering technical analysis ineffective.
The semi-strong form efficiency hypothesis asserts that a security's price incorporates all publicly available information, including historical price data and details from an issuer's prospectus This level of market efficiency encompasses the weak efficiency hypothesis, indicating that all market information—such as stock prices, interest rates, and transaction volumes—must be publicly acknowledged Additionally, public information encompasses non-market factors like earnings announcements, dividend payouts, price-to-earnings ratios, and political economic data According to this hypothesis, investors cannot achieve above-average returns when making decisions based on newly released information, as stock prices quickly adjust to reflect all publicly available data While a moderately efficient market implies weak efficiency, it can still exhibit weak efficiency before reaching optimal efficiency levels, allowing investors to potentially earn extraordinary returns based on public information However, the quicker the market reacts to such information, the lower the potential profits for investors.
The strong form efficiency hypothesis posits that stock prices fully reflect all information, including non-public data from within the business This hypothesis integrates the principles of both weak-form and semi-strong efficiency, suggesting that efficient markets not only incorporate publicly available information but also have access to all information simultaneously In a strongly efficient market, moderate efficiency is a prerequisite; however, it is possible for a market to exhibit moderate efficiency without being highly efficient.
24 efficient At that time, non-public information can be used to generate extraordinary profits, but once this information is made public, extraordinary profits cannot be achieved.
The method of principal component analysis (PCA) proposed by Karl Pearson in
Principal Component Analysis (PCA), developed by Hotelling in 1933 and later refined by Jolliffe in 2002, is a technique for transforming data into a new coordinate system, where the new variables, known as principal components, are linear combinations of the original variables This method effectively reduces the complexity of the data set by focusing on its essential components and eliminating unnecessary information In the context of behavioral finance, the momentum effect observed in the market arises from investors' delayed reactions, which occur in the short to medium term and can lead to overreactions and subsequent reversals in the long term.
Hong and Stein (1999) introduced a behavioral model featuring two types of traders: newswatchers, or news investors, and momentum traders, or trend investors This model illustrates how private information disseminates slowly among news traders, prompting a gradual correction in stock prices that creates momentum without surpassing fair value As prices begin to move, momentum traders enter the market, amplifying the momentum effect Eventually, the increased activity from trend-followers drives the market price above fair value, resulting in an overreaction and subsequent reversal This phenomenon was further evidenced by Hong et al (2000) in their analysis of the US stock market.
Momentum in investing is driven by conservative sentiment among investors, as noted by Barberis et al (1998) and Doukas & McKnight (2005) This mentality leads investors to cling to past beliefs, undervalue new information, and resist making significant behavioral adjustments Consequently, their reluctance to adapt results in a sluggish response to changing market conditions.
Some other theoretical bases on factors affecting the stock market
Inflation occurs when there is an increase in the money supply relative to the availability of goods and services, leading to higher prices Research on the relationship between inflation rates and stock returns has yielded mixed findings Some studies, such as those by Chen et al (1986) and Fama and Schwert (1977), indicate a negative correlation, where higher inflation rates result in lower stock returns Conversely, other research, including work by Binswanger and Fare (1989), suggests a positive correlation between inflation and stock returns over the long term.
27 and Senhadji (2001) found that inflation rate has a positive effect on stock returns in the short run.
The exchange rate represents the relative value of foreign currencies, indicating the price of one country's currency in terms of another Fluctuations in exchange rates significantly influence the stock market by affecting import costs, inflation, and investor sentiment Research reveals mixed results regarding the relationship between exchange rates and stock market performance; some studies, like those by Fatum and Hutchison (2002) and Nasseh and Strauss (2000), indicate a negative correlation, particularly in emerging markets Conversely, other research, including findings by Bollerslev et al (1992) and Eun and Shim (1989), suggests a positive correlation in various countries, demonstrating that exchange rate volatility can enhance stock market performance in both developed and developing economies.
The Price to Earnings (P/E) ratio is a widely used metric for assessing a company's stock value Research indicates a correlation between P/E ratios and stock market performance; notably, Yardeni (2019) discovered that lower P/E ratios are often associated with strong market performance, whereas higher P/E ratios correlate with weaker performance Additionally, studies by Hussain et al reinforce this relationship, highlighting the significance of P/E ratios in investment decisions.
Research indicates varying correlations between the price-to-earnings (P/E) ratio and stock market returns across different countries A study conducted in Pakistan in 2019 revealed a negative correlation between the P/E ratio and stock market returns In contrast, research by Lai and Chen in 2016 demonstrated a positive impact of the P/E ratio on stock market returns in Taiwan Additionally, Chandra et al (2012) identified a positive relationship between the P/E ratio and stock market performance in India.
Earnings per share (EPS) is a crucial financial metric that indicates a company's profitability by dividing net income by the number of outstanding shares Numerous studies have explored the correlation between EPS and stock market performance, revealing significant insights into how EPS influences investor decisions and stock valuations.
28 positive correlation between EPS and stock market performance For example, Kao et al.
Research indicates a mixed relationship between Earnings Per Share (EPS) and stock market returns across different regions A study by 2015 demonstrated a positive impact of EPS on stock market returns in Taiwan, while Fung and Yau (2014) reported similar findings in Hong Kong Conversely, other studies, such as Martinez et al (2016), identified a negative correlation between EPS and stock market performance in Spain This highlights the varying effects of EPS on market returns depending on the geographical context.
(2018) found that there is a negative relationship between EPS and stock market returns in Malaysia.
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Research design is a structured framework aimed at answering specific research questions and managing variance (Dulock, 1993) It is essential to establish the research method prior to data collection to effectively achieve research objectives This framework can be developed through either qualitative or quantitative approaches In this study, a quantitative approach will be utilized, focusing on the relationships between variables through the analysis of historical data.
The quantitative approach offers several advantages, including repeatability, which minimizes disputes regarding its validity (Devault, 2020) Its straightforward nature reduces the likelihood of errors, and the data collection and analysis processes remain unbiased This approach is characterized by its use of deductive logic to test theories and determine their applicability, resulting in data points that support standard arguments Additionally, the quantitative method aims to generalize findings through the use of large and random samples (Wright et al., 2016).
The study is a combination of two methods of qualitative research and quantitative research:
The author will utilize a qualitative method by gathering reputable documents and reviewing previous research papers relevant to the topic, which will provide in-depth information about the research subject This approach will establish a solid theoretical foundation and a well-reasoned argumentative framework for the study.
The quantitative method involves modeling variables using POLS, FEM, and REM models after gathering essential information and data sets This approach aims to analyze the impact of independent variables on the dependent variables within the model.
3.2 Econometric model: Panel Regression Model
To test the appropriate regression model, the author follows the following steps:
- Step 1: Perform regression with Pooled OLS model Then, perform a defect test in the model:
The Pooled Ordinary Least Squares (OLS) model, as noted by Alam (2020), is widely utilized for analyzing panel data This model estimates data similarly to cross-sectional models, but it overlooks variations in dimensions By pooling data from various individuals, the Pooled OLS model does not account for individual differences that may affect coefficient values To mitigate these coefficient discrepancies, certain assumptions are made, including the constancy of intercepts and slopes across individuals, consideration of heterogeneity, and the absence of time effects The model's equation encapsulates these principles.
The equation Rự = a + B,EPU + B2PE + B3EPS + BẠDP + B3DI + Bg FI + B7TO + sự represents a model where 'i' denotes individual units and 't' indicates time periods In this context, Rj signifies the observation of the individual 'i' at time 't', while R; denotes the independent variable for the same individual and time The model includes an intercept (o) and coefficients (f), which are assumed to be constant over time, implying no time effect Additionally, pit represents the error term in the model, capturing the variability not explained by the independent variables.
+ Test for multicollinearity: Using the coefficient VIF
- Step 2: Perform regression with two models FEM and REM.
The Fixed Effect Model (FEM) builds upon the Pooled OLS model, which posits that while intercepts vary among individuals, the slope remains constant Adjusting these assumptions modifies the equation accordingly.
The equation Rig = œ + BỊEPU + B2PE + B3EPS + ByDP + B;DI + BoFI + B;T0 + eit represents a model where 'i' denotes individual units and 't' indicates time periods In this model, Rj signifies the observation of individual 'i' at time 't', while Rj represents the independent variable for the same individual and time The subscript 'i' attached to 'a' indicates that different individuals may have varying intercepts, and the coefficient Ă to ỉ reflects the relationship between the independent variable and the dependent variable.
Twumasi-Ankrah, Ashaolu, and Ankrah (2015) suggest that the optimal method for estimating equation (3.23) involves incorporating a dummy variable for each individual, effectively transforming the equation.
The equation Rự = a + B,EPU + B;PE + B3EPS + BẠDP + B3DI + Bg FI + B;T0 + tự represents a model where R refers to the observation of an individual unit (i) at a specific time period (t) In this context, the variables included in the equation are essential for analyzing the impact of independent variables on the dependent variable for each individual unit over time.
31 individual at the time period t Besides that, 6, to ; refers to the coefficient of the independent variable Finally, pit refers to the error term of the model.
The Random Effect Model (REM) incorporates random individual differences into its framework, as highlighted by Twumasi-Ankrah, Ashaolu, and Ankrah (2015) This model features a fixed component that represents the population average, defined by the intercept parameter 'a' Additionally, the model includes an error term to account for random individual variations, enhancing its explanatory power.
The equation Rig = œ + BEPU + B;PE + B3EPS + ByDP + B;DI + BoFI + B;T0 + Ei describes a model where 'i' represents individual units and 't' indicates specific time periods In this model, R;¿ denotes the observation of individual 'i' at time 't', while Rj represents the independent variable associated with individual 'i' at the same time The parameter 'o' signifies the intercept, reflecting the average fixed component of the population, and 'f' denotes the coefficients for the independent variables Additionally, pit represents the model's error term, and ¢€; refers to the random error term for individuals.
- Step 3, perform Hausman test to choose between 2 models
According to Saada, Haniffb and Alic (2016), Hausman test is developed by Hausman
In 1978, a study was conducted to determine the suitability of the Fixed Effect Model (FEM) versus the Random Effect Model (REM) for interpreting results, specifically by analyzing the correlation between the dependent variable and the independent variables The null hypothesis is formulated accordingly.
H0: Cov( A; , Riz) which means that there is no correlation between the Ai and the independent variables, in other words, REM is preferable.
The Hausman test-statistic follows the Chi-squared distribution with k degree of freedom with k degree of freedom.
The formula H = (6 FE - #RE)[Var(#FE) - Var(#RE)] -1 (B FE - ỉ RE)~xk 2 calculates the relationship between the beta values of Fixed Effects Model (FEM) and Random Effects Model (REM) In this equation, f FE denotes the beta value for FEM, while BRE represents the beta for REM Additionally, Var(#FE) and Var(#RE) signify the variance of the beta values for both models.
Reject the null hypothesis if the Hausman test-statistic is greater than the critical value, which concludes that REM is preferable.
- After selecting a suitable model, test the defects in the model:
The Breusch-Pagan Lagrangian multiplier (BP-LM) test, developed by Breusch and Pagan in 1980, is utilized to assess the appropriateness of the Pooled-OLS model for data analysis, as noted by Saada, Haniffb, and Alic (2016).
RESEARCH RESULTS OF THE RELATIONSHIP BETWEEN ECONOMIC
Overview of the Vietnamese stock market and the uncertainty of economic policy in
4.1 Overview of the Vietnamese stock market and the uncertainty of economic policy in Vietnam
4.1.1 Overview of the Vietnamese stock market
With the initial establishment of the State Securities Commission (November 28,
Vietnam's stock market officially began operations on July 28, 2000, with the VN Index set at a base value of 100 points Over the past 20 years, this investment channel has demonstrated its effectiveness, achieving an average annual return of 12.5% The VN Index's performance from 2003 to 2023 highlights its growth and potential as a lucrative investment option.
Figure 4.1: VN-Index in the period 2003-2023
The transformation of the Ho Chi Minh City Stock Exchange Center into the Ho Chi Minh City Stock Exchange, as established by Decision No 599/QD on May 11, 2007, marks a historic milestone in HOSE's development This era has seen the introduction of innovative solutions aimed at enhancing market liquidity and offering numerous benefits for investors, including continuous order matching and collaborations with media outlets.
Securities Investment held the Annual Report Voting for Listed Enterprises in 2008; proactive relationship; international cooperation.
Launched in 2009, the online trading method on HOSE significantly enhanced market liquidity and was complemented by the acquisition of ISO 9001:2008 certification, improving operational activities including auctions The introduction of the VN30 index in February 2012, which comprises the top 30 enterprises and employs a new calculation method, further refined market representation HOSE's membership in the World Federation of Stock Exchanges in 2013 solidified its global standing and facilitated adherence to international standards In early 2014, HOSE introduced the HOSE-Index, featuring various indices such as VNMidcap, VN100, and VNSmallcap, catering to different market segments The launch of Exchange Traded Funds (ETFs) in October 2014 marked another significant advancement in investment options By 2017, the Vietnamese stock market experienced remarkable growth, with the VN-Index rising by 48% and daily trading values increasing by 64% However, the market faced challenges in the second quarter of 2018, correcting sharply due to the US-China trade war, resulting in a nearly 20% decline.
Vietnam's stock market has experienced significant fluctuations influenced by the economic situation and the COVID-19 pandemic At the beginning of 2020, the market faced a sharp decline, with the VNIndex dropping to 659.21 points on March 24, 2020, marking a 31.4% decrease from the end of 2019 and the lowest level in two years Despite ongoing challenges in 2021 due to the pandemic, the market continued to navigate these turbulent conditions.
In 2021, Vietnam's stock market experienced significant growth, achieving record highs in both index and transaction value despite the challenges posed by the COVID-19 pandemic By October 2021, the VN-Index surged to 1,444.27 points, marking a remarkable 30.8% increase from the end of 2020 The following year, 2022, continued to showcase notable trading activities in various stocks and corporate sectors.
In 2022, 39 bonds were identified for manipulating transactions, concealing information, and profiteering, highlighting significant limitations in market expression that prompted decisive action from authorities While challenges from 2022 may extend into 2023, the outlook is optimistic as inflation is expected to decline, interest rates are likely to peak and then decrease, and exchange rate pressures will ease Consequently, monetary policy may become more accommodating, leading to increased liquidity as valuations reach attractive levels.
4.1.2 Overview of Economic Policy Uncertainty in Vietnam
The economic uncertainty index for Vietnam, derived from the World Uncertainty Index established by Ahir et al (2018), highlights fluctuations in economic stability over time As illustrated in Figure 4.2, this index spans from the first quarter of 2003 to the first quarter of 2023, providing valuable insights into the country's economic landscape during this period.
Figure 4.2 Economic Policy Uncertainty Index in Vietnam
FRED of = World Uncertainty Index for Vietnam
Sources: Ahir, Hites; Bloom, Nick; Furceri, Davide fred.stlouisfed.org
Source: World Uncertainty Index for Vietnam, fred stlouisfed.org
Since the global financial crisis in 2008, Vietnam's economic uncertainty index has risen significantly In response to this crisis, the government adopted loose monetary and fiscal policies in 2009 to bolster the domestic economy However, these measures resulted in increased inflation in early 2010, prompting the government to issue Resolution 18/NQ-CP on April 6, 2010, which aimed to prioritize macroeconomic stability and curb high inflation.
From April to August 2010, inflation was controlled and began to decrease; however, this period revealed signs of underestimating the risks associated with high inflation Consequently, inflation rose again later in 2010, contributing to heightened inflation uncertainty and an increase in the uncertainty index.
40 of economic policy in the third quarter of 2010 Before the sharp increase of inflation in 2010,
2011, the State Bank (SBV) implemented a tight monetary policy to control inflation.
Between 2011 and 2014, Vietnam experienced significant fluctuations, particularly concerning its banking system and the issue of bad debt The State Bank of Vietnam reported a bad debt ratio of 8.82%, while Fitch estimated it to be as high as 13% in 2012, surpassing figures from commercial banks Economic policy instability peaked in the third quarter of 2014 Following a period of decline and stability, the economic policy uncertainty index rose again in late 2020 and mid-2021, largely due to the impacts of the Covid-19 pandemic.
In 2022, Vietnam experienced a rise in its economic policy uncertainty index, reaching levels comparable to the third quarter of 2010 This increase can be attributed to external factors, including heightened global inflation and the US Federal Reserve's interest rate hikes, which have exerted pressure on the exchange rate.
The corporate bond market is facing challenges, as a lack of confidence among corporate leaders is hindering businesses from issuing bonds to raise capital This situation is contributing to a liquidity crisis within the financial system, posing significant risks to the market Similarly, the stock market has experienced a dramatic decline, with trading values dropping by 4-5 times from their peak Research indicates that economic policy uncertainty adversely affects the financial system, leading to decreased investment, slower growth, and higher unemployment rates To mitigate this uncertainty, it is crucial for economic policies to prioritize transparency, consistency, and feasibility, which is particularly relevant for Vietnam in its economic policy development and implementation.
Variable Observation Mean Standard Min Max
Table 4.1 provides summary statistics for the key variables analyzed, covering data from Q1 2003 to Q1 2023 The analysis includes measures of Economic Policy Uncertainty, represented by the natural logarithm of the relevant variables.
R EPU PE EPS DP DI FI TO
Table 4.2 presents the correlation coefficients between the primary variable, Economic Policy Uncertainty (EPU), and other key variables including Price Earnings (PE), Earnings Per Share (EPS), Dividend Payout (DP), Dividend Income (DI), Financial Investment (FI), and Turnover (TO) The analysis incorporates the natural logarithm of these variables to assess their relationships effectively.
4.2.3 Result of Panel Regression Model
The models shown below are the models that had been formulated in Chapter 3 To deliver the specific objective, which is impact of EPU and variables on market return.
Riz = œ + B+EPU + B;PE + B3EPS + BẠDP + B5DI + Bg FI + B;T0 + se Table 4.3 shows the result of the Pooled OLS model, FEM model and REM model.
Table 4.3 : Result of POLS, FEM, REM model
Notes: The rejection of null hypothesis at 10%, 5% and 1 % significance level are represented by *, **, *** respectively The parentheses value is the P-value.
The analysis of Economic Policy Uncertainty (EPU) reveals significant findings, with p-values of 0.003, 0.004, and 0.004 across three regression models, indicating a 1% significance level In the pooled OLS model, a 1% increase in previous returns correlates with a 0.029851% increase in current returns, while the Fixed Effects Model (FEM) shows a 0.029101% increase under the same conditions Similarly, the Random Effects Model (REM) also indicates a 0.029851% increase in current returns for a 1% rise in previous returns, holding other variables constant.
CONCLUSION 00115
Summary of research results 8 .e
The study analyzed the relationship between the Economic Policy Uncertainty (EPU) index, Price Earnings (PE), Earnings Per Share (EPS), Dividend Payout (DP), domestic and foreign investors, and turnover concerning stock market volatility using regression models such as POLS, FEM, and REM Findings indicate that economic policy instability adversely affects Vietnam's stock market, demonstrating a clear inverse relationship with stock market returns This underscores the significance of government economic policies in influencing market dynamics, as these policies regulate various financial markets The constructed EPU and related indicators serve as predictive tools for future stock market trends The research revealed a strong correlation between policy uncertainty and market volatility, particularly during fiscal conflicts and tight monetary policies The EPU consistently showed a negative coefficient across all models, signifying its influence on stock market returns, while the null hypothesis of a relationship between the VNIndex and EPU was rejected due to p-values below the 10% significance threshold Other variables like DP, EPS, and foreign investment showed no significant relationship with stock market returns Ultimately, EPU, PE, domestic investors, and turnover were linked to market returns, with EPU, DP, domestic investors, and turnover exhibiting negative correlations, whereas PE, EPS, and foreign investment had positive associations.
Research analyzing the impact of Vietnam's Economic Policy Uncertainty (EPU) index on the stock market from Q1 2003 to Q1 2023 reveals a negative correlation, indicating that rising EPU levels lead to decreased stock market returns in Vietnam This finding underscores the sensitivity of the country's volatile stock market to short-term EPU shocks These results align with previous studies by Antonakakis et al (2013), Guo et al (2018), and Kundu and Paul (2022), which also identified a significant negative relationship between EPU and stock returns.
Empirical studies indicate that economic policy uncertainty (EPU) adversely affects financial systems and spills over into the real economy, leading to decreased investment, slower growth, and higher unemployment rates To mitigate EPU, it is crucial for economic policies to prioritize transparency, consistency, and feasibility, which is particularly relevant for Vietnam's policy development High levels of EPU create significant investment risks, as unpredictable economic policies make it challenging for the public to make informed decisions Additionally, research shows that the EPU index tends to decline rapidly following events that trigger heightened uncertainty.
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The government must closely monitor global economic policy fluctuations, particularly among key partners and strategic allies Given the complexities of international trade influenced by the Covid-19 pandemic and the unique policy responses from various countries, this vigilance will enable Vietnam to proactively formulate effective economic policies that foster growth and support the nation's development.
Regular updates on global economic policy fluctuations are essential for businesses and investors Understanding these changes plays a crucial role in shaping effective business plans, ultimately empowering enterprises to take proactive measures in their operations.
The Vietnamese government must enhance regulations and policies to enable businesses to effectively leverage bilateral and multilateral trade agreements While active participation in these agreements has significantly strengthened Vietnam's position in international trade, it also exposes the country to global economic fluctuations To mitigate this vulnerability, it is crucial for Vietnam's legal and policy framework to be robust, transparent, and aligned with international standards.
Investors carefully evaluate multiple factors when making investment decisions, with risk being a significant consideration, particularly for risk-averse individuals who seek to mitigate potential losses Additionally, informed investors explore various alternatives to optimize their investment strategies.
When economic policy uncertainty (EPU) rises, it negatively impacts stock market returns, indicating that investors may hesitate to invest during periods of heightened uncertainty, such as the COVID-19 pandemic, wars, financial crises, and significant market volatility.
Individual investors must enhance their reading comprehension and analytical skills regarding the financial statements of publicly listed companies Key indicators such as Return on Sales (ROS), Price-to-Earnings (P/E) ratio, and Beta coefficient can serve as valuable tools for informed decision-making However, it is crucial to approach seemingly favorable financial statements with caution, as they can be deceptively manipulated by skilled accountants, particularly in Vietnam and globally Thus, investors should not rely solely on these statements for their investment decisions.
5.3 Research limitations and directions for future studies
Despite thorough efforts in reviewing prior research, formulating a topic, choosing appropriate research methods, and designing comprehensive research models, this study still faces inherent limitations.
The author aims to enhance their understanding of the research topic and objectives to better align with the critical needs of stakeholders in the stock market and address Economic Policy Uncertainty.
The study faces challenges in data collection and limitations, resulting in a narrow focus on the Ho Chi Minh City Stock Exchange rather than the broader market This restricted scope hinders the ability to generalize the impact of the EPU index on the entire stock market Additionally, the use of quarterly data yields a limited number of observations, which may lack sufficient variation, potentially leading to undesirable outcomes in the research model.
The author's limited analytical skills may lead to errors in constructing research models The stock market is influenced by various factors beyond the EPU index, including PE ratios, EPS, dividend payout ratios, domestic and foreign investors, and turnover, indicating that the model may not fully capture the complexities of the market.
The author suggests that future research should focus on gathering historical exchange rate data to improve the robustness of the uncertainty index measurement By extending the time period of data collection, researchers can capture significant past events, such as the Asian Financial Crisis of 1997, the US Savings and Loan Crisis of 1989, and Black Wednesday in 1987 Analyzing these major crises offers valuable insights that can enhance the understanding of the uncertainty index.
Future researchers should confidently choose proxies for measuring the uncertainty index By examining macroeconomic factors such as government monetary policies and GDP growth, alongside microeconomic variables like domestic savings and the supply and demand for local currency, they can gain fresh insights into uncertainty.
Al-Thageb, S.A and Algharabali, B.G., 2019 Economic Policy Uncertainty: A literature review The Journal of Economic Asymmetries, 20, p.e00133.
Asafo-Adjei, E., Agyapong, D., Agyei, S.K., Frimpong, S., Djimatey, R and Adam, A.M., 2020 Economic Policy Uncertainty and stock returns of Africa: a wavelet coherence analysis Discrete Dynamics in Nature and Society, 2020, pp.1-8.
Baker, S.R.; Bloom, N.; Davis, S.J Measuring Economic Policy Uncertainty Q J. Econ 2016, 131, 1593-1636.
Chan, Z.P., Chee, C.J., Kong, L.Z., Leong, C.O and Yen, K.Y., 2017 What Drives Exchange Rate Uncertainty? From the Perspective of Global and Domestic Factors (Doctoral dissertation, UTAR).
Chen, J., Jiang, F and Tong, G., 2017 Economic Policy Uncertainty in China and stock market expected returns Accounting & Finance, 57(5), pp.1265-1286.
Darsono, S.N.A.C., Wong, W.K., Nguyen, T.T.H and Wardani, D.T.K., 2022 The Economic Policy Uncertainty and its effect on sustainable investment: A panel ARDL approach Journal of Risk and Financial Management, 15(6), p.254.
Duan, Y.; Chen, W.; Zeng, Q.; Liu, Z Leverage effect, Economic Policy Uncertainty and realized volatility with regime switching Phys A Stat Mech Its Appl 2018, 493, 148-154.
Gabor, E.; Georgarakos, D Economic Policy Uncertainty and Stock Market Participation CFS Work Pap Ser 2018, 1-42.
Iqbal, U., Gan, C and Nadeem, M., 2020 Economic Policy Uncertainty and firm performance Applied Economics Letters, 27(10), pp.765-770.
Liu, L and Zhang, T., 2015 Economic Policy Uncertainty and stock market volatility. Finance Research Letters, 15, pp.99-105.
Luo, Y and Zhang, C., 2020 Economic Policy Uncertainty and stock price crash risk. Research in International Business and Finance, 51, p.101112.
Maqulera, C.P., Espinosa-Méndez, C and Gahona-Flores, O., 2023 How does Economic Policy Uncertainty (EPU) impact copper-firms stock returns? International evidence Resources Policy, 81, p.103372.
Nguyen, M.H and Trinh, V.Q., 2023 UK Economic Policy Uncertainty and innovation activities: A firm-level analysis Journal of Economics and Business, 123, p.106093.
Pastor, L.; Veronesi, P Political uncertainty and risk premia J Financ Econ 2013,
Phan, D.H.B., Sharma, S.S and Tran, V.T., 2018 Can Economic Policy Uncertainty predict stock returns? Global evidence Journal of International Financial Markets, Institutions and Money, 55, pp.134-150.
Raza, S.A.; Zaighum, I.; Shah, N Economic Policy Uncertainty, equity premium and dependence between their quantiles: Evidence from quantile-on-quantile approach. Phys A Stat Mech Its Appl 2018, 492, 2079-2091.
Xu, Y., Wang, J., Chen, Z and Liang, C., 2021 Economic Policy Uncertainty and stock market returns: New evidence The North American journal of economics and finance, 58, p.101525.
Yuan, D., Li, S., Li, R and Zhang, F., 2022 Economic Policy Uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis Energy Economics, 110, p.105972.
1 Kiều, V.T.T., 2023 Quan hệ nhân quả giữa lạm phát và sự bất định lạm phát: Nghiên cứu tại một số nước Đông Nam A TAP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHO HO CHÍ MINH-KINH TE VA QUAN TRI KINH DOANH, 180), pp.18-30.
Nghiên cứu của Lộc (2014) phân tích các yếu tố ảnh hưởng đến sự thay đổi giá cổ phiếu, dựa trên các bằng chứng từ Sở Giao dịch Chứng khoán Thành phố Hồ Chí Minh Bài viết được đăng trong Tạp chí Khoa học Trường Đại học Cần Thơ, số 33, trang 72-78, cung cấp cái nhìn sâu sắc về các yếu tố kinh tế và tâm lý thị trường tác động đến giá cổ phiếu.
Bài viết của Lý, T.T.H và Trà, M.T.T (2021) nghiên cứu về sự bất định trong chính sách kinh tế của Trung Quốc và tác động của nó đến việc nắm giữ tiền của các công ty tại khu vực Đông Nam Á Nghiên cứu được công bố trên Tạp chí Nghiên cứu Kinh tế và Kinh doanh Châu Á, chỉ ra rằng sự biến động trong chính sách kinh tế của Trung Quốc có thể ảnh hưởng đáng kể đến quyết định tài chính của các doanh nghiệp trong khu vực Các tác giả phân tích mối liên hệ giữa chính sách của Trung Quốc và chiến lược tài chính của các công ty Đông Nam Á, nhấn mạnh tầm quan trọng của việc hiểu rõ các yếu tố kinh tế vĩ mô trong việc ra quyết định đầu tư.