ABSTRACT The thesis titled "FACTORS AFFECTING STOCK PRICES OF COMMERCIAL BANKS LISTED IN VIETNAM’S STOCK MARKET" will examine the relationship between bank specific factors, and macro fa
INTRODUCTION
General objectives
The general objectives of the research are to investigate factors affecting stock prices of commercial banks listed in Vietnam’s stock market, thereby proposing some related implications based on the findings in this research.
Specific objectives
To achieve the general objectives, the thesis has to solve the following specific objectives:
Firstly, identifying the factors that affect stock prices of commercial banks listed in Vietnam’s stock market
Secondly, analyzing the influence of the determinants of stock prices of commercial banks listed in vietnam’s stock market
Thirdly, proposing and recommending policy implications according to the result of the forecast
To achieve the following specific objectives, the thesis has to solve the following specific questions:
What are the factors affecting the stock prices of commercial banks listed in Vietnam’s Stock Market?
How do these factors affect the stock price of commercial banks listed in Vietnam’s stock market?
What are critical policy implications for investors, bank managers based on the relationship between stock prices and factors affecting stock prices of commercial banks listed in Vietnam’s stock market?
Research subject
The research subject of this study is stock prices and determinants affecting stock prices of commercial banks listed in Vietnam’s stock market.
Research scope
Scope of spatial research: There are 27 commercial banks listed in Vietnam’s stock market However, the study will use data from 18 commercial banks that are listed on the Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) in the period from the quarter 1 of 2015 to the quarter 4 of 2023 The reason the author choose data from HNX and HOSE since the data of commercial banks listed in these stock exchanges maintaining stability over time There are some commercial banks having plans to trade shares in a new stock exchange in 2023, this may lead to sharp fluctuations in stock prices
Scope of time research: The research secondary dataset was collected from the quarter 1 of 2015 to the quarter 4 of 2023 This is the period help the paper to investigate the fluctuation of stock prices of commercial banks in recent years in accordance with many economic events in Vietnam in particular and in the world in general.
Data collection methods
Secondary data includes data on 18 commercial banks listed on the Vietnam’s stock market from the quarter 1 of 2015 to the quarter 4 of 2023 Firm – specific data is collected and calculated based on financial reports of selected banks; while a few macroeconomic variables such as gross domestic product growth rate, inflation rate and exchange rate collected from the World Bank, VietstockFinane and the General Statistics Office of Vietnam (GSO) The stock prices are taken from data at the end of each quarter during the research period from VietstockFinance.
Data analysis methods
This study employs the quantitative analysis method to examine the correlation between factors and stock prices of commercial banks listed in Vietnam's stock market STATA 17.0 software will be used to estimate the model based on panel data The study will conduct descriptive statistics (means, standard deviations, minimum and maximum) andcorrelation matrix Then, the author will use Pooled Ordinary Least Squared model approach, Fixed Effect model approach, Random Effect Model approach to estimate regression analysis with the secondary panel data Furthermore, in order to ensure the truthful regression of the model, the author will utilize F-test, Breusch-Pagan test, Hausman test to determine whether to employ Pooled OLS, FEM or REM and check the detection of variables including autocorrelation, multicollinearity, heteroscedasticity test.
Theoretical contribution
The research will provide additional empirical evidence on the impact of four firm – specific factors and three macroeconomic factors on the stock prices in the banking sector listed on the Vietnam’s stock market accompanied with evaluating the tendency of those factors Moreover, by conducting multiple tests and identifying the variables that impact stock prices in the banking sector, the thesis reinforces the earlier theory and offers insights that can be utilized in analyzing other stock prices.
Practical contribution
The research results will provide valuable information to assist investors in being more proactive in analyzing and evaluating the benefits and correlations between factors when selecting strategies and developing suitable investment methods Additionally, assuming from the research findings, the thesis will suggest practical measures for policymakers to effectively utilize the market, making the stock market a supply channel and a capital-efficient resource for businesses in the economy The research results can also serve as a reference for bank managers to devise appropriate strategies to increase stock prices and solidify their position in the stock market
Structure of research CHAPTER 1 INTRODUCTION
This study summarizes research reasons, research objectives, research questions, research subjects and scope, research methodology, research contributions and research structure
CHAPTER 2 LITERATURE REVIEW AND EMPIRICAL RESEARCH
This chapter provides definitions of stock prices and discusses factors influencing them Additionally, it examines relevant literature and empirical studies on the effects of both bank characteristics and macro factors on the stock prices of commercial banks listed in the stock market
The chapter outlines the data collection process from financial statements of domestic banks, selection of research variables, and formulation of research hypotheses based on prior theory and empirical evidence It also suggests methods for analyzing variables, detailed research procedures, and research methods to be employed
CHAPTER 4 RESEARCH RESULTS AND DISCUSSION
The chapter analyzes and synthesizes results from the regression module using STATA 17 software, establishes the relationship between independent and dependent variables, and draws conclusions regarding the impact coefficient of these variables
CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS
This paper will draw the conclusions, and propose some recommendations based on (i) theoretical framework; (ii) the final result of the influence between macroeconomic, firm – specific variables and stock price of commercial banks listed in Vietnam’s stock market This study also presents limitations and suggests directions for further research.
LITERATURE REVIEW AND EMPIRICAL RESEARCH
Macroeconomic factors
Gross domestic product is calculated by taking into account a variety of economic factors, such as consumption; investment by businesses; government purchases of goods and services; and foreign demand GDP is often selected as one of the best measures of economic health of a nation (Indra, 2014) High level of GDP will lead to the increase in people’s consumption that result in the rise in the company’ s net profit which can attract investors Bakhri et al (2023) There is much empirical research that reveals that GDP affects stock prices in a positive and significant way (Bakhri et al (2023), Nguyen (2023), Nguyen & Le (2020), Chadi & Rasha (2022)
The stock market perceives inflation as unfavorable news because increasing inflation leads to higher discount rates used to evaluate equities, resulting in a decrease in stock prices due to declining sales and net income for companies (Talla,
2013) According to a study by (Siang and Rayappan, 2023), as inflation rises, investors typically opt to hold onto their funds, leading to a drop in stock prices, creating a buying opportunity (Siang and Rayappan, 2023) Therefore, whether inflation is at a positive or negative level will impact the votatility in stock prices on the stock market
Foreign currency exchange is considered a key determinant of a nation's economic activity The investment decisions of investors are positively influenced by fluctuations in the exchange rate (Kalam, 2020) An exchange rate represents the value of one country's currency relative to another (Indra, 2014) USD/VND signifies the amount paid by the United States in relation to Vietnam's currency Numerous empirical studies indicate a negative correlation between the exchange rate and share prices.
Bank specific factors
Earnings Per Share (EPS) indicators in financial statements can provide investors with important signals (Sustain et al., 2021) The EPS ratio is a tool that offers insights into a company's strength, giving an overview of key information for making investment decisions It includes net income, revenue earned per share, and earnings generation Changes in EPS can impact stock prices, with a positive and significant effect on the share prices of commercial banks listed on the stock market (Chad & Tasha, 2022)
Return on assets (ROA) is a percentage that assesses a company's profit generation from its total assets (Saputra, 2022) A higher ratio indicates better performance, leading to increased shareholder returns This, in turn, enhances investor interest and boosts demand for the stock, ultimately driving up the bank's stock price Mohammad et al (2022) proves that ROA plays a crucial role in influencing share prices within the banking sector
The company's value in signaling theory, as indicated by the price-to-earnings ratio, is essential for communicating information to external parties In signaling theory, the price-to-earnings ratio can convey positive signals to investors, providing insights into a company's growth potential relative to its market price (Kumar, 2017) The price-to-earnings ratio, related to corporate earnings, shows the level of investment that investors are ready to make, reflecting their confidence in the company's future performance A higher price-to-earnings ratio indicates stronger investor confidence (Kadima et al., 2020) Companies with significant growth prospects typically have higher price-to-earnings ratios compared to those with limited growth Fast-growing companies often generate high returns for investors, attracting more investment and driving up stock prices (Sihaloho et al., 2021; Kumar,
The bank's size significantly influences its stock price A larger bank holds a stronger position in the competitive market, offering better service facilities to customers compared to smaller banks Moreover, bank shares are actively traded on the stock market due to their higher payouts As a result, investors find bank shares more appealing as an increase in share purchasing power leads to a rise in market price (Nguyen & Le, 2020) Mohammed et al (2020) discovered that large firms are more diversified than small ones and encounter lower risk Furthermore, large firms incur lower bankruptcy costs and are well-known, facilitating their entry into the stock market When firms exhibit the same profitability, the larger firm tends to maintain a relatively low level of debt In addition, they noted that the issue of information asymmetry is less obvious in larger firms, and the information costs are also lower compared to smaller firms Size is proved to be a key predictor of stock prices in the banking sector (Mohammad et al., 2022).
Foreign Research
Chhetri (2023) investigated the factors influencing the share price of commercial banks in Nepal The study selected independent variables affecting market price per share as a dependent variable, including firm-specific independent variables, specifically (1) earnings per share (EPS), (2) price-earnings ratio (P/E), (3) book value per share (PBV), (4) return on assets (ROA), and (5) size of the company (S), and external factors including (1) inflation, (2) broad money supply, and (3) gross domestic product (real) With pooled cross-sectional data covering 13 out of 21 commercial banks in Nepal for the period from 2012 to 2022, the study is based on results from multiple regression models and a causal-comparative method, which show that variables such as (1) EPS, (2) P/E, (3) PBV, and (4) ROA display positive relationship with stock prices and have a statistical significance on stock prices, except that (5) S has a negative effect on the context of the joint venture commercial bank of Nepal Concerning macroeconomic variables, (1) inflation, (2) broad money supply, and (3) gross domestic product (real) insignificantly associated with share prices, while (3) gross domestic product has an insignificantly negative relationship with market price per share
Mohammad et al (2022) examined the impact of financial determinants on the stock market prices of 13 Jordanian commercial banks scrutinized from 2014 to 2019 The study utilized two statistical analyses, descriptive statistics (means, standard deviations, minimum, and maximum) and regression diagnostics (Hausman test, multicollinearity test for the variables over the study period), to ascertain the correlation between a specific group of macroeconomic and microeconomic variables and the market price per share (MPS) The findings indicate that company-specific factors such as earnings per Share (EPS), dividend per Share (DPS), return on Assets (ROA), and size (S) have a positive and statistically significant influence on MPS, making them key predictors of stock prices in the Jordanian banking sector The research also identified volume as the most crucial determinant affecting stock prices among the microeconomic factors In terms of macroeconomic factors, gross domestic product (GDP) and money supply (MS) emerged as the major variables impacting the share price of commercial banks in Jordan during this period
Additionally, the price earnings ratio, book value per share, and inflation had a minor impact on the MPS
Research by Azmeh & Hamada (2022) has been carried out to examine the effect of seven of the most important internal determinants on stock prices for all listed banks in the Dubai and Abu Dhabi stock markets and investigate the differences in results for the internal determinants affecting the stock prices between two stock exchanges in the United Arab Emirates and between banks listed on both markets The study used pooled least squares, fixed effects, and random effects models to analyze data from 23 banks between 2014 and 2017 The results revealed that earnings per share (EPS) and dividend per share have a positive and statistically significant impact on the market price of shares in all markets and only in Abu Dhabi, respectively On the contrary, there is evidence that return on equity (ROE), dividend yield (DY), and price-earnings (P_E) all have a negative impact on the market price of the stock in all markets Of greater significance, the research findings indicated that there are differences in the effects of dividend policy variables on the share prices of the two markets investigated in the United Arab Emirates
Another study is that of Bustani et al (2021) who investigated the impact of earning per share, price to book value, dividend payout ratio and net profit margin on the stock price in Indonesia Stock Exchange The population of the study is 12 companies from 26 companies in in the period five years (2014-2018) in a sub - sector of food and beverage companies The study based on data analysis with bootstrapping using statistical equation modeling reveals that Earning Per share (EPS), Price to Book (PB) and Dividend Payout Ratio significantly affect stock prices, except for Net Profit Margin These results suggest that information on EPS, PBV, DPR, and NPM ratios can be valuable for investment decisions However, the study is limited to the fundamental factors of companies and does not consider external factors like inflation and government policies Future research should incorporate these external factors to provide a more comprehensive understanding of stock price determinants
The study carried out by Talla (2013) used unit root test, Multivariate Regression Model computed on Standard Ordinary Linear Square method and Granger causality test to determine the impact of changes in selected macroeconomic variables on stock prices in the period 1993 - 2012 and based on monthly data of The findings indicate that inflation and currency depreciation have a statistically significant negative impact on stock prices Additionally, the study concluded that there is a negative relationship between interest rates and changes in stock prices, while money supply is positively correlated with stock prices However, interest rates and money supply were not found to be statistically significant in the model.
Domestic study
Similar to countries around the world, Vietnam also has a number of studies related to factors affecting share prices of commercial banks on the stock market
Nguyen and Le (2020)conducted on a research sample including 9 joint stock commercial banks in the period from 2010 to 2018 through applying The Random Walk theory of stock prices, and the theory about dividend policy and its impact on bank stock prices and using STATA software to analyze the relationship between factors affecting the market value of commercial bank stocks in Vietnam Results from the study show that earnings per share (EPS), P/E ratio (PE), GDP growth (GDP), bank size (SIZE) have a positive and significant relationship This means that this is consistent with the theoretical basis and previous research results Meanwhile, research results show that the B/M ratio (BM) and inflation rate (INF) have a negative and significant relationship with stock prices; The remaining independent variables such as the difference in duration (DGAP) in the model, although not statistically significant, partly show their impact on the market value of shares of listed commercial banks
Dinh et al (2020) conducted a study to determine and analyze the impacts of seven macro factors on Vietcombank stock price (VCB) from 2014 to 2019 The factors included inflation, GDP growth, market interest rate, risk-free rate, VNIndex,
S&P500, and exchange rate Using Eview software for quantitative research, the study found that an increase in GDP growth, lending rate, and risk-free rate significantly increased VCB stock price, with GDP growth having the highest impact coefficient On the other hand, a decrease in the exchange rate and a slight decrease in S&P500 had a negative impact on VCB stock price
Nguyen (2021) examined the influence of macroeconomic factors and their interaction with institutional performance on 13 Vietnamese bank share prices from the first quarter of 2009 to the third quarter of 2020 The study utilized The Efficient Market Theory, The Arbitrage Pricing Theory, and The Capital Asset Pricing Model (CAPM), building upon previous research to assess the impact of macroeconomic variables and their interaction with bank performance variables on share prices Using the Two-Stage Least Squares (2SLS) method, the research reveals that GDP growth can elevate bank share prices, particularly when banks excel in return on assets (ROA), capital adequacy (CAR), and non-performing loans (NPLs) management Additionally, interest rates (R) positively affect bank share prices, with further increases when NPLs, leverage (LEV), and stock beta (Beta) are optimized However, the study indicates that fluctuations in foreign exchange (FX) and gold prices (GP) do not significantly influence bank share prices, especially without effective NPL and LEV management Moreover, equity capital (E), deposit amounts (D), and loan amounts (L) show no substantial impact on bank share price movements
Overall, many researchers used different measurement methods and periods to examine the best reliable results Most of the empirical research shows positive and negative relationships between many micro, and macroeconomic factors and stock prices for different sectors, mainly the banking sector, in different countries These researchers always based on the results concluded, suggest some recommendations to not only the investors but also the banks and government having right decision in investment and implementing policies to ensure the long – term sustainable development of the economy However, there are still some limitations that the number of observation variables or the period is not enough to analyze This current study is based on these empirical studies to examine seven external and internal determinants affecting the stock prices of commercial banks listed on two main stock exchanges in Vietnam, namely Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) According to the findings of prior research, table 2.2 demonstrates the influences of selected variables affecting stock prices in various research
Based on results concluded by many empirical research, the author gives a table that summarize seven factors affecting stock prices of commercial banks with high frequency appeared in prior studies (above twice) Therefore, this study examines seven factors affecting stock prices of commercial banks listed in Vietnam’s stock market
Table 2.1: Summary of the variables from empirical studies
Stock prices GDP INF FX ROA EPS PE Chhetri (2023) x x x x x x
Source: Synthesized by the author
Table 2.2:The influences of the selected variables affecting stock prices from various empirical research summary
Factors affecting stock prices of commercial banks listed in
Positive direction Negative direction No statistical significance
Gross domestic product growth rate
Inflation rate Nguyen and Le
Earnings per share Nguyen and Le
Price to earnings ratio Nguyen and Le
Source: Synthesized by the author
RESEARCH METHODOLOGY
Research model
This study is based on theories mentioned above, about factors and stock prices to achieve quantitative analysis target through a regression model that analyzes the impact of firm - specific, macroeconomic factors on stock prices, thereby suggesting some recommendations to the study
Based on results concluded by many empirical research and theories mentioned above, a research model is propsosed This paper chooses the proxy variable, which was used to examine the effect of banks’ stock prices founded on the empirical literature This paper analyzes and builds a regression model to consider the nexus between dependent variable stock prices of commercial banks and seven independent variables, as follows: (1) Gross domestic product growth rate (GDP), (2) inflation rate (INF), (3) exchange rate (EX), (4) earnings per share (EPS), (5) price earnings ratio (PE), (6) return on assets, (7) bank size (SIZE)
The linear regression model illustrates the effect of independent variables on the bank’s stock price listed in Vietnam’ s stock market
𝑃 𝑖𝑡 is dependent variable that represent price share of commercial bank i at time t
𝐺𝐷𝑃 𝑡 is an indicator to represent the gross domestic product growth rate studied at time t GDP is extracted from reliable source like World Bank
𝐼𝑁𝐹 𝑡 is a variable to represent the inflation rate at time t INF is collected from reliable sources like the General Statistics Office of Vietnam (GSO) and FinanceVietstock
𝐸𝑋 𝑡 is a variable to represent the rate between US dollars/ VND at time t EX is collected from a reliable source like FinanceVietstock
𝐸𝑃𝑆 𝑖𝑡 is a variable to represent earnings per share of each bank i at time t Earning per share is denoted as division between return after tax and end – of – period common shares outstanding
𝑃𝐸 𝑖𝑡 is a variable to represent price earning ratio of each bank i at time t Price to earnings ratio is defined by stock price divided into earning per share of that respective stock
𝑅𝑂𝐴 𝑖𝑡 is a variable to represent the return on assets of each bank i at time t Return on assets percentage is calculated by division between return after tax and total assets
𝑆𝐼𝑍𝐸 𝑖𝑡 is a variable to represent the size of each bank i at time t Banks’ size is determined by the natural logarithm of total assets of each bank
𝛽 1 , 𝛽 2 , 𝛽 3 , 𝛽 4 , 𝛽 5 , 𝛽 6 , 𝛽 7 : coefficients for the independent variables, which are used to indicate the influence of independent variables on the dependent variable i: refer to the commercial bank studied while (t) to time period t: is time period studied in this thesis
Hypotheses
Based on the proposed model, this thesis gives a range of hypotheses to test the impact of selected factors on the stock prices in the banking sector listed in Vietnam’s stock market
GDP is a crucial factor when analyzing the influence on stock prices in various studies Many empirical studies indicate that GDP significantly affects stock prices, especially in the banking sector (Nguyen, 2023; Nguyen & Le, 2020; Chadi & Rasha,
2022) Nguyen et al (2020) demonstrate that an increase in GDP growth notably leads to the rise in Navibank (NVB) stock price, showing the highest coefficient of influence However, Bakhri (2023) suggests that GDP does not have a substantial impact on stock value Building on previous research, hypothesis 1 proposes that GDP plays a crucial and positive role in influencing the stock prices of commercial banks listed on the Vietnamese stock market
The first hypothesis ( 𝑯 𝟏 ) : GDP affected positively to the stock price
Inflation is investigated to have a negative relationship with the share price of commercial banks (Nguyen & Le, 2020), (Chadi & Rasha, 2022), (Nguyen, Dinh & Pham, 2020) On the other hand, inflation was found not to be statistically significant in many previous studies (Rjoub, 2017) In addition, Mohammad et al (2022) observed that inflation had no significant impact on the banks’ market price in the Jordan market price Overall, hypothesis supposes that there is a significant and negative relationship between inflation and stock prices of commercial banks listed in Vietnam’s stock market
The second hypothesis (𝑯 𝟐 ) : Inflation affected negatively to the stock price
Study conducted by Nguyen et al (2020), the increase in NaviBank (NVB) stock price results from a slight decrease in the exchange rate (USD/ VND) in the Vietnamese market Similarly, USD/ VND is negatively in accordance with the bank's share in the long run in the Vietnamese market (Nguyen, 2023) In many oversea studies such as Antono et al (2019), exchange rate is found to have a negative and insignificant effect on stock price
The third hypothesis (𝑯 𝟑 ) : Exchange rate affected negatively to the stock price
Based on signaling theory, earnings per share (EPS) is a reliable signal, offering valuable insights into changes in share price and volume, as well as informing investors' decision-making (Bustani et al., 2021) Bakhri et al (2023) and Chadi & Rasha (2022) have demonstrated the positive impact of EPS on stock prices, highlighting its significance for investors In essence, a higher EPS corresponds to a higher share price, while a lower EPS results in a lower share price
The fourth hypothesis (𝑯 𝟒 ) : EPS affected positively to the stock price
Price to Earning ratio high or low will lead to the result of decision making for investors A low P/E ratio for the company's shares may indicate that the shares are undervalued, which allows investors to buy undervalued shares at a discount and profit when the stock price rises Conversely, a high P/E ratio may indicate that the shares are being overvalued, and investing in overvalued shares carries a risk of losing money (Kumar, 2017) Price to earning ratio shows that there is a significant and positive relationship with market price share and one of the major determinants of the share price (Chhetri, 2023) Price earning ratio has experienced a significant and positive effect on stock price (Sihaloho and Rochyadi PS, 2021) According to Kumar (2017), price earning ratio is directly positive to the prediction of market price of share On the other hand, price earning ratio has no statistically meaningful relationship with the stock price (Saputra, 2022)
The fifth hypothesis (𝑯 𝟓 ) : PE affected positively to the stock price
An analyst, manager, or investor can determine a company's level of asset utilization efficiency based on return on assets (ROA) It is a metric to assess a business's profitability in relation to its total assets Return on assets showed a strong positive correlation with stock price, indicating that a rise in profitability will result in an increase in price (Mohammed et al (2020), Chhetri (2023), Sholichah et al (2020)) By contrast, other studies demonstrate that there is no relationship between return on assets and stock prices (Saputra, 2022 and Sausan, 2020)
The sixth hypothesis (𝑯 𝟔 ) : ROA affected positively to the stock price
Nguyen & Le (2020) conducted concluded that size has statistical significant and negative impact on the share price of commercial banks Size was found to be positively and statistically significant in explaining stock prices (Rjoub (2017) and Mohammad et al (2022)) The size of the bank proved to be significant on stock price with a positive direction (Wuryani et al., 2022) However, following the study conducted by Chandra & Osesoga (2021), ROA are proved to have negative significant relation with stock prices
The seventh hypothesis (𝑯 𝟕 ) : Size affected positively to the stock price
Quantitative methodology Step 1: Descriptive statistics
Statistical information represents the number of observations, mean value, maximum value, minimum value and standard deviation of the variables, the author summarize and give general statements
The study uses panel data (a combination of cross – sectional and time – series data) to investigate the influence of the independent variables on the dependent variable in the model The results of the correlation matrix initially assess the relationship between the independent and the dependent variables Then, the author utilizes the variance inflation factor VIF (Variance Inflation Factor) to check the multicollinearity phenomenon According to Voss (2004), multicollinearity is a common phenomenon in analyzing multiple regression models To identify the phenomenon of multicollinearity, we can apply a very simple test that relies on the variance inflation factor (VIF) to determine the correlation between independent variables and the power of the variable (Shrestha, 2020)
VIF is calculated using the formula:
Following to the study conducted by Shrestha (2020), where, (1-𝑅 2 ) is the inverse of VIF The lower the (1-𝑅 2 )
• If the VIF value > 2 shows signs of multicollinearity
• If the VIF value is < 2 shows that there is no multicollinearity phenomenon
• If the VIF value ≥ 10 shows that the regression coefficients definitely have multicollinearity
Step 3: Estimating the regression models and testing the regression hypothesis
The author conducts quantitative analysis on three models: Pooled OLS, FEM and REM, and determines which regression model (Pooled OLS or FEM or REM) is the most appropriate through tests Then, the study checks the autocorrelation and heteroscedasticity of the model If the methods in the author’s research report do not violate the hypothesis after screening for model defects The conventional regression method will be used in this study However, the Feasible Generalized Least Squares (FGLS) method will be used if the model includes violations (Autocorrelation and Heterokesdaticity) This method is very helpful when attempting to defect multicollinearity or autocorrelation or heteroscedasticity
Pooled ordinary least square (Pooled OLS) model is a basic regression model used for estimating panel data This model overlooks the time and space dimensions of the data, leading to constant coefficients that do not vary over time or across different enterprises Consequently, this frequently leads to autocorrelation issues due to the low Durbin Watson coefficient
In FEM, the intercept in the regression model is allowed to differ among individuals to reflect to the unique feature of individual units Fixed effect model
(FEM) represents the observed quantities in terms of explanatory variables that are all treated as non - random
REM may be used as an alternative if the FEM method is not suitable for the analysis One advantage of this model is that it is more economical than the FEM model in terms of the number of parameters estimated REM is appropriate in situations where the (random) intercept of each cross-sectional unit is uncorrelated with the regressors
Three following tests to determine the most suitable regression model among three recommended options
The Hausman test is utilized by the author to select between two models: REM and FEM The purpose of this test is to determine whether the residuals and the independent variables are correlated
H0: There is no correlation between random error and independent variable H1: A correlation exists between random error and independent variables
When the P_value is less than 0.05, indicating a correlation between random errors and independent variables, we reject H0 and choose the fixed effects model Conversely, we employ the random effects model
• Breusch-Pagan Lagrange Multiplier (LM) Test
This test is performed to choose between POLS and REM with the following hypothesis
H0: Pooled OLS model is suitable
H1: Random effect model (REM) is suitable
Accept the null hypothesis (H0) and select the Pooled OLS model if Prob > Chi - square is greater than 0.05 By contrast, we reject the null hypothesis and REM model is more efficient than Pooled OLS model.
SUMMARY
Chapter 3 outlines the research methodology of the subject The author formulates hypotheses regarding the correlation between variables and stock prices, based on prior theoretical and empirical research on capital structure Following data collection and analysis, it will be processed using STATA software The research findings will be evaluated using analytical methods, descriptive statistics, correlation, and regression analysis The next chapter will present the research outcomes.
RESULTS AND DISCUSSION
Autocorrelation test
Wooldridge test for autocorrelation in panel data
The author tests the autocorrelation of residuals in the regresson model through Wooldridge test to find the optimal solution to fix the model defect by the xtserial command structure with the hypothesis:
H0: the model does not have autocorrelation
H1: The model has autocorrelation phenomenon
Wooldridge test H0: No first – order autocorrelation
Source: The author’s summary from STATA 17.0
The testing result from Table shows that autocorrelation is detected in regression model due to Prob = 0.000 < 0.05.
Unrestricted Heteroscedasticity test
The study continuously tests heteroskedasticity for selected FEM model
Heteroskedasticity can cause the reliability of each observation to be different, causing loss of reliability of the coefficient and an ineffective estimation model This study uses Breusch and Pagan Lagrangian multiplier test to test the change in variance with the hypothesis
Hypothesis H0: The model does not have heteroskedasticity
Hypothesis H1: The model has heteroskedasticity
If Prob > F is greater than 0.05, then the hypothesis H0 is accepted and the FEM model will not suffer from heteroskedasticity The results are presented in the table as follows:
Table 4.10: Breusch and Pagan Lagrangian multiplier test for heteroskedasticity
Breusch and Pagan Lagrangian multiplier test for random effects
Source: The author’s summary from STATA 17.0
Based on the results, we see that prob > chi 2 = 0.000 is less than 0.05, rejecting H0 and concluding that the model suffers from heteroskedasticity Thus, after performing the necessary tests to detect defects in the random effect model (REM), the test results show that the random effect model (REM) exists aucorrelation and heteroskedasticity phenomenon Therefore, to be able to analyze regression results accurately and achieve high reliability, it is necessary to overcome the current defects of the FEM fixed effects model.
Fixing regression model defects
Through analysis and model evaluation results, it shows that the random effects model (REM) is the most suitable; however, the research model has an autocorrelation phenomenon, and the error variance changes through testing Wooldridge test, Breusch and Pagan Lagrangian multiplier test respectively To overcome the two phenomenon mentioned above, the author uses a feasible generalized least squares (FGLS) model for the research model
Table 4.11: Feasible generalized least squares (FGLS) model
Price Coef Std Err t P > |t| [95% Conf.Interval]
Source: The author’s summary from STATA 17.0
The results after the author performed generalized least squares (FGLS) model estimation for the research model, showed that the statistically significant variables were EPS, SIZE, INF
From the results table of the feasible generalized least squares (FGLS) model, it shows that the variables that affect the stock prices of commercial banks in Vietnam in the period from the first quarter of 2015 to the fourth quarter of 2023
P = -3.586 + 0.0003 EPS +0.187 SIZE + 0.031 INF + 𝐮 𝐢𝐭 Research discussion
After using STATA 17.0 software to process research data, the study synthesizes 4 research results for the research model and the feasible generalized least squares (FGLS) estimation model is selected as the most suitable model for the author's research seas
The research results are summarized by the author in the following table:
Table 4.13: Summary all models studied in this research
Source: The author’s summary from STATA 17.0
From the estimation’s result, we have the final model as follows:
For earnings per share (EPS), the relationship with the stock price of commercial banks listed on Vietnam's stock market is positive This is evident from the positive coefficient value of 0.0003 and statistical significance at the 1% level This result indicates that when all the other factors remain unchanged, there is one- unit increase in the earnings per share ratio leads to a 0.003 units increase in the stock price of commercial banks The results of the descriptive research of the EPS variable show that while the total number of outstanding shares stays constant, the earnings value of common shares fluctuates often An important indicator of how well the business is performing in its operational activities is the increase or decrease in EPS from year to year Investors can predict future dividend payments with the use of changes in the EPS value A high EPS indicates that the company may be able to provide some level of success for its owners The study's findings can serve as a model for companies looking to improve performance to raise stock earnings, or EPS, and draw in investors The favorable impact of EPS on stock prices has demonstrated that EPS is a significant factor that investors should take into consideration Therefore, investors may take the study's findings into account when making judgments The findings of this study align with the research conducted by Bustani et al (2021), Bakhri et al (2023), and Chadi & Rasha (2022)
It can be seen from table 4.13, banks’ size (SIZE) has a positive relationship with the stock price, which is designated by the positive value of coefficient, and this result has a significant level of 1% The coefficient of the banks’ size (SIZE) equal to 0.187 which means that when all the other factors remain unchanged, if the logarithm of assets increases by 1 unit, the stock price of commercial banks will increase by 0.187 units The stronger a bank's position in the competitive market, the larger its size Bank shares are actively traded on the stock market because they offer higher payouts to investors; consequently, investors find bank shares more attractive, which increases the market price of the shares The conclusion drawn from this research is also similar to the prior research, which is the research of Rob (2017), Mohammad et al (2022) and Aryan (2022)
The inflation rate (INF) has a coefficient of 0.031, remarking the positive correlation with the stock prices of commercial banks This means that when all the other factors remain unchanged, if inflation rate (INF) increases by 1%, the stock price will increase to 0.031% respectively The results from table 4.13 show that there is a statistically significant relationship between the inflation rate and banks’ stock price This result is not similar to the expected hypothesis and many previous research such as Rob (2017) and Mohammad et al (2022) As a consequence, inflation rate has no significant impact on stock prices of commercial banks listed on Vietnam’s stock market in this research
According to the final model, gross domestic product growth rate (GDP) positively affects the stock price of commercial banks with a regression coefficient of 0.212 This means that when all the other factors remain unchanged, if gross domestic product (GDP) increases by 1%, stock price will increase by 0.212% This result is consistent with the expected hypothesis and prior research conducted by Nguyen (2023), Nguyen & Le (2023) and Chadi & Rashi (2023) However, there is no statistically significant correlation between GDP and stock prices
The estimation result of the price to earnings ratio (PE) shows a negative impact on the stock prices of commercial banks listed in Vietnam's stock market When all the other factors remain unchanged, the coefficient value is -0.007, indicating that a decrease of 1 unit in the price to earnings ratio results in a decrease of 0.007 units in the stock price of commercial banks This result contradicts the expected hypothesis and previous research by Chhetri (2023), Kumar (2017), and Sihaloho and Rochyadi (2021) The study finds that the PE ratio has an insignificant impact on the stock prices of commercial banks during the studied period
Return on assets (ROA) depicts the negative correlation with the stock price of commercial banks listed in Vietnam’s stock market The influence of return on assets is measured to be equal -7.462 The result is read as when all the other factors remain unchanged, if the return on assets, increases by 1 unit, the stock price of commercial banks will decrease by 7.462 units The results are not consistent with the expected hypothesis and many prior research like Saputra (2022) and Mohammad et al (2022) However, this result is connected to the conclusion derived from the prior study conducted by Chandra & Oses (2021) Return on assets (ROA) does not affect banks’ stock prices in the research time because table 4.13 depicts that there is no statistically significant relationship between ROA and stock prices
The exchange rate (EX) is found to be negatively correlated with the stock prices of commercial banks listed in Vietnam’s stock market The estimation of currency exchange rate (EX) variable is -13.095 As a result, when all the other factors remain unchanged, if the currency exchange rate increases by 1%, then the stock price will decrease to 13.095% Moreover, the estimated result of the currency exchange rate show that there is no statistically significant impact on banks’ stock price The conclusion of this study is consistent with the research conducted by Antonio et al
SUMMARY
Chapter 4 details the research process, which includes analyzing the characteristics of the panel data through descriptive and correlation analysis to identify multicollinearity This chapter also involves conducting regression models and testing for model defects such as autocorrelation or unrestricted heteroscedasticity The panel data underwent testing with three models - Pooled OLS,
FEM, and REM - to select the most suitable model, followed by further testing for autocorrelation and heteroskedasticity Despite the chosen model having defects, the author opted to use FGLS estimation to address the issues and ensure optimal model estimation The results indicate that two independent variables - banks' size (SIZE), earnings per share (EPS) - have positively and statistically significant influences on the stock price of commercial banks listed on Vietnam's stock market While return on assets (ROA), price to earnings ratio (PE), inflation (INF), gross domestic product growth rate (GDP) and currency exchange rate (EX) estimations were found to have no significant impact on stock price in this research Additionally, Chapter 4 offers partial answers to the research questions posed earlier in the study.
CONCLUSION AND IMPLICATION
Investors
Based on the positive relationship between earnings per share and commercial bank stock prices, investors are drawn to commercial banks with high and stable EPS
A high EPS indicates that the enterprise effectively utilizes equity capital and conducts profitable business However, there remains a tendency to manipulate and embellish accounting data Therefore, investors must stay vigilant and clear-headed, monitoring the market and recalculating the indicators related to the enterprise's reasonable revenue and expenses to assess the EPS index accurately Investors should consider purchasing shares of commercial banks that have consistently maintained a high revenue growth rate, ideally exceeding the industry average In practice, enterprises with stable net revenue growth tend to have high EPS and stable dividends Consequently, shares of such enterprises are favored in the market, leading to higher prices and giving investors optimistic expectations regarding future cash flow and capital gains Furthermore, when net profits are robust, commercial banks showcase their capacity to reinvest and expand operations, making them worthy candidates for investors' funds
According to the research findings, the size of the bank positively influences the price of bank shares; the larger the bank, the higher the price of its shares When considering investments in securities, investors are not only concerned with fluctuations in scale factors but must also accept the potential for personal risks Since not all banks aim to expand their scale, their equity capital increases In fact, many banks opt to use debt to raise additional capital for production and business expansion, which helps them avoid ownership dilution but simultaneously heightens the risk of default In conclusion, while analyzing the impact of scale on stock prices, investors must also assess the overall condition of the bank's business in the current period to determine which bank's stock is most suitable for their investment capacity
For market factors such as the inflation rate, GDP growth rate, and other macroeconomic elements, investors need to clearly understand their concepts and their influence on stock fluctuations to gain an overall perspective and speculate on trends in stock price fluctuations To avoid potential losses due to changes in stock prices, investors also require an effective plan This involves reviewing and adjusting investment portfolios based on fluctuations in market variables, particularly GDP growth rates and interest rates Rising interest rates alongside increasing GDP growth can reduce the volatility of bank stocks Therefore, to predict trends in stock price fluctuations, investors must also consider economic information related to the market
Additionally, investors can mitigate risks by diversifying their investments across various industries or by investing in gold and foreign currencies Conversely, professional investors can capitalize on investment opportunities arising from changes in stock price fluctuations to buy or sell bank stocks at favorable prices Finally, investors must periodically and continuously update themselves on market news to remain informed about changing market factors and optimize their investment strategies.
Banks’ managers
The proportional relationship between EPS and P has been verified in many empirical studies and in many markets in many different stages of the economy Enterprises that want to increase their stock prices must definitely increase their earnings per share Because investors' psychology is that when they see a business continuously increasing its profits, they will trust and have higher expectations about the value of that business, promoting an increase in stock prices To increase EPS earnings per share, enterprises have two options, one is to increase net revenue, the other is to cut costs However, to achieve high business profits or at least maintain stable profits, especially in today's competitive environment, is very difficult Because revenue is affected by many factors, from internal factors of the business to external factors, especially market demand Therefore, in addition to controlling sales, another way to increase EPS is that businesses should focus on controlling their business costs With the development of science and technology, especially in the 4.0 era, banks should use technology as an inseparable part of their operations Use software to support customer care, reduce personnel costs Or enhance software to support management accounting, especially develop a cost accounting department to control costs effectively, which is also a trend that successful businesses are applying Diversification is also an effort to cut costs
According to the research findings, the size of the bank positively influences the price of bank shares; the larger the bank, the higher the price of its shares Expanding the size of the bank enables it to diversify its financial activities and offer a broader range of goods and services, thereby providing a competitive edge Furthermore, joint stock commercial banks must implement a comprehensive growth strategy, adapt to expansion, and utilize leverage judiciously, as these factors impact the price of the bank's shares, ensuring that additional risks associated with controlled expansion are mitigated For bank managers, when scaling up the bank, it is crucial to focus on developing human resources in terms of quantity, qualifications, and management capacity to prevent a scenario where the emphasis is solely on expanding the operational network, increasing the number of branches and transaction offices, while human resources fail to meet the requirements, which can easily lead to risks for the bank Avoid the situation where expanding the scale leads to increased risks that surpass the control of the bank's management board Additionally, banks should consistently monitor and restructure their asset portfolio in an optimal manner to ensure safe and effective operations
In the relationship between banks and investors, banks should convey information to investors to help them better understand the market and stock price fluctuations Facilitate the exchange of information and opinions between parties through organizing events and seminars to meet investors by providing financial reports and transparent market information The above suggestions and recommendations not only help banks manage concerns of stock price fluctuations but also enhance the stability and sustainability of the entire banking system However, each bank's conditions and market situation require the application to be specifically adjusted.
Limitations of research
Firstly, the limitation in this thesis is the research sample Although the data is derived from the financial statements of all 18 commercial banks from the first quarter of 2015 to the fourth quarter of 2023, the number of observations is relatively limited This may not be sufficient to fully represent all the crucial factors influencing stock prices in the banking sector in Vietnam While the thesis offers valuable insights into the determinants impacting banks' stock prices during the study period, its applicability to future scenarios is not guaranteed
Secondly, the research model only considers independent variables that represent both macroeconomic and microeconomic characteristics impacting banks’ stock prices, which have been identified as the most influential variables in prior studies However, stock prices in the banking sector can also be influenced by various other macro factors such as the economy's interest rate, the political climate of a country, etc., as well as specific firm characteristics like the bank's liquidity, non-debt tax, etc The absence of these variables results in the model only partially explaining the impact of determinants on stock prices Furthermore, the model has limitations in practical application due to the missing variables, potentially leading to inaccurate influence estimations Nevertheless, the variables utilized in the research are drawn from previous empirical studies to ensure the effectiveness of the model's outcomes
Thirdly, the study focused on secondary data to conclude the final result and did not include the preference of different investors and other stakeholders.
Recommendation of further research
The research has fully addressed the research question and examined the research hypotheses regarding the correlation between macroeconomic and microeconomic factors and the stock price of commercial banks listed in Vietnam's stock market Further research should consider additional macro factors such as interest rates and the political climate of a country, as well as more firm-specific variables like dividend policies and bank profitability, to enhance the accuracy of the model This can be beneficial for practical applications such as bank management or analyzing primary data like investor preferences or stakeholder interests Additionally, while this study focused solely on the banking sector in Vietnam's stock market, future research should explore other sectors to yield optimal results and recommendations.
SUMMARY
In chapter 5, the factors affecting the stock price of commercial banks listed on the Vietnam stock market are outlined using regression results and previous research Furthermore, suggestions are offered based on the estimation results of the regression model for variables to propose solutions that may enhance the value of commercial banks' shares This chapter ends with an examination of the limitations of the thesis, such as sample size constraints, along with recommendations for future research directions
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APPENDIX Appendix 1: List of 18 commercial banks listed in Vietnam’s stock market
Number Name of Banks Code
1 Asia Commercial joint Stock Bank - ACB ACB
2 Bac A Commercial Joint Stock Bank BAB
3 Joint Stock Commercial Bank for Investment and
Development of Vietnam - BIDV BID
4 Vietnam Joint Stock Commercial bank for Industry and Trade – VietinBank CTG
5 Vietnam Export Import Commercial Joint Stock
6 Ho Chi Minh Housing Development Commercial
Joint Stock Bank – HDBank HDB
7 LienViet Post Joint Stock Commercial Bank -
8 Military Commercial Joint Stock Bank – MBBank MBB
9 Vietnam Maritime Commercial Joint Stock Bank –
10 National Citizen Commercial Joint Stock Bank -
11 Orient Commercial Bank Joint Stock Bank - OCB OCB
12 Saigon – Hanoi Commercial Joint Stock Bank –
13 Southeast Asia Commercial Joint Stock Bank -
14 Sai Gon Thuong Tin Commercial Joint Stock Bank
15 Vietnam Technological and Commercial Joint
16 Joint Stock Commercial Bank For Foreign Trade of
17 Vietnam International Commercial Joint Stock
18 Vietnam Prosperity Joint Stock Commercial Bank
Source: The author’s summary from STATA 17.0
Appendix 3: Correlation matrix at 0.1 significant level
Source: The author’s summary from STATA 17.0
Appendix 4: Correlation matrix at 0.05 significant level
Source: The author’s summary from STATA 17.0
Appendix 5: Correlation matrix at 0.01 significant level
Source: The author’s summary from STATA 17.0
Appendix 6:Regression results according to Pooled OLS Model
Source: The author’s summary from STATA 17.0
Appendix 7: Variance inflator factor (VIF)
Source: The author’s summary from STATA 17.0
Appendix 8: regression results according to Fixed Effect model – FEM
Source: The author’s summary from STATA 17.0
Appendix 8: Regresion results according to Random Effect model –
Source: The author’s summary from STATA 17.0
Source: The author’s summary from STATA 17.0
Source: The author’s summary from STATA 17.0