INTRODUCTION
Problem statement
Finance plays a vital role in a nation's economic growth and prosperity Banks are essential financial institutions that significantly influence the financial activities of businesses, manufacturing firms, and individuals, ensuring the economy operates smoothly and sustainably By facilitating the flow of capital and investing in cash-intensive projects, banks contribute to a more balanced market and enhance overall market development.
The banking industry plays a crucial role in the economy, yet its profitability has been less than satisfying Annual data indicates that from 2010 to 2015, bank earnings were significantly impacted by the economic recession of 2012, resulting in low profitability levels However, from 2015 to 2020, bank profits surged dramatically, achieving record-high levels just before the onset of the Covid-19 pandemic in 2020.
The profitability of commercial banks in Vietnam is influenced by various factors, prompting an analysis of these elements through the CAMEL model This study aims to identify the key factors affecting bank profitability and their interactions, while also evaluating the governance capacity of commercial banks, which plays a significant role in determining their financial performance.
This thesis aims to identify and analyze the key factors influencing profitability in Vietnamese commercial banks By gaining a comprehensive understanding of these factors, the research seeks to provide precise insights and actionable solutions that can enhance profitability Ultimately, the goal is to assist Vietnamese banks in maximizing their profits and improving the overall profitability of the banking sector in Vietnam.
Research objectives
Determining the influencing factors and their impact on commercial bank profitability in Vietnam, as well as suggesting strategies to boost profits for Vietnamese commercial banks
Firstly, determine the internal and external factors affecting commercial bank profitabilityin Vietnam
Secondly, determine the impact of internal and external factors on commercial bank profitability in Vietnam
Finally, provide solutions to help increase the profitability of commercial banks in
Research questions
(1) What are the internal and external factors affecting the profitability of commercial banks in Vietnam?
(2) How internal and external factors impact on commercial bank profitability in Vietnam?
(3) What solutions aid in increasing the profitability of commercial banks in Vietnam?
Research subjects
The 16 listed commercial banks in Vietnam selected by the author are those with large market capitalization, accounting for about 80% of the total capitalization of the Vietnam banking industry.
Research scope
This study focuses on 16 Vietnamese commercial banks listed on the Ho Chi Minh Stock Exchange (HSX) and the Hanoi Stock Exchange (HNX) It utilizes secondary data derived from the audited consolidated financial statements of these banks, alongside information from the General Statistics Office of Vietnam.
The topic's research period is from 2010 to the end of 2020.
Research content
The CAMEL model is utilized to analyze the factors affecting the profitability of commercial banks in Vietnam, focusing on 16 listed banks This study evaluates the varying degrees of impact from both internal and external factors to identify those that most significantly influence profitability Based on the quantitative findings, the research offers practical solutions aimed at enhancing the profit margins of commercial banks in Vietnam.
Methodology
The study employs a quantitative research method utilizing the CAMEL evaluation framework to analyze internal and external factors impacting bank profitability Secondary data is sourced from banks' financial statements and annual reports, while macroeconomic variables are obtained from the General Statistics Office A multivariate regression technique with panel data is applied to assess the influence of various factors on bank profitability Additionally, the research compares three models: the Pooled Regression Model (POLS), Fixed Effects Model (FEM), and Random Effects Model (REM) The quantitative analysis will be conducted using Stata 14.
Thesis structure
Chapter 1 is an overview about the thesis.
VIETNAM OF BANKING SYSTEM AND LITERATURE
This article provides an overview of Vietnam's banking system, discussing the theoretical framework of banking operations and relevant influencing factors It also reviews previous research on this topic to identify existing gaps that this study aims to address.
Chapter 3: Empirical strategy and data
Chapter three focuses on the data, research methods, models, and hypotheses of the study It aims to employ quantitative methods and CAMEL models to formulate research hypotheses and evaluate the profitability of commercial banks in Vietnam, utilizing secondary data from 16 listed commercial banks and the General Statistics Office.
This chapter presents the results obtained using Stata 14, along with the researcher's insights and discussions, aimed at enhancing readers' comprehension of the issue at hand.
In this final chapter, the conclusion for Chapter 4 will be presented, as well as some solutions to assist banks in increasing their profitability
CHAPTER 2 VIETNAM OF BANKING SYSTEM AND LITERATURE
2.1 Vietnam’s banking system: An overview
EMPIRICAL STRATEGY AND DATA
Empirical strategy
From the literature review in chapter 2, author have built a separate regression model appropriate to the research problem:
Where the cross – sectional dimension across banks is presented by subscript i, and the time dimension is presented by t, α is a constant term, and ε is the error term
The author analyzes the profitability of 16 listed commercial banks in Vietnam by examining key financial metrics such as Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM) This diverse approach enhances the thesis's understanding of the Vietnamese banking system, leading to more comprehensive and reliable results in evaluating bank profitability.
The author identifies several independent variables to analyze the profitability of commercial banks in Vietnam, including CAP (Capital ratio), CAR (Capital to risk-weighted assets), AQ (Asset quality), ME (Management efficiency), SIZE (Bank size), LQ1 (Loans to deposits), and LQ2 (Loans to assets) Additionally, to provide a comprehensive understanding of the macroeconomic factors influencing profitability, the study incorporates GDP (Growth in gross domestic product) and IFL (Inflation rate) as further independent variables.
This study employed quantitative research methods to analyze the relationship and correlation between variables within the context of Vietnamese commercial banks.
To ensure precise responses to research inquiries, this study will utilize several established methods in panel data estimation, including correlation analysis, Pooled Ordinary Least Squares (POLS), Random Effects Model (REM), and Fixed Effects Model (FEM).
The Pooled Ordinary Least Squares (POLS) model, characterized by constant coefficients, assumes that intercept and slope coefficients remain unchanged across different time periods and countries This model posits that the error term accounts for variations over time and across entities (Nickell, 1981) However, the lack of distinction between time and entities, coupled with the potential for the error term to embody unique characteristics of each entity, leads to a violation of the assumption that regressors are uncorrelated with the error term (Clark & Linzer, 2015) Consequently, POLS results are frequently biased and inconsistent.
Factor analysis Interpret analysis results
The Random Effects Model (REM) is a hierarchical framework that utilizes random variables as parameters, assuming that the variation among entities is random and not influenced by independent variables This model allows for the inclusion of time-invariant variables, but caution is warranted, as their impact on the dependent variable may lead to biased results (Clark & Linzer).
The Fixed Effects Model (FEM) effectively controls for all time-invariant differences between entities, ensuring that estimated coefficients remain unbiased despite the absence of time-invariant variables (Nickell, 1981) However, it is not suitable for exploring time-invariant causes of dependent variables, making it most appropriate for examining the effects of variables that fluctuate over time.
To determine the most appropriate model for analyzing the impact of variables on bank profitability, we will employ the Breusch-Pagan Lagrangian multiplier test to compare the Pooled Ordinary Least Squares (POLS) and Random Effects Model (REM) Subsequently, we will utilize the Hausman test to assess the suitability of REM versus Fixed Effects Model (FEM), with the null hypothesis stating that there is no correlation between the unique errors and the regressors.
RESULTS AND DISCUSSIONS
Descriptive statistics result of the variables
Prior to executing the estimates, the author employs descriptive statistics to gain a thorough understanding of the data, enabling the identification of various observations within the sample size The data analysis is conducted using Stata 14 software, with the descriptive statistics for both dependent and independent variables presented in Table 4.1.
Table 4.1 Descriptive statistics of variables
Table 4.1 presents a summary of descriptive statistics derived from data collected on Vietnam's banking industry between 2010 and 2020 The analysis encompasses 176 observations from 16 commercial banks, highlighting the descriptive statistics for all variables within the sample dataset.
Correlation analysis
The article employs correlation analysis to examine the relationships among various variables, ensuring their independence within the model This analysis, along with the assessment of multicollinearity, enhances the reliability of the findings The study investigates the correlation between the capital adequacy ratio (CAP, CAR), asset quality (AQ), management efficiency (ME), liquidity (LQ1, LQ2), bank size (SIZE), gross domestic product growth (GDP), and inflation (IFL) A model is deemed acceptable if the correlation coefficient between independent variables is below 0.8; otherwise, it indicates multicollinearity (Kenedy, 2008) The Pearson correlation coefficient (r) is utilized to measure the strength of these correlations Results, based on 176 observations from 2010 to 2020, are presented in Table 4.2.
Table 4.2 Correlation matrix analysis CAP CAR AQ ME LQ1 LQ2 SIZE GDP IFL CAP 1.000
The correlation matrix results shown in Table 4.2 show that the correlation coefficient between each pair of explanatory variables (between the independent variables) is less than 0.8, the model is accepted
The model results indicate that there is no significant correlation among the independent variables, with the highest correlation value recorded at 0.6864 between SIZE and LQ2 This finding confirms the appropriateness of the variables included in the model.
Multicollinearity among explanatory variables can result in misleading correlation coefficients, inflated standard errors, and inaccurate model estimates To address this issue, the author utilizes two methods: a correlation matrix and the Variance Inflation Factor (VIF) test It is essential to verify the critical assumption of no multicollinearity before proceeding with regression model estimation.
Variables VIF SQRT VIF Tolerance R-Squared
The analysis of the VIF coefficients in Table 4.3 reveals a mean VIF value of 1.90, which is significantly below the threshold of 10 Additionally, the individual VIF values for each variable are under 5, indicating the absence of multicollinearity The relationship among the explanatory variables shows similarities to autocorrelation, suggesting a strong interdependence among the independent variables Based on the correlation coefficients and VIF values, it can be concluded that multicollinearity is not present in the model, as supported by Hoang Trong and Chu Nguyen Mong Ngoc (2008).
CONCLUSION AND RECOMMENDATION
Conclusion and recommendation
The research article presents significant findings on the factors affecting the profitability of commercial banks in Vietnam from 2010 to 2020, analyzing 12 variables, including 3 dependent and 9 independent factors Among these, 9 are internal variables, while 3 external factors—bank size, GDP, and inflation ratio—are also considered Chapter 4's tests reveal that 6 independent variables significantly influence overall bank profitability, with two key factors identified as having a substantial impact By focusing on these critical variables, banks can enhance their profitability.
Based on the study's findings, the author has proposed some solutions to help commercial banks in Vietnam increase their profits
Research indicates that the ratios of loans to deposits and loans to assets have a similar effect on enhancing bank profitability To boost profits, it is recommended that commercial banks leverage financial resources effectively in their lending and investment strategies However, improper allocation of funds can expose banks to risks, particularly when utilizing short-term capital for long-term loans Maintaining a reasonable interest rate differential is crucial for maximizing bank profits Additionally, in light of the challenges posed by the Covid-19 pandemic, banks must implement strategic business plans to mitigate potential adverse outcomes.
Bank size significantly influences the profitability of commercial banks, as larger banks with substantial capital, competitive advantages, and strong credit reputations tend to gain greater trust and popularity among customers This trust allows for the expansion of business activities and higher profit generation A key marketing strategy for banks is to enhance their presence through multiple branches and large transaction offices, which boosts brand awareness and convenience for customers Increased customer satisfaction leads to more business opportunities and improved profitability In the digital age, it is essential for banks to embrace and promote digital banking systems that align with market demands.
To optimize the benefits of a favorable macroeconomic environment, banks should enhance their service activities during prosperous economic times while scaling back during downturns By adapting to market fluctuations, banks can minimize losses and mitigate credit risks Additionally, proactive measures to anticipate inflation can help safeguard against profit declines, enabling banks to seize opportunities for increased profitability.
Limitation of the thesis and future research direction
While the article's findings align with the author's expectations, it presents several limitations The study focuses on 16 commercial banks in Vietnam from 2010 to 2020, selected due to the economic fluctuations during that period, which affected the stability of many banks Consequently, only those with the most stable operations were included to represent the broader commercial banking system Additionally, the quality of the original data was subpar, significantly influencing the results Despite the author's attempts to gather audited financial statements, some crucial information remained elusive and may not accurately reflect reality.
The author aims to enhance this article through further research, focusing on the future development of Vietnam's commercial banking system By expanding the research scope and diversifying samples, the study will investigate the influence of sensitivity factors within the CAMELS framework on the profitability of Vietnamese commercial banks Additionally, incorporating the effects of the global economic crisis will provide a clearer understanding of its impact on the profitability of these banks, ultimately improving the feasibility and relevance of the research findings.
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