VIETNAM NATIONAL UNIVERSITYUNIVERSITY OF ECONOMICS AND BUSINESS FINANCE & BANKING THE INFLUENCE OF CORPORATE FINANCIAL VARIABLES ON SYSTEMATIC RISK: A STUDY IN THE VIETNAMESE BANKING SEC
LITERATURE REVIEW 0 0ĐS2
The theoretical foundation regarding the influence of financial factors on systematic risk 8 1 Overview of systematic riSÌK e sex rkrrrrke 8 2 Modern Portfolio Theory (MPT) -sss+cxsscrxxtrrkrttrkettrkirttrkrrrrrirrrrrrrrrkirrrrrrrrrrrerrrrerrrrke 9 3 Theoretical basis about efficient Market scsssssssssssessssssessssesssssssssssessssstassssessssssesssssses 10
Systematic risk has been widely debated, yet a unified definition remains elusive, with various interpretations highlighting different aspects (Smaga, 2014) Furthermore, Haldane and May contribute to this ongoing discourse, emphasizing the complexities involved in understanding systematic risk.
(2011) underscore that systematic risk is growing progressively intricate and challenging to manage, primarily owing to the expanding diversity of contemporary financial instruments and tools.
The focus on systematic risk has grown significantly since the 2007-2008 global financial crisis, as its unforeseen consequences impact both the financial system and individual banks Systematic risk, as defined by Engle & Ruan (2009) and Brunnermeier (2009), refers to the risk that escalates when the failure of one or more organizations triggers a domino effect, potentially threatening the stability of the entire financial system due to the interconnectedness of these entities.
Systematic risk in banking systems arises from interbank relationships, as highlighted by Elsinger et al (2006) and Kaufman & Scott (2003) According to Elsinger et al (2006), this risk has two main sources: first, a bank's failure to meet its interbank payment obligations can trigger a "domino effect," potentially leading to insolvency among multiple banks (Cont et al., 2010) Second, adverse economic shocks can cause significant losses in banks' financial asset portfolios, resulting in the simultaneous collapse of several institutions.
Systematic and unsystematic risks are often interconnected, as highlighted by Hu et al (2012) In risk assessment, these risks, along with total risk, are essential metrics, according to K Gupta et al (2022) Total risk can be divided into systematic risk, which affects the entire market, and unsystematic risk, which is specific to individual firms Investors can reduce or eliminate unsystematic risk by maintaining a well-diversified portfolio.
Systematic risk refers to how a firm's performance correlates with the overall economy, as defined by Kingsley (2008) This type of risk, also known as beta, is calculated using the market model.
Systematic risks are unique factors that affect individual investments and can be mitigated through diversification In developed markets, where diversification is assumed to be effective, systematic risk remains as the residual risk that requires managers to focus on its identification, measurement, and the implementation of preventive strategies Various theoretical models have been developed to quantify systematic risk effectively.
Systematic risk, often measured by beta, holds significant relevance in the realm of accounting research, especially within capital markets research (Gwangcheon Hong,
In their 2022 study, Vilayphone Vongphachanh and Khairunisah Ibrahim identified two primary types of risk: systematic and unsystematic Unsystematic risk, also known as diversifiable risk, can be managed or reduced, while systematic risk, associated with market fluctuations, is uncontrollable and cannot be diversified away For firms and investors, systematic risk is of greater significance, as it requires strategic management and mitigation efforts to address its inherent challenges.
In 1952, Markowitz introduced the groundbreaking "Portfolio Selection" article, establishing the foundation for Modern Portfolio Theory (MPT), which has become essential in international business MPT provides a strategic framework for risk-averse investors to construct investment portfolios that optimize returns while managing market risk The theory emphasizes that a diversified portfolio, consisting of various asset classes, can yield consistent higher returns, though it comes with increased risk Consequently, diversification is a fundamental principle of Modern Portfolio Theory, helping risk-averse investors to effectively create a well-rounded portfolio.
Modern Portfolio Theory (MPT) emphasizes the importance of diversification in investment strategies, aiming to create a portfolio of assets that collectively lower risk compared to individual investments This principle is effective because different asset types often experience value fluctuations in different directions Importantly, diversification can still reduce risk even when asset returns are positively correlated, highlighting its significance in effective risk management.
Modern Portfolio Theory (MPT) emphasizes that assets in an investment portfolio should not be selected in isolation, as their optimization relies on the interdependence of their price movements It is essential to analyze how the price fluctuations of each asset correlate with those of other assets in the portfolio to achieve optimal performance.
1.2.3 Theoretical basis about efficient market
The Efficient Market Theory is a cornerstone of modern financial theory, significantly influencing investment strategies for over thirty years, from the early 1960s to the mid-1990s Its practical implications have shaped how investors understand market dynamics and asset pricing.
Efficient markets, as defined by Fama (1970), are characterized by the presence of numerous rational profit maximizers who actively compete to forecast future market values of individual securities, with vital current information readily accessible to all participants Karz (2012) supports this view, emphasizing the compelling nature of Fama's argument regarding market efficiency.
In an active market with knowledgeable investors, securities are accurately priced to reflect all available information, as argued by Fama He identified three forms of market efficiency: Strong-form efficiency, where stock prices incorporate all information—public, personal, and confidential—preventing any investor from gaining a competitive edge; Semi-strong form efficiency, where prices adjust to reflect publicly available financial data, such as company announcements and balance sheets; and Weak efficiency, which indicates that current prices account for all past stock prices and trading activity, making it impossible to predict future price movements based on historical data.
Factors affecting the risk system of industry groups bank in the stock market
The Liquidity Ratio (LIQ) is a crucial factor affecting a company's valuation, reflecting its ability to convert assets or inventory into cash This ratio is calculated using a specific formula, highlighting its importance in assessing financial health.
Liquidity, defined as the ratio of current assets to current liabilities, plays a critical role in financial analysis M.C Jensen (1986) posits a positive correlation between liquidity and systematic risk, a view supported by Pham Tien Minh (2017) However, contrasting findings from a study by Loo Sin Chun, Meharani Ramasamy, Rashed Nawaz, and W Ahmed (2017) suggest that liquidity does not significantly influence beta, or systematic risk.
Financial leverage in a business indicates how much borrowed capital is used to enhance Return on Equity (ROE) It is measured by the debt-to-assets (D/A) ratio, which quantifies the level of financial leverage employed by a company.
11 debt-to-equity (D/E) ratio In this study, the author employs the formula introduced by Hassan and Bashir (2003):
LEV= Total liability / Total asset
A 2017 study by Pham Tien Minh reveals that increasing financial leverage (LEV) heightens the burden of debt repayment, leading to a greater risk of failing to meet financial obligations and subsequently increasing systematic risk In contrast, research by Loo Sin Chun and Meharani Ramasamy indicates that leverage and liquidity ratios do not significantly impact financial outcomes.
Bank size is a vital financial metric that reflects a bank's financial strength It is commonly assessed using total asset data over a designated timeframe, with its measurement achieved through the natural logarithm of total assets.
Bank size = Log (Total asset)
Research by Kieu and Nhien (2020) highlights a positive correlation between firm size and stock prices This finding is supported by Naveed and Ramzan (2013), who, using the Fixed Effects Model (FEM), analyzed data from 15 banks in Pakistan over a four-year period from 2008 to 2011.
Operating efficiency is the capability of a company to minimize operational costs while effectively meeting its goals through a strategic blend of skilled workforce, optimized processes, and cutting-edge technology To evaluate a company's operating efficiency, one can calculate it by summing all operating expenses and dividing that total by the overall revenue.
Research by Le Truong Niem (2022) indicates that operational performance negatively impacts systematic risk This suggests that companies exhibiting high operational performance are generally more stable, which in turn helps to mitigate systematic risk.
In a study by Pham Tién Minh (2017), it was found that effective asset management by enterprises is viewed positively by the market, resulting in a decrease in the systematic risk linked to those businesses.
Return on Assets (ROA) is a key financial metric that measures a company's profitability by dividing its after-tax profit by the average total assets over a specific operational period This ratio indicates how effectively a company generates after-tax profit for each dollar invested in its total assets, highlighting the efficiency of asset utilization and overall asset profitability.
ROA = Net profit / Total asset
Research by Pham Tien Minh (2017) highlights that Return on Assets (ROA) is a crucial financial factor that effectively reduces systematic risk in banks An increase in ROA signals enhanced profitability, leading to a positive market perception and a subsequent decrease in the bank's systematic risk.
Growth rates represent the percentage change of a variable over a designated period, indicating whether the variable is increasing or decreasing Originally utilized in biology to analyze population changes, growth rates are now essential in assessing economic trends, corporate performance, and investment returns The methods for calculating growth rates vary based on the specific insights or information required.
GROW = % growth of total assets each year
Research by Hyunjoon Kim, Jiyoung Kim, and Zheng Gu (2010) demonstrates that GROW influences systematic risk, supporting the conclusions of Muhammad Junaid Iqbal and S Shah (2012).
The market control variable is assessed by determining the standard deviation of daily returns on the VNIndex By analyzing both volatility and beta, investors gain a deeper understanding of how market conditions influence the performance of particular sectors or stocks, including those in the banking sector.
The research results of Do Thu Hang and Pham Thi Hoang Anh (2020), showed that the volatility (VOL) of the previous period will reduce systematic risk in the subsequent period.
Table 1 Summary factors affecting the risk system of industry groups bank in the stock market from different country
Country Source LIQ | LEV |OE | ROA | SIZE | GROW | VOL
Malaysia Loo Sin Chun and
Michael Jarvela, James Canada Kozyra, and Carla +
Li Zhang, N Nielson, and Joseph D Haley (2019)
Muhammad Junaid Iqbal and S Shah (2012)
Rashed Nawaz and colleagues (2017); Sajid Iqbal (2015)
Vilayphone Vongphachanh and Khairunisah Ibrahim's study (2020)
I Kadek, Rian Mahendra, and A Gst.Ngr.Suaryana (2023)
Nguyen Thi Kim (2003) and Pham Tien Minh et al.(2017)
Do Thu Hang and Pham Thi Hoang Anh (2020)
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RESEARCH METHODOLOGY .ssssssssssssesssssstessssssesessnseesessnseeesssseeeesnseeesssnseesessueenssnsensesnnesessnnness 16 2.2 Research model and method of data Collection sssssssssssssessssesssssesssssssssssesssseseesssesssseeeessans 16 2.2.1 Research MOE] vsscsssssssssssssssseessssseessssssessssssessssssesessssseesssseesssssseessseueessssseessssieesssssuessssetensssaessssnteesssaes 16 2.2.2 Research 0 hố ẽ .ẽ.ẽẽ
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The study analyzed a sample of 18 firms listed on the Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) from 2017 to 2022 The criteria for inclusion required that companies be listed before January 1, 2017, have complete financial reports available, and remain continuously traded without delisting during the study period, with any temporary trading suspensions lasting less than three months.
Table 22 Stock codes in the research sample fears [fs Piomes—To
Joint Stock Commercial 10 Southeast AsiaBank for Investment and Commercial Joint StockDevelopment of Vietnam Bank
4 Military Commercial Joint MBB 13 Orient Commercial Joint | OCB
5 | Vietnam Technological and | TCB 14 | Vietnam Export Import | EIB
Commercial Joint Stock Commercial Joint Stock Bank
Asia Commercial Joint Stock | ACB TienPhong Commercial | TPB Bank Joint Stock Bank
LienViet Commercial LPB Joint Stock Bank
Ho Chi Minh city National Citizen bank NVB Development Joint Stock
The research combines two methods: qualitative research and quantitative research.
The research team will utilize qualitative methods by gathering credible materials and reputable articles from prior studies related to the topic, which helps in building a strong theoretical foundation and logical argument for the research subject.
The quantitative method involves modeling information variables that affect systematic risk through regression analysis, utilizing a comprehensive dataset to identify the most impactful variables over a designated time frame Hsiao C (2003) highlights key models in panel data analysis, including Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM), to analyze the influence of financial variables on systematic risk.
The Pooled Ordinary Least Squares (POLS) regression model assumes equal treatment of cross-sectional data points, the Fixed Effects Model (FEM), and the Random
The Random Effects Model (REM) is used to forecast the influence of specific financial variables To determine the suitable model, the Hausman test is performed to compare Fixed Effects Model (FEM) and REM Additionally, the Durbin-Watson test assesses autocorrelation, while tests for heteroscedasticity are conducted using Stata 16 software.
2.3.1 Data analysis method Panel Data
2.3.1.1.The Ordinary Least Squares regression model (Pooled OLS)
Eit = do + o1LIQit + a2LEVit + a30Eit + a4ROAit + œsSI2ZEi+ + a6GROWit+ a7VOLit +eit œ1 œ7: the coefficients of the respective independent variables Index i represents each company, and index t represents the observation year;
Eix: Systematic risk of company i in year t; eit! error term with a normal distribution, varying by i and t;
LIQit, LEVit, OEit, ROAit, SIZEit, GROWit, a7VOLit: respectively denote liquidity, financial leverage, operating efficiency, profitability, size, and growth rate of bank iin year t.
The Pooled Ordinary Least Squares (POLS) model analyzes panel data by treating it as uniform, neglecting the unique characteristics of individual entities, such as banks This limitation can result in biased estimates, as it fails to capture the distinct influences of factors like culture and attributes on the target variable In contrast, the Random Effects Model (REM) and Fixed Effects Model (FEM) effectively account for these individual differences, providing a more accurate analysis.
The Fixed Effects Model (FEM) examines the relationship between the residuals of each observation and the explanatory variables, effectively controlling for time-invariant characteristics This approach isolates the impact of these specific traits, allowing for a more accurate estimation of how the explanatory variables influence the dependent variable.
Based on Anderson's research & Minnema (2018), Tu & Nguyen (2014) and Shukeri et al (2012), we have the regression equation of the fixed effects model:
Bit = do + Œ1LIQ¡+ + a2LEVit + a30Eit + a4ROAIt + ASSIZEit + AGGROWi t+ a7VOLit + uit
Where: ơi a7: the coefficients of the respective independent variables;
Index i represents each company, and index t represents the observation year ;
In the analysis of bank performance, key variables include white noise as the residual, the bank-specific intercept coefficient, and factors such as liquidity, financial leverage, operating efficiency, profitability, size, and growth rate for bank i in year t.
The author utilizes the Random Effects Model (REM) when the variability of individual observations is not related to the explanatory variables In contrast to the Fixed Effects Model (FEM), REM assumes that the differences between subjects are random and do not correlate with the predictors or independent variables in the model.
Research-based dynamic by Anderson & Minnema (2018), Tu & Nguyen (2014) and Shukeri et al (2012), Regression equation of the operating model random is formulated as follows:
Bi,t = œ0 + œ1LIQi,t + a2LEVi,t + œ3OEi,t + a4ROAI,t + œ5SIZEl,t + A6GROWi,t + a7VOLit + Eitt Uit
The coefficients a1 to œ7 correspond to the independent variables in the analysis In this model, index i denotes each company, while index t indicates the observation year The term uit represents the white noise or residuals, capturing random variations Additionally, ai encompasses all unobservable factors that differ across entities but remain constant over time, whereas €iô accounts for unobservable factors that fluctuate among entities over time.
21 ® LIQit, LEVit, OEit, ROAit, SIZEit, GROWit, a7VOLit: respectively denote liquidity, financial leverage, operating efficiency, profitability, size, and growth rate of bank iin year t.
3.1 Overview of the Vietnamese banking sector
3.1.1 Introduction to Vietnamese banking sector
Commercial banks are essential for the socioeconomic development of both developed and developing nations, as they significantly contribute to economic growth by efficiently allocating financial resources Their pivotal role is fundamental to the sustainability of the financial system within the economy.
Over the past 60 years, Vietnam's banking system has reached significant milestones and positively impacted the economy Established in 1976 with the founding of the State Bank of Vietnam, the banking sector is relatively young compared to other Asian nations However, it has mirrored the country's swift economic growth, showcasing one of the fastest growth rates in the world.
3.1.2 Current situation of systematic risks in Vietnamese banking sector in the period 2017-2022
The Vietnamese banking sector presents a mixed landscape, with certain banks demonstrating robust growth while others face significant risks This analysis, informed by data from the central bank and various organizations, will delve into the systematic risks affecting the banking industry in Vietnam.
Figure 3.1 Systematic risk and VNIndex from 2017 to 2022
Chart 3.1 demonstrates that systemic risk in the banking sector exhibited instability during the period from 2017 to 2022 The chart indicates that over a 6-year period, the rate of return for the VN-Index stock market index was at its lowest point in
In 2018, the VN-Index fell below the 900-point mark, marking a nearly 10% decline from its peak in 2017, while market liquidity remained low Over the next three years, the VN-Index experienced stable growth but faced another decline in 2022 Notably, the banking sector's beta was at its lowest in 2017 and peaked in 2022.
2020, the beta of the banking sector remained below 1, indicating that the volatility of banking sector stock prices was lower than that of the overall market However, from
From 2021 to 2022, the beta of the banking sector rose above 1, indicating that the price volatility of banking stocks surpassed that of the overall market This suggests that while banking sector stocks have the potential for higher returns, they also carry a greater level of systematic risk.
3.2.1 Descriptive statistics of the variables.
The relationship between the dependent variable and the independent variable
A scatter diagram visually represents pairs of numerical data, plotting one variable on the x-axis and another on the y-axis to identify potential relationships between them When the variables are correlated, the data points will align closely along a line or curve, indicating a stronger correlation Each point in the scatter diagram corresponds to an individual observation, making it an effective tool for analyzing the relationship between two variables.
Figure 3.2: Description of the correlation between LIQ and Beta oO
Figure 3.3 : Description of the correlation between LEV and Beta ©°
Figure 3.4: Description of the correlation between OE and Beta e e e s
Figure 3.5: Description of the correlation between ROA and Beta e oO
Figure 3.6: Description of the correlation between SIZE and Beta
Figure 3.7: Description of the correlation between GROW and Beta
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Figure 3.8: Description of the correlation between VOL and Beta ® VOL Fitted values
The analysis of the seven charts reveals that the scattered points are tightly clustered, suggesting a correlation between the independent variables and the dependent variable, Beta However, this correlation is relatively weak.
The LEV chart demonstrates a strong positive correlation with the dependent variable Beta, as evidenced by the points clustering along a straight line that extends horizontally from the origin This relationship suggests that changes in LEV directly influence Beta, indicating that an increase in LEV is likely to result in a corresponding increase in Beta.
The charts for LIQ, OE, SIZE, GROW, ROA, and VOL in relation to the dependent variable Beta reveal a consistent pattern The data points cluster along the horizontal axis, indicating a linear relationship with a zero slope This suggests that as the independent variables increase, the fluctuations in the value of Beta remain minimal.
Quantitative research reSults 1 6 ẽẽốẽẽẽẽ
3.3.1 Results of the POLS model.
3.3.1.1 Results of the POLS model.
Table 3.3: Regression model Pooled OLS
Beta Coef Std Err t P>|t| [95% Conf Interval]
The analysis of the Pooled OLS model, based on historical data, reveals an R-squared value of 50.13%, indicating a satisfactory fit Among the independent variables, return on asset (ROA), bank size (SIZE), growth rate (GROW), and volatility (VOL) significantly influence the dependent variable Beta, as evidenced by their p-values below 0.05 Notably, the growth rate (GROW) exhibits a negative relationship with systematic risk (Beta), while ROA and SIZE also show a negative effect Additionally, with a Prob>F value of 0.0000, the Fixed Effects Model (FEM) is deemed more suitable than the Pooled OLS model, prompting the author to proceed with both FEM and Random Effects Model (REM) analyses, followed by a Hausman test for model selection.
3.3.1.2 Testing the weaknesses of the POLS model.
The research author evaluated the limitations of the Pooled OLS model by employing several statistical tests, including the White test to identify heteroskedasticity, the Breusch-Godfrey test to assess autocorrelation, and the Variance Inflation Factor (VIF) test to detect multicollinearity.
Table 3.4: Variance inflation factor - VIF
OE 1.44 0.695400 GROW 1.25 0.801192 VOL 1.18 0.848772 ROA 1.11 0.899601 LEV 1.11 0.902506 Mean VIF 1.40
To assess multicollinearity in the Pooled PLS model, the research team utilized the Variance Inflation Factor (VIF) test According to standard research literature, a VIF value of less than 10 indicates the absence of multicollinearity However, this threshold applies specifically when the model is based on data collected through a Likert scale.
The study utilizes a dataset derived from historical stock market data of the VNIndex, analyzing the potential for multicollinearity in the variables According to Table 3.4, the White test results indicate no evidence of multicollinearity, as all variables exhibit a Variance Inflation Factor (VIF) of less than 2.
3.3.2 The results of the Fixed Effects Model (FEM) and the Random Effects Model
Beta Coef Std Err t P>|t| [95% Conf Interval]
The analysis in Table 3.5 reveals that Operating Efficiency (OE), Company Size (SIZE), and Growth Rate (GROW) significantly influence systematic risk, with p-values below 5% This supports the author's hypothesis regarding their impact on the dependent variable Notably, an increase in Company Size correlates with a rise in systematic risk due to its inverse relationship with Beta In contrast, both Operating Efficiency and Growth Rate exhibit a negative effect on Beta, indicating that as Growth Rate increases, systematic risk decreases.
The groups of independent variables, including liquidity (LIQ), leverage (LEV) and return on asset (ROA), do not have a significant impact on the dependent variable
(Systematic Risk) All of these variable groups have p-values greater than the 5% alpha level.
Beta Coef Std Err t P>|t| [95% Conf Interval]
The Random Effects Model (REM) regression analysis revealed that four independent variables—Return on Assets (ROA), bank size (SIZE), growth rate (GROW), and volatility (VOL)—significantly influence the dependent variable (Beta), with all exhibiting significance levels below 5%.
The independent variables liquidity (LIQ), leverage (LEV), and operating efficiency (OE) are not significant, as their significance levels exceed the 5% alpha threshold, indicating they do not influence systematic risk (Beta) This aligns with the findings of Loo Sin Chun and Meharani Ramasamy (1989) from Malaysia.
Table 3.6 reveals that the growth rate (GROW) negatively impacts systematic risk (Beta), indicating that an increase in GROW leads to a decrease in systematic risk Conversely, the return on assets (ROA), bank size (SIZE), and volatility (VOL) positively influence Beta, suggesting that increases in these variables result in higher systematic risk.
Compared to the Fixed Effects Model (FEM), the Random Effects Model (REM) has more variables affecting systematic risk (Beta).
The Random Effects Model presented in Table 3.6 demonstrates an R-squared value of 0.4993, which signifies that the independent variables in the regression model account for 49.93% of the variation in the dependent variable.
After conducting the regression with the retained variables, the regression model is as follows:
The author notes that return on assets (ROA) positively influences systematic risk, indicating that a one-unit increase in a bank's assets leads to a 22.76-unit rise in systematic risk These findings align with Hong Nhung Do (2017) but contrast with the research of Nguyen Thi Kim (2003), Pham Tien Minh et al (2017), and Le Truong Niem (2020).
The size of a bank (SIZE) positively influences systematic risk, with a one-unit increase in a bank's assets leading to a 0.3442394 unit rise in systematic risk This indicates that expanding a bank's scale can negatively impact its risk profile if not managed properly, as larger banks may face heightened systemic risk These findings align with previous research by Kieu and Nhien (2020) and Ramzan (2013), as well as Rashed Nawaz et al.
The author finds that an increase in the GROW index negatively impacts the systematic risk (Beta) of a bank, indicating that a 1-unit rise in GROW leads to a decrease of 1.331625 units in systematic risk This suggests that a higher growth rate is beneficial for banks, as it correlates with an increase in assets and indicates successful operations The findings align with both empirical and theoretical expectations, as larger banks typically possess more resources, a stronger market presence, and greater expertise These factors enhance their ability to diversify and manage challenges, ultimately reducing systemic risk.
35 that operate successfully will reduce their systematic risk This outcome aligns with the findings of studies by Hyunjoon Kim, Jiyoung Kim, Zheng Gu (2010), Muhammad Junaid Iqbal, and S Shah (2012).
Table 3.6 indicates that the variable VOL positively influences the dependent variable Beta, suggesting that a 1-unit increase in the market control variable leads to a 1.47054-unit rise in the bank's systematic risk This finding is in contrast to the results reported by Do Thu Hang and Pham Thi Hoang Anh in their 2020 study.
The Hausman (1978) specification test is employed to determine whether to use the Fixed Effects Model (FEM) or the Random Effects Model (REM) in statistical analysis The null hypothesis (H0) posits that the appropriate model is the Random Effects Model (REM).
The Alternative Hypothesis (H1) suggests a correlation between the explanatory variables and the random component, indicating that the Fixed Effects Model (FEM) is more suitable than the Random Effects Model (REM).
Coef Chi-square test value 15.45
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The study investigates the impact of financial variables on systematic risk within the Vietnamese banking sector, utilizing a combination of qualitative and quantitative data collection methods It analyzes data from 18 publicly traded commercial banks in Vietnam, covering the period from 2017 to the end of 2022 A panel data regression model was developed using balanced panel data, applying Pooled Ordinary Least Squares (POLS), Fixed Effect Models (FEM), and Random Effect Models (REM) for comprehensive analysis The research also incorporated the Hausman test and other diagnostic assessments through Stata 16 software, leading to several significant findings detailed in Chapter 3.
The Hausman test results indicate that the Random Effect Model (REM) is more suitable than the Fixed Effect Model (FEM) for analyzing the relationship between financial information and systematic risk in banks In the context of the Vietnamese banking sector, the growth rate (GROW) is identified as a financial factor that mitigates systematic risk, whereas return on assets (ROA), bank size (SIZE), and volatility (VOL) are associated with an increase in systematic risk.
To effectively reduce systematic risk, banks should prioritize maintaining a stable growth rate (GROW) Research indicates that banks demonstrating consistent growth are generally more stable, which aids in mitigating systematic risk Therefore, it is essential for banks to implement strategies and operations that not only sustain but also enhance their growth rate in a sustainable manner.
As banks expand and scale their operations, it is crucial to prioritize the optimization of asset and risk management This approach helps to manage financial resources effectively and mitigate risks, ensuring that growth is achieved without increasing systemic risk unnecessarily.
Overall, the study highlights that the group of variables: return on asset (ROA), growth rate (GROW), bank size (SIZE), and volalitily (VOL) have an impact on systematic
41 risk, which aligns with the research auhtor's expectations Meanwwhile, these variables liquidity (LIQ), financial leverage (LEV) and operating efficiency (OE) do not have an impact on systematic risk (Beta).
While this study has yielded certain results, it is important to acknowledge its limitations Time constraints and limited resources hindered the accuracy of assessments and arguments regarding the collected data The research faced challenges such as restricted access to relevant information and a focus solely on commercial banks listed on the stock market, excluding non-listed and foreign banks.
To enhance future research, the author should broaden the study's scope by incorporating a more diverse sample and utilizing various models for thorough analysis, leading to a comprehensive and detailed research outcome.
The research topic is a crucial starting point for broader research objectives, especially within the banking sector, which plays a vital role in a nation's economy Additionally, it is essential to explore the economic variables and their influence on systematic risk to guide the development of this research.
In their 2012 study published in the Scientific Bulletin - Economic Sciences, Anastasios Konstantinidis, Androniki Katarachia, George Borovas, and Maria Eleni Voutsa explore the transition from the efficient market hypothesis to behavioral finance They investigate whether behavioral finance can emerge as the new dominant model for investing, highlighting its implications for understanding market dynamics and investor behavior This research contributes to the ongoing debate about the efficacy of traditional investment theories versus the insights offered by behavioral finance.
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Liu, D Y., Wu, Y C., Lu, W M., & Lin, C H (2017) The Matthew effect in the casino industry: A dynamic performance perspective Journal of Hospitality and Tourism Management, 31, 28-35.
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Minh, P T., Bich, B H H., & Thao, N T T Impact of Financial Factors on Systematic Risk - A Study in the Industry Groups in the Ho Chi Minh Stock Market 20(Q4-2017), pp.88-94
Nguyen Thi Minh Hue, Tran Dang Kham, Tran Thi Lan Huong (2015) Application of value at risk methodology for measuring systematic risk in Vietnam stock market Economic & Development, 218(II), pp.19-26.
Nguyen Thi Thanh Huyen (2015) Application of modern financial theory to measure the risks in investing shares on vietnam's stock market Danang University Journal of Science and Technology, 12(97), pp.89-93
Nong Thi Hai Yen, Vu Thi Thuy Van, and Le Hoang Anh (2018) explore the measurement of systematic risk within financial institutions in the Vietnamese stock market, highlighting its significance in investment strategies Additionally, Olibe, K.O., Michello, F.A., and Thorne, J (2008) examine the relationship between systematic risk and international diversification, providing empirical insights that underscore the importance of understanding risk in global finance Together, these studies contribute to a deeper comprehension of systematic risk and its implications for investors and financial analysts.
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Rashed Nawaz, Wagar Ahmed, Imran, Sabeela Sabir, Muhammad Arshad, Tayyaba Rani and Adnan Khan (2017) Financial Variables and Systematic Risk Chinese Business Review, 16(2).
The study by Sharif et al (2016) investigates the factors influencing systematic risk in both isolated and pooled estimation contexts within Pakistan's banking, insurance, and non-financial sectors The research provides empirical evidence highlighting the distinct impacts of various determinants on systematic risk across these industries By analyzing data from multiple sectors, the authors emphasize the importance of understanding sector-specific risks and their implications for financial management and investment strategies This comprehensive examination contributes valuable insights to the fields of accounting, finance, and risk management, particularly in the context of emerging markets like Pakistan.
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Vo Xuan Vinh, Dang Quoc Thanh (2014) Vo Xuan Vinh, Dang Quoc Thanh (2014). Economic & Development, 209 (11/2014), pp.102-111
Vongphachanh, V., & Ibrahim, K (2020) The Effect of Financial Variables on Systematic Risk in Six Industries in Thailand ABC Journal of Advanced Research, 9(2), 63-68.
Vu, V.T.T., Phan, N.T and Dang, H.N (2020) Impacts of Ownership Structure on Systematic Risk of Listed Companies in Vietnam The Journal of Asian Finance, Economics and Business, [online] 7(2), pp.107-117.
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