Khóa luận tốt nghiệp: The influence of corporate financial variables on systemic risk: A study in the Vietnamese banking sector

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

VIETNAM NATIONAL UNIVERSITY

UNIVERSITY OF ECONOMICS AND BUSINESS

FINANCE & BANKING

THE INFLUENCE OF CORPORATE FINANCIAL VARIABLES ON

SYSTEMATIC RISK: A STUDY IN THE VIETNAMESE BANKING

SECTOR

Instructor : — TS Vu Quoc Hien

Class : QH 2020E TCNH CLC 3

Ha Noi —- 2023

VIETNAM NATIONAL UNIVERSITY

UNIVERSITY OF ECONOMIC AND BUSINESS

FINANCE & BANKING

THE INFLUENCE OF CORPORATE FINANCIAL VARIABLES ON

SYSTEMATIC RISK: A STUDY IN THE VIETNAMESE BANKING

SECTOR

Instructor : — TS Vu Quoc Hien

Class : QH 2020E TCNH CLC 3

Ha Noi —- 2023

During the time of researching and implementing this research, I have received veryenthusiastic help, valuable words of encouragement With all respect and gratitude, Iwould like to express our sincere thanks to: The Board of Directors of the University ofEconomics and Business - Vietnam National University has built a learning environment

that helps me as well as the students of the whole university to be motivated,

opportunities to access and practice scientific research I sincerely thank the teachersand experts of the Faculty of Finance and Banking and other faculties in the university

for taking the time to answer and analyze the questions, contributing to creating a foundation to help me confidently implement this research I would like to express my

deep gratitude to Vu Quoc Hien - the person who directly guided me He is a verydedicated and enthusiastic teacher with new directions, detailed communication, franksuggestions and especially he is always conscious to show me that the greatest value Ireceive after completing my research I feel very fortunate to receive his support For thefirst time conducting research, | still have many limitations and can't be blamed formistakes I look forward to receiving comments from teachers and readers to improve

the research.

Ha Noi, Octorber 30", 2023

Signature of Student

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3.1 Overview of the Vietnamese banking S€CtOL sssssssssssssssssessssetsssssiessssstecsssmessssiessssnieenssaes 23

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3.1.2 Current situation of systematic risks in Vietnamese banking sector in the period

POLS Pooled regression modeROE Return on Equity

ROA Return on Assets

EBIT Earnings Before Interest and Taxes

mm CAPM Capital Asset Pricing Model

Modern Portfolio Theory

iv

LIST OF TABLES

index [Name

the stock market from different country

LIST OF FIGURES

Figure 2.1 Model to study factors affecting sytematic risk of Vietnamese banking

from 2017 to 2022Figure 3.1 Systematic risk from 2017 to 2022

Figure 3.2 Description of the correlation between LIQ and Beta

Figure 3.3 Description of the correlation between LEV and Beta

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2022 The research employs three estimation models: Ordinary Least Squares (OLS),Random Effects Model (REM), and Fixed Effects Model (FEM) The test results indicatethat the REM model is the most appropriate To enhance the reliability and effectiveness

of the model, tests for model misspecification are conducted The results reveal the

presence of autocorrelation, and the Generalized Least Squares (GLS) model is used to

address this issue Experimental results on banks in the HOSE and HNX stock markets indicate that return on assets (ROA), bank size (SIZE), and volatility (VOL) have positive

impacts on systemic risk, while growth rate (GROW) exerts a negative influence

Keywords: Beta, financial variables, industry, OLS model, FEM, REM, systemic risk.

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1 The relevance of the research topic

Conducting research to assess the systematic risk of stocks is indispensable forboth investors and fund managers due to its direct influence on the anticipated VOLs of

investments In addition to addressing the challenges associated with quantifying

systematic risk, it is equally essential to explore the factors that influence this coefficient

- essentially, what variables can modify the systematic risk of stocks Multiple empiricalstudies have demonstrated that variations in systematic risk among companies are a

consequence of distinct financial decisions, substantiated by diverse financial data.

Notably, most of the impact studies concerning financial information and its effect on thesystematic risk of enterprises are predominantly carried out in developed countries likethe United States, Canada, and European countries Nevertheless, Vietnam has seenlimited empirical research on this subject, particularly in the banking sector

As a result, the primary goal of this study is to investigate the influence offinancial information (in the form of financial variables) on the systematic risk of stocks

in the banking sector in Vietnam This research contributes an empirical perspective to

financial theory, particularly regarding systematic risk, within the context of anemerging and rapidly growing market like Vietnam Simultaneously, the study addresses

a limitation observed in previous research by considering the unique characteristics ofindividual enterprises and evaluating model deficiencies, thus ensuring the mostappropriate framework for analysis This approach is aimed at producing cohesive andrepresentative results that accurately depict the dynamic nature of the industry and the

crucial factors within it Additionally, the research focuses on the Vietnam banking

sector, an industry of paramount importance for the nation's long-term development as

it strives towards industrialization and modernization

The research helps investors to have better judgments and reasonable policies

when deciding to invest in banking stocks The first focus of this study is to identify,consider and evaluate the influencing factors and determine the extent of the impact ofmacro and micro factors on the share value of the banking sector in the Vietnam stockmarket from 2017 to 2022 Therefore, the topic " The Influence of Corporate FinancialVariables on Systematic Risk: A Study in the Vietnamese Banking Sector" was selected toconduct the research

2 The research subjects:

The central objective of this study is to comprehensively investigate the financialfactors that play a pivotal role in shaping the systematic risk profile within the domain ofthe banking industry on the Vietnamese stock market This research endeavor is poised

to offer a deeper understanding of the intricate relationships and mechanisms thatcontribute to the systematic risk experienced by banks in Vietnam By examining these

financial factors, the study seeks to shed light on how economic and financial conditions

impact the stability and performance of the banking sector This analysis is particularly

relevant given the banking industry's crucial role in the country's economic landscape,

where it acts as a key intermediary and driver of financial stability

3 The research questions:

In light of author’s stated research objectives, the author have devised thefollowing research questions to guide our study:

First and foremost, the author endeavors to discern the corporate financial

variables that wield a significant influence on the systematic risk observed within

industry groups related to banking on the Vietnamese stock market This line of inquiry

is of paramount importance as it delves into the intricacies of the financial components

that impact the stability and risk profile of the banking sector, a critical player in

Vietnam's economic landscape By meticulously identifying and examining thesevariables, the author aims to gain a comprehensive understanding of the multifacetedfactors that underpin systematic risk within this specific industry segment

Furthermore, the study seeks to unravel the extent of influence that theseidentified financial variables exert on the systematic risk within the banking sector Thisaspect of the author's investigation is instrumental in providing a nuancedunderstanding of the role and significance of each financial variable in shaping theoverall systematic risk By quantifying and assessing the relative importance andmagnitude of these variables, the author can elucidate their contributions to thesystemic risk and gain a deeper insight into the dynamics governing systematic riskwithin this particular context This exploration not only enhances our comprehension ofsystematic risk but also aids in the formulation of informed strategies for riskmanagement and decision-making within the banking industry on the Vietnamese stock

market, which, in turn, has broader implications for the nation's economic stability and

growth.

4 Research scope

This study centers its focus on the comprehensive examination of systematic risk

within the Vietnamese banking sector Systematic risk, a crucial element in the realm of

finance, carries significant implications for the overall stability and performance ofbanks operating in Vietnam Understanding and dissecting the intricacies of systematicrisk within this specific financial domain is essential to not only financial professionalsbut also policymakers and stakeholders aiming to ensure the sector's sustainable

growth and stability.

The primary research object of this study is the assessment of how corporatefinancial variables influence systematic risk within the Vietnamese banking sector Thebanking industry serves as a cornerstone of the nation's economic landscape, playing avital role in intermediating financial activities and contributing to overall economicdevelopment By exploring the impact of corporate financial variables on systematicrisk, this research aims to shed light on the factors that shape the risk profile of banks inVietnam This knowledge is indispensable for both financial practitioners and regulatorsseeking to make informed decisions and implement effective risk management

strategies in this dynamic and crucial sector.

It's essential to emphasize that the study's scope is deliberately confined tostocks listed on the HOSE (Ho Chi Minh Stock Exchange) and HNX (Hanoi StockExchange) between the years 2017 and 2022 This specific timeframe and spatialconstraint have been imposed to facilitate a more focused and comprehensive

examination of the financial factors that influence systematic risk within this particular

subset of the Vietnamese stock market This temporal boundary will allow for an depth exploration of the dynamics, trends, and financial variables that shaped thebanking industry during these years, providing a valuable and contextualized insightinto the industry's performance and risk profile within this defined period

in-5 Structure of the thesis

The subsequent sections of this research have been thoughtfully organized toprovide a structured exploration of the study's objectives and findings

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In Part 1, the author journey through the historical theories that form thefoundation of our research and trace their development over time This section not onlypresents a comprehensive overview of the theoretical framework within which theauthor’s study is situated but also demonstrates the progression of ideas that have

contributed to the understanding of systematic risk and financial markets.

Part 2 the author provides a thorough explanation of the methodologies thathave been employed in this research, demystifying their functions and highlighting theirrelevance in the study of systematic risk By comprehensively detailing these methods,

we aim to equip readers with a clear understanding of the tools and techniques we have

employed to investigate the key financial variables influencing systematic risk

In Part 3, the author delve deeper into the specific context of the Vietnamesestock market, shedding light on research conducted in this market and the efficiency ofits operations Understanding the nuances of the local stock market is essential forcontextualizing this research findings within the unique dynamics of Vietnam's financiallandscape Then the author presents the results of the study and engage in extensive

discussions regarding the implications and interpretations of these results This section

forms the core of our research, offering valuable insights into the relationship betweencorporate financial variables and systematic risk in the Vietnamese banking sector

Finally, in Part 4, the author bring this research to a close with a comprehensiveconclusion The author not only summarize the key findings but also propose practicalrecommendations for commercial bank This section serves as a valuable guide forindividuals and entities navigating the intricate world of investment in the Vietnamesebanking sector, ultimately contributing to informed decision-making and portfoliomanagement strategies

PART 1: LITERATURE REVIEW

1.1 Research overview

1.1.1 Systematic risk problem

Firstly, Loo Sin Chun and Meharani Ramasamy's research (1989) offeredsubstantiating evidence that financial ratios, particularly profitability ratio and activityratios, play a significant role as determinants of a common stock's systematic risk Their

study did not find statistical significance for other crucial financial dimensions of a firm,

such as leverage and liquidity ratios

Additionally, Michael Jarvela, James Kozyra, and Carla Potter's research (2009)

highlighted a robust connection between market risk and accounting risk metrics Their

findings emphasize the growing importance of accountants in the accurate reporting offinancial information Their analysis demonstrated that as leverage rises, beta alsoincreases, although earnings variability displayed the weakest correlation among thethree variables examined In contrast, the study conducted by Beaver, Kettler, andScholes (1970) yielded notably different outcomes Their research revealed the mostsignificant correlations among the variables investigated

In a separate investigation, conducted by Hyunjoon Kim and Ji Young (2010) analyzed the risk characteristics of hotel firms and understanding the factors influencing

their systematic risk The study involved a sample of 31 hotels and spanned a 4-yearperiod, from 2004 to 2008 The results showed that while debt leverage and growth are

positively related to systematic risk, firm size is negatively associated with such risk.

Moreover, in a study by Muhammad Junaid Iqbal and S Shah (2012), eightfinancial variables were investigated as determinants of systematic risk The results,based on the data of 93 non-financial firms listed on the Karachi Stock Exchange from

2005 to 2009, revealed that liquidity, leverage, operating efficiency, dividend payout,

and market value of equity displayed inverse associations with systematic risk (beta),while profitability, firm size, and growth exhibited positive correlations with systematic

yielded analogous findings, demonstrating that liquidity, leverage, operating efficiency,

dividend payout, and market value of equity were inversely associated with systematic

risk, while profitability, firm size, and growth were positively related to systematic risk(beta)

Subsequently, a study by Sajid Iqbal (2015) delved into the factors contributing

to systematic risk Liquidity, leverage, operating efficiency, profitability, and firm sizewere examined as determinants of systematic risk All of the hypotheses put forward inthe study were validated, except for one which was refuted The results of the research

demonstrated the significance of all hypotheses when measured using systematic risk

proxy, namely beta The only hypothesis that did not align with the findings in thecurrent investigation was the lack of a significant correlation between leverage andsystematic risk

In the same year, Day-Yang Liu's research (2015) delved into the connection

between systematic risk (beta) and six critical financial variables within the U.S casino

industry These variables encompassed firm size, liabilities as a percentage of assets,

asset turnover, return on assets, growth rate, and current ratio.

In addition, in the research conducted by Li Zhang, N Nielson, and Joseph D.Haley (2019), it was established that profitability, leverage, and the types ofmanagement compensation exhibited significant correlations with both total risk andsystematic risk Additionally, the size of the firm was positively associated with

systematic risk.

Moreover, Vilayphone Vongphachanh and Khairunisah Ibrahim's study (2020)

scrutinized the primary factors influencing systematic risk in six industries withinThailand The research covered a 15-year period, spanning from 2002 to 2016, and

encompassed a total of 372 non-financial listed firms The overarching conclusions underscore that certain key financial variables, including financial leverage, liquidity,

firm size, firm growth, and profitability, are identified as the primary determinantsinfluencing systematic risk in the Thai consumer goods, technology, telecommunication,utilities, and healthcare sectors

Afterwards, in a study conducted by K H Quao (2022), an analysis was carriedout using financial data spanning five years (2016-2020) from 14 multi-sector non-financial firms listed on the Ghana Stock Exchange The findings of this research

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revealed that liquidity, leverage, operating efficiency, dividend payout, and market value

of equity exhibited negative relationships with systematic risk, while profitability, firm

size, and growth displayed positive relationships with it

In a different research study conducted by I Kadek, Rian Mahendra, and A Gst.Ngr.Suaryana (2023), the impact of financial leverage, firm size, and dividend payout

ratio on the systematic risk (beta) of stocks was analyzed, with the Covid-19 pandemicserving as a moderating variable The findings of the study revealed the followingresults: 1) Financial leverage had a positive influence on the systematic risk (beta) of

stocks 2) Firm size did not demonstrate an effect on the systematic risk (beta) of stocks.

3) Dividend payout had a negative impact on the systematic risk (beta) of stocks 4) TheCOVID-19 pandemic amplified the impact of financial leverage on the systematic risk

(beta) of stocks 5) The COVID-19 pandemic did not have a moderating effect on the

impact of firm size on the systematic risk (beta) of stocks 6) The COVID-19 pandemicreinforced the influence of the dividend payout ratio on the systematic risk (beta) ofstocks

1.1.2 Review of studies in Vietnam

Next, we will analyze the research on corporate finance variables and systematicrisk in Vietnam Nguyen Thi Kim (2003) and Pham Tien Minh et al.(2017) conductedresearch on the overview of risk-return and the CAPM model, and applied it to calculatethe Beta coefficient on the Vietnamese stock market This study show that for industrialenterprises, the financial factors that primarily reduce systematic risk are operatingefficiency (OE) and return on asset (ROA), while the only financial leverage factor (LEV)increases systematic risk Van Thi Thuy Vu (2019) indicates systematic risk is the riskthat has impacts on every sector, fields or types of assets and cannot be excluded based

on the diversification of investment portfolios

According to the research findings of Hong Nhung Do (2017), it is shown that the

return on assets (ROA) and Size are two factors that have a positive impact on

systematic risk Additionally, the research results of Do Thu Hang and Pham Thi Hoang

Anh (2020), using data from 9 listed commercial banks in Vietnam from June 2019 to

the end of 2019, indicate that an increase in the Return on Equity (ROE) and the

volatility (VOL) of the previous period will reduce systematic risk in the subsequentperiod

industries, resulting in varied findings due to the distinct characteristics unique to each

industry

1.2 The theoretical foundation regarding the influence of financial factors on

systematic risk

1.2.1 Overview of systematic risk

Systematic risk has been a subject of extensive discussion over time Presently, acomprehensive consensus on the concept of systematic risk remains elusive, as eachdefinition accentuates distinct facets (Smaga, 2014) Additionally, Haldane and May

(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 spotlight on systematic risk has intensified, particularly in the aftermath ofthe global financial crisis of 2007-2008 (Borio & Drehmann, 2009) This heightenedattention is a result of the unforeseeable repercussions affecting not only the overallfinancial system but also individual banks As defined by Engle & Ruan (2009) andBrunnermeier (2009), systematic risk is the type of risk that amplifies when the failure

of one or a group of organizations can set off a domino effect, potentially leading to thecollapse of the entire financial system due to the interconnectedness among these

entities.

Systematic risk within banking systems has its origins in interbank relationships,

as noted by Elsinger et al (2006) and Kaufman & Scott (2003) Elsinger et al (2006)propose that systematic risk encompasses two primary sources: 1) A bank's incapacity

to fulfill its interbank payment obligations to other banks, setting off a "domino effect"

where multiple banks may face insolvency (Cont et al., 2010) 2) An adverse economic

shock that can lead to substantial losses in the financial asset portfolios of banks,

resulting in the simultaneous collapse of numerous banks.

Crucially, it should be emphasized that these two origins of systematic risk arefrequently intertwined, as underscored by Hu et al (2012) In risk assessment,

systematic, unsystematic, and total risks serve as pivotal measures, as proposed by K.

Gupta et al (2022) The total risk associated with a firm can be partitioned into twodistinct components: systematic risk and unsystematic risk Unsystematic riskrepresents firm-specific risk, and through a well-diversified portfolio, investors can

mitigate or eliminate this portion of the overall risk.

Systematic risk is the degree to which the firm's performance covaries with the

economy as a whole (Kingsley, 2008) On the other hand, systematic risk (beta) is

derived from the market model expressed in the form:

Ri = ait EiRm + ei

Systematic risks represent specific risk factors that individually impact an

investment and can be alleviated through diversification Consequently, in a developedmarket where effective diversification is assumed, systematic risk becomes the residualrisk that necessitates managers’ attention for identification, measurement, and theimplementation of preventive measures Numerous theoretical models have beenexplored to quantify systematic risk

Systematic risk, often measured by beta, holds significant relevance in the realm

of accounting research, especially within capital markets research (Gwangcheon Hong,

2010) Vilayphone Vongphachanh and Khairunisah Ibrahim (2022) categorized risk intotwo main types: systematic risk and unsystematic risk Unsystematic risk, also referred

to as diversifiable risk, can be mitigated or controlled In contrast, systematic risk,denoting market-related risk, remains beyond control or diversification Between thesetwo risk categories, systematic risk takes precedence for firms and investors since itcannot be eliminated or diversified away, necessitating strategic management andmitigation

1.2.2 Modern Portfolio Theory (MPT)

In 1952, Markowitz authored the influential "Portfolio Selection" article, layingthe groundwork for Modern Portfolio Theory (MPT), which garnered significantrecognition in the realm of international business MPT is renowned as a framework thatempowers risk-averse investors to strategically build an investment portfolio thatoptimizes returns or maximizes expected profits while maintaining a specified level ofmarket risk Essentially, it underscores that portfolios comprising various asset classes,offering adequate diversification, can deliver consistent higher Returns, albeit with a

commensurate increase in risk Therefore, diversification stands as a pivotal tenet

within the Modern Portfolio Theory This theory holds exceptional importance because

it aids risk-averse investors in academically crafting a diversified portfolio

Modern Portfolio Theory (MPT) illustrates the principle of diversification ininvestment, with the goal of assembling a collection of investment assets that, as awhole, exhibit lower risk than any individual asset The feasibility of this concept is

apparent, as various asset types often move in value in contrasting directions Nonetheless, diversification diminishes risk, even when asset Returns are not inversely

correlated In fact, diversification can mitigate risk even in cases where the assets are

positively correlated.

In summary, Modern Portfolio Theory (MPT) asserts that individual assets within

an investment portfolio should not be chosen in isolation, as their optimization cannot

be achieved independently Rather, it is crucial to evaluate how each asset's price

movements correlate with the price movements of every other asset in the portfolio.

1.2.3 Theoretical basis about efficient market

The Efficient Market Theory carries significant practical implications and is

regarded as the foundational pillar of modern financial theory It held sway as the predominant investment theory for over three decades, spanning from the early 1960s

to the mid-1990s

According to Fama (1970), efficient markets are markets where ‘there are largenumbers of rational profit maximizers actively competing, with each trying to predictfuture market values of individual securities, and where important current information

is almost freely available to all participants’ Karz (2012) states that ‘Fama persuasively

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made the argument that in an active market that includes many well-informed andintelligent investors, securities will be appropriately priced and reflect all availableinformation’ Also, Fama distinguished efficiency in three different forms:

¢ Strong-form Information (public, personal, even confidential) contributes to

stock pricing, and, therefore, does not enable investors to achieve a competitive

advantage in investing processes Strong form efficiency refers to a market where shareprices fully and fairly reflect not only all publicly available information and all pastinformation, but also all private information (insider information) as well In such a

market, it is not possible to make abnormal gains by studying any kind of information.

e Semi-strong form Stock prices reflect public financial information(announcements of listed companies, balanced sheets, assets etc.)

e Weak efficiency All past stock prices are integrated in current prices includingcontinuous price changes, income rates, trading volume, and other general information

such as retail transactions, large block trades, and transactions by foreign exchange

experts or exclusive groups; therefore, they cannot be used for future predictions

1.3.Factors affecting the risk system of industry groups bank in the stock market

1.3.1 Liquidity (LIQ)

The Liquidity Ratio (LIQ) of a stock is recognized as a pivotal determinantinfluencing the business's valuation It serves as an indicator of the capacity to transformassets or products into cash, calculated using the following formula:

LIQ= Current asset/ Current liability

According to the agency theory, M.C Jensen (1986) suggests that liquidity andsystematic risk have a positive relationship, which aligns with the findings of Pham TienMinh (2017) In contrast, the study conducted by Loo Sin Chun, Meharani Ramasamy,Rashed Nawaz, and W Ahmed (2017) indicates that liquidity does not have a significantrelationship with beta (systematic risk)

1.3.2 Financial Leverage (LEV)

Financial leverage within a business reflects the degree to which a companyemploys borrowed capital to amplify its Return on Equity (ROE) The calculation of theextent of financial leverage is derived from the ratio between the debt-to-assets (D/A) or

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debt-to-equity (D/E) ratio In this study, the author employs the formula introduced byHassan and Bashir (2003):

LEV= Total liability / Total asset

The empirical results from Pham Tien Minh's study in 2017 demonstrate that ascompanies increase their financial leverage (LEV), the burden of repaying debt andinterest escalates, raising the risk of potential failure to meet financial obligations and,consequently, elevating systematic risk However, the research conducted by Loo SinChun and Meharani Ramasamy suggests that leverage and liquidity ratios did notemerge as statistically significant variables

1.3.3 Bank size (SIZE)

Bank size is a crucial financial variable that serves as an indicator of financialstrength In research, it is typically determined by using data from total assets during aspecific period, and it is calculated by taking the natural logarithm of the total assets

Bank size = Log (Total asset)

The research findings of Kieu and Nhien (2020) emphasize that firm size has apositive relationship with stock prices Similarly, Naveed and Ramzan (2013) concurwith this perspective, based on the Fixed Effects Model (FEM) analysis after studying

data from 15 banks in Pakistan over a four-year period during the 2008-2011

timeframe

1.3.4 Operating Efficiency (OE)

Operating efficiency refers to a company's ability to reduce operational costs while efficiently achieving its objectives through a well-balanced combination of skilled

personnel, streamlined processes, and advanced technology To assess a company'soperating efficiency, it can calculated by adding up all the operating expenses and thendividing that sum by the total revenue

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OE = Revenue / Total asset

According to the research findings of Le Truong Niem (2022), the operationalperformance has a counteracting effect on systematic risk In other words, companieswith high operational performance tend to be stable, thereby reducing systematic risk

Similarly, in the study conducted by Pham Tién Minh (2017), when enterprises

efficiently manage and utilize their assets (OE), the market perceives it as a positivesignal, leading to a reduction in the systematic risk associated with those enterprises

1.3.5 Return on Asset (ROA)

Return on Assets (ROA) is a financial ratio computed by dividing after-tax profit

by the average total assets over a company's operational period This ratio reveals theextent of after-tax profit generated for each dollar invested in the company's total assets,thereby reflecting the asset's profitability or the efficiency of asset utilization by thecompany The formula for calculating this ratio is as follows:

ROA = Net profit / Total asset

The research findings of Pham Tien Minh (2017) indicate that the financial factor

that significantly reduces systematic risk is the Return on assets (ROA) When a bank

increases its profitability on these assets (ROA), the market perceives it as a positive

signal, and the bank's systematic risk decreases This aligns with the results of a study by

Le Truong Niem (2022)

1.3.6 Growth rate (GROW)

Growth rates are defined as the percentage variation of a particular variable

during a specified timeframe These rates can be either positive or negative, contingent

on whether the variable's magnitude is expanding or contracting over time Initially,growth rates found their application in the field of biology when researchers werestudying changes in population sizes However, they have since found utility in theexamination of economic trends, corporate management, and investment returns.Calculating growth rates can take various forms, contingent on the specific message orinformation that needs to be conveyed

GROW = % growth of total assets each year

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The research results of Hyunjoon Kim, Jiyoung Kim, and Zheng Gu (2010)

indicate that GROW has an impact on systematic risk, which aligns with the findings of

Muhammad Junaid Iqbal and S Shah (2012)

1.3.7 Volatility (VOL)

The market control variable is measured by calculating the standard deviation ofdaily return on VNIndex The combination of volatility and beta provides a

comprehensive view of the relationship between market conditions and the

performance of specific sectors or stocks, such as the banking sector

_ {i n — xỒA

o= h yer i — #)^2

The research results of Do Thu Hang and Pham Thi Hoang Anh (2020), showedthat the volatility (VOL) of the previous period will reduce systematic risk in thesubsequent 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

Meharani Ramasamy's X Xresearch (1989)

Michael Jarvela, James

Canada Kozyra, and Carla +

Li Zhang, N Nielson, and

Thailand

VilayphoneVongphachanh andKhairunisah Ibrahim'sstudy (2020)

Nguyen Thi Kim (2003)

and Pham Tien Minh et

al.(2017)

Vietnam Hong Nhung Do (2017)

Do Thu Hang and PhamThi Hoang Anh (2020)

CHAPTER 2: RESEARCH METHODOLOGY

2.2 Research model and method of data collection

2.2.1 Research model

The regression model of financial variables affecting systematic risk:

B = œo + ơiLIQ + a2LEV + a30E + a4ROA + asSIZE + asGROW + C

Figure 2.1 Model to study factors affecting sytematic risk of Vietnamese banking from

2017 to 2022

Liquidity

Leverage

Operating efficiency

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The author then employs the Hausman test, as proposed by Hausman (1978), to

choose the suitable model between the Fixed Effects Model (FEM) and Random Effects

Model (REM) The Breusch & Pagan Lagrangian test is utilized to assess the presence ofheteroscedasticity, and the Durbin Watson test is employed to examine autocorrelation

Additionally, the article conducts tests for autocorrelation and calculates the Variance

Inflation Factors (VIF) to detect multicollinearity in the model The study also employs

“robust” techniques to enhance the robustness of the estimation results

The variables in the model are categorized into two groups: dependent and

independent variables The proposed model in the research comprises a dependent

variable, Beta, along with seven independent variables in the table below

Table 2.1 Summary of financial factors that affect systematic risk

Symbol Variable name Calculation formula

LIQ Liquidity Current asset/ Current liability

LEV Leverage Total liability / Total asset

OE Operating efficiency Revenue / Total asset

ROA Return on assets Net profit / Total asset

SIZE Bank size Natural logarithm of total assets

GROW Growth rate % growth of total assets each year

VOL Volatility Standard Deviation of daily returns

Source: Author’s analysis

2.1.1.1 Dependent variable

The researchers selected fixed time periods with historical data over a 6-year

period from January 2017 to December 2022 After determining the specific time points,the authors collected data on LIQ, LEV, OE, ROA, SIZE, GROW, and VOL from 18 bankslisted on the Vietnam Stock Exchange (HOSE and HNX) through the financial reports of

each bank and the Investing data platform.

2.1.1.2 Independent variable

17

Systematic risk is measured using historical stock price information in the stock

market over a 6-year period, and it is calculated by the Slope function, which calculates

the daily price difference divided by the closing price of the previous trading day Thisindicates the level of risk for that stock compared to the overall market risk

Beta coefficient is a useful tool to evaluate the level systematic risk of a security or a

portfolio invest in relation to the entire market

® B=0: The price volatility of this security is entirely independent of the market

® £ <0 (negative beta): This means the stock tends to rise when the market is in a

downward trend.

® <1: The price volatility of this security is lower than the market's volatility

B = 1: The price volatility of this security will be equal to the market's volatility

® £ > 1: The price volatility of this security is higher than the market's volatility,

indicating that the stock is likely to generate high returns but also carries higherrisk

2.2.3 Method of data collection

The sample was constructed based on 18 firms traded on both the Ho Chi MinhStock Exchange (HOSE) and Hanoi Stock Exchange (HNX) during the period from 2017

to 2022 The data for the study met the following criteria: i) Companies that were listed

before January 1, 2017, and had complete financial reports published; ii) Companies that

were not delisted between 2017 and 2022, with their stocks traded continuously duringthis period, or if trading was temporarily suspended, the suspension period was lessthan three months

Table 22 Stock codes in the research sample

fears [fs Piomes—To

Joint Stock Commercial 10 Southeast Asia

Bank for Investment and Commercial Joint StockDevelopment of Vietnam Bank