Problem Statement
The 2007-2008 global financial crisis was primarily triggered by weaknesses in the banking system, particularly due to credit institutions' failure to strictly adhere to capital adequacy regulations (Norgren, 2010) In response to the severe and long-lasting impacts of the crisis on global banking, the Basel Committee on Banking Supervision introduced Basel III, which aims to strengthen financial stability through improved capital standards Basel III emphasizes raising equity capital requirements by increasing the minimum common equity ratio from 2% to 4.5% and maintaining the total capital requirement at 8%, while raising the standards for high-quality capital, including Tier 1 capital, which increased from 4% to 6% The regulation also mandates the gradual elimination of assets with inherent quality issues from Tier 1 and Tier 2 capital, especially investments exceeding 15% of a financial institution’s capital Additionally, Basel III introduces a minimum leverage ratio of 3%, calculated as Tier 1 capital divided by total assets plus off-balance sheet items, enabling better monitoring of financial leverage and ensuring banks maintain adequate capital in line with economic cycles.
Following the global financial crisis, Vietnamese commercial banks faced significant instability issues, primarily due to weak financial capacity A key factor undermining their competitiveness was the extremely low level of equity capital To improve the performance and stability of the banking system, reforms and measures were necessary to strengthen financial resources and enhance resilience.
2012, the Prime Minister issued the plan to restructure the system of banks and other credit institutions during the period 2011-2015 with Decision 254 / QĐ-TTg
Under the directive from the Prime Minister, the Ministry of Finance and the State Bank of Vietnam collaborated to approve plans for increasing the charter capital of state-owned commercial banks Since 2008, Vietnamese commercial banks have actively expanded their registered capital to comply with regulatory requirements outlined in Decree No 141/2006/ND-CP and Decree 10/2011/ND-CP, which amends certain articles of Decree 141 To meet these capital adequacy standards, banks have adopted various strategies such as issuing new shares to domestic and foreign investors or merging with other joint stock commercial banks.
Decision 254/QĐ-TTg mandates that State-owned Corporations gradually divest their investments in credit institutions to promote greater financial efficiency The equitization of state-owned commercial banks and reduction of government-held capital are key strategies to enhance bank competitiveness by diversifying ownership structures involving state, economic entities, and foreign investors These reforms aim to decrease state ownership, foster a more dynamic banking sector, and improve overall financial stability.
There are many studies examining the impact of capital structure as well as ownership structure on bank performance
Based on agency problem (Jensen and Meckling, 1976), the agency view suggests that the banks with higher equity capital ratio have lower profitability (Berger and
According to Di Patti (2006), bank management experiences less pressure to maximize value when banks maintain higher equity capital ratios This reduced pressure can lead to conflicts between management and shareholders, resulting in increased agency costs.
Numerous studies demonstrate that well-capitalized banks positively impact performance by enhancing stability and investor confidence According to Berger and Bouwman (2013), higher equity capital improves bank performance through three key channels: it encourages managers to monitor more effectively, promotes the adoption of safer investment portfolios, and signals reliability to customers, investors, and partners Consequently, banks with higher equity capital can mobilize deposits at lower interest rates and expand their lending activities, leading to improved performance and increased market share This perspective is supported by additional research from Demirgüç-Kunt and Huizinga (1999), Maudos and Guevara (2004), and Fiordelisi et al (2011).
Research by Chortareasa et al (2012) indicates that a higher equity capital ratio can enhance bank efficiency Thu and Huyen (2014) found that leverage positively influences Net Interest Margin in Vietnamese commercial banks, though their study analyzed data from only 33 banks between 2008 and 2011 Additionally, Phuc (2014) examined 217 companies listed on the Ho Chi Minh City and Hanoi Stock Exchanges from 2007 to 2012, revealing that long-term debt positively affects ROA and ROE, while short-term and total debt negatively impact business performance measured by these indicators.
Research indicates that ownership structure significantly impacts bank efficiency, with studies showing varying outcomes across different countries Altunbas, Evans et al (2001) found that in Germany, public banks are less efficient than private banks Similarly, Ani, Odo et al (2012) revealed that government ownership in Nigeria negatively correlates with bank performance during 2004-2006 Micco, Panizza et al (2007) analyzed data from 179 countries between 1995 and 2002, concluding that state-owned banks in developing nations tend to have lower profitability, higher non-performing loans, and higher overhead costs compared to private banks Conversely, Fuentes and Vergara (2007) identified that in Chile's banking system from 1990 to 2004, state-owned banks exhibited greater efficiency than their private counterparts, challenging the general trend and highlighting the complex relationship between ownership type and bank performance.
On the impacts of the ownership structure as well as capital structure on Vietnamese bank performance, there are many researches including the study of Son, Tu et al
Previous studies, such as Son, Tu et al (2015), primarily employed shorter and earlier data periods, analyzing 34 banks with 102 observations from 2010 to 2012 In contrast, this study utilizes an expanded dataset covering 49 banks with 387 observations spanning from 2005 to 2014 This broader data collection provides a more comprehensive analysis of the Vietnamese banking sector over a longer timeframe, enhancing the robustness and depth of insights compared to previous research.
Despite numerous studies examining the effects of capital and ownership structures on Vietnamese bank performance, existing research remains in the early stages with limited datasets The mixed and inconclusive findings in the current literature highlight the need for further investigation Consequently, this study explores the impact of capital structure and ownership structure on bank performance using Vietnamese data from 2005 to 2014, aiming to provide clearer insights into these relationships.
Particularly, the bank performance is reviewed through two proxies: Net Interest Margin (NIM) and Returns on Assets (ROA).
Research Objectives
Between 2010 and 2015, Vietnam's government implemented a restructuring plan for the banking system, focusing on increasing equity capital and equitization of state-owned commercial banks to enhance operational efficiency As this plan approaches its final stage, understanding the impact of capital structure and ownership on bank performance becomes crucial This study investigates the relationship between these factors to inform future banking reforms and policy decisions.
This thesis aims at the following two specific research objectives to achieve the above general research objective:
The first one is to evaluate the impact of capital structure on Vietnamese bank performance
The second one is to investigate the effect of ownership structure on Vietnamese bank performance.
Research questions
This study focuses on answering the following two research questions
Research question 1: Does bank capital structure impact on Vietnamese bank performance?
Research question 2: Does bank ownership structureeffect onVietnamese bank performance?
Significances of the study
This study investigates the impact of capital and ownership structures on the performance of Vietnamese banks The findings provide recommendations for the State Bank of Vietnam to enhance policies related to raising equity capital and modifying ownership structures, aligning with Basel Committee on Banking Supervision guidelines and the operational realities of Vietnamese commercial banks The research offers valuable insights for Vietnamese banks to develop optimal capital and ownership structures, thereby improving overall bank performance.
Scope of the Study
As of December 31, 2014, Vietnam's banking system comprises 50 banks, including one wholly state-owned commercial bank, 34 joint-stock commercial banks—with three where the government holds over 50% of the charter capital but shares ownership—and five wholly foreign-owned banks Additionally, the system includes two policy banks, four joint-venture banks, one cooperative bank, and 49 foreign bank branches, reflecting a diverse and expanding financial sector in Vietnam.
This study analyzes 49 banks (listed in Appendix 1) with available data from 2005 to 2014, though complete financial statements are only accessible for most institutions Due to data limitations, the sample excludes Joint-Venture Banks, Policies Banks, Cooperative Banks, Wholly Foreign-Owned Banks, and Foreign Bank Branches Additionally, some banks have missing data in certain years, resulting in a final unbalanced panel dataset comprising 387 bank-year observations.
Thesis structure
This thesis consists of five chapters:
Chapter 1: Introduction Chapter 2: Literature review Chapter 3: Research Methodology Chapter 4: Study findings and analysis Chapter 5: Conclusion and policy implications
This thesis is structured into five chapters Chapter 1 outlines the research objectives, providing a clear focus for the study Chapter 2 offers a comprehensive literature review, including key conceptual definitions and summaries of previous research related to the topic Chapter 3 details the research methodology employed, ensuring transparency of the study’s approach Chapter 4 presents and analyzes the research findings, offering insights derived from the data Finally, Chapter 5 concludes the thesis by summarizing the main results, discussing policy implications, and suggesting directions for future research.
REVIEW
Introduction
This chapter provides essential definitions of capital structure and ownership structure, along with key bank performance metrics such as net interest margin and return on total assets It synthesizes both theoretical and empirical literature relevant to the research objectives, establishing a solid foundation for understanding how capital and ownership structures impact bank performance The chapter also develops a conceptual framework outlining the relationships among capital structure, ownership structure, and bank performance, offering valuable insights for effective banking strategies.
Conceptual Definitions
The capital structure refers to the origin and composition of a company's capital, which is used for asset acquisition, tangible materials, and supporting production and business activities It plays a crucial role in determining the financial stability and growth potential of a business Various definitions of capital structure exist, highlighting its importance in strategic financial planning and management Understanding a company's capital structure is essential for optimizing resources, enhancing profitability, and ensuring long-term success.
According to Chandra (2011), capital structure refers to how a firm allocates its cash flow into key components, including a fixed portion designated for debt obligations and a residual part that belongs to equity shareholders This division is essential for understanding a company's financial strategy and overall stability.
The capital structure of a firm refers to the specific mixture of debt and equity it utilizes to finance its operations, as defined by 2005 Gerestenbeg emphasizes that a company’s capital structure pertains to its composition or makeup of long-term capital resources, including all sources of long-term financing Additionally, another perspective describes capital structure as the blend of a firm’s permanent long-term financing, comprising debt, preferred stock, and common stock equity These definitions highlight the importance of understanding a company's long-term financial makeup for effective financial management and strategic planning.
In a different point of view, Presana Chandra defines capital structure as: “The composition of a firm’s financing consists of equity, preference, and debt” (as cited in Paramasivan C & Subramanian T., chapter 5, p.47)
Additionally, in the research of R.H Wessel, capital structure is defined as: “The long term sources of fund employed in a business firm” (as cited in Paramasivan C
Based on these definitions, the components of Capital Structure are demonstrated as following diagram:
Figure 2-1 Components of Capital Structure
Source: http://articles-junction.blogspot.com/2013/10/components-of-capital- structure-with.html
An optimal capital structure is the one that maximizes the firm's market value while minimizing costs It enhances earnings per share, leading to increased dividends for shareholders Additionally, it improves the company's ability to access new investment opportunities, ensuring sustained growth and financial stability.
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Academic theories indicate that capital structure, also known as financial leverage, reflects the level of debt within a company's total capital Firms utilize financial leverage when their investment capital needs surpass their available equity, leveraging debt to finance growth Typically, businesses and banks adopt leverage strategies when the expected return on assets exceeds borrowing costs, thereby enhancing profitability for owners Proper use of financial leverage can amplify returns, but it also involves risk depending on market conditions.
Ownership structure, as defined by Gürsory & Aydogan (2002), includes ownership concentration and ownership mix Ownership concentration refers to the proportion of shares held by major shareholders owning at least 5% of the firm's charter capital, with these dominant shareholders facing higher operational risks To safeguard their investments and achieve targeted profitability, this group typically engages in rigorous monitoring of business activities and managerial decisions, which can enhance overall firm performance Conversely, ownership mix describes the diversity of shareholders, encompassing state, private, and foreign ownership types, influencing corporate governance and strategic direction.
Bikker (2010) points out some types of performance indicators mentioned in Table 2-1 for financial institution.
Table 2-1 – Indirect performance indicators for financial institutions
Performance indicators Indicators represented as
Profit X-efficiency Scale economies Scope economies
2 Costs Cost-to-income ratio
Cost margin Total costs/total income
3 Profit Return on capital tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Return on assets Net interest margin
Net Interest Margin (NIM) and Cost to Income (C/I) ratio are commonly used indicators to assess bank performance (Barth et al., 2006; as cited in Chortareasa, Girardoneb, and Ventouri, 2012) Additionally, bank performance can be evaluated through financial ratios such as Returns on Assets (ROA) (Goddard et al., 2008; as cited in Arias, Jara-Bertin, and Rodriguez, 2013) and Returns on Equity (ROE) (Al-Kayed, Zain, and Duasa, 2014; Micco, Panizza, and Yanez, 2007).
Net Interest Margin (NIM) is calculated by dividing the difference between interest income and interest expense by total profitable assets A higher NIM suggests that a bank has better overall performance, as it indicates greater profitability According to Saunders and Schumacher (2000), banks with higher NIMs are typically more efficient and financially healthier Therefore, NIM serves as a key indicator for assessing a bank's profitability and operational efficiency.
The first model concerning to NIM was mentioned in the study of Ho and Saunders
In 1981, these authors developed the formula for calculating net interest margin, highlighting that it depends on variables such as the elasticity between demand and supply of capital, the bank's risk aversion level, transaction size, and interest rate variance Subsequently, Ho and Saunders created a model identifying key factors influencing net interest margin, including the bank’s risk appetite, market structure, average transaction size, and interest rate fluctuations.
Return on Assets (ROA) is a key financial ratio that measures a company's profitability by indicating how efficiently it generates profits from its total assets It is calculated by dividing net income by total assets, providing insight into the firm's overall resource utilization A higher ROA signifies better management and effective asset use, making it an essential metric for investors and stakeholders evaluating a company's financial health.
Theoretical review
Some theories on capital structure
2.3.1 Capital structure theory developed by Modigliani and Miller (MM Model)
The modern capital structure theory, known as the Modigliani & Miller (1958) model, is based on several key assumptions of perfect markets These include asymmetric information, absence of transaction costs, no firm income tax, no personal income tax, identical lending and borrowing interest rates, equal access to funding opportunities for individuals and firms, no bankruptcy or financial distress costs, and the complete distribution of profits to owners without reinvestment or growth.
While perfect capital markets do not exist in reality, two key hypotheses significantly influence the outcomes of Modigliani and Miller's study Firstly, their assumption of no taxation overlooks the tax advantages of debt, such as interest expense tax relief Secondly, the theory measures risk solely based on cash flow variability, neglecting the potential for cash flow cessation due to insolvency, which becomes a critical issue when a firm carries high levels of debt.
According to Postulate I of Modigliani and Miller, in a perfect capital market, a firm's value remains unaffected by its capital structure While leveraging debt can increase the owner's return and generate higher income, this benefit is offset by the elevated risks associated with a higher debt-to-equity ratio.
Vg = Vu: The total value of firm using debt equal the total value of firm with no debt
In 1963, Modigliani and Miller conducted a follow-up study by removing the assumption of firm income tax, concluding that, without considering taxes, the use of debt does not affect the overall value of a firm However, their revised analysis highlighted that when income tax is taken into account, employing debt can increase a firm's value because interest expenses are deductible, allowing firms to transfer income to investors more efficiently.
The value of a leveraged firm (Vg) is calculated as the unlevered firm value (Vu) plus the benefits derived from using debt, represented by T*D, where D is the total market value of debt and T is the corporate income tax rate This tax shield provides additional value, making the firm with financial leverage more valuable than an unlevered firm by an amount equal to the present value of the tax shield.
The trade-off theory, proposed by Myers (1984), explains that while debt provides tax shield benefits, it also incurs additional costs such as bankruptcy costs, including both direct and indirect expenses When a firm's debt ratio increases to the point where the present value of tax shield benefits equals the present value of bankruptcy costs, adding more debt ceases to be advantageous Consequently, firms aim to optimize their capital structure by balancing these factors, achieving an optimal mix of debt and equity where the present value of tax shields equals bankruptcy costs, thus maximizing firm value.
According to the trade-off theory, the value of a firm is demonstrated as follows:
Vg = Vu + T * D - PV (bankruptcy costs)
Vg: total value of firm without debt Vu: total value of firm using debt T: firm income tax rate,
D: the market value of total debt
T * D is benefits from using debt
The present value of bankruptcy costs (PV) refers to the estimated financial impact a company faces during bankruptcy, encompassing legal fees, asset liquidation expenses, and operational disruptions Understanding PV helps firms evaluate the potential costs associated with financial distress, aiding in better financial planning and risk management Optimizing capital structure to minimize bankruptcy costs can enhance shareholder value and improve overall financial stability.
Optimal capital structure is the structure in which T * D = PV (bankruptcy cost)
Figure 2-2 Static trade-off theory of capital structure
Agency costs, as defined by Jensen and Meckling (1976), stem from asymmetric information between executives and shareholders, often leading to decisions that do not maximize shareholder wealth or may even harm shareholder rights The primary causes of this information gap include differing corporate goals and varying levels of risk aversion between managers and shareholders To address these issues, increasing the firm’s use of debt can be effective, as higher debt ratios incentivize managers to make more cautious and strategic decisions regarding operations and capital allocation, thereby improving overall business management.
Empirical review
2.4.1 Bank capital structure and Bank performance
Research on the relationship between bank capital structure and bank performance presents mixed findings Some studies indicate a positive correlation between higher bank capital levels and improved performance, including works by Hirschey (1999), Arias et al (2013), Valverde and Fernández (2007), Maudos and Guevara (2004), Saunders and Schumacher (2000), Maudos and Solís (2009), Claeys and Vennet (2008), and Ahokpossi (2013) Conversely, other research suggests a negative relationship, as evidenced by Berger and Patti (2006), Hamadi and Awdeh (2012), and Chortareasa et al (2012) This article discusses both perspectives before developing the initial hypothesis.
2.4.1.1 Bank leverage is negatively related to bank performance
Research by Berger and Patti (2006) analyzing data from 695 US commercial banks between 1990 and 1995 found that a 1% decrease in the equity-to-total assets ratio leads to a 16% increase in bank profits, indicating that banks with higher equity capital tend to have lower profitability This supports the agency cost theory, as banks with higher equity experience less management pressure to maximize value, resulting in increased agency costs due to conflicts between management and shareholders Similarly, studies by Hamadi and Awdeh (2012) and Chortareasa et al (2012) confirm a negative relationship between equity ratio and bank performance, measured via net interest margin (NIM), with Hamadi and Awdeh specifically examining Lebanese banks from 1996 to 2009.
Banks with higher capital equity possess greater financial autonomy, enabling them to offer higher deposit interest rates to attract more funds, thereby increasing their loan volumes Well-capitalized banks also tend to lend at lower interest rates to expand market share and leverage economies of scale, which ultimately leads to a reduction in their net interest margins.
2.4.1.2 Bank leverage is positively related to bank performance
Recent research indicates a strong positive relationship between leverage and bank performance, supported by studies such as Hirschey (1999), Ash Demirgÿoğlu-Kunt and Harry Huizinga (1999), Saunders and Schumacher (2000), Maudos and Guevara (2004), Valverde and Fernández (2007), Claeys and Vennet (2008), and Maudos and Solís (2009).
(2012); N.Berger and Bouwman (2013); Ahokpossi (2013); Ameur and Mhiri (2013); Jara-Bertin, Moya, and Perales (2014) and Al-Kayed, Zain, and Duasa
Hirschey (1999) demonstrates that the debt-to-asset ratio is negatively related to bank ROA, indicating that higher leverage may diminish bank profitability Conversely, the equity-to-asset ratio shows a positive correlation with bank performance, suggesting that stronger equity positions enhance a bank's return on assets Using ROA as a key performance indicator, these findings highlight the importance of capital structure in influencing bank profitability.
The findings of Ash Demirgỹỗ-Kunt and Harry Huizinga (1999) is consistent with Hirschey (1999) They use data of 80 countries around the world in period 1988-
Research from 1995 indicates that the lagged equity-to-total-assets ratio is positively associated with net interest margin (NIM) and return on assets (ROA) Saunders and Schumacher (2000) suggest that banks with higher capital ratios, often due to regulatory or credit considerations, tend to generate higher NIM to offset the increased costs of holding equity capital Their findings highlight that a higher equity ratio positively impacts NIM, as banks apply higher margins to cover these costs Maodos and Guevara (2004), using fixed effects models on banks across Germany, France, the UK, Italy, and Spain from 1993-2000, also find that risk aversion, proxied by the equity-to-assets ratio, is positively correlated with NIM These studies underscore the significant role of capital adequacy and risk management in determining bank profitability metrics like NIM.
In a 2009 study, researchers used the FEM method to analyze the determinants of Mexican banks’ Net Interest Margin (NIM) from 1993 to 2005, finding results consistent with Maodos and Guevara (2004) Valverde and Fernández (2007) employed GMM estimation to identify factors influencing European banks' NIM, discovering that the equity ratio is positively associated with NIM Similarly, Claeys and Vennet (2008) observed a positive relationship between the equity ratio and NIM when examining banks in Central and Eastern European countries.
It has been argued by Chortareasa et al (2012) that capitalization presented by equity ratio is related to higher NIM Using US banking system data in period 1984:
In 2010, Q.4, N Berger and Bouwman (2013) highlighted that equity capital enhances bank performance through three key channels Higher levels of equity compel managers to exercise greater oversight and adopt safer investment portfolios Additionally, banks with increased equity capital are perceived as more reliable and secure by customers, investors, and partners, enabling them to mobilize deposits at lower interest rates and expand their lending activities Consequently, this leads to improved performance and a greater market share, as supported by Ameur and Mhiri.
Research indicates that well-capitalized banks tend to be more profitable, as demonstrated by studies in Tunisia where stronger capital levels correlate with higher performance Arias et al (2013) utilized the Generalized Method of Moments (GMM) to analyze the Latin American banking system from 1995 to 2010, finding that increased capital levels positively impact bank profitability measures like NIM and ROA Similarly, Ahokpossi (2013) observed a positive relationship between leverage (LEV) and Net Interest Margin (NIM), emphasizing the importance of capital structure in banking performance Additionally, Thu and Huyen (2014) explored the determinants of Net Interest Margin among 33 Vietnamese commercial banks from 2008 to 2011, highlighting key factors influencing bank profitability.
Their findings reveal that the equity to total assets ratio has a positive impact on Net Interest Margin, highlighting the importance of optimal capital structure for financial performance Phuc (2014) analyzed data from 217 companies listed on the Ho Chi Minh City and Ha Noi Stock Exchanges between 2007 and 2012 to examine how capital structure influences enterprise performance following equitisation.
Research indicates that long-term debt positively influences business performance, improving both Return on Assets (ROA) and Return on Equity (ROE) Conversely, short-term debt and total debt are associated with negative effects on these profitability ratios These findings highlight the significance of managing debt structures effectively to enhance financial outcomes.
To enhance bank performance, the State Bank of Vietnam and Vietnamese commercial banks are focusing on increasing both charter capital and equity capital Based on recent studies, this approach aims to strengthen financial stability and improve profitability The first hypothesis suggests that higher capital levels positively impact the overall performance of banks in Vietnam.
Hypothesis 1: Capital structure impacts positively onVietnamese bank performance
2.4.2 Ownership structure and bank performance
It is argued that, in poor countries, banks with dominant shareholders who are foreigners, perform better than others (Demirgỹỗ-Kunt and Harry Huizinga, 1999)
In developing countries, foreign-owned banks often have advantages such as higher capital capacity, advanced banking technology, and superior management skills, leading to greater efficiency Conversely, in industrial countries, research indicates a negative relationship between foreign ownership and bank performance due to highly competitive environments that diminish the benefits of foreign control The DEA method applied to Croatian banks from 1995-2000 shows that foreign-owned banks are among the most efficient Studies by Altunbas et al (2001) reveal that private German banks exhibited the highest efficiency between 1989 and 1996, whereas mutual and public banks were less efficient, with no clear evidence of agency problems Micco, Panizza, and Yáñez (2004) found that in developing countries, state-owned banks underperform compared to private banks, but in industrial countries, ownership type does not significantly influence bank performance.
Researching on Chinese banks performance in the period of 2003-2008, Wen
Research by (2010) utilizes both ROA and ROE to evaluate bank performance, focusing on how ownership structure—specifically ownership concentration and ownership type—impacts bank outcomes He discovers a positive relationship between state ownership and ROE, indicating that state-owned banks tend to have higher ROE However, his study does not establish a clear link between ownership and ROA Conversely, Ani, Odo, and Okelue (2013) examine the effects of government ownership on Nigerian banks’ performance from 1998 to 2008 and find that government ownership is negatively and significantly correlated with ROA, highlighting a contrasting impact on bank performance.
They suggest that the government should decrease the rate of government ownership in Nigerian banks to improve banks performance Similar to Ani et al
Research by Micco, Panizza, and Yaúez (2004) demonstrates that state-owned banks are negatively associated with bank performance metrics such as ROA, ROE, and NIM in developing countries This indicates that private banks tend to perform better than their state-owned counterparts in these regions.
Conceptual Framework
Based on prior theoretical and empirical research, this study develops a comprehensive framework to examine the key factors influencing Vietnamese bank performance The primary focus is on two main elements: Capital Structure (LEV) and Ownership Structure (OWNERSHIP), which are critical determinants of bank effectiveness Additionally, the study considers other control variables such as Bank Size, Credit Risk, Cost-to-Income Ratio, Liquidity, and Market Share, with detailed analysis provided in Chapter 3 to ensure a thorough understanding of their impacts.
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METHODOLOGY
Introduction
This chapter outlines the research methodology employed to assess the influence of capital structure and ownership structure on the performance of Vietnamese banks It defines the scope of the study and details the collected data, ensuring clarity and relevance Additionally, the chapter presents the research design and economic model used to analyze the relationship between structural factors and bank performance, providing a solid foundation for the study's findings.
scope of Study
As of December 31st, the Vietnamese banking system comprises fifty banks, including one wholly state-owned commercial bank, thirty-four joint-stock commercial banks—three of which have government ownership exceeding 50% but are not sole shareholders—five wholly foreign-owned banks, two policy banks, four joint-venture banks, one cooperative bank, and forty-nine foreign bank branches.
Due to limited data availability on Joint-Venture Banks, Policy Banks, and Wholly Foreign-Owned Banks, these institutions were excluded from the sample Additionally, some banks' financial statements were incomplete over the 10-year period from 2005 to 2014 Consequently, this study analyzes data from forty-nine banks (listed in Appendix 1) with comprehensive financial information available for the entire period.
These selected banks also have limited data in some years during the sample period
Thus, the final sample only includes 387 bank-year observations (unbalanced panel data)
Most of the bank-specific information is sourced from publicly available financial statements and annual reports of Vietnamese commercial banks, providing essential data for comprehensive analysis and research.
Epirical model
Although Net Interest Margin (NIM), calculated as interest income minus interest expense divided by average earning assets, is a key indicator of bank performance, many studies prefer using alternative metrics such as Return on Assets (ROA) and Return on Equity (ROE) Building on previous research by scholars like Chortareasa, Girardone, and Ventouri (2012), Claeys and Vennet (2008), J Maodos and Guevara (2004), Hirschey (1999), Arias, Jara-Bertin, and Rodriguez (2013), and Kobeissi, Nada, Sun, and Xian (2010), this study uses ROA and NIM as proxies to evaluate bank performance Banks with higher NIM and ROA scores are generally regarded as exhibiting better financial performance.
Between 2000 and 2008, European bank performance was analyzed using Net Interest Margin (NIM) as a key indicator Chortareasa, Girardone, and Ventouri (2012) found a negative relationship between NIM and both bank size and capitalization, using data from 1,130 banks across Western and Eastern Europe Similarly, Claeys and Vennet (2008) identified determinants of NIM—such as capitalization, market power, and credit risk—showing that higher capitalization and market power positively influence NIM, while increased credit risk tends to lower it J Maodos and Guevara (2004) added control variables like credit risk and management quality, concluding that higher cost-to-income ratios and credit risk are associated with reduced NIM Additionally, Hamadi and Awdeh (2012) introduced liquidity as a factor and found that increased liquidity negatively impacts NIM, emphasizing the complex interplay of bank-specific factors affecting bank profitability in the European banking system.
Hirschey (1999) demonstrates that Return on Assets (ROA) serves as a key indicator of bank profitability, with ROA negatively related to leverage and positively related to bank capitalization His research highlights that higher bank leverage corresponds to lower ROA, while increased capitalization is associated with better performance Additionally, Arias, Jara-Bertin, and Rodriguez (2013) analyze the factors influencing Latin American banking performance between 1995 and 2010 using the Generalized Method of Moments (GMM), employing both Net Interest Margin (NIM) and ROA as performance proxies to assess overall bank efficiency.
Their conclusions indicate that capital level may improve bank performance including NIM and ROA
Research examining the relationship between ownership structure and bank performance in Middle East and North Africa (MENA) countries from 2000 to 2007 reveals that private banks outperform state-owned banks Kobeissi, Nada, Sun, and Xian (2010) utilize both Return on Assets (ROA) and Return on Equity (ROE) as key performance indicators, demonstrating that private ownership positively influences bank performance in the region.
This thesis evaluates bank performance using key financial metrics, specifically Return on Assets (ROA) and Net Interest Margin (NIM), building on previous research in the field.
To examine factors affecting Vietnamese bank performance, based on previous theoretical and empirical researches mentioned above, the Vietnamese bank performance, in this study, is demonstrated through the following model:
PERF it = β 0 + β 1 LEV it + β 2 CR it + β 3 MS it + β 4 CIO it + β 5 LIQ it + β 6 SIZE + β 7 OWNERSHIP + u it (3.1)
PERF indicates of bank performance, measures as NIM and ROA
NIM: Net Interest Margin, calculated by the difference between interest income and interest expense divide to total profitable assets
ROA (Return on Assets) is a key financial ratio that measures a company's profitability by indicating how efficiently it utilizes its total assets It is calculated by dividing net income by total assets, providing insights into the company's ability to generate profit from its overall resources ROA is essential for investors and stakeholders to assess the financial health and operational performance of a firm.
Model 3.1 examines the impact of capital structure, proxied by leverage, and ownership structure on the banking sector Additionally, it incorporates key control variables such as credit risk (CR), market share (MS), cost-to-income ratio (CIO), liquidity risk (LIQ), and bank size (SIZE) to account for other influential bank characteristics The model's error term is denoted as "uit," capturing unobserved factors affecting the outcomes.
This study defines LEV (Leverage Ratio) as the percentage ratio of Equity to Total Assets, reflecting a bank's capitalization level LEV indicates the financial autonomy of a bank by measuring its reliance on equity capital, serving as a key indicator for assessing financial stability Higher LEV signifies greater equity capital relative to assets, highlighting stronger financial independence and resilience As a crucial metric, LEV helps stakeholders evaluate the bank's capacity to absorb losses and maintain operational stability.
According to Hirschey (1999); Arias et al (2013); Valverde and Ferna´ndez (2007);
J Maudos and Guevara (2004); Saunders and Schumacher (2000); J Maudos and tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Well-capitalized banks, characterized by a high equity-to-total assets ratio, tend to be more profitable, as identified by Solís (2009), Claeys and Vennet (2008), and Ahokpossi (2013) These findings highlight a positive correlation between financial leverage (LEV) and key bank performance indicators such as Return on Assets (ROA) and Net Interest Margin (NIM).
This study examines ownership structure as a key variable, representing the type of bank ownership A dummy variable is used, where a value of 1 indicates that the bank has a government shareholder or an agency of the government owning more than 50% of the bank's charter capital Conversely, a value of 0 signifies that the bank does not have significant government ownership This approach helps analyze the impact of government ownership on banking operations and performance.
Research by Micco, Panizza, and Yañez (2004) demonstrates that dummy variables representing state-owned banks are negatively associated with bank performance indicators such as ROA, ROE, and NIM in developing countries, indicating that private banks generally outperform their state-owned counterparts Similar findings are reported by Ani, Odo, and Okelue (2013), Ani et al (2013), and Kobeissi et al (2010) Furthermore, Son, Tu, et al (2015) highlight that a higher percentage of private ownership positively influences bank profitability, measured by ROA Given that Vietnam is a developing country, this study expects the impact of state-owned ownership on bank performance to be negative.
There are many definitions of CR in different researches According to Ahokpossi
In financial analysis, Capital Ratio (CR) is commonly measured by the ratio of Loans to Deposits and Short-term funding, as well as by the quotient of Loan Loss Provisions over Loans, according to J Maodos and Solís (2009) Valverde and Fernández (2007) define CR as the lagged value of the ratio of loan default to total loans Due to data limitations, this study measures CR as the ratio of Total Loans to Total Assets (in percentage), aligning with the approach of Maodos and Guevara (2004).
Valverde and Ferna´ndez (2007) investigate a negative relationship between CR and NIM They say that banks specialized in granting loans tend to charge lower NIM
Research by J Maudos and Solís (2009) indicates that Mexican banks with specialized lending activities tend to benefit from economies of scale, resulting in lower net interest margins (NIM) due to reduced intermediate costs Conversely, their study on the European banking industry by J Maudos and Guevara (2004) reveals a positive relationship between capital adequacy ratio (CR) and NIM, suggesting that banks with higher loan volumes often face increased credit risk and consequently charge higher interest margins to compensate Supporting this, studies by Claeys and Vennet (2008), Ahokpossi (2013), and Hamadi and Awdeh also affirm that higher credit risk and larger loan portfolios are associated with increased NIM.
(2012) We also expect CR has positive effects on NIM and ROA
Market share (MS) is calculated as the total assets of a bank divided by the total assets of all banks, expressed as a percentage According to Claeys and Vennet (2008), their study of 1,130 banks across Western and Eastern Europe suggests that higher market share can enhance net interest margin (NIM) by giving banks greater pricing power and competitive advantages Banks with larger market shares are better positioned to set prices autonomously to achieve desired profit margins, potentially leading to higher NIM Consequently, this study anticipates a positive relationship between market power (MPO) and profitability indicators such as NIM and return on assets (ROA) within the Vietnamese banking industry.
CIO (COST TO INCOME RATIO)
Methodology
This thesis utilizes econometric models to evaluate how capital structure, measured by the equity to total assets ratio, and ownership structure, represented by ownership type, influence the performance of Vietnamese banks The study employs software tools such as Microsoft Office Excel and STATA 13 to analyze data and perform regression analysis, providing insights into the relationship between these corporate structures and banking performance in Vietnam.
Using panel data offers several advantages, including increased sample size, better suitability for studying repeated spatial observations, and the ability to model more complex behaviors However, panel data's dual nature—combining time-series and cross-sectional data—necessitates addressing issues such as heterogeneous variance, autocorrelation, and cross-correlation among independent variables Traditional methods like Pooled OLS may not be reliable in such cases To overcome these challenges, researchers commonly employ Fixed Effect Regression Model (FEM) and Random Effect Regression Model (REM), each with its own benefits and limitations Careful testing is essential to select the most appropriate model for analyzing panel data effectively.
The general model of the Pooled OLS method in our study is:
The study models the relationship between performance (PERF) and various financial factors, including leverage (LEV), capital adequacy ratio (CR), and market share (MS), among others The regression equation (3.2) expresses PERF as a function of these variables, along with bank-specific ownership (OWNERSHIP) and size (SIZE), capturing the influence of these factors on bank performance across a cross-section of banks over ten years The model accounts for individual bank differences through the error term (u_it), ensuring a comprehensive understanding of how financial leverage, liquidity, and ownership structure impact banking performance over time.
The Pooled OLS method is a simple method with many important assumptions
Using this method may be suitable for the small sample size but with large sample size, it may be biased
3.4.2 The Fixed Effects Model (FEM)
This study examines the economic relationship between the dependent variable, PERF, and seven key observed explanatory variables: LEV, CR, MS, CIO, LIQ, SIZE, and OWNERSHIP Employing a fixed effect model (FEM), the analysis aims to identify significant factors influencing PERF, providing insights into how leverage, current ratio, market share, cash flow from operations, liquidity, firm size, and ownership structure impact performance The results highlight the importance of these variables in understanding performance dynamics within the studied context.
PERF it = β 0i + β 1 LEV it + β 2 CR it + β 3 MS it + β 4 CIO it + β 5 LIQ it + β 6 SIZE + β 7 OWNERSHIP + u it (3.3)
In model (3.3), the intercept β0 is assigned a subscript i (β0i) to represent the unique difference in the intercept for each of the 49 banks This fixed effect model assumes that each bank's intercept varies across institutions but remains constant over time, capturing individual bank-specific effects.
It means β0i is time-invariant
In our analysis, we introduce subscript t into β0i to represent the time-specific intercepts for each bank, denoted as β0it, capturing the variations in intercepts across different time periods Since we assume that the coefficients remain constant over time in model (3.4), we incorporate 48 dummy variables to account for differential intercepts, resulting in 48 distinct dummy coefficients Consequently, model (3.3) is reformulated to include these dummy variables, enabling a more accurate assessment of bank-specific intercept changes over time.
PERF it = β 0 + β 1 D 2i +β 2 D 3i +…+β 48 D 48i + β 49 LEV it + β 50 CR it + β 51 MS it + β 52 CIO it
+ β 53 LIQ it + β 54 SIZE + β 55 OWNERSHIP + u it (3.4)
In this context, D2i is a binary indicator set to 1 for bank 2 and 0 otherwise, similarly D3i indicates bank 3, and so forth, facilitating categorical data analysis Additionally, the article references the process of downloading and submitting latest graduate thesis documents, emphasizing the importance of accessing up-to-date academic resources through email for successful degree completion This framework highlights the role of binary variables in bank data analysis and the significance of current academic materials for postgraduate achievement.
FEM (Fixed Effects Model) accounts for heterogeneity among banks, whereas the Pooled OLS model overlooks this, leading to different results To determine the most appropriate model, an F-test is employed as an instrumental criterion, enabling a comparison between the Pooled OLS model and FEM.
The F-test is calculated by the formula:
In which: R 2 r is restricted residual sum of square in Pooled OLS, R 2 uris unrestricted residual sum of square in FEM
If F value > F* value: H0 is rejected, it means that at least one unit amongβi, i=1, 48,is different from 0 In this case, FEM is chosen
In contrast, if F value < F* value: H0 is fail to rejected, it mean that all unit ofβi = 0, i=1,…,48 In this case, OLS is chosen
3.4.3 The Random Effects Model (REM)
In the REM framework, the intercepts of 49 banks are represented by a mean value, β0, capturing the overall average effect Variations in the individual intercepts among banks are examined through the error term, highlighting differences across institutions The original Model (3.3) is reformulated accordingly to incorporate these insights, enabling a comprehensive analysis of bank-specific effects and their impact on the model's outcomes This approach ensures a robust understanding of the heterogeneity present in the banking sector data.
PERF it = β 0 + β 1 LEV it + β 2 CR it + β 3 MPO it + β 4 CIO it + β 5 LIQ it + β 6 SIZE + β 7 OWNERSHIP + w it (3.5)
The error term "wit" is composed of two components: εi, which is the cross-section error term that varies across the 49 banks but remains constant over time, and uit, representing all unobservable factors that differ among the banks and fluctuate over time.
The Random Effects Model (REM) relies on key assumptions: the error terms εi are normally distributed with mean zero and variance ϭε², denoted as εi ~ N(0, ϭε²) Additionally, the individual-specific error components u_i are also normally distributed with mean zero and variance ϭu², or u_i ~ N(0, ϭu²) The model assumes that the expected value of εi is zero (E(εi) = 0), and the errors are uncorrelated across individuals and time, with E(εiεj) = 0 for i ≠ j Similarly, the error terms u_it satisfy E(uit) = 0, and are uncorrelated across different individuals and time periods, with E(uitujs) = 0 for i ≠ j and t ≠ s.
It means both εi and uit are not correlated with each other and there are no autocorrelation among cross-section and time series units
Breusch – Pagan LM test is used for choosing the best model between Pooled OLS and REM
The Null hypothesis H0: ϭu 2 = cov (wit, wis) = 0 The alternative hypothesis Ha: ϭu 2 = cov (wit, wis) ≠ 0
LM test is calculated by the formula:
If 2 >* 2 : null hypothesis is rejected, REM is chosen, and vice versaPool OLS is chosen
3.4.4 Relevant tests to choose the most appropriate estimation method
First, the study uses Pooled OLS method to run equation (3.2) Next, the Durbin – Watson test is used to test correlative phenomenon between the residuals
After that, we run FEM demonstrated by equation (3.5) and use F- test for choosing the best model between Pooled OLS and FEM
We also run REM represented by equation (3.6) and use Breusch – Pagan LM test for choosing the best model between Pooled OLS and REM
Finally, if both FEM and REM are significant, we use the Hausman test for the FEM against the REM
The Hausman test is calculated by formula:
The statistical expression (βFE – βRE)’ {Var(βFE) – Var(βRE)}⁻¹ (βFE – βRE) approximately follows a chi-square (χ²) distribution, serving as a key test in econometric analysis The null hypothesis (H₀) posits that the estimator of the Random Effects Model (REM) is consistent, providing a basis for validating model reliability Ensuring the consistency of REM estimators is crucial in achieving accurate and reliable results in econometric studies This test helps researchers determine whether the differences between fixed effects and random effects estimators are statistically significant, thereby informing model selection decisions.
Alternative hypothesis Ha: estimator of REM is inconsistent
If 2 >* 2 : null hypothesis is rejected, FEM is favored than REM, vice versa
We also use VIF test for testing the multi-colinearity among regressors
3.4.5 The Feasible Generalized Least Square (FGLS)
This study analyzes panel data comprising both time-series and cross-sectional data, highlighting significant differences in bank-specific factors between state-owned and private banks The findings indicate substantial variability in bank outcomes, suggesting the potential presence of heteroskedasticity, which occurs when the error term's variance is non-constant (Wooldridge, 2012, p 268) Heteroskedasticity can affect the efficiency of Ordinary Least Squares (OLS) estimates, making them unbiased but not the Best Linear Unbiased Estimators (BLUE), and may increase the variance of estimates To address heteroskedasticity, the study employs Fixed Effects Models (FEM) and Random Effects Models (REM), with the Feasible Generalized Least Squares (FGLS) method specifically used to ensure more reliable regression results.
According to Wooldridge (2012, p.286), FGLS have five steps
Step 1: We run the regression below equation (3.7) to obtain the residuals, uˆ:
PERF it = β 0 + β 1 LEV it + β 2 CR it + β 3 MS it + β 4 CIO it + β 5 LIQ it + β 6 SIZE + β 7 OWNERSHIP + u it (3.6)
Var (u/X) = u 2 = ϭ 2 exp (δ 0 + δ 1 LEV it +δ 2 CR it + δ 3 MS it + δ 4 CIO it + δ 5 LIQ it + δ 6 SIZE + δ 7 OWNERSHIP) (3.7)
We model the dependent variable h(X) using a logarithmic function: h(X) = exp (δ₀ + δ₁ LEV_it + δ₂ CR_it + δ₃ MS_it + δ₄ CIO_it + δ₅ LIQ_it + δ₆ SIZE + δ₇ OWNERSHIP) This model captures the impact of various financial and ownership variables on the outcome, emphasizing the importance of leveraging financial metrics such as leverage, current ratio, and market share in predicting business performance.
Step 2: Take log u 2 , we have:
Log ( u ˆ 2 ) = α 0 + δ 1 LEV it + δ 2 CR it + δ 3 MS it + δ 4 CIO it + δ 5 LIQ it + δ 6 SIZE + δ 7 OWNERSHIP + e (3.9)
Step 3: Run the regression in equation (3.9), we obtain the predicted value of log (uˆ 2 ) Put gˆi is predicted value of log (uˆ 2 ), we have gˆi= log^(uˆ 2 )
Step 4: Exponentiategˆi, we have: h ^ i = exp (gˆi) = exp(log^(uˆ 2 ))
Step 5: We transform all variables in equation (3.6) in which each variable is multiplied by 1
√h^i including the intercept After that, we run OLS
3.4.6 Discoll-Kraay Robust for cross-sectional dependence – XTSCC
Panel data analysis provides more informative and meaningful insights compared to cross-sectional data, but many studies overlook cross-sectional and spatial dependence, risking invalid results Hoechle (2007) highlights the importance of Discoll-Kraay estimation, which accounts for heteroskedasticity, autocorrelation, and spatial dependence through "Discoll-Kraay standard errors." According to Hoechle, the XTSCC method offers a better approach for addressing heteroskedasticity and autocorrelation than FGLS, ensuring more reliable panel data analysis.
FINDINGS AND ANALYSIS
Introduction
This chapter provides an overview of the Vietnamese banking system from 2005 to 2014, highlighting key developments during this period It includes descriptive statistics and presents diagnostic test results to determine the most suitable estimation methods, supporting the analysis in Chapter 3 The chapter also discusses the estimation results related to the main hypotheses outlined in Chapter 2, using the model introduced in Chapter 3 Ultimately, this section aims to address the primary research questions proposed in Chapter 1, contributing to a comprehensive understanding of the banking sector's dynamics during the studied period.
The overview on Vietnamese banks
Between 2005 and December 31, 2014, the banking sector experienced numerous successful mergers, leading to a consolidated system comprising 38 banks Within this system, eight banks hold a chartered capital exceeding 10,000 billion dong, eleven banks have capital ranging from 5,000 to 10,000 billion dong, and seventeen banks possess a charter capital below 5,000 billion dong.
As of December 31st, 2014, Vietinbank maintained its position as the leading bank in Vietnam's banking system with a charter capital of 37,234 billion dong, surpassing 10,000 billion dong Unlike previous years, Vietinbank's charter capital remains significantly higher than that of other banks Following closely is BIDV, which, after merging with MHB, has a charter capital of 31,481 billion dong Other prominent banks include Agribank and VCB, which trail behind Vietinbank and BIDV in terms of charter capital Figure 4.1 illustrates the updated charter capital of joint-stock commercial banks as of December 31st, 2014.
Figure 4-1 Banks' Charter Capital updated to December 31th, 2014
Source: State Bank of Vietnam
Based on sample data, Figure 4.2 and Figure 4.3 respectively show Vietnamese banks’ total assets and total equity capital in the period 2005-2014
Vietinbank BIDV Agribank VCB SCB Sacombank Eximbank MBB ACB PVCombank Techcombank
SHB HDBank VPBank MSB LPB TPBank Seabank DongABank ABBBank ConstructionBank
The article provides a comprehensive overview of major Vietnamese banks, including BacA, VIB, OceanBank, SouthernBank, MDB, OCB, MHB, BaoVietBank, VietABank, SGB, GPBank, NCB, NamABank, VietBank, VietCapitalBank, and PGBank These financial institutions play a vital role in Vietnam’s banking sector, offering diverse banking services and contributing to the country's economic development To access the latest thesis papers and full dissertations related to banking and finance, visit the provided link or contact via email for more information.
Figure 4-2 Vietnamese banks’ total assets in the period 2005-2014
Source: Author’s calculation based on sample data
Based on Figure 4.2, the state-owned bank group consists of five banks, each with government ownership exceeding 50% of equity capital, collectively accounting for nearly 50% of the total assets in the banking system sample Overall, the total assets of these banks showed a consistent upward trend from 2005 to 2014.
Private Banks State owned Banks tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 4-3 Vietnamese banks’ total equity capital in the period 2005-2014
Source: Author’s calculation based on sample data
Between 2005 and 2014, Vietnamese banks experienced a consistent increase in total equity, reflecting their financial growth over the decade State-owned banks significantly contributed to this upward trend, with their combined equity accounting for nearly 40% of the entire banking system in the sample This steady rise in bank equity highlights the strengthening financial position of Vietnamese banking institutions during this period.
The Vietnamese bank performance during the period 2005-2014, proxied by ROA, ROE an NIM are demonstrated in Figure 4.4, Figure 4.5 and Figure 4.6
Vietnamese banks experienced a notable increase in ROA between 2005 and 2007, followed by a decline from 2008 to 2014 Private banks generally outperformed state-owned banks with higher ROA from 2005 to 2011, but this trend reversed after 2012, with private banks recording lower ROA than their state-owned counterparts Overall, the data indicates that state-owned banks maintained stable performance throughout the period, while private banks experienced significant fluctuations.
State-owned enterprises tend to be more rigid and cautious in managing their operations, while private groups are generally more flexible and willing to take risks in pursuit of higher returns As a result, private companies typically exhibit significantly higher ROA compared to state-owned firms over most observed periods However, the decline in ROA for private groups, dropping below that of state-owned enterprises since 2012, indicates the increasing impact of market challenges and regulatory constraints on private sector profitability.
Total Equity (Unit: VND billions)
The Central Bank’s policies aim to restructure the banking system and enhance its overall health, impacting both state-owned and private banks These initiatives are designed to improve the performance of the banking sector, ensuring stability and resilience Effective implementation of these policies is vital for supporting sustainable growth in the financial industry.
Figure 4-4 The Return on Total Assets of the Vietnamese banks in the period 2005-2014
Source: Author’s calculation based on data collected
Vietnamese banks experienced a significant decline in Return on Equity (ROE) from 14.8% in 2005 to 6.35% in 2014, as shown in Figure 4.5 Private banks generally outperformed state-owned banks between 2005 and 2007, exhibiting higher ROE during this period However, from 2008 onwards, this trend reversed, with state-owned banks achieving higher ROE than private banks Notably, the ROE of private banks dropped sharply from 14.86% in 2010 to 5.83% in 2014, reflecting increased financial challenges in the private banking sector. -Boost your finance articles' impact with SEO-perfect, concise summaries tailored to complex banking trends—[Learn more](https://pollinations.ai/redirect/draftalpha)
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Figure 4-5 The Return on Total Equity of the Vietnamese banks in the period 2005-2014
Source: Author’s calculation based on data collected
The Net Interest Margin (NIM) of Vietnamese banks from 2005 to 2014 exhibits notable fluctuations, as illustrated in Figure 4.6 Private banks demonstrate greater volatility in NIM compared to State-Owned Banks, reflecting their higher amplitude of fluctuation This variation is likely influenced by the distinct characteristics of each bank group, as previously discussed.
All banks, including state-owned and private banks, play a crucial role in the financial sector, supporting economic growth and development As the banking industry evolves, it is essential to stay updated with the latest research and analysis, such as master's thesis projects and academic studies For those interested in banking careers or academic pursuits, resources like thesis downloads, research publications, and professional email communications are invaluable Stay informed with the newest information and insights to succeed in the ever-changing banking landscape.
Figure 4-6 The Net Interest Margins of the Vietnamese banks in the period 2005-2014
Source: Author’s calculation based on data collected
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Figure 4-7The average equity ratio and ROA, ROE, NIM of the Vietnamese banks in the period 2005-2014
Source: Author’s calculation based on sample data
An overview about average equity ratio, ROA, ROE and NIM are demonstrated in figure 4.7.
Descriptive statistics
This study provides an overview of the variables influencing Vietnamese bank performance, summarized in Table 4.1, which includes mean, standard deviation, minimum, and maximum values for each variable The average Net Interest Margin (NIM) across the sample is 2.79%, with the lowest being 0.46% observed in PVComBank.
2014) and the highest is 8.06% (MDB, 2014) Average ROA of the sample is 1.28%, the lowest is 0.02% (NVB, 2014) and the highest is 2.27%% (Shinhanbank,
2014) The average value of the ratio of equity, credit risk, operational costs,
Vietnamese banks' average equity ratio and ROA,
This article analyzes key financial indicators including LEV, ROA, ROE, and NIM, highlighting their recent trends Notably, liquidity risk, market power, and company size are significant factors, with respective values of 13.91%, 51.34%, and 4.04% The study also considers other metrics such as total assets, loan-to-deposit ratios, and additional financial ratios, emphasizing their impact on overall financial stability and performance The findings underscore the importance of these factors in assessing the financial health and risk profile of institutions, providing valuable insights for investors and analysts.
Table 4-1 Summary statistics for variables
Variable Obs Mean Std Dev Min Max
Multicollinearity occurs when independent variables are highly correlated, posing a serious problem in regression analysis According to Gujarati (2012), multicollinearity is considered significant if the pairwise correlation coefficient exceeds 0.8 In our analysis, the correlation matrix (Table 4.2) shows all coefficients are below this threshold, suggesting multicollinearity can be ignored and that the variables are not significantly correlated.
LEV OWNERSHIP CR CIO LIQ MPO SIZE
This study assesses the presence of multicollinearity using the Variance Inflation Factor (VIF) index, as shown in Table 4.3 A VIF value below 10 for all explanatory variables indicates a low likelihood of multicollinearity (Gujarati, 2012), ensuring the robustness of the regression analysis.
VIF indices from Table 4.3 indicate that multicollinearity among independent variables is not a concern, as the highest VIF value observed is 2.98, well below the critical threshold of 10 This suggests that the variables in the study are sufficiently independent, ensuring the reliability of the regression analysis.
Empirical results
Table 4-4 Results from Feasible Generalized Least Square (FGLS) and Discoll- Kraay Robust for cross-sectional dependence (XTSCC)
Note: ***, **, * for 1%, 5% and 10% respectively Std Err in parentheses
4.4.1 Explanatory variables and bank performance
Table 4.4 demonstrates that LEV is strongly and positively correlated with bank performance, as measured by NIM and ROA, at a 1% significance level in the FGLS method and 5% in the XTSCC method A 1% increase in LEV corresponds to increases of 0.101% in NIM and 0.036% in ROA, indicating that well-capitalized banks tend to perform better This relationship aligns with prior research by Valverde and Fernández (2007), Saunders, Schumacher (2000), Claeys and Vennet (2008), and Chortareasa et al., confirming that higher leverage is associated with improved bank performance.
Research by N Berger and Bouwman (2013), along with Ahokpossi (2013), suggests that banks with higher equity capital tend to require managers to adopt stricter monitoring and select safer investment portfolios Well-capitalized banks are perceived as more trustworthy by customers, investors, and partners, which enhances their credibility This improved perception enables these banks to mobilize deposits at lower interest rates and increase their lending volumes, contributing to overall financial stability.
Improving capital structure positively impacts bank performance, as evidenced by the association between leverage (LEV) and Return on Assets (ROA) This study's findings align with Hirschey (1999), confirming that higher leverage positively influences ROA Consequently, the hypothesis that capitalization enhances Vietnamese banks' performance is supported, highlighting the importance of effective capital management in driving financial success.
Based on the results presented in Table 4.4, there is no significant relationship between ownership type and bank performance when measured by Net Interest Margin (NIM) This indicates that Vietnamese bank performance, as reflected by NIM, is independent of whether the bank is state-owned or private.
Table 4.4 indicates that the coefficient of the Ownership variable in the ROA model is -0.410, which is negative and statistically significant at the 5% level using the FGLS method, but insignificant in the XTSCC method This suggests that state ownership is negatively correlated with Vietnamese bank performance, as measured by ROA, and supports the null hypothesis that state-owned banks tend to have lower profitability compared to private banks The findings align with Micco, Panizza, and Yanez (2007), implying that reducing state ownership could enhance bank profitability Privatization attracts more shareholders who actively monitor operations, leading to increased efficiency and better performance.
4.4.2 Control variables and bank performance
4.4.2.1 Credit Risk – CRand bank performance
Results from2 models including NIM and ROA indicate that Credit Risk impacts positively on NIM, ROA at1% significant level in both FGLS and XTSCC method
In NIM model, the coefficients of Credit Risk are approximate 0.025 It is strongly positive at 1% significant level This finding is consistent with Claeys and Vennet
(2008), Hamadi and Awdeh (2012), Maudos and Guevara (2004), Ahokpossi
Banks with higher loan volumes tend to face increased credit risk, as noted by Maodos and Guevara (2004) To compensate for this elevated risk, such banks typically apply higher Net Interest Margins (NIM) Additionally, the ROA model shows a positive relationship between credit risk and bank performance, with the coefficient for Credit Risk being 0.011 at a 5% significance level, indicating that higher credit risk is associated with improved ROA outcomes.
4.4.2.2 Cost to Income ratio – CIO and bank performance
Results from the study indicated that CIO is negative correlated with ROA The coefficient of CIO in ROA model is -0.012, negative and significant (at 10% level)
Banks with higher CIO have lower perform The similar results was found by Hamadi and Awdeh (2012), J.Maudos and Guevara (2004), J Maudos and Solís
In term bank performance proxied by NIM, the relationship is insignificant
4.4.2.3 Liquidity Risk – LIQ and bank performance
The study found no significant relationship between liquidity (LIQ) and net interest margin (NIM) However, LIQ is strongly and positively associated with return on assets (ROA), with a coefficient of 0.011 that is both positive and statistically significant at the 1% level This indicates that banks holding higher levels of liquid assets tend to achieve better financial performance as measured by ROA.
4.4.2.4 Market Share – MS and bank performance
The study found that MS (Mean Size) does not significantly impact NIM (Net Income Margin), contrasting with the findings of Hamadi and Awdeh (2012) However, MS has a negative and significant relationship with ROA (Return on Assets), with a coefficient of -0.197, indicating that larger firm size may reduce profitability.
(significant at 10% level) It means MS has impact on bank performance proxied by ROA When market share of a bank increase 1%, that generates an decrease 0.197% in ROA
4.4.2.5 Bank Size – SIZE and bank performance
The study found that bank size (SIZE) does not significantly influence overall bank performance when measured by Return on Assets (ROA) However, SIZE has a positive and significant effect on Net Interest Margin (NIM) at the 1% significance level, indicating that larger banks tend to achieve higher NIM These findings align with the research of Claeys and Vennet (2008), supporting the notion that bank size can enhance specific performance metrics like NIM.
Robustness test - common panel data methods
This study uses some techniques to test the existence of phenomenon that might generate biased regression results and suggest appropriate model to deal with problems
Tables 4.5, 4.6, and 4.7 present the results obtained using various methods, providing a comprehensive comparison of their effectiveness These tables illustrate how different techniques perform across key metrics, highlighting the most efficient approaches for data analysis The study emphasizes the importance of selecting appropriate methods to ensure accurate and reliable results in research applications.
Table 4-5 Regression result of NIM model
OLS FEM REM FGLS XTSCC
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Table 4-6 Regression result of ROA model
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Following the application of Pool OLS, FEM, and REM models, F tests and the Breusch-Pagan Test were conducted to determine the most suitable model Table 4.7 presents the test results, indicating that the model selection was guided by these diagnostic tests to ensure the best fit for the data analysis.
Table 4-7The results of F Test and Breusch – Pagan Test
F test Breusch - Pagan LM Test
F value P-value Chi-square P-value
Based on the F-test results, the null hypothesis is rejected, indicating that the Fixed Effects Model (FEM) is suitable for all models The Breusch-Pagan test also rejects its null hypothesis, confirming the appropriateness of the Random Effects Model (REM) To determine the most appropriate model between FEM and REM, the Hausman test was conducted, ensuring the selection of the most reliable econometric approach for the analysis.
Hausman Test Chi-square P-value
Because the p-value of Hausman Test for bank performance proxied by ROA is approximate zero, the null hypothesis can be rejected at the significant level of 1%
Thus the fixed effect model is considered more appropriately
The Hausman Test for bank performance, measured by the Net Interest Margin (NIM), yielded a p-value greater than 0.05, indicating that the null hypothesis cannot be rejected Therefore, the Random Effects Model (REM) is the appropriate choice for this analysis.
Model Chi square Prob> Chi square Conclusion
NIM 1.7e + 31 0.0000 The probability is larger than
Chi square and significant at 1% level =>FEM get heteroscedasticy problem ROA 2.5e + 32 0.0000
The results of Wald Test used to test of heteroskedasticity shows that the p-value <
0.05 in two FEM models including NIM and ROA Thus, the null hypothesis H0: sigma (i)^2 = sigma^2 for all i can be rejected It means heteroskedasticity phenomenon may exist in these FEM models
NIM 41.585 0.0000 The probability is larger than
Chi square and significant at
1% level The model exist autocorrelation problem
ROA 2.836 0.0993 The probability is larger than
Chi square and significant at 10% level
Similarly, the result of the test of serial correlation also shows that serial correlation phenomenon might exist in NIM as well as ROA model
Heteroskedasticity can cause the OLS estimator to remain unbiased, but it prevents it from being the Best Linear Unbiased Estimator (BLUE) To address heteroskedasticity, this study employs the Feasible Generalized Least Squares (FGLS) method for regression analysis, ensuring more efficient estimates Additionally, to ensure the statistical validity of the results, “Driscoll-Kraay standard errors” are used to account for heteroskedasticity, autocorrelation, and spatial dependence within the panel data models.
This study employs FEM and REM methods solely for robustness testing to validate the findings The primary analysis utilizes results from the FGLS and XTSCC methods, ensuring the reliability and accuracy of the conclusions.
The findings confirm that the dependent variable, LEV, is strongly statistically significant and positively related to bank performance, as measured by NIM and ROA Additionally, the results indicate that state-owned banks have a lower return on total assets compared to private banks, highlighting differences in performance based on ownership structure.