Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.

Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.Biến động tỷ suất sinh lợi thị trường chứng khoán, hiệu quả và rủi ro bằng chứng thực nghiệm từ các ngân hàng thương mại Việt Nam niêm yết.

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY  NGUYEN THANH HUNG STOCK MARKET RETURN VOLATILITY, EFFICIENCY AND RISK: EMPIRICAL EVIDENCE FROM LISTED COMMERCIAL BANKS IN VIET NAM SUMMARY OF PhD THESIS Ho Chi Minh City, 2022 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY  NGUYEN THANH HUNG STOCK MARKET RETURN VOLATILITY, EFFICIENCY AND RISK: EMPIRICAL EVIDENCE FROM LISTED COMMERCIAL BANKS IN VIET NAM MAJOR: FINANCE & BANKING (BANKING) Code: 93.40.201 SUMMARY OF PhD THESIS Ho Chi Minh City, 2022        CHAPTER 1: INTRODUCTION 1.1 Research background and motivations The research perspective of SMV-performance and SMV-risk has very few previous empirical studies, and there are many research gaps that need to be addressed, such as: (i) A lack of theoretical mechanisms to explain the above-mentioned linkages; (ii) The variable's measurement methods are not diverse enough to compare the robustness of research results; (iii) Among the few empirical studies on this impact, the research context is under-researched in emerging countries—which have dynamic developing financial markets and the banking and stock market sectors as important capital conduits in the economy; and (iv) These studies fail to capture bank-specific factors driving the effect of market return volatility on bank performance and risk? Thus, the thesis focuses on exploiting the influence of SMV on risk and efficiency in the banking sector to enrich the empirical knowledge of the effects of SMV 1.2 Research objectives Objective 1: To study the impact of SMV on the performance of a joint stock commercial bank listed on HOSE Objective 2: To explore the impact of SMV on the risk of a joint stock commercial bank listed on HOSE Objective 3: To examine the impact of SMV on the performance of a joint stock commercial bank listed on HOSE in the context of different sizes of the bank Objective 4: To investigate the impact of SMV on the risk of a joint stock commercial bank listed on HOSE in the context of different sizes of the bank Objective 5: To detect the impact of SMV on the performance and risk of joint stock commercial banks listed on UPCOM and the role of bank’s size in this impact of SMV 1.3 Research question Does SMV affect the performance of banks listed on HOSE? Does SMV affect the risk of banks listed on HOSE?  Is the effect of SMV on performance affected by the size of the bank listed on HOSE?  Is the influence of SMV on risk affected by the size of the bank listed on HOSE?  Is there any difference in the effect of SMV on risk and performance when considering the sample of banks listed on UPCOM? And what is the role of size in this influence of SMV? 1.4 Objects and scope of the thesis 1.4.1 Research objects The research object of the thesis is the stock market return volatility, performance and risk of listed Vietnamese joint-stock commercial banks Besides, the thesis also exploits the aspect of the bank’s size and examines the role of size on the impact of SMV on the performance and risk of banks 1.4.2 Research scope The thesis studies the influence of SMV on performance and risk and the role of size in this effect 1.4.3 Research sample The thesis focuses on 14 Vietnamese joint-stock commercial banks that are publicly traded on the Ho Chi Minh City Stock Exchange (HOSE) 1.4.4 Research period The thesis uses the period from nd Quarter of 2006 – 1st Quarter of 2021 Within this period, the data is relatively stable and fully accessible Previous studies investigating the determinants of performance and risk of joint stock commercial banks mainly employ the yearly data; so this is also one of the new contributions of the thesis 1.5 Research method The thesis uses a quantitative approach including the fixedeffect regression method (FEM), random-effect regression method (REM), and the classical linear regression method (OLS), and the two-step system generalized method of moments (S-GMM) 1.6 Theoretical and empirical contributions The thesis contributes the following main points to the theoretical and empirical research understanding Firstly, through theoretical and empirical research reviewed, this is the first empirical work to consider the influence of SMV on risk and performance in Vietnam, especially in the banking sector Secondly, arguments from theoretical work show that an increase in SMV will help reduce a bank’s risk However, this result of the thesis is contrary to the explanations of economic theories related to the relationship between stock market return volatility and risk Thirdly, the role of bank size has been mentioned in many empirical studies However, only the study by Rashid and Ilyas (2018) mentions the role of bank’s size in the influence of SMV on bank performance, and the author has not found any research which refers to the role of size in the effect of fluctuations in stock market return on bank risk 1.7 Structure of the thesis The content of the thesis is divided into parts, the detailed content of each part is as follows: Chapter 1: Introduction; Chapter 2: Theoretical background and empirically related studies; Chapter 3: Research methods; Chapter 4: Research results; Chapter 5: Conclusion and policy implications CHAPTER 2: THEORETICAL BACKGROUND AND EMPIRICALLY RELATED STUDIES 2.1 Research concept 2.1.1 Stock market return volatility Movements in stock market returns are considered as macro uncertainties of the economy According to Schwert (1990), the SMV is the dispersion of market returns as measured by the standard deviation of stock return series In addition, SMV is simply defined as the volatility of a market stock’s price; this price can sometimes be high or low The SMV is measured using market index volatility, which is characterized by the unpredictable volatility of the stock market index over time 2.1.2 Bank performance From the perspective of input-output approach, efficiency is the balanced combination of input units to create an optimal output unit (Banya and Biekpe, 2018; Segun et al., 2013) From a traditional revenue-profit perspective (Athanasoglou et al., 2008), a bank’s efficiency can be considered through its ability to generate profits (revenue, interest, and profit margin) 2.1.3 Bank risk Risk is defined from different perspectives The first perspective was mentioned by Willett (1901) who indicates that risk is the uncertainty arising from some unexpected event Besides, Apătăchioae (2015) argues that events or the probability of the occurrence of damage are considered risks The risk in the bank’s operation can be perceived as an unexpected event that, if it occurs, will lead to asset damage, a decrease in profit compared to the plan, or an increase in costs allowing banks to be able to perform a certain financial operation (Nguyen Minh Kieu and Bui Kim Yen, 2009) 2.1.4 Bank’s size Size indicates corporate governance through the number of total assets owned by a firm relative to other firms in the same industry (Sritharan, 2015) Bank’s size is often used to measure economies of scale or non-economies of scale in the banking sector Banks with large total assets can benefit more from cost reduction by taking advantage of economies of scale (Kolapo et al., 2012) 2.2 Theoretical background and empirical evidence on the impact of SMV on bank performance and risk 2.2.1 Theoretical background and empirical evidence on the impact of SMV on bank performance 2.2.1.1 Theoretical background on the impact of SMV on bank performance The effect of SMV on performance can be explained through prospect theory When the SMV increases (implying that the uncertainty in market returns increases), this causes a shift in investment funds from the stock market to bank deposits, which is essentially safer and characterized by a guaranteed profit margin In the context of increased risk, investors will be less concerned with low-probability outcomes and more concerned with higherprobability outcomes (Kai Ineman and Tversky, 1979) These funds have an impact on the liquidity and profitability of banks (GarcaHerrero et al., 2009) 2.2.1.2 Related empirical evidence Preliminary research on the direct effect of SMV on bank performance is implied in the work of Angbazo (1997) Accordingly, movements in the federal funds rate are addressed to represent the SMV As a result, an increase in SMV enhances the bank’s profit margin In addition, from the review of existing studies, the thesis provides 05 research articles that directly mention the influence of SMV on bank efficiency 2.2.2 Theoretical background and empirical evidence on the impact of SMV on bank risk 2.2.2.1 Theoretical background on the impact of SMV on bank risk The influence of SMV on bank risk can be implied based on the financial instability hypothesis of Minsky (1992), which discusses the correlation between financial stability and financial crisis In this theory, prolonged stabilization (closely associated with low levels of SMV) could lead to an increase in optimism This optimism is related to the emergence of increased risk-taking behavior for economic actors in the market (Huang, 2017) 2.2.2.2 Related empirical evidence From the review for theoretical and empirical research, the author has not found any direct results for the effect of SMV on risk Therefore, the author gives some indirect arguments related to this correlation to elucidate the possible impact mechanism of SMV on risk: (i) SMV → Collateral value → Risk; (ii) SMV → Lending interest rate → Risk; (iii) SMV → Credit accumulation and leverage → Risk; (iv) SMV → Efficiency → Risk; (v) SMV → Domestic stock market volatility → Risk 2.2.3 Theoretical background and empirical evidence on the impact of SMV on bank performance and risk with the driving role of bank’s size 2.2.3.1 Theoretical background on the role of bank’s size The theory of optimal bank size, which basically describes the importance of bank size in determining profitability and risk when overall non-diversified risks of banks exist (Krasa and Villamil, 1992) This theory implies that differences in the size of banks can have an impact on bank efficiency and risk 2.2.3.2 Related empirical evidence According to a review of existing studies, only the study by Rashid and Ilyas (2018) directly mentions the role of bank size in the influence of SMV on bank efficiency SMV directly addresses the role of bank size in navigating the influence of SMV on risk Depositors trust large banks more than small banks Therefore, they will tend to deposit their investment funds at large banks to ensure safe returns This can be effective because the banks could use this capital to finance operating activities, avoiding the situation of lack of liquidity 2.3 Research gaps There are a few studies that address the effect of SMV on efficiency and these studies are conducted in the context of developed countries (for example, 10 major industrialized countries in Europe, Pakistan and China) However, the direction of predicting the influence of SMV on efficiency is different This research gap leads to the following first research question (RQ): ► RQ1: Does SMV affect the bank’s performance? Regarding the influence of SMV on risk, the author has not found empirical evidence directly referring to the influence of SMV on bank risk From theoretical reasoning and indirect empirical evidence, the second research question is displayed as follows: ► RQ2: Does SMV have an impact on bank risk? The role of bank size has been addressed in many empirical studies, including Al-Tamimi and Charif (2011), Sufian (2009), and Kosmidou (2008) However, these studies have not mentioned the role of the bank’s size in the impact of SMV on efficiency and risks 10 of banks Only the study of Rashid and Ilyas (2018) mentioned the role of bank size in the effect of SMV on efficiency The research question about the role of the size factor on the effect of SMV on efficiency can be stated as follows: ► RQ3: Is the effect of SMV on performance different between large and small banks? The author has not found studies that address the impact of size on the effect of SMV on risk From the theoretical argument, the next research question is formulated as follows: ► RQ4: Does the influence of SMV on risk differ between large and small banks? The thesis makes a comparison between banks listed on HOSE and banks listed on exchanges other than HOSE (specifically UPCOM) with the goal of examining whether the influence of SMV on efficiency and risk can be changed In other words, we investigate whether there is any other implication when considering the factor of difference in the stock exchange on which the banks are listed The final research hypothesis is put forward as follows: ► RQ5: How is the effect of SMV on efficiency and risk different when changing the sample to banks listed on UPCOM? 2.4 Research hypotheses 2.4.1 The hypothesis of the effect of stock market returns on bank performance During a period of increased volatility in market returns (which implies increased systemic risk), capital flows from investors will navigate toward banks, where capital is safe with a certain rate of return As a result, banks have abundant liquidity and increased operational efficiency Based on the analysis above, the author proposes the following research hypothesis H1 based on the majority of empirical results: ► H1: An increase in SMV has a positive impact on bank performance 2.4.2 The hypothesis of the effect of stock market returns on bank risk The increase in SMV causes capital flow to shift to banks Excess deposits make bank managers overconfident that banks will 12 2.4.5 The hypothesis that the impact of stock market return on risk varies among listed bank samples The author expects that the influence of SMV on the performance and risk of banks listed on HOSE will be statistically significant, and that the influence of SMV on efficiency and risk will not be statistically significant for samples of banks not listed on HOSE Research hypotheses H5a and H5b are presented as follows: ► H5a: The influence of SMV on performance and risk exists only for the sample of banks listed on HOSE ► H5b: The effect of size on SMV-performance and SMV-risk exists only for the sample of banks listed on HOSE CHAPTER 3: RESEARCH METHODOLOGY 3.1 Research models 3.1.1 SMV-performance model The research model of the thesis is developed from the research of Tan and Floros (2012b), Tan and Floros (2012a), Tan and Floros (2013) and Rashid and Ilyas (2018) A regression model for the effect of SMV on bank performance is constructed in the following specification: = ++ + + + (1a) Model (1a) was estimated using OLS, FEM, and REM Following the approach of Tan and Floros (2012b), the thesis adds the lagged variable of the dependent variable in the model (1b) to control the bank performance persistence and the dynamics of panel data Specifically, the thesis introduces the lagged variable into the model (1a) and exploits the regression approach to estimate GMM dynamic panel data to achieve the regression coefficients as in the model (1b): = +++ + ++ (1b) 13 Table 3.1: Description of the research variables used in the model (1a) and (1b) Expected Variables Symbol Calculation Reference sign Dependent variables: Bank performance Tan and Floros (2012b); Albertazzi and Gambacorta Return on Equity ROE Earning after tax/Equity (2009); Dao and Nguyen (2020); Adesina (2021) Return on Total Rashid and Ilyas ROA Earning after tax/Total assets assets (2018) Tan and Floros (2012b); Heffernan Net income Net interest income/ interest and Fu (2010); NIM margins earning assets Moudud-Ul-Huq (2021); Huong et al (2021) Independent variables Stock market SMV1 Tan and Floros + volatility in which: (2012b); Võ Xuân and is the number of trading Vinh and Võ Văn days in quarter t Phong (2016); French 14 Variables Symbol Calculation Reference Expected sign et al (1987); Khan et al (2017) Standard deviation of SMV SMV2 Lau et al (2013) Control variables Bank size SIZE Logarithm of total assets Liquidity LIQ (Total liability – Loans)/Total assets Capitalization CAP Equity/Total assets Tan and Floros (2012b); Vu and Nahm (2013); Fadzlan and Kahazanah (2009); Pasiouras and Kosmidou (2007) Tan and Floros (2012b); Rashid and Ilyas (2018); Pasiouras and Kosmidou (2007) Tan and Floros (2012b); Bouzgarrou et al (2018); Rashid and Ilyas (2018) + - + 15 Variables Symbol Cost efficiency CE Non-traditional activity NTA Bank solvency/ Bank stability/ distance to default ZSCORE Scale of banking industry LOTA GDP growth GDPG Calculation Reference Expected sign Tan and Floros (2012b); Maudos and Operating cost/Total assets De Guevara (2004) and Hawtrey and Liang (2008) Tan and Floros Non-interest income/ Gross (2012b); Meslier et + profit al (2014); Chiorazzo et al (2008) Hunjra et al (2020); ZSCORE takes logarithmic Kabir et al (2015) + form to reduce the high skewness Tan and Floros Log of total assets of all (2012b); Albertazzi studied bank and Gambacorta (2009) Tan and Floros (2012b); Dietrich and The growth rate of GDP + Wanzenried (2014); Trujillo‐Ponce (2013) Source: Compiled by the author 16 3.1.2 SMV-risk model The author continues to use the above model in the studies of Tan and Floros (2012b), Tan and Floros (2012a), Tan and Floros (2013) and Rashid and Ilyas (2018) Accordingly, the research model of the impact of SMV on risk is constructed by replacing the dependent variable measuring bank performance with a variable measuring bank risk The SMV-risk model is stated as follows: = + + + ++ (2a) Model (2a) was estimated using OLS, FEM, and REM However, to control the inertia of bank risk and the dynamics of panel data (Tan and Floros, 2012b), the thesis introduces the lagged variable and uses S-GMM to estimate the model (2b) as follows: = + + + + ++ (2b) Table 3.2 Description of the research variables used in the model (2a) and (2b) Expected Variables Symbol Calculation Reference sign Dependent variables: Bank risk Delis et al (2014); Houston et al (2010); Laeven and Bank solvency/ Levine (2009); Bank stability/ ZSCORE ZSCORE takes logarithmic Adesina (2021); distance to default form to reduce the high Isnurhadi et al skewness (2021); Moudud-UlHuq (2021) 17 Variables Risks due to uncertainty in returns on equity Symbol DROE Risks due to uncertainty in returns on total assets DROA Stock market volatility SMV1 Standard deviation of SMV) Calculation Reference Expected sign Lepetit et al (2008); Standard deviation Return Lee and Hsieh on equity (2013); Tan and Floros (2013) Lepetit et al (2008); Standard deviation Return Lee and Hsieh on asset (2013); Tan and Floros (2013) Independent variables Tan and Floros (2012b); Võ Xuân in which: Vinh and Võ Văn and is the number of Phong (2016); trading days in quarter t French et al (1987); Khan et al (2017) SMV2 Lau et al (2013) Control variables + 18 Variables Symbol Calculation Bank size SIZE Logarithm of total assets Liquidity LIQ (Total liability – Loans)/Total assets Capitalization CAP Equity/Total assets Cost efficiency CE Operating cost/Total assets Reference Tan and Floros (2012b); Vu and Nahm (2013); Fadzlan and Kahazanah (2009); Pasiouras and Kosmidou (2007) Tan and Floros (2012b); Rashid and Ilyas (2018); Pasiouras and Kosmidou (2007) Tan and Floros (2012b); Bouzgarrou et al (2018); Rashid and Ilyas (2018) Tan and Floros (2012b); Maudos and De Guevara (2004) and Hawtrey and Liang (2008) Expected sign + + + + 19 Symbol Calculation Reference Expected sign Non-traditional activity NTA Non-interest income/ Gross profit Tan and Floros (2012b); Meslier et al (2014); Chiorazzo et al (2008) + Net income margins NIM Net interest income/ interest earning assets Dwumfour (2017) + Variables Tan and Floros (2012b); Albertazzi LOTA and Gambacorta (2009) Tan and Floros (2012b); Dietrich and GDP growth GDPG The growth rate of GDP Wanzenried (2014); + Trujillo‐Ponce (2013) Source: Compiled by the author Note: The expected sign of the independent variable used for the dependent variable is DROA and DROE This result is similar to the case of the variable ZSCORE but has the opposite sign Scale of banking industry Log of total assets of all studied bank 20 3.1.3 The model capturing the role of bank size in SMVperformance According to the work of Rashid and Ilyas (2018), the thesis uses the interaction variable between two measures for banks’ size (SIZE and DUM_SIZE) with SMV Models with interactive variables are estimated by OLS, FEM and REM estimation methods as follows: = + + + + + (3a) Similar to the approach of Tan and Floros (2012b), the author adds the lagged variable of the effective variable () to control the effective inertia as well as the dynamics of the panel data, and then conduct S-GMM regression The specific equation is as follows: = ++ + + + + (3b) 3.1.4 The model capturing the role of bank size in SMV-risk The model is developed from the perspective of Rashid and Ilyas (2018) to consider the role of SMV in the bank’s performance with a change in banks’ size In this thesis, the performance variables are replaced by measure variables for risk The thesis will continue to estimate the following model: = + + + ++ (4a) Similar to the approach of Tan and Floros (2012b), the author adds the lagged variable of the bank risk variable () to control the phenomenon of risk inertia as well as the dynamics of panel data, then conduct S-GMM regression The specification is as follows: = + + + + ++ (4b) 3.2 Data and methodology 3.2.1 Research data The research sample includes data from 14 joint-stock commercial banks listed on HOSE in the period from Q2/2006 to Q1/2021 In addition, in order to compare how the regression results change when changing the research sample, the thesis further employs the dataset of 07 banks listed on UPCOM as a basis for comparison, thereby somewhat reinforcing the research results 21 3.2.2 Estimation methods The thesis uses Ordinary Least Square (OLS), the fixed-effect regression method (FEM), and the random-effect regression method (REM) to examine the effect of SMV on bank performance and risk In addition, in order to control the potential endogeneity and dynamic nature of the research model when including the lagged measure of the main dependent variable (Tan and Floros, 2012b), the thesis uses two-step generalized method of moments (S-GMM) to check the robustness of the above research results CHAPTER 4: EMPIRICAL RESULTS 4.1 Descriptive statistics of research variables Table 4.1: Descriptive statistics of research variables 4.2 Pairwise correlation between research variables Table 4.2: Pairwise correlation between research variables 4.3 Research results 4.3.1 The effect of stock market return volatility on bank performance Table 4.3 shows that all the regression coefficients of SMV on performance measures such as ROA, ROE, and NIM are positive and have very strong statistical significance, supporting research hypothesis H1 As for the control variables in the model, the research results show that high liquid assets (LIQ) will help banks benefit through more efficient operations In addition, other control variables such as capital size (CAP), cost efficiency (CE), risk (ZSCORE), and economic growth (GDPG) have inconsistent signs depending on the measures used In addition, no evidence has been found for the effect of bank size (LOTA) and size (SIZE) on bank performance 4.3.2 The effect of stock market return volatility on bank risk Table 4.4 shows that the regression coefficients of SMV on the risk measures DROE and DROA are positive, and the coefficients of SMV2 on ZSCORE are negative This implies that SMV may cause an increase in bank risk (this does not support hypothesis H2) With respect to other dependent variables, the increased cost efficiency ratio will expose the bank to more risks (DROE and DROA) and reduce the stability (ZSCORE) in banking operations, implying that the bank does not have good cost management, which leads to the use of operating costs with 22 additional risks Engaging in non-interest income-generated activities (NTA) may increase a bank’s risk The growing size of the banking industry (LOTA) can reduce risks and increase stability for banks Economic growth (GDPG) poses increased risks for banks Besides, there is no statistical evidence showing the influence of size (SIZE), liquidity (LIQ) and capital (CAP) on risk and stability of banks 4.3.3 The impact of stock market return volatility on bank performance: A comparison between large and small banks Table 4.5 and Table 4.6 show that, for the NIM, the regression sign remains negative but no statistical significance is found However, the signs of the interactive variables SMV 1*SIZE and SMV2*SIZE are negative and statistically significant with respect to the ROA and ROE This shows that the effect of SMV on bank performance is stronger for small banks (in contrast to hypothesis H3) 4.3.4 The impact of stock market return volatility on bank risk: A comparison between large and small banks Based on the regression results of Table 4.7 and Table 4.8, the author has not found any cases where the interaction variable SMV2*SIZE affects the risk measures However, the sign of this interactive variable is consistent for measures of risk (negative sign for ZSCORE and positive sign for DROE and DROA) Although not statistically significant, this result implies that an increased SMV affects the bank’s risk Accordingly, fluctuations in market stock returns pose risks to banks, especially large banks (which does not support the research hypothesis H4) 4.3.5 Effect of SMV on performance and risk: Comparing results using a sample of banks listed on UPCOM Table 4.9 shows that increased SMV (SMV and SMV2) enhances bank performance This result is similar to the regression sample of banks listed on HOSE Table 4.10 shows that an increase in SMV reduces bank stability (ZSCORE) and increases risk from volatility in profitability (DROA and DROE) This result is again similar to the results for the sample of banks listed on HOSE Table 4.11 shows the role of the size factor that has no statistically significant influence on the impact of SMV on bank efficiency Table 4.12 shows that the influence of SMV on risk is stronger for large- 23 sized banks on UPCOM However, this result has weak statistical significance, existing only in the model with DROA and SMV2 as the dependent and independent variable of interest, respectively To some extent, this result is similar to the influence of SMV on risk for the sample of banks listed on HOSE CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 5.1 Conclusion Some of the results achieved by the thesis are as follows: Firstly, an increase in SMV has a positive impact on bank performance Second, despite having a positive effect on bank efficiency, SMV also causes risks for the bank, implying the “passthrough” of risks from SMV into the bank's risk Third, size has a different role in the effect of SMV on the performance of large and small banks According to the research results, the positive effect of SMV on efficiency is stronger for small-sized banks than for largesized banks Fourth, a certain level of risk arising from SMV has a stronger “pass-through” effect on large-sized banks than small-sized peers 5.2 Contributions of the thesis 5.2.1 Contribution to evidence on the effect of SMV on bank performance In view of the effect of SMV on bank performance, the author finds that there are few empirical studies on this topic, especially for listed banks in emerging markets And among these few empirical studies, the direction of the effect of SMV on performance was mixed The research results in the thesis show that there is statistical evidence to support the view that SMV can improve the performance of Vietnamese joint stock commercial banks listed on the stock market This is a significant contribution to the scarce empirical evidence on the role of SMV - considered as one of the important influencing factors on the bank performance 5.2.2 Contribution to evidence on the effect of SMV on bank risk The author has not found any works mentioning the influence of SMV on risk Meanwhile, the explanation for this effect can be implied through the financial instability hypothesis of Minsky (1992) and the volatility paradox of Adrian and Shin (2014) The results of 24 the thesis are contrary to the predictions of these viewpoints Accordingly, SMV can potentially cause a “pass-through” of a certain level of risk into the banking sector 5.2.3 Contribution to evidence on the impact of SMV on performance and risk considering the size of the bank The role of size on bank risk and efficiency has been widely studied However, the role of size and its influence on the impact of SMV on bank performance and risk remains scarce in previous studies The results of this thesis support further empirical evidence that the negative effect of SMV on performance is stronger for large banks because of their greater ability to transfer systemic risk (an increase in SMV on lending interest rates reduces the borrowing needs of economic agents, thereby reducing the bank’s profits) Besides, the author has not found any research showing the influence of SMV on risk in the comparison between large-sized banks and small-sized ones The research results show that the risks of large banks are more strongly affected by the increase in SMV, thereby suggesting evidence of risk “pass-through” from systematic risk (caused by an increase in SMV) to the risk of large-sized banks 5.3 Research implications 5.3.1 Implications for the effect of SMV on bank performance Recognizing stock market signals through overall fluctuations in market stock returns will have important implications for bank executives in forecasting or anticipating capital flows from the stock market channel to the banking industry The banking channel helps banks to have an abundance of mobilized capital in the form of savings deposits, thereby improving the bank’s operational efficiency 5.3.2 Implications for the effect of SMV on bank risk Banks can monitor stock market return volatility through its volatility information to make appropriate predictions for movements in systemic risk, thereby controlling “pass-through” risk from volatility dynamic in SMV It should be possible that the lending interest rate will be set to suit the borrowing needs of the market because, at this time, economic agents who need capital through bank’s loan credit will also have the same perception of increased systemic risk and limit borrowing of loans with fixed interest rates 25 5.3.3 Implications for the impact of SMV on performance and risk: Comparison between large and small banks The policy of stimulating banks to grow in size should address the bank’s risk- taking behaviors, which may stem from the excessive use of “soft power” from the “too big to fail” advantage and over-diversifying activities based on their large-sized assets Bank administrators may need to have an appropriate cost allocation plan through designing a system to monitor market stock return fluctuations so that they can quantify the influence of SMV factors on bank performance and risk in the context of opening up their operations through increasing asset-based size LIST OF RESEARCH WORKS RELATED TO THE THESIS No Title of articles Stock market volatility and operational efficiency in the banking sector: The case of banks on Vietnam's stock market Co-authors Publish ed year Nguyễn Thành Hưng Phạm Quốc Phu Thân Thi Thu Thủy 2021 Mai Đức Xuân Conference Proceedings ICBF 2021: UEH International Conference on Business and Finance 2021 27-28 September 2021 Ho Chi Minh City, Vietnam ISBN: 978-604325-669-7 Stock market volatility and bank risk: The case of banks on Vietnam's stock market Nguyễn Thành Hưng Phạm Quốc Phu Thân Thi Thu Thủy 2021 Mai Đức Xuân Đặng Kiếm Bửu ICBF 2021: UEH International Conference on Business and Finance 2021 27-28 September 2021 Ho Chi Minh City, Vietnam ISBN: 978-604325-669-7 ... return volatility through its volatility information to make appropriate predictions for movements in systemic risk, thereby controlling “pass-through” risk from volatility dynamic in SMV It... banks are more strongly affected by the increase in SMV, thereby suggesting evidence of risk “pass-through” from systematic risk (caused by an increase in SMV) to the risk of large-sized banks 5.3... Floros (2012b); Rashid and Ilyas (2018); Pasiouras and Kosmidou (2007) Tan and Floros (2012b); Bouzgarrou et al (2018); Rashid and Ilyas (2018) + - + 15 Variables Symbol Cost efficiency CE Non-traditional