Factors affecting the performance of commercial banks

Master. LE HONG NGA (Faculty of Economics, Bac Lieu University)


This study analyzes data of 29 joint-stock commercial banks in Vietnam over the period from 2007 to 2019 to test the impact of factors affecting the profitability of commercial banks. Fixed effects (FE) and random effects (RE) regression methods were used to conduct this study. Then, a suitable research model was selected via the Hausman test. The results show that banking characteristics have significant impacts on the profitability of commercial banks. Based on the study’s findings, some solutions are proposed to improve the performance efficiency of commercial banks.

Keywords: performance efficiency, commercial banking, panel data.

 1. Introduction

 The banking system is critical to the development of the economy because it allows capital to be circulated from excess sources and in demand. As a result, the commodity budget's stability is regarded as a critical aspect of the economy's development. Currently, in economic terms, Vietnam is a member state of the United Nations, the World Trade Organization, the International Monetary Fund, the World Bank Group, the Asian Development Bank, the Asian Development Bank, the International Monetary Fund, the Asia-Pacific Economic Cooperation, and the ASEAN. Vietnam participates in multilateral free trade agreements with ASEAN countries, Korea, Japan, and China. Vietnam has also signed a bilateral economic partnership agreement with Japan. For the monetary and banking sector, the integration process is associated with the financial market liberalization process, bringing many opportunities, but also many challenges. From that reality, the study analyzes factors affecting the performance of Vietnamese commercial banks in the current period. Based on the study’s findings, some practical solutions are proposed to improve the performance of Vietnamese commercial banks.

2. Literature Review

Commercial banks, according to Rose (2004), are also regarded as business groups that operate for the purpose of maximizing profits while minimizing risk. Profitability, on the other hand, is an aim that banks are interested in, since high income would assist banks to conserve capital, expand market share, and attract investment capital. The impact of factors on operational efficiency has been shown through several domestic and foreign studies, such as Le Thi Huong (2002) with research on improving the investment performance of Vietnamese commercial banks; Ariss's (2010) study used OLS and Tobit models to explore how market power affects the efficiency and stability of the system in the context of developing economies.

According to William (2012), the SFA, 2SLS, and Tobit models are used to analyze the relationship between market power and bank efficiency in Latin America. Most of the studies on the efficiency of banking activities focus mainly on developed countries.

Athanasoglou et al (2008) examined the internal, sectoral, and macro factors affecting the ROA and ROE of the Greek banks. The results show that, except for the size variable, the variables reflecting bank characteristics such as capital adequacy, credit risk, production capacity, cost management, and scale all affect profitability.

Osuagwu (2014) studies the profitability of commercial banks in developing countries, specifically Nigeria. The findings show that bank-specific factors are important in determining bank profitability, while industry factors have a negligible influence and macro factors fail the multi-collinearity test.

3. Research Methods and Data

3.1 Research methods

Theoretically, as well as with empirical evidence, there are many different measures and representations of performance. The author's research point of view: Choose the ROE ratio to measure the performance of joint-stock commercial banks and it is the dependent variable. Based on the theory of several factors affecting bank performance and empirical studies at home and abroad related to the impact of factors affecting performance. The author proposes a research model:

ROEit = β0 + β1SIZEit + β2TCTRit + β3DLRit + β4ETAit + β5NPLit + uit

3.2 Research data

Research data was collected from the annual financial statements of 20 joint-stock commercial banks operating as of the end of the 2019 accounting year. The results of Table 2 show that most of the variables, such as: ROE, NPL, SIZE, TCTR, ETA all have relatively low dispersion. The DLR variables, on the other hand, produce the opposite result.

 Table 1: Description of variables


(Sources: Compiled by the author)

4. Result

Table 2: Descriptive Statistics


 (Source:  Work’s estimation from STATA 15)

Table 3: Regression results


 (Source:  Work’s estimation from STATA 15)

The standard errors of variables are put into parentheses. 

*, **, *** stand for the significance level at 10%, 5% and 1% respectively.

From the FE and RE regression results, we see that the variables TCTR, ETA, DLR, NPL, and size always have an impact on the ROA. All regression models are statistically significant and have an R-square of 19%. The Hausman test is used to choose between the FE and RE models, and the test results show that Prob > Chi 2 = 0.039 = 0.05. Therefore, we accept hypothesis H0, the FE model is more suitable than RE. The Breusch - Pagan test for the FE model gives the result that Prob < Chi 2 = 0.000 < = 0.05, so the model has a variable variance. At the same time, the Wooldridge autocorrelation test for Prob > Chi 2 = 0.07 > = 0.05, so the model does not have any autocorrelation, multicollinearity test

Table 4: Variance Inflation Factor (VIF) results

variance_inflation_factor_vif_results (Source:  Work’s estimation from STATA 15)

Their VIF values are less than 10, suggesting that there is no multicollinearity among them (Any VIF greater than 10 indicates a multicollinearity issue (Hair et al. 2010). The magnitude of the correlation coefficients indicates that multicollinearity in the regression model is unlikely. The FGLS (feasible generic least square) approach is then used to address the phenomena of variable variance, and the results are shown in Table 5.

Table 5: The result FGLS

the_result_fgls (Source:  Work’s estimation from STATA 15)

The standard errors of variables are put into parentheses. 

*, **, *** stand for the significance level at 10%, 5% and 1% respectively.

When testing the model's fit, the value of the F test yields the result Prob (F-statistic) = 0.000 = 0.05, so we reject hypothesis H0 and accept hypothesis H1 that the research model is adequate. The independent variables account for approximately 20% of the variation in ROE. As a result, the model is free of flaws, ensuring its dependability.

5. Conclusions

The regression coefficient of the scale variable (SIZE) is 0.003. It shows that the bank size has a positive effect on the performance and is significant at the 5% level. The larger the bank's scale, the easier it is to equip it with more modern technology to diversify its services. The research results of Ho Thi Hong Minh and Nguyen Thi Canh (2015) show that there is an evidence that income diversification positively affects profitability. Therefore, this study also predicts that the bank size has a positive effect on the dependent variable. The regression coefficient of the variable cost to revenue (TCTR) is -0.001. The results of this study show that the cost-to-revenue ratio harms operational efficiency and has statistical significance at 5%. This finding is consistent with the author's expectations and it is supported by Rahman et al (2015). DLR is statistically significant at the 1% level. This shows that if banks make good use of mobilized capital, they can increase their operational efficiency.

The regression coefficient of the variable equity ratio (ETA) is 0.079. This result shows that the ratio of equity to total assets has a positive effect on performance and has a statistical significance of 1%. When equity is high, they can lend more, which contributes to an increase in operational efficiency. This finding is consistent with the research of Rahman et al (2015). NPL ratio (NPL) is statistically significant at the 5% level of significance. This variable reflects the quality of the bank's lending assets. This result is consistent with the studies of Ayanda et al. (2013), Osuagwu (2014).

Furthermore, to optimize profitability, commercial banks must strike a balance between costs and revenue. Furthermore, it is critical to make good use of the mobilized money because the input capital has a high cost that affects the bank's profit. Furthermore, rising equity must be considered because increasing equity is also a component of generating profit.



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Khoa Kinh tế, Trường Đại học Bạc Liêu


Nghiên cứu này phân tích dữ liệu của 29 ngân hàng thương mại cổ phần (NHTMCP) tại Việt Nam từ năm 2007 đến năm 2019 nhằm kiểm định tác động của các nhân tố ảnh hưởng đến lợi nhuận của các ngân hàng thương mại. Phương pháp hồi quy hiệu ứng cố định (FE) và hiệu ứng ngẫu nhiên (RE) được sử dụng trong nghiên cứu này. Từ đó, một mô hình nghiên cứu phù hợp đã được lựa chọn thông qua bài kiểm tra Hausman. Kết quả cho thấy các đặc điểm của ngân hàng có tác động đáng kể đến lợi nhuận của các ngân hàng thương mại. Dựa trên kết quả nghiên cứu, một số giải pháp được đề xuất nhằm góp phần nâng cao hiệu quả hoạt động của ngân hàng thương mại.

Từ khóa: hiệu quả hoạt động, ngân hàng thương mại, dữ liệu bảng.

[Tạp chí Công Thương - Các kết quả nghiên cứu khoa học và ứng dụng công nghệ, 

Số 13, tháng 6 năm 2021]