Profitable Investment Channels of Vietnamese Commercial Banks (2018–2024)

Abstract

The Law on Credit Institutions 2010, amended and supplemented, was applied on 15 January 2018, causing many changes in senior personnel in Vietnamese banking. The period (2018–2014) had many changes. This was also a period of many business difficulties. Four commercial banks had to carry out mandatory transfers at the request of the State Bank to ensure the development of the Vietnamese banking system in 2024. Profitable investment channels of commercial banks sometimes generate income and, at other times, suffer losses. Managers often analyze and make investment decisions by observing developments recorded on graphs and estimating the future fluctuation trends of each profitable investment channel. However, no research has been conducted on how the simultaneous implementation of all information from investment channels affects the final profit results of commercial banks. This study investigates all banking activities, from trading to investing, to consider which investment channel has a stable impact on bank profits over a long period. The S-GMM estimation method is used, due to the consideration of endogenous variables in quarterly panel data of 27 Vietnamese commercial banks from the first quarter of 2018 to the third quarter of 2024. This study provides statistical evidence indicating that all investment channels of commercial banks contribute to increased profits, except for short-term securities trading channels and capital contributions to subsidiaries. This study also reveals that economic growth and systemic risk affect commercial bank profits. Several solutions are proposed for commercial banks to develop future profitable investment channels.

1. Introduction

Commercial banks are enterprises that sell and purchase special products, particularly currency. In addition to the function of receiving deposits and lending, commercial banks also provide non-cash payment services for the entire economy. With this characteristic activity, commercial banks play a vital role in the economy, because they are the intermediary bridge to transfer capital from places with surplus to places with shortage (Xiong et al., 2017). In addition to lending and providing payment services that bring income to banks, banks also have other profitable investment activities, such as investing in securities, investing in real estate, investing in subsidiaries, etc. All activities can be profitable or unprofitable, depending on the business period. Therefore, each activity’s contribution to the bank’s total profit is different. Bank managers often evaluate the impact of each activity through analyzing the level of its profit contribution to the total profit after each business period. At that time, depending on the actual results, each bank will have its own strategy, suitable to the business reality at its unit.

In 2024, in the context of the weak performance of four private commercial banks in Vietnam (GPBank, CBBank, Oceanbank, and DongA Bank), the State Bank of Vietnam mandatorily transferred these four banks to large commercial banks for management (P. D. Nguyen, 2024). This shows that banking activities need to be better managed and compete more healthily to ensure the safety of the entire system.

In business operations, banks also compete with each other with regard to lending and deposit interest rates to attract customers (van Leuvensteijn et al., 2011; Biswas & Koufopoulos, 2020). This competition brings benefits to customers when increasing deposit interest rates, and helps businesses to increase the efficiency of technological innovation when reducing lending interest rates (Liu & Zhao, 2024). However, it will reduce bank profits and increase the risk for banks when accepting subprime loans. (Joaquim et al., 2023; Canta et al., 2023). Thus, competition between banks to increase profits will also increase risks. Instead of relying on analysis of past data from a single bank to inform managers’ decisions, research on data from many banks is very valuable for bank managers. Therefore, it is necessary to add more new research from scientists that considers overall data to evaluate which commercial bank activities bring stable profits to banks.

As bank lending activities provide capital for the economy, they play an extremely important role in promoting production and business in enterprises. Most studies have focused on analyzing the impact of credit on the economy (Duican & Pop, 2015; Armeanu et al., 2015; Hussein et al., 2022). Many studies have also focused on analyzing the impact of credit risk in commercial banking activities (Gizaw et al., 2015; Poudel, 2018; Anh & Phuong, 2021). In addition to credit activities, the issues of banks mobilizing deposits for businesses and ensuring bank payments must also be of primary concern. Other studies are concerned with the impacts of credit risk and liquidity risk on the performance of commercial banks (Imbierowicz & Rauch, 2014; Sari, 2021).

Many studies have examined the impact of micro- and macro-variables on the profitability of commercial banks. In particular, the micro-variables considered in the literature concern lending activities, deposit activities, and investment in assets of commercial banks (Jayaraman et al., 2021; Yuan et al., 2022). Besides credit activities, other investment activities of commercial banks, such as securities investment, real estate investment, and investment in subsidiaries, also contribute to increasing profits for banks (Nyachwaya & Nyanga’u, 2020; Zhang et al., 2018; Salman et al., 2020). However, no study has been conducted in relation to how all bank activities—from business to investment—affect bank profitability. Therefore, this study evaluates the impacts of all investment and business activities of commercial banks over a long period, in order to determine which activities contribute to increasing the profit level of Vietnamese commercial banks.

The structure of the research paper is presented in five sections. After the introduction of the research problem comes the theoretical review of related studies. The research method and model are presented in the third section. The fourth section is a discussion of the research results. Finally, the conclusion proposes solutions and future research directions.

2. Literature Review

Commercial banks are credit institutions that mainly use debt as their capital for business. According to Circular 10-NH5, the banking sector’s operations are allowed to mobilize capital up to 20 times higher than the owner’s equity. Thus, the business results of commercial banks must be considered based on all the capital used by the bank during the period in which the capital is mainly mobilized from debt. Therefore, the indicator used to evaluate the level of bank profits achieved during this period must be compared to the total capital used by the bank. The ROA indicator for return on total assets, which evaluates the rate of commercial bank profits achieved from using all capital during this period, represents the dependent variable for the level of commercial bank profits, similarly to the studies of Flamini et al. (2009), Phan et al. (2020), Yuan et al. (2022), and P. D. Nguyen (2024).

With its characteristic activities of capital mobilization and re-lending, the commercial banking system acts as a bridge to transfer capital in the economy from surplus to deficit. The role of bank credit in the economy has received attention from many studies (Duican & Pop, 2015; Armeanu et al., 2015; Hussein et al., 2022). Credit activities are also the main profit-making activities of commercial banks. The difference between deposit interest rates and lending interest rates creates the credit operating profit of commercial banks (Yuan et al., 2022; Kohlscheen et al., 2018). Deposit mobilization activities generate capital costs, while credit activities generate income for banks. When commercial banks compete with each other in terms of interest rates, this reduces bank profits and increases credit risks (Liu & Zhao, 2024; Joaquim et al., 2023; Canta et al., 2023). Thus, credit activities generate income, but at the same time, increase risks for banks.

When lending capital, the risk of not being able to recover interest or not being able to recover the principal amount is a risk that causes the bank to incur losses. In addition, when a bank mobilizes customer deposits, the bank must ensure that it can repay the customer at any time upon request. Therefore, liquidity risk and credit risk are of primary concern to commercial banks in their banking operations (Dang et al., 2024; Haris et al., 2024; Saleh & Abu Afifa, 2020).

In addition to the main business activities of capital mobilization and re-lending, investment activities also generate income for commercial banks, as do securities investment, real estate investment, and investment in subsidiaries of commercial banks, as has been confirmed by researchers in various countries (Nyachwaya & Nyanga’u, 2020; Zhang et al., 2018; Salman et al., 2020). Diversifying the investment portfolio also helps commercial banks to increase their profits (Hailu et al., 2018; Salman et al., 2020). The most recent study in Vietnam was conducted by Giang et al. (2024), who considered the impact of investment in stocks, investment in bonds, investment in subsidiaries, and investment in real estate on the Return on Assets (ROA) and Net Interest Margin (NIM). The study only considered these four investment activities in relation to financial leverage and bank size. The study was conducted on data from 24 commercial banks in the period from 2018 to 2022, using the least squares method. The findings indicated that the proportion of investment in bonds positively affected financial performance, while investment in real estate had a negative impact on bank performance. Investment in stocks did not have a significant impact on bank performance during this period, due to market fluctuations. Investment in subsidiaries and other enterprises only affected the ROA. Thus, the simultaneous consideration of business activities that generate income and investment activities that generate income for commercial banks has not yet been carried out by researchers.

The operation and management of commercial banking activities are part of the individual strategy of each bank. Banks will compete with each other in capital mobilization and lending through attraction policies and interest rates (Jayaraman et al., 2021; Tiep et al., 2022). In terms of investment activities, each bank chooses and allocates capital to investment channels. Therefore, after each business period, depending on the business strategy of each commercial bank in the period, the profit results will be different. Bank managers analyze their own bank’s past data to make decisions for the future period. Past studies focused on determining the factors affecting the performance of commercial banks have considered all investment banking assets, risks in banking operations, and macroeconomic factors to seek evidence of their impact (Tiep et al., 2022; Jayaraman et al., 2021; Vu & Nahm, 2013). However, no study has simultaneously examined how a commercial bank’s business and investment activities contribute to its overall profitability.

The Structure–Conduct–Performance (SCP) theory can be applied to explain the monopoly power of commercial banks in concentrated markets, which is created by low deposit mobilization costs at the input while lending at higher interest rates. In concentrated markets, SCP explains how commercial banks can derive significant monopoly profits or benefits from trading in monetary products. Banks have an advantage in providing very distinctive banking services that other institutions do not possess. Applying the SCP model helps researchers to explain how commercial banks’ business activities (such as lending and non-cash payment fee collection) and investments (foreign exchange trading, securities trading, and investments in subsidiaries) contribute to the bank’s overall profit.

Based on the financial statements of listed joint stock commercial banks in Vietnam, seven business and investment activities generate profits. This study aims to evaluate the impacts of all the investment and business activities of Vietnamese commercial banks over a long period which contribute to increasing their profit level. Instead of analyzing a single bank’s historical data, based on which managers make decisions, this study relies on data from multiple banks. This is also a pioneering study, conducting evaluations in relation to the simultaneous contribution of investment and business activities of each bank over a certain period. The research results will constitute an important basis for managers in considering the structure and scale of investment and business needed to maximize profits for their bank.

3. Research Model

According to a commercial bank’s business performance report, profitable investment activities that generate profits for the bank include the seven activities listed below. The bank’s total profit includes all seven of these activities.

(1)

Lending activities: Individuals and economic organizations in need of capital will borrow it from commercial banks, creating income from interest on lending activities. The capital source for banks to use for lending is customer deposits, so there is an interest expense on deposits. The profit from this activity is the difference between loan interest and deposit interest arising during the period. The profit from lending activities does not yet offset the provision for credit risk. The contribution of profit from lending activities is determined by the ratio of net interest income (i.e., net profit) to the total net income of all banking activities. From this, hypothesis H₁ is formulated: The net interest income ratio has a positive impact on the ROA of commercial banks.

(2)

Service activities: Commercial banks provide non-cash payment services, guarantee services, factoring services, etc., to customers. Commercial banks charge customers fees for all of these activities, generating income for the bank. The contribution of profit from service activities is determined by the ratio of the net income (i.e., net profit) from service activities to the total net income of all banking activities. Based on this, hypothesis H₂ is formed: The ratio of net income from service activities has a positive impact on the ROA of commercial banks.

(3)

Foreign exchange trading activities: These are special activities of Vietnamese commercial banks when they are allowed to reserve buy and sell all foreign currencies for profit purposes. From the fluctuations in foreign currency prices according to the international market, the difference between the buying price and the selling price will be the source of profit from these activities. The contribution of profit from foreign exchange trading activities is determined by the ratio of the net income (i.e., net profit) from this activity to the total net income of all banking activities. Based on this, hypothesis H₃ is formed: The ratio of net income from foreign exchange trading activities has a positive impact on the ROA of commercial banks.

(4)

Trading securities activities: Banks repurchase and sell securities, mortgage and discount securities with maturity dates, and create short-term valuable papers with the aim of increasing liquidity and creating more profit for the bank. This activity is a short-term investment of the bank. The contribution of profits from trading securities is determined by the ratio of the net income (i.e., net profit) from this activity to the total net income of all banking activities. Based on this, hypothesis H₄ is formed: The ratio of the net income from trading securities has a positive impact on the ROA of commercial banks.

(5)

Investment securities trading activities: These activities involve the bank selecting a number of securities with good profit opportunities to buy for long-term investment in order to enjoy interest, dividends, and the opportunity for stock price increases in the future. These activities are a long-term investment of the bank. The contribution of profit from investment securities trading activities is determined by the ratio of the net income (i.e., net profit) from this activity to the total net income of all banking activities. Based on this, hypothesis H₅ is formed: The ratio of the net income from investment securities trading activities has a positive impact on the ROA of commercial banks.

(6)

Capital contribution to buy shares: Commercial banks also participate in capital contribution activities to invest in subsidiaries, joint ventures, and associates in the form of buying shares for investment. Every year, the bank will receive interest and dividends from this investment capital contribution activity. The contribution of profit from equity investment activities is determined by the ratio of the net income (i.e., net profit) from this activity to the total net income of all banking activities. Based on this, hypothesis H₆ is formed: The ratio of the net income from equity investment activities has a positive impact on the ROA of commercial banks.

(7)

Other unusual activities are activities that do not belong to the six groups of investment and business activities mentioned above. They are usually fines, contract compensation, or debts recovered after debt settlement. The contribution of profit from other activities is determined by the ratio of the net income (i.e., net profit) from other activities to the total net income of all banking activities. Based on this, hypothesis H₇ is formed: The ratio of the net income from other activities has a positive impact on the ROA of commercial banks.

From the total operating income of the bank in each period, its total operating expenses and credit risk provision will be its total profit before tax. In banking operations, with the typical function of mobilizing capital and then lending it out, it is mandatory to set up provisions to ensure safety according to state regulations. The income from lending activities has not yet been used to offset the provision. Therefore, the after-tax profit margin on total investment capital (ROA) will depend on the profit ratio achieved by each activity compared to the total operating income and credit risk provision ratio. Therefore, the research model includes the provision ratio variable. The higher the provisioning ratio, the more it reduces the profit of commercial banks (Thanh et al., 2022; Ahamed, 2017; Pervan et al., 2015). Therefore, the research model includes the influence of the provisioning ratio variable on the performance of banks. Based on this, hypothesis H₈ is formed: The provisioning ratio has a negative impact on the ROA of commercial banks.

Macroeconomic factors, such as inflation and economic growth, affect the profitability of commercial banking activities (Niraula & Maharjan, 2024; Phan et al., 2020). When inflation increases, market interest rates increase, causing deposit interest rates and lending interest rates to rise, affecting the profitability of commercial banks’ lending activities. Therefore, inflation has a negative impact on the profitability of commercial banks during the period. Accordingly, hypothesis H₉ is determined as follows: “The Inflation rate has a negative impact on the ROA of commercial banks”. Similarly, when the economy grows well, the investment activities of individuals and businesses will be more favorable. During this time, individuals and businesses will expand their business activities and be able to access bank loans, so economic growth positively impacts the profitability of commercial banks. Therefore, the research model includes the influence of variables of economic growth on banking performance, similarly to the studies of Niraula and Maharjan (2024) and Phan et al. (2020). Hypothesis H₁₀ is constructed as follows: “Economic growth has a positive impact on the ROA of commercial banks”.

The Law on Credit Institutions 2010, amended and supplemented, effective from 15 January 2018, has greatly affected the issue of senior personnel in the banking industry. Accordingly, the chairman of the board of directors, the chairman of the board of members, and the general director of credit institutions cannot concurrently be the chairman and a member of the board of directors, the chairman and a member of the board of members, a general director, a deputy general director, or another equivalent position at any other enterprise. In 2018, commercial banks changed a series of senior personnel, which affected the operation of the banks’ business activities. This is also a suitable starting period to choose for researching profitable investment channels of commercial banks. This study was conducted from the first quarter of 2018 to the third quarter of 2024, which was a period of systemic risk, due to the COVID-19 pandemic. The COVID-19 pandemic limited all the investment activities of enterprises, negatively affecting the profits of commercial banks. Therefore, this study adds the systemic risk variable into the research model, similarly to the studies of Eken et al. (2012) and T. Nguyen (2024). Based on this, hypothesis H₁₁ is formed: “Systemic risk has a negative impact on the ROA of commercial banks”.

The research model of the business and investment activities of commercial banks for the period (2018–2024) considered in the context of the Vietnamese economy is proposed to include eight variables related to business and profitable investment channels of commercial banks, and three macroeconomic variables related to the economy, in the research period.

Due to market imperfections, or high sensitivity to the impact of macroeconomic factors on the operations of commercial banks, the profits of banks are unstable. To achieve a sustainable profit trend over time, this study applies a dynamic feature in the research model: the appearance of a lag variable of the dependent variable (Flamini et al., 2009; Dietrich & Wanzenried, 2014; Trujillo-Ponce, 2013). Over time, the profit of commercial banks will converge to an average value in the long run. α also shows the speed of adjustment of commercial banks’ profits to the equilibrium value; the closer this value is to 0, the faster the adjustment and the higher the level of competition in the commercial banking industry, and vice versa (Athanasoglou et al., 2008). The research model has the following form:

ROA_i,t = β₀ + α₀ROA_i,t−1 + β₁*X_1,i,t + β₂*X_2,i,t + β₃*X_3,i,t + β₄*X_4,i,t + β₅*X_5,i,t + β₆*X_6,i,t + β₇*X_7,i,t + β₈*X_8,i,t + β₉*X_9,i,t + β₁₀*X_10,i,t + β₁₁*X_11,i,t + μ_i,t

In this research model, the variables are measured as detailed in

Table 1. Description of variables according to research model.

Sign	Variable	Measure	Source
ROA	Return on Assets	ROA = ${\displaystyle \frac{\mathrm{E}\mathrm{a}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{a}\mathrm{f}\mathrm{t}\mathrm{e}\mathrm{r} \mathrm{t}\mathrm{a}\mathrm{x}}{\mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$	Yuan et al. (2022), P. D. Nguyen (2024)
X1	Net interest income ratio (from lending activities)	X1 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{t} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X2	Net profit margin from service activities	X2 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{s}\mathrm{e}\mathrm{r}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{e} \mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X3	Net profit margin from foreign exchange trading	X3 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{f}\mathrm{o}\mathrm{r}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{n} \mathrm{e}\mathrm{x}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{e} \mathrm{t}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X4	Net profit margin from trading securities	X4 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{t}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{s}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X5	Net profit margin from trading investment securities	X5 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{t}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{s}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X6	Net profit margin from other operations	X6 = ${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{o}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r} \mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X7	Rate of income from capital contribution to purchase shares	X7 = ${\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{b}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{t}\mathrm{o} \mathrm{p}\mathrm{u}\mathrm{r}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{s}\mathrm{e} \mathrm{s}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$	The bank’s business performance report
X8	Loan loss provision ratio	X7 = ${\displaystyle \frac{\mathrm{L}\mathrm{o}\mathrm{a}\mathrm{n} \mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}{\mathrm{C}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{r} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{n}\mathrm{s} \mathrm{O}\mathrm{u}\mathrm{t}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}}$	Thanh et al. (2022), Ahamed (2017), Pervan et al. (2015)
X9	Inflation	Consumer price index	Niraula and Maharjan (2024), Phan et al. (2020)
X10	Economic growth	X10 = ${\displaystyle \frac{{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{t}}-{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{t}-1}}{{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{t}-1}}}$	Niraula and Maharjan (2024), Phan et al. (2020)
X11	Systemic risk	Value 1 is obtained during the COVID-19 period, from Q1 2020 to Q2 2022; otherwise, value 0 is obtained	This is the timeline of the COVID-19 pandemic in Vietnam

Source: collected from author.

. The model determines the ROA as the dependent variable and the one-period lagged variable (ROA_t−1) as the independent variable.

This study used data from 27 joint stock commercial banks in Vietnam, with fully listed financial statements from the first quarter of 2018 to the third quarter of 2024. Variables Y and X₁ to X₈ were calculated, according to the formula in Table 1, from the financial statement data of the commercial banks. Variables X₉ and X₁₀ were collected from the quarterly socio-economic reports of the General Statistics Office of Vietnam. Variable X₁₁ is a binary variable determined by the research period, as shown in Table 1.

First, we conducted a descriptive statistical analysis of variables to eliminate outliers. Next, the correlation coefficient between pairs of research variables was checked to provide an early warning of the possibility of multicollinearity between variables. Estimation of regression coefficients for the impact level of independent variables on dependent variables was carried out step by step, according to appropriate tests. Next, the multicollinearity, autocorrelation, and endogeneity of the research data were tested. If the model had endogeneity, the appropriate estimation method was used to overcome this issue.

In the research model, adding a lagged variable of the dependent variable itself to explain the aggregate impact in the past on the dependent variable in the current period is considered effective. However, adding a lagged variable of the dependent variable to the model causes an endogeneity risk, and there is a possibility of correlation with the aggregate error of the model. To simultaneously solve the above problems, Hansen (1982) proposed the Generalized Method of Moments, which allows for the estimation of models with endogeneity and over-identification in many cases.

With panel data from 27 commercial banks over the 27 research periods, the independent variable is a one-period lagged variable of the dependent variable, so Ordinary Least Square (OLS), Random Effects Model (REM), and Fixed Effects Model (FEM) estimates are not appropriate (Sasaki, 2015). In addition, the research data sample has a research period (T = 27) that does not exceed the number of subjects (N = 27), so the S-GMM estimation model (Blundell & Bond, 1998) is suitable for use on the dynamic panel model. This technique can estimate the endogenous model, and is more suitable than the fixed effect method (Arellano & Bond, 1991; Baltagi, 2005; Sasaki, 2015). With regard to the difference GMM method and the system GMM, each type has two versions: one-step GMM and two-step GMM. This study chooses to apply two-step system GMM with standard error correction, according to Windmeijer (2005), to obtain more accurate estimation results than the one-step system GMM. At the same time, the two-step system GMM overcomes the defects of heteroscedasticity, autocorrelation, and endogeneity (if any). In addition, the study also presents the IV-GMM estimation results in ivreg2 with robustness, to further check the robustness of the research results (Roodman, 2009).

4. Results and Discussion

The statistical analysis of the level of variation in the variables in the research model (measured as percentages) is shown in

Table 2. Descriptive statistics of variables.

Variable	Y (%)	X1 (%)	X2 (%)	X3 (%)	X4 (%)	X5 (%)	X6 (%)	X7 (%)	X8 (%)	X9 (%)	X10 (%)	X11
Mean	0.307	78.227	10.20	2.286	0.387	3.902	4.482	0.312	0.304	2.972	5.511	0.259
Std. Dev	0.240	23.934	11.017	14.318	1.542	9.107	30.024	1.641	0.422	1.415	1.841	0.483
Min	−0.36	−71.31	−116.58	−353.09	−15.37	−87.18	−404.54	−0.87	0	0.29	0.39	0
Max	1.750	356.97	105.97	43.48	15.03	84.64	451.31	37.38	7.55	5.56	7.83	1

Source: research data processed from Stata 16.

. The average value for each quarter, standard deviation, minimum value, and maximum value for each quarter of each variable fluctuated quite significantly. All seven business and investment activities of commercial banks experienced losses at certain points during the research period. Compared to total operating income, income from other unusual activities (X₆) experienced the largest fluctuation, from −404.54% to 451.31%. In addition, the ratio of net interest income from lending activities (X₁) and the ratio of income from service activities (X₂) also fluctuated strongly, from the lowest value of −116.58% to the highest value of 356.97%. The operating loss ratio fell between the third quarter of 2020 and the first quarter of 2022. This was also the period when the economy was affected by the COVID-19 pandemic.

The correlation coefficients between pairs of variables in the research model are shown in

Table 3. Correlation coefficients between pairs of variables.

	ROA	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
X1	−0.187 (0.000)
X2	0.277 (0.000)	−0.480 (0.000)
X3	−0.029 (0.436)	−0.261 (0.000)	0.056 (0.130)
X4	0.164 (0.000)	−0.168 (0.000)	0.083 (0.026)	−0.019 (0.601)
X5	0.046 (0.214)	−0.310 (0.000)	−0.013 (0.720)	−0.043 (0.251)	0.128 (0.001)
X6	0.058 (0.120)	−0.461 (0.000)	0.046 (0.219)	−0.009 (0.817)	0.116 (0.002)	−0.106 (0.004)
X7	0.067 (0.072)	−0.117 (0.001)	0.115 (0.002)	0.214 (0.000)	0.213 (0.000)	−0.084 (0.023)	0.153 (0.000)
X8	0.229 (0.000)	−0.231 (0.000)	0.142 (0.000)	0.004 (0.709)	0.174 (0.000)	−0.001 (0.019)	0.265 (0.000)	0.036 (0.327)
X9	−0.047 (0.203)	0.086 (0.021)	−0.068 (0.067)	−0.089 (0.016)	−0.048 (0.198)	−0.114 (0.002)	0.017 (0.637)	0.030 (0.418)	0.008 (0.829)
X10	0.127 (0.001)	−0.006 (0.877)	0.063 (0.090)	−0.040 (0.280)	−0.060 (0.105)	0.036 (0.336)	−0.029 (0.430)	−0.027 (0.464)	0.046 (0.210)	0.011 (0.000)
X11	−0.057 (0.122)	0.068 (0.064)	−0.017 (0.643)	−0.013 (0.723)	−0.117 (0.002)	−0.130 (0.000)	0.020 (0.595)	0.003 (0.295)	−0.014 (0.695)	0.419 (0.000)	−0.304 (0.000)

Source: research data processed from Stata 16.

. The data in Table 3 show that the variables in the model have correlation coefficients lower than 0.6, meaning that the variables satisfy the independence condition, and there is no possibility of multicollinearity (Evans, 1996).

According to

Table 4. Results of autocorrelation and endogeneity tests.

No	Test	p-Value	Conclusion
1	Breusch–Pagan/Cook–Weisberg test for heteroskedasticity	chi2 (1) = 29.26 Prob > chi2 = 0.000 < 5%	Accept H1: heteroscedasticity
2	White’s test for heteroscedasticity Skewness Kurtosis	p-value = 0.033 < 5% p-value = 0.6452 > 5% p-value = 0.0757 > 5%	-Heteroscedasticity -Data is normally distributed
4	Wooldridge test for autocorrelation in panel data	F(1, 26) = 0.234 Prob > F = 0.633 > 5%	No autocorrelation
5	Test for endogeneity	Wu-Hausman F(1, 687) = 58.885 p-value = 0.000 < 5%	There is an endogenous phenomenon

Source: research data processed from Stata 16.

, the Breusch–Pagan test for heteroskedasticity with the hypothesis H₀: constant variance had a p-value < 5%, so it was rejected. Thus, the model had the phenomenon of changing variance. The skewness and kurtosis values in White’s test had p-values > 5%, proving that the variables in the model were symmetrical, and did not have many outliers. Thus, White’s test showed that the model had changing variance, but the cause for this was not asymmetry or outliers in the distribution of the variables. The Wu–Hausman test gave a p-value < 5%, indicating that the research data had endogeneity. This study used the S-GMM estimation technique on a dynamic panel model to overcome the endogeneity phenomenon (Arellano & Bond, 1991; Baltagi, 2005).

The results of estimation of the regression coefficients of the research model by the S-GMM method are shown in

Table 5. Results of regression coefficient estimation by S-GMM and GMM2S robust.

ROA	G_GMM			GMM2S Robust
ROA	Coef	Std. Err.	p > |z|	Coef	Robust Std. Err.	p > |z|
L1.ROA	−0.072	0.1369	0.597
X1	0.070	0.0264	0.008	0.009	0.0022	0.000
X2	0.061	0.0260	0.018	0.013	0.0024	0.000
X3	0.073	0.0262	0.006	0.012	0.0023	0.000
X4	0.014	0.0348	0.687	0.025	0.0055	0.000
X5	0.070	0.0268	0.009	0.006	0.0024	0.009
X6	0.070	0.0263	0.008	0.010	0.0022	0.000
X7	−0.008	0.0399	0.830	−0.001	0.0038	0.723
X8	−0.219	0.0250	0.000	−0.216	0.0413	0.000
X9	0.003	0.0049	0.552	0.008	0.0082	0.330
X10	0.059	0.0161	0.000	0.085	0.0227	0.000
X11	−0.010	0.0043	0.023	−0.007	0.0047	0.111
cons	−6.460	2.6273	0.014	−0.732	0.2232	0.001
Number of groups			27	Hansen J statistic (overidentification test of all instruments): 201.510
Number of instruments			25
Arellano–Bond test for AR(1) in first differences: z = −1.97			Pr > z = 0.049	Chi-sq(1) p-val = 0.000
Arellano–Bond test for AR(2) in first differences: z = 1.53			Pr > z = 0.126	Included instruments: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11
Sargan test of overid. restrictions: chi2(12) = 5.08			Prob > chi2 = 0.955	Excluded instruments: L.roa
Hansen test of overid. restrictions: chi2(12) = 12.22			Prob > chi2 = 0.428	Duplicates: X1

Source: research data processed from Stata 16.

. With the constraint that the number of instrumental variables is smaller than the number of groups, the first-order autocorrelation test has a p-value < 5%, and the second-order correlation test has a p-value > 10%, indicating that there is no second-order serial autocorrelation in the first-order correlation constraint. The Hansen test for excessive constraints (with a p-value > 10%) shows that there is no correlation between the instrumental variables and the error. Thus, the instrumental variables are reasonable.

In addition, the estimation results using the GMM2S robust method are presented in Table 5 to verify the robustness of the model and the stability of the direction of the impact of the independent variables on the dependent variable. The research results show that the direction of the impact of the dependent variables (from X₁ to X₁₁) on the independent variable, the ROA, is completely consistent in both methods. The statistical evidence for the impact of the independent variables on the dependent variable is also similar in the two estimation results. For variables X₄ (net profit rate from trading securities) and X₁₁ (systematic risk), the two methods do not have the same statistical evidence. The assessment of the comprehensiveness also acknowledges that the research results ensure high stability and efficiency.

With a significance level of 5% and a confidence level of 95%, the estimated results show that lending activities (X₁), service activities (X₂), foreign exchange trading activities (X₃), investment securities trading (X₅), and other unusual activities (X₆) had a positive impact on the return on total invested capital (ROA) of commercial banks. With a model that includes all business and investment activities that affect commercial bank profits, the results of this study provide a different verification method compared to previous studies. This shows that managers should focus on developing these activities to contribute to increasing bank profits. The provision ratio (X₈) had a negative impact on the profitability of commercial banks, similarly to the research results of T. Nguyen (2024) and Haris et al. (2024). When banks finance many subprime loans, the risk will increase, and banks must set up more provisions, which will reduce bank profits.

In the research period (2018–2024), according the S-GMM estimation method, the trading of securities (X₄), the capital contribution to buying shares (X₇), and inflation (X₉) were not statistically significant. Investment in securities is a short-term investment of banks, involving pledging, discounting of securities that are still valid, and repurchasing securities in the investment portfolio to increase profits for the bank. Variable X₄ demonstrated statistical evidence of a positive impact on commercial bank profitability, according to the GMM2S Robust method. Capital contribution to buy shares (X₇) is an investment activity of banks in subsidiaries and other businesses. The data do not provide evidence that these activities have an impact on the profitability of commercial banks, which means that they are unstable, and do not contribute regularly to these banks’ profits. Therefore, this is not an investment channel for managers to focus on in the aim of increasing profits for commercial banks. This result is different to that seen in the study of P. D. Nguyen (2024) on commercial bank data from 2018 to 2022, which found evidence that equity capital contribution has an impact on ROA. It is possible that the research data were supplemented with many business and investment activities of commercial banks at the same time, so this obscured the evidence of the impact of capital contribution activities in subsidiaries and other enterprises (called capital contribution to buy shares—X7), which, inherently, do not account for a large proportion of bank profits.

This study also found evidence of a positive impact of economic growth (X₁₀) on the profitability of commercial banks, similarly to the research results of (Yuan et al., 2022; Phan et al., 2020). Systemic risks (X₁₁) limit their profitability, similarly to the research results of T. Nguyen (2024) and Eken et al. (2012). This shows that economic and political instability will affect the business activities of commercial banks. Good economic growth will help individuals and businesses to operate more conveniently, use more banking services, and borrow more capital to expand investment, thereby contributing to increasing profits for commercial banks.

The results of this empirical study, when simultaneously adding all business activities and investment activities to the final profit results of commercial banks, show that hypotheses H₁, H₂, H₃, H₄, H₅, H₆, H₇, H₁₀, and H₁₁ are accepted. Hypotheses H₇ and H₉ require no statistical evidence to be accepted or rejected. Thus, simultaneously considering the impact of all the business activities and investments of the bank on the final business results of the bank will help managers to gain an overview of the inter-relationships between all of these activities. The research results will help managers to come up with appropriate management strategies for banks in the future.

5. Conclusions

This study used the panel data of 27 listed joint stock commercial banks, reporting quarterly financial statements from the beginning of 2018 to the third quarter of 2024. Using the S-GMM and GMM2S robust estimation methods to handle endogenous phenomena, we found the following results. Commercial lending and payment services are still the main activities that contribute a large amount of profit to commercial banks. In addition, foreign exchange trading and long-term securities investment also contribute significantly to increasing their profits. In contrast, the credit risk provision ratio has a strongly negative impact on the increase in bank profits. Thus, bank managers need to focus on and better control credit risks to increase safe profits for banks. At the same time, banks should also increase investment in foreign exchange trading activities and long-term securities investment to contribute to increasing bank profits.

Securities trading activities and capital contributions to subsidiaries of commercial banks in the past have been lackluster, with no convincing contribution to their profits. An objective reason for this is that the stock market in 2018–2024 experienced unstable fluctuations. Therefore, short-term securities investment should still be treated with caution, with a low contribution to the total operating income of commercial banks, averaging 0.38%. Subsidiaries of commercial banks include financial leasing companies and finance companies. The activities of financial leasing companies in Vietnam are strongly competed against by foreign financial leasing companies, so there is no stable market. Therefore, this is a market with a lot of future potential, one that commercial banks need to invest in more to make new contributions, thus increasing commercial bank profits.

Evidence of the impact of each business activity and investment activity on the final profit of commercial banks shows that the management of banking activities is very important. Lending is the main profit channel of commercial banks, so it is necessary to focus on issuing loan safety policies and increasing loan sales. Banks should add a team of business consultants for bank customers, especially for individual customers and newly established business customers. Service activities and foreign exchange trading activities also contribute significantly to increasing profits for commercial banks. These are also two exclusive activities of commercial banks compared to other types of businesses. Therefore, commercial banks should provide more assistance to customers in using payment services through banks, installment purchases, discounting valuable documents, etc. Banks should provide many forms of spot and forward contracts on foreign currencies, and widely disseminate information to the public, in order to attract more investors to participate in transactions. This too will contribute to increasing profits for banks.

This study also showed a positive impact of economic growth and a negative impact of systemic risk on bank profits, suggesting that policies to promote the economic growth and political stability of a country play an important role in stabilizing and developing the banking system. Building a digital economy, disseminating information to the entire population about identifying and preventing online fraud, and implementing a project to restructure the credit institution system associated with bad debt handling during the period of 2021–2025, are practical solutions that the government can make that will have a strong impact on economic development in general, and on the banking system in particular.

This study was conducted on Vietnamese commercial banks listed on the Vietnamese stock market, so the number of banks investigated was limited. Vietnam’s financial market also has the participation of many foreign commercial bank branches. Foreign banks strongly develop investment activities in subsidiaries in the financial leasing sector. This is the weakest segment of Vietnamese commercial banks. In the future, when commercial banks develop more financial leasing activities, future studies may find statistical evidence for their impact on commercial bank performance.