Ảnh hưởng của hoạt động mua bán và sáp nhập đến hiệu quả hoạt động của các ngân hàng thương mại Việt Nam

Nội dung text: Ảnh hưởng của hoạt động mua bán và sáp nhập đến hiệu quả hoạt động của các ngân hàng thương mại Việt Nam

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 IMPACTS OF MERGERS AND ACQUISITIONS ACTIVITIES ON BANKING EFFICIENCY: THE CASE OF VIETNAM ẢNH HƢỞNG CỦA HOẠT ĐỘNG MUA BÁN VÀ SÁP NHẬP ĐẾN HIỆU QUẢ HOẠT ĐỘNG CỦA CÁC NGÂN HÀNG THƢƠNG MẠI VIỆT NAM Le Ngoc Quynh Anh, Nguyen Tien Nhat University of Economics, Hue University lnqanh@hce.edu.vn ABSTRACT This study applies a combination of DEA (Data Envelopment Analysis) model and the SFA (Stochastic Frontier Analysis) model to measure the impacts of M&A activities on the banking efficiency in Vietnam from 2011 to 2018. The results show that merge and acquisition (M&A) activities had a positive effect that reflects in an increase of the index of banking efficiency from 2011 to 2013. However, from 2015 to 2017 they created a negative impact. In addition, this research shows that there are two groups of banks suffering opposite impacts of M&A activities during the research period. The finding also points out that in 2011, 2012 and 2015, commercial banks were affected and adjusted quite significantly by the efficiency index under the impacts of M&A activities. Keywords: M&A, banking efficiency, DEA, SFA, Vietnam. TÓM TẮT Nghiên cứu đã sử dụng kết hợp 2 mô hình DEA (Data Envelopment Analysis) và mô hình SFA (Stochastic Frontier Analysis) để đo lường ảnh hưởng của hoạt động mua bán và sáp nhập đến hiệu quả hoạt động của các ngân hàng thương mại Việt Nam có thực hiện M&A trong giai đoạn 2011 - 2018. Kết quả chỉ ra rằng việc mua bán và sáp nhập có ảnh hưởng tích cực làm tăng chỉ số hiệu quả trung bình của các ngân hàng trong giai đoạn 2011 - 2013 và có ảnh hưởng tiêu cực làm giảm chỉ số hiệu quả trung bình của các ngân hàng trong giai đoạn 2015 - 2017. Ngoài ra nghiên cứu cho thấy có 2 nhóm ngân hàng chịu tác động tích cực và tiêu cực từ hoạt động mua bán và sáp nhập trong giai đoạn nghiên cứu. Nghiên cứu cũng chỉ ra được các năm 2011, 2012 và 2015 các ngân hàng chịu sử ảnh hưởng và điều chỉnh khá lớn đối với chỉ số hiệu quả dưới tác động của hoạt động M&A. Từ khóa: M&A, hiệu quả ngân hàng, DEA, SFA, Vietnam. 1. Introduction In recent years, in accordance with an implementation of restructuring credit institutions in Vietnam towards reducing the number of banks and strengthening the banking system, several M&A deals of Vietnamese commercial banks have been done. Promoting M&A is not only for the purpose of reducing the number of banks, but also enhancing the competitiveness of each bank by increasing the amount of total assets and operational efficiency for all aspects of banking activities. As a result, the number of mergers and acquisitions (M&A) deals in Vietnamese banking system has dramatically increased since 2011. However, up to 2014 the practices of M&A activity related to Vietnamese commercial banks has been still unprofessional reflecting in small quantities, unplanned agreement. The mechanism and regulation of the legal documents have not taken the economic benefits of banks and the economy into account yet, thus there have been a lack of experience and information. The banking efficiency reflects a relationship between the profit and cost as banks allocate and incorporate their resources. There are two types of the banking efficiency, including an absolute efficiency and a relative efficiency. In great detail, the former one is calculated by the difference between the profit and the cost, but it can be simply used to measure the efficiency of an individual bank. Meantime the latter one can be used to compare the effectiveness between commercial banks that have varied sizes. In addition, how M&A activities affect the performance of commercial banks has not been neither evaluated nor compared to that in the pre-M&A period in a proper way. According to (Hawkins, 1999), M&A deals yield good results if large-size banks acquire small-size banks that are in trouble. The result also indicates that M&A deals are likely to be delayed or hindered in the common period more often than that in the crisis period, and they often incur low costs in the process of restructuring banking system. 1
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 (Krishnasamy, 2004) provides much evidence about how to improve the banking efficiency in Malaysia before and after M&A from 2000 to 2001. The result shows an increase of banking efficiency due to the advancement of banking technology. (Peng, 2004) indicates that M&A deals in the field of finance and banking lead to an increase in the banking efficiency of Taiwanese banks. For these reasons, it is necessary to measure the business performance of Vietnamese commercial banks after time of doing M&A in order to provide much-needed policies that might improve the operational efficiency of commercial banks. This paper examines the impacts mergers and acquisitions activities on banking efficiency in Vietnam form 2011 to 2018. Particularly, we investigate: (i) whether mergers and acquisitions activities enhances efficiency of banks; (ii) bank efficiency is measured using a combination of DEA and SFA models to evaluate the performance of banking system including three steps and (iii) analyze the influence of M&A on efficiency of banking groups. 2. Literature Karim (2001), who used the stochastic frontier analysis (SFA) to assess efficiency of banking industries in four South East Asian countries prior to the crisis in 1997, provides different results from the aforementioned studies. The author indicates that cost efficiencies in South East Asian banks tend to decline over the year preceding the crisis, and suggests that the problem of bank failures may have been related to inefficiency. Berger et al. (1999) indicate that mergers may also improve efficiency if greater diversification improves the risk – return tradeoffs. They suggest that regulators may act to encourage consolidation in periods of financial crisis. Krishnasamy et al. (2004) have documented improvement in production efficiency of Malaysian post-merger banks in 2000–2001. The authors note that the overall rise in total factor productivity was driven more by technological progress of the banking system than individual bank technical efficiency. Peng and Wang (2004). Cost efficiency and the effect of mergers on the Taiwanese banking industry. The study suggests that bank mergers could have enhanced cost efficiency of Taiwanese banks. Mukesh Kumar & Vincent Charles (2012) uses DEA model to evaluate the banking efficiency of Indian banks before and after the global financial crisis. Enticed by the reform of Indian banking sector in the early 1990s and further slowdown in the economy as a result of global financial crisis in late 2000s, the current study analyzes the performance of Indian banks using data envelopment analysis. The performance is measured in terms of technical efficiency, returns-to-scale, and Malmquist productivity index for a sample of 33 banks, consisting of 19 public sector and 14 private sector banks during the period spanning 1995-1996 to 2009-2010. The jackknifing analysis, followed by the dummy variable regression model is used to identify the outlier and its possible impact on overall efficiency trends. Findings reveal that efficiency scores are robust in the sense that the inclusion of outlier does not affect the overall efficiency trends. The public sector bank is faintly doing better than the private sector banks in terms of (i) technical efficiency since 2003-2004 and (ii) scale efficiency from 2000-2001 onwards. There is growing tendency of public banks operating under increasing returns to scale, implying that substantial gains could be obtained from altering scale via either internal growth or consolidation in the sector. The difference in the Total Factor Productivity (TFP) change between these two types of banks is found to be statistically significant in favour of public sector banks. The technological change has been the dominating source of productivity growth, whereas, the contribution of pure efficiency change and scale change are found to be negligible in Indian banking sector during the period of study. The reform in Indian banking sector has clearly re-energized the Indian banking sector as a whole, resulting in a positive change in TFP through technological change possibly as a result of adoption of latest technology and new business practices in post reform period. However, there is evidence of shrink in the market resulting in movement of the banks towards increasing returns-to-scale as well as negative growth in TFP in both the sectors during the period of global financial crisis. 2
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Said Gattoufi (2017) also apply DEA model to answer a question of whether M&A activities will improve the banking efficiency from 2003-2017. The result indicates that the M&A activities have a positive although the impacts are considered to be limited due to the overall technical efficiency of banks. Huong (2017) applies DEA model to evaluate the banking efficiency of 21 commercial banks in Thai Nguyen Province of Vietnam from 2011 to 2015. The study was based on the method of Data Envelopment Analysis (DEA) to estimate the performance of 21 commercial banks in Thai Nguyen province in the period of 2011-2015. The results revealed that commercial banks’ uses of inputs are relatively efficiency with the average technical efficiency of 94%. Malmquist index (MI) was also used to analyze the change of commercial banks’ performance over time. The study showed that technological change is the main reason of MI changes. Tobit model was then applied to estimate the impact of different factors on the performance of commercial banks in Thai Nguyen province. It was found that the four factors affecting technical efficiency of commercial banks include: return on assets, nonperforming loan, total assets and the number of enterprises operating in the province Tran Hoang Ngan (2015) evaluates the banking efficiency in awareness of the influence of reconstruction process including the equitization of state-owned commercial banks, M&A activities and governmental interventions. The results show that, the banking efficiency increases or decreases in an irregular way in the restructuring process. Some commercial banks significantly improved their banking efficiency, but others recorded the dramatical decrease in the efficiency ratios compared to that before the period of restructuring because of the impact of ineffective M&A deals. 3. Methodology and data 3.1. Methodology This research applies a methodology that some previous studies used. (Avkiran, 2008) and (Thoraneenitiyan, 2009) use a combination of DEA and SFA models to evaluate the performance of banking system including two steps as follows: 1st step: Use the DEA model to determine the banking efficiency without considering the impacts of M&A deals. In step 1, assuming that banks in the sample try to minimize inputs and maximize inputs simultaneously, the original inputs and outputs are used in the non-oriented variable returns to scale SBM. The fractional program to estimate the efficiency is shown in equation (1), where r is the scalar that reports efficiency after capturing non-radial inefficiencies. I Subject to  1 λ ≥ 0, s- ≥ 0, s+ ≥ 0 (1)  j 1 j where x ≥ 0 is a DMU’s N x 1 vector of inputs, y ≥ 0 is a DMU’s M x 1 vector of outputs, X = [x1, ,xi] is an N x I matrix of input vectors in the sample, Y = [y1, ,yi] is an M x I matrix of output vectors in the sample, si and si are input and output slacks, respectively, and Xλ and Yλ represent benchmark input consumption and output production. Inputs and outputs for the unit evaluated are indicated by the superscript ‘o’ and the linear program is solved once for each unit in the sample. Imposing the constraint introduces variable returns to scale. A bank is rated as efficient if the optimal value for the objective function equals one. That is, the efficient bank will have zero input and output slacks. 3
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 2nd step: The purpose of step 2 is to decompose input and output slacks (i.e., inefficiencies) obtained from step 1 into environmental effects. Input and output slacks are separately regressed on environmental variables using SFA. When parameters from SFA regressions are obtained, observed inputs are adjusted for the impact of the environment and statistical noise. Thus, banks enjoying relatively favourable operating environments and statistical noise would find their inputs adjusted upwards and efficiency scores lowered. These adjustments vary both across banks and across inputs. Similarly, banks suffering from relatively unfavourable operating environments and statistical noise would have their outputs adjusted upwards (thus, raising efficiency scores). It should be noted that to avoid negative inputs and outputs, we do not adjust downwards. 3st step is a repetition of the non-oriented SBM analysis first undertaken in stage 1, but using adjusted input and output data obtained from step 2. The results from step 3 represent DEA analysis of bank efficiency where the influences of the operating environment and statistical noise have been removed. 3.2. Data This study applies the DEA model to evaluate the performance of banking system, with using three main factors: labor, assets and capital. However, identifying input and output variables of banks is considered to be difficult and inconsistent as shown in previous studies. The selection of these factors mainly depends on the ability to collect data, the views and requirements of bank administrators. The traditional banking activities, for example lending and capital mobilization, play a leading role in Vietnamese banking system. Therefore, the income and interest expenses account for high proportions of total income and total expenses. For this reason, along with the model and approach mentioned above, the variables are determined as follows (Thao, 2015): Input variables: these variables represent the inputs used in the banking businesses. This model refers to three factors including fixed assets (K), Labor capital (L) and Deposite (D); Output variables: these variables represent the income and profit generated in the banking businesses. Two output factors used in the model are interest income (Y1) and non-interest income (Y2). M&A variable (zj) may falsify the analysis of initial performance. This variable is a dummy variable which equals to 1 if a certain commercial bank makes M&A deal at time t and equals to 0 in the opposite case. According to previous studies, the author expects the sign of M&A variable to be negative. The research data includes 19 banks that already conducted M&A activities from 2011 to 2018. Table 1: Descriptive statistics of input and output variables Variables (mlVND) Mean Standard deviation Min Max Y1 7.954.746,789 7.764.243,441 -159.573 30.955.331 Y2 5.179.321,217 5.762.332,576 -1.278.079 28.366.140 K 2.896.577,533 2.806.873,034 68.366 11.436.527 D 196.481.813,9 209.643.230 6.242.227 989.671.155 L 2.447.192,691 2.690.535,309 106.531 14.530.020 4. RESULTS 4.1. The estimation of banking efficiency index The estimation includes three steps. 1st Step: DEA analysis The Figure 1 shows that the average value of banking efficiency of 19 banks that performed M&A from 2011 to 2018 are quite small, between 0.84 and 0.92. As the matter of fact, in 2011 Vietnamese banking system encountered many difficulties, in which the rate of non-performance loans increased up to 8.6% (according to SBV’s report). Therefore, the credit quality sharply reduced and the liquidity was 4
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 insufficient. That’s why the average value of banking efficiency index was quite low, 0.886. Since 2011, some commercial banks started to implement M&A deals, leading to the fact that the banking efficiency index somewhat became prosperous from 2012 to 2013. In great detail, the average value of banking efficiency index of 2012 increased by 2.85% compared to that of 2011, and the figure of 2013 raised by 1.33% compared to that of 2012. From 2013 to 2015, the average value of banking efficiency index showed a slight decrease. However, in the period of 2015-2017, the average value of banking efficiency index tended to be negative. Specifically, the average value of banking efficiency index in 2016 decreased by 3.54% compared to that of 2015, and the figure of 2017 decreased by 2.53% compared to that of 2016. As shown in Figure 1, since 2018 the average value of banking efficiency index improved significantly, with increasing by 7.54% compared to that of 2017 and reaching a peak of 0.913. However, the initial DEA analysis just calculate the average value of banking efficiency index based on the input and output variables of the model, but not to consider the M&A impacts factor. This estimation also has ineffective input and output variables, especially Input Slacks and Output Slack. Figure 1: The average value of banking efficiency 2nd step: SFA results The results in Table 2 suggest that merger and acquisition activities do indeed exert a statistically significant influence on bank inefficiency. Independent variable is significant in the two regressions on input slacks and the three regressions on output slacks. The five gamma estimates in Table 2, ranging from 0.034 to 0.042, are also statistically significant, indicating that part of the variations in predicted slack reflects primarily the effect of managerial inefficiency. The main point of interest from Table 2 is the coefficients of merger and acquisition variables (M&A). M&A apppears to have a negative relationship with most bank input and output slacks. In other words, merged banks appear to be more efficient in managing resources to produce their output. Table 2: SFA regression results Dependent Variables Indeppendent Input Slacks Output Slacks Variables Y1-Slack Y2-Slack K-Slack D-Slack L-Slack Constant 130,638 292,844 285,167 12,331,432 207,108 M&A -8,875 -93,722* -40,388 -3,013,369 -100,193* Gamma 0.036 0.034 0.042 0.043 0.035 Log likelihood -2.154 -2,265 -2,195 -2,760 -2,236 function Y1-interest income; Y2-non-interest income; K-fixed assets; L-Labor capital; D-Deposits; M&A-a dummy variable for banks under merger and acquisition activities “*” and “ ” indicate 10% and 5% one-tailed significance levels. 5
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 3rd Step: SBM results on adjusted data The result of 3rd step shows a more considerable impact of the M&A variable on the banking efficiency index of banks. In the period of 2011 to 2018, commercial banks conducted several M&A deals in 2011 and 2015. However, as similar as the analysis of the banking efficiency index in 1st step, the result of 3rd step also shows two opposite effects produced from current M&A deals in the banking system. In great detail, the implementation of M&A deals in 2011 created a positive impact, with enhancing the average value of banking efficiency index in a significant way. However, in the period of 2015 to 2017, it brought about a negative effect that reflects in a significant decrease in the banking efficiency index of banks that conducted M&A deals. Figure 2: Average value of banking efficiency indicators before and after involving the M&A factor 4.2. The influence of M&A activities on the banking efficiency The result shows that there were five banks having an increase in the banking efficiency after conducting M&A deals, including BIDV, VCB, ABB, VIB and TPB (figure3). Among these five banks, VCB and BIDV had a decrease in the banking efficiency index after adjustmenting under the impact of environmental factors (i.e. M&A factor) of the banking system during the research period. Both of them had a decrease in banking efficiency by scale (DSR). The other three banks, specifically ABB, VIB and TPB, observed an increase in their banking efficiency indices after making adjustments under the impact of M&A deals. They all experienced an increase in the banking efficiency by Increase returns to scale (IRS) in the research period. 6
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Figure 3: The group of banks has increased their effectiveness when conducting M&A 7
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Figure 4: The group of banks has decreased their effectiveness when conducting M&A On the other hand, there are ten commercial banks that reported a decrease in the banking efficiency index when conducting M&A deals, including CTG, SHB, AGR, MSB, LPB, MBB, PGB, TCB, EIB and SCB (Figure 4). In which, AGR, CTG, LPB and TCB had an increase in the banking efficiency index under the impact of environmental factors (i.e. M&A variable) of the banking system during the research period. They all were considered to be DRS banks (Decrease returns to scale). The other banks, including SHB, MSB, MBB, PGB, FIB and SCB, experienced a reduction in the banking efficiency index under the impact of M&A activities. These banks can be seen as IRS banks in the research period (especially MBB is an effective bank having an increase by the scale of stage after the M&A implementation). The result also reveals the group of banks that were not significant affected after the implementation of M&A deals, including ACB, STB, VPB and HDB. The Table 3 shows that a large amount of M&A deals was conducted by commercial banks and the enterprises in Vietnam in 2011, 2012 and 2015. As a result, the performance of banking system greatly volatilized under the influence of M&A activities, especially in 2011 the influence was greatest in size, thus the adjusted efficiency index decreased by 0.174. In great detail, TPB achieved the largest increase in banking efficiency at 0.228 in 2012, and PGB was heavily influenced by M&A activities with the banking efficiency index increasing by 0.272 in 2015. Table 3: The adjustment of the efficiency index after considering the M&A factor 2011 2012 2013 2014 2015 2016 2017 2018 BIDV -0.002 -0.072 0 0 -0.017 0 0 -0.014 HDB -0.013 0.158 -0.129 0 -0.006 0 0 0 MSB -0.018 0.001 0 0 0.083 0 0 0 STB 0 0 0 0 0 0 0 0 SHB -0.055 0.134 0 0 0.049 0 0 0 TCB 0 -0.112 0 0 0 0 0 0 VPB 0 -0.095 0 0 0 0 0 0 8
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 SCB 0.051 0.053 0 0 0.042 0 0 0.003 LPB 0 -0.024 0 0 -0.032 0 0 0 CTG 0 -0.004 0 0 -0.017 0 0 -0.001 ACB -0.01 -0.079 0 0 -0.036 0 0 0.004 VIB 0 0 0 0 0.046 0.011 -0.028 0 ABB 0.013 0.112 0.015 0 0.091 0 0 -0.002 MBB 0.011 0 0 0 0 0 0 0 AGR -0.174 0.001 0 -0.005 -0.025 0 0 0 TPB 0 0.228 0 0 0 0.171 0 0 PGB -0.006 0 0 0 0.272 0 0 -0.006 EIB 0 0.002 0 0 0.053 0 0 0 VCB -0.013 0.004 0 0 -0.037 0 0 -0.013 5. Conclusion This study shows how integrated DEA and SFA approarch can account for conducting merger and acquisition to measure bank technical efficiency in Vietnam during the period of 2011 – 2018. The result shows that the M&A activities have a significant impact on the banking efficiency of Vietnamese banks that implemented M&A from 2011 to 2018, with reflecting in two opposite directions in different periods, specifically from 2011 to 2013 and from 2015 to 2017. After performing two steps including DEA and SFA examinations, the finding indicates that the M&A factor has greatly statistical influences on the input and output variables packed in the research model. The result also shows that there are only five over nineteen banks that have an increase in the banking efficiency after making M&A deals, meaning that only 26.3% of selected banks benefit from M&A deals. This figure indeed implies that the implementation of M&A deals in the banking system from 2011 - 2018 was really ineffective. However, concerning the significance of influence stemming from M&A activities from 2011 to 2018, most Vietnamese commercial banks were affected by them especially in 2011, 2012 and 2015 The key limitations of this paper consist of unvailable data for some commercial banks. The limited sample size hampers the study to conduct the bootstrap procedures. REFERENCES 1 Avkiran, N. K. (2008). How to better identify the true managerial performance: State of the art using DEA. Omega, 36(2), 24-317. [2] Berger e. (1999) The consolidation of the financial services industry: causes, consequences and implications for the future. Journal of Banking and Finance, 23, 94-135 [3] Hawkins, J. &. (1999). Bank restructuring in practice: An overview. BIS Policy Papers 6, 6-105. [4] Huong, N. T. (2017). Operating efficiency of commercial banks in Thai Nguyen province - Vietnam country. Can Tho university Science magazine, PartD, 52-62. [5] Karim MZA (2001). Comparative bank efficiency across select ASEAN countries: ASEAN Economic Bulletin, 18, 289-304. [6] Krishnasamy, G. R. (2004). Malaysian post merger bank's productivity: application of malmquist productivity index. Managerial Finance, 30 (4), 63-74. [7] Peng, Y. &. (2004). Cost efficiency and the effect of mergers on the Taiwanese banking industry. The Service Industries Journal, 24 (4), 21-39. 9
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 [8] Said Gattoufi, S. A.-M.-K. (2017). The impact of mergers and acquisitions on the efficiency of GCC banks. Semantic Scholar. [9] Thoraneenitiyan, N. &. (2009). Measuring the impact of restructuring and country-specific factors on the efficiency of post-crisis East Asian banking systems: Integrating DEA with SFA . Socio- Economic Planning Science, 43 (4), 52-240. [10] Tran Hoang Ngan, e. (2015). The impact of restructuring on the performance bank: The case of Vietnam. Economic development, 26(2), 26-47. [11] Vincent Charles, M. K. (2012). Data Envelopment Analysis and Its Applications to Management. Cambridge Scholar Publishing. APPENDIX Year Acquiror Target company 3/2018 Warburg Pincus LLC Techcombank (TCB) 5/2018 Alp Asia Finace Vietnam Ltd Asia commercial Bank (ACB) 1/2018 Estes Investments Asia commercial Bank (ACB) 7/2017 Vietnam International Commercial - Comonwealth Bank of Australia (CBA) Joint Stock Bank (VIB) - VIB 8/2016 PYN Elite Fund Tien Phong Commercial Joint Stock Bank (TPB) 10/2015 Saigon Thuong Tin Commercial - Southern Bank Joint Stock bank (STB) - STB 2015 Vietinbank PG Bạnk 2015 NHNN (AGR) - VNCB - OceanBank - GP Bank (Global Petroleum Commercial JS Bank) 04/2015 NHTMCP Đầu tƣ và Phát triển (BIDV) - NHTMCP Phát triển Nhà Đồng bằng sông Cửu Long (MHB) - NHTMCP Đầu tƣ và Phát triển (BIDV) 2015 NHTMCP Đầu tƣ và Phát triển (BIDV) - CTTC Bƣu điện (PTF) 07/2015 NHTMCP Hàng hải Việt Nam (MSB) - NHTMCP Phát triển Mê Kông (MDB) - NHTMCP Hàng hải Việt Nam (MSB) 2015 Maritime Bank Vietnam Textile and Garment Finance JSC. 2015 Techcombank Vietnam Chemical Financial JSC (VCFC) 2015 State Capital Investment Corporation MBB 2015 NHTMCP Quân đội CTTC Sông Đà (SDFC) 2015 SHB - CTTC cổ phần Vinaconex Viettel - SHB 2015 Credit Saigon NHTMCP Phát triển TP. HCM (HDB) 06/2014 NHTMCP Việt Nam Thịnh Vƣợng CTTC than khoán Việt Nam (CMF) (VPB) 10
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 11/2013 NHTMCP Phát triển TP.HCM (HDB) - NHTMCP Đại Á (DaiAbank) - NHTMCP Phát triển TP.HCM (HDB) 09/2013 NHTMCP Đại chúng Việt Nam - NH Phƣơng Tây (Western Bank) (PVcomBank) - Tổng công ty cổ phần Tài chính Dầu Khí Việt Nam (PVFC) - NHTMCP Đại Tín (Trustbank) - NHTMCP Xăng dầu Petrolimex (PGbank) 2013 - IFC - ABB - Maybank 08/2012 NHTMCP Sài Gòn – Hà Nội (SHB) - NHTMCP Phát triển Nhà Hà Nội (HabuBank) - NHTMCP Sài Gòn – Hà Nội (SHB) 2012 MMB Viettel 2012 Eximbank Sacombank 2012 Bank of Tokyo Mitshubishi Vietinbank 2012 Tập đoàn DOJI TPBank 07/2011 NHTMCP Bƣu điện Liên Việt (LPB) - Công ty dịch vụ tiết kiệm bƣu điện VNPT (VPSC) - NHTMCP Liên Việt (LienVietbank) 12/2011 NHTMCP Sài Gòn (SCB) - NHTMCP Sài Gòn (SCB) - NHTMCP Đệ nhất (FicomBank) - NHTMCP Tín Nghĩa (TNB) 9/2011 Mizuho corporate bank LTD Vietcombank 7/11 IFC Vietinbank 2011 HSBC Tecombank 6/11 MR CHANG HEN JUI (TAIWAN) Sacombank 11