View of Banking Sustainability for Economic Growth and Socio-Economic Development – Case in Vietnam

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Research Article

Banking Sustainability for Economic Growth and Socio-Economic Development – Case

in Vietnam

Dinh Tran Ngoc Huya_{, Tran Thi Binh An}b_{, Tran Thi Kim Anh}c _{, and Pham Thi Hong Nhung}d

a

PhD candidate, Banking University HCMC, Ho Chi Minh city Vietnam – International University of Japan, Japan

b_{Master (corresponding)}

Thai Nguyen University of Economics and Business Administration (TUEBA), Vietnam

c_{Master, Thai Nguyen University of Economics and Business Administration}

(TUEBA), Vietnam

d_{Master, Ho Chi Minh College of Economics, Vietnam}

Article History: Received: 11 January 2021; Accepted: 27 February 2021; Published online: 5 April 2021

Abstract: In the context China-US commerce war and Covid 19 and Industry 4.0, wht happens to banking sustainability in

emerging markets such as Vietnam?

By using both quantitative analysis with statistics, charts and comparison, combined with qualitative analysis with synthesis, inductive and explanatory methods, research results show us that June is the month banks experience highest or lowest values of market risks, and during pre low inflation time , more beta values (max, mean, median) are equal to 1 or lower than 1. Whereas during post low inflation stage, several more beta values (max, mean, median) higher than 1.

Then, Main findings could be used for socio-economic policy implications in Vietnam. And research model can be applied for other countries, esp. Emerging markets.

Keywords: banking sustainability, Vietnam, economic growth, socio-economic development, risk management

JEL: M21, G30, O11

1. Introduction

Sustainable economic development of Vietnam is contributed by banking industry, among them are Five big listed joint stocks in Vietnam including: Saigon Hanoi Bank (SHB), Sacombank (STB), Asia Commercial Bank (ACB), Navibank later become National Citizen bank NCB, and Eximbank (EIB).

There are many factors affecting banking sustainability such as: Bank CSR to environment and community, management excellence, credit risks, market risk, risk management, etc.

Any bank risks happening might cause big declines in economic development and sustainability, just see domino effects from Lehman Brothers collapse case in 2008.

This study will use practical approach with real data and practices in banking industry in Vietnam, based on the viewpoint that better risk management will contribute much more to sustainable bank operation and management.

Banking sustainability will affected much more by risk levels, esp. Market risk level in banking industry. Therefore, this paper will estimate beta CAPM in 2 special periods in Vietnam, then we can use beta results to estimate Weighted Beta CAPM for further researches.

From that formula of Weighted Beta CAPM we recommend in the conclusion part, we can measure macro factors effects on Weighted beta CAPM then propose macro policies for banking sustainability and for economic development sustainability.

The study structured with introduction, research questions, literature review, Methodology and data, Main results, Discussion and Conclusion.

2. Literature review

Gunarathna (2016) revealed that whereas firm size negatively impacts on the financial risk, financial leverage and financial risk has positive relationship.

Then, Hami (2017) showed that financial depth has been affected negatively by inflation in Iran during the observation period.

In addition to, Khan et al (2018) investigated thorough analysis of stocks from different sectors in order to estimate beta values and thus creating optimum portfolio of estimated low β values. The researchers have considered beta to be measured of different stocks taken from various sectors in the stock market

Next, Kayo et al (2020) seeks to analyse and propose alternative procedures to estimate the cost of equity through the capital asset pricing model (CAPM) in the context of electricity transmission in Brazil. Results show that the desirable beta stability may be reached with the use of a Brazilian pure-play global beta estimated with an 11-year estimation window.

Thang, N.C et al (2020) indicated that the selection of portfolio construction, estimation technique, and news about economic conditions significantly affects the view whether or not beta should be considered as a valid measure of systematic risk.

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Then, Oseni and Olanrewazu (2020) discovered that building & construction, manufacturing, quarry & mining, communication, transportation, education and utilities sectors have been having lower volatility, that is, in boosting the economy over the last 15 years.

Last but not least, Suarez et al (2020) proposed a time‐varying beta CAPM in order to control for the variable nature of beta risk to changes in the market liquidity, using the variation of the Amihud illiquidity measure to account for the degree of trading activity. Their results show that the pricing errors of the CAPM have significantly decreased with respect to those of previous literature. Furthermore, the time‐varying beta model performs similarly to the Fama–French models in most cases. These results are consistent with increased trading activity that reduces arbitrage opportunities and, therefore, enhances market efficiency.

For a better overview or previous studies for futher assessment of macro factors on Beta CAPM or risk, see the below table:

Table 1 - Summarize previous studies on Beta CAPM Domestic researches Authors name Results, contents 1.Systemic risk and the

problem of determining Beta coefficient in Vietnam

Vương Đức Hoàng Quân (2012)

In the first stage, in general, the information from the Vietnam stock market is not sufficient in quantity and quality to estimate the beta coefficient according to the traditional method, which is regression analysis of stock returns volatility compared to indices. VN-Index to value the listed companies and stocks.

2.Fama-French 3-Factor Model: The empirical evidence from HCM city

Trương Đông Lộc and Dương Thị Hoàng Trang (2014)

The research results show that earnings of stocks are positively correlated with market risk, firm size and the book value to market value (BE / ME) ratio. In other words, the Fama - French 3-factor model is suitable in explaining the change in profits of stocks listed on HOSE.

3.The econometric model for stock prices in the period 2008-2011 - Case of stock prices ACB, VNIndex, Rf

Đinh Trần Ngọc Huy (2015)

Analyze the impact of VNIndex and internal and external macro variables on the stock price of ACB.

4.The theory of average return of K.Marx and model of capital asset pricing

Nguyễn Thị Hường (2017)

The limitation of Vietnam's stock market is the lack of beta in stock analysis. However, as the market portfolio matures, beta will keep pace with the development of the market.

5. Book chapter by Dinh Tran Ngoc Huy (2021, Springer Verlag book chapter) “ Macro effects on stock price in real estate industry in Vietnam”

Đinh Trần Ngọc Huy (2021)

Presenting a regression model analyzing the impact of internal macro variables (inflation in Vietnam, lending rate, risk-free rate) and external (US inflation, exchange rate, S&P 500) on stock prices Vingroup is as follows:

Stock price_VIC = -245.13 * Inflation_CPI + Lendingrate - 815.06*Rf_rate

USD_VND_rate+0.07*SP500 -

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6. Systemic risks in banking

business - periods of crisis

Nguyễn Thanh Bé, Bùi

Quang Hưng (2019) Presented in Vietnam, the risk management system at commercial banks has been paid attention to a certain extent in the past few years, but due to its structural and technical limitations, this system has not can meet the complex requirements of a modern commercial bank operating in the current risky

environment.

7. Factors affecting the return rate of listed stocks from the Fama French 5-factor model

Trịnh Minh Quang et al (2019)

Referring to factors of market change will strongly affect the share prices of large companies

International researches (summary)

Authors name Results

1. Macro effects on market risk

Patro et al (2002)

They found that a number of variables including imports, exports, inflation, market capitalization, dividend yield, and a book-to-book price ratio significantly influence a person's world market risk at national level. 2. Industrial analysis on stock

return

Butt et al (2010)

The results revealed that market returns are primarily changes in stock returns, but macroeconomic variables and industry-related variables add explanatory power in describing volatility. stock returns.

3.Responses from US bank risks

Claudia et al (2010)

The risk of about a third of US banks increases in response to monetary easing.

4.Macro effects on Pakistan banks

Saeed và Akhter (2012)

In Karachi stock market, Regression results show that exchange rate and short-term interest rate have a significant impact on the Banking index. Macroeconomic variables such as money supply, exchange rate, industrial production and Short-term interest rate and exchange rate have a negative effect on banking index while oil price has a positive effect on the bank index. Banking index.

5.The case of Istanbul exchange

Arnes (2014)

Their analysis has shown that for investors interested in Turkey, first of all, be careful not to assume that relationships that existed in the past will continue into the future. We also find that depending on the sector, the effects of

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3. Method and data

Values of Beta CAPM are calculated rom data of stock price on HOSE and HNX stock market during 2011-2015 and 2011-2015-2020. This is pre low and post low (L) inflation time and China-US commerce war.

We use analytical and synthesis methods and dialectical materialism method. Analytical data is from the situation of 5 big listed bank (SHB, STB, NVB, EIB and ACB) in Vietnam stock exchange.

Weekly data collected from 2011-2020 for 5 banks stock price to measure Beta and other macro data from reliable sources such as the General Statistics Office and commercial banks.

Based on that, Macro policies and risk management plans are recommended for bank system, SBV, relevant organizations and government.

4. Main results 4.1 Initial results

We can infer from the below chart 1 and 2 that: during pre low inflation time , more beta values (max, mean, median) are equal to 1 or lower than 1.

Whereas during post low inflation stage, several more beta values (max, mean, median) higher than 1. The sustainability of the banking industry and whole economy will reflex via the stable beta values (mean) around 1.

Chart 1 - Volatility of beta CAPM of 5 big banks during post-low (L) inflation time

changes in macroeconomic variables will also differ. For policymakers and lawmakers, however, our findings indicate that keeping interest rates low has been a good policy for the past 20 years.

6.Bank and financial stability Emilios (2015)

The leverage cycle can cause financial

instability and the impact of limited leverage on bank governance performance.

7. Macro effects on 4 countries Gay (2016)

According to the hypothesis, the relationship between the exchange rate and the security's price should be in the same direction. 8. Case of German market Celebi and Honig (2019)

In Germany, the aggregate index (OECD), the Economic Research Institute's Export Expectations index, the climate index, exports, CPI, as well as the 3-year German government bond yield has a delayed effect on stock returns 9. Macro variables effects on

Starbucks.

Kumaresan (2019) Indicates that compared to internal corporate factors, macroeconomic factors (exchange rate) have a greater effect on firm performance.

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Chart 2- Volatility of beta CAPM of 5 big banks during pre-low (L) inflation time

4.2 Main findings We can see:

Table 2 – Mean, Max, Min, Median of Beta CAPM in 5 big listed joint stock banks in Vietnam during pre and post-L inflation time comparison

Post-L inflation stage

Beta

EIB

Beta

ACB

Beta

SHB

Beta

NVB

Beta

STB

Mean

1.388

0.991

0.635

0.649

1.125

Median

1.425

0.805

0.862

0.515

0.993

Max

2.501

3.374

1.126

3.536

2.654

Min

0.386

0.405

-1.459

-0.358

0.560

Pre-L inflation stage

Beta

EIB

Beta

ACB

Beta

SHB

Beta

NVB

Beta

STB

Mean

1.005

0.547

0.893

0.100

0.448

Median

0.922

0.492

0.926

0.180

0.569

Max

4.792

1.421

1.649

0.688

0.936

Min

-0.453

0.008

-0.067

-1.590

-0.180

Table 3 – Statistic data of Beta CAPM of 5 big listed joint stock banks in Vietnam 2011-2015 period (pre – L Inflation)

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Pre – L

inflation

Beta

SHB

Beta ACB

Beta EIB

Beta NVB

Beta STB

Thg6-11

0.903

0.278

4.792

0.327

0.170

Thg12-11

1.255

0.293

0.152

0.535

0.156

Thg6-12

1.457

0.762

1.119

0.394

0.686

Thg12-12

1.649

1.421

1.029

0.172

0.730

Thg6-13

0.825

0.613

0.815

0.161

0.516

Thg12-13

0.305

0.047

-0.453

-1.590

-0.180

Thg6-14

0.948

0.372

0.197

0.189

0.621

Thg12-14

-0.067

0.008

-0.023

0.688

0.008

Thg6-15

0.965

0.708

1.080

0.054

0.936

Thg12-15

0.692

0.970

1.341

0.075

0.835

Table 4 – Statistic data of Beta CAPM of 5 big listed joint stock banks in Vietnam 2015-2020 period (post – L Inflation)

Post – L

inflation

Beta

SHB

Beta ACB

Beta EIB

Beta NVB

Beta STB

Thg6-15

0.965

0.708

1.080

0.054

0.936

Thg12-15

0.692

0.970

1.341

0.075

0.835

Thg6-16

0.838

0.493

1.140

1.145

0.850

Thg12-16

1.126

0.405

0.580

-0.358

0.560

Thg6-17

-1.459

3.374

2.501

3.536

2.654

Thg12-17

0.663

0.755

2.000

0.633

1.108

Thg6-18

1.073

1.037

1.512

0.611

1.115

Thg12-18

1.010

1.093

1.585

0.811

1.434

Thg6-19

0.687

0.856

1.680

0.422

1.050

Thg12-19

0.956

0.598

1.384

0.607

0.894

Thg6-20

0.182

0.523

0.386

-0.012

0.855

Thg12-20

0.886

1.081

1.080

0.258

1.209

4.3 Charts of statistical results

We can infer from the below chart 1 that: market risk of ACB reached highest value of 3.37 in June 2017 and lowest value in Dec 2014.

Then from chart 2, market risk of EIB reached highest value in 2011 and lowest values in 2013-2014. Chart 3 shows us market risk of SHB got highest value in 2013 and lowest values in 2014, June 2017. Chart 4 tell us that market risk of NVB reached lowest value in 2013 and highest values in June 2017. Last but not least, we see from chart 5 that market risk of STB got highest value in June 2017 and lowest value in 2013.

We pay attention to June : the time point some banks reached highest or lowest values. Chart 1- Beta ACB fluctuation 2011-2020 period

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Chart 2- Beta EIB fluctuation 2011-2020 period

Chart 3 – Beta SHB fluctuation 2011-2020 period

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Chart 5 – Beta STB fluctuation 2011-2020 period

Chart 6 – Comparison of 5 banks beta CAPM values 2011-2020

4. Discussion

From the above chart 6, we can infer that market risk level of EIB reached the highest of 4.79 in June 2011 while that of STB is the lowest, then beta CAPM of SHB reached the highest of 1.65 in Dec 2012 while that of NVB got the lowest, then in Dec 2015 (low inflation year) market risk of EIB goes to the highest of 1.34 while that of NVB gt the lowest, and in June 2017 beta CAPM of NVB became the highest of 3.54 while that of SHB has the lowest and finally, beta of EIB reached the highest of 1.68 and 1.47 in June 2019 and in Dec 2020, while that of NVB got the lowest values.

Developing and emerging countries such as China, Brazil, India and Bangladesh have built the main framework for environmental and socialand market risk management towards a green economy. The policy framework is designed to guide the banking system in implementing the roadmap to achieve sustainable development.

In Vietnam, sustainable banking development is in the early stages, some banks are paying attention and gradually integrating environmental issues, with social and market risk and internal operations. However, the commercial banking system has not had a complete management policy, many commercial banks have not built a proper risk model in evaluating and classifying projects on environmental, market and social risks, including potential risks, Within the scope of this study,The paper examines countries' experiences in developing a risk policy recommendation for the sustainable development of the commercial banking system.

5. Conclusion

First we suggest big listed banks need to build a proper model to evaluate market risks in 2 special stages of economy. Then we can recommend risk management solutions for banking sustainability as if higher risks, lower sustainability.

Second, State Bank of Vietnam (SBV) and government agencies need to control bank risk better with risk evaluation model for sustainability.

For better banking sustainability , not only we adapt to Document 22/2019/TT-NHNN with regulations on reducing the maximum proportion of short-term capital sources used for medium and long-term loans and adjust credit portfolio according to a reasonable structure, thereby contributing to promoting the sustainable development of the banking system, but banks also need to evaluate and compare their market beta to other industries benchmark: real estate, manufacturing, etc.

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The beta CAPM results estimated in the model can be used to estimate Weighted Beta CAPM for the whole banking industry with the application of market value of each joint stock bank following the below formula:

Weighted beta CAPM whole industry = (beta CAPM at time t x market value bank i +…) / (total market value of banking industry)

From that formula, we can measure macro factors effects on Weighted beta CAPM then propose macro policies for banking sustainability and for economic development sustainability.

Limitation of research: we can expand this model for other countries, esp. Emerging markets. References

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