Invention Journal of Research Technology in Engineering & Management (IJRTEM) ISSN: 2455-3689 www.ijrtem.com Volume 3 Issue 3 Ç March-April 2019 Ç PP 01-08
Factor determining the business capital structure in Vietnam: Studying the efficiency of business chain 1
Diep Dang Ngoc, 2Minh Duong Anh
1
2
Department of Business Administration, Dayeh University, Changhua, Taiwan, Department of Human Resources and Public Relations, Dayeh University, Changhua, Taiwan
ABSTRACT : This article empirically examines the typical factors of businesses that affect capital structure and macroeconomic environment. Using the balance sheet of 200 companies in Vietnam over 10 years (from 2007 to 2016), the author found empirical evidence to support the hypotheses related to asymmetric information theory, agency cost theory, trade off theory in determining the capital structure of enterprises in an emerging market economy. Special macroeconomic cycle significantly affects the financial decisions and capital structure of the company.
KEYWORDS: capital structure, GMM method, typical factors of businesses. I.
INTRODUCTION
The choice of capital structure of companies is a research topic that attracts researchers in developed markets as well as in emerging markets like Vietnam. Although in Vietnam there are many research topics related to the structure of corporate capital, the topic of the author examines the relationship between the choice of capital structure and the performance of businesses. , this study was conducted to determine the determinants of capital structure and business performance and their linkage in the context of macroeconomic cycle in Vietnam. This study provides empirical evidence of the role of macroeconomic conditions affecting the choice of the company's capital structure through interest rates. Besides determining the determinants of capital structure of enterprises, and their linkage in the context of macroeconomic cycle in Vietnam to answer questions? Firstly, does the business cycle as well as macro policies affect the capital structure of the company? Second, which specific factors determine the capital structure of companies? Responding to the above questions, the author performed the research article "Factors determining capital structure of enterprises in Vietnam: Researching the Business Cycle Effect". Debt variables (or leverage) are endogenous, so the author applies transformative econometric methods (GMM) the first two steps of Arellano and Bond (1991) to deal with potential endogenous problems. hidden. In this way, the GMM method not only solves endogenous problems on debt variables but also other explanatory variables by using a series of instrument variables created by late variables (Roodman , 2006).
II.
LITERATURE REVIEW
Capial structure : The capital structure of an enterprise is a combination of debt and equity in the total longterm capital that businesses can mobilize to finance investment projects.Modern capital structure theory began with the article by Modigliani - Miller in 1958 (referred to as MM theory for short). According to MM theory, the choice between equity (equity) and debt is not related to company value. And modern capital structure theory continues to be developed over the following years Previous studies: Some specific factors affect the financial risk and the company's capital structure choice. Since the capital structure model of Modigliani and Miller (1958), financial theorists have provided some possible explanations for the financial decisions of companies. The focus of most explanations of capital structure is on the most important factors that lead to a financial mix for a company, with an expected cash flow. In capital structure documents, related variables explain the capital structure of enterprises based on three main theoretical models of capital structure:trade off theory, agency cost theory and pecking order theory.Asymmetric information theory supports a negative relationship between scale and debt of the business. Large companies are more likely to be diverse and less bankrupt (Rajan and Zingales, 1995). Titman and Wessels (1988) and Bevan and Danbolt (2002) have found a negative relationship between firm size and leverage with large companies choosing to fund equity instead of debt because Relatively low fees relate to it because large companies have low asymmetric information. Ezeoha (2008) found order patterns in the financial selection of Nigerian companies explaining the inverse relationship between firm size and leverage.
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Factor determining the business capital structure‌ However, Stephan et.al (2010) found that small companies have higher investment opportunities but less tangible assets to secure and therefore less leverage. According to Titman and Wessels (1988) and Booth et al. (2001) and Stephan et al. (2011), the author uses natural logarithm of total assets as an indicator of firm size to find out the determinants of capital structure of companies. Agency cost theory shows that agency costs arise from the relationship between shareholders and managers or between creditors and shareholders (Jensen and Meckling, 1976). The nature of the company growth opportunities are important factors in determining these agency costs. The signaling hypothesis indicates that higher tangible assets mean higher liquidation values and companies can take advantage of that during a time of financial stress by giving signals to marketers. School that they are more stable than the company. Therefore, the higher the tangible assets, the higher is the leverage as was experimentally found by Titman and Wessels (1988) and Harris and Raviv (1991). The author follows Rajan and Zingales (1995) and uses the ratio of total fixed assets to total assets as a measure of tangible assets. Empirical research also shows that a macroeconomic shock worsens the balance of the company (Bernanke and Gertler, 1989, 1990, Kiyotaki and Moore, 1997). McNamara and Duncan (1995) find that GDP growth has a positive impact on corporate performance measured by profit on assets. Experimental research (Opler and Titman, 1994, Campello, 2003) shows that capital structure of companies becomes an important parameter affecting the performance of enterprises in the market of transparent products. research process, recession in the macroeconomic cycle. Their research shows that the use of debt has a negative impact on the revenue growth of companies in sectors where rivals do not benefit in the recession period. According to Michael J. Barclay and Clifford S. Smith (1995), firm size is positively correlated with debt numbers for a number of reasons. Issuing costs for public issues have a large fixed component that creates large economies. A growth company is also more likely to issue private debt with higher priority and more restrictive terms. Also according to Murray Z. Frank and Vidhan K. Goyal (2009) Profitable companies face lower expected costs of financial hardship and seek more valuable tax shields. . Therefore, the angle of tax and bankruptcy costs predicts that profitable companies use more debt. Larger and more diverse businesses face lower risks. In addition, older companies that have a better reputation in the debt market face lower debt-related agency costs. Order theory is often understood as predicting an inverse relationship between leverage and firm size and between leverage and company age. Big companies are better known, because they have been longer. In addition, old companies had the opportunity to retain income. Table 1: Overview of previous studies
Research hypothesis: Based on empirical studies of the determinants of capital structure and business performance and their linkages in the context of the macroeconomic cycle, the paper supports the following hypotheses: Hypothesis 1: Retained earnings, Company age and Company size affect capital structure. Specifically, Retained earnings, age and company size have a negative impact on capital structure.
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Factor determining the business capital structure… Hypothesis 2: Quality of the company, tangible assets and liquidity are important in explaining the capital structure decisions of enterprises. Specifically, company quality and tangible assets positively affect capital structure while liquidity is opposite. Hypothesis 3: Company policy can be better forecasted in economic downturns, from which managers can adjust capital structure more effectively to increase profitability of the company.
III.
METHODS AND RESEARCH DATA
Collect and process data : The data used for empirical testing from the company level include the Income Statement and the Balance Sheet of 200 listed companies on the HOSE and HNX from 2007 to 2016, with 2615 observations by year are taken from the available data from Bao Viet Securities Joint Stock Company (source: http://www.bvsc.com.vn). The above two stock exchanges have long enough operating time and development scale to conduct research. Listed companies before December 31, 2005 are selected and no company has been merged or excluded from listing on the two stock exchanges in the research period. The author excludes financial companies from the data sample because of the unique characteristics of this type of company. Companies with abnormal data in the sample are also removed to produce more accurate results. Research models: The author conducts the following regression model to examine the factors affecting the capital structure and performance of the company: Yit = α0 + α1 LNTAit + α2 LNAGEit + α3 TANGIB_TAit + α4 SALESTAit + α5 CRit + α6 PB_BSEt + α7 PROFTAit +α8 (DDOWN×TB)it + (βi +βt) + uit Inside: Index i = 1, 2, 3, ..., 2615 is the number of businesses in the sample. Index t = 1, 2, ..., 10 is the number of years. α0, α1, ..., α8 are the coefficients of the explanatory variables. βi and βt are respectively specific fixed effects and time effects. Uit is an error term. Yit represents the dependent variable, the company's leverage ratio is rotated for the ith company in time t. (Table 2). Model theory : Through the arguments of Arellano and Bond (1991), Arellano and Bover (1995), Blundell and Bond (1998), the author uses the General method of moments (GMM) to regression for dynamic panel data model. GMM is the method considered as a modern and general method of many popular estimation methods such as OLS, MLE, FE, RE, GLS, IV ... Even in the context of endogenous hypothesis is violated, autocorrelation, variance change, GMM method gives sustainable estimates, not biased, accurate distribution and effective. Variable name
Measurement formula
Expected sign
Dependent variables BORR_TA
Total debt on total assets
(LTBR_TA)
Total long-term debt on total assets
BNKBR_TA
Total bank debt on total assets
LTBNKBR_TB
Total long-term bank debt on total debt
Independent variables LNAGE
Company age
Log (Company age)
-
LNTA
Company size
Log (Company size)
-
TANGIB_TA
Tangible fixed assets
+
SALES_TA
Revenue
-
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Factor determining the business capital structure‌ CR
Current ratio
-
PB_BSE
Market price on book value
-
PBIT_TA
Profit
-
Indicate the state of degradation
-
Dummy variables DDOWNĂ—TB
IV.
RESEARCH RESULTS
Data statistics description : Descriptive statistics table helps to analyze the basic characteristics of the data collected and make initial comments about the research data series, specifically, descriptive statistics table will describe data including: mean, standard deviation, minimum value, median and maximum value. Table 3: Statistical description of data Variable
N
Mean
Std. dev
Min
P50
Max
Total debt on total assets (BORR_TA) Total long-term debt on total assets (LTBR_TA) Total bank debt on total assets (BNKBR_TA) Total long-term bank debt on total debt (LTBNKBR_TB) Company age (LNAGE) Company size (LNTA) Tangible fixed assets on total assets (TANGIB_TA) Total revenue on total assets (SALES_TA) Current ratio (CR) Market price on book value (PB_BSE)
2615
0.2512754
0.1942703
0
0.2386799
0.772701
2615 2615
0.0772521 0.2364205
0.124204 0.1886233
0 0
0.0153348 0.2210221
0.755439 0.771835
2615
0.1972646
0.2900648
0
0.0184674
1
2615 2615
1.70536 26.97675
0.4799677 1.448455
0.6931472 23.22041
1.791759 26.92719
2.484907 31.92201
2615 2615 2615 2615
0.2831143 1.301137 2.200518 1.02945
0.2088181 1.139915 4.028497 0.7368514
0 0.0021475 0.106327 0.1260081
0.2331304 1.046077 1.405088 0.8418873
0.977917 12.73354 145.1005 8.136346
Profit on total assets (PBIT_TA) Monetary policy index (TBILL)
2615 2615
0.0695651 0.0796908
0.0888428 0.0292321
-0.653146 0.0422142
0.053232 0.0953683
0.9928 0.122289
According to the Descriptive statistics table, the average value of total debt on total assets is 25.13% with a standard deviation of 19.24%. It means that the BORR_TA ratio can be deviated from the average value on both sides 19.24%. The maximum value of BORR_TA is 77.27% and the minimum value is 0%. The long-term debt ratio on the total assets of Vietnamese companies averages about 77.25% with a standard deviation of 12.42%. The maximum value of LTBR_TA is 75.54% while the smallest value is 0%. Similar to bank debt on total assets and long-term bank debt on total debt. The average value of BNKBR_TA is 23.64% with a standard deviation of 18.86%. The largest value of bank debt on the company's total assets is 77.18% and the minimum value is 0%. Long-term bank debt ratio on the average total debt is about 19.73% with a standard deviation of 29.00%. The maximum value of LTBR_TA is 100% while the smallest value is 0%. The company's age has an average value of 1.7, a median of 1.79 with a standard deviation of 0.48 days. The average age of companies of Vietnam is about 50 years. Current ratio has average value of 2.2 with standard deviation of 4.03. The maximum value 145.1 and the minimum value is 0.1. Correlation check: Coefficient of correlation are used to estimate the degree of linear relationship between variables. The absolute value of this coefficient is nearly 1 meaning that the higher the degree of tightness and the closer to 0 means the lower the degree of tightness.
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Factor determining the business capital structure‌ Table 3: Test of autocorrelation
Borr Ltbr Bnkbr Ltbnkb r
Lnage Lnta
Borr 1 0.583 5 0.958 6 0.279 8 0.003 4 0.417 3
Ltbr
1 0.474 8 0.722 1 0.071 2 0.363 1
Pbr
0.260 3 0.073 2 0.270 6 0.108 5
Profta
0.369
0.542 1 0.217 3 0.110 2 0.019 9 0.174 9
Tbill
0.003 3
0.066 6
Tangib
Sales
Cr
Bnkbr
Ltbnkb r
1 0.288 2
1
0.013 1
0.0684
0.381
0.2963
0.225 3
0.4641
1 0.164 6 0.110 2
0.2127
-0.005
-0.053 0.269 4 0.109 4 0.358 1 0.004 5
0.0766
0.0362 0.0434
0.0704
Llnag e
0.028 2 0.049 9 0.111 1 0.740 8
Tangi b
Sales
Cr
0.097 0.170 3 0.125 9
1 0.084 4 0.118 2
1 0.061 6
1
0.164 8 0.011 2 0.086 3
0.019 9 0.025 8
0.057 9
0.043
1
0.120 8
0.035 2
0.106 6 0.049 9
0.460 1 0.118 5
Lnta
Pbr
Profta
Tbil l
1
0.134 9
1 0.070 6
1
According to the Test autocorrelation table, total debt represents a positive and significant correlation with company size, company age and tangible fixed assets. This result shows that if the size of a large company, the age of a large company or tangible fixed assets is large, the debt ratio will decrease. This is true for arguments about control variables in hypothesis 1. In addition, this table also helps the author to see whether the data is multicollinearity. To test, the author examines the coefficient of correlation between variables, which are almost very small compared to +/- 1. Thus, there is no multicollinearity between variables and can put into the model. Regression results : The regression results of the model are presented in Table 4. The model considers the relationship between factors that are considered to affect the capital structure of the company and debt indicators.
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Factor determining the business capital structure‌ Table 4: Regression results VARIABLE
BORR_TA
LTRB_TA
BNKBR_TA
LTBNKBR_TB
Lnage
-0.124 (0.022)***
-0.101 (0.026)***
-0.107 (0.019)***
-0.111 (0.101)
Lnta
0.108 (0.0182)***
0.099 (0.0196)***
0.084 (0.021)***
0.275 (0.102)***
Tangib
-0.264 (0.129)***
-0.188 (0.147)
0.222 (0.073)***
0.605 (0.129)***
Sales
0.032 (0.037)
0.074 (0.043)*
-0.016 (0.020)
0.034 (0.076)
Cr
0.001 (0.003)
-0.005 (0.004)
0.0397 (0.011)***
-0.003 (0.002)*
Pbr
0.058 (0.016)***
0.057 (0.018)***
0.0255 (0.01)***
0.015 (0.035)
Profta
-1.565 (0.275)***
-1.471 (0.315)***
-0.801 (0.222)***
4.916 (1.351)***
Downtbill
-0.389 (0.136)***
-0.465 (0.162)***
-0.797 (0.342)***
-2.36 (0.978)**
Constant
-2.346 (0.4795)***
-2.238 (0.514)***
-2.104 (0.537)***
-7.57 (2.632)***
N
2615
2615
2615
2615
AR(2)
0.631
0.858
0.421
0.523
Hansen 0.121 0.126 0.136 Note: ***, **, * denotes the significance levels of 1%, 5% and 10%.
0.174
The author finds that the company's age has a significant negative impact on the company's debt structure. When companies increase their age, debt in capital structure decreases significantly. Because older businesses accumulate income in the past, they tend to borrow less. Firm size has a significant negative impact on the overall debt of the business as well as long-term debts. Large companies use significantly less overall debt, especially long-term debts. Large companies have financial strength and are in a better position to ensure refinancing so the company can choose short-term debts at cheaper cost. However, the results also show that large companies use long-term bank debt because large companies have lower direct bankruptcy costs. Tangible fixed assets have a positive and significant impact on the capital structure of the company. Companies with more tangible fixed assets are more likely to borrow more money by using physical assets as collateral. Revenue/asset ratio has a significant negative impact on the company's capital structure. High quality companies have the ability to issue short-term debt to signal their market for better quality and have the motivation to choose less long-term debt to avoid the additional burden on long-term interest payments. The current rate is negative for all equations so the current ratio of payments has a negative effect on long-term debt. Companies with a short-term debt ratio are more risk to liquidity, extend the debt term to minimize the risk of refinancing and thus choose longer-term debt.The price-book value ratio has a significant negative impact on the company's debt structure, suggesting that high growth companies have less long-term debt in the company's capital structure. Retained earnings have a significant negative impact on the capital structure of the company but have a positive impact on long-term bank debt. When profits keep rising, the company chooses less external financing in the short term but the demand for long-term bank financing still remains there. Dummy variable has a significant negative impact on the capital structure of the company. This result shows that the increase (decrease) of interest rates during economic downturn makes companies use less (more) debt in the capital structure and the direct impact of interest rate reductions makes companies to easy access with total debt and bank debt. Therefore, to better control the economic cycle during a economic downturn, the central bank can use monetary policy strategically to positively impact the growth model of the business through interest rate channel. In addition to checking the suitability of the GMM method in regression, the author used 2 tests. Hansen test to determine the appropriateness of instrument variables in the model. The Hansen test with Ho is not correlated with the error of the main model, so the large p value is good. P-Value > 0.05 so accept. Arelleno and Bond test to determine the autocorrelation properties of variance of GMM model error. Since the erroneous sequence has AR (1), the test result is ignored. Similarly, the AR (2) test value has a P-Value > 0.01 so it is acceptable.
V.
DISCUSSION
In the paper, the author has reviewed many corporate financial theorems regarding the determinants of capital structure of enterprises. The author's results show that the choice of capital structure varies according to specific factors of the company as well as the condition of the macroeconomic cycle. The author has carried out these
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Factor determining the business capital structure‌ analyzes based on the balance sheet data of 200 businesses in 10 years (2006 to 2017). This ten-year period is divided into two phases: the extended period and the transition period of the business cycle. The author finds the size, age, quality of the company, tangible fixed assets, liquidity, and retained earnings in determining debt options. Companies with many growth options have less long-term debt in their capital structure. The author finds that older companies borrow less often because they have more information about them and they also accumulate higher income levels in the past. Companies with higher tangible assets use more debt to meet the signal hypothesis. Better quality companies (with a higher turnover rate) may issue more short-term debt to avoid the burden of long-term payments. Retained earnings have a significant negative impact on the company's debt structure, consistent with order theory. As the retained earnings of businesses increase, they prefer less external financing, except for long-term banking finance. In addition, the study also shows the importance of capital structure during economic recession. The policy implications of the company can be better forecasted in economic downturns, from which managers can adjust capital structure more effectively to increase the profitability of the company. Economic cycle affects the profitability of the company, when the economy recedes, many companies fall into economic recession, lack of operating cash flow, capital and credit are gradually becoming depleted, customers have difficulty in payment, suppliers do not accept late payments for the purchase of materials from the company thereby causing revenue and profit of the company decreased. Therefore debt management is an essential issue in the company's overall strategy to create value for shareholders. Finally, in the GMM regression results, the author found strong empirical evidence on the factors affecting the capital structure of the company. For banking finance, the coefficient of change in leverage is positive and significant. The author's evidence suggests that low information costs related to bank debt due to debt monitoring reduce the financial costs of companies allowing them to be more profitable in the product market. Moreover, increasing leverage can lead to reduced capital spending and improved cash flow and thus improve the performance of businesses. In Vietnam, banks as financial intermediaries facilitate the provision of low-cost financing for businesses and thus increase market competitiveness.
VI.
LIMITATION
Although the purpose and research results have been achieved, the article still has some limitations as follows: First, the Vietnamese stock market has just been established in recent years so the stock market has not really developed, the transparency of information is not high, the quality of listed companies is not good, there are many Big company has not been on the floor yet. The data collected from the financial statements of the company, though audited, still do not fully reflect information about the company, the difference in accounting standards in Vietnam also affects the results of the study. assist. Secondly, the data collection of companies in Vietnam to get a sample of data is very small, only 200 companies on two central securities exchanges are Ho Chi Minh Stock Exchange (HOSE) and Department of Hanoi Stock Exchange (HNX). Data that can be collected is limited because it has to be on the floor since 2006, and must be fully present during the study period from 2007-2016, no company has ever been merged or excluded books listed on two stock exchanges focused in the research period. The data sample does not reflect the current state of the Vietnamese economy in the research period. Third, the article only focuses on internal factors of the company and the economic cycle, not considering external factors such as macro conditions such as inflation, exchange rate fluctuations, major instability. treatment, economic factors ...
VII.
FUTURE RESEARCH DIRECTIONS
The above limitations are also suggestions for further research directions: First, due to the limitations of the number of research samples, the results do not fully reflect on the relationship between management of circulating capital and profitability of the company in other economic cycles. each other, so later studies have more advantages in data collection, can expand the larger sample size will give a clearer, more reliable impact, thereby determining the exact image elements towards capital structure to help managers bring the highest efficiency for the company and benefit for shareholders. Secondly, it is possible to add macroeconomic and microeconomic variables affecting revenue and profit of the company such as exchange rate fluctuations, inflation rates, operating cash flows ... to comprehensively evaluate the situation of companies in different economic cycles.
REFERENCES [1] [2]
Arellano, M. and S. Bond (1991), Some tests of specication for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies 58, 277-298. Modigliani, F., & Miller, M. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review. 48(3), pp. 261–297.
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Factor determining the business capital structure… [3] [4] [5] [6] [7] [8] [9] [10]
Ezeoha, A. E. (2008). Firm size and corporate- financial leverage choice in a developing economy: Evidence from Nigeria. The Journal of Risk Finance. 9(4), pp. 351-364. Booth, L., V. Aivazian, A. Demirguc-Kunt & V. Maksimovic. (2001). Capital structures in developing countries. Journal of Finance. 55(1), pp. 87–130. Campello, M. (2003). Capital structure and product market interactions: Evidence from business cycles. Journal of Financial Economics. 68(3), pp. 353-378. Barclay, M., & Smith, W. (1995). The Maturity structure of corporate debt. Journal of Finance. 50(2), pp. 609-631. Bevan, A. & Danbolt, J. (2002). Capital structure and its determinants in the UK – A decompositional analysis. Applied Financial Economics. 12(3), pp. 159-70 Titman, S. & Wessels, R. (1988). The determinants of capital structure choice. Journal of Finance. 43(1), pp. 1-19. Arellano, M. and O. Bover (1995), Another Look at the Instrumental Variable Estimation of ErrorComponents Models, Journal of Econometrics 68, 29-51. Blundell, R. and S. Bond, 1998, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics 87, 115-143.
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