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Effect of Macroeconomic Factors on Capital Structure of the Firms in Vietnam: Panel Vector Auto-regression Approach (PVAR)

  • Nguyen Ngoc Thach
  • Tran Thi Kim Oanh
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 753)

Abstract

The article examines the impact of macroeconomicfactors on capital structure during the period of economic recession and economic recovery. The authors collected data from the financial statements of 82 firms listed in Vietnam stock market during the Quarter 1/2007-Quarter 2/2016 and using PVAR. The results demonstrate that during economic recession, the economic growth, the bond market, credit market positively impacted the capital structure whereas the stock market showed negative impacts on the capital structure. During economic recovery, economic growth positively impacted on the capital structure and the remaining macroeconomic variables negatively impacted on the capital structure. In addition, capital structure was affected bymicroeconomic variables such as profitability, asset structure, size, growth and liquidity.

Keywords

Capital structure Economic recession PVAR 

1 Introduction

Maximizing profits and business value are important targets of enterprises. In order to achieve those targets, managers must employ right decisions in choosing investment opportunities as well as optimally organize and manger their businesses. Capital structure, one of financial tasks in corporate governance, plays a key role.

After witnessing a period of high economic growth in the first half of 2000s, the global economy experienced an economic recession from 2007 to 2010, which negatively affected the business activities of Vietnam’s enterprises. The impact was demonstrated by a sharp increase in number of enterprises ceasing their operation during the period. According to the General Statistics Office of Vietnam (2015), in 2014, there were 58,322 enterprises the faced with difficulties and dissolved, 14.5% rising compared to that of the previous year. One of the main causes for the situation was the volatility in macroeconomic environment, which propelled businesses into financial difficulties. However, in the context of such traumatized economy, most Vietnam’s enterprises lacked a specific and long-term plan for capital restructure but still relied on subjective decisions regarding capital structure, ignoring the circumstances of the economy in each specific period.

Over the last several years, many studies on capital structure have been published. Most of them are about the impacts of micro-economic variables on the capital structure of businesses in different countries. These studies applied different approaches and methodologies, but mainly the Pooled OLS, the Fixed effect model (FEM), the Random effect model (REM) and the General method of moments (GMM). In this report, the authors use PVAR to analyze and compare the impact of those variables on the capital structure of Vietnam’s businesses in the two periods of recession and recovery of the world economy.

2 Rationale and Empirical Studies

2.1 Rationale

Most studies regarding the capital structure focus on the following theories:

The MM theory

The theory was proposed by Modigliani and Miller (1958) based on the theory of perfect markets with the absence of taxes, concluding that the business value and the weighted average cost of capital (WACC) are independent of the capital structure. The theory was continued to be further studied in tax environment (1963), drawing a conclusion that the value of the business would increase if it utilizes debt from the benefits of tax shield. The weighted average cost of capital (WACC) of businesses utilizing debts is lower than that of debt-free businesses. However, the theory was based on unrealistic premises (perfect competitive market, absolute rationality, perfect information). Still, the theory serves as the basis for the emergence of more realistic theories later.

The pecking-order theory (POT)

The theory proposes a hierarchy of priorities in selecting funding options but does not address the existence or non-existence of an optimal capital structure for businesses. The POT theory states that the capital structure accords with the following funding order: internal capital from retained earnings, debts, last, new equity (Donaldson 1961).

Trade-off theory (TOT)

The static Trade-off theory was initiated by Kraus and Litzenberger (1973) and further developed into the dynamic TOT (Myers and Majluf 1984). According to the static TOT, enterprises can easily and quickly achieve their optimal capital structures, reflecting the tradeoff between debt’s benefits from tax shield and the cost of capital exhaustion. Each enterprise has only one optimal capital structure. Conforming to the dynamic TOT, under the impacts of microeconomic and macroeconomic environment, enterprises cannot immediately reach the optimal capital structure without experiencing a gradual adjustment. Also, the optimal capital structure will vary in each specific period. Despite the difference in view, there is a common approach of the two theories based on the tradeoff between cost and benefit for business to obtain its optimal capital structure and maximize its value.

The theory of agency cost

The theory was proposed by Jensen and Meckling (1976). Agency cost incurs due to the asymmetry of information between enterprise’s managers and owners. Therefore, enterprises tend to increase the use of debt in order to reduce agency cost because once doing so, managers must be more cautious in their financial decisions, business risks would be lessened and the efficiency of business activities would improve.

The signaling theory

The signaling theory was also developed based on the information asymmetry between enterprises and investors. Investors usually analyze enterprise’s activities, speculate on the current situation and forecast the prospect of the enterprises. They believe issuing of debt is a positive signal about the business prospect, thus the stock price would go up. In contrast, that enterprises issue new equity indicates a negative signal, which would make stock price to fall. From the perspective of investors, only a business has not good prospects want to be funded by equity in order to share the risk of the business with new investors (Asquith and Mullins 1983).

The Market-timing theory

This theory was originated from the study of Baker and Wugler (2002), stating that the difference between market value and book value is the determining factor for enterprise’s capital structure. In case of a high price-to-book ratio (P/B), enterprises will issue new equity to mobilize capital. Meanwhile, enterprises usually use debt when P/B is low.

The above mentioned theories have various views, however they do not conflict but rather complement each other in comprehensively explaining the manager’s decisions of funding sources.

2.2 Other Related Studies

Although theoretical and empirical research on capital structure varies in perspectives and methodologies, they generally focus on the following aspects: capital structure is influenced by micro variables (Truong and Nguyen 2015; Vatavu 2015); the combined effect of micro and macro variables to capital structure (Jong et al. 2008; Nor et al. 2011; Khanna et al. 2015); capital structure impacts business value or determines the optimal capital structure threshold (Ahmad et al. 2012; Wang and Zhu 2014). However, the common limitation of these studies is that they merely focus on the capital structure of businesses in long term without analyzing specific economic contexts in each period (stage) of the economic cycle.

Since the 1970s, economic crises have occurred at high frequency, intensity and complexity, causing severe socio-economic consequences for many nations all over the world. This trend has resulted in studies on capital structure in combination with global and regional economic crises such as 1997–1998, 2007–2010. However, the number of these works remains modest. Other noticeable studies are Ariff et al. (2008); Fosberg (2013); Alves and Francisco (2015); Iqbal and Kume (2014). In Vietnam, currently only Truong and Nguyen (2015) refer to this issue. However, studies in Vietnam and abroad mainly focus on addressing and giving solutions to fix the impacts of economic downturns on capital structure without thoroughly analyzing the capital structure adjustment of businesses in response to the context of the economy during recession and recovery. This is a scientific gap in studying capital structure in Vietnam and abroad.

In terms of methodology, most of the published studies on this subject only use Pooled OLS, FEM, REM or GMM. The purpose of using these models is simply to verify the positive or negative impacts of macroeconomic variables on capital structure. However, they are unable to analyze the mechanism driving the effects of these variables to the capital structure decisions of enterprises as well as unable to explain how a macroeconomic shock impacts on the behavior of adjusting the capital structure of enterprises and how long this impact will last. Only Khanna and Associates (2015) used PVAR in panel data to study the impacts of macroeconomic variables such as economic growth, inflation and stock indexes on capital structure. In Vietnam, according to the authors, there has not been any studies applying PVAR to study this issue. This is a gap in study methodology because socio-economic characteristics in the context of unstable economic recession and recovery require the use of an appropriate method to study of the impact of microeconomic and macroeconomic factors to the financial situation, especially the capital structure of the business. Therefore, in the present study, the authors analyze the impact of micro variables and the mechanism of the impact of macro variables on the capital structure of Vietnam’s enterprises in the period of recession (from Q1/2007 to Q4/2010) and economic recovery (from Q1/2011 to Q2/2016) by using PVAR.

3 Data, Model and Study Methodology

3.1 Data

Based on grounded theories and empirical researchers, variables affecting the capital structure are selected to build the model (as shown in Table 1).
Table 1.

Variables and measurement.

Symbol

Variables

Expectation

Theory

Measurement

Studies

Endogenous variables

TDR

Capital structure

\(+\)

Total liabilities/Total assets

Vo et al. (2014); Vătavu (2015)

GDP

Economic growth

\(+\)

TOT, agency cost POT

Quarterly growth rates of real GDP

Ariff et al. (2008); Jong et al. (2008); Khanna et al. (2015)

RATE

Loan interest rate

\(+\)

MM POT, market-timing, TOT

Average loan interest

Nor et al. (2011); Allayannis et al. (2003); Zerriaa and Noubbigh (2015)

LNVNINDEX

Stock market

Market timing

Natural logarithm of VNINDEX

Jong et al. (2008); Alves and Francisco (2015); Khanna et al. (2015)

BOND

Bond market

\(+\)

TOT, agency cost

Market capitalization value/GDP

Jong et al. (2008); Nor et al. (2011)

Exogenous variables

SIZE

Firm size

+

TOT, Agency cost, POT

Natural logarithm of total assets

Jong et al. (2008); Vo et al. (2014)

TANG

Asset structure

\(+\)

TOT, POT Agency cost

Fixed assets/Total assets

Vo et al. (2014); Vătavu (2015)

GRO

Firm growth

\(+\)

POT Agency cost

Quarterly growth rate of total assets

Jong et al. (2008); Vo et al. (2014)

LIQ

Short-term liquidity

\(+\)

TOT POT, Agency cost

Short-term assets/Short-term liabilities

Vo et al. (2014); Vătavu (2015)

VOL

Business risk

\(+\)

TOT POT

Standard deviation (EBIT/Total assets)

Vo et al. (2014); Vătavu (2015)

MTR

Coiporate income tax

\(+\)

TOT MM

Corporate income tax/Profit before tax

Jong et al. (2008); Vătavu (2015)

Note: (+) is positive relationship between capital structure and explanatory variables. (−) is negative relationship between capital structure and explanatory variables. Source: Author’s compilation.

The authors used balance sheets extracted from financial statements of 82 randomly selected enterprises from those listed on the Vietnam Stock exchanges, which were continuously in operation from Q1/2007 to Q2/2016 (\(82\times 38=3,116\) observations). The samples were highly representative, provided by Ban Viet Capital Securities (VCSC). In addition, macroeconomic variables werecollected from IMF, ADB and AsianBondsOnline.

3.2 Models and Research Methodology

PVAR for economic recession period (Model 1) and economic recovery (Model 2) with latency k are described as follows:
$$\begin{aligned} Y_{it} = \mu _0 + A_1 Y_{it-1} + \ldots + A_k Y_{it-k} + \beta _x X_it + e_{it}, \quad \forall i=1,2,\ldots ,N, \, t =1,2,\ldots ,T. \end{aligned}$$
Where \(Y_{it} =(TDR_{it}, GDP_{it}, RATE_{it}, LNVNINDEX_{it}, BOND_{it})\): is a random vector level of dependent variables; \(Y_{it-p}\): vector level of dependent variables lentency; \(A_1, A_2,\ldots , A_k\): matrices \(k \times k\); \(X_{it}\): exogenous vectors level (\(1 \times k\)), including variables listed in Table 1; \(\beta _x\): matrices (\(l \times k\)) coefficient estimation; \(e_{it}\): Fixed effects due to unobservable characteristics of enterprises and constant effects over time, \(e_it | y_{it-1} \sim N(0; \sigma _e^2)\).

The classical VAR model is applied to the stationery and non-coherent time series, originated from Sims’s study (1980) on the transmission mechanism of macro variables. Eakin et al. (1988) continued to propose VAR model to process panel data (PVAR). Since PVAR was proposed based on the classical VAR model, there were still some shortcomings such as the deviated estimated parameters or loss of observations when taking lags. To fix this disadvantage, Love and Zicchino (2006) introduced and used PVAR based on the application of GMM to ensure the uniformity of balance variances, preventing self-correlation and maintaining data conservation.

3.3 Basic Tests

3.3.1 Stationery Test

When estimating PVAR, the variables in use must be stationery. The authors used the Augmented Dickey-Fuller (ADF) to test the variables. Table 2 shows that all variables are stationery at 0.
Table 2.

PVAR unit root test results.

VARIABLES

STATISTIC T

VARIABLES

STATISTIC T

TDR

526,1311\(\mathrm{^{***}}\)

TANG

291,4663\(\mathrm{^{***}}\)

ROE

2405,4509\(\mathrm{^{***}}\)

LIQ

702,1896\(\mathrm{^{***}}\)

VOL

1858,4298\(\mathrm{^{***}}\)

LNVNINDEX

381,9070\(\mathrm{^{***}}\)

SIZE

493,4786\(\mathrm{^{***}}\)

BOND

207,7378\(\mathrm{^{***}}\)

MTR

1638,3719\(\mathrm{^{***}}\)

RATE

207,7378\(\mathrm{^{***}}\)

GRO

1343,1092\(\mathrm{^{***}}\)

GDP

561,0923\(\mathrm{^{***}}\)

Note: \(\mathrm{^{***}}\)corresponds to 1% of significance level. Source: Author’s calculation.

3.3.2 Optimal Latency Test

Andrews and Lu (2001) proposed to use the Moment Model Selection Criteria (MMSC) with determination coefficient CD and J-Pvalue statistics to determine the optimal latency. The results shown in Table 3 indicate that PVAR optimal latency in model 1 is 2 and model 2 is 3.
Table 3.

Lags criteria results of Model 1 and Model 1.

Latency

CD

Statistic J

Model 1

1

0.9997

874.1953

2

0.9998

567.1583\(\mathrm{^{*}}\)

3

0.9999

867.7019

Model 2

1

0.9979

1,286.025

2

0.9980

1,173.926

3

0.9986

510.0104\(\mathrm{^{*}}\)

Note: \(\mathrm{^{*}}\)represents the selected latency corresponding with criteria. Source: Author’s calculation

3.3.3 Model Stability Test

Research conducted AR test. Figure 1 shows that all the solutions of Model 1 and Model 2 are in the unit circle. PVAR model ensures stability and sustainability.
Fig. 1.

AR root test results of Model 1 and Model 2.

Source: Author’s calculation.

4 Study Outcomes

Table 4 represent the regression results of Model 1 and Model 2.
Table 4.

PVAR results of Model 1 and Model 2

Criteria

Model 1

Model 2

L.TDR

0.726\(\mathrm{^{***}}\)

0.722\(\mathrm{^{***}}\)

[8.30]

[7.99]

L.LNVNINDEX

−0.178\(\mathrm{^{***}}\)

−0.0726\(\mathrm{^{***}}\)

[−3.74]

[2.85]

L.BOND

3.597\(\mathrm{^{***}}\)

−0.495\(\mathrm{^{***}}\)

[5.35]

[−5.03]

L.RATE

1.231\(\mathrm{^c}\)

−0.0316

[4.90]

[−0.56]

L.GDP

2.669\(\mathrm{^{***}}\)

0.105

[3.53]

[0.84]

ROE

−0.251\(\mathrm{^{**}}\)

−0.0549\(\mathrm{^{*}}\)

[−2.05]

[−1.50]

VOL

−0.26

0.219

[−1.12]

[1.06]

SIZE

−0.328\(\mathrm{^{***}}\)

0.0369\(\mathrm{^{*}}\)

[−3.09]

[0.62]

MTR

−0.131

−0.0356\(\mathrm{^{*}}\)

[−1.63]

[−1.91]

GRO

−0.00804\(\mathrm{^{**}}\)

0.00186\(\mathrm{^{*}}\)

[−2.07]

[0.46]

TANG

0.299\(\mathrm{^{***}}\)

0.0810\(\mathrm{^{*}}\)

[2.85]

[1.67]

LIQ

−0.00202\(\mathrm{^{*}}\)

0.000261\(\mathrm{^{*}}\)

[1.53]

[0.93]

N

1230

1722

Note: \(\mathrm{^{*}}\), \(\mathrm{^{**}}\), \(\mathrm{^{***}}\) correspond to the significance level of 10%, 5% and 1%; [] value of standard deviation. Source: Author’s calculation.

4.1 Economic Recession

In order to analyze the mechanism and direction of the impact of macroeconomic variables on the capital structure of Vietnam’s enterprises when shocks happen, the authors analyzed the push function (Fig. 2).
Fig. 2.

Impulse response function Model 1.

Source: Author’s calculation.

4.1.1 Impacts of Bond Market on the Capital Structure

As the bond market went up by one standard deviation, the bond market improved, enterprises increased 3.597% of debts in the first quarter and dampened in the fourth quarter. This result was consistent with the theory TOT and Jong et al. (2008); Nor et al. (2011).

4.1.2 Impacts of Economic Growth on the Capital Structure

Figure 2 shows that GDP has a positive impact on TDR. When GDP increased by one standard deviation, corporate debt use increased by 2.669% and the increase in debt use declined gradually as of Q3, in line with Ariff and Associates (2008).

4.1.3 Impacts of Credit Market on Capital Structure

With the shrinkage of credit market, credit balance declined, loan conditions became more difficult and RATE increased by one standard deviation but positively impacted TDR. TDR increased by 1.231% at 5% significance level. This increase lasted for nine quarters. The result is consistent with MM theory and Allayannis et al. (2003); Zerriaa and Noubbigh (2015).

4.1.4 Impacts of Stock Market on the Capital Structure

When the stock market increased by one standard deviation, TDR fell by 0.178% at 1% significance level. The decline of debt using lasted in 1 quarter, in agreement with Khanna et al. (2015) and Alves and Francisco (2015).

As analyzed above, there is a relation between TDR and macroeconomic variables in the context of economic downturn. However, TDR does not only depend on macroeconomic shocks but also under the effect of microeconomic variables.

The capital structure of the previous period positively influences the capital structure of the later one. This indicates that a rise in debt using in the previous period would make the debt using in the later period increase by 0.726%, harmonizing with Nor et al. (2011) and Khanna et al. (2015).

Profitability negatively correlated with the capital structure at 1% statistical significance level. The results was explained by POT, Nor et al. (2011) and Truong and Nguyen (2015). However, the results also demonstrated an inefficacy in using debts of Vietnam’s enterprises, lowering businesse’s profitability.

Firm size and scale negatively influences the capital structure. This indicates that during economic downturn, large-scale businesses usually have high profit, large equity, good reputation and financial capacity can easily issue new equity to the market. The result is analogous to Fosberg (2013) and Proenca et al. (2014).

Firm growth negatively affects the capital structure, which is similar to the Agency cost theory and Proenca et al. (2014) when studying the correlation in the economic recession period.

Asset structure has positive correlation with capital structure. Indeed, enterprises whose fixed assets are large when issuing secured debts or mortgaged debts are more likely to have access to loans and better policies. This results match with POT, TOT, Alves and Francisco (2015) and Iqbal and Kume (2014).

Solvency has negative impact on capital structure, consistent with POT, Nor et al. (2011) and Proenca et al. (2014) when studying this correlation in the economic recession period.

4.2 Economic Recovery

Similarly, Fig. 3 demonstrates the push function of Model 2.
Fig. 3.

Impulse response function Model 2.

Source: Author’s calculation.

4.2.1 Impacts of Bond Market on Capital Structure

Table 3 shows that when the bond market expanded by one standard deviation, TDR fell by 0.495% and this trend prolonged in 10 quarters. This result contradicts with the study on economic recession and TOT but consents to Vo et al. (2014). The reason for the result is the characteristics of bond markets of developing countries in general and Vietnam in particular, which is small in scale, undiversified products mainly comprising of Government bonds (which account for 95.4% of the market value). Besides, the market capitalization value of the government bond market increased from 13.68% GDP (economic recession) to 19.03% GDP during the observation period. In contrast, the scale of corporate bond market slightly decreased with market capitalization value fell from 1.01% GDP to 0.92% GDP. The cause of such development was the fact that credit market remained as a traditional mobilization source or Vietnam’s enterprises, therefore when loan conditions loosened; there was a shift from issuing debts towards borrowing directly from financial institutions.

4.2.2 Impacts of Economic Growth on Capital Structure

Similar to the economic downturn period, Table 3 shows that when GDP increased by one standard deviation, TDR rose by 0.105% in Q1 and slowly diminished as of Q6. This implies a rise in consumption demand in that period, encouraging businesses to expand their production, which led to high capital demand. Furthermore, the low cost of capital exhaustion and reduction in bankruptcy risk made enterprises increase the use of debts to take advantages of the tax shields. This result is consistent to TOT, Jong et al. (2008); Nor et al. (2011) and Khanna et al. (2015).

4.2.3 Impacts of Credit Market on Capital Structure

In contrast with the study in economic recession, Table 3 shows that when credit market shrank, RATE increased by one standard deviation, TDR dropped by 0.312% at 5% of significance level. This trend lasted for 6 quarters. It indicates that the credit market positively correlates with TDR, consistent to POT and Nor et al. (2011)

4.2.4 Impacts of Stock Market on Capital Structure

Similar to the economic recession, Fig. 4.3 exhibits a decrease of TDR by 0.073% when the stock market went up by one standard deviation. The trend prolonged for 2 quarters since the economic shock. The result conforms with the market-timing theory and Vo et al. (2014).

The results of Model 2 also indicate that in economic recovery period, other variables such as solvency, scale and speed of growth positively influence the capital structure, contravening the results of the study in economic recession period. This result could be explained by Keynes (1936). That is, when the economy recovers, global and domestic demand for goods and services increases. Hence, in order to meet the demand of the market and the new business cycle, Vietnam’s firms expand their operations, increase their asset investment and actively seek new business opportunities. However, the result in this period also points out that Vietnam’s businesses should prioritize their investment in short-term projects and/or assets with high profitability to increase their short-term solvency as well as minimize risks arising under the context of unstable and unsustainable economic recovery. Besides, businesses should also control and recalculate their taxable income in this period. This is also a significant source of capital enhancing capital resources for businesses.

5 Conclusion and Policy Recommendations

5.1 Conclusion

The PVAR regression results show that TDR is affected by macroeconomic variables such as bond market, economic growth, credit market and stock market. However, the direction and magnitude of the impact of macroeconomic variables on TDR varies. Vietnam economy under the global economic recession, macroeconomic variables had a strong impact on TDR, which suggests that managers were cautious during this period. However, as the global economy recovered and the process influenced the macroeconomic movements in the country, managers did not properly address the macroeconomic variables. Macroeconomic variables showed weak impacts on TDR, specifically:

The bond market has a positive impact on TDR during the economic downturn. A shock would increase the impact of the bond market, making businesses increase their debt use by 3.597%. Conversely, as the economy recovers, TDR fell by 0.495% under the impact of the bond market.

Economic growth has a positive impact on TDR in both economic recession and recovery. In particular, the strongest impact was during the economic downturn. Despite the difficulties of the period, economic growth still made TDR rise by 2.699%, in accordance with the characteristics of Vietnam economy during this period, whose growth rate was high due to recent WTO accession.

The credit markethas a negative impact on TDR during economic recession. Conversely, as the economy recovered, credit markets positively influenced TDR. Thereby we find that, despite the economic downturn, difficult operating conditions, financial exhaustion and bankruptcy risk, businesses still increased their use of debt. This demonstrates the fact that Vietnam’s enterprises have low financial autonomy, depends heavily on loans as well as undiversified channels for capital mobilization.

The stock market has a negative impact on capital structure in both economic recession and recovery, in line with the Market-timing theory. However, the strongest impact was during the economic downturn, when a stock market shock made the TDR decrease by 0.178%.

In addition, the estimated PVAR model also indicates that during the economic downturn, the pace of TDR adjustment of Vietnam’s enterprises (0.726%) is faster than that when the economy recovers (0.722%). This result reaffirms the content of the TOT theory that the capital structure of a business varies from time to time, however, this theory does not yet indicate the rate of firm’s adjustment of target capital structure in each specific economic context. This study, therefore, provides additional evidences for the argument of the TOT theory. That is, during economic downturn, managers adjusts capital structure faster than in economic recovery to achieve the target capital structure, ensuring financial security and increasing the value of the company.

In addition, asset structure has a positive impact, while profitability has a negative impact on TDR. Other variables including solvency, firm growth and size have a negative impact on TDR in the context of economic recession. Conversely, as the economy recovers, these variables have a positive impact on corporate TDR. Furthermore, during the economic recovery period, businesses need to control expenses and especially recalculate their taxable income.

5.2 Policy Recommendations

For enterprises

First, enterprises should diversify their forms of capital mobilization to reduce debts, make the most of capital sources and increase the use of financial instruments.

Second, the capital restructure of businesses must be associated with each stages and target of development, specific financial situation, business size, domestic and foreign macroeconomic environment in line with stages of economic cycle- economic recession and recovery.

Third, restructure capital in the direction of increasing owner’s equity and self-financing capacity of businesses. The study outcomes show that the profitability of enterprises negatively influences the capital structure. This proves that profitability is an important capital sources that can help firms actively meet their capital needs while still keeping control. Additionally, businesses should actively mobilize equity from outside such as issuing shares, doing joint ventures and associates.

Fourth, capital restructuring should be associated with the restructuring of investment portfolios, especially investment in fixed assets. However, enterprises should also note that the increase of fixed assets must be associated with capital structure adjustment in the direction of strengthening long-term source, avoiding financial imbalances. Besides, the study results also show that Vietnam’s enterprises should prioritize investments in short-term projects or assets with high yields that can enhance their short-term liquidity and help avoid risks arising from the economic conditions of unstable and unsustainable recovery.

Fifth, capital restructure must be in line with the recalculation of taxable income and tax planning in order to optimize the amount of tax payable within the legal framework. It means that firms should recalculate their taxable income to achieve tax deduction that are higher than their reduction of business income.

For the Government and related agencies

First, the Government should simultaneously implement polices to stabilize the macro economy, curb inflation, ensure rational economic growth, create favorable business environment and assist enterprise to better access to the funds serving their purpose of restructuring.

Second, the Government should improve regulations and policies to promote and facilitate the development of financial markets, especially the bond market and the stock market. They are not only the channels that help mobilize capital for enterprises but also the channels for capital withdrawal under the market mechanism. Therefore, sound and developed stock and bond markets are significant conditions to ensure the success of the capital restructuring process of enterprises. Thus, the capital restructuring of enterprises must be associated with the development of these markets. The Government should employ policies to strengthen, stabilize and soundly develop credit markets.

Third, the corporate income tax is 22%, which is relatively higher than that of other countries in the region such as Singapore, Hong Kong (17%) and Taiwan (16.5%). The Government should adjust the corporate income tax to improve competitiveness and support enterprises in capital restructuring.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Head of Research InstituteBanking University of Ho Chi Minh CityHo Chi Minh CityVietnam
  2. 2.Banking University of Ho Chi Minh CityHo Chi Minh CityVietnam

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