Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Impact of Financial Leverage, Firm Performance on Systemetic Risks in Developing Countries: An Analysis on Vietnamese Listed Firm

Pham Thai Ha

Vietnam Government Office, Hanoi, Vietnam

Abstract: In this paper, we study the link between financial leverage, firm performance on the systemetic risks in developing countries, especially in the case of a developing country in Asia like Vietnam. Although there are many papers on this topic, the difference between them and this paper is that we have conducted the study in Vietnam that have not yet been studied together. The data used were collected from Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) in the period of 2013 – 2018. Based on the results of the panel data regression, the study can demonstrate that a firm used more current assets can positively impact on systemetic risk. Furthermore, a firm owned more performance can certainly decrease systemetic risk. Regarding firm size, and financial leverage, the results demonstrate that firm size, and financial leverage are insignificantly correlated with systematic risk.

Keywords: leverage, firm performance, systematic risk, panel data

Received December 10, 2019; Revised January 10, 2020; Accepted January 18, 2020

Citation: Pham Thai Ha, Impact of Financial Leverage, Firm Performance on Systemetic Risks in Developing Countries: An Analysis on Vietnamese Listed Firm. Journal of Finance and Accounting, 8 (1). 11-15. January 2020. doi:10.12691/jfa-8-1-2

1. Introduction

In the background of the economic development in each country, the stability of economic growth has greatly contributed on relieve of systematic risks and help the economy sustainabe development. In each country, the management of systemic risk is one of the issues paid attention to ensure the stability and sustainability of the overall financial system in general and the stability market for every firm in the country in particular. The Capital Asset Pricing Model (CAPM) of Sharpe [1] and Lintner [2] has significantly contributed more attention in managing systematic risks and financial market development.

Vietnam has greatly achieved rapid economic growth to be a socialist-oriented market economy in connection to a lower middle-income country. Real GDP growth rate increased by 7.10% during 1990 to 2018, in which, foreign direct investment firms have been identified as an important energy of financing for a developing country like Vietnam. It enhances economic growth and growth model reform [3]. According to the census, there were more than 700,000 businesses operating in Vietnam so far this now, up 100.000 new businesses each year since 2016. However, large companies accounted for roughly 2 percent of the total and 98 percent of its total from SMEs. Up to date, there are approximately 10,000 large firms.

It is evident that the determinants of the systematic risk has been widely discussed in a large number of studies across the globe and has frequently inspired academics to find a number of empirical studies in both developing countries and developed countries. Indeed, finding factors that influence systematic risk has been at the center of the relevant previous studies [4,5,6,7]. In which, Sharp [1] has boardly developed the model based on Capital Asset Pricing Model (CAPM) which describes the linkage between systematic risk and for assets, particularly stocks, as the key factors to develop other studies in recent years.

In this study, proposing an approach to apply in Vietnam in order to estimate systematic risk (the beta) in a CAPM. The paper focuses on one of the fastest growing economies in Asia, named Vietnam. The general objective of the study is to examine the determinants of systematic risk, in particular by other prime factors such as financial leverage, firm performance, and other control factor as liquidity, firm size in the Vietnamese listed companies over the 2013 – 2018 period and based on the approach of fixed effects method (FEM) and random effects method (REM), and pooled ordinary least squares (pooled OLS). Therefore, the main aim of this study is to examine what most important factors that impact on systematic risk. The importance of the paper is to provide to the researchers, policy makers and Vietnamese government in general, and investors in particular.

The rest of the paper is organized as follows: Section 2 presents the literature review. Section 3 discusses the data collection, research model and methodology as well as the estimation techniques. Further, Section 4 presents the results and discussion. Section 5 gives the main conclusion.

2. Literature Review

In recent years, there has been a very large number of studies which study the correlation between financial leverage, firm performance on the systemetic risks in developing countries. In the theory in macroeconomics, there are many types of risk, but it is easy to understand that systematic and unsystematic risk can be focused. Basically, unsystematic risk is firm specific or industry/sector specific risk. This is risk in relation to the specific attribution of an individual investment or a specific group in the economic environment. In fact, this is not associated with returns of stock market. In contrast, systematic risk is as a risk consistent with market returns, and can be attributed to broad factors. With systematic risk, it is described that a portfolio in investment cannot be impacted by the specific risk of individual investments. Further, some reasons of systematic risk may be generated by almost macroeconomic factors as economic growth, inflation, shocks in interest rates, shocks in currencies, recessions, conflits and wars, etc. Macro factors which affect direction and fluctuation of whole market could be systematic risk. An individual company, a firm, a specficic based sector cannot release systematic risk.

To evaluate systematic risk, Sharp [1] has construced the model based on Capital Asset Pricing Model (CAPM) which describes the linkage between systematic risk and for assets, particularly stocks. CAPM is widely used throughout finance for pricing risky and generating expected returns for assets given the risk of those assets and .

The formula for calculating the expected return of an asset given its risk is as follows:

This fundamental equation follows from the proposition that only systematic risk, measured by beta (β), matters.

where:

Rs = the stock’s expected return (and the company’s cost of equity capital).

Rf = the risk-free rate.

Rm = the expected return on the stock market as a whole.

β s = the stock’s beta.

This model was also developed by Markowitz [8]. In which, the coefficient of of a potential investment is a measure of how much risk the investment will add to a portfolio that looks like the market. If a stock is riskier than the market, it will have a beta greater than one. If a stock has a beta of less than one, the formula assumes it will reduce the risk of a portfolio.

To discuss about systemetic risk, the Efficient Market Hypothesis, or EMH can be focused as the fundamental theory for investment, is the hypothesis in financial economics that describes that prices of asset could reflect all available information. EMH further indicates that a stock normally trades at a fair value on the stock exchanges, it is significantly impossible for investors or traders to either exchanged stocks or in the inflated prices.

The determinants of the systemetic risks has been widely discussed in developing and developed countries as well as countries in transition. The main hypothesis of this paper is to establish the link between financial leverage, firm performance on the systemetic risk in the case of Vietnamese listed companies. More specifically, a large number of empirical studies have confirmed this relationship [4,5,6,7].

In the study on Norway’s economy, Yang et al. [4] describes that Norway was the country to force some regulations for board of management of publicly traded firms, especially regulation for gender rate. The study has further expanded the previous debate on endogeneity problem, and applying the approach of difference-in-difference to discuss the effects of its regulation on performance of firm. For the control group, those are firms from other neighboring countries such as Finland, Sweden, and particularly in Denmark. The results indicate a negative impact of representation of mandated female on firm performance as well as on firm risk.

Sensoy [9] conduct on a study in the emerging market and especially based on the argument that the institutional ownership can generate systematic risk in liquidity via robustness of liquidity commonality. Using a various database in emerging stock markets, the study describes that a new source of systematic risk in liquidity for a singular group of companies can be found. Further, commonality can decrease with generating number of investors at the level of any size of firm; indicating that as the investor base significantly becomes larger, views of market stakeholders can become more complex, which can entirely provide an alternative way to decline the systematic risk in liquidity.

Muijsson and Satchell [5] conduct on a study on Australian bank – a developed country in Oceanian. According to the study, the study has a link between systematic risk and factors of balance sheet and recognize the dependencies in covariance structure. Consequently, funding structure, and factors of balance sheet significantly affect systematic risk in Australian banks.

In respect to Huh [7] in a study about the determinants of systematic risks, the very suitable indicators are representative for systematic risk could be easily found by adding daily returns on a large range of total assets into network of bottleneck. Additionally, the study has a comparison of performance between this analysis and the previous models based on the sample of S & P 100 components. As a result, it is much worthy to confirm that it can create the performance as best comparably estimated models can do.

As shown by Beltrame et al. [6] who has a study in commercial banks in Europe, using a data between 2005 and 2016 and covering on 11 European countries. The data is retrieved from 97 listed banks in the region. In this paper, the study does three adjustments in leverage for doctoring the impact of provisioning and incorporating as well as the impact of non-performing loans (NPLs) and total exposure of credit risk. In addition to control for size of bank, results describe that the combination of leverage, and asset quality is known as a component of systematic risk. The coefficient of NPLs is found to be a significant factor of market risk. Results also indicate that leverage is pointless in the process to verify a financial riskiness in a bank.

3. Data Sources and Methodology

3.1. Data Sources

The study has focused on the secondary data in the period of 2013 – 2018. The data is collected from Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) and over 60 companies. The data of a firm is retrieved from balance sheet, income statement, and cash flow statement. As a result, HOSE and HNX have frequently published the financial data of every firm on there.

In order to evaluate return on the stock Rs, firstly, we collect data for Vietnam 10-year bond yield and representative for Rf, secondly, return on the market Rm is based on the average return of VN-Index, and βs is calculed as below:

3.2. Methodology

Financial leverage, firm performance on systemetic risks has been focused in numerous of developing countries. Followed by the studies of and other empirical studies, we have:

[picture] | (1)

[picture] | (2)

Table 1. Measurement of Variables Used in the Study

4. Results and Discussion

4.1. Descriptive Statistics

Table 2 indicates the descriptive statistics of variables used in the study in terms of number of observations, mean, standard deviation, minimum, and maximum values. This analysis is based on panel data, including time-series data and crossectional data. The results describe that, the level of liquidity of Vietnamese firms is fluctuated between 0.15 and 7.82 but the average liquidity is roughly 1.76, it is evident that a large number of firms are available to ensure liquidity in operation. In addition to financial leverage, firm performance measured by return on assets is roughly 6% on average, however, there exists some firms with negative performance.

Regarding financial leverage in the capital structure, firms are supported by 53% debt and 47% equity. The data further said that some firms are entirely donored by shareholdes’ capital. In this study, a large number of firms are small-medium sized.

Table 2. Descriptive Statistics

4.2. Correlation Analysis

In this study, we analyze based on three techniques, which are discussed on the studies of Yang (2019); Muijsson and Satchell (2019); Beltrame et al. (2018); Huh (2019). In conclusion, multicollinearity can take part in the study when two or more predictors are significantly consistent in the model. It is further discussed that VIF is used for multicollinearity test, if VIF value is excess of 10, a problem with multicollinearity cannot be found. In addition, detection of multi-collinearity can be found if the correlation coefficient is 0.8 and more, serious multicollinearity may be appeared if absolute value of pairwise correlations among independent variables may be high.

Table 3 describes about the correlation matrix in the model, we see that all correlation coefficients between independent variables are less than 0.8. It is evident that the multi-collinearity cannot be present.

Table 3. Correlation Matrix

4.3. Estimation Model

4.3.1. Discussion about Estimation Approach

Followed by the models of Yang [4]; Muijsson and Satchell [5]; Beltrame et al. [6]; Huh [7], Tandelilin [12], and Chun and Ramasamy (1989), the estimation model can be estimated based on either Fixed-effect model (FEM) or Random-effects model (REM), especially by pooled ordinary least squares (pooled OLS). However, Wooldridge [13] demonstrated that pooled OLS should be employed in the situation if the study can select a different sample for each period of the panel dataset. In contrast, in the case of the same sample of entity, we should follow REM or FEM.

Regarding FEM, which is more preferred if individual specific effect is significantly related to independent variables. It assumes that there is a true effect size which underlines studies, and differences in observed effects can occur because of sampling error. Further, REM is more preferred if the true effect can significantly fluctuate from this study to another study. Generally, effect size could be either higher or lower in studies. Because of difference in the mixes of participants and in the implementations of interventions, and other reasons, it is consistent to indicate that there might be different effect sizes underlying different analyses.

In order to select either FEM or REM used in the study, Hausman tests have been applied in oder to select the best model. In addition, the select model is FEM if Pro>Chi2, otherwise is REM, the hypothesis as follows:

H0: The null hypothesis - the preferred model is random effects (REM)

Ha: The alternate hypothesis - the model is fixed effects (FEM).

4.4. Estimation Results

Table 4 presents the results of the relationship of financial leverage, firm performance on systemetic risks in the case of Vietnam. Results are:

Table 4. Estimation Results

Breusch-Pagan Lagrangian test have statistical meaning, that is, the study should follow random effect or pooled estimation. From the test, the Pooled OLS is worse than the others. To select either FEM or REM, Hausman tests should be used, from that test, the study can choose either a or a random effects model. As a result, we find that FEM is the suitable model.

Followed by FEM model with the explanatory variable of systemetic risks, the study describes that the coefficients of LIQ, and ROA are statistically significant. Further, a positive impact from liquidity on systemetic risks can be found while a negative impact for firm performance. In addition to financial leverage, and firm size, no impact can be found in this study because coefficients of FLE, and SIZE are insignificant.

The following regression result is given as below:

[picture] | (3)

4.5. Interpretation of the Results

From the results, indicating that the determinants of systemetic risks in the listed companies in Vietnam can be found in terms of some evidence, as follows:

Firstly, liquidity has a significant and positive impact on the systemetic risk of Vietnamese listed firms. It means that the higher ability of liquidity, which is measured by a higher level of the quotient of current assets to short term debt could significantly inspired the systemetic risk. In general, a firm used more current assets can positively affect systemetic risk. It is evident that a firm with a larger current assets has to operate more efficient their current assets in order to generate more profit and narrow systemetic risk in the financial market. It is in line with Tandelilin [12] in Indonesian stocks market. Indonesia is an emerging economy in Asia, Indonesian company in this country could be supported so as to diversify and upgrade before relieving systemetic risks in the process of operation.

Secondly, a firm with more performance has a negative and significant impact on the systemetic risk. It can be explained that a higher profitability of a firm can decrease systemetic risk. In fact, a firm with a higher profitability has continually generated in the wake of a higher level of economic development and business environment. For this reason, investors and shareholder can predominantly believe in expansion of business and start up because less systemetic risk is in relation to less cost of capital. This evidence is in line with Tandelilin [12]. Further, Chun and Ramasamy [10] also found the same evidence in Malaysia, namely Kuala Lumpur Stock Exchange (KLSE). Chun and Ramasamy [10] confirmed that financial factors in the balance sheet, such as profitability, effectiveness are very important determinants of systematic risk of a common stock.

Regarding firm size, and financial leverage and its impact on systematic risk, the study demonstrates that firm size, and financial leverage are insignificantly associated with systematic risk. Specifically, finding about firm size in this study is not in line with Tandelilin [12] in Indonesia. This is called by scope of economies in a firm, it is evident to recognize that a larger firm might obviously have a lower cost of bankruptcy and generate a higher level of performance and less risk. Further, financial leverage cannot be found any impact on systematic risk, this is not correlated with Chun and Ramasamy [10] with a negative link.

5. Conclusion

The determinants of the systematic risk has been widely discussed in a large number of studies across the globe. In general, systematic risk normally refers to the risk inherent to the whole market or a market segment. In some situations, systematic risk has been also known as undiversifiable risk which can impact on overall market, not just a specific firm or industry. In recent years, there has been a very large number of papers which study the correlation between financial leverage, firm performance and systemetic risks. However, there is a very significant difference between the results obtained in these different papers.

A firm always thinks about using debt in its capital structure in order to make more robustness of tax shield benefits into firm value. The finding obtained from the study is described that a firm used more current assets can positively affect systemetic risk. Further, a firm owned a higher profitability can decrease systemetic risk. Regarding firm size, and financial leverage and its impact on systematic risk, the results demonstrate that firm size, and financial leverage are insignificantly correlated with systematic risk. It means that the scope of economies in a firm could not be found, a larger firm might not perfectly have a lower cost of bankruptcy and generate a higher level of performance and less risk.

References

[1]   | Sharpe, W. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance 19, 425–442.
[2]   | Lintner, J., 1965. The valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics. 47 (1), pp 13-37.
[3]   | OECD (2002). Foreign Direct Investment for Development: Maximum Benefits and Minimum Costs. OECD Publications Service, Paris, France.
[4]   | Yang et al. (2019). Women directors, firm performance, and firm risk: A causal perspective. The Leadership Quarterly. 30 (5), 101297.
[5]   | Muijsson and Satchell (2019). The role of bank funding in systematic risk transmission. Finance Research Letters. In Press, Corrected Proof.
[6]   | Beltrame, F et al. (2018). Systematic risk and banks leverage: The role of asset quality. Finance Research Letters. 27, pp 113-117.
[7]   | Huh, J. (2019). Measuring systematic risk with neural network factor model. Physica A: Statistical Mechanics and its Applications. Physica A, 123387
[8]   | Markowitz, H. (1952). Portfolio selection. The Journal of Finance. 7 (1), pp 77-91.
[9]   | Sensoy, A. (2017). Firm size, ownership structure, and systematic liquidity risk: The case of an emerging market. Journal of Financial Stability. 31(C), pp 62-80.
[10]   | Chun, L. S. and Ramasamy, M. (1989). Accounting variables as determinants of systematic risk in Malaysian common stocks. Asia Pacific Journal of Management. 6 (2), pp. 339-350.
[11]   | Nguyen, V. (2020). Human capital, capital structure choice and firm profitability in developing countries: An empirical study in Vietnam. Accounting. 6(2), pp 127-136.
[12]   | Tandelilin, E. (1997). Determinants of Systematic Risk: The Experience of Some Indonesian Common Stock. Kelola. Gadjah Mada University Business Review, 16(IV).
[13]   | Wooldridge, J. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press.