The Effect of Capital Structure on Profitability in State-Owned Construction Companies Listed on IDX in 2009 – 2018 Period

Aninditya Yumna Maisyaroh

Mursalim Nohong Abdullah Sanusi

ABSTRACT

The finances of State-Owned Enterprises (SOEs) in construction sector are currently attracting public attentiiom through its dramatic boost in external funding. Firms need an optimum capital structure in order to inflate profits while maintaining ability to handle increasingly competitive environment. This research aimed to identify the impact of capital structure on firms’ profitability. This research examined the effect of Debt to Equity Ratio (DER) and Debt to Asset Ratio (DAR) on Return on Equity (ROE) and Return on Assets (ROA). The study analyzed secondary data using panel data method and specifically chose State-Owned Enterprises (SOEs) in construction sector listed on Indonesia Stock Exchange (IDX) in 2009 – 2018 as research population. The result of hypothesis showed that DER and DAR simultaneously have positive and significant effect on both ROE and ROA. Partially, DER has positive and significant effect on ROE and has no effect on ROA. Meanwhile, DAR partially does not has significant effect on both ROA and ROE. Keywords: Capital Structure, Profitability, Debt to Equity Ratio (DER), Debt to Asset Ratio

(DAR), Return on Equity (ROE), Return on Asset (ROA). Keuangan perusahaan Badan Usaha Milik Negara (BUMN) di sektor konstruksi sedang menarik perhatian publik dengan peningkatan drastis pada pendanaan eksternal. Perusahaan memerlukan struktur modal yang optimal untuk meningkatkan laba dan mempertahankan kemampuan untuk menangani lingkungan yang semakin kompetitif. Penelitian ini bertujuan untuk mengidentifikasi dampak struktur modal terhadap profitabilitas perusahaan. Penelitian ini menguji pengaruh Debt to Equity Ratio (DER) dan Debt to Asset Ratio (DAR) terhadap Return on Equity (ROE) dan Return on Assets (ROA). Studi ini menganalisis data sekunder menggunakan metode data panel dan secara khusus memilih perusahaan konstruksi BUMN yang terdaftar pada Bursa Efek Indonesia (BEI) pada tahun 2009 – 2018 sebagai populasi penelitian. Hasil hipotesis menunjukkan bahwa DER dan DAR secara simultan berpengaruh positif dan signifikan terhadap ROE dan ROA. Secara parsial, DER berpengaruh positif dan signifikan terhadap ROE dan tidak berpengaruh pada ROA. Sementara itu, DAR secara parsial tidak memiliki pengaruh signifikan terhadap ROA dan ROE. Kata kunci: Struktur Modal, Profitabilitas, Debt to Equity Ratio (DER), Debt to Asset Ratio

(DAR), Return on Equity (ROE), Return on Asset (ROA). INTRODUCTION Research Background Construction industry is considered one of the main drivers and platforms for economic growth, especially in developing countries. The fundamental role of the industry could be seen through its activities that preserve the attainment of socio-economic development objectives to provide proper shelter, infrastructure, and employment to society (Anaman & Osei-Amponsah, 2007).

The sector affects most sectors within the economy and becomes a crucial donor to the process of infrastructure development that provides a physical foundation on which development attempts and advancement of living standards could be realised (Khan, 2008). Indonesia has been intensively pushing the development of construction to meet the needs of infrastructure growth,

which becomes a priority for these past few years. There are compelling needs for investment in infrastructure associated with roads, highways, ports, airport railways, water, and power plants. In respond to the needs, there are boosts in priority infrastructure projects, such as revitalization of airports, water power plants, highways, Mass Rapid Transit (MRT) and manufacture of new ports, and upgrades of existing ports (PwC, 2016). National construction projects are generally handled by State-owned Enterprises (SOEs). SOEs are all industrial and commercial firms, mines, utilities, transport companies, and financial intermediaries regulated by government to some extent (Shirley, 1983). The finances of the SOEs in the construction sector are currently attracting public attention through its dramatic boost of external funding. The Jakarta Post addresses the raising concerns over the worsening balance sheets, primarily those working in construction massive projects. It is added that the companies heavily rely on external funding sources to obtain fresh funds, putting themselves in hazard with a serious upward trend of debt to equity ratio for the past few years (Aisyah R., 2018). Standard & Poor’s, a credit rating agency, highlights that the debt of the four largest construction SOEs increase dramatically. The construction-related expenditure would push the debt levels to continuously increase, causing the financial ratio weaken (Dangra et al., 2019) SOEs involved in extensive infrastructure projects require large amount of money. Therefore, debt is one of the sources of financing undertaken by the SOE. Moreover, the government's commitment to infrastructure investment for 2014 to 2019 would not be fully funded by them. Determining the right funding source is the main key in optimizing the capital structure of a company (Kristianti, 2018). The capital structure significantly influences the burden and availability of capital so that it affects the company's performance. Companies need an optimum capital structure in order to inflate profits while maintaining the company's capability to handle the increasingly competitive environment. If the

operational cash flow is minus, it means the company utilizes more internal cash to fund the infrastructure projects. If the internal cash is not enough, the company needs to borrow funds from banks or other funding alternatives and take some corporate actions in order to reap fresh funds. Fund from third parties could increase and potentially decrease profit if it is not used properly. The greater the usage of debt in the company's capital structure not only will increase the installment payments and interest that becomes the company's obligation, but also the risk of the company's inability to meet these obligations. One of the major causes of bankruptcy is financial problems occurred from the non-fulfillment of obligations that must be paid by the company to creditors. An increase in debt signifies an increase in obligations that must be met by the company. Therefore, it is necessary to have an adequate assessment to determine the health condition of the company (Dinarjito, 2018). This research aims to see the relationship between Debt to Equity Ratio (DER) and Debt to Asset Ratio (DAR) to profitability that represented by Return to Asset (ROA) and Return to Equity (ROE) variables. Based on the background stated above, researcher was interested in conducting a study entitled "The Effect of Capital Structure on Profitability in State-Owned Construction Companies listed on IDX in 2009 – 2018 Period.”

Research Questions

1. Do DER and DAR have positive and

significant effect on ROE? 2. Do DER and DAR have positive and

significant effect on ROA? 3. Does DER has a positive and significant

effect on ROE? 4. Does DAR has a positive and significant

effect on ROE? 5. Does DER has a positive and significant

effect on ROA? 6. Does DAR has a positive and significant

effect on ROA?

LITERATURE REVIEW 1. Financial management

Financial management is an elemental component of the overall management. It is involved with financial managers’ main function within organizations and associated with effective finance management in the business (Paramasivan & Subramanian, 2009).

It is related to analyzing financial situations, making financial decisions, setting financial objectives, and formulating financial plans to achieve those objectives while providing an effective financial control system for the company (McMenamin, 1999). Financial management is the use of financial data, skills, and approach to compose the best application of organization's resources.

2. Capital Structure

Capital structure refers to how a company funds its operations with a combination of debt and equity capital

(Ross, Westerfield, & Jordan, 2010). It is

the scale of resources attributed to the company via different origins, including internal and external financers (Martis, 2013). Baker & Martin (2011) claimed that capital structure is a major aspect of financial management and refers to the sources of financing employed by firm, including debt, equity, and hybrid securities that the firm employs to finance its assets, operation, and future growth. According to the definitions above, it is clear that capital structure is a funding decision taken by a company to determine the proportion of debt and capital in funding operations and other company activities.

3. Capital Structure Theories Modigliani Miller

Modigliani and Miller claimed that capital structure does not affect the cost of capital so that the size of the company will not change despite the changes in the proportion of debt and capital. According to Ghosh (2017), MM built their proposition model based on these assumptions:

1. The capital market is perfect 2. No taxes 3. No bankruptcy and transaction

costs 4. Investors could borrow at the same

rate as firms 5. Investors have the same

information as the management regarding the firm’s future investment opportunities

With these assumptions, Modigliani and Miller proposed two prepositions (Ross S., 2019): 1. Proposition I

This proposition claims that capital structure has no relation to the value of the firm. The value of two identical companies would remain the same and will not be affected by the financial alternatives adopted to finance the company assets. The value of a company depends on the expected future earnings when there is no tax.

2. Proposition II This proposition says that when tax information is available, financial leverage gains company’s value and reduces WACC.

Pecking Order Theory According to Myers, adverse selection implies that retained earnings are better than debt and debt is better than equity (Eckbo, 2008). The pecking order theory begins from the concept of asymmetric information, also known as information failure, that is a situation where one party carries better information than other party and leads to an imbalance in transaction power.

Managers of the company commonly have more information regarding the company’s performance, prospects, risks, and future outlook than outsider parties such as creditors and investors. Consequently, there is a demand for higher return as a compensation for the information asymmetry and its risk. Retained earnings financing comes straight from the company’s internal fund and

minimizes information asymmetry. It is way cheaper and a more convenient source of funding compared to external financing.

Trade-Off Theory

Trade theory of leverage is the development of MM theory, but it focuses more on the effects of taxation and bankruptcy costs. It illustrates the situation when the company trades off benefits of debt financing, favorable corporate tax treatment, against higher interest rate and bankruptcy costs. The theory claims that the value of a levered firm equals to the value of an unlevered firm plus the value of any side effects, including tax shield and expected costs occurred from financial distress (Ehrhardt & Brigham, 2009). Bradley et al. (1984) make the following conclusion based on their static trade-off model: 1. An increase in the costs of financial

distress reduces the level of optimal debt.

2. An increase in non-debt tax shields reduces the level of optimal debt.

3. An increase in personal tax rate on equity increases the level of optimal debt.

4. At the optimal capital structure, an increase in the marginal bondholder tax rate decreases the level optimal of debt.

5. The risk has an ambiguous effect even if uncertainty is assumed to be normally distributed. The relationship between debt and volatility is negative.

Signaling Theory

Signaling means signs given by a company to outsiders or investors with the expectation of market would change their perspective regarding company’s current situation (Gumanti, 2009). It is added that the theory also started with the concept of asymmetric information between corporate insiders and external parties, where managers and directors have better information regarding company’s

current condition and its prospect. According to Birgham & Daves (2007), there are two major assumptions of actions taken by a company regarding its stocks: 1. A firm with highly positive prospect

would avoid selling its stock and prefer to issue excessive debt for their required capital.

2. A firm with negative prospect would prefer to sell its stocks.

In conclusion, a company that announces a sudden stock offering is a signal that the company does not has a bright prospects. In contrast, debt offering by a company signals that the company is confident of their good prospects.

4. The Components of Capital

Structure Long-term Debt

Sundjaja & Barlian (2007) define long-term debt as one of financing types that matures in more than a year. Long-term debt could be in the form of bonds, note, bank loans, or other types of debt with a maturity of about five to twenty years. Equity Capital Equity capital is the funds given by investors in exchange for common or preferred stock (Bragg, 2018). Equity is part of capitalization that is not classified as debt. It is the interests of ownership, residual claims for assets and earnings, and in contrast to the debt that represents the first and fixed claims on both assets and earnings (Anthony, 1960). Equity capital is represented by two main types of security: preferred stock and common stock.

5. Profitability According to Munawir (2014), profitability is the company's ability to generate profits for a certain period. He added that profitability is measured by the ability to manage its assets productively.

RESEARCH METHODOLOGY

Research Design

Based on the purpose and nature, this quantitative study is categorized as explanatory research. This study examines the proposed hypothesis in order to explain the effect of independent variables (Debt to Equity Ratio and Debt to Assets Ratio) on the dependent variable (Return on Equity and Return on Assets), both partially and simultaneously. Meanwhile, from the aspect of time horizon, this study belongs to panel data study, involving time series data and cross-sectional data. Place and Time of Research

This research is conducted by taking data on financial reports of construction State-owned Enterprises (SOEs) listed on IDX with input data from 2009 to 2018. The data utilized in this study were obtained from Indonesian Capital Market Directory (ICMD) and the official website of IDX (www.idx.co.id). This research was conducted in October 2019 to March 2020. Population and Sample The population in this study is state-owned companies that are engaged in the construction sub-sector. 1. PT. Adhi Karya (Persero) Tbk (ADHI). 2. PT. PP (Persero) Tbk (PTPP). 3. Wijaya Karya (Perseo) Tbk (WIKA). 4. Waskita Karya (Perseo) Tbk (WSKT). This research adopts a non-probability sampling technique, that is purposive sampling. Based on the study purpose, the researcher determines several criteria in selecting samples to be applied in this study: 1. State-owned Enterprises (SOEs)

engaged in the construction sector and listed in BUMN20 on IDX.

2. Construction SOEs that annually publish complete financial statements of their companies.

3. Construction SOEs that have available data on IDX and ICMD within the period set by the researcher, which is 2009 – 2018.

Based on the criteria above, the following are the names of companies concluded as samples in the study of the effect of capital structure on profitability of state-owned construction companies listed on IDX. 1. PT. Adhi Karya (Persero) Tbk (ADHI). 2. PT. PP (Persero) Tbk (PTPP). 3. Wijaya Karya (Perseo) Tbk (WIKA). Research Variables and Operational Definitions Independent Variables 1. Debt to Equity Ratio (DER) (X1)

DER is the ratio used to find out the proportion of the amount of funds provided by creditors and shareholders.

Total Debt

Total Equity

2. Debt to Asset Ratio (DAR) (X2)

DAR is the ratio used to measure how much the company's assets are financed by total debt.

Total debt

Total assets

Dependent Variables 1. Return on Equity (ROE) (Y1)

ROE is the measure of a company’s annual return (net income) divided by the value of its total shareholders’ equity.

Net Income

Shareholders Equity

2. Return on Asset (ROA) (Y2)

ROA is a ratio that used to see the ability of companies to manage each value of assets they have to generate net income after tax.

Net Profit After Tax

Assets

DATA ANALYSIS TECHNIQUE Model Selection Methods

There are three approach models to estimate the model parameters with panel data, namely Common Effect Model, Fixed Effect Model, and Random Effect Model.

According to Basuki (2016), in order to choose the most appropriate model for managing panel data, there are several tests that could be conducted, namely: 1. Chow Test

The Chow Test aims to determine the most suitable panel data analysis model to be used between Fixed Effect Model (FEM) and Common Effect Model (CEM). If the test result shows a Chi-square probability that is greater than 0.05, then the model chosen is common effect. Conversely, if the Chi-square probability is less than 0.05, the model that should be used is fixed effect.

2. Hausman Test

The test aims to determine the model that should be used between fixed effect model (FEM) and random effect model (REM). If the p-value < 0.05, then H0 is rejected and FEM is selected. In contrast, if p-value > 0.05, then H0 is accepted and REM is selected.

Data Panel Regression Model There are two panel data regression models in this study, where the first regression model is the effect of DER and DAR on ROE and the second regression model looks at the effect of DER and DAR on ROA.

Hypothesis Regression Model I

ROE = α + β1 DERit + β2 DARit + εit

Hypothesis Regression Model II

ROA = α + β1 DERit + β2 DARit + εit

Classical Assumption

The classical assumption test used in linear regression with OLS approach includes normality test, multicollinearity test, autocorrelation test, and heteroscedasticity test. However, the autocorrelation test will not be conducted in this study because the test is only intended for time series data. 1. Normality Test

Normality test aims to test whether in a regression, the dependent variable, the independent variable, or both variables have normal distribution or not. A good regression model is having normal or near normal data distribution. If the probability value of Jaque-Bera > significance level (0.05), then the

research residuals are normally distributed.

2. Multicollinearity Test

The test is applied to determine the existence of high correlation between variables in the regression model. A good regression model is characterized by no correlation between independent variables. The test could be conducted through Pearson Correlation Test. If the coefficient correlation between independent variables is greater than 0.8, it could be concluded that there is a multicollinearity problem within the model. Conversely, coefficient correlation that is less than 0.8 implies that the regression model is multico-free.

3. Heteroscedasticity Test

Heteroscedasticity test is used to test whether there is a regression model residual variance inequality from one observation to another. The regression model is considered good if there is not heteroscedasticity. If the probability value < level of significance (0.05), then heteroscedasticity symptoms occur in the research model. In contrast, if the probability value > level of significance (0.05), then heteroscedasticity symptom does not occur in the research model.

Hypothesis Test 1. Coefficient Determination (R²)

R² is used to measure how far the ability to explain variations in the dependent variable (Ghozali, 2009). The value of determination is between 0 and 1. Small score indicates the limited ability of independent variables in explaining the variation of dependent variables. On the other hand, score that closes to one illustrates the ability of independent variables in providing all necessary information to predict the dependent variables.

2. F-statistic Test Simultaneous significance test (F – Test) is performed to indicate whether the independent variables within the model have an influence simultaneously on the dependent variables (Ghozali, 2009). If F p-value < 0.05, it means that the independent

variables influence the dependent variable simultaneously. If F p-value > 0.05, it means that the independent variables does not affect the dependent variable simultaneously.

3. t-statistic Test

The test is conducted to show how far the influence of one explanatory or independent variable individually in explaining the variation of the dependent variable (Ghozali, 2009). If t p-value < 0.05, it means that the independent variable partially influences the dependent variable. If t p- value > 0.05, it means that the independent variable partially does not affect the dependent variable.

RESEARCH RESULTS Chow Test Chow Test for Panel Data Regression Model I

Chow Test for Panel Data Regression Model II

Based on the test results, it is known that the probability values of cross-section F and cross-section chi-square from the two models are less than 0.05, where chow test for panel data regression model I results in 0.0276 and 0.0002 for chow test on panel data regression model II. Therefore, fixed effect model is selected.

Hausman Test Hausman Test for Panel Data Regression Model I

Hausman Test for Panel Data Regression Model II

The Hausman Test results show that the probability values from the Hausman Test on both panel data regression models result in probability value that is less than 0.05. It implies that H0 is rejected and the panel data regression analysis equation would utilize fixed effect model. Classical Assumption Results

Normality Test for Panel Data Regression Model I

Normality Test for Panel Data Regression Model II

Table 4.2 and table 4.3 show that the Jaque-Bera probability values of both panel data regression models are greater than the level of significance (0.05). it implies that the residuals generated by regression model I and regression model II are normally distributed. Multicollinearity Test

DER DAR

DER 1.000000 0.354674

DAR 0.354674 1.000000 Source: Data Processed, 2020.

Based on the Pearson Correlation table, it is known that in this study, the correlation coefficient between the independent variables valued below 0.8. Thus, the independent variables in both regression model I and model II of this study are declared to have no multicollinear symptoms. Heteroscedasticity Test

Source: Data Processed, 2020.

The result shows that the probability value of independent variables are greater than 0.05. it implies that the regression model I and regression model II have homogeneous variations and meet the assumption of heteroscedasticity.

Hypothesis Test Hypothesis Regression Model I

Source: Data Processed, 2020.

ROE = 2.078 + 3.845DER + 0.494DAR + εit 1. Constant value of 2.078 indicates that if

the value of DER and DAR are zero, then the ROE that occurs is 2.078%.

2. Coefficient of DER of 3.845 indicates that DER has a positive influence on ROE. This means a one point increase on DER would increase ROE by 3.845 %.

3. Coefficient of DAR of 0.494 indicates that DAR has a positive influence on ROE. This means a one point increase on DAR would increase ROE by 0.494%.

Hypothesis Regression Model II

Source: Data Processed, 2020.

ROA = 3.809 + 0.149DER – 0.989DAR + εit 1. Constant value of 3.809 indicates that if

the value of DER and DAR are zero, then the ROA that occurs is 3.809%.

2. DER coefficient of 0.149 indicates that DER has a positive influence on ROA. This means a one point increase on DER would increase ROE by 0.149%.

3. DAR coefficient of 0.989 indicates that DAR has a positive influence on ROA. This means a one point increase on DAR would increase ROA by 0.989%.

Coefficient Determination (R2)

The coefficient determination generated by Regression Model I is 0.712. It implies that the variations of ROE could be represented by DER and DAR by 71.2%. In other words, DER and DAR have a contribution of 71.2% to ROE, while the remaining 28.8% is contributed by other factors that are not discussed in this study. Meanwhile, the coefficient determination generated by Regression Model II is 0.386.

It indicates that the variations of ROA could be represented by DER and DAR by 38.6%. In other words, DER and DAR have a contribution of 38.69% to ROA, while the remaining 61.31% is contributed by other factors that are not discussed in this study.

F-statistic Test F-statistic Test For Regression Model I

The result shows that the panel data regression with fixed effect model results in F statistic value of 18.942 with a probability of 0.00. The test result shows that the F statistic > F table (3.35) and the probability value is smaller than the level of significance (0.05). It could be concluded that there is a positive and significant effect of DER and DAR on ROE. F-statistic test For Regression Model II

Table 4.13 indicates that the panel data regression with fixed effect model results in F statistic value of 5.562 with a probability of 0.002. The result signifies that the F statistic

F table (3.35) and the probability value is smaller than the level of significance (0.05). It is implied that there is a positive and significant effect of DER and DAR on ROA. t-statistic Test t-statistic Test For Regression Model I

1. DER variable obtains t-test statistic of 2.757 with a probability of 0.010, meaning that DER variable has a positive and significant effect on company’s profitability projected with ROE.

2. DAR variable obtains t-test statistic of 0.017 with a probability of 0.0985, meaning that DAR variable has a no significant effect on company’s profitability projected with ROE.

t-statistic Test For Regression Model I 1. DER variable obtains t-test statistic of

0.472 with a probability of 0.640, meaning that DER variable has no significant effect on company’s profitability projected with ROA.

2. DAR variable obtains t-test statistic of -0.157 with a probability of 0.876, meaning that DAR variable has no significant effect on company’s profitability projected with ROA.

DISCUSSION

Hypothesis Regression Model I Regression Model I explains the relationship between Debt to Equity Ratio (DER) and Debt to Assets Ratio (DAR) to Return on Equity (ROE). Based on the results, the DAR and DER simultaneously have a

positive and significant effect on company’s ROE. The finding supports the research conducted by Violita and Sulasmiyati (2017) and Yusuf et al. (2014). Partially, the result indicates that DER has a positive and significant effect on ROE. Greater capital structure, in this case is DER, would increase the level of profitability of the company. Conversely, lower capital structure would reduce the profitability. (Violita and Sulasmiyati, 2017). The study supports The Effect of Corporate Taxes Theory by Modigliani and Miller in 1963, where both researchers stated that there is a different treatment towards corporate taxes and dividend payment. Corporation is allowed to deduct the interest payment as an expense, but dividend payment to investors is not deductible. It means that interest payment is able to cut the taxes that need to be paid by company. The lessen money paid to government, the greater the cash flow available for stockholders. A study by Mehotra, Mikkelson, and Partch (2003) finds that more asset-intensive companies tend to have higher levels of debt. Asset-intensive industry is the industry that requires a large, or above-average, capital in order to operate. Construction projects indeed require a large amount of capital. Construction State-Owned Companies (SOEs) need to issue debt in great amount due to many operational needs and limited government funding in certain circumstances. Yusuf et al. (2014) found that there is a significant relationship in almost all firms observed between ROE and DER which justifies that a highly geared firm tends to have high profitability. According to Horner (2013), highly-geared firm is a firm that has greater proportion of debt compared to its equity. However, this research contradicts several results of studies with the similar topic and variables, such as research performed by Kristanti (2018) and Efendi & Wibowo (2017), where both studies conducted research on the financial sector. Both studies found that that DER has negative and significant effect on ROE. On the other hand, the partial test concludes that DAR has no significant effect on ROE.

The finding supports the studies conducted by Bustami et al. (2019), Efendi & Wibowo (2017), and Yusuf et al. (2014), which also found the insignificant relationship between the two variables. The results of this study is contrast with the result of a research from Kristanti (2018), where it is stated that DAR has a positive and significant effect on ROE. This research suggests that if the total debt increases with the assumption that the total assets are fixed, then the proportion of equity will decrease and ROE would increase under these conditions. Hypothesis Regression Model II Regression Model II shows the relationship between Debt to Equity Ratio (DER) and Debt to Assets Ratio (DAR) to Return on Assets (ROA). Based on the results of regression equation in model II, DAR and DER simultaneously have a positive and significant effect on ROA. However, apart from the significant simultaneous result, the finding shows that DER and DAR partially have insignificant effect on ROA

The result indicates that the company’s ROA would only be affected by debt proportion only if DER and DAR are simultaneously reduced or increased. Based on the coefficient determination, DER and DAR only represent ROA for 38.62%, means that there are other factors that have greater, and possibly significant, contribution towards ROA. Yusuf et al (2014) explain that anecdotal evidences and literatures established prove that highly-geared companies tend to have significant relationship with ROE while insignificant with ROA. This result of study contradicts the result of research conducted by Kristanti (2018) which states that DER has a negative and significant effect on ROA, while DAR has a positive and significant effect on ROA. Conversely, this study results in positive and insignificant relationship from DER on ROA and negative and insignificant relationship between DAR on ROA.

The contradiction is mainly caused by the different nature of each industry where the research is taken. According Brigham and Daves (2007), capital structures are

varied considerably across industries. Food and beverages companies or pharmaceutical companies generally have very different capital structures than SOEs construction companies. Moreover, capital structures also vary among firms within a given industry. CONCLUSION

1. Capital structure measured by Debt to Equity Ratio (DER) and Debt to Assets Ratio (DAR) have a positive and significant effect on profitability measured by Return on Equity (ROE).

2. Capital structure measured by DER and DAR have a positive and significant effect on profitability measured by Return on Assets (ROA).

3. Capital structure measured by DER has a positive and significant effect on ROE. This means an increase in DER would also increase ROE.

4. Capital structure measured by DAR has no significant effect on ROE. This means an increase in DAR would not affect ROE.

5. Capital structure measured by DER has no significant effect on ROA. This means an increase in DER would not affect ROA.

6. Capital structure measured by DAR has no significant effect on ROA. This means an increase in DAR would not affect ROA.

LIMITATIONS

1. The previous results of studies within this topic were diverse and not consistent due to different natures and characteristics of different industries that affects each industry’s optimal capital structure.

2. Despite of the broad scope of capital structure, this study only focused on the proportion of debt taken within the companies that measured by DER and DAR and how this decision affects their profitability.

SUGGESTION

1. Future studies in construction industry might update the research period to see the newest trend and it might be better to take all construction companies into population to have greater amount of samples and broader insight of result.

2. Future studies might choose different sectors or sub-sectors to find out the differences in the effect of capital structure on company’s profitability in other sectors.

3. The companies might need to pay attention to their funding sources and optimal proportion of debt to obtain greater profitability and reduce business risks.

4. Investors might need to pay attention and carefully examine how companies manage capital and debt because it is related to profitability and financial risk.

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