FACTORS INFLUENCE ON DEBT MATURITY STRUCTURE (IN MANUFACTURING COMPANIES REGISTERED ON THE IDX FOR THE 2019 - 2021 PERIOD)

Abstract


INTRODUCTION
The maximum source of funding is something that must be managed by a financial manager.It is important for industries to have adequate sources of funds for industrial development.Financial managers should be able to manage which sources of funds can optimally carry out industrial operational activities.The sorting of existing sources of funds must be carried out by the financial manager, which is based on costs and their nature and as much as possible will reduce the cost of capital to be financed by the industry.The industry will use external funding sources (debt) if internal funding (from retained earnings) cannot fund industrial activities.To finance industrial operating activities, apart from own capital, one of the funding options is from debt.Terra (2011) explains that research on the maturity structure of industrial debt is still very limited because previous research has focused more on capital structure and dividend policy.The tradeoff hypothesis explains that the maximum debt maturity structure is determined by the tradeoff between short-term debt turnover costs and interest rates, which are generally borne by long-term debt.Transaction costs such as flotation and rollover are a weakness of the debt financing structure.While the advantages have the benefit of tax protection.Whereashaving the benefit of tax protection is the advantage of a debt financing structure.
In the decision on the debt maturity structure, Magri (2010) creates the fact that large listing industries are more in control of their creditworthiness issues than lenders who are affected by conflicts of interest.Modigliani and Miller's (1958) research laid the foundation for what is conventionally considered modern industrial finance.In the following years Miller and Modigliani (1961); Modigliani and Miller (1963); Miller (1977) defines a situation in which the industry will be more concerned about its sources of financing.
The debt maturity structure is an industry tool for solving agency problems experienced by various industry stakeholders.Agency theory suggests that the industry chooses the maximum debt maturity structure to resolve data asymmetries that lead to underinvestment.In conjunction with asymmetric data, the debt maturity structure can also provide credible signals to the market as a way to convey data to the market thereby reducing the cost of industry capital (Myers, 1977;Jensen and Meckling, 1976).
When the industry chooses debt as a source of funding, then the industry also needs to think about the maturity of the debt.Determination of debt policies and debt maturity policies must be considered together.There is a conflict of interest between shareholders and bondholders regarding the determination of industrial debt policies.This conflict is intertwined because there are different pay-off structures and risk levels.Shareholders will get income from excess obligations that need to be paid to bondholders, otherwise bondholders will get a steady income from interest and repayments on loans.Judging from the level of risk experienced, when management carries out activities with great risk, the level of risk experienced by bondholders is far greater than that of shareholders.
For Rahmawati (2016) maturity is the maturity period for obligations that the industry wants to use.Debt maturity is divided into 2, namely short-term debt and longterm debt.The general phenomenon of this research is that the industry must be able to determine whether the industry will use short-term debt or the industry will use longterm debt as a source of industry funding.Debt maturity is one of the important aspects for the industry to make financing decisions, which is used as a provider of data for quality, credibility, and future plans of the industry.
This research examines the relationship between earnings management, size, asset maturity and leverage on the debt maturity structure for manufacturing industries listed on the IDX.Industries with free cash flow agency problems tend to take negative discretionary accounting accruals.Greater accounting quality increases the expected cash flow and conversely lower accounting quality due to the emergence of earnings management lowers the expected cash flow thereby making it difficult to access longterm debt financing.So that earnings management should have a negative effect on the structure of debt maturity.In terms of industry size, the bigger the industry, the easier it is to obtain long-term obligations.So that the size of the industry should have a positive influence on the structure of debt maturity.
The industry matches debt maturity structures with legacy maturities.If the maturity of the debt is shorter than the legacy, the company may not have enough available cash to pay its financial obligations when they fall due.But if the debt has a longer term, payment of the debt is always due when the cash flow from the legacy ends.Myers (1977) commented that this risk and the problem of underinvestment can be overcome by matching asset maturity with debt maturity.So that asset maturity should have a positive effect on the debt maturity structure.In terms of leverage, very large leverage will make it difficult for the industry to find access to other long-term debt financing.So that levearge should have a negative effect on the structure of debt maturity.
Previous studies still produce gap studies in the form of research results that are still changing.The following is the resulting gap study

Research design
The method used in this research is a quantitative approach, which means that the data to be obtained are numbers and the analysis is carried out using statistics.The use of data types in the form of secondary data.

Population & Sample
Populationfor this research are all publicly listed companies on the IDX for the 2019-2021 period.The samples were taken based on purposive sampling, which means the criteria used are as follows: 1. Manufacturing companies listed on the IDX in 2019-2021.
2. Financial reports are accessible.
3. The required variable data is available.

Research variable
The independent variables used in this study are Earnings Management, Firm Size, Asset Maturity, Leverage and the dependent variable in this study is Debt Maturity Structure.

Method of collecting data
The method used is the documentation method, namely the collection of annual report information that will be released through the IDX website and the Company's official website.The types of materials studied at the IDX consist of quantitative data for the 2019-2021 annual reports.

Analysis Techniques
Data analysis techniques in this study used multiple regression with the help of SPSS 24.According to Ghozali (2011), regression analysis can be used to measure the strength of the correlation between two or more variables, as well as to identify the relationship between the dependent variable and the independent variable.Multiple regression analysis is a useful tool for understanding how different independent variables can affect the dependent variable.

Descriptive statistics
This statistical analysis is a statistic that is used to perform data analysis by simply describing the collected information without generalization (Ghozali, 2009).This research sample is 494 companiesmanufacturing listing on the IDX in 2019-2021.After going through the normality test process, there were 157 outlier data so that 337 normal data were obtained with the following descriptive statistics.The debt maturity structure variable (DEBTMAT) has a minimum value of 0.018 and a maximum value of 0.686.This shows that the minimum range of debt maturity structure values is 0.018 and the maximum is 0.686.The average value of the debt maturity structure is 0.27920 and the standard deviation is 0.162347.This means that the average composition of long-term debt in the total debt of the sample companies in this study is 27.92% of the total debt.
Earnings management variable (DA) has a minimum value of 0.0003 and a maximum value of 0.983.This shows that the minimum range of earnings management is 0.0003 and the maximum is 0.983.The average value of earnings management is 0.08036 and the standard deviation is 0.093784.This means that the average earnings management that occurs when managers can choose accounting policies from applicable accounting standards, is common because choosing policies that maximize company utility in this study is 8.04% of total assets.
The company size variable (SIZE) has a minimum value of 25.391 and a maximum value of 33.537.This shows that the minimum range of company size values is 25.391 and the maximum is 33.537.The average value of firm size is 28.57318 and the standard deviation is 1.552208.This means that the average scale for determining the size of a company as measured by the natural logarithm of total assets in this study is 28.57.
The asset maturity variable (ASMAT) has a minimum value of 0.030 and a maximum value of 0.790.This shows that the minimum range of asset maturity values is 0.030 and the maximum is 0.790.The average asset maturity value is 0.39452 and the standard deviation is 0.173439.This means that the average degree of flexibility of fixed assets in generating company cash as measured by the formula for the proportion of fixed assets to total assets in this study is 39.45% of total assets.
The leverage variable (DAR) has a minimum value of 0.109 and a maximum value of 0.892.This indicates that the value rangeleveragethe minimum is 0.109 and the maximum is 0.892.The average leverage value is 0.44275 and the standard deviation is 0.178076.This means that the average equity owned by the company to guarantee the company's debt as measured by the formula for the proportion of debt to total assets in this study is 44.28% of total assets.

Heteroscedasticity Test
Testing whether there is a state of occurrence of a fixed variance error.Testing using the Glejser test.If the significant value of each independent variable is > 0.05, there is no heteroscedasticity (Ghozali, 2009).All independent variables each provide a significance value of > 0.05 so that all independent variables are free from heteroscedasticity problems.

Multicollinearity Test
Test whether there is a correlation between all independent variables.The test uses the tolerance value and VIF (Variance Inflation Factor).If the tolerance value is > 0.1 and the VIF value is < 10, then there is no multicollinearity (Ghozali, 2009).All independent variables each provide a tolerance value of > 0.1 and a VIF value < 10 so that all independent variables are free from multicollinearity problems.

Autocorrelation Test
Test whether in the linear regression model there is a correlation between the confounding errors in the previous period (t-1).Testing by comparing the Durbin-Watson values.If the Durbin-Watson value is between du and 4-du then there is no autocorrelation (Ghozali, 2009).The Durbin-Watson value of 2.161 is between 1.809 (du) and 2.191 (4-du) so that the research data is free from autocorrelation problems.

Model Fit Test
This fit test aims to investigate whether the model compiled is adequate or suitable for estimating the dependent variable.Statistical calculations are said to be significant if the statistical test value is <0.05 (Ghozali, 2009).2023) sig F value of 0.000 <0.05 so that all independent variables can explain the dependent variable: DEBTMAT (debt maturity structure).

Determination Coefficient Test
Shows the ability of the independent variable to explain the variation of the dependent variable.Coefficient values vary from 0 -1.If they are close to one, it means that the independent variables provide almost all the data needed to estimate the dependent variable (Ghozali, 2009).2023) MarkadjustedR² 0.359 so that it can be concluded that the explanatory power of all independent variables on the dependent variable: DEBTMAT (debt maturity structure) is 35.9% while the remaining 64.1% is explained by other factors.

Hypothesis testing
Using multiple linear regression.Profit management(DA) has a sig value of 0.027<0.05and β1 -0.170<0, so H1 is accepted, meaning that earnings management has a negative effect on the structure of debt maturity.

Hypothesis 2
Firm size (SIZE) has a sig value of 0.000<0.05and β2 0.018>0, so H2 is accepted, meaning that firm size has a positive influence on the structure of debt maturity.

Hypothesis 3
Asset maturity(ASMAT) has a sig value of 0.000 <0.05 and β3 0.528> 0, so H3 is accepted, meaning that asset maturity has a positive influence on the structure of debt maturity.4. Hypothesis 4 leverage(DAR) has a sig value of 0.510> 0.05, so H4 is rejected, meaning that leverage has no effect on the structure of debt maturity.

Discussion
Profit Management and Debt Maturity Structure H1 in this research is accepted, meaning that earnings management has a negative effect on the structure of debt maturity.The concept of earnings management is based on agency theory, where earnings management practices arise from an unbalanced distribution of information between creditors (banks) and debtors (companies).Information asymmetry can lead to opportunistic behavior (moral hazard) by companies to hide information from the bank, hide or reduce the risk of default.If the company requires long-term external funding, the manager as the decision maker for what happens in the company reduces the steps to implement earnings management.This is done so that the bank feels safe that the loan can be repaid.
The company's ability to pay off obligations can be seen from the company's ability to create cash flows in the future (De Meyere et al. (2018).However, the existence of large information differences makes forecasting future cash flows difficult, and this difficulty becomes more difficult because of the horizon.the calculation of cash flows becomes longer.To protect against bankruptcy risk, banks usually offer loans with shorter maturities.Ray et al.(2020) found that long-term debt is profitable for companies because companies have flexibility in paying debts.
According to Healy and Wahlen (1999), earnings management is carried out when management influences financial statements by making judgments and preparing transactions in a way that can mislead users of financial statements.In terms of loan maturity, business leaders determine accounting policies that can hide the company's solvency.Creditors can protect themselves from possible erroneous financial reports by issuing debt instruments with shorter repayment terms.
The results of this research are in accordance with previous research.Ray et al. ( 2020) examined the debt maturity structure of Italian companies and found a negative effect of earnings management on the debt maturity structure.From Meyer et al. ( 2018) examined the debt maturity structure of Belgian companies and obtained the same research results as Ray et al.

Company Size and Debt Maturity Structure
H2 in this research is accepted, meaning that company size has a positive influence on the debt maturity structure.Agency theory explains the conflict of interest between creditors (banks) and debtors (companies).Bigger company size means it needs more funds to grow.In this case the debtor (company) requires a much larger amount of financing through loans to creditors (banks).Company size shows the size of the company in terms of the final number.The bigger the assets, the bigger the company and the easier it is to obtain long-term liability financing (Seo et al., 2017).
Large companies have a lower bankruptcy risk, which is why lenders offer long loan terms.In contrast, lenders offer short loan terms to minimize loan risk to small businesses that have a higher risk of bankruptcy.So the bigger the company, the longer the financing received (Brockman et al., 2010).
The results of this research are in accordance with previous research.Ray et al. ( 2020) examined the debt maturity structure of Italian companies and found that company size had a positive effect on the debt maturity structure.Abadi et al. (2013) examined the debt maturity structure of listed manufacturing companies in Indonesia and found that company size had a positive effect on the debt maturity structure.Lemo et al. ( 2020) examined the debt maturity structure of open companies in Johannesburg and obtained the same results as the results of the study above.

Asset Maturity and Debt Maturity Structure
H3 in this research is accepted, meaning that asset maturity has a positive influence on the structure of debt maturity.Agency theory explains the conflict of interest between creditors (banks) and debtors (companies).When debtors (companies) expand and usually require large amounts of fixed assets (construction of new factories, purchases of new machines, increased demand for trucks and vehicles), additional longterm debt from creditors (banks) is required.
Based on Taleb and Shubir (2011), asset maturity shows the level of flexibility of fixed assets in the formation of money.Companies that have short-term asset maturity can generate cash from their assets.Current assets usually have shorter maturities than fixed assets.The time balance of liabilities and assets is perceived as the main factor in deciding whether to take long or short term loans.Taleb and Shubiri (2011) examine the duration of liabilities and assets, and obtain that the longer the duration of assets, the more long-term debt an entity uses, and conversely, the shorter the duration of assets, the more current liabilities.Only companies with financial strength and flexibility can benefit from short-term debt capital that is not exposed to interest rate risk and refinancing.
For making financial decisions, companies must adjust the maturity of assets and liabilities because companies run the risk of running out of money when the maturity of the assets is longer than the maturity of the liabilities.This is because if the company's liabilities are shorter than the assets, then when the company's obligations are due, the company does not have the amount of money to pay off the amount of liabilities because the assets have not created cash flows.If the life of the asset is longer than the life of the liability, the company will not have a certain amount of funds to pay off long-term debt when the due date is reached.Likewise, if the life of the liability is longer than the age of the assets, then the cash flow of the assets can be used to pay and pay off debts, in this case the age of the debt is faster (Abadi et al. et al. 2013).
The results of this research are in accordance with previous research.Seo et al. (2017) examined the debt maturity structure of US companiesas well as obtaining the conclusion that asset maturity has a positive effect on the structure of debt maturity.Abadi et al. (2013) examined the maturity structure of the liabilities of listed manufacturing companies in Indonesia and found that the maturity of assets has a positive effect on the maturity structure of liabilities.Brockmann et al. (2010) studied the maturity structure of US corporate debt and found that asset maturity yields had a positive effect on the debt maturity structure.

Leverage and Debt Maturity Structure
H4 in this research is rejected, meaning that leverage has no effect on the structure of debt maturity.Either the higher or the lower the leverage, it has no effect on the debt maturity structure.This is because both leverage and debt maturity structure use a formula that uses total debt.The leverage formula is total debt divided by total assets, while the debt maturity structure formula is total long-term debt divided by total debt.The results of this study are in accordance with previous research, Brockman et al. (2010) found that leverage has no effect on the structure of debt maturity.

CONCLUSION
Below are the research conclusions: 1. Earnings management has a negative effect on the debt maturity structure.2. Firm size has a positive influence on the debt maturity structure.
3. Asset maturity has a positive influence on the structure of debt maturity.4. Leverage has no effect on the maturity structure of the debt.
The multiple linear regression equation model formula used by researchers is: Using multiple linear regression with the model: DEBTMATs = β0 + β1DAs + β2SIZEs + β3ASMATs + β4DARs +e DEBTMAT = Debt Maturity Structure DA

table . Table 1 Gap Research Gap Research Results Prior Research
Smirnov sig value is 0.000 <0.05 so the data is not normal.To normalize the data, the outlier data is removed.