The Effect of Economic Determinants on Capital Structure with Market Conditions as a Control Variable (Case Study of IDX-Listed LQ 45 Manufacturing Company in 2015-2019)

ABSTRACT


Introduction
Competitive competition with the increasing years in the era of globalization in the world and the sophistication of technology in this era, makes companies around the world have a goal to earn profits and increase company value (Damayanti, 2013). In addition, the company must have capital in order to live and develop its business so that it can finance all company operations.
In increasing the competitiveness of a company's economic growth rate, Indonesia must be able to overcome all challenges, due to the faster pace of economic growth in manufacturing companies in various sectors. In addition, companies must be required to be able to see the situation that will occur, this can reduce risk. Companies must also be able to carry out good management functions, namely in the fields of marketing, production, human resources, and finance so that the company's condition is healthy and superior in the competition faced in this era of globalization (World Bank). In the context of companies that want to develop their business, companies must require sufficient capital. In meeting capital, companies can obtain capital through funding activities, this is the main activity of the financial manager function (Sartono, 2010). The company's priority task to financial managers when making funding decisions or capital structure decisions, namely a financial decision related to the composition of debt that must be used by the company and will affect the company's operational activities to the risks found in the company. In addition, the type of funding chosen must influence the company's investment decisions (Brigham & Houston, 2006).
Capital structure can be measured by the level of Debt to Equity Ratio (DER) which is a comparison of the total debt owned by the company with its own capital. The greater the DER, the greater the risk that must be faced by the company, because the use of debt as a source of funding is much greater than its own capital (Fahmi, 2012).
The optimal corporate capital structure is a condition in which a company can use a combination of debt and equity ideally, which balances the value of the company and the cost of its capital structure. The optimal capital structure can change over time, which can affect weighted average cost of capital. Furthermore, changes in the cost of capital will affect capital budget decisions and will ultimately affect the company's stock price (Firnanti, 2011).
There are capital structure guidelines, one of which is the vertical capital structure guidelines. The guidelines for the vertical capital structure provide a ratio limit that must be maintained by a company regarding the amount of loan or debt capital with the amount of its own capital (Julita, 2013). Based on the assumption that healthy spending must initially be built on the basis of own capital, the capital structure guidelines stipulate that the amount of loan or debt capital in a company under any circumstances should not exceed the amount of its own capital (Nurmadi, 2013). The debt coefficient, which is the ratio between the amount of foreign capital or debt with own capital, must not exceed 1:1. In essence, the optimal capital structure must prioritize the interests of shareholders. Therefore, companies should fund their business with their own capital (YB & Bambang, 2010).

Figure 1 Movement of Debt Equity Ratio (DER) Value Increases from 2016-2018 in Manufacturing Companies on the Indonesia Stock Exchange for the 2015-2019 Period
In the picture above shows the DER value has increased from 2016 -2018 manufacturing companies listed on the IDX are above one. The average debt to equity ratio for 2016 is 1.76%; in 2017 by 2.77%; and in 2018 it was 3.18%. Researchers Brigham and Houston (2011), suggest that if an organization that has a DER value exceeds the value of one, then the company uses corporate debt as its operational activity is greater than the amount of its own capital. "This is not in accordance with the theory of optimal capital structure, where the amount of debt should not exceed the company's own Journal of Social Sciences, Vol. 3, No. 1, January 2022 capital". However, "many investors are more interested in companies that invest their capital in the form of investments in companies that have a DER whose value is less than one." (Listijowati, 2012).
Amiriyah and Andayani (2014) stated "The greater the capital structure ratio indicates the greater the number of long-term loans, which causes more part of operating profit to be used to pay fixed interest expenses, and more cash flow is used to pay loan installments. This will result in a decrease in the amount of net profit after tax that will be obtained by the company (Alipour, Mohammadi, & Derakhshan, 2015).
Many studies have analyzed the influence of various factors on capital structure, but the results of these studies have not shown consistent results. Based on this research, the researcher aims to select the factors that are considered dominant in influencing the capital structure. The factors that will be raised in this study to examine the effect of capital structure on these factors include: Profitability, company size, company growth, asset structure, liquidity, effective tax rate, and business risk with samples on the IDX in 2015-2019 in Manufacturing companies.
In general, investors who are longterm oriented are not so affected by market issues, because they have carried out a fundamental analysis of the company chosen to invest. in contrast to investors who are short-term oriented. Short-term oriented investors tend to follow market trends, when stock prices decline, investors decide to buy, and when stock prices are high, investors decide to sell the shares. Behavioral finance is the application of psychology to financial decision making and financial markets. Behavioral finance is also a transformation of the financial paradigm with a psychologically based framework (Shefrin, 2010). According to Statman (1999) quoted by Chandra (2012) According to Kole and Dijk (2010) in Ramadhan (2016), in the financial market that many investors have the same expectations, at the same time, on future prices and yields. of that perception. Bullish is an up market condition characterized by positive (high) market returns. On the other hand, bearish is a downward market condition marked by negative (low) market returns. in the Composite Stock Price Index in 2013-2015 already includes bullish and bearish market conditions.

Method Population and Sample
The research object population is the LQ 45 Manufacturing company listed on the IDX according to those circulated on IDX. Pooled data used in this study using 135 data observations (5 years x 27 companies).

Results And Discussion A. Overview of the Samples Used
The sample used in this study is the LQ 45 Manufacturing Company for the 2015-2019 period on the condition that the company has a positive profit. Retrieval of data related to the data related to this research was obtained from the IDX website, namely www.idx.co.id. Details of the number of sample companies required are 27 companies that have previously been observed with data specifications.

Descriptive Statistical Analysis
The following is a descriptive statistical analysis on a sample of LQ 45 Manufacturing companies in each year.

Figure 1 Descriptive Statistics of Variables
Source: Processed secondary eviews data (2021)

Chow Test (Chow Test)
Redundant Fixed Effects or Likelihood ratio (Chow Test). Chow test is used to determine whether the selected model is pooled least square or fixed effects. H0 is rejected if the value of the probability F is less than alpha, which is less than 0.05, where H0 is the pooled least squares model and H1 is the fixed effects model. If the prob value. Crosssection chi-square <0.05 then we will choose a fixed effect.

Hypothesis: H0 : Common Effects H1 : Fixed Effects
The results of the redundant fixed effect or likelihood ratio for this model have a probability value of F of 0.6486 which is greater than alpha 0.05, so that H0 is accepted and H1 is rejected, the appropriate model from this result is common effects.

Hausman Test (Hausman Test)
The Hausman test is a test used to see whether fixed effects or random effects are the best method. If the Hausman test accepts H1 or p value <0.05, the method we choose is fixed effect. Test Summary

Prob.
Crosssection random 7.555909 7 0.3734 Output Interpretation: Hypothesis: H0 : The model follows random effects H1 : The model follows the fixed effects Based on the results of the Hausman test showing a significance value of 0.3734 (significance > 0.05), then H0 is rejected and H1 is accepted, so it can be interpreted that the random effects model is better than the fixed effects model.

Langrange Multiplie (LM)
Langrange Multiplier (LM) is a test to determine whether the right model is used by random effects or common effects. This test was developed by Breusch Pagan. The Breusch Pagan method for the random effect significance test is based on the residual value of the OLS method.
If the p value is greater than 0.05 then accept H0 which means the best estimation method is the common effect. The output results above show the Breush-Pagan (BP) probability value of 0.0000. The hypothesis is that if the Breush-Pagan (BP) probability is greater than alpha (0.0000 > 0.05) then H0 is rejected and H1 is accepted, so the correct model in the above results is random effects.

Normality test
"This study uses statistical analysis with the Kolmogorov-Smirnov test". Can be seen in Table 4 Variable Normality Test Based on Table 4, it can be seen that the value of the Kolmogorov-Smirnov statistical test is 0.263 and is not significant which indicates the "Asymp Sig. (2-tailed) of 0.000 which is smaller than 0.05". Then the residual data with normal distribution is rejected, which means the residual data is not normally distributed.

Multicollinearity Test
"Multicollinearity testing can be seen from the tolerance value and Variance Inflation Factor (VIF)".

Multicollinearity Test with VIF Coefficientsa
Figure 4 Source: Eviews 11 secondary data processed in 2021 Based on table 5, the results of the calculation of the tolerance value produce no independent variables whose tolerance value is less than 0.10, which means that there is no correlation between independent variables whose value is more than 0.950. Then the results of the VIF calculation are also no more than a value of 10. So there is no multicollinearity.

Conclusion
The conclusion that the author can draw in chapter four, namely the results and discussion is that the research data has a normal distribution, there is no multicollinearity, there is no autocorrelation, and there is no heteroscedasticity. in addition, there are three hypotheses that pass (accept) from the seven hypotheses given. the following are the details of the seven hypotheses of this research: The first hypothesis produces "roa variable has a significant and negative effect on the debt to equity ratio, then the first hypothesis is accepted".
The second hypothesis produces "the size variable does not have a significant and negative effect on the debt to equity ratio, so the second hypothesis is rejected".
The third hypothesis produces "the sagr variable does not have a significant and positive effect on the debt to equity ratio, so the third hypothesis is rejected".
The fourth hypothesis results "sa variable has a significant and negative effect on the debt to equity ratio, then the fourth hypothesis is accepted".
The fifth hypothesis produces "the wcta variable does not have a significant and positive effect on the debt to equity ratio, so the fifth hypothesis is accepted".
The sixth hypothesis produces "the etr variable does not have a significant and positive effect on the debt to equity ratio, then the sixth hypothesis is accepted".
The seventh hypothesis produces "risk variable has a significant and negative effect on the debt to equity ratio, so the seventh hypothesis is rejected".