Document Type : Original Article
Author
 Peyman Sarhadi ^{}
PhD Student in Financial Economics, University of Tehran, Iran
Abstract
This study investigates the relationship between macroeconomic variables and the structure of capital in the Iranian capital market. Undoubtedly, today the largest amount of capital in the world is exchanged through capital markets in countries, and the economy and the capital market are undoubtedly and strongly influenced by each other. And what is especially important for investors is to increase their wealth in the shortest time, and with the separation of company management from investors and the discussion of agency theory, management decisions benefit from a high reflection among shareholders, including this decision. Financial decisions and financing strategies of managers in companies, according to the decisions of managers in the type of financing, impose different costs on the company, which ultimately affects the profitability and efficiency of companies. Discussion of capital structure and choice of type of financing is one of the concerns of managers in companies, hence the researcher has done much research in relation to choosing the optimal capital structure and type of financing and many factors inside and outside the company that affect it. Macroeconomic factors are among these external factors. Given that in developing countries, including Iran, these macroeconomic variables have been very volatile during the last three decades, it is clear that these fluctuations have an adverse effect on the economy and the capital market will undoubtedly affect the decisions of managers. They have cast a shadow and made them have serious doubts about this.
Keywords
Main Subjects
Introduction
In today's investment world, decisionmaking is perhaps the most important part of the investment process, in which investors need to make the most optimal decisions in order to maximize their profits and wealth. In this regard, the most important factor is the information of decision process. Information can have a significant impact on the decisionmaking process. Because it causes different decisions in different people. In companies, decisions on capital structure are also influenced by information. Economists consider economic information as one of the information resources of financial managers [1].
For this reason, one of the purposes of preparing economic variables is to provide information to facilitate decision making. In developed countries, due to economic stability, future economic conditions are predictable for most individuals and companies, and by considering these conditions, they try to optimize their decisions, but in underdeveloped countries, the development of this issue due to fluctuations and turbulence in the economic environment is far from the mind or impossible and during various researches, the relationship between macroeconomic variables and accounting variables has been studied and evaluated. The question is whether macroeconomic variables affect the structure of capital in the Iranian capital market or not. Therefore, the main approach in this research is the experimental test of the effect of economic variables with debt ratio.
Background of Research
Sinai (2005) studied the effect of corporate characteristics on the capital structure and named the size of the company, profitability, growth opportunities and tangible assets as the most important parameters within the company affecting the financial leverage of companies. Namazi (2005) reported that there is a positive relationship between capital structure and profitability of companies that this relationship is influenced by the type and also an industry [2].
Nemati (2009) showed that in Iran, as in the countries of East Asia, there is a relationship between the ratio of fixed assets to total assets and the size of the company to the capital structure and there is a negative relationship between profitability and current ratio with capital structure. Sajjadi (2010) approached the effect of macroeconomic variables on the total stock index in the Tehran Stock Exchange and found a negative relationship between inflation and liquidity with the growth rate of the stock price index.
In a study, Riotis (2010) investigated the effect of companies' characteristics on their capital structure in Greece and found a significant relationship between the current ratio and the interest rate and value of the company with the capital structure.
Bokpin (2010) studied the effect of macroeconomic variables on financial decisions of the capital structure and found a significant relationship between GDP and interest rates with decisions about the structure of capital and financial leverage in companies. Imran (2018) reported that there is a positive relationship between capital structure and company size.
In examining the structure of capital in Portugal, Cerasquier (2017) has proved the existence of a significant relationship between size and fixed assets with the structure of capital or the amount of longterm debt to total assets in companies. In his research, Viviani (2017) examined the determining and effective factors in the capital structure of commercial companies in France and considered the number of longterm debts in companies to be different depending on the type of industry. Amido (2016) also studied the factors affecting the capital structure in Ghanaian banks and found a significant relationship between tax rate, growth rate and asset structure, the size of banks and the capital structure.
Data Analysis
Descriptive indicators of variables
In order to better understand the nature of the population studied in the study and become more familiar with the research variables, before analyzing the statistical data, it is necessary to describe the data [2].
Therefore, before testing the research hypotheses, the research variables are briefly examined in Table (1). This table contains indicators to describe the research variables. These indices include central indices, dispersion indices and distribution shape indices.
Table 1. Indicators describing research variables, central indices, dispersion indices and distribution shape indices
Bank interest rates 
dollar price 
Liquidity rate 
The inflation rate 
Debt ratio 
Variables Indicators 
625 
625 
625 
625 
623 
Number 
13 
9399/4 
1620600 
15/5 
0.614 
average 
0.06405 
12/634 
19794/2 
0.22727 
0.0077 
Mean standard error 
1/60128 
3/1586e2 
4/94855e5 
5/69173 
./19342 
Standard deviation 
2/564 
9/977e4 
2/44e11 
32/396 
0.037 
Variance 
4 
897 
1/43e6 
14/60 
1/66 
Domain changes 
15/8 
9920 
2/36e6 
25/4 
1/73 
The highest 
11/8 
9023 
921019 
10/8 
1/06 
the lowest 
Table 1 shows the characteristics of the research variables. The first line of this table states that the number of all data for all variables studied is equal to 125 per year and up to 625 per year. And the second line shows the average of the collected variables separately, which is, for example, the average return on the debt ratio by 0.614. The fifth line shows the variance and scatter of variables around the mean, which is the debt ratio variance by 0.037. The sixth line describes the changes of the largest and smallest numbers as the amplitude of the changes [3].
Analysis of research hypotheses
Checking the assumption on the normality of the variables
Since the normality of the variables depends on the normality of the remainder of the model; It is necessary to check the normality of the model before fitting it.
The null hypothesis and the hypothesis opposite to the normality test are as follows:

Data distribution is normal 

Data distribution is not normal 
The KolmogorovSmirnov test was used to test the above hypothesis. In this test, when the significance level is less than 5%, the null hypothesis is rejected at the 95% confidence level.
Table 2. Kolmogorov  Smirnov test (KS) for dependent variable (debt ratio)
Meaningful level 
Kalmogrof Smirnov 
Most Deviation negative 
Most Deviation Positive 
Absolute value Most Standard deviation 
Standard deviation 
Average 
Number 
0.369 
0.918 
0.037 
0.028 
0.037 
0.19342 
0.614 
623 
Based on the values presented (Table 2), since the values of the significance level for the debt ratio in the model are more than 5% (Sig.> 0.05 or Pvalue); therefore, the assumption of zero, i.e. the normality of the variables, is not rejected.
Table 3. Correlation coefficient, determination coefficient, adjusted coefficient of determination and CameraWatson test between debt ratio and bank interest rate
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.764 
0.19338 
0.000 
0.002 
0.044 
1 
According to Table 3, Pearson correlation coefficient between the two variables of debt ratio and bank interest rate is 0.044. This number at the level of 5% error indicates a lack of significant relationship between the two variables of debt ratio and bank interest rate. According to SPSS software outputs, the tables show that since the calculated adjustment coefficient shows the number 0.000, which is a very small number, it does not provide a good fit of the changes in the debt ratio variable by the bank interest rate variable.
One of the assumptions of independence regression is errors; if the hypothesis of error independence is rejected and the errors are correlated with each other, regression cannot be used. CameraWatson statistics are used to check the independence of errors that if the value of the CameraWatson statistics falls in the distance of 1.5 to 2.5, the assumption of correlation between errors is rejected and regression can be used. The value of the CameraWatson statistic is 1.764, based on Table 3 and this number shows that the errors are independent of each other and there is no correlation between the errors and the assumption of correlation between the errors is rejected and regression can be used [4].
Table 4. Regression analysis of variance for debt ratio and bank interest rate variables
ANOVA ^{b} 

Meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of squares 
Model 

0.271 
1,215 
0.045 
1 
0.045 
1 




0.037 
621 
23.224 
Residual 



622 
23.269 
Total 
Table 4 shows the analysis of variance between the bank interest rate variable as an independent variable and the debt ratio as a dependent variable. According to this output, since sig is more than five percent, the H0 hypothesis is at the error level five. The percentage is confirmed and the correlation between this variable is hardened.
Table 5. Correlation coefficient, determination coefficient, adjusted determination coefficient and CameraWatson test between debt ratio and bank interest rate with the presence of control variables
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.86 
0.138894 
0.485 
0.489 
0.699 
1 
Then, the above hypothesis was examined with the presence of control variables. Based on Table (5), Pearson correlation coefficient between the two variables of debt ratio and bank interest rate with the presence of control variables is 0.699.
This number at the error level of 5% shows a significant relationship between debt ratio and bank interest rates and control variables. With respect to SPSS software outputs, the tables show that since the calculated adjusted coefficient shows the number 0.485, which is a good number, it provides a good fit of the changes in the debt ratio variable by the independent variables.
One of the assumptions of independence regression is errors; if the hypothesis of error independence is rejected and the errors are correlated with each other, regression cannot be used. CameraWatson statistics are used to check the independence of errors that if the value of the CameraWatson statistics lies in the distance of 1.5 to 2.5, the assumption of correlation between errors is rejected and regression can be used. The value of the CameraWatson statistics, as depicted in Table 5, is 1.86 and this number shows that the errors are independent of each other and there is no correlation between the errors and the assumption of correlation between the errors is rejected and regression can be used [5].
Table 6. Regression analysis of variance for debt ratio and bank interest rate and control variables
ANOVA ^{b} 

meaningful level 

F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 

0.00 
117.848 
2.275 
5 
11.375 
Regression 



0.019 
616 
11.891 
Residual 




621 
23.266 
Total 
According to the obtained results, the first subhypothesis, i.e. return on equity, return on assets, ratio of earnings per share and fixed assets ratio affect the relationship between bank interest rate and capital structure, is confirmed at 95% confidence level. According to this output, the overall significance of the regression model is tested by ANOVA table through the following statistical hypotheses:

Data distribution is normal 

Data distribution is not normal 
Given that sig is less than five percent, the assumption that the relationship between the variables is linear is confirmed. Finally, we will discuss how to fit multiple regression by the Enter method as follows Table 7.
Table 7. Summary of multiple regression findings of subhypothesis one by Enter method
meaningful level 
Statistics t) 
Standardized coefficients 
Nonstandardized coefficients 
Model 


B 


0.000 
10.816 
10,816 
 
0.499 
(Constant) 
0.126 
1.531 
1,531 
0.044 
0.044 
Fixed Asset Ratio 
0.00 
9,391 
9,391 
0.433 
0.07 
Earnings per share ratio 
0.00 
21.4 
21.4 
0.981 
0.015 
Return on total assets 
0.00 
5,169 
5,169 
0.152 
0.00 
Return of capital owners 
0.000 
4.8 
4.8 
0.14 
0.017 
Bank interest rates 
Table 8. Correlation coefficient, determination coefficient, adjusted coefficient of determination and CameraWatson test between debt ratio and inflation rate
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.762 
0.19349 
0.000 
0.001 
0.03 
1 
According to Table 8, Pearson correlation coefficient between the two variables of debt ratio and inflation rate is 0.03. This number at the 5% error level indicates a lack of a significant relationship between the two variables of debt ratio and inflation rate. According to SPSS software outputs, the tables show that since the calculated adjustment coefficient shows the number 0.000, it is a very small number and does not provide a good fit of the changes in the debt ratio variable by the inflation rate variable. One of the assumptions of independence regression is errors; if the hypothesis of error independence is rejected and the errors are correlated with each other, regression cannot be used. CameraWatson statistics are used to check the independence of errors and if the value of the CameraWatson statistics falls in the distance of 1.5 to 2.5, the assumption of correlation between errors is rejected and regression can be used. The value of the CameraWatson statistic according to Table (8) is 1.762 and this number shows that the errors are independent of each other and there is no correlation between the errors, so the assumption of correlation between the errors is rejected and regression can be used [6].
Table 9. Regression analysis of variance for debt ratio and inflation rate variables
meaningful level 

F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 

0.455 
0.56 
0.021 
1 
0.021 
Regression 



0.037 
621 
23.248 
Residual 




622 
23.269 
Total 
Table 9 shows the analysis of variance between the inflation rate variable as an independent variable and the debt ratio as a dependent variable. According to this output, since sig is more than five percent, assumption H0 at the five percent error level is confirmed.
Table 10. Correlation coefficient, determination coefficient, adjusted coefficient and CameraWatson test between debt ratio and inflation rate with the presence of control variables
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.8 
0.14119 
0.468 
0.472 
0.689 
1 
The above hypothesis was examined with the presence of control variables. According to Table (10) Pearson correlation coefficient between the two variables debt ratio and inflation rates with the presence of control variable is 0.689. This number at the 5% error level shows a significant relationship between the debt ratio and inflation rate and control variables. According to SPSS software outputs, the tables show that the calculated adjustment coefficient shows the number 0.468, which is a good number and provides a good fit of the changes in the debt ratio variable by the independent variables.
The value of the CameraWatson statistic according to Table (10) is 1.80 and this number shows that the errors are independent of each other and there is no correlation between the errors, so the assumption of correlation between the errors is rejected and regression can be used.
Table 11. Regression analysis of variance for debt ratio and inflation rate and control variables
ANOVA ^{b} 

meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 

0.00 
110.234 
2.197 
5 
10.987 
Regression 




0.02 
616 
12.279 
Residual 



621 
23.266 
Total 
According to the obtained results, the second subhypothesis (return on equity, total return on assets, earnings per share ratio and fixed assets ratio affects the relationship between inflation rate and capital structure) is confirmed at 95% confidence level.
Table 12. Summary of multiple regression findings of the second subhypothesis by enter method
Model 
Nonstandardized coefficients 
Standardized coefficients 
Statistics t) 
Level Meaningful 

B 
Std.Error 

(Constant) 
0.741 
0.019 
 
38.414 
0.000 
Fixed Asset Ratio 
0.047 
0.029 
0.047 
1.604 
0.109 
Earnings per share ratio 
0.07 
0.000 
0.422 
9.02 
0.00 
Return on total assets 
0.015 
0.001 
0.966 
20.663 
0.00 
Return of capital owners 
0.00 
0.00 
0.158 
5.277 
0.00 
The inflation rate 
0.002 
0.001 
0.049 
1.689 
0.09 
According to Table 12, the fixed value and the coefficient B related to each variable, in the general model, have been decided according to the level of significance. The assumption that the regression coefficient is equal to zero (assumption H0) is confirmed and must be removed from the regression equation, showing that there is no significant relationship between the ratio of fixed assets and inflation rate to debt ratio [1].
Table 13. Correlation coefficient, determination coefficient, adjusted determination coefficient and CameraWatson test between debt ratio and dollar exchange rate
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.762 
0.19347 
0.000 
0.001 
0.032 
1 
According to Table 13, Pearson correlation coefficient between the two variables of debt ratio and dollar exchange rate is 0.032. This number at the 5% error level indicates a lack of significant relationship between the two variables of debt ratio and dollar exchange rate. According to SPSS software outputs, the tables show that the calculated adjusted coefficient shows the number 0.000, which is a very small number and does not provide a good fit of the changes in the debt ratio variable by the dollar exchange rate variable.
The value of the CameraWatson statistic according to Table 13 is 1.762 that shows that the errors are independent of each other, and there is no correlation between the errors, so the assumption of correlation between the errors is rejected and regression can be used.
Table 14. Regression analysis of variance for debt ratio and dollar exchange rate variables
ANOVA ^{b} 

meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 


0.422 
0.647 
0.024 
1 
0.024 
Regression 





0.037 
621 
23.245 
Residual 




622 
23.269 
Total 

Table (14) shows the analysis of variance between the dollar exchange rate variable as an independent variable and the debt ratio as a dependent variable.
Table 15. Correlation coefficient, determination coefficient, adjusted coefficient and CameraWatson test between debt ratio and dollar exchange rate with the presence of control variables
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.82 
0.14026 
0.475 
0.479 
0.692 
1 
Then the above hypothesis was examined with the presence of control variables. According to Table (15), Pearson correlation coefficient between the two variables debt ratio and dollar rates with the presence of control variable is 0.692. This number at the 5% error level shows a significant relationship between the debt ratio and the dollar exchange rate and control variables. According to SPSS software outputs, the tables show that the calculated adjustment coefficient shows the number 0.475, which is a good number and provides a good fit of the changes in the debt ratio variable by the independent variables.
The value of the CameraWatson statistic according to Table (15) is 1.82 that shows that the errors are independent of each other and there is no correlation between the errors, so the assumption of correlation between the errors is rejected and regression can be used.
Table 16. Regression analysis of variance for debt ratio and dollar exchange rate and control variables
ANOVA ^{b} 

meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 

0.00 
113.234 
2.23 
5 
11.148 
Regression 




0.02 
616 
12.118 
Residual 



621 
23.266 
Total 
According to the results, the third subhypothesis (return on equity, total return on assets, earnings per share and fixed assets ratio affects the relationship between the dollar exchange rate and capital structure) is confirmed at 95% confidence level.
Table 17. Summary of Multiple Regression Findings of the Third SubHypothesis by Enter Method
Model 
Nonstandardized coefficients 
Standardized coefficients 
Statistics t) 
Level Meaningful 

B 
Std.Error 

(Constant) 
1,288 
0.173 
 
7,461 
0.000 
Fixed Asset Ratio 
0.047 
0.029 
0.047 
1,617 
0.106 
Earnings per share ratio 
0.07 
0.000 
0.437 
9.36 
0.00 
Return on total assets 
 0.015 
0.001 
 0.992 
 21.032 
0.00 
Return of capital owners 
0.00 
0.00 
0.154 
5,166 
0.00 
dollar price 
 0.06 
0.00 
 0.099 
 3.328 
0.01 
According to the table number (17) about the fixed value and the coefficient B related to each variable, in the general model has been decided according to the level of significance.
Since in this output, the significance level of the regression coefficient equality test related to the fixed asset ratio variable is greater than five percent; Therefore, the assumption that the regression coefficient is equal to zero (assumption H0) is confirmed and should be removed from the regression equation and shows that there is no significant relationship between the ratio of fixed assets to debt ratio. But for other variables, the assumption The equation of the regression coefficient is rejected by zero (assumption H0) and they should not be excluded from the regression equation.
Table 18. Correlation coefficient, coefficient of determination, adjusted coefficient of determination and CameraWatson test between debt ratio and liquidity rate
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.76 
0.19356 
0.001 
0.000 
0.012 
1 
According to Table (18), Pearson correlation coefficient between the two variables of debt ratio and liquidity rate is 0.012. This number at the 5% error level indicates a lack of significant relationship between the two variables of debt ratio and liquidity rate. According to SPSS software outputs, the tables show that the calculated adjustment coefficient shows the number 0.001, which is a very small number and does not provide a good fit of the changes in the debt ratio variable by the liquidity rate variable.
CameraWatson statistics are used to check the independence of errors that if the value of the CameraWatson statistics in the distance of 1.5 to 2.5, the assumption of correlation between errors is rejected and regression can be used [7].
Table 19. Regression analysis of variance for debt ratio and liquidity rate variables
ANOVA ^{b} 

meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 


0.455 
0.56 
0.021 
1 
0.021 
Regression 





0.037 
621 
23.248 
Residual 




622 
23.269 
Total 

Table 19 shows the analysis of variance between the liquidity rate variable as an independent variable and the debt ratio as a dependent variable. According to this output, since sig is more than five percent, assumption H0 at the five percent error level is confirmed and the existence of a correlation between these variables is rejected.
Table 20. Correlation coefficient, determination coefficient, adjusted coefficient and CameraWatson test between debt ratio and liquidity rate with the presence of control variables
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.84 
0.13977 
0.479 
0.483 
0.695 
1 
The above hypothesis was examined with the presence of control variables. According to Table (20), Pearson correlation coefficient between the two variables of debt ratio and liquidity rates with the presence of control variable is 0.692. This number at the error level of 5% shows a significant relationship between debt ratio and liquidity rate and control variables.
According to SPSS software outputs, the tables show that the calculated adjustment coefficient shows the number 0.479, which is a good number and provides a good fit of the changes in the debt ratio variable by the independent variables.
Table 21. Regression analysis of variance for debt ratio, liquidity rate and control variables
ANOVA ^{b} 

meaningful level 
F statistics 
Mean Square 
Degrees of freedom 
Sum of Squares 
Model 

0.00 
114.972 
2.246 
5 
11.231 
Regression 




0.02 
616 
12.035 
Residual 



621 
23.266 
Total 
According to the obtained results, the fourth subhypothesis (return on equity, total return on assets, earnings per share ratio and fixed assets ratio affects the relationship between liquidity rate and capital structure) is confirmed at 95% confidence level.
Since economic variables are strongly correlated with each other, their simultaneous use will lead to strong alignment, so none of the variables is significant in the multiple models, and their sign indicates the direction of correlation will be reversed in some cases [8].
Table 22. Correlation coefficient between explanatory variables
Explanatory variables 
The inflation rate 
Liquidity rate 
dollar price 
Bank interest rates 

The inflation rate 
Solidarity 
1 
0.266 
0.154 
 0.554 
meaningful level 

0.00 
0.00 
0.00 

Liquidity rate 
Solidarity 
0.266 
1 
0.976 
 0.877 
meaningful level 
0.00 

0.00 
0.00 

dollar price 
Solidarity 
0.154 
0.976 
1 
 0.758 
meaningful level 
0.00 
0.00 

0.00 

Bank interest rates 
Solidarity 
 0.554 
 0.877 
 0.758 
1 
meaningful level 
0.00 
0.00 
0.00 

As can be seen, the correlation coefficient between the variables is very high, so for the simultaneous use of variables, the factor analysis method will be used. Using this method, a combination of the main variables can be obtained to interpret them. It depends on the relationship between the variables of each factor.
Table 23. KMO sample adequacy criterion and Bartlett test
KaiserMeyerOlkin sample adequacy criterion 
0.39 

Bartlett test for unit unity of correlation matrix KaiserMeyerOlkin sample adequacy criterion 
The amount of kai  approximately two 
3e4.926 
Degrees of freedom 
6 

meaningful level 
00 
Table 24. Percentage of changes expressed by agents
Component 
Special values 

Total 
Percentage of changes 
Percentage of cumulative changes 

1 
2,909 
72,714 
72,714 
2 
0.959 
23,987 
96,701 
3 
0.131 
3,274 
99,975 
4 
0.001 
0.025 
100.00 
Table 25. Component matrix (factor coefficient matrix)
Variables 
Component 
The inflation rate 
0.484 
Liquidity rate 
0.97 
dollar price 
0.912 
Bank interest rates 
 0.949 
Table 26. Correlation coefficient, coefficient of determination, adjusted coefficient of determination and CameraWatson test between macroeconomic variables and debt ratio
Camera  Watson 
Estimation criterion error 
Adjusted coefficient of determination 
Determination coefficient 
Correlation coefficient 
Model 
1.76 
0.19356 
0.001 
0.00 
0.012 
1 
According to Table (26), the Pearson correlation coefficient between the two factors is 1 and the debt ratio is 0.012. This number at the 5% error level indicates the absence of a significant relationship between the debt ratio variable as a dependent variable and factor 1 as an independent variable. According to the outputs of SPSS software, since the calculated adjusted coefficient shows the number 0.001, it does not provide a suitable fit of the changes in the debt variable by the independent variable.
The value of the CameraWatson statistic according to Table (26) is 1.760. This number shows that the errors are independent of each other and there is no correlation between the errors, so the assumption of correlation between the errors is rejected and regression can be used.
Table 27: Summary of multiple regression findings of main hypothesis one by enter method
Model 
Nonstandardized coefficients 
Standardized coefficients 
Statistics t) Level 
Statistics t))Level 

B 
Std.Error 

(Constant) 
5,842 
2,025 
 
2,885 
0.004 
Fixed Asset Ratio 
0.044 
0.029 
0.045 
1,549 
0.122 
Earnings per share ratio 
0.07 
0.000 
0.434 
9,428 
0.00 
Return on total assets 
 0.015 
0.001 
 0.988 
 21.27 
0.00 
Return of capital owners 
0.00 
0.00 
0.148 
5,052 
0.00 
Liquidity rate 
0.008 
0.00 
2,079 
2,921 
0.004 
The inflation rate 
0.005 
0.002 
0.16 
3,061 
0.002 
Bank interest rates 
0.119 
0.033 
0.987 
3,639 
0.00 
dollar price 
0.00 
0.00 
 1.403 
 2.796 
0.005 
Since in this output, the significance level of the regression coefficient equality test related to the fixed asset ratio variable is greater than five percent, the assumption that the regression coefficient is equal to zero (assumption H0) is confirmed and must be removed from the regression equation, showing that there is no significant relationship between the ratio of fixed assets to debt ratio.
Conclusion
Information on economic conditions is useful for investors and managers to make financial decisions. Information is useful in itself if it changes the beliefs and behaviors of investors and managers. In addition, the amount and degree of usefulness can be measured by the extent of volume and price changes following the dissemination of information. Investors and corporate executives are required to increase their capital day by day and maximize it, and for this reason, they are looking for ways to reduce costs, including financing costs and increase revenues. To achieve this goal, they need tools and criteria to be identified. These criteria must be sufficiently reliable so that managers can make decisions based on them and invest their capital in business activities. This is where the knowledge of accounting and financial management and economic information comes to the aid of managers to make decisions. One of the pieces of information that managers and analysts around the world attach great importance to is information that reflects the economic situation of countries. And one of the factors that affect the economic situation of countries is that financial managers in companies are interested in knowing information that indicates the degree of stability or instability of the economic situation. In this study, inflation rate, cash rate, bank interest rate and dollar rate were studied as macroeconomic variables and its effect on the capital structure of companies was investigated.
In this research, after collecting the necessary information and data of the sample companies, the relationship between macroeconomic variables and the capital structure of companies and other control variables of the research was measured using Pearson correlation coefficient and then the test (T) With degree of freedom df = n2 and 95% confidence level was used to determine the significance of the correlation between the above criteria.
According to the tests and analyses obtained through regression and correlation, it can be said that there is no correlation coefficient between the two variables of bank interest rate and debt ratio in companies admitted to the Iranian capital market and its value is 0.002; the correlation is weak and in fact the mentioned variable cannot affect the debt ratio independently. Studies have shown that the above hypothesis has not been confirmed, and there is no linear and positive relationship between bank interest rates and capital structure, which is consistent with Sajjadi's (2018) findings stating there is no relationship between bank interest rates and total stock index. With the introduction of control variables, the lack of relationship between the ratio of fixed assets and the return on equity with the structure of capital was revealed, which was also the result of research by Partners’ brothers (2019).
Further, the coefficient of determination obtained from the twovariable regression between the two variables of inflation rate as independent variables and debt ratio as a dependent variable in the Iranian capital market is 0.001, showing the lack of correlation between the two variables is mentioned. Studies have also shown that the above hypothesis has not been confirmed, and there is no linear and positive relationship between inflation and capital structure, which is related to the volatile market of the country's economy, affected by various factors every month. This result does not correspond to the findings of Sajjadi's (2010) research voicing there is no relationship between bank interest rates and the total stock index. And with the introduction of control variables, the lack of relationship between the ratio of fixed assets and return on equity with the capital structure was revealed. It has also been the result of the research of the partners' brothers (2017).
The coefficient of determination obtained from the twovariable regression between the two variables of the dollar exchange rate as independent variables and the debt ratio as a dependent variable in the Iranian capital market is 0.001, showing the lack of correlation between the two variables. Studies have also shown that the above hypothesis has not been confirmed, and there is no linear and positive relationship between the dollar exchange rate and the capital structure, which is consistent with the findings of Sajjadi's (2018) research saying there is no relationship between exchange rate and total stock index. The introduction of control variables revealed the lack of relationship between the ratio of fixed assets and return on equity to the structure of capital, which has been the result of research by Partners’ brothers on 2010.
The coefficient of determination obtained from the twovariable regression between the two variables of liquidity rate as independent variables and debt ratio as a dependent variable in the Iranian capital market is 0.001 that shows the lack of correlation between the two variables. Studies have also shown that the above hypothesis has not been confirmed, and there is no linear and positive relationship between liquidity rate and capital structure, which also indicates the mismatch between the growth of money and capital markets and their ownership differences. This result is not consistent with that of Sajjadi's (2018) research stating there is no relationship between the liquidity rate and the total stock index.
According to the tests and analyses that were presented through regression and correlation and using the method of factor analysis and creating a factor or a factor as macroeconomic variables, the coefficient of determination obtained by twovariable regression between two variables of factor one as variables Independent and debt ratio as a dependent variable in the Iranian capital market is 0.001 that shows that there is no relationship between the two variables. Studies have also shown that the above hypothesis has not been confirmed, and there is no linear and positive relationship between macroeconomic variables and the structure of capital, which is due to the volatile market of the country's economy, affected by various factors every month. Because the managers of companies, due to the current fluctuation of the country's economy, are not able to predict and analyze future events in the environment around companies and cannot consider the best way to arrange the capital structure in companies than the cost, capital financing should be reduced.
What is present in the general conclusion of the test of research hypotheses is that there is no significant relationship between the independent variable and the capital structure of listed companies. And macroeconomic variables have no significant relationship with the debt ratio, which is almost in line with the results of Sajjadi's (2016) research and also the control variables of total return on assets and earnings per share ratio affect this relationship. The results obtained in this study are consistent with those of Chahab and Bukpin’s (2010).
Citation P. Sarhadi. Effectiveness of Correctional Education System in Georgia State Prisons: Labeling Theory as Sociological Approach. Int. J. Adv. Stu. Hum. Soc. Sci. 2022, 11(2):111123.
https://doi.org/10.22034/IJASHSS.2022.2.5
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