International Journal of Economics.
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ISSUING SUKUK AT INDONESIA STOCK EXCHANGE
Nurmalita1*.
Zaki Mubarak2.
Novita Alfinuri3 Faculty of Islamic Economics and Business.
UIN Antasari Banjarmasin.
Banjarmasin3 E-mail: litanurmalita9@gmail.
com1 zakimubarak@uin-antasari.
id2 novitaalfinuri@uin-antasari.
Abstract:
This study aims to analyze the effect of profitability, solvency, liquidity, leverage, and activity variables on sukuk ratings in Indonesia.
The method used is logistic regression with sample data totaling 50 sukuk samples published on the Indonesia Stock Exchange in 2017-2021.
This research contributes to understanding factors that affect sukuk ratings in Indonesia.
The implication of this research is the importance of companies improving their financial performance in order to obtain better and higher sukuk ratings.
Keywords:
Profitability.
Solvency.
Liquidity.
Leverage.
Activity.
Sukuk Rating Submitted: 2024-12-04.
Revised: 2025-03-21.
Accepted: 2025-03-23 Introduction A Sharia capital market is a capital market whose entire mechanism of activity, especially regarding issuers, and the types of securities traded, follows Sharia principles.
The Islamic capital market covers market behaviour, market infrastructure, transaction mechanisms, and securities that have been transacted that have fulfilled Islamic principles.
Through the Islamic capital market, investors can invest in several companies through several purchases of securities traded in the capital market, especially the Islamic capital market, while companies that need funds will use these funds to develop their companies.
At the same time.
Sharia securities are securities intended for the regulation of legislation in the field of Capital Market whose contracts, company management, and issuance methods comply with Sharia principles based on Islamic teachings whose determination is carried out by DSN-MUI (National Sharia Council of the Indonesian Ulema Counci.
in the form of fatwas.
The number of investors in the capital market, judging from data from the Indonesian Central Securities Depository (KSEI) as of December 2021, experienced a significant increase in investors, reaching 7.
49 million single investor identification (SID).
Year to date (YTD), this figure experienced a growth of 92.
99% when compared to 2018 to 2020 of around 3.
million SID.
The following graph shows the number of capital market investors from 20182021, where the number of investors has increased yearly.
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Indonesia is the world's largest Muslim country, so Muslim investors at home and abroad are expected to be more interested in investing in Indonesia.
The strategy that can be applied to optimize the capital market in this country is to optimize the government's role in identifying assets, prioritizing investor moral standards, and being able to coordinate training provisions for legislators on the sukuk approach.
Within the scope of investment, there are always the possibilities of investment experiencing risk.
The risk in investing, such as in bonds, is if the bond-issuing company cannot fulfill predetermined promises.
For example, the risk of default, where the sukuk issuer cannot fulfill its obligation to pay the principal of the sukuk, is another example of the risk of investment in the sukuk (Darmadji and Fakhruddin, t.
Several factors can affect the rating of Sukuk, including profitability, solvency, liquidity, leverage, and activity.
Profitability is an indicator that can determine the sukuk rating.
Investors will see how much profit the company gets because the greater its ability to pay off its debts, including sukuk, will affect the assessment of sukuk rating.
This is supported by research (February 2.
profitability by proxy ROA influences the sukuk rating because the higher the ratio, the higher the net profit obtained.
However, there is a gap with research (Rukmana and Laila 2.
that profitability with ROA proxy does not affect the corporate sukuk rating because every increase or decrease in one company's ROA number does not affect the rise and fall of corporate sukuk.
This is also reinforced by the research of Winanti.
Nurlaela, and Titisari .
found that profitability proxied with ROA does not affect the rise and fall of sukuk ratings.
Solvency is an indicator that can affect the rating of sukuk, and this is reinforced by research (Setiyani and Baihaqi 2.
because if there is an increase or high solvency, it will increase sukuk yield.
Meanwhile, there is a gap with other studies (Masykurah and Gunawan 2.
that solvency does not affect the rating of the sukuk because the rating of the sukuk does not affect the size of the funds obtained from debt.
Liquidity is an indicator that can affect the sukuk rating.
This is reinforced by research (Wisesa 2.
That liquidity affects the rating of the sukuk because the higher the level of a company's liquidity, the better the possibility of the sukuk rating.
However, there are inconsistencies with the research (Sari 2.
which states that liquidity can negatively affect Sukuk ratings because the ability of the company to fulfil its short-term obligation cannot be said to be a guarantee that the company can get a good sukuk Leverage is an indicator that can affect the sukuk rating.
This is reinforced by research (Raimuna and Mutia 2.
that sukuk rating companies use liquidity ratios in assessing sukuk security levels.
However, there are inconsistencies with the research (Nurakhiroh and Jayanto International Journal of Economics.
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in his research which state that leverage does not affect the sukuk rating because every increase in every single DER number does not affect the increase or decrease in the sukuk Activity is an indicator that can determine the sukuk rating.
A good company if it can make efficient use of its possessions.
The activity ratio is still classified as a ratio that is still little used in other studies.
Activity ratio in the study (Andika 2.
mentioned that intervening activity variables positively affect sukuk rating.
Meanwhile, other studies (Wisesa 2.
showed that the activity ratio did not affect the sukuk rating.
This research was motivated by the lack of research that raised the rating of sukuk in Indonesia.
The reason for taking the above variables is because there are inconsistencies between other studies and other studies.
Therfore, this research investigate the influence of profitability, solvency, liquidity, leverage, and activity variables on sukuk ratings in Indonesia Research Method 1 Data and Data Sources This study used secondary data from as many as 50 samples.
The population of this study is companies that issue sukuk on the Indonesia Stock Exchange for the 2017-2021 period totalling ten companies, namely PT FKS Food Sejahtera Tbk.
PT Perusahaan Listrik Indonesia.
PT XL Axiata Tbk.
PT.
Indosat Tbk.
PT.
Aneka Gas Industri Tbk.
PT.
Summerecon Agung Tbk.
PT Sampoerna Agro Tbk.
PT.
Global Mediacom.
PT.
Mora Telematics Indonesia Tbk, and PT Angkasapura Tbk.
The source of this research data is financial statements published by the company on the Indonesia Stock Exchange (IDX) website and the official website of PT.
Indonesia Securities Rating Agency (Pefind.
2 Technical Data Analysis This study used the logistic regression method.
The logistic regression method is an approach to making predictive models just like linear regression.
This study uses the ordinal logistic regression method because the dependent variable of this study is categorical.
in this study, 8 for sukuk rating with ratings of 7 for a, 6 for AA, 5 for A, 4 for b, 3 for BB, 2 for B, 1 for c, and 0 for D.
3 Descriptive Statistical Analysis The data obtained are tested using descriptive statistical tools.
Statistical testing is carried out to provide an overview or description of the variables in the study.
The descriptive statistics used in this study are the determination of the mean .
, maximum value, minimum value, and standard deviation.
4 Assessing Model Fit The first initial stage is to assess the fitting information against the data.
This information explains whether including independent variables in the model will contribute to the model (Yamin and Kurniawan 2.
Meanwhile, the statistics used are based on the Likelihood The likelihood (L) of the model is the probability that the hypothesized model is zero, and the alternative hypothesis.
L, is transformed into -2LogL.
With an alpha of 5%, how to assess this fit model is as follows:
If the value of -2LogL < 0.
05 means that the model fits with the data.
If the value of -2LogL > 0.
05 means that the model does not fit with the data.
The reduction in the value between -2LogL and -2LogL in the next step indicates that the hypothesized model fits the data.
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The Goodness of Fit Test tests the modern compatibility between the null hypothesis as the data predicted by the model and empirical data .
here is no difference between the model and the If the Goodness of fit test value >0.
05 (Person and Deviance significance value > 0.
then the model formed is fit or suitable for use (Yamin and Kurniawan 2.
6 Pseudo R-Square The Pseudo R Square value is used to determine the value of the coefficient of The Pseudo R Square value can be interpreted as the R Square value in multiple regression (Ghozali2.
Pseudo R Square describes variations in dependent variables that independent variations can describe.
The degree of variability is indicated by the magnitude of the value McFadden (Ghozali 2.
7 Parallel Lines Test The feasibility of the regression model was assessed using the test of parallel lines.
The test of parallel lines tests whether the assumptions of all categories have the same parameters or not.
If the statistical value of the test of parallel lines is more significant than 0.
05, it can be interpreted that the model used is appropriate.
Vice versa, if the statistical value of the test of parallel lines is smaller than 0.
05, the model used is unsuitable.
8 Parameter Estimation and Interpretation Parameter estimation can be seen with the regression coefficient of each variable tested, showing the relationship between the independent and dependent variables (Ghozali 2.
Hypothesis testing is done by comparing the probability value with the significance level.
Meanwhile, the value of probability in ordinal regression can be known from the value of the Wald Statistic.
The criteria for acceptance or rejection of the hypothesis are as follows:
If the probability value > 0.
05, then H0 is accepted .
he independent variable does not affect the dependent variable.
If the probability value O 0.
05 then H0 is rejected .
he independent variable has an effect on the dependent variabl.
The hypothesis model is as follows:
Logit .
1 p2 A.
= 1 X1 2 X2 3 X3 4 X4 5 X5 6 X6 e Keterangan:
P: Bond Rating Probability P1 = sukuk rating probability is quite good .
P2 = probability of good sukuk rating .
d A) P3 = very good sukuk rating probability (AA i.
P4 = Best sukuk rating probability .
ID) 1-5
: Constanta
: Coefficient de rygression : ROA
: ROE
: DER
: Cash Ratio
: DAR
: TATO
: Coefficient de rygression International Journal of Economics.
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Data analysis used in ordinal logistic regression tests is descriptive statistical analysis, assessing fit models, assessing the feasibility of regression models .
oodness of fit tes.
, pseudo-r-square, parallel lines tests, parameter estimation, and Descriptive Statistical Analysis The following are descriptive statistics of each independent variable in this study.
Table 1.
Descriptive Statistics Minimum Maximum Mean Std.
Deviation ROA
-,19
1,06
,0519
,18756
ROE
-5,11
2,85
,0318
,93469
DER
-2,11
53,20
2,9756
7,38154
,02
1,91
,2933
,31359
DAR
,35
2,90
,7706
,44927
TATO
,08
1,18
,4264
,22106
Valid N .
Source: Test SPSS 22 data, data processed The results of data processing based on descriptive statistical analysis in the table show that:
ROA (Return on Asse.
The variable return on assets has the lowest value of -0.
19 and the highest value of 1.
This shows that the company's ability to generate a net profit using its total assets has a reduced value of -0.
19, while the highest value is 1.
The mean indicates a value of 0.
The standard deviation shows a value of 0.
The standard deviation value exceeds the mean, so the data spread is heterogeneous.
ROE (Return on Equit.
The variable return on equity has the lowest value of -5.
11 and the highest value of 2.
This shows that whether or not the company is good at generating revenue using its total assets has a reduced value of -5.
11, while the highest value is 2.
The mean indicates a value of 0.
The standard deviation shows a value of 0.
The standard deviation value is greater than the average value, so the data spread is heterogeneous.
Debt to Equity Ratio The variable debt-to-equity ratio has the lowest value of -2.
11 and the highest value of 53.
These results show that out of 50 samples, the company's use of debt from its capital was the lowest at -2.
11 and the highest value at 53.
The average value is 2.
9756, indicating that the average company has a debt of 2.
9756 times.
The standard deviation indicates a value of 1.
Cash Ratio The variable debt-to-equity ratio has the lowest value of 0.
02 and the highest of 1.
These results show that out of 50 samples, the company's ability to meet its short-term obligations was the lowest at 0.
02 and the highest value at 1.
The average value shows a value of 2933, indicating that the average ability of the company to fulfil its short-term obligations 2933 times.
The standard deviation indicates a value of 0.
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In this variable, the debt-to-asset ratio has the lowest value of 0.
35 and the highest value of These results show in 50 samples the company's ability to utilize its assets with the lowest value of 0.
35 or 35% and the highest value of the company's ability to utilize its assets or wealth of 2.
The average value is 0.
7706, which shows the company's ability to utilize its assets on average 0.
7706 times.
The standard deviation value is 0.
TATO (Total Asset Turnove.
In the Total Asset Turnover variable, it has the lowest value of 0.
08 and the highest value This result shows from 50 samples of companies that the company's ability to streamline its assets with the lowest value of 0.
08 or 8% and the highest value of 1.
The average value shows a value of 0.
Meanwhile, the standard deviation value is 0.
Table 2.
Case Processing Summary Marginal Percentage Sukuk Ratings D 6,0% 4,0% 42,0% 10,0% a 19
38,0%
Valid
100,0%
Missing Total Source: Test SPSS 22 data, data processed Based on the Case Processing Summary table, it can be seen that the number of samples used in Based on the Case Processing Summary table, it can be seen that the number of samples used in this study is 50 samples from 10 non-financial companies multiplied by the observation time for five years from 2017-2021 with details of 3 samples of sukuk 0 .
dD) rating, two samples of sukuk 3 .
dBB) rating, sukuk rating 5 .
dA) as many as 21 samples, sukuk rank 6 .
dAA) as many as five samples and sukuk rank 7 .
as many as 19 samples.
Model Fit Testing Table 3.
Model Fitting Information Model
-2 Log Likelihood
Chi-Square Df
Intercept Only 125,985 Final
89,921
36,064
Link function: Logit.
Source: Test SPSS 22 data, data processed Sig.
,000
Table 4.
5 compares the -2 Log like hood value in the intercept-only and final models.
The table above shows that the value of -2 Log like hood in the intercept-only model is 125.
Then Return on Equity, debt-to-equity ratio, current ratio, debt-to-asset ratio, and total asset turnover are included in the model, and the value of -2 Log like hood drops to 89.
921, and this decrease is significant at a significant value of 0.
This shows that the model with the addition of the variables Return on Equity, debt to equity ratio, current ratio, debt to asset ratio, and total asset turnover is better than the intercept-only model.
So it can be concluded that the model fit.
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Goodness-of-fit Chi-Square Sig.
Pearson
107,049
1,000
Deviance 89,921 1,000 Link function: Logit.
Source: Test SPSS 22 data, data processed Table 4.
Shows the values of Chi-Square .
and Chi-Square (Devianc.
The value of Chi-Square (Pearso.
and Chi-Square (Devianc.
is 1,000 and 1,000, where the p-value> 0.
This means that the model formed is fit or feasible to use.
Pseudo R-Square Table 5.
Pseudo R-Square Cox and Snell
,514
Nagelkerke ,559 McFadden
,286
Source: Test SPSS 22 data, data processed Based on table 5, it can be seen from the table above that the Pseudo R-Square value of 286 or 28.
6% means that as much as 28.
6% of the variation in sukuk rating can be explained by variations in Return on Equity, debt to equity ratio, current ratio, debt to asset ratio, and total asset turn over and the remaining 71.
4% is explained by other unrelated variables in this research model.
Test Parallel Lines Table 6.
Parallel Lines Model -2 Log Likelihood Chi-Square Null Hypothesis 89,921 General 88,878b 1,043c Source: Test SPSS 22 data, data processed Sig.
1,000
Based on Table 6, it can be seen that the significance value of the parallel lines test is above 05, which is 1.
From this value, it can be interpreted as the resulting model having the same parameters or the relationship between the independent variable and logit is the same for all logit equations, so the selection of the link function is appropriate.
Parameter Estimation and Interpretation Table 7.
Parameter Estimates Estimate Threshold [Y = ,.
-9,942 [Y = 3,.
-7,996 [Y = 5,.
-3,777 [Y = 6,.
-3,230 Std.
Error
Wald
2,236
1,676
,956
,928
19,777
22,774
15,605
12,128
95% Confidence Interval Df Sig.
Lower
Upper
Bound
Bound
1 ,000 -14,324
-5,560
1 ,000 -11,281
-4,712
1 ,000 -5,650
-1,903
1 ,000 -5,048
-1,412
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Source: Test SPSS 22 data, data processed -,675 1,337 ,052 3,014 -,166 -2,314 Based on the test results with Ordinal Regression, it is known that the independent variables that significantly affect the prediction of sukuk rating are liquidity (Cash Rati.
and activity (TATO).
Based on Table 4.
9, the regression equation is obtained as follows:
Logit .
=-9,942 Ae 8,065 (ROA) 0,419 (ROE) Ae 0,44 (DER) Ae 0,999 (CR) Ae 1,995 (DAR) - 6,264 (TATO) Logit .
1 p.
=-7,996 -8,065 ROA) 0,419 (ROE) Ae 0,44 (DER) Ae 0,999 (CR) Ae 1,995 (DAR) - 6,264 (TATO) Logit .
1 p2 p.
= -3,777 -8,065 (ROA) 0,419 (ROE) Ae 0,44 (DER) Ae 0,999 (CR) Ae 1,995 (DAR) - 6,264 (TATO) Logit .
1 p2 p3 p.
= -3,320 -8,065 (ROA) 0,419 (ROE) Ae 0,44 (DER) Ae 0,999 (CR) Ae 1,995 (DAR) - 6,264 (TATO) Discussion Hypothesis Test The Effect of Profitability on Sukuk Rating The research results with ordinal regression analysis show that the company's profitability, measured by return on assets, significantly affects the sukuk rating.
This is based on the results of ordinal regression analysis, which provides information that the profitability variable measured by return on assets (ROA) has a significance value of 0.
> 0.
, then H1 is Meanwhile, the results of ordinal regression analysis show different results that profitability, as measured by return on equity, shows different results from ROA proxies, where ROE gives results that do not affect the sukuk rating.
These results are based on the results of ordinal regression analysis, which provides information that the profitability variable measured by return on equity (ROE) has a significance value of 0.
> 0.
, then H2 is rejected.
Thus, it can be concluded that profitability, as measured by ROA, shows that the results affect the sukuk rating, and ROE shows that the results do not affect the sukuk rating.
The signal theory that shows profitability ratio to ROA proxy affects the sukuk rating is correct.
A high ROA can indicate a company's ability to generate substantial enough profits to pay back its debts quickly.
Thus, this result can be one of the signals for investors to pay attention to the ratio of profitability proxied to ROA.
Meanwhile, profitability proxied by ROE proxy does not affect the sukuk rating, this can occur by several factors, such as changes in financial market conditions, and the company's risk profile can impact the ROE effect on the sukuk rating.
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, where profitability by proxy ROA affects the sukuk rating.
However, it is not following the research (Rukmana and Laila 2.
and (Winanti.
Nurlaela, and Titisari 2.
where ROAproxied profitability does not affect sukuk rating.
The Effect of Solvency on Sukuk Rating The research with ordinal regression results shows that solvency measured using the debtequity ratio (DER) does not significantly affect the sukuk rating.
This is based on the results of ordinal regression analysis, which provides information that the solvency variable measured by DER has a significance value of 0.
> 0.
, then H1 is rejected.
Thus, it can be concluded that the solvency variable does not affect the sukuk rating.
The signal theory that states that solvency ratios determine sukuk ratings is imprecise.
This is because the proportion of debt to equity does not significantly affect the credit scoring by the party giving the sukuk This result follows Masykurah's research .
which states that solvency does not affect sukuk rating.
Meanwhile, this result is not following Winanti's .
research which states that solvency variables affect sukuk rating.
Effect of Liquidity on Sukuk Rating The research results with ordinal regression analysis show that the company's liquidity, measured by cash ratio, does not affect the sukuk rating.
Based on the ordinal regression analysis results, which show that the liquidity variable has a significance value of 0.
< .
H3 is rejected.
Thus, it can be concluded that liquidity does not significantly affect the sukuk rating.
These results suggest that signal theory stating that liquidity is decisive in sukuk ratings is incorrect.
This is because the company's liquidity as measured by cash ratio does not affect the sukuk rating, so this can be interpreted that the company's liquidity level as measured by cash ratio is not an essential factor in providing sukuk rating.
This factor is also supported by factors where each company's liquidity level is different for each industry.
both financial sector companies and the non-financial sector have different liquidity levels.
This makes the cash ratio not an indicator of PEFINDO in determining the rating of sukuk in liquidity and cash Thus, signals from liquidity do not need to be the primary consideration for investors investing in sukuk.
This study's results follow the research conducted by (Nurakhiroh dkk.
However, this study does not support the results of the study (Pebruary 2.
(Rukmana and Laila 2.
and (Winanti.
Nurlaela, and Titisari 2.
Leverage Affects Sukuk Ratings The research results with ordinal regression analysis show that the company's leverage, measured by debt to asset ratio, affects the sukuk rating.
The results of regression testing show that leverage proxied by debt to asset ratio (DAR) has a significance value of 0.
> 0.
meaning that it can be concluded that H4 is successfully supported, so hypothesis 4 is accepted.
The test results show that hypothesis 4 is supported because leverage significantly influences the likelihood of sukuk obtaining a high rating.
This can be interpreted that the size of the leverage of a company will affect the sukuk rating.
This can be interpreted that the proportion of a company's debt to its total assets affects the credit assessment by rating agencies in assigning sukuk ratings.
If an investor finances a company with a high level of debt, it will increase the debt burden owned by the company and impact the company's difficulty in International Journal of Economics.
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Investors can pay attention to signals of leverage in investing in sukuk.
This study's results follow the research (Elhaj.
Muhamed, and Ramli 2.
, which found that leverage affected sukuk ratings.
Meanwhile, the study contradicts (Nurakhiroh.
which found that leverage did not affect sukuk ratings.
Effect of Activity on Sukuk Rating The research results with ordinal regression analysis show that company activities measured by total asset turnover (TATO) significantly affect the sukuk rating.
The results of regression testing showed that the results of activity measurement with a TATO proxy showed a significance value of 0.
<0.
That is, it can be concluded that hypothesis 5 is The results of testing the activity variables proxied by the company's sales with the average total assets show that the company's size significantly affects the high sukuk rating.
Thus, it means that the greater the activity of a company, the greater the rating of the company sukuk it issues to get a high rating.
The higher the TATO ratio, the more efficiently the company uses its assets to generate sales, so the better its ability to pay back its debts.
This can increase the possibility of the company using its assets to generate sales, thus the worse the company's ability to repay a good credit rating from the rating agency.
Thus, this signal is useful for lowering investors' uncertainty levels.
This study's results follow the research (Andika 2.
, which shows the results of activity variables with intervening variables that positively affect the sukuk rating.
However, this study contradicts the research (Wisesa 2.
, which shows that the activity variable negatively affects the sukuk rating.
This means that the lower the company's activity ratio, the higher the rating assessment of the company's sukuk.
Conclusion Based on the results of testing profitability, solvency, liquidity, leverage, and activity on sukuk ratings in companies issuing sukuk on the Indonesia Stock Exchange (IDX) for the 2017-2021 period using ordinal regression, the author can draw the following conclusions:
The ordinal regression test results prove that the profitability variable with ROA proxy affects the sukuk rating, and ROE does not affect the sukuk rating.
These results do not match the study's results (Rukmana and Laila 2.
and (Winanti.
Nurlaela, and Titisari 2.
, which confirm that profitability proxied with ROA does not affect the sukuk rating.
The ordinal regression test results prove that the solvency variable does not affect the sukuk These results contradict the study (Winanti.
Nurlaela, and Titisari 2.
, which confirms that the solvency variable affects the sukuk rating.
Based on testing using ordinal regression proves that liquidity variables do not affect the sukuk rating.
This result is not in line with research conducted by (Pebruary 2.
, (Rukmana and Laila 2.
, and (Winanti.
Nurlaela, and Titisari 2.
, which states that liquidity affects sukuk rating.
Based on the test results using ordinal regression shows that the leverage variable affects the sukuk rating.
These results do not align with research conducted by (Nurakhiroh dkk.
, which found that variable leverage did not affect the sukuk rating.
Based on the test results using ordinal regression shows that activity variables affect sukuk These results do not match the studies conducted by (Andika 2.
, which state that activity variables negatively affect sukuk ratings.
International Journal of Economics.
Bussiness and Accounting Research (IJEBAR) Page 171 International Journal of Economics.
Business and Accounting Research (IJEBAR) Peer Reviewed Ae International Journal Vol-9.
Issue-1, 2025 (IJEBAR) E-ISSN: 2614-1280 P-ISSN 2622-4771 https://jurnal.
stie-aas.
id/index.
php/IJEBAR References