The Effect of Leverage, Company Size and Bond Age on Bond Ratings in the Financial Sector on the Indonesia Stock
Exchange (IDX)
Nurpadilaa, Djamaluddin Kadira, Kodding Hasenga
a Universitas Muslim Indonesia
nurpadila.nurpadila@umi.ac.id, djamaluddin.kadir@umi.ac.id kodding.haseng@umi.ac.id
I. Introduction
The capital market as a market of various long-term financial instruments (securities) that can be traded, performs economic and financial functions that can support economic and financial development in a country. Therefore, the capital market is also an indicator of the country's economic progress (Linandarini, 2010). Along with economic growth, the capital market is the main choice for people who have the desire to invest with the aim of getting profits in the future. Investments in the financial sector have a very high risk according to changing conditions, but have very high returns as well. Therefore, not a few people choose to invest in the financial sector (Mahfudhoh, 2014). There are various types of financial sector investments offered by the capital market, one of which is bonds.
Bonds are long-term, transferable debt securities that contain a promise from the issuing party to pay rewards in the form of interest and pay off the principal at a predetermined time to the bond buyer (Indonesia Stock Exchange, 2010).
The increasing number of companies issuing bonds on the Indonesia Stock Exchange (IDX) indicates that the bond market in Indonesia is growing. Bond rating is one of the important indicators used by investors to evaluate the risk of the bonds offered. In this context, there are several factors that affect bond ratings, including leverage, company size, and bond age. This article will discuss the influence of these factors on bond ratings in the financial sector on the IDX in the last five years.
Leverage or the ratio of debt to capital is one of the factors that affect bond ratings. According to a study conducted by Ali, Yulianti, and Maulana (2019), leverage significantly affects bond ratings. The higher the debt to capital ratio, the lower the bond rating given by the rating agency. The results of this study are consistent with the findings of previous studies such as those conducted by Bagus (2017) and Handoko and Jati (2017). Therefore, companies that have a high debt ratio must pay attention to financial risks that can affect the rating of the bonds offered. In addition, company size also affects bond ratings. According to research conducted by Lestari, Hadiwidjojo, and Purwanto (2018), company size has a positive effect on bond ratings. This means that the larger the size of the company, the higher the bond rating given by the rating agency. This finding is consistent with previous research such as that conducted by Dharmawan, Pratiwi, and Khaira (2018). This shows that large companies have a better reputation and financial capabilities so that they get higher bond ratings.
ARTICLE INFO A B S T R A C T
Article history:
Received 12 June 2022 Revised 6 Nov 2022 Accepted 29 Dec 2022
This study aims to examine the effect of Leverage, Company Size and Bond Age on Bond Ratings in financial sector companies on the Indonesia Stock Exchange (IDX). Based on the research results previously described, the following conclusions can be drawn: (1) Leverage has no significant effect on bond ratings. Based on these results, it shows that the first hypothesis is rejected. (2) Company size has a significant effect on bond ratings. Based on these results, it shows that the second hypothesis is accepted. (3) Bond Age has a significant effect on bond ratings. Based on these results, it shows that the third hypothesis is accepted.
Copyright © 2022 International Journal of Artificial Intelligence Research.
All rights reserved.
Keywords:
Leverage, Company Size, Bond Age, Bond Rating
Bond age also affects bond ratings. According to research conducted by Sasmito and Sari (2019), the longer the age of the bond, the lower the bond rating given by the rating agency. This finding is consistent with previous research such as that conducted by Budiarto, Adji, and Wulandari (2018).
This means that bonds that are more than one year old are considered more risky because they have a higher probability of default. Therefore, investors should pay attention to the age of bonds when considering investing in bonds. Overall, leverage, company size, and bond age are factors that affect bond ratings in the financial sector on the IDX. Companies that have high debt ratios should pay attention to their bond rating.
According to Sari & Badjra (2016) Investment in bonds is in great demand by investors because bonds have fixed income obtained from interest that will be received periodically and bond principal at maturity. For issuers, bonds are relatively safer securities compared to stocks because their issuance costs are cheaper than stocks. Investment in bonds is indeed safer, but bonds still have risks, namely interest rate risk and the risk that the company will not be able to pay bond coupons or principal. The phenomenon of bond default risk occurs in many companies that are quite popular with the public. PT Mobile-8 Telecom Tbk, has defaulted twice for coupons of March 15, 2009 and June 15, 2009 with bonds worth Rp 675 billion due in March 2012. PT Davomas Abadi Tbk, a 235 million dollar bond due 2011 has defaulted 13.09 million dollars for the May 5, 2009 coupon. PT Central Proteinprima which is the largest shrimp producer and processor in Indonesia has defaulted on its December 28, 2009 interest coupon of 17.9 million dollars due June 28, 2012 worth 325 million dollars (Kompas, 2010). One signal that can be used to determine the risk of bond default is the bond rating. According to Veronica (2013) bond ratings are very important for investors because they are able to provide informative statements and provide signals about the possibility of a company's debt failure. Another benefit that investors get from bond ratings is the cost and time savings of doing their own analysis and getting information directly. The safety of a bond is indicated by the ability of a company to pay interest and pay off the principal so that investors get information about bond ratings by using the services of bond rating agencies (Fauziah, 2014). The rating given is one of the references of investors when deciding to buy a bond. Tandelilin (2010: 251) states that, bond ratings vary from one rating agency to another. In Indonesia, there are 2 (two) bond rating agencies, namely PT PEFINDO (Indonesian Securities Rating Agency) and PT Moody's Indonesia. These rating agencies assess and evaluate publicly traded corporate debt securities, both in the form of ratings and changes in bond ratings which are then announced to the capital market. In general, bond ratings are categorized into two, namely the investment-grade category (AAA, AA, A and BBB) where companies or countries have sufficient ability to pay off their debts and the non-investment-grade category (BB, B, CCC and D), namely companies or countries that are not worth investing in for investors. Sari and Badjra (2016) state that there are many factors that influence bond ratings from both financial and non-financial factors. Financial factors include liquidity ratio, solvency ratio, profitability ratio, leverage ratio and company growth. Non-financial factors include company size, collateral, auditor reputation and bond age. This study uses several variables that have an influence on bond ratings, namely leverage, company size and bond age. The author uses these variables because previous research results still have research gaps.
There are several studies that examine the factors that influence bond ratings, including Yuliana (2011), Magreta & Nurmayanti (2009) and Widowati et al (2013), which show that leverage has a negative and insignificant effect on bond ratings. These findings are not in accordance with research conducted by Fauziah (2014) and Sari & Badjra (2016) which show the results that leverage has a positive influence on bond ratings. Magreta & Nurmayanti (2009) show that company size has a negative effect on corporate bond ratings. These results are different from research conducted by Veronica (2013) and Alfiani (2013) which show that company size has a positive influence on corporate bond ratings. Veronica (2013) and Vina (2021) show that bond age has an effect on bond ratings. These results are different from Kustiyaningrum et al. (2016) and Prastika (2021) which show that bond age has no effect on bond ratings. Some previous research results show that the variables that affect bond ratings are very varied. For this reason, this study will test leverage, company size and bond age with different sample periods. This research sample uses objects in the financial sector listed on the Indonesia Stock Exchange, because bonds in the financial sector dominate the issuance of corporate bonds throughout 2019-2021 and have bond ratings issued by PT. PEFINDO. Based on the background of the problem and the differences in results between fellow previous studies that have been described in the background subchapter of the problem, the problem formulations in this study are: (1) Does leverage affect bond ratings in the financial sector on the IDX; (2) Does firm size affect
bond ratings in the financial sector on the IDX; (3) Does bond age (maturity) affect bond ratings in the financial sector on the IDX.
II. Methods
This research is in the form of associative research and uses a quantitative approach. This research was conducted at the Indonesia Stock Exchange (BEI). The research time taken in carrying out and completing this activity is scheduled within 4 (four) months, namely from January to April 2022. The population in this study were financial sector companies listed on the Indonesia Stock Exchange (BEI) in 2019-2021 totaling 83 companies. The number of samples used based on the length of experience for 3 years is 23x3 = 69. The criteria used are as follows:
Table 1. Sample Selection Based on Criteria
No Description Number of
Companies
1. Financial sector companies listed on the IDX 83
2. Financial sector companies that are not listed in the bond rating issued
by PT. PEFINDO (50)
3. Financial sector companies that do not have complete financial reports
(Leverage, Company Size and Bond Age) (10)
Sample Quantity 23
Table 2. List of financial sectors listed on the Indonesia Stock Exchange (IDX) as research samples.
No Firm Company Name Obligation Rating
2019 2020 2021
1 ADMF PT Adira Dinamika Multi Finance Tbk. AAA AAA AAA
2 ASDF PT Astra Sedaya Finance AAA AAA AAA
3 BACA PT Bank Capital Indonesia Tbk. BBB BBB BBB
4 BBIA PT Bank UOB Indonesia AAA AAA AAA
5 BBKP PT Bank Bukopin Tbk. BBB BBB BBB
6 BBRI PT Bank Rakyat Indonesia (Persero) Tbk. AAA AAA AAA
7 BBTN PT Bank Tabungan Negara (Persero) Tbk. AA AA AA
8 BEXI Lembaga Pembiayaan Ekspor Indonesia AAA AAA AAA
9 BFIN PT BFI Finance Indonesia Tbk. AA AA AA
10 BIIF PT Maybank Indonesia Finance Tbk. AA AA AA
11 BMRI PT Bank Mandiri (Persero) Tbk. AAA AAA AAA
12 BNGA PT Bank CIMB Niaga Tbk. AAA AAA AAA
13 BNII PT Bank Maybank Indonesia Tbk AAA AAA AAA
14 BPFI PT Batavia Prosperindo Finance Tbk BBB BBB BBB
15 FIFA PT Federal International Finance AAA AAA AAA
16 IMFI PT Indomobile Finance Indonesia A A A
17 NISP PT Bank OCBC NISP Tbk. AAA AAA AAA
18 PNBN PT Bank Pan Indonesia Tbk. AA AA AA
19 SANF PT Surya Artha Nusantara Finance AA AA AA
20 SMFP PT Sarana Multigriya Finansial (Persero) AAA AAA AAA
21 TAFS PT Toyota Astra Financial Services AAA AAA AAA
22 TUFI PT Mandiri Tunas Finance AA AA AA
23 WOMF PT Wahana Ottomitra Multiartha Tbk AA AA AA
The type of data used in this research is quantitative data. The data source used in this research is secondary data. In this study, secondary data was obtained from the documentation data technique of annual financial reports from financial sector companies that issue bonds listed on the Indonesia Stock Exchange and the Indonesian Capital Market Directory (ICMD) and bond ratings obtained from the site www.pefindo.com.
Operational Definition and Measurement of Variables a. Bond Rating
This variable is seen based on the rating issued by PEFINDO, which in this study is divided into two categories, namely investment grade (AAA, AA, A and BBB), non-investment grade (BB, B, CCC and D). The measurement scale is an ordinal scale with reference to the bond rating classification conducted by Surya (2009). The bond rating from PT Pefindo will be given an assessment with a number 0 to number 7 which indicates the higher the bond rating, the higher the number given. The bond rating classification used in this study is as follows:
Table 3: Bond Rating Classification
No Bond Rating Classification
1. AAA 7
2. AA 6
3. A 5
4. BBB 4
5. BB 3
6. B 2
7. CCC 1
8. D 0
b. Leverage
Leverage measurement in this study uses debt to equity ratio. The debt to equity ratio referred to in this study is the ratio between total debt and equity. The calculations are:
𝐷𝑒𝑏𝑡 𝑡𝑜 𝐸𝑞𝑢𝑖𝑡𝑦 𝑅𝑎𝑡𝑖𝑜 = Total Debt 𝐸𝑞𝑢𝑖𝑡𝑦
c. Company Size
Firm Size is an indicator that can show the condition or characteristics of the company. Company size can be assessed in various ways, including: total assets owned, total sales earned, total equity used and others such as stock market value. Company size can be measured by the logarithm of total assets:
Company size = 𝐿𝑛 (Total Assets
d. Bond Age
The bond age in this study is the period from the issuance of the bond until the bond maturity date.
The measurement scale uses a nominal scale because it is also a dummy variable. Measurement is done by giving a value of 1 if the bond has an age between one and five years and 0 if the bond has an age of more than five years.
Table 4. Operational Definition of Variables, Measurement and Scale
Variables Proxy Reference
Source Data Scale
Bond Rating Rank Classification (Surya, 2009) Ordinal
Leverage Debt to Equity Ratio (Fauziah, 2014) Rasio
Company Size Ln Sales (Sari & Badjra,
2016) Rasio
Bond Age
code 1 if the bond has an age between 1-5 years and code 0 if the bond has an age of more than 5 years.
(Widowati et al.,
2013) Nominal
Classical Assumption Test
Based on the explanation above, the researcher uses testing, as follows:
a. Autocorrelation Test
The test method with Durbin Watson (DW test) has the following decision-making basis:
Table 5. Decision-making on the presence or absence of autocorrelation
Null Hypothesis Decision Situation If
No positive autocorrelation Total 0 < d < dL
No positive autocorrelation No decision dL ≤ d ≤ dU
No negative autocorrelation Total 4 - dL< d < 4 No negative autocorrelation No decision 4 - dU ≤ d ≤ 4 - dL No positive/negative autocorrelation Not rejected dU < d < 4 - dU Source Ghozali (2011)
1) If the DW (Durbin Watson) value lies between the upper bound (dU=durbin Watson upper) and (4 - dU), then the autocorrelation coefficient is equal to zero, meaning there is no autocorrelation.
2) If the DW value is lower than the lower bound (dL=durbin Watson lower), then the autocorrelation coefficient is greater than zero, meaning there is positive autocorrelation.
3) If the DW value is greater than (4 - dL), then the autocorrelation coefficient is smaller than zero, meaning there is negative autocorrelation.
4) If the DW value lies between the upper limit (dU) and the lower limit (dL) or DW lies between (4 - dU) and (4 - dL), then the results cannot be concluded.
b. Multicollinearity Test
Multicollinearity occurs if there is a linear relationship between the independent variables involved in the model. The classic assumption test such as multicollinearity can be carried out by regressing the analysis model and testing the correlation between the independent variables using the Variance Inflation Factor (VIF).
c. Normality Test
The normality used in a study includes the kormologrov-smirnov test, the test can be done with the SPSS version 23 program, namely the normal probability plot. The basis for decision making is if the probability value is greater than the 5% error rate (0.05), it can be concluded that the residual value of the regression model is normally distributed (Syahruddin et al., 2019).
d. Heteroscedasticity Test
A good regression model should not have heteroscedasticity. Heteroscedasticity test is carried out using the Glejser test (Syahruddin et al., 2019).
Data Analysis Methods
a. Descriptive Statistical Analysis
Descriptive statistics serve to describe or provide an overview of the object under study through sample or population data as it is without analyzing and making general conclusions. This analysis is used to find the mean value to state the average population in the study.
b. Inferential Statistical Analysis
Thus, the multiple regression equation in this study is as follows (Kustiyaningrum, et al., 2016):
Y = a + b1 X1 +b2 X2 + b3 X3 + e
Description:
Y = Bond Rating X1 = Leverage X2 = Company Size X3 = Bond Age a = Constant
b1 - b3 = Regression coefficient e = Error
Hypothesis Test
To test the hypothesis regarding "The Effect of Leverage, Company Size and Bond Age on Bond Rating" partial hypothesis testing is used with the t test and the coefficient of determination.
Coefficient of Determination (R2)
A small R2 value means that the ability of variations in the independent variables to explain the dependent variable is very limited. A value close to one means that the independent variables provide almost all the information needed to predict the variation in the dependent variable. And if on the contrary the coefficient value is 0, the independent variable cannot explain the variation that occurs in the dependent variable at all with the following formula:
R2 = Coefficient of determination R = Correlation Coefficient
Partial Test (t test)
The hypothesis is carried out as follows:
a. If t-count> table, then HO is rejected and Hα is accepted. This means that there is an influence of the independent variable on the dependent variable.
b. If t-count < table, then HO is accepted and Hα is rejected.
This means that there is no influence of the independent variable on the dependent variable. The entire data processing was carried out using the help of the Statistical Product and Standard Solution (SPSS) version 23 program.
III. Result and Discussion
The data used in this study are financial statements of financial sector companies. Sampling in this study was carried out using purposive sampling where 23 financial sector companies were collected with an observation time of three years, 2019-2021. Based on the results of the normality test, the independent variables with the initial data of 69 observations have an abnormal distribution because the significance value is less than 0.05. To normalize the data, the extreme data is removed, so that the following amount of data is obtained:
Table 6. Number of Testable Data
Total Initial Data 69
Data is not normal (23)
Number of testable data 46
Data that is not normally distributed is found in the 2019 research year, so in this study the data is processed with an observation time of 2 years, namely 2019-2021 with a total number of observations of 23x2 = 46. The following is company data based on variables in the company:
R2 = (R)2 x 100%
Table 7. Financial Sector Companies Listed on the IDX
No Company Name (Code) Year DER LN
(SIZE) Maturity Rating 1 PT Adira Dinamika Multi Finance Tbk.
(ADMF)
2019 4.45 17.13 5 AAA
2021 4.13 17.20 5 AAA
2 PT Astra Sedaya Finance (ASDF) 2019 4.44 17.26 3 AAA
2021 4.15 17.20 5 AAA
3 PT Bank Capital Indonesia Tbk. (BACA) 2019 9.80 16.47 7 BBB
2021 10.61 16.61 7 BBB
4 PT Bank UOB Indonesia (BBIA) 2019 7.70 18.36 5 AAA
2021 7.70 18.37 7 AAA
5 PT Bank Bukopin Tbk. (BBKP) 2019 10.05 18.47 5 BBB
2021 14.75 18.48 7 BBB
6 PT Bank Rakyat Indonesia (Persero) Tbk (BBRI)
2019 5.84 20.73 5 AAA
2021 5.73 20.84 5 AAA
7 PT Bank Tabungan Negara (Persero) Tbk.
(BBTN)
2019 10.20 19.18 5 AA
2021 11.06 19.38 5 AA
8 Lembaga Pembiayaan Ekspro Indonesia (BEXI)
2019 4.77 18.43 5 AAA
2021 4.19 18.52 5 AAA
9 PT BFI Finance Indonesia Tbk. (BFIN) 2019 1.93 16.34 5 AA
2021 2.36 16.62 3 AA
10 PT Maybank Indonesia Finance Tbk (BIIF) 2019 3.79 15.65 5 AA
2021 3.50 15.77 5 AA
11 PT Bank Mandiri (Persero) Tbk. (BMRI) 2019 5.38 20.76 10 AAA
2021 5.22 20.84 10 AAA
12 PT Bank CIMB Niaga Tbk. (BNGA) 2019 5.95 19.30 5 AAA
2021 6.21 19.40 5 AAA
13 PT Bank Maybank Indonesia Tbk. (BNII) 2019 7.65 18.93 3 AAA
2021 7.34 16.40 5 AAA
14 PT Batavia Prosperindo Finance Tbk.
(BPFI)
2019 1.06 13.85 2 BBB
2021 1.53 14.27 3 BBB
15 PT Federal International Finance (FIFA) 2019 4.94 17.20 3 AAA
2021 4.77 17.24 3 AAA
16 PT Indomobile Finance Indonesia (IMFI) 2019 5.82 16.06 4 A
2021 6.31 16.16 5 A
17 PT Bank OCBC NISP Tbk. (NISP) 2019 6.08 18.74 3 AAA
2021 6.06 18.85 3 AAA
18 PT Bank Pan Indonesia Tbk. (PNBN) 2019 4.82 19.11 5 AA
2021 4.88 19.18 7 AA
19 PT Surya Artha Nusantara Finance (SANF) 2019 3.70 15.73 3 AA
2021 2.92 15.55 5 AA
20 PT Sarana Multigriya Finansial (Persero) (SMFP)
2019 1.01 16.39 3 AAA
2021 0.99 16.57 3 AAA
21 PT Toyota Astra Financial Services (TAFS) 2019 7.84 16.92 3 AAA
2021 8.47 16.94 3 AAA
22 PT Mandiri Tunas Finance (TUFI) 2019 6.74 16.25 5 AA
2021 7.34 16.51 5 AA
23 PT Wahana Ottomitra Multiartha Tbk.
(WOMF)
2019 7.18 15.71 3 AA
2021 6.83 15.86 3 AA
Table 8. Autocorrelation Test Results
Table 9. Decision on Autocorrelation Test Results
Based on table 9 above, it is known that the Durbin-Watson (d) value is 2.366. Furthermore, we will compare this value with the Durbin-Watson table value at 5% significance with the formula (k;
N). The number of independent variables is 3 or "k" = 3, while the number of samples or "N" = 46, then (k; N) = (3; 46). We then look at this number in the Durbin Watson table value distribution. Then the dL value is found to be 1.3912 and dU is 1.6677.
The Durbin-Watson (d) value of 2.366 is greater than the limit (dU) of 1.3912 and less than (4-dU) 4 - 1.3912 = 2.608. So as the basis for decision making in the Durbin Watson test above, it can be concluded that there are no autocorrelation problems or symptoms.
Table 10. Multicolonierity Test Results Coefficientsa
Model Collinearity Statistics
Tolerance VIF
1
(Constant)
Leverage (X1) .998 1.002
Size (X2) .922 1.085
Maturity (X3) .924 1.083
a. dependent variable: Rating
Based on the calculation results of table 10, the Tolerance value of the Leverage variable (X1) of 0.998 is greater than 0.10, Size (X2) of 0.922 is greater than 0.10 and Maturity (X3) of 0.924 is greater than 0.10. Meanwhile, the VIF value of Leverage (X1) of 1.002 is smaller than 10.00, Size (X2) of 1.085 is smaller than 10.00 and Maturity (X3) of 1.083 is smaller than 10.00 so it can be concluded that between the independent variables there is no multicolonierity problem.
Table 11. Normality Test Results One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 46
Normal Parameters a,b Mean .0000000
Std. Deviation .47206261
Model Summaryb
Model Durbin-Watson
1 2.366
a. Predictors: (Constant), X3, X1, X2 b. Dependent Variable: Y
Null Hypothesis Decision If Condition
No positive autocorrelation Total 0 < d < dL 0 < 2.366 < 1.391 No positive autocorrelation No decision dL ≤ d ≤ dU
1.391 ≤ 2.366 ≤ 1.677 No negative autocorrelation Total 4 - dL< d < 4
2.608 < 2.366 < 4 No negative autocorrelation No decision 4 - dU ≤ d ≤ 4 – dL
2.3323 ≤ 2.366 ≤ 2.608 No positive/negative autocorrelation Not rejected dU < d < 4 – dU
1.677 < 2.366 > 2.3323
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual Most Extreme Differences
Absolute .084
Positive .071
Negative -.084
Test Statistic .084
Asymp. Sig. (2-tailed) .200c,d
a. Test distribution is Normal.
b. Calculated from data.
Based on the table 11, it is known that the significance value of all variables is 0.200, this value is greater than 0.05. So, it can be concluded that the data tested is normally distributed. This means that the research variables have a probability value of 0.200 which is greater than the significance value of 0.05. The basis for decision making in this test is as follows:
a. If the significance value is greater than 0.05, the conclusion is that there is no heteroscedasticity.
b. If the significance value is smaller than 0.05, the conclusion is that heteroscedasticity occurs Table 12. Heteroscedasticity Test Results
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients t Sig.
B Std. Error Beta
1
(Constant) .305 .494 .619 .540
Leverage (X1) .000 .051 .001 .004 .997
Size (X2) .008 .026 .051 .323 .748
Maturity (X3) -.087 .121 -.115 -.720 .475
a. Dependent Variable: Heterocedastisity
Based on table 12, it is known that the significance value of the Leverage variable (X1) of 0.997 is greater than 0.05, Size (X2) of 0.748 is greater than 0.05 and Maturity (X3) of 0.475 is greater than 0.05. Which means that heteroscedasticity does not occur. Thus it can be concluded that the leverage data (X1), company size (X2) and bond age (X3) have a value above 0.05 which has the assumption of heteroscedasticity, so the data can be tested in the next test.
Descriptive Statistical Analysis Results
Table 13. Descriptive Static Analysis Results
Based on the data in the output of SPSS version 23, it shows that the amount of data studied (N) is 46 data. The 46 data can be explained as follows:
Bond Rating (Y)
Based on table 13, it can be seen that the minimum value in the bond rating variable measured using the rating classification is 4 in the Bank Capital Indonesia Tbk. company in 2019 and the maximum value is 7 in the Adira Dinamika Multi Finance Tbk company in 2019 with an average (mean) of 0.1173 at a standard deviation of 0.59804.
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Rating (Y) 46 4 7 .1173 .59804
Leverage (X1) 46 .99 14.75 -.2826 .83435
Size (X2) 46 13.85 20.84 17.5717 1.70808
Maturity (X3) 46 2 10 .85 .363
Leverage (X1)
Based on table 13, it can be seen that the minimum value in the leverage variable as measured using the debt to equity ratio is 0.99% in the Sarana Multigriya Finansial (Persero) company in 2021 and the maximum value is 14.75% in the Bank Bukopin Tbk. company in 2021. This shows that the value of leverage in this study ranges from 0.99% to 14.75% with an average (mean) of -0.2826 at a standard deviation of 0.83435.
Company Size (X2)
Based on table 13, it can be seen that the minimum value in the company size variable as measured using Ln Sales is 13.85% in the Batavia Prosperindo Finance Tbk company in 2021 and the maximum value is 20.84% in the Bank Mandiri (Persero) Tbk. company in 2021. This shows that the value of company size in this study ranges from 13.85% to 20.84% with an average (mean) of 17.5717 at a standard deviation of 1.70808.
Bond Age (X3)
Based on table 13, it can be seen that the minimum value in the bond age variable is 2 in the Batavia Prosperindo Finance Tbk company in 2019 and the maximum value is 10 in the Bank Mandiri (Persero) company in 2021 with an average (mean) of 0.85 at a standard deviation of 0.363.
Table 14: Multiple Linear Regression Analysis Results
Based on the data in table 14, it can be seen that the regression coefficient values of leverage (X1), size (X2) and maturity (X3) on bond rating (Y) are -0.114 (X1), 0.207 (X2), 0.505 (X3) and a constant value of -3.977. Thus the following regression equation is formed:
The regression equation above can be explained as follows:
1. The constant value of -3.977 means that if the level of Leverage, Company Size and Bond Age is constant or equal to zero (0), then the Bond Rating is equal to -3.977.
2. The Leverage variable (X1) has a negative regression coefficient of -0.114, this means that the more the Leverage value increases by 1%, the bond rating will decrease by - 0.114.
3. The Company Size variable has a positive regression coefficient of 0.207, this means that the more the Company Size value increases by 1%, the bond rating will increase by 0.207.
4. The Bond Age variable (X1) has a positive regression coefficient of 0.505, this means that the more the Bond Age value increases by 1%, the bond rating will increase by 0.505.
Determination Test (R2)
Table 15. Test Results of the Coefficient of Determination (R2) Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .614a .377 .332 .48863
a. Predictors: (Constant), Maturity, Leverage, Size b. Dependent Variable: Rating
Coefficients
Model Unstandardized Coefficients Standardized
Coefficients T Sig.
B Std. Error Beta
1
(Constant) -3.977 .849 -4.684 .000
Leverage (X1) -.114 .087 -.160 -1.309 .197
Size (X2) .207 .044 .591 4.656 .000
Maturity (X3) .505 .209 .307 2.419 .020
a. Dependent Variable: Rating (Y)
Y = -3,977 – 0,114 X1 + 0,207 X2 + 0,505 X3
Table 15 above shows that the coefficient of determination determines how much the Independent variable can explain the dependent variable. Based on the table, the coefficient of determination (R2) in this study is 0.377 or 37.7%, which means that the variables of leverage, company size and bond age affect the bond rating by 37.7% while the remaining 62.3% is explained by other factors not carried out in this study.
Partial Test (t-test)
Table 16: Partial Test Results (t Test) Variables t-calculated
Value
t-estimated
value Results
Leverage (X1) -1,309 1,680 t-calculated < t-estimated
Size (X2) 4,656 1,680 t- calculated > t- estimated
Maturity (X3) 2,419 1,680 t- calculated > t- estimated Based on the table 16, it can be explained as follows:
1. Leverage Effect on Bond Rating
Based on the results of the calculation of the regression coefficient partially seen in table 4.11, it is obtained from the tit value of the Leverage variable of -1.309. The t-estimated value with a = 0.05 and free degree = 46-1-1 = 44, the t-estimated value is 1.680. Therefore, the tit value for the Leverage variable coefficient of -0.197 is smaller than t-estimated (-0.197 < 1.680), so Ho is accepted and Ha is rejected. This means that Leverage has no effect on bond ratings.
2. The Effect of Company Size on Bond Rating
Based on the results of the partial regression coefficient calculation seen in table 4.11, it is obtained from the tit value of the company size variable of 4.656. The t-estimated value with a = 0.05 and free degree = 46-1-1 = 44, the t-estimated value is 1.680. Therefore, the tit value for the company size variable coefficient of 4.656 is greater than t-estimated (4.656> 1.680), so Ho is rejected and Ha is accepted. This means that company size has an effect on bond ratings.
3. The Effect of Bond Age on Bond Rating
Based on the results of the partial regression coefficient calculation seen in table 4.11, it is obtained from the tit value of the bond age variable of 2.419. The t-estimated value with a = 0.05 and free degree = 46-1-1 = 44, the t-estimated value is 1.680. Therefore, the thit value for the coefficient of the bond age variable of 2.419 is greater than t-estimated (2.419> 1.680), so Ho is rejected and Ha is accepted. This means that bond age has an effect on bond ratings.
Discussion
Based on tests that have been carried out using multiple linear regression analysis techniques to obtain a comprehensive picture of the effect of the independent variable on the dependent variable, the following results are obtained:
1. Leverage Effect on Bond Rating
The results of testing the hypothesis of the effect of Leverage (X1) on bond ratings show that leverage has a negative and insignificant effect on bond ratings. The results of this test can be proven by the value of the standardized coefficients obtained from the negative direction of -0.160 and the value of tit -1.309 < t table 1.679 with a significance value of 0.197 greater than 0.05. This means that the hypothesis is rejected. This means that the hypothesis is rejected. Leverage has a negative and insignificant effect on bond ratings, it can be said that the lower the leverage, the less likely the company's bond rating. Leverage has two sides of the assessment, namely risks and benefits. On the one hand, a high increase in debt can increase the potential for losses and even bankruptcy which cannot be avoided (Hanafi, 2004 in Arifman, 2013). On the other hand, increased debt also brings benefits in the form of tax savings (Hanafi, 2004 in Arifman, 2013). Therefore, researchers argue that leverage is not relevant to be used as an indicator in determining bond ratings due to the substitution effect of potential losses with tax savings. The results of this study are in line with the results of
research conducted by (Yuliana, 2011) and (Arifman, 2013) which suggest that leverage has no significant effect on bond ratings.
2. The Effect of Company Size on Bond Rating
The results of testing the hypothesis of the effect of company size (X2) on bond ratings show that company size has a significant positive effect on bond ratings. The results of this test can be proven by the value of the standardized coefficients obtained from the positive direction of 0.591 and the value of tit 4.656> t-estimated 1.679 with a significance value of 0.000 smaller than 0.05. This means that the hypothesis is accepted. This means that the hypothesis is accepted. Company size has a significant positive effect on bond ratings, it can be said that the higher the company size, the higher the bond rating of a company. The results of this study are in line with the results of research conducted by (Sari & Badjra, 2016) which shows that there is a significant positive effect on bond ratings. These results indicate that rating companies tend to pay attention to company size as a variable that can affect the size of the bond rating in companies on the Indonesia Stock Exchange. The results showed that the larger the company size, the higher the bond rating given, this is due to the high level of investor confidence in companies with large sizes.
3. The Effect of Bond Age on Bond Rating
The results of hypothesis testing for the bond age variable (X3) on bond ratings show that bond age has a significant positive effect on bond ratings. The results of this test can be proven by the standardized coefficients value obtained from the positive direction of 0.307 and the value of tit 2.419> t table 1.679 with a significance value of 0.020 smaller than 0.05. This means that the hypothesis is accepted. This means that the hypothesis is accepted. Bond age has a significant positive effect on bond ratings, it can be said that the long or short age of the bond will affect the bond rating given by the rating company.
The results of this study are in line with the results of research conducted by Veronica (2013) showing that bond age affects bond ratings. This means that bonds with shorter maturities have better ratings than bonds with long maturities. Investors tend to dislike bonds with a longer age because the risk that will be obtained will also be greater, so the shorter the age of the bond will get a better bond rating.
IV. Conclusion
This study aims to examine the effect of Leverage, Company Size and Bond Age on Bond Ratings in financial sector companies on the Indonesia Stock Exchange (IDX). Based on the research results previously described, the following conclusions can be drawn: (1) Leverage has no significant effect on bond ratings. Based on these results, it shows that the first hypothesis is rejected. (2) Company size has a significant effect on bond ratings. Based on these results, it shows that the second hypothesis is accepted. (3) Bond Age has a significant effect on bond ratings. Based on these results, it shows that the third hypothesis is accepted.
Based on the description of the background, research results and discussion, the following suggestions are made: (1) For companies it is advisable to improve or improve their financial performance, with increasing financial performance the company will be able to improve its bond rating, a good bond rating will have high selling power. (2) Pefindo is advised to be more thorough and complete in assessing companies whose bonds are included in the rating list by looking at bond rating criteria, namely in terms of financial ratios, mortgage provisions, subordination provisions, guarantee provisions, repayment funds, and maturity. (3) It is suggested that further research can use other categories of companies as research and not only bonds in the financial sector but all bonds rated by rating companies so that the results can be used as a comparison and can be used as a source of further reference.
References
[1] Afifuddin, A. (2022). The Regression Model Effect of Financial Ratio on Construction and Building Stock Price. Golden Ratio of Finance Management, 2(1), 43-60.
[2] Ali, R. S., Yulianti, E., & Maulana, M. F. (2019). The Effect Of Leverage On Bond Rating In Indonesia: Empirical Study On Lq-45 Companies Listed On The Indonesia Stock Exchange. Journal Of Accounting And Investment, 20(2), 173-182.
[3] Amin, M. (2021). The Regression Effect of Capital Structure and Firm Growth on the Firm value. Golden Ratio of Finance Management, 1(1), 33-50.
[4] Bagus, P. (2017). The Effect Of Leverage On Bond Rating In Indonesia: Empirical Study On Manufacturing Companies Listed On The Indonesia Stock Exchange. Journal Of Accounting And Investment, 18(2), 130-141.
[5] Budiarto, A., Adji, R. A., & Wulandari, A. (2018). The Effect Of Bond Age On Bond Rating In Indonesia. International Journal Of Economics And Financial Issues, 8(2), 15-21.
[6] Dharmawan, S., Pratiwi, H., & Khaira, R. (2018). The Effect Of Company Size On Bond Rating In Indonesia. Journal Of Accounting And Investment, 19(1), 50-60.
[7] Fauziah, Y. (2014). Pengaruh Likuiditas, Leverage Dan Umur Obligasi Terhadap Prediksi Peringkat Obligasi. Ekonomi.
[8] Lestari, P., Hadiwidjojo, D., & Purwanto, E. (2018). The Effect Of Company Size On Bond Rating In Indonesia. Accounting Analysis Journal, 7(4), 432-439.
[9] Malikah, A. (2021). Comparison of Financial Performance Before and During COVID-19:
Case Study of Hospitality Business, Indonesia. Golden Ratio of Finance Management, 1(1), 51-60.
[10] Sapiri, M., Hamzah, F. F., Putra, A. H. P. K., & Hadi, A. (2022). The Effect of Financial Performance on Firm Value with Financial Distress as an Intervening Variable. International Journal of Artificial Intelligence Research, 6(1.1).
[11] Sari, K. R., Martini, R., Almira, N., Hartati, S., & Husin, F. (2022). Prediction of Bankruptcy Risk Using Financial Distress Analysis. Golden Ratio of Finance Management, 2(2), 77-86.
[12] Sari, N. M. S. K., & Badjra, I. B. (2016). Pengaruh Likuiditas, Ukuran Perusahaan, Leverage Dan Jaminan Terhadap Peringkat Obligasi Pada Sektor Keuangan. Jurnal Manajemen, 5(8), 5041–5069.
[13] Sasmito, H., & Sari, L. M. (2019). The Effect Of Bond Age And Financial Performance On Bond Rating: Empirical Study On Lq-45 Companies Listed On The Indonesia Stock Exchange. Journal Of Applied Accounting And Taxation, 3(1), 1-7.
[14] Surya, E. I. (2009). Pengaruh Ukuran Perusahaan (Firm Size), Profitabilitas, Likuiditas, Produktivitas, Dan Leverage Terhadap Peringkat Obligasi. Jurnal Bisnis Dan Akuntansi, 11(1), 33–56.
[15] Widowati, D., Nugrahanti, Y., & Kristanto, A. B. (2013). Analisis Faktor Keuangan Dan Non Keuangan Yang Berpengaruh Pada Prediksi Peringkat Obligasi Di Indonesia (Studi Pada Perusahaan Non Keuangan Yang Terdaftar Di Bei Dan Di Daftar Peringkat Pt Pefindo 2009-2011). Jurnal Manajemen Maranatha, 13(1), 35–54.
