JRSSEM 2023, Vol. 02, No. 7, 1445 – 1462
E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494
DOI: 10.36418/jrssem.v2i07.389 https://jrssem.publikasiindonesia.id/index.php/jrssem
LOGISTIC REGRESSION WITH NON-FINANCIAL
LIABILITY RATINGS ON THE INDONESIA STOCK
EXCHANGE
Muklis Kanto
1
Annas Lalo
2
1,2
Sekolah Tinggi Ilmu Ekonomi Bongaya STIEM Bongaya Makassar, Indonesia
*
e-mail: mukliskanto1@gmail.com, annas.lalo@stiem-bongaya.ac.id
*Correspondence: mukliskanto1@gmail.com
Submitted
: 15
th
January 2023
Revised
: 12
th
February 2023
Accepted
: 25
th
February 2023
Abstract: Companies that issue bonds have an obligation to pay interest regularly according to a
predetermined period of time and the principal at maturity. This study aims to determine and
analyze the effect of profitability on bond ratings in non-financial companies listed on the IDX for
the 2018-2021 period. The analytical method used in this study is a quantitative analysis method
using Microsoft Excel 2016 software and SPSS (Statistical Package for Social Sciences) version 26.0
as tools to test data. The purpose of this analysis is to get the relevant information contained in the
data and use the results to solve a problem. The results of this study state that a positive liquidity
value indicates that companies with high liquidity are most likely to be in efficient conditions, for
example, companies do not use financing through bonds because companies have large internal
funds and tend to choose to use internal funds first compared to external financing sources such
as issuance of bonds resulting in high corporate value and affect the bond rating. These results
indicate that liquidity has a positive influence on bond ratings of non-financial companies listed on
the Indonesia Stock Exchange in 2018-2021, thus supporting the research hypothesis. This is
because liquidity shows a positive direction, where the higher the level of liquidity, the greater the
acquisition of a non-financial company's bond rating.
Keywords: Logistic Regression; Non-Financial Liability; Bursa Efek Indonesia.
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INTRODUCTION
Bond investment is a type of
investment that is in great demand by
capital owners (investors) because bonds
have a fixed income. The fixed income is
derived from the principal of the bond and
interest that will be received periodically at
maturity. The profit derived from bond
investment is that the bondholder has the
first right to the company's assets if the
company in question undergoes liquidation
because the company has entered into a
contract to be able to pay off the bonds
that have been purchased by the
bondholder. Bond investment is relatively
better and safer compared to stock
investment. Bonds are preferred by
companies that need additional funds
because they are easier to obtain. However,
bonds are a type of investment that has
some risks for investors. One of the risks
that can arise is the company's inability to
pay off bonds to investors or it can be called
bad bonds (Ilmiawan et al., 2018).
The advantage of investing in bonds
compared to stocks is in terms of paying
returns. The income received from shares
comes from dividends and capital gains.
Dividend payments are given when coupon
bond payments have been made. If from
the payment of coupon bonds there is no
remaining for dividends, then shareholders
do not benefit from the shares owned.
Another advantage derived from bond
investments is that bondholders have the
first right to the company's assets if the
company goes into liquidation. This
happens because the company has an
agreement contract to pay off the bonds
that have been purchased by the
bondholders. In other words, bond
investment is relatively better (safe) than
stock investment (Sumiyati & Hartono,
2017)
Companies that issue bonds have the
obligation to pay interest regularly in
accordance with the predetermined period
and the principal of the loan at maturity.
Bonds are basically debt securities offered
to the public. Although bonds are
considered a safe investment, they still have
risks. One such risk is the company's
inability to pay off bonds to investors.
Before being offered, bonds must be rated
by an agency or bond rating agency (Rating
Agency). A bond rating agency is an
independent agency that provides risk-
scale rating information, one of which is
bond securities as a clue as to the extent of
a bond's security for investors. Such
security is indicated by the ability of a
company to pay interest and pay off the
principal of the loan. So that investors can
use the services of the bond rating agent to
get information about bond ratings. This
rating process is carried out to assess the
company's performance, so that the rating
agency can state whether or not the bond
is worth investing (Hidayat, 2018).
Bond ratings represent the risk scale of
all traded bonds. This scale shows how safe
a bond is for investors as shown by the
company's ability to pay interest and
principal on loans. Bond ratings are one of
the references from investors when
deciding to buy a bond. When an entity's
bond rating is in the high category
(investment grade) it means that the rating
agency considers the company's
performance to be good. This information
will be responded to by investors by
1447 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
allocating their funds to the company
because investors think that the company
can improve its welfare, as a result,
abnormal returns will increase. On the other
hand, if there is a low bond rating (
non-
investment grade
) indicates that the
company's performance has decreased. As
a result, investors are less interested in
investing their funds into the company.
Information from bond ratings will certainly
be very useful for investors in making
investments, especially investments in
bonds. Investing in bonds has three
components of profit that investors
consider in choosing investments in bonds,
namely interest income,
capital gains
, and
special future gains
(Dwitayanti & Zahara,
2018).
A person who wants to invest in bonds
needs information that is used as a basis for
his decision-making. A bond rating is one
of the information used as a basis for
consideration to decide whether the bond
is worth investing in and knowing the level
of risk. Bond ratings announced to the
public can reduce information asymmetry
between bond issuing companies and
investors. Bond ratings have an important
role as a signal of a company's
performance. This signal is used as one of
the basis for decision-making of
information users. The Financial
Accounting Standards Board seeks to draw
up appropriate standards, so that the
financial statements produced by the
company reflect the reality of a business
entity. In reality, the looseness of the
established standards is often misused by
the management to carry out engineering.
One of the bond ratings is determined from
the results of the company's financial
statements, so that if a company's
performance is good, the bond will also
have a good rating, so many investors are
interested in the bond (Romhadhoni et al.,
2019). There are several studies that
examine the factors that affect bond ratings
including profitability, liquidity, leverage,
bond lifespan, company size against bond
ratings.
The bond rating phenomenon can be
seen in the case of one of the issuers,
namely Mobile 8 Telekom, Tbk where in
2018 this company had failed to fulfill its
obligation to pay the 12th interest and 9th
interest and fine for Mobile 8 bonds which
continued to decline from year to year,
causing the company not to have sufficient
funds to pay its bonds. This bond default
problem is not the first time that has
occurred, in March 2017 the IDX also
suspended FREN shares and bonds as the
company did not pay interest on its bonds
of Rp. 675 billion. With the default, rating
agency PEFINDO downgraded the
company's bond rating to "D‟ from "CC‟
(Astuti & Fitria, 2019; Ikhsan, 2020).
One of them is the existence of a
company ranked by PT PEFINDO but has
defaulted, which raises a question about
the accuracy of rating agents in Indonesia.
As happened to the taxi company once late
in paying its bond interest debt that had
overdue on March 26, 2018. Pefindo
downgraded the bond rating from BB- to D
due to a default issued in 2014. However, in
April 2018, TAXI had paid the interest debt
and Pefindo downgraded TAXI's rating from
BB- to SD (Selective Default). SD rating is a
default bond at maturity but akyn
continues to make payment of the bond on
Muklis Kanto
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time there are other obligations
(www.finance.detik.com, December 13,
2018). The phenomenon of default risk is
also in the case of PT Bakrie Telecom Tbk.
(BTEL), namely in 2016 it did not pay ßunga
coupons on guaranteed senior notes bonds
issued by its subsidiary, Bakrie Telecom Pte
Ltd around RPI 63.72 billion with a principal
debt of 250 million US dollars with a
coupon of 11.50% with a fall in time o
ligation on May 7, 2015
(http://bisniskeuangan.kompas.com).
There is also the company AISA or PT
Tiga Pilar Sejahtera Food Tbk, which pays
interest and instruments from bonds
maturing on June 26, 2018 and sukuk
maturing on July 19, 2018Qléngan values of
Rp 600 billion and Rp 300 billion
respectively (ww,v.finance.de .com, July 25,
2019). In addition to the Three Pillars of
Sejahtera Food, there is also PT Kawasan
Industri Jababeka Tbk (KIJA) which is
threatened with default on bonds caused
by the replacement of the management
structure (Idonesia.com, July 18, 2019).
Profitability shows a company in
making its profit in a period. This ratio can
be seen from the return
on
assets (ROA)
where the company makes its profit by
utilizing the assets it owns, while in the
return on
equity (ROE) the company
generates its profit by utilizing the equity or
capital owned by the company. Profitability
is the best indicator in showing the health
of a company, but actually bond investment
has no effect on the profit of a company
because no matter how much profit a
bondholder company will only receive
according to the specified interest rate. The
better the level of profitability of a
company, the better the company in
making a profit, the company can fulfill its
obligations on time. Previous research
according to (Astuti & Fitria, 2019) stated
that the profitability ratio has a positive
effect on bond ratings, while according to
(Putri, 2018), profitability has no effect on
bond ratings, and according to (Sumendap
et al., 2018) profitability ratios negatively
affect bond ratings.
leverage
is the amount or proportion
of the use of debt in financing its capital
investment, this ratio can be shown from
debt to total asset ratio, debt to equity ratio
(DER),
Long-term to total assets,
etc. The
leverage
ratio shows how much a company
uses external debt to finance its operations
and expansion.
Leverage
is often
interpreted as boosting a company's
performance and is synonymous with debt.
The reason is, debt and loans can indeed
boost the company's performance than if
the company only relied on the strength of
its own capital. If
the leverage level of a
company is high, it shows that the company
uses a large amount of debt in the
company's performance, the lower the
leverage level of a company the better the
company's performance
and it is likely that
the company will fulfill its obligations. There
are previous studies that stated that the
leverage ratio negatively affects the bond
rating, namely according to Restuti (2020)
dissenting from (Apritasari, 2018), which
states that the leverage ratio does not
affect the bond rating, but according to
Satoto (2019)
leverage
has a positive effect
on bond ratings.
There are several studies that examine
the factors that affect bond ratings,
including profitability. Profitability is the
ability of management to make a profit
1449 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
(Utari. (2014). Previous studies on
profitability affect bond ratings according
to Widiyastuti (2016), Henny (2016), Suwarti
& Kurniawan (2015), stated that profitability
assessed using ROA had a significant
positive effect on bond ratings. In contrast
to the research of other variables, namely
liquidity variables. Liquidity is the
company's ability to fulfill all its maturing
obligations (Utari (2014). According to the
results of Azani's research, Khairunnisa &
Dillak (2017), Hidayat (2018), that the test
results using logistic regression proved that
the liquidity variable was measured using
the current ratio indicator on bonds rated
by PT. PEFINDO from 2011 to 2015 had a
significant positive effect on bond ratings.
The next variable is the leverage variable,
which is a description of a company's ability
to meet and maintain its ability to always be
able to fulfill its obligations in paying debts
on time, Fahmi. (2013). On leverage
variables according to Azani khairunnisa &
Dillak.. (2017), Widiyastuti (2016),
Dwitayanti (2018), Mardiyati et al. (2015),
Sakinah et al. (2017), stated that leverage
has a significant positive effect on bond
ratings. The fourth variable there is the life
of the bond. The life of the bond is maturity
value or also known as maturity value is the
value promised to be paid at the time the
bond matures, Anandasari & Sudjarni,
(2017). Meanwhile, research according to
Faizah (2019) and Widiastuti & Rahyuda
(2016), states that maturity has a significant
positive effect on bond ratings. In the last
independent variable, the size of the
company is the size of a company which
can be expressed by total assets or by total
net sales. The larger the total assets, the
larger the size of a company. The larger the
assets, the greater the capital invested,
while the more debt turnover in the
company (Hery, 2017). According to Sari &
Badjra (2016), Pinanditha & Suryantini
(2016), stated that the size of the company
proxied by size has a significant positive
effect on bond ratings. Here are some
explanations related to bonds, bond
ratings, factors that affect bond ratings.
Based on the background above, the
author is interested in researching the ratio
of financial to bond ratings of companies.
This research is a modification of previous
researchers, namely Ni Made Estiyanti and
Gerianta Wirawan Yasa (2017) and Periklis
Gogas, Theophilos Papadimitriou and Anna
Agrapetidou (2019) about financial ratios
that affect bond ratings, researchers
decided to research with the title "The
Effect of Profitability,
Leverage and
Liquidity on Bond Ratings in Non-Financial
Companies Listed on the IDX for the 2018-
2021 Period.
MATERIALS AND METHODS
This research uses a quantitative
research approach. Quantitative research is
a study that basically uses a deductive-
inductive approach. This approach departs
from a theoretical framework, the ideas of
experts, and the understanding of
researchers based on their experience, then
developed into problems posed to obtain
justification (verification) or rejection in the
form of field empirical data documents.
The quantitative approach aims to
test the theory, build facts, show
relationships between variables, give a
statistical description, assess and forecast
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the results. Research designs that use a
quantitative approach must be structured,
standard, formal and designed as carefully
as possible beforehand. The design is
specific and detailed because the design is
a research design that will be carried out
actually. This study is to test the effect of
the Profitability, Leverage, and Liquidity
variables on the Bond Rating variables.
Meanwhile, to analyze the influence of each
variable using multiple linear regression
analysis techniques.
RESULTS AND DISCUSSION
A. Overview of Research Objects
The data used in this study is
secondary data sourced from the
company's annual report for the period
2018 to 2021 obtained through the official
website of the Indonesia Stock Exchange at
the address of the www.idx.co.id, the
Company's official website, the Indonesian
Securities Rating (PEFINDO) at the
https://www.pefindo.com address, taking
from articles, journals, previous research,
and other relevant sources. The data used
are related to the profitability, leverage, and
likwidity of the company as well as bond
ratings. In this study, the purposive
sampling method was used to determine
the sample. Purposive sampling indicates
that the sample used in the study is a
representation of the existing population
and is in accordance with the purpose of
the study. The analysis method used in this
study is a quantitative analysis method
using the help of Microsoft Excel 2016
software and SPSS (Statistical Package for
Social Sciences) version 26.0 as a tool to
test data. The purpose of this analysis is to
obtain the relevant information contained
in the data and use the results to solve a
problem. Here's a table with company
names as follows:
Table 1.
Research Samples
Sample Criteria
Bond Amount
Bonds listed on the IDX during the 2018-2021
observation year
105
Bonds issued by companies that are not
listed on the IDX during the 2018-2021 observation year
(33)
Bonds not rated by Pefindo during
observation year 2018-2021
(17)
Bonds that do not publish financial statements
during the observation year 2018-2021
(9)
Number of Observations 22 companies
28
Total sample 22 x 4 years =
112
Data processed 2022
Table 2.
Research Data Samples
No
Code
Non-Financial Company Name
1451 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
1
HIGH
PT Tri Banyan Tirta Tbk
2
CAMP
PT Campina Ice Cream Industry Tbk
3
WAITING
PT Wilmar Cahaya Indonesia Tbk
4
CLEO
PT Sariguna Primatirta Tbk
5
DLTA
PT Delta Djakarta Tbk
6
HOCKEY
PT Buyung Poetra Sembada Tbk
7
ICBP
PT Indofood CBP Sukses Makmur Tbk
8
INDF
PT Indofood Sukses Makmur Tbk
9
MLBI
PT Multi Bintang indonesia Tbk
10
MYOR
PT Mayora Indah Tbk
11
PCAR
PT Pratama Cakrawala Abadi Tbk
12
PSDN
PT Prasidha Aneka Niaga Tbk
13
BREAD
PT Nippon Indosari Corpindo Tbk
14
SKLT
PT Sekar Laut Tbk
15
STTP
PT Siantar Top Tbk
16
ULTJ
PT Ultra Jaya Milk Industry
17
GGRM
PT Gudang Garam Tbk
18
HMSP
PT Hm Sampoerna Tbk
19
RMBA
PT Bentoel Internasional Investama Tbk
20
WIIM
PT Wismilak Inti Makmur Tbk
21
DVLA
PT Darya Varia Laboratoria Tbk
22
INAF
PT Indofarma Tbk
23
HIGH
PT Tri Banyan Tirta Tbk
24
CAMP
PT Campina Ice Cream Industry Tbk
25
WAITING
PT Wilmar Cahaya Indonesia Tbk
26
CLEO
PT Sariguna Primatirta Tbk
27
DLTA
PT Delta Djakarta Tbk
28
HOCKEY
PT Buyung Poetra Sembada Tbk
29
ICBP
PT Indofood CBP Sukses Makmur Tbk
30
INDF
PT Indofood Sukses Makmur Tbk
31
MLBI
PT Multi Bintang indonesia Tbk
32
MYOR
PT Mayora Indah Tbk
33
PCAR
PT Pratama Cakrawala Abadi Tbk
34
PSDN
PT Prasidha Aneka Niaga Tbk
35
BREAD
PT Nippon Indosari Corpindo Tbk
36
SKLT
PT Sekar Laut Tbk
37
STTP
PT Siantar Top Tbk
38
ULTJ
PT Ultra Jaya Milk Industry
39
GGRM
PT Gudang Garam Tbk
40
HMSP
PT Hm Sampoerna Tbk
41
RMBA
PT Bentoel Internasional Investama
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Tbk
42
WIIM
PT Wismilak Inti Makmur Tbk
43
DVLA
PT Darya Varia Laboratoria Tbk
44
INAF
PT Indofarma Tbk
45
HIGH
PT Tri Banyan Tirta Tbk
46
CAMP
PT Campina Ice Cream Industry Tbk
47
WAITING
PT Wilmar Cahaya Indonesia Tbk
48
CLEO
PT Sariguna Primatirta Tbk
49
DLTA
PT Delta Djakarta Tbk
50
HOCKEY
PT Buyung Poetra Sembada Tbk
51
ICBP
PT Indofood CBP Sukses Makmur Tbk
52
INDF
PT Indofood Sukses Makmur Tbk
53
MLBI
PT Multi Bintang indonesia Tbk
54
MYOR
PT Mayora Indah Tbk
55
PCAR
PT Pratama Cakrawala Abadi Tbk
56
PSDN
PT Prasidha Aneka Niaga Tbk
57
BREAD
PT Nippon Indosari Corpindo Tbk
58
SKLT
PT Sekar Laut Tbk
59
STTP
PT Siantar Top Tbk
60
ULTJ
PT Ultra Jaya Milk Industry
61
GGRM
PT Gudang Garam Tbk
62
HMSP
PT Hm Sampoerna Tbk
63
RMBA
PT Bentoel Internasional Investama
Tbk
64
WIIM
PT Wismilak Inti Makmur Tbk
65
DVLA
PT Darya Varia Laboratoria Tbk
66
INAF
PT Indofarma Tbk
67
HIGH
PT Tri Banyan Tirta Tbk
68
CAMP
PT Campina Ice Cream Industry Tbk
69
WAITING
PT Wilmar Cahaya Indonesia Tbk
70
CLEO
PT Sariguna Primatirta Tbk
71
DLTA
PT Delta Djakarta Tbk
72
HOCKEY
PT Buyung Poetra Sembada Tbk
73
ICBP
PT Indofood CBP Sukses Makmur Tbk
74
INDF
PT Indofood Sukses Makmur Tbk
75
MLBI
PT Multi Bintang indonesia Tbk
76
MYOR
PT Mayora Indah Tbk
77
PCAR
PT Pratama Cakrawala Abadi Tbk
78
PSDN
PT Prasidha Aneka Niaga Tbk
79
BREAD
PT Nippon Indosari Corpindo Tbk
80
SKLT
PT Sekar Laut Tbk
1453 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
81
STTP
PT Siantar Top Tbk
82
ULTJ
PT Ultra Jaya Milk Industry
83
GGRM
PT Gudang Garam Tbk
84
HMSP
PT Hm Sampoerna Tbk
85
RMBA
PT Bentoel Internasional Investama
Tbk
86
WIIM
PT Wismilak Inti Makmur Tbk
87
DVLA
PT Darya Varia Laboratoria Tbk
88
INAF
PT Indofarma Tbk
89
HIGH
PT Tri Banyan Tirta Tbk
90
CAMP
PT Campina Ice Cream Industry Tbk
91
WAITING
PT Wilmar Cahaya Indonesia Tbk
92
CLEO
PT Sariguna Primatirta Tbk
93
DLTA
PT Delta Djakarta Tbk
94
HOCKEY
PT Buyung Poetra Sembada Tbk
95
ICBP
PT Indofood CBP Sukses Makmur
Tbk
96
INDF
PT Indofood Sukses Makmur Tbk
97
MLBI
PT Multi Bintang indonesia Tbk
98
MYOR
PT Mayora Indah Tbk
99
PCAR
PT Pratama Cakrawala Abadi Tbk
100
PSDN
PT Prasidha Aneka Niaga Tbk
101
BREAD
PT Nippon Indosari Corpindo Tbk
102
SKLT
PT Sekar Laut Tbk
103
STTP
PT Siantar Top Tbk
104
ULTJ
PT Ultra Jaya Milk Industry
105
GGRM
PT Gudang Garam Tbk
106
HMSP
PT Hm Sampoerna Tbk
107
RMBA
PT Bentoel Internasional Investama
Tbk
108
WIIM
PT Wismilak Inti Makmur Tbk
109
DVLA
PT Darya Varia Laboratoria Tbk
110
INAF
PT Indofarma Tbk
111
HIGH
PT Tri Banyan Tirta Tbk
112
CAMP
PT Campina Ice Cream Industry Tbk
B. Results of Descriptive Statistical Analysis
According to Ghozali (2018)
descriptive statistical analysis provides an
overview or description of a data seen from
the
mean
, standard deviation, maximum,
and minimum values.
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Table 3.
Descriptive Statistical Results
Descriptive Statistics
Mini
mum
Maxi
mum
Mea
n
Std.
Deviation
Profitability
1.00
748.0
0
81.10
23
79.2194
Leverage
52.0
0
2201.
00
734.6
705
346.4044
1
Liquidity
343.
00
994.0
0
311.1
023
258.4967
2
Pering_Obli
gasi
.00
15.00
.9773
.5608
Valid N
(listwise)
Source : Data processed 2022
From the results of descriptive
statistical calculations in the table above,
the analysis can be explained as follows:
1. The table above explains the variable
profitability of non-financial
companies listed on the Indonesia
Stock Exchange in 2018-2021. The
highest profitability of non-financial
companies listed on the Indonesia
Stock Exchange in 2018-2021 is
748.00, while the minimum
profitability of non-financial
companies listed on the Indonesia
Stock Exchange in 2018-2021 is 1.00.
The average Profitability of non-
financial companies Listed on the
Indonesia Stock Exchange in 2018-
2021 is 81.1023, and the standard
deviation of Profitability of non-
financial companies Listed on the
Indonesia Stock Exchange in 2018-
2021 is 79.2194.
2. The table above explains the variable
leverage of non-financial companies
listed on the Indonesia Stock
Exchange for 2018-2021. The highest
leverage of non-financial companies
listed on the Indonesia Stock
Exchange in 2018-2021 is 2201.00,
while the minimum leverage of non-
financial companies listed on the
Indonesia Stock Exchange in 2018-
2021 is 52.00. The average Leverage
of non-financial companies Listed on
the Indonesia Stock Exchange in
2018-2021 is 734.6705, and the
standard deviation of Leverage from
non-financial companies Listed on
the Indonesia Stock Exchange in
2018-2021 is 346.40441.
3. The table above explains the Liquidity
variables of non-financial companies
listed on the Indonesia Stock
Exchange in 2018-2021. The highest
liquidity of non-financial companies
listed on the Indonesia Stock
Exchange in 2018-2021 is 994.00,
while the minimum liquidity of non-
financial companies listed on the
Indonesia Stock Exchange in 2018-
1455 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
2021 is 343.00. The average Liquidity
of non-financial companies Listed on
the Indonesia Stock Exchange in
2018-2021 is 311.1023, and the
standard deviation of Liquidity from
non-financial companies Listed on
the Indonesia Stock Exchange in
2018-2021 is 258.4967.
4. The table above describes the
variable bond ratings of non-financial
companies listed on the Indonesia
Stock Exchange for 2018-2021. The
highest Bond Rating of non-financial
companies listed on the Indonesia
Stock Exchange for 2018-2021 is
15.00, while the minimum Bond
Rating of non-financial companies
listed on the Indonesia Stock
Exchange for 2018-2021 is .00. The
average Bond Rating of non-financial
companies Listed on the Indonesia
Stock Exchange in 2018-2021
is .9773, and the standard deviation
of Bond Ratings of non-financial
companies Listed on the Indonesia
Stock Exchange for 2018-2021
is .5608.
C. Uji Kelayakan Model
(Goodness of
Fit
The feasibility of the regression
model was assessed using Hosmer
and Lemeshow's Goodness of Fit
Test. Hosmer and Lemeshow's
Goodness of Fit Test tests the null
hypothesis that empirical data match
or fit the model (there is no
difference between the model and
the data so the model can be said to
be fit).
Table 4.
Model Feasibility Test Results
Hosmer and Lemeshow Test
St
ep
Chi-
square
df
Itself
.
1
7.939
8
.439
Source : Data processed, 2022
Table 5.4 shows that the statistical
value of Hosmer and Lemeshow's
Goodness of Fit Test is 7,939 with a
significance of .439. So with a significant
level of .439 whose value is greater than
0.05 shows that the model is able to predict
the value of observations in research or it
can be said that the model is acceptable
because it matches the observation data.
D. Model Fit
To assess the entire model (
Overal
model fit
) it can be seen from the value of -
2log likelihood at the beginning (block
number = 0) and the value of -2 log
likelihood at block number = 1. If the value
of -2 log likelihood of block number = 0 is
greater than the value of -2 log likelihood
in block number = 1. So it shows that the
hypothesized model fits the data. Here are
the results of the likelihood log -2 test,
which is more in the table, below:
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Table 5. Overall Model Fit Test 1
Iteration History
a,b,c
Iteration
-2 Log
likelihood
Coefficients
Constant
Step
0
1
83.449
1.504
Source : Data processed 2022
The overall assessment of the model is
done by comparing the value between -2
Log Likelihood at the beginning (Block
Number = 0), where the model enters only
constants, with the value -2 Log Likelihood
at the end (Block Number = 1), where the
model enters constants and free variables.
This table shows that the statistical value of
2 Log Likehood (block number = 0) without
variables, only a constant of 83,449. It can
be said that models without variables are
not fit.
Table 6. Overall Model Fit Test 2
Iteration History
a,b,c,d
Iteration
-2 Log
likelihood
Coefficients
Cons
tant
Profitab
ility
Leve
rage
Likwi
ditas
St
ep 1
1
68.035
3.01
1
-.001
-.002
-.001
2
62.063
4.66
9
-.002
-.003
-.002
3
61.294
5.55
4
-.001
-.004
-.003
4
61.262
5.76
6
-.001
-.004
-.003
5
61.262
5.77
6
-.001
-.004
-.003
6
61.262
5.77
6
-.001
-.004
-.003
Initial -2 Log Likelihood: 83.449
The initial -2Likelihood value is 83,449
and after the inclusion of all three
independent variables, the final -2
Likelihood value decreases to 61,262 This
decrease in the value - 2Likelihood
indicates that the addition of an
independent variable to the model can
improve the model so that the model is said
to be fit.
E. Coefficient of Determination
(Nagelkarke R Square)
The Nagelkarke value of R² can be
interpreted as the value of R² in multiple
regression. The Nagelkarke R² value seen in
1457 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
snell's cox n value can be used to measure
the model's ability to describe dependent
variables. The following are the results of
the Nagelkarke R² value, more presented in
the table:
Table 7. Nagelkarke R Square Test Results
Model Summary
St
ep
-2 Log
likelihood
Cox &
Snell R
Square
Nagelker
ke R Square
1
61.262
a
.223
.564
a. Estimation terminated at iteration number 6
because parameter estimates changed by less
than .001.
The magnitude of the value of the
coefficient of determination in the logistic
regression model is indicated by the value
of Nagelkerke R square. Based on the
results of the tests carried out, the value of
the R square agelkerke is 0.564 which
means that the variability of the dependent
variables that can be explained by the
independent variables namely, profitability,
leverage and liquidity is 56.2%, while the
remaining 43.8% is explained by other
variables outside the research model.
F. Multicholinearity Test
The multicholinearity test aims to test
whether a regression model found a
correlation between free (independent)
variables. A good regression model is a
regression in the absence of symptoms of a
strong correlation among its free variables.
Multicholinearity testing in logsitic
regression using correlation matrices
between independent variables and
calculation of Tolerance and VIF values. Test
results are shown in Table 5.8.
Table 8 Multicholinearity Test Results
Coefficients
a
Model
Unstandardized
Coefficients
Collinearity
Statistics
B
Std.
Error
Tolera
nce
BRIG
HT
1
(Consta
nt)
1.124
.099
Profitabi
lity
.019
.001
.961
1.04
1
Leverag
e
.000
.000
.931
1.07
4
Liquidity
.000
.000
.947
1.05
6
a. Dependent Variable: Peringkat_Oblig
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The results of the multicholinearity
test show that there are no independent
variables that have a Tolerance value of less
than 0.10 which means that there is no
correlation between independent variables.
The results of calculating the VIF value also
show that there are no independent
variables that have a VIF value of more than
10. So it can be concluded that there is no
multicholinearity between independent
variables in the regression model.
G. Formed Regression Models and
Hypothesis Testing
A logistic regression model can be
formed by looking at the estimated value of
the paramater in Variables in The Equation.
The regression model formed based on the
estimated value of parameters in Variables
in The Equation is as follows : RATING =
5.776 + .001PROFIT + .004LEVE
– .003LIKUID + εi
Table 9. Hypothesis Test Results
Variables in the Equation
B
S
.E.
Fo
rest
d
f
It
self.
Ex
p(B)
95%
C.I.for EXP(B)
L
ower
U
pper
Ste
p 1
a
Profit
ability
0
.001
0
.012
0.0
07
1
0
.033
.99
9
.
976
1
.023
Levera
ge
0
.004
0
.001
6.7
57
1
0
.009
.99
6
.
993
.
999
Likwid
itas
0
.003
0
.001
5.1
42
1
0
.023
.99
7
.
995
1
.000
Const
ant
5
.776
1
.418
16.
583
1
0
.000
32
2.503
a. Variable(s) entered on step 1: Profitabilitas, Leverage, Likwiditas.
Hypothesis testing is carried out by
comparing the significance level (
sig
) with
the error rate
(β)
= 5%. The results of the
hypothesis test are as follows:
1). Hypothesis 1. (Profitability
positively affects bond ratings)
H1. states that profitability has a
positive effect on bond ratings. Based on
the results of the hypothesis test, it can be
seen that the value of the regression
coefficient is . 001 with a significance value
of 0.033 < 0.05. Since the significance value
is less than 0.05 then H1 is accepted. This
means that profitability has a significant
positive effect on bond ratings.
2). Hypothesis 2. (Leverage
positively affects bond ratings)
H2. states that leverage has a positive
effect on bond ratings. Based on the results
of the hypothesis test, it can be seen that
the value of the regression coefficient
is .004 with a significance value of 0.009 <
0.05. Since the significance value is less
than 0.05 then H2 is accepted. This means
that leverage has a significant positive
effect on bond ratings.
3). Hypothesis 3. (Liquidity positively
affects bond ratings)
1459 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
H3. states that liquidity can have a
positive effect on bond ratings. Based on
the results of the hypothesis test, it can be
seen that the coefficient value is .003 with a
significance value of 0.023 < 0.05. Since the
significance value is greater than 0.05 then
H3 is accepted. This means that the
liquidity ratio has a significant positive
effect on bond ratings.
B. Interpretation of Research Results
1. Effect of Profitability Terh a dap
Bond Rating
Profitability is one of the
measurements for company performance.
The profitability of an enterprise indicates
the ability of an enterprise to make a profit
over a certain period. In this study,
profitability calculated how much profit the
company's assets generate. Based on
hypothesis testing conducted in this study,
it is stated that profitability has a significant
positive influence on bond ratings with a
regression coefficient of 0.001 and a
significance level of 0.033 < 0.05, which
means that a 1 percent increase in
profitability will increase the chances of a
bond rating.
The test results show a positive
regression coefficient so that the effect of
profitability on bond ratings is positive,
where the higher the profitability
generated by the company, the better the
bond rating of a company, and vice versa. It
can also be seen from the descriptive
statistical test where companies with the
high investment grade category have a
high level of profitability compared to
companies with the
low investment grade
category or an average value greater than
the standard deviation of (81.1023 >
79.2194).
High profitability reflects good
performance so it can be said that
profitability is a good indicator in assessing
the health of the company. High profit
indicates the company's ability to fulfill its
obligations on time. This has an impact on
the bond rating assessment set by
PEFINDO where the high profitability will
also be better.
The results of this study are in
accordance with research conducted by Siti
Hariyati (2019), Arifman (2017), Manurung,
et.al (2016) and Barkah Rian (2015) that
companies that have high profits are
considered capable of fulfilling their
obligations, so that the possibility of
default risk of these companies becomes
lower. The company's high profitability
indicates that the loans provided by the
creditors have been used well by the
company so that they are able to generate
high profits.
2. Effect of Leverage Terh a dap Bond
Rating
Leverage is a ratio used to measure
how much of an asset a company has
derived from debt or capital. The leverage
of a company calculates the share of its own
capital that is used as collateral for the
entire debt. Based on the results of
hypothesis testing conducted by this study,
it is stated that leverage has a significant
negative influence on bond ratings with a
regression coefficient of 0.004 and a
significance level of 0.009 < 0.05, which
means that it can be concluded that the
higher the liquidity of a company, the
better its bond rating.
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Variables in the Equation
B
S
.E.
Fo
rest
d
f
It
self.
Ex
p(B)
95%
C.I.for EXP(B)
L
ower
U
pper
Ste
p 1
a
Profit
ability
0
.001
0
.012
0.0
07
1
0
.033
.99
9
.
976
1
.023
Levera
ge
0
.004
0
.001
6.7
57
1
0
.009
.99
6
.
993
.
999
Likwid
itas
0
.003
0
.001
5.1
42
1
0
.023
.99
7
.
995
1
.000
Const
ant
5
.776
1
.418
16.
583
1
0
.000
32
2.503
a. Variable(s) entered on step 1: Profitabilitas, Leverage, Likwiditas.
The test results show a negative
regression coefficient, where the higher the
leverage, the lower the company's bond
rating, and vice versa. Based on descriptive
statistical tests conducted, it is stated that
companies with the high investment grade
category have a higher level of leverage
than companies with the low investment
grade category. This indicates that the high
level of leverage results in the company
being faced with the risk of company failure
because it tends to have a low ability to pay
off its obligations and the bond rating has
dropped.
The results of this study are in
accordance with research conducted by Adi
Wira Pinandhita (2016), Saputri (2016)
which states that the smaller the company's
leverage, the more its bond rating
increases. The results of this study
corroborate research from Sari (2018)
Leverage, this high ratio means that most
assets are funded with debt and this causes
the company to be faced with a default risk
problem so that the possibility of the
company getting a bond rating is not good.
Thus, these findings are consistent with the
research conducted by Amalia (2017).
However, these results contradict the
research of Magreta & Nurmayanti (2019)
and Manurung, et al. (2018).
Companies with a low
leverage
ratio
are the higher the likelihood of obtaining a
higher bond rating (Adams et al., 2017).
Researchers think that external parties tend
to look at companies that have low
leverage
because the risks owned by these
companies are relatively small given the
comparison of capital needed to cover the
company's debt is low. If
the company's
leverage is too high, it is feared that there
will be a default when issuing bonds later
because the debt burden owned is already
quite heavy.
3. Effect of Likwiditas Terh a dap Bond
Rating
Liquidity is one of the tools used to
measure a company's ability to meet
1461 | Logistic Regression With Non-Financial Liability Ratings on The Indonesia Stock Exchange
obligations that are due soon. Liquidity
compares current assets with current debt
of the company. Based on hypothesis
testing in this study, a regression coefficient
of. 003 showed a positive relationship to
bond ratings. The results suggest that the
third hypothesis is accepted. The test
results show a positive regression
coefficient so that the effect of liquidity on
bond ratings is positive, where the higher
the liquidity generated by the company, the
more it will increase the bond rating of a
company, and vice versa.
This result was in line with what was
expected as liquidity was thought to have
an influence on the bond's rating. A liquid
company is considered to be able to fulfill
its obligations in a timely manner so as to
avoid the risk of default. However, this
result occurs because investors tend to
choose to consider other risks arising from
investment grade
bondsas they are
considered more important. The results of
this study are in accordance with research
by Sari (2016) and (Dali et al., 2015) stated
that a positive liquidity value indicates that
the company has high liquidity is most
likely to be in an efficient condition, for
example the company does not use
financing through bonds because the
company has large internal funds and tends
to choose to use internal funds first
compared to external sources of financing
such as The issuance of bonds resulted in
a high company value and affected the
rating of bonds. However, this study
contradicts the research that (Almilia &
Budisusetyo, 2017)
CONCLUSIONS
After data analysis and hypothesis testing,
the results of the research discussed in
Chapter V were obtained.
1. Profitability has a significant positive
influence on bond ratings in non-
financial companies listed on the
Indonesia Stock Exchange in 2018-2021.
The significance level is 0.033 < 0.05 and
the regression coefficient is .001. These
results show that the higher a non-
financial company generates a profit, the
better its bond rating will be. The high
profit achieved by the company
indicates that the company is able to
fulfill its obligations on time.
2. Leverage has a significant negative
influence on bond ratings in non-
financial companies listed on the
Indonesia Stock Exchange for 2018-2028
1. The significance level is 0.009 < 0.05
and the regression coefficient is -.004.
These results show that the lower the
company's leverage level, the better the
bond rating will be. The high level of
leverage indicts that companies use a lot
of debt in funding their company's
activities so that they tend to have a risk
of failure in fulfilling their obligations.
Liquidity has a significance level of. 0.023 >
0.05 and a regression coefficient of .003.
The results show that liquidity has a
positive influence on bond ratings in non-
financial companies listed on the Indonesia
Stock Exchange for 2018-2028 1, thus
supporting the research hypothesis. This is
because liquidity shows a positive
direction, where the higher the level of
liquidity, the greater the bond rating of a
non-financial company
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© 2023 by the authors. Submitted
for possible open access publication
under the terms and conditions of the Creative
Commons Attribution (CC BY SA) license
(https://creativecommons.org/licenses/by-sa/4.0/).