Business Management Vol.
5 No 1 Februari 2026 p-ISSN:2828-7606, e-ISSN:2828-8203 DOI: 10.
58258/bisnis.
10195/https://ejournal.
org/index.
php/Bisnis The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings Muh.
Rusli1.
Andi Firdania2.
Andi Masniah3 Akademi Sekretari Manajemen Indonesia Publik.
Makassar.
Indonesia Institut Ilmu Sosial Dan Bisnis Manajemen Andi Sapada.
Parepare.
Indonesia Article Info Abstract Article history:
Accepted: 26 December 2025 Publish: 1 February 2026 Introduction: Bond ratings are key indicators of corporate credit risk and reduce information asymmetry in debt markets.
In Indonesia.
PT PEFINDO ratings differentiate issuers with stronger credit quality .
nvestment grad.
from riskier issuers .
on-investment grad.
This study examines whether earnings management, financial ratios, and corporate governance mechanisms affect bond ratings of IDX-listed issuers rated by PEFINDO.
Material and Methods: This quantitative explanatory study uses secondary data from annual reports/financial statements and PEFINDO bond ratings.
Using purposive sampling, 11 non-financial firms were observed for 2018Ae2022 .
firm-year Bond ratings were coded as a binary variable .
nvestment grade = 1.
non-investment grade = .
and analyzed using binary logistic regression.
Research Results: Most observations were investment grade .
1%).
The model fits well (HosmerAeLemeshow Sig.
= 0.
with strong explanatory power (Nagelkerke RA = 0.
Earnings management negatively affects the probability of achieving investment grade, while liquidity, managerial ownership, and audit quality (Big-4 prox.
positively affect it.
Total asset turnover, priceAe earnings ratio, institutional ownership, and independent commissioners are not significant.
Conclusion: Bond ratings are more closely related to reporting credibility, liquidity strength, and selected governance signals than to market-based and activity ratios in this sample.
Issuers should enhance reporting quality, liquidity management, and audit/governance credibility to support higher bond ratings.
future studies should expand samples, periods, and include additional credit-risk controls and alternative rating models.
Keywords:
Bond Ratings.
Earnings Management.
Financial Ratios.
Corporate Governance Mechanisms.
Logistic Regression.
This is an open access article under the Lisensi Creative Commons Atribusi-BerbagiSerupa 4.
0 Internasional Corresponding Author:
Muh.
Rusli Akademi Sekretari Manajemen Indonesia Publik.
Makassar.
Indonesia Email Coresspondent: roeslicully@gmail.
INTRODUCTION
The corporate bond market has become an important financing channel because it provides firms with medium- to long-term funding beyond bank loans while offering investors instruments with measurable riskAereturn profiles.
Nevertheless, the main risk remains the issuerAos ability to meet coupon and principal payments, making credit-risk information essential for investment decisions.
In Indonesia, rating agencies such as PT PEFINDO play a central role in providing credit-risk signals through bond ratings.
Default monitoring evidence indicates that defaults still occur and tend to be concentrated in lower rating categories.
PEFINDO reported default events in 2024 with cumulative default values reaching hundreds of billions of rupiah, while the highest rating categories show longhorizon consistency without defaults ((PEFINDO) PT Pemeringkat Efek Indonesia, 2.
40 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 Regulators also publish regular capital market statistics to strengthen transparency and supervision, reinforcing the need to enhance information quality and investor protection through market infrastructure, including ratings (Otoritas Jasa Keuangan, 2.
International governance standards further emphasize that effective corporate governance and transparent disclosure support market confidence, financial stability, and the protection of investors and creditors, including bondholders (OECD, 2023, 2.
Earnings management is a key concern because it can reduce reporting quality and weaken the ability of investors and rating agencies to assess credit risk accurately.
Recent literature confirms that earnings management remains a major research issue, including bibliometric evidence highlighting its market consequences and the importance of control mechanisms (Bui, 2.
In the bond-rating context, accounting manipulation may distort performance indicators used in credit assessment and therefore be associated with higher or lower rating outcomes.
Indonesian evidence also commonly tests earnings management alongside financial and governance variables as determinants of bond ratings (Oktaviyani.
Financial ratios .
, liquidity, leverage/solvency, and profitabilit.
capture issuersAo fundamentals and are directly related to default probability.
Recent studies show that liquidity measures can help distinguish investment-grade bonds from high-yield categories, supporting classification-based modeling of ratings (Armanda & Wibowo, 2.
Other Indonesian studies continue to find that profitability and solvency indicators explain rating variation, indicating that financial ratios remain core signals in credit-risk evaluation (Sumantri et al.
, 2.
Corporate governance mechanisms .
, board structure, audit committee effectiveness, institutional/managerial ownership, and monitoring qualit.
are designed to reduce agency conflicts and improve accountability and transparency.
Global governance principles stress the governance relationship among management, boards, shareholders, and stakeholdersAiincluding creditorsAiand highlight transparency as a prerequisite for healthy access to financing (OECD, 2.
OECDAos Indonesia-focused governance review also notes the role of OJK regulation and capital-market strengthening in disclosure and investor protection (OECD, 2.
Moreover, modern risk assessment increasingly recognizes governance/ESG considerations as part of credit-risk evaluation, making governance variables increasingly relevant in bond-rating research (Kusumowinahyu, 2.
Recent Indonesian studies generally assess bond-rating determinants by combining earnings management, financial ratios, and corporate governance variables.
For example, evidence suggests that these three groups of factors are relevant in explaining bond-rating variation among Indonesian issuers (Oktaviyani, 2.
Other work reports that solvency/financing structure and governance may be associated with ratings, although the direction and significance can differ across samples, sectors, and time periods (Hartono et , 2.
Additional studies confirm that bond ratings are not driven solely by financial figures but also by governance quality (Sumantri et al.
, 2.
At the same time, recent findings indicate that conclusions depend on how variables are operationalized and how ratings are classified.
Liquidity proxy studies, for instance, show that different liquidity measures vary in their ability to separate investment-grade and high-yield categories (Armanda & Wibowo, 2.
Other research links governance and bond characteristics .
, liquidity and maturit.
to bond ratings that then mediate yield outcomes, implying that ratings reflect both firm fundamentals and instrument features (Alinto et al.
, 2.
Governance research also shows its role in constraining earnings management, potentially improving the quality of information used by rating agencies (Mismiwati et al.
, 2.
Bibliometric reviews further reinforce the continued importance of reporting quality and 41 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 control mechanisms in understanding the market consequences of earnings management, including credit risk (Bui, 2.
Given these developments, the study is urgent because bond investors require risk assessment that does not rely on a single information set.
Defaults still occur and are more frequent at lower rating levels, making it important to identify factors influencing ratings for risk mitigation ((PEFINDO) PT Pemeringkat Efek Indonesia, 2.
Policy trends toward transparency and stronger governance frameworks also call for empirical evidence on whether accounting information .
ncluding earnings managemen.
, financial ratios, and governance mechanisms meaningfully contribute to rating outcomes (Otoritas Jasa Keuangan, 2.
Because ratings are often mapped into categories .
nvestment grade vs non-investment grad.
, classification approaches such as logistic regression are particularly suitable for producing practical evidence for investors, issuers, and regulators.
Using IDX-listed firms rated by PEFINDO and a purposive sample .
firms, 4-year perio.
, this research addresses: .
whether earnings management affects bond ratings.
whether financial ratios affect bond ratings.
whether corporate governance mechanisms affect bond ratings.
whether earnings management, financial ratios, and corporate governance simultaneously affect bond ratings among IDX firms rated by PEFINDO (Oktaviyani, 2021.
(PEFINDO) PT Pemeringkat Efek Indonesia, 2.
MATERIAL AND METHODS
Bond ratings are a crucial creditAarisk signal because they reflect default probability and an issuerAos ability to meet coupon and principal obligations.
From an information asymmetry perspective, ratings reduce uncertainty between issuers and investors.
under signalling theory, strong performance and governance signal credibility and may support higher Prior evidence shows that credit ratings are shaped not only by financial performance .
but also by reporting quality .
, earnings managemen.
and governance mechanisms that mitigate agency conflicts (Alkhawaldeh et al.
, 2.
In this study, bond ratings (Bond Ratin.
are converted into a binary scale, where investment grade is coded as 1 and non-investment grade is coded as 0.
This classification is consistent with the common practice in rating-agency studies that distinguish Auinvestment-worthyAy versus Aunon-investment-worthyAy categories .
ncluding logistic regression tests using high vs low rating group.
Operationally, the measurement is defined ycycnycoyca ycycaycycnycuyci .
ayaya, yaya, ya, yaAyaAyaA} .
cnycuycyceycycycoyceycuyc yciycycayccyc.
yaAycIycnyc = { 0, ycycnycoyca ycycaycycnycuyci .
aAyaA, yaA, yayaya, y.
cuycuycu Oe ycnycuycyceycycycoyceycuyc yciycycayccyc.
This binary categorization is also appropriate for logistic regression, which models the probability that an issuer belongs to a higher versus lower rating class (Livia et al.
, 2.
Earnings management refers to managerial intervention in financial reporting through accrual choices or real activities to achieve specific targets such as performance perception, financing costs, or financing access.
In debt markets, firms may have incentives to manage earnings to appear more creditworthy and improve credit assessments, both during initial evaluations and following rating changes (Enjolras & Yue, 2.
Earnings management in this study is proxied by a Healy-based managed accrual estimate .
ccrual varian.
scaled by total assets to reduce firm-size distortion.
Operationally:
Managed accrual estimate (EDA) 42 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 yayayaycnyc = Total accruals (TA) ycNyaycnyc yaycnycOe1 ycNyaycnyc = ycAyayc Oe yayaycCyc where ycAyayc is net income in period yc, yayaycCyc is operating cash flow in period yc, and yaycnycOe1 is total assets in period ycOe1.
The use of accrual-based proxies for earnings management and comparative discussions of discretionary accrual models .
ncluding the Healy/accrual approac.
are widely summarized in methodological and comparative studies in modern accounting research (ACAR & COSKUN, 2.
Financial ratios are treated as indicators of financial strength and operating efficiency that influence credit-risk perceptions.
Under signalling logic, stronger ratios imply better short-term solvency and cash-generation capacity, potentially increasing the likelihood of higher ratings.
Evidence from rated firms .
ncluding PEFINDO-rated issuer.
suggests that combinations of ratios can explain rating variation, although effects may differ across sectors and periods (Setiawati et al.
, 2.
Liquidity (Current Ratio/CR) Liquidity measures a firmAos ability to meet short-term obligations using current Operationally:
yaycIycnyc = yaycycycyceycuyc yaycycyceycycnyc yaycycycyceycuyc yaycnycaycaycnycoycnycycnyceycycnyc Several rating-based studies indicate that CR is often used as a potential predictor of bond ratings, although its significance may depend on sample characteristics and control variables (Livia et al.
, 2024.
Setiawati et al.
, 2.
Activity (Total Asset Turnover/TAT) This ratio reflects how effectively a firm utilizes its assets to generate sales.
Operationally:
ycAyceyc ycIycaycoyceycycnyc ycNyaycNycnyc = ycNycuycycayco yaycycyceycycnyc Theoretically, higher asset turnover can strengthen cash-generating capacity and, in turn, influence perceived credit risk and ratings (Taradong et al.
, 2.
Market value (Price Earning Ratio/PER) PER reflects how the market values a firmAos earnings prospects .
rowth expectations/ris.
through the stock price relative to earnings per share.
Operationally:
ycEycycnycayce ycyyceyc ycIEaycaycyceycnyc ycEyaycIycnyc = yaycaycycuycnycuyciyc ycyyceyc ycIEaycaycyceycnyc Within a market-information framework, price-based ratios can capture investorsAo expectations about earnings quality and sustainability, making them relevant to analyze alongside other rating determinants (Livia et al.
, 2.
43 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 Corporate governance is conceptualized as a monitoring and control system that aligns management with shareholders and creditors, reduces agency costs, and improves disclosure Credit-rating research argues that effective governance enhances creditworthiness by constraining opportunistic behavior .
ncluding aggressive earnings managemen.
and strengthening decision discipline (Alkhawaldeh et al.
, 2.
Institutional ownership (INST) Institutional ownership is commonly viewed as an external monitoring mechanism that strengthens managerial discipline.
It is measured as:
yaycAycIycNycnyc = ycIEaycaycyceyc Eayceycoycc ycayc yaycuycycycnycycycycnycuycuycycnyc ycNycuycycayco ycIEaycaycyceyc ycCycycycycycaycuyccycnycuyciycnyc International evidence suggests that institutional investorsAo horizons and characteristics can be associated with governance quality and credit assessments .
redit rating.
(Driss et al.
Managerial ownership (MAN) Managerial ownership reflects an alignment of interests between managers and owners and is proxied using a dummy variable:
ycycnycoyca ycyceycyccycaycyycayc ycoyceycyyceycoycnycoycnycoycaycu ycoycaycuycaycyceycycnycayco ycAyaycAycnyc = { 0, ycycnycoyca ycycnyccycayco ycyceycyccycaycyycayc ycoyceycyyceycoycnycoycnycoycaycu ycoycaycuycaycyceycycnycayco In the governance literature, ownership structure including managerial ownership may strengthen or weaken monitoring effectiveness depending on control dominance and institutional context (Ahmad et al.
, 2.
Independent commissioners (BOARD_IND) Independent commissioners are an internal governance mechanism intended to enhance monitoring independence.
It is measured as:
ycAycycoycayceyc ycuyce yaycuyccyceycyyceycuyccyceycuyc yaycuycoycoycnycycycnycuycuyceycycycnyc ycNycuycycayco yaycuycoycoycnycycycnycuycuyceycycycnyc Credit-rating composition/independence, can correlate with credit ratings because they affect risk control and reporting credibility (Alkhawaldeh et al.
, 2.
yaAycCyaycIya_yaycAyaycnyc = Audit quality (AUDQUA) Audit quality is used as a proxy for the credibility of financial statements, which is important for creditors and risk evaluators.
In this study, audit quality is proxied by a Big-4 dummy:
ycycnycoyca ycaycyccycnycycuyc yaAycnyci 4 yaycOyaycEycOyaycnyc = { 0, ycycnycoyca ycaycyccycnycycuyc ycuycuycu Oe yaAycnyci 4 44 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 Modern audit literature supports that auditor characteristics .
ncluding reputation and industry expertis.
are linked to audit quality and reporting corrections, and the Big-4 vs non-Big-4 distinction remains a practical proxy especially in emerging markets with limited audit-metric data (Dekeyser et al.
, 2.
This study adopts an explanatory quantitative design to examine the effects of earnings management, financial ratios, and corporate governance mechanisms on bond ratings.
uses secondary data from annual financial statements of firms listed on the Indonesia Stock Exchange (IDX/BEI) and bond ratings published by PT PEFINDO.
Because the dependent variable is classified into two categories .
nvestment grade vs non-investment grad.
, the hypotheses are tested using binary logistic regression, which is appropriate for modeling a binary outcome and interpreting effects through odds ratios (Harris, 2.
The population includes all public firms that issue bonds, are listed on IDX, and have bonds rated by PEFINDO.
The sample is selected via purposive sampling and consists of 11 non-financial firms observed over four years .
firm-year observation.
The selection criteria include: .
IDX-listed firms with PEFINDO-rated bonds.
exclusion of financialsector issuers .
anking, insurance, financin.
due to different rating characteristics.
complete annual reports/financial statements with relevant bond-related accounts.
fiscal year ending 31 December and financial statements reported in IDR.
Data are collected through document review, extracting financial and governance information from annual reports/financial statements available via IDX and related capital market databases, and matching them with PEFINDO bond ratings for the corresponding Operational definitions and measurement formulas are presented in the previous Briefly, bond rating (BR) is coded as a binary dependent variable.
management is proxied by managed accruals (EDA).
financial ratios include liquidity (CR), activity (TAT), and market valuation (PER).
and governance mechanisms include institutional ownership (INST), managerial ownership .
, independent commissioners (BOARD_IND), and audit quality (Big Four dumm.
Logistic regression is employed because the dependent variable is binary and the predictors combine metric and non-metric measures.
ycoycu ( ycyycnyc ) = yu0 yu1 ycAyaycnyc yu2 yayaycEycnyc yu3 ycNyaycNycnyc yu4 ycEyaycIycnyc yu5 yaycAycIycNycnyc 1 Oe ycyycnyc yu6 yaAycCyaycIya_yaycAyaycnyc yu7 ycAyaycAycnyc yu8 yaycOyaycEycOyaycnyc where ycyycnyc is the probability that firm ycn in year yc receives an investment-grade rating .
aAycI=.
Coefficients are interpreted using the odds ratio .
, which reflects the change in the odds of yaAycI=1 BR=1 for a one-unit increase in a predictor, holding other variables constant (Harris, 2.
The study was conducted through the following steps: .
pre-study design .
onstructs, proxies, operational definitions, and data-extraction templat.
identifying the population of IDX-listed bond issuers rated by PEFINDO.
purposive sampling of non-financial firms meeting data-eligibility criteria.
collecting documentary data from annual reports/IDX and PEFINDO rating databases.
coding dummy variables and computing ratios/accrual measures.
statistical analysis .
escriptive statistics, logistic estimation, and model-fit/goodness-of-fit test.
drawing conclusions based on coefficient significance, direction, and odds-ratio interpretation for investment-grade This workflow follows standard guidance for binary logistic regression emphasizing outcome definition, predictor coding.
OR interpretation, and goodness-of-fit evaluation (Nattino et al.
, 2.
45 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
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Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606
RESULTS
This study uses a purposive sample of non-financial bond issuers listed on the Indonesia Stock Exchange (IDX/BEI) and rated by PEFINDO, totaling 44 firm-year observations .
firms y 4 years.
2018Ae2.
The sample firms are ADHI.
APOL.
BLTA.
FREN.
EXCEL.
INDF.
ISAT.
LTLS.
PWON.
SCTV, and SMRA.
The results section begins with the distribution of bond-rating categories, followed by descriptive statistics, model-fit assessment, and binary logistic regression to test how the explanatory variables affect the probability of receiving an investment-grade rating.
Table 1 reports the dependent variable distribution after coding bond ratings into investment grade .
and non-investment grade .
Table 1.
Bond Rating Categorical Distribution (Dependent Variabl.
Kategori Non investment grade Investment grade Total Kode Frekuensi Persentase (%) Based on Table 1, from the 44 observations, 26 .
1%) are investment grade and 18 .
9%) are non-investment grade.
This indicates that most bonds in the sample are investment grade, while the sizable share of non-investment grade observations highlights the importance of examining the factors that explain rating differences.
To describe the data characteristics prior to model estimation, the study reports descriptive statistics for all variables.
This summary highlights central tendency, dispersion, and the minimumAemaximum range for each measure.
Table 2.
Descriptive Statistics of Research Variables Variable Mean Std.
Dev.
Min Earnings management (ML) -0,092 0,20402 -0,39 Liquidity (LIQ) 1,339 0,94696 Activity (TAT) 0,46 0,34533 0,08 Market value (PER) 8,174 9,07992 -4,46 Institutional ownership (INST) 60,45 24,24366 Managerial ownership (MANJ) 0,294 0,32581 0,0001 Independent commissioners 0,429 0,09713 (BOARD_IND) Max
0,41
3,55
1,39
1,01
0,66
As shown in Table 2, the sample averages are ML = -0.
LIQ = 1.
TAT = 0.
PER = 8.
INST = 60.
MANJ = 0.
294, and BOARD_IND = 0.
The observed ranges and standard deviations indicate sufficient variability across firms and years, supporting the use of logistic regression to test the relationships among variables.
In addition to the numeric variables, the study reports the distribution of the dummy governance proxy for audit quality (AUDQUA).
This distribution is important because dummy-coded predictors affect how logistic regression coefficients are interpreted.
Table 3.
Audit Quality (AUDQUA) Distribution Category Code Frequency Percentage (%) Non Big-4 Big-4 Total 46 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
Rusl.
Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 As shown in Table 3, most observations .
7%) are audited by Big-4 firms .
, 3% are audited by nonAeBig-4 firms .
Although Big-4 audits dominate the sample, there is still sufficient variation to assess whether audit quality is associated with a higher probability of receiving an investment-grade bond rating.
Before interpreting the logistic regression estimates, the study evaluates overall model Because logistic regression does not require normality assumptions like OLS, normality checks are only supporting information.
the main assessment relies on model fit and goodness-of-fit indicators.
The results are summarized in Table 4.
Table 4.
Logistic Regression Model Fit Summary Indicator Value -2 Log Likelihood .
wal / Step .
59,534 -2 Log Likelihood .
khir / Step .
20,316 HosmerAeLemeshow Sig.
0,907
Cox & Snell R Square 0,59 Nagelkerke R Square 0,795 As shown in Table 4, the Oe2 Log Likelihood decreases from 59.
534 (Step .
(Step .
, indicating improved fit after adding the predictors.
The HosmerAeLemeshow significance value .
907, > 0.
suggests the model fits the data well.
The model also shows strong explanatory power, with Nagelkerke RA = 0.
795 (Cox & Snell RA = 0.
implying that the predictors collectively explain a substantial portion of the variation in bond-rating classification within the sample.
After confirming that the model is adequate, the next step is to test the effect of each predictor using binary logistic regression.
Estimated coefficients, significance levels, and odds ratios (Exp(B)) are reported in Table 5.
Table 5.
Logistic Regression Results (Variables in the Equatio.
Variable Wald Sig.
Exp(B) -9,706 4,901 3,922 0,048 LIQ
3,642
1,452
6,292
0,012
38,16
TAT
-0,435
2,195
0,039
0,843
0,647
PER
0,068
0,137
0,248
0,619
1,07
INST
-0,02
0,042
0,235
0,628
0,98
MANJ
5,974
2,996
3,975
0,046
BOARD_IND
7,215
8,898
0,657
0,417
AUDQUA
4,54
2,087
4,73
0,03
93,696
Konstanta
-10,828
5,068
4,565
0,033
At the 5% significance level, four variables significantly affect bond-rating classification: earnings management (ML), liquidity (LIQ), managerial ownership (MANJ), and audit quality (AUDQUA).
In contrast.
TAT.
PER.
INST, and BOARD_IND are not significant .
> 0.
Specifically.
ML has a negative and significant effect (B = Oe9.
= 0.
, indicating that higher earnings management reduces the likelihood of obtaining an investment-grade rating.
LIQ has a positive and significant effect (B = 3.
p = 0.
Exp(B) = 38.
, suggesting that stronger short-term solvency increases the probability of 47 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
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Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 investment grade.
MANJ is also positive and significant (B = 5.
p = 0.
Exp(B) = .
, implying that the presence/level of managerial ownership is associated with better rating odds.
Finally.
AUDQUA is positive and significant (B = 4.
p = 0.
Exp(B) = .
, meaning firms audited by Big-4 auditors are more likely to receive an investmentgrade rating.
Overall, the results suggest that, for this sample and period, bond-rating variation is more sensitive to reporting quality, liquidity strength, and selected governance signals than to activity, market valuation, institutional ownership, or the proportion of independent commissioners.
Based on the binary logistic regression results for 44 firm-year observations, the study finds that earnings management (ML), liquidity (LIQ), managerial ownership (MANJ), and audit quality (AUDQUA) significantly affect the probability that a bond is classified as investment grade, while asset turnover (TAT), market value (PER), institutional ownership (INST), and independent commissioners (BOARD_IND) are not significant.
Statistically.
ML is negative and significant .
= 0.
LIQ is positive and significant .
= 0.
MANJ is positive and significant .
= 0.
, and AUDQUA is positive and significant .
= 0.
Earnings management (ML) has a negative and significant effect on bond ratings (B = Oe9.
p = 0.
, implying that more intensive accrual-based earnings management lowers the likelihood of receiving an investment-grade rating.
Substantively, this supports the credit-risk argument that aggressive reporting can reduce financial statement credibility, increase information risk, and raise perceived default risk.
Recent evidence also suggests that rating events are related to earnings-management behavior: firms tend to engage in income-decreasing earnings management after credit-rating downgrades (Koerniadi, 2.
, and downgrades together with regulatory/accounting-standard factors can influence earnings-management incentives (Zhee Lim et al.
, 2.
Thus, the negative ML result can be interpreted as a signal that lower reporting reliability reduces the probability of a stronger bond rating.
Liquidity (LIQ) .
urrent rati.
is positive and significant (B = 3.
p = 0.
Exp(B) = 38.
, indicating that stronger short-term solvency increases the probability of investment grade.
Conceptually, rating agencies treat liquidity as a buffer that reduces nearterm default risk, particularly for non-financial issuers dependent on operating cash stability.
This aligns with recent international evidence that fundamental ratios, including liquidity, remain relevant predictors of corporate credit quality (Michalski & Low, 2.
Managerial ownership (MANJ) is positive and significant (B = 5.
p = 0.
Exp(B) = 392.
From an agency perspective, managerial shareholding can align managerial and owner interests, encourage more prudent decisions, and reduce opportunistic behavior that could harm creditorsAithereby increasing the likelihood of investment-grade Empirically, ownership structure has been linked to disclosure quality and credit ratings, with auditor choice moderating this relationship (Chen et al.
, 2.
, reinforcing the role of ownership-based governance as a rating-relevant signal.
Audit quality (AUDQUA) is positive and significant (B = 4.
p = 0.
Exp(B) = 93.
, suggesting that firms audited by Big-4 auditors are more likely to obtain investment-grade ratings.
Mechanistically, higher-quality audits can reduce misstatement risk and strengthen the credibility of accounting information, lowering information risk for bond investors and rating agencies.
This interpretation is consistent with recent findings that audit quality .
nd disclosure qualit.
can influence rating assessments, especially in emerging-market contexts (Driss et al.
, 2.
By contrast.
TAT is not significant .
= 0.
, likely because asset efficiency does not necessarily translate directly into debt-servicing capacity across industries with different cost structures and working-capital needs.
PER is not significant .
= 0.
because 48 | The Effect of Earnings Management.
Financial Ratios, and Corporate Governance Mechanisms on Bond Ratings (Muh.
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Business Management e-ISSN : 2828-8203, p-ISSN: 2828-7606 market-based ratios are highly sensitive to sentiment and cycles, while rating agencies emphasize cash-flow-backed repayment capacity.
INST is not significant .
= 0.
potentially because institutional monitoring may be ineffective under ownership concentration or short-term orientation.
the information value of institutional ownership may be context-specific (Salem et al.
, 2.
Finally.
BOARD_IND is not significant .
= .
, which may indicate that meeting minimum independent-commissioner requirements alone is insufficient for effective monitoring if influence or competence is limited.
governanceAerating link often depends on institutional context and implementation strength, not only board proportions (Zhao et al.
, 2.
CONCLUSION
This study concludes that bond ratings of non-financial firms listed on the Indonesia Stock Exchange and rated by PEFINDO .
observations, 2018Ae2.
are significantly influenced by a combination of reporting quality, liquidity conditions, and selected corporate governance mechanisms.
The logistic regression results indicate that earnings management has a negative effect on the probability of receiving an investment-grade rating, suggesting that more intensive earnings management reduces the likelihood of a higher rating.
In contrast, liquidity has a positive effect, meaning stronger short-term repayment capacity increases the probability of investment grade.
From a governance perspective, managerial ownership and audit quality (Big-4 prox.
also show positive effects, implying that managerial shareholding and reputable auditing are associated with higher investment-grade likelihood.
Meanwhile, asset turnover (TAT), market value ratio (PER), institutional ownership, and the proportion of independent commissioners are not significant in this sample and period.
The implications highlight that bond ratings are not driven solely by market indicators or operating efficiency, but are more sensitive to financial reporting credibility, liquidity resilience, and governance signals that strengthen confidence in information qualityAi particularly through high-quality audits and interest alignment via managerial ownership.
For managers, the results emphasize improving reporting quality and limiting aggressive earnings management to support credit perceptions, funding costs, and bond-market access.
For investors and creditors, strong liquidity, credible audits, and effective governance can serve as practical cues for identifying firms more likely to be investment grade.
For regulators and rating agencies, the findings reinforce the importance of monitoring reporting quality and strengthening governance and audit standards to reduce information risk in the corporate bond market.
For future research, scholars should expand the sample size and time horizon, test sectoral differences, and include more direct credit-risk variables such as leverage, profitability, interest coverage, maturity, collateral, and issue size.
Future studies may also apply more comprehensive earnings-management measures .
ombining accrual-based and real earnings managemen.
and richer governance indicators .
, audit committee features, board expertise, ultimate ownershi.
Methodologically, panel logit or alternative classification models .
, ordered logit for ordinal rating level.
may better capture time dynamics and finer rating distinctions.
ACKNOWLEDGMENTS
We would like to express our gratitude to all parties who have provided support and contributions throughout the preparation of this research
REFERENCE