Perbandingan Risiko Gagal Bayar Perusahaan Penerbit Surat Utang Berperingkat Layak Investasi Berdasarkan Sektor Usaha
-
Aulia Ikhsan
*
Universitas Sultan Ageng Tirtayasa, Cilegon,
Indonesia
- Aditya Rahadian Fachrur Universitas Sultan Ageng Tirtayasa, Cilegon, Indonesia
- Fikri C Permana KB Valburi Sekuritas, Jakarta, Indonesia
- Ayu Nurulhaq Putri Universitas Negeri Jakarta, Jakarta, Indonesia
- Zahra Mahendra Universitas Sultan Ageng Tirtayasa, Cilegon, Indonesia
- Chaesa Panji Wicaksono Universitas Sultan Ageng Tirtayasa, Cilegon, Indonesia
- Heggy Septian Universitas Sultan Ageng Tirtayasa, Cilegon, Indonesia (*) Corresponding Author
Abstract
Ratings for debt-issuing companies (issuers) are conducted to provide information on credit quality to investors and prevent default risk, which is the main risk in investing in corporate debt securities. Issuers are generally classified into financial services and non-financial services sectors due to the different risk characteristics between these two sectors. If a company from each sector has the same rating, then the credit quality of both companies will be considered the same. However, research by PEFINDO, S&P, and Fitch shows that the non-financial services sector has a relatively greater potential default risk than the financial services sector. Based on this, this study aims to examine the risk comparison between these two sectors in the investment-grade rating group based on rating transitions and test the assumption that the non-financial services sector has a relatively greater default risk than the financial services sector. The data used in this study are sample data on issuer rating transitions in Indonesia during the period 2007 - 2024 from both sectors. The data analysis technique uses Markov Chains to create a rating transition matrix for both sectors, as well as a comparison test of two proportions using the Z test used to compare the two matrices. A comparison of the transition matrix indicates that the non-financial services sector has a greater probability of default than the financial services sector, with ratings A (0.0078 vs. 0.0045) and BBB (0.0429 vs. 0.0143). Hypothesis testing results indicate that, at the 5% significance level, the non-financial services sector has a greater proportion of rating downgrades than the financial services sector, meaning the risk of default is greater than that of the financial services sector.
Downloads
References
Aida, Hermina, D., & Norlaila. (2025). Jenis Data Penelitian Kuantitatif. Jurnal Ilmiah Keislaman Dan Kemasyarakatan, 10(1), 31–40.
Aslam, M. A. (2020). Does the Percentage of Investment Grades Given by Rating Agencies Impact their Market Share? Financial Markets, Institutions and Risks, 4(1), 5–31. https://doi.org/10.21272/fmir.4(1).5-31.2020
Attwood, K., Park, S., & Hutson, A. D. (2022). Practical and robust test for comparing binomial proportions in the randomized phase II setting. Pharmaceutical Statistics, 21(2), 361–371. https://doi.org/10.1002/pst.2174
Bednarek, P., & Franke, G. (2024). Dynamics of Probabilities of Default (Issue 32). http://dx.doi.org/10.2139/ssrn.4930492
Belkhir, M., Naceur, S. Ben, Candelon, B., & Wijnandts, J.-C. (2022). Macroprudential Regulation and Sector-Specific Default Risk. In IMF Working Papers (Vol. 2022, Issue 141). https://doi.org/10.5089/9798400215421.001
Cao, J., Guan, F., Zhang, X., Nam, W. H., Leng, G., Gao, H., Ye, Q., Gu, X., Zeng, J., Zhang, X., Huang, T., & Niyogi, A. D. (2023). Multiple Markov Chains for Categorial Drought Prediction on the U.S. Drought Monitor at Weekly Scale. Journal of Applied Meteorology and Climatology, 62(10), 1415–1435. https://doi.org/10.1175/JAMC-D-23-0061.1
Cipresso, P., Borghesi, F., & Chirico, A. (2023). Affects affect affects: A Markov Chain. In Frontiers in Psychology (Vol. 14). https://doi.org/10.3389/fpsyg.2023.1162655
Fabozzi, F. J. (Johns H. C. B. S., & Fabozzi, F. A. (Stevens I. of T. (2021). Bond Markets, Analysis, and Strategies (Tenth). The MIT Press.
Fitch Ratings. (2025). 2024 Transition and Default Studies.
Gzyl, H., & Mayoral, S. (2025). Determining a Credit Transition Matrix from Cumulative Default Probabilities. An Entropy Minimization Approach. Computational Economics. https://doi.org/10.1007/s10614-025-11108-7
Kahn, W. N. (2024). Debtors’ Rights in the Age of Mass Securitization. University of Wisconsin-Madison.
Kurbonov, S., Nasriddinov, B., & Mulbah, K. T. (2024). The Impact of Credit Rating Adjustments on Bond Spreads: Evidence from China. European Scientific Journal ESJ, 28, 1–14. https://doi.org/10.19044/esipreprint.4.2024.p1
Liu, S. (2024). Risk in Bond Investment and Risk Management Methods. Transactions on Economics, Business and Management Research, 14, 551–555. https://doi.org/10.62051/wyseg667
McKenna, L., & Sommers, C. (2025). Comparative Analysis Methodologies in Nursing Research: A Scoping Review. Nursing Open, 12(9), 1–28. https://doi.org/10.1002/nop2.70300
Parulian, P., Meilana, A., & Tan, E. (2023). Analisis Kinerja Keuangan Terhadap Resiko Gagal Bayar Obligasi Perusahaan. Margin: Jurnal Lentera Managemen Keuangan, 1(01), 26–33. https://doi.org/10.59422/margin.v1i01.30
PEFINDO. (2024). Rating Definition. https://www.pefindo.com/services
Pemeringkatan Efek Bersifat Utang Dan/Atau Sukuk (2020).
PT Pemeringkat Efek Indonesia. (2024). The Default Study of Corporate and Corporate Debt Securities Rated by Pefindo.
S&P Global Ratings. (2025). Default, Transition, and Recovery: 2024 Annual Global Corporate Default And Rating Transition Study. 1–72. https://www.spglobal.com/ratings/en/research/articles/250327-default-transition-and-recovery-2024-annual-global-corporate-default-and-rating-transition-study-13452126
Song, Y., Hongtao, X., Wang, H., Zhu, Z., Kang, X., Cao, X., Bin, Z., & Zeng, H. (2024). An adaptive transition probability matrix with quality seeds for cellular automata models. GIScience and Remote Sensing, 61(1). https://doi.org/10.1080/15481603.2024.2347719
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & Statistics for Engineers & Scientist (Ninth). Prentice Hall.
Xiong, L., & Duncan-Williams, S. (2022). Generalized Linear Model for Predicting the Credit Card Default Payment Risk. Advances in Science, Technology and Engineering Systems Journal, 7(3), 51–61. https://doi.org/10.25046/aj070306
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Risiko Gagal Bayar Perusahaan Penerbit Surat Utang Berperingkat Layak Investasi Berdasarkan Sektor Usaha
Pages: 522-528
Copyright (c) 2025 Aulia Ikhsan, Aditya Rahadian Fachrur, Fikri C Permana, Ayu Nurulhaq Putri, Zahra Mahendra, Chaesa Panji Wicaksono, Heggy Septian
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
