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Abstract

This research entails an examination of credit risk assessment (credit rating) with respect to credit risk assessment models utilizing accounting data and market data for non-financial corporations in Indonesia between the years 2013 and 2022. The sample comprises credit rating values of 20 companies listed on the Indonesia Stock Exchange and included in the IDX 80 index, possessing credit ratings from PEFINDO over a ten-year period from 2013 to 2022. Panel data analysis is conducted, employing the multiple linear regression method. The findings of this study reveal that the accounting data-based model demonstrates the significance of liquidity, capital structure, and interest payment ability indicators, while the market data-driven model highlights the influence of equity volatility and market multiples indicators in respect on credit ratings. Moreover, the study concludes that the combined model incorporating accounting data-based and market data-driven models provides a more comprehensive and holistic explanation of credit ratings.

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Bahasa Abstract

Penelitian ini merupakan analisis penilaian risiko kredit (credit rating) ditinjau dari model penilaian risiko kredit berbasis data akuntansi dan data pasar, terhadap korporasi non-keuangandi Indonesia pada periode 2013 - 2022. Sampel yang digunakan adalah nilai peringkat kredit dari20 perusahaan yang terdaftar di Bursa Efek Indonesia dan masuk dalam indeks IDX 80, sertamemiliki peringkat kredit dari PEFINDO selama sepuluh tahun pada periode 2013 - 2022. Data yang dianalisis merupakan data panel dan metode yang digunakan adalah regresi linear berganda. Hasil penelitian ditemukan bahwa dari model berbasis data akuntansi; indikator likuiditas, struktur modal, dan kemampuan membayar bunga; dan dari model berbasis data pasar; indikatorvolatilitas ekuitas dan market multiples berpengaruh secara signifikan terhadap peringkat kredit. Hasil penelitian juga menyimpulkan bahwa model penilaian yang menggabungkan model berbasis data akuntansi dan data pasar, menjelaskan peringkat kredit secara lebih komprehensifdan menyeluruh.

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