CREDIT RATING ANALYSIS OF COAL PRODUCTION INDUSTRY USING LOGISTIC REGRESSION MODEL

Arief Tirtana; Aditio Wahyudi; Prima Naomi

Authors

Arief Tirtana Magister Management, Faculty of Economics and Business Universitas Paramadina
Aditio Wahyudi Magister Management, Faculty of Economics and Business Universitas Paramadina
Prima Naomi Magister Management, Faculty of Economics and Business Universitas Paramadina

Keywords:

Credit Rating, Logistic Regression, Financial Distress, Coal Companies

Abstract

Purpose: The main idea of this study is to identify the credit rating of the coal-producing sub-industry through the utilization of binary logistic regression analysis. Method: This research used 19 companies of IDX (Indonesia Stock Exchange) as samples with purposive sampling method. This paper use financial ratio data in October 2022. The variables used are six types of financial ratios, namely Price to Earning Ratio (PER), Return on Equity (ROE), Return on Asset (ROA), Net Profit Margin (NPM), Price to Book Value (PBV), and Debt to Equity Ratio (DER). This study uses binary logistic regression analysis. Result: The empirical result shown that financial ratio such as PBV, PER, and DER can be used prediction model with accuration rate of 0.95 and ROC 1.00. Based on the result, the companies with the best credit rating (AAA) is Bukit Asam Tbk (PTBA), Indika Energy Tbk (INDY), Baramulti Suksessarana Tbk (BSSR), and Adaro Energy Indonesia Tbk (ADRO). Novelty: The research presented in this study is distinguished by its utilization of a two-stage approach. The initial phase involves the application of the K-Means method to replicate the clustering of the company in order to identify binary performance data. The subsequent phases involve the utilization of binary logistic regression, incorporating six distinct financial ratios.

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