Determinants of Financial Distress in Basic Materials Companies: Evidence from Indonesian Listed Firms
DOI:
https://doi.org/10.29080/jai.v12i1.2445Keywords:
Financial Distress, liquidity, operating capacity, Firm Size, cash flowAbstract
Purpose: This study examines the influence of liquidity, operating capacity, firm size, and cash flow on financial distress in basic materials manufacturing companies listed on the Indonesia Stock Exchange during 2021–2024. Financial distress represents a critical phase prior to bankruptcy and remains a significant concern, particularly in the basic materials sector, which experienced performance pressures during the post-pandemic recovery period.
Methodology/approach: This study contributes to the literature by specifically examining the basic materials industry during the 2021–2024 period and by integrating liquidity, operating capacity, firm size, and cash flow within a single panel data regression framework, thereby providing a more comprehensive and updated empirical perspective on financial distress in a capital-intensive sector. This study employs purposive sampling, resulting in 80 companies with 320 panel data observations. Data were analyzed using panel data regression through the Random Effect Model using EViews 12 software.
Findings: The results indicate that liquidity and operating capacity have a significant negative effect on financial distress, suggesting that higher liquidity and more efficient asset utilization reduce the likelihood of financial difficulties. Meanwhile, firm size and cash flow do not exhibit a statistically significant partial effect. However, the joint significance test indicates that all variables collectively influence financial distress.
Practical Implications: These findings highlight the importance of comprehensive financial performance evaluation to support managerial decision-making in the early prevention of financial distress.
Originality/value:
Focuses on the basic materials sector during 2021–2024 and integrates multiple financial variables within a single panel data regression framework.
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