Financial Ratios as Predictors of Financial Distress Risk for Transportation, Logistics, and Infrastructure Companies on the Indonesia Stock Exchange for the 2022–2024 Period - M. Rendy Al-Fath Andika Pulun; Henny Wirianata (Devotion : Journal of Research and Community Service, 2026)
This study aimed to examine and analyze the effects of liquidity, leverage, and profitability on financial distress risk among companies in the transportation, logistics, and infrastructure sectors listed on the Indonesia Stock Exchange during the 2022–2024 period. The study used secondary data obtained from annual financial reports published on the official website of the Indonesia Stock Exchange (www.idx.co.id). The sample was selected using a purposive sampling technique, resulting in a final sample of 105 observations from 63 companies over the three-year observation period. The data were analyzed using Statistical Package for the Social Sciences (SPSS) version 26 through multiple linear regression analysis. Financial distress risk was measured using the Modified Altman Z”-Score model, while liquidity was proxied by the Current Ratio (CR), leverage was proxied by the Debt-to-Equity Ratio (DER), and profitability was proxied by Return on Assets (ROA). The results indicate that liquidity had a positive and significant effect on financial distress risk, leverage had a negative and significant effect on financial distress risk, and profitability had a positive and significant effect on financial distress risk. These findings provide important implications that liquidity, leverage, and profitability ratios can serve as indicators for the early detection of financial distress risk among companies in the transportation, logistics, and infrastructure sectors. For companies, these results emphasize the importance of maintaining healthy working capital, managing debt structures effectively, and improving operational profitability to avoid financial distress conditions. For investors and creditors, these three financial ratios, together with the Modified Altman Z”-Score model, can be utilized as an early warning system to support investment and credit decision-making.