Evaluasi Metode Deteksi Financial Distress pada Perusahaan LQ45

Abstract

Objectives: This study aims to evaluate the accuracy of three financial distress prediction models (Springate, Zmijewski, Grover) on LQ45 companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2023 period. The focus is to assess the models' effectiveness in addressing contemporary financial challenges such as debt restructuring and financial distress indicators amidst dynamic global economic conditions.Design/method/approach: This quantitative research utilizes secondary data from 37 LQ45 companies selected through purposive sampling, resulting in 148 financial statements for the 2020–2023 observation period. The analysis employs financial ratios and prediction scores from each model using Microsoft Excel for computation.Results/findings: The Grover model demonstrates the highest accuracy rate (95%) with a type error of 6%, outperforming Zmijewski (accuracy 89%, type error 11%) and Springate (accuracy 59%, type error 41%). This suggests that the Grover model is superior in detecting financial distress.Theoretical contribution: This study identifies the Grover model as the most accurate method for predicting financial distress in Indonesia. It contributes to the development of more relevant financial distress prediction models for the capital market.Practical contribution: The findings provide critical insights for shareholders and stakeholders to detect financial risks in LQ45 companies.Limitations: The study is limited by its reliance on classical prediction models, focus on LQ45 companies, and analysis restricted to the 2020–2023 period. Modern approaches like machine learning, which could enhance prediction accuracy, are not explored.Type: empirical, review (conceptual and viewpoints).