Evaluating the efficacy of financial distress prediction models in Malaysian public listed companies

• Published in: International Journal of Advanced and Applied Sciences
• Main Author: Nayan A.B.; Ilias M.R.; Ishak S.S.; Rahim A.H.B.A.; Morat B.N.
• Format: Article
• Language: English
• Published: Institute of Advanced Science Extension (IASE) 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192736153&doi=10.21833%2fijaas.2024.02.001&partnerID=40&md5=34971ee15da1eaba7cb523f86e93f8b4

This research critically examines the precision of financial distress prediction models, with a particular focus on their applicability to Malaysian publicly listed companies under Practice Note 17 (PN17) from 2017 to 2021. Financial distress, defined as the imminent risk of bankruptcy evidenced by an inability to satisfy creditor demands, presents a significant challenge in corporate finance management. The study underscores the necessity of an efficient prediction model to strategize preemptive measures against financial crises. Unlike prior research, which predominantly compared prediction models without assessing their accuracy, this study incorporates an accuracy analysis to discern the most effective model. Utilizing the Grover and Zmijerski models, it assesses whether companies listed under PN17 are experiencing financial distress. A noteworthy finding is the substantial correlation between the return on assets (ROA) and the prediction of financial distress in these companies. Furthermore, the Grover model demonstrates a remarkable 100% accuracy rate, indicating its exceptional efficiency in forecasting financial distress. This research not only contributes to the existing body of knowledge on financial distress prediction but also offers practical insights for companies and stakeholders in the Malaysian financial market. © 2024 The Authors. Published by IASE.