International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 2 (February 2024), Pages: 1-7

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 Original Research Paper

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

 Author(s): 

 Asmahani Binti Nayan 1, Mohd Rijal Ilias 2, *, Siti Shuhada Ishak 2, Amirah Hazwani Binti Abdul Rahim 1, Berlian Nur Morat 3

 Affiliation(s):

 1College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) Kedah Branch, Sungai Petani Campus, Merbok, Kedah, Malaysia
 2College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
 3Academy of Language Studies, Universiti Teknologi MARA Kedah Branch, Merbok, Kedah, Malaysia

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-6226-2389

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.02.001

 Abstract

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.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Financial distress, PN17, Financial ratio, Grover model, Zmijerski model, Logistic regression, Accuracy

 Article history

 Received 16 March 2023, Received in revised form 10 November 2023, Accepted 15 January 2024

 Acknowledgment 

The work was funded by the Fundamental Research Grant Scheme (FRGS/1/2019/SS01/UITM/03/1) from the Ministry of Education, Malaysia.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Nayan AB, Ilias MR, Ishak SS, Abdul Rahim AHB, and Morat BN (2024). Evaluating the efficacy of financial distress prediction models in Malaysian public listed companies. International Journal of Advanced and Applied Sciences, 11(2): 1-7

 Permanent Link to this page

 Figures

 Fig. 1  

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 

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