Classification of Malaysian public listed companies as PN17 based on selected financial indicators using Logit model / Basheer Azdi Shahizan, Maisarah Mohd Redwan and Wardina Humaira’ Rostam - Shahizan, Basheer Azdi; Mohd Redwan, Maisarah; Rostam, Wardina Humaira’ (2024)
Introduction: Companies are classified as PN17 if they fail to comply with the Bursa Malaysia laws. The PN17 is said to be a synonym for companies with financial issues. Problem: It is difficult to determine the financial status of the PN17 without reviewing the company’s reports and undergoing procedures. This study seeks to explore the complexities of PN17 classifications, with a specific focus on the subset related to financial distress. Objective: The research identifies and analyzes key financial indicators to the listed Malaysian public companies as PN17 limited to these financial distress factors: stock volatility, leverage, liquidity, profitability, and the probability of default. Employing the Logit model, the study aims to classify the Malaysian Public Listed Companies as PN17 or Non-PN17 using selected financial indicators. Methodology: This study employs financial data from companies listed on Bursa Malaysia that have been classified as either PN17 or Non-PN17 for the years 2017-2022. Logistic regression analysis is utilized to identify the most significant financial indicator for the PN17 classification. The derived logistic function is applied to the financial indicators to determine their significance and ability to predict the PN17 classification. Findings: The study reveals that 2 out of the 5 financial indicators identified are the most significant for classifying PN17 companies through Logistic Regression. However, the classification accuracy for PN17 companies is lower (11.3%) compared to Non-PN17 companies (98.6%) among Malaysian Public Listed Companies when using the Logit function derived from the selected financial indicators. Conclusion: This study successfully identified stock volatility and leverage as the most significant financial indicators for classifying PN17 companies. It employed the logistic function with an accuracy of 81.8% to classify Malaysian public companies as either PN17 or Non-PN17 based on the selected financial indicators.