Identifying the determinants of financial distress for public listed companies in Malaysia - Ahmad Monir Abdullah, (Penerbit Universiti Kebangsaan Malaysia, 2020)
Companies that face financial distress are always regarded as the root cause of enormous financial and economic losses for many stakeholders and at the same time, contribute to social unrest within the society. Identifying the determinants of financial distress in advance will bring many advantages to stakeholders so that they can manage their companies effectively. This study aimed to identify the determinants of financial distress for Malaysian public listed companies (PLC) by utilising financial ratios and market data. Additionally, this study focuses on finding a better distress prediction model between the traditional statistical approach that utilises a logistic regression and an artificial neural networks (ANN) model. Sixteen ratios were selected in the study and two techniques were used to assess the data of 192 Malaysian PLC. The empirical findings from this research show that current assets turnover (CAT), working capital to total assets (WCTA,) and retained earnings to total assets (RETA) display the highest ability to distinguish between financially distressed and non-distressed groups. The results also indicate that the mentioned variables possessed a high discriminant and predictive power. This study also found that the ANN model has a higher predictive accuracy compared to the logistic regression model.