Financial distress prediction has been a topic of great interest over decades, not only to managers, but also to the external stakeholders of a company. The aim of this study is to examine the ability of the adaptive neuro-fuzzy inference... more
Financial distress prediction has been a topic of great interest over decades, not only to managers, but also to the external stakeholders of a company. The aim of this study is to examine the ability of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the financial distress of public listed companies in Malaysia. The analysis is based on a sample of companies classified as PN17 by Bursa Malaysia over the period 2010-2015. The financial data of the distressed and financially healthy companies was collected for five years prior to classification as a PN17 company. Five financial ratios exist in the Altman model were used as the input variables. The results of this study indicate that the adaptive neuro-fuzzy inference system model has an accuracy rate of 86% prior to financial distress. This study will be useful to financial institutions, investors, creditors and auditors to identify companies that are likely to fall into financial distress.
