Prediction Financial Distress: The Pro-Technology Technique of Altman Z-Score Model

• Published in: Journal of Information Technology Management
• Main Author: Zainudin Z.; Hassan H.; Jaafar Sidik M.H.; Zaini S.M.
• Format: Article
• Language: English
• Published: University of Tehran 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172031285&doi=10.22059%2fjitm.2023.92334&partnerID=40&md5=de1d22d1ecfd1bd053ddf72d6e682641

The Covid-19 outbreak has had a severe effect on the world economy. The company's business operations and profitability are damaged during the covid 19 outbreak. This deterioration is not only threatening the company’s survival position but also destroy the investor’s investment return. Therefore, it is vital to establish an effective early prediction technical method to foresee a corporate distress by a Pro-technical measurement to enhance the corporate sustainability. This study applies Altman Z-Score Model to as a Pro-Technology technique to the financial distress prediction of Malaysia’s Government Linked Plantation Companies (GLC-P) over a period of 10 years starting from 2012 to 2021. The significant contribution of the study is that the Z-Score Model provides an advanced indication tool regarding the financial stability of the respective GLC-P companies. The findings indicate that Financial Distress Prediction was dependent via in-time application of leverage, liquidity, activity, and profitability to the Altman Z-Score Model. Profitability and leverage were found to be superior prediction tool to financial distress. Copyright © 2023, Zalina Zainudin, Haslina Hassan, Morni Hayati Jaafar Sidik and Syeliya Md. Zaini.