Evaluation of bankruptcy prediction models and the effectiveness on listed companies in the stock market in Malaysia

• Main Author: Ong, Gek Hwa
• Format: Thesis
• Language: English
• Published: 2021
• Subjects: HG Finance
• Online Access: https://etd.uum.edu.my/11142/1/s826698_01.pdf; https://etd.uum.edu.my/11142/

Investors need to understand the background of the business before they started to invest in the stock market. Good business management can help the business to perform better. Each of the businesses leads to a different result this is based on the business management decision making as well as the business process flow. Investors have to understand the business type and the function of each business carefully to avoid misallocation of their funds. Business when into financial bankruptcy when the business debts are higher than assets there will be total bankruptcy which the business falls into bankruptcy and legal steps are taking place to ensure creditors or liquidation occurs. This is to ensure that the investment plan can make a profit for investors rather than making a loss. The value of the investment affected the confidence level of the investors by putting in their funds in the stock market. Prediction makes formerly is to prevent a business from sudden fall into bankruptcy. Altman Z-score and Logit model are implemented to predict the chance of bankruptcy. 40 companies were taken for prediction, 20 out of 40 companies are financial distress companies and the other 20 companies are non-financial distress companies. This paper is to help investors to have a good analysis for their investment decision. Logit model shows that the result from the calculation probability which is more than 0.5 as financial distress, less than 0.5 consider as non-financial distress, and showed that the result is predicted 92% of the analyses. The results show that there is a significant 5% level are sales to total assets, shareholders’ fund to total liabilities, cash from operating to total liabilities in the logit model for prediction.