Altman Z-Score as a tool to predict Financial Distress of Junior Mines in Southern Africa
Many mines, both locally and internationally, have embarked on mining projects which have come to a point where they have to shutdown, slowdown or be put on hold with a view to be relooked at further down the line. This study intends to explore the possibility of predicting financial distress in mining companies using the Altman Z-Score prediction technique. This technique is simple and uses financial ratios to determine a Z-Score. Data was collected from financial statements of mining companies listed on various stock exchanges. An Excel model was developed to calculate the ZScores of the captured data. Results were then compared to the actual outcomes. Statistical tests were performed on the sets of Expected data and Actual data and the main finding was that the Altman Z-Score could not be used to predict financial distress in companies in the mining sector. Modifying the Altman Z-Score technique, by changing the coefficients and the boundary, as well as introducing variables that were of a subjective nature could improve the results. That is, the model used to predict financial distress should not be based purely on financial data.
MBA thesis - WBS
Mines and mining, Financial distress in companies