Altman Z-Score as a tool to predict Financial Distress of Junior Mines in Southern Africa
Date
2012-01-17
Authors
Munien, Neville
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Abstract
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.
Description
MBA thesis - WBS
Keywords
Mines and mining, Financial distress in companies