Abstract
Financial distress is a socially and economically important problem that affects mining
companies the world over. Having the power to better understand – and hence aid mining
businesses from failing, has the potential to save not only the company, but potentially prevent
economies from sustained downturn. Mining in Australia generates around $138 billion
annually, making up more than half of total goods and services. This paper uses financial data
from hundreds of Australian companies in the mining sector to work on advancing financial
distress modelling by utilizing cutting-edge stochastic models, namely: decision trees,
stochastic gradient boosting, and random forests, to develop the most accurate technique in
forecasting insolvency risk. Our results indicate that stochastic gradient boosting was the best
technique at correctly classifying the successful and distressed companies within the mining
sector. Our model showed that net gearing, return on equity, and book value per share ratios
were found to be the variables with the best explanatory power pertaining to predicting
financial distress of mining companies.
companies the world over. Having the power to better understand – and hence aid mining
businesses from failing, has the potential to save not only the company, but potentially prevent
economies from sustained downturn. Mining in Australia generates around $138 billion
annually, making up more than half of total goods and services. This paper uses financial data
from hundreds of Australian companies in the mining sector to work on advancing financial
distress modelling by utilizing cutting-edge stochastic models, namely: decision trees,
stochastic gradient boosting, and random forests, to develop the most accurate technique in
forecasting insolvency risk. Our results indicate that stochastic gradient boosting was the best
technique at correctly classifying the successful and distressed companies within the mining
sector. Our model showed that net gearing, return on equity, and book value per share ratios
were found to be the variables with the best explanatory power pertaining to predicting
financial distress of mining companies.
| Original language | English |
|---|---|
| Publication status | Published - Nov 2017 |
| Event | The 29th Asian-Pacific Conference on International Accounting Issues - Kuala Lumpur, Malaysia Duration: 5 Nov 2017 → 8 Nov 2017 Conference number: 29th |
Conference
| Conference | The 29th Asian-Pacific Conference on International Accounting Issues |
|---|---|
| Country/Territory | Malaysia |
| City | Kuala Lumpur |
| Period | 5/11/17 → 8/11/17 |