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Assessing the State of Financial Distress of Listed Gold and Platinum Mining Companies in South Africa

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  • Sam Ngwenya

    (University of South Africa)

Abstract

The main objective of this study was to assess the state of financial distress of listed gold and platinum mining companies in South Africa during the period 2011 to 2015. Mining has been the driving force of the economy in South Africa, and has played a vital role in the country's economic development. Most studies conducted on financial distress in South Africa focussed on manufacturing companies, and none could be found that focussed on mining companies. Out of a population of 8 listed gold mining companies and 11 listed platinum mining companies, only 5 gold mining companies and 5 platinum mining companies were selected to form a total sample of 10 gold and platinum mining companies. Standardised financial statements of the sampled gold and platinum mining companies was downloaded from iNET BFA database and analysed making use of Altman Z-score and Altman Z' (EM) score models as proxies to predict financial distress. The results revealed that gold miningcompanies are more financially distressed than platinum mining companies. It is recommended that management in gold and platinum mining companies should conduct regular ratio analyses and take corrective action where necessary to improve the financial health of the companies.

Suggested Citation

  • Sam Ngwenya, 2018. "Assessing the State of Financial Distress of Listed Gold and Platinum Mining Companies in South Africa," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 14(4), pages 655-677, AUGUST.
    • Handle: RePEc:dug:actaec:y:2018:i:4:p:655-677
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    References listed on IDEAS

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    1. Shane Magee, 2013. "The effect of foreign currency hedging on the probability of financial distress," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1107-1127, December.
    2. Kun Xu & Qilan Zhao & Xinzhong Bao, 2015. "Study on Early Warning of Enterprise Financial Distress – Based on Partial Least-squares Logistic Regression," Acta Oeconomica, Akadémiai Kiadó, Hungary, vol. 65(supplemen), pages 3-16, December.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    6. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. E. Nur Ozkan-Gunay & Mehmed Ozkan, 2007. "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, Emerald Group Publishing, vol. 8(5), pages 465-480, November.
    Full references (including those not matched with items on IDEAS)

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