Forecasting the financial distress of mining companies: Tool for testing the key performance indicators
There are numerous studies and research work related to the forecasting of financial distress of companies. Developed theoretical and practical models were used for forecasting such problems. Application of specific model is relatively novel analytical approach and represents an indicator which some...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Mining and Metallurgy Institute, Bor
2016-01-01
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| Series: | Mining and Metallurgy Engineering Bor |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/2334-8836/2016/2334-88361601073Z.pdf |
| _version_ | 1797225411707404288 |
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| author | Zlatanović Dragan Bugarin Mile Milisavljević Vladimir Zlatanović Vukašin |
| author_facet | Zlatanović Dragan Bugarin Mile Milisavljević Vladimir Zlatanović Vukašin |
| author_sort | Zlatanović Dragan |
| collection | DOAJ |
| description | There are numerous studies and research work related to the forecasting of financial distress of companies. Developed theoretical and practical models were used for forecasting such problems. Application of specific model is relatively novel analytical approach and represents an indicator which sometimes could have large importance for decision makers. Indicators for production and business aspects are represented by one of the most suitable synthetic parameters - Altman Financial Distress Ratio, which is sum of weighted individual parameters. The aim of this paper is to present a method for forecasting the financial distress, mainly based on financial parameters of a company. Calculation of financial parameters was based on the public annual financial reports of companies included in the example. Authors applied the Altman Z-score model on sample of two mining companies, to establish accuracy of this model and possibility for application on other mining companies. |
| first_indexed | 2024-04-24T14:08:35Z |
| format | Article |
| id | doaj.art-4aba018d19394f7291fbdfa3ec17c18c |
| institution | Directory Open Access Journal |
| issn | 2334-8836 2406-1395 |
| language | English |
| last_indexed | 2024-04-24T14:08:35Z |
| publishDate | 2016-01-01 |
| publisher | Mining and Metallurgy Institute, Bor |
| record_format | Article |
| series | Mining and Metallurgy Engineering Bor |
| spelling | doaj.art-4aba018d19394f7291fbdfa3ec17c18c2024-04-03T09:39:03ZengMining and Metallurgy Institute, BorMining and Metallurgy Engineering Bor2334-88362406-13952016-01-0120161738010.5937/mmeb1601073Z2334-88361601073ZForecasting the financial distress of mining companies: Tool for testing the key performance indicatorsZlatanović Dragan0https://orcid.org/0000-0002-1095-1913Bugarin Mile1https://orcid.org/0000-0002-6992-5154Milisavljević Vladimir2https://orcid.org/0000-0002-3713-0138Zlatanović Vukašin3Innovation Centre, Faculty of Mechanical Engineering, University of Belgrade, SerbiaMining and Metallurgy Institute, Bor, SerbiaFaculty of Mining and Geology, University of Belgrade, SerbiaFEFA - Faculty of Economy, Finance and Administration, Singidunum University, Belgrade, SerbiaThere are numerous studies and research work related to the forecasting of financial distress of companies. Developed theoretical and practical models were used for forecasting such problems. Application of specific model is relatively novel analytical approach and represents an indicator which sometimes could have large importance for decision makers. Indicators for production and business aspects are represented by one of the most suitable synthetic parameters - Altman Financial Distress Ratio, which is sum of weighted individual parameters. The aim of this paper is to present a method for forecasting the financial distress, mainly based on financial parameters of a company. Calculation of financial parameters was based on the public annual financial reports of companies included in the example. Authors applied the Altman Z-score model on sample of two mining companies, to establish accuracy of this model and possibility for application on other mining companies.https://scindeks-clanci.ceon.rs/data/pdf/2334-8836/2016/2334-88361601073Z.pdffinancial distressmining companyaltman z-scoreperformance indicators |
| spellingShingle | Zlatanović Dragan Bugarin Mile Milisavljević Vladimir Zlatanović Vukašin Forecasting the financial distress of mining companies: Tool for testing the key performance indicators Mining and Metallurgy Engineering Bor financial distress mining company altman z-score performance indicators |
| title | Forecasting the financial distress of mining companies: Tool for testing the key performance indicators |
| title_full | Forecasting the financial distress of mining companies: Tool for testing the key performance indicators |
| title_fullStr | Forecasting the financial distress of mining companies: Tool for testing the key performance indicators |
| title_full_unstemmed | Forecasting the financial distress of mining companies: Tool for testing the key performance indicators |
| title_short | Forecasting the financial distress of mining companies: Tool for testing the key performance indicators |
| title_sort | forecasting the financial distress of mining companies tool for testing the key performance indicators |
| topic | financial distress mining company altman z-score performance indicators |
| url | https://scindeks-clanci.ceon.rs/data/pdf/2334-8836/2016/2334-88361601073Z.pdf |
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