Issue |
E3S Web Conf.
Volume 177, 2020
XVIII Scientific Forum “Ural Mining Decade” (UMD 2020)
|
|
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Article Number | 05006 | |
Number of page(s) | 7 | |
Section | Economics and Management | |
DOI | https://doi.org/10.1051/e3sconf/202017705006 | |
Published online | 08 July 2020 |
Analysis of computed discount rates for the largest companies of the mineral resources sector of Russia
1 Ural state mining University, Department of Economics and management, 620144, 30, Kuibyshev St., Ekaterinburg, Russia
* Corresponding author: Podkorytov.V@m.ursmu.ru
The article provides a comparative analysis of discount rates for the largest companies in the mineral resources sector of Russia, which are calculated on the basis of statistical data from the US and Russian securities markets. Using the CAPM model (Capital Asset Pricing Model) for each selected company, various ruble discount rates were obtained. Calculations based on statistical data from the Russian securities market showed higher rates, and this, according to the authors, can negatively affect the assessment of potential investment projects in terms of their effectiveness. According to the results of the study, it was concluded that when calculating discount rates, it is advisable to use statistical data from the US securities market, since they give more objective results. The appropriateness of their use in forecasting the return on investment is largely due to the length of the retrospective period when calculating the premium for the risk of investing in stocks (from 1928 to the present), smoothing out market volatility at certain crisis times. The Russian securities market has a short retrospective, uneven dynamics of indicators, which does not allow full use of its statistical information. The authors see the prospect of further research in constructing special stochastic models for discount rates forecasting to evaluate investments in companies of the mineral resources sector of Russia.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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