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Оценка систематического риска в инвестиционном проекте: проблема определения
Aim. The presented study aims to analyze existing approaches and develop recommendations for the numerical assessment of the magnitude of risk associated with the implementation of investment projects.
Tasks. The authors describe traditional approaches to systematic risk assessment; systematise the practice of determining the beta coefficient as a risk measure of investment projects; analyse alternative approaches to assessing discount rate and systematic risk; develop recommendations for leveling the systematic risk measure.
Methods. This study uses general scientific methods of analysis and synthesis, induction and deduction, comparison and description, as well as special methods of financial mathematics and economic-mathematical modeling.
Results. Determining the investment attractiveness of a business or project involves forecasting and discounting future cash flows. Obvious risks are accounted for by adjusting cash flows, and most other risks and uncertainties are reflected in the discount rate. One of the specific components of the discount rate is a measure of systematic risk – the beta coefficient (β). The authors consider the traditional approach to assessing the cost of capital and systematic risk, alternative approaches that have methodological advantages, but are often not applicable in practice
due to additional difficulties in calculations, and most importantly – in comparison between projects/ companies/results for different settlement dates. The study also provides a critical analysis of publicly available analytical data through the example of A. Damodaran’s information and analytical resource and highlights problems in the use of statistics for the valuation of projects and companies in the long term due to the significant volatility of the beta coefficient.
Conclusions. In the absence of other reliable sources, analysts should be more careful about the values of published analytical materials, in some cases independently rechecking the data of publicly available analytics using the recommendations presented in this study.