The objective of this study is to propose an approach that would increase the efficiency and efficacy of project financial risk management. The starting point of this research is an original detailed list of project financial risk categories, as it was observed that financial risk is described in the literature far too generally. Following a survey of project managers, it is shown that all the identified project financial risk categories are significant and early warning signals may play an important role in their prevention or mitigation. Additionally, the main causes for project financial risks are identified and their importance assessed. Following a literature review on metrics-based and financial risk management in projects, as well as an analysis of the causes assessed in the survey, it is hypothesised that sustainability principles, combined with metrics-based management, may increase the efficiency and efficacy of project financial risk management. A corresponding method is proposed, which should be embedded into the traditional process of project financial risk management. This method consists of generating metrics with a warning and preventive potential for each combination of three elements (financial risk category, risk cause, sustainability principle). This approach introduces into project financial risk management elements going beyond the financial optics, which may considerably increase its potential.

The aim of the article is to propose a new method of valuation of a company, considering its ownership relations with other companies. For this purpose, the concept of the Shapley value from cooperative game theory is used as the basis for assessing such dependent companies. The paper presents proposals for Shapley value calculation algorithms for our model. We expand our model by discussing personal relations in addition to ownership relations and point out how intuitionistic fuzzy sets may be helpful in this context. As a result, we propose two new expanded models. In the first probabilistic model, we apply Pearson’s correlation coefficient, in the second, we use a correlation coefficient between intuitionistic fuzzy sets to determine the personal relationships. Finally, we present and interpret results for a real-world economic network with 17 companies.