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The article presents the results related to the evaluation of the effectiveness of investment activities in the dairy subcomplex. It is established that investments are an important component of the economic development of the dairy subcomplex, the effectiveness of which depends on a set of factors of the macro- and microeconomic environment, its organizational and economic characteristics, the investment strategy of enterprises, objects and sources of investment, the period for which capital is invested, the certainty of its future effect and the associated risk level. It was found out that in the conditions of low competitiveness of dairy subcomplex enterprises, there is a need to use effective investment projects, which significantly strengthened the role of investment efficiency analysis as a tool within which it is possible to justify, evaluate and select priority investment projects. It is determined that the task of analyzing the effectiveness of investments is the calculation of profits and cash flows, the assessment of their effectiveness, the identification of the influence of uncertainty factors on their profitability, the selection of optimal investment projects. It is advisable to organize the analysis of the effectiveness of investments in the following areas: the definition of subjects and objects of analysis; the choice of organizational forms of analysis, depending on the organizational structure and the distribution of responsibilities between individual performers; the preparation of the analysis program and its information support; analytical data processing, design and generalization of the analysis results. The proposed methodological approach to the analysis of investment efficiency takes into account the conditions of an unstable market environment (variable macroeconomic conditions, insufficient supply of dairy raw materials, uncertainty about wholesale purchase and sale prices), makes it possible to determine the effectiveness of investments, conduct risk analysis by simulation and predict data for risk analysis using regression methods.
Постникова et al. (Mon,) studied this question.