MODELING THE SUCCESS OF COMPLETING MERGERS AND ACQUISITIONS IN THE ELECTRICITY INDUSTRY USING LOGISTIC REGRESSIONS

The paper considers mergers and acquisitions to be often the only possible option for implement-ing long-term development strategies due to the limited possibilities for creating new production capacity in electric power companies. The study aims to identify the factors that influence the success of mergers and acquisitions in the electric power industry. The success of a deal is crucial for decision making due to the long period of its implementation. The main hypothesis of the study is that not only the deal parameters themselves, the strategy implemented by the participants, but also the economic, social and political fac-tors of the environment influence the successful completion of deals. The authors use logistic regressions in the form of single-layer neural networks with a sigmoidal activation function as an economic and mathematical toolkit. The constructed models show that larger deals are less likely to be successful. The authors also conclude that political factors have a significant impact on the success of a deal. This reflects the modern realities of long-term decision making. The models are tested on the example of large mer-gers and acquisitions in the Russian energy industry; the results obtained allow us to conclude that the hypothesis is verified and the models are applicable to strategic planning. The models can be used in the planning and execution of M&A deals both at the stage of preliminary selection of counterparties and for the purpose of choosing the best time for the deal execution.