Гибридная модель оценки инвестиционной привлекательности компаний с использованием нечетких многокритериальных методов

This study develops a novel algorithm to assess the investment attractiveness of companies using multi-criteria decision-making methods (MCDM) such as the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). This approach integrates both quantitative and qualitative aspects, addressing the main limitations of traditional models that often focus solely on internal factors and overlook crucial external variables and qualitative metrics necessary for a comprehensive evaluation. Furthermore, existing models often fail to incorporate adequate risk assessments and typically disregard individual investors’ preferences. The algorithm employs fuzzy sets to integrate diverse data, thereby enhancing assessment accuracy in the presence of uncertainty and incomplete information. The model incorporates a range of traditional financial metrics, other quantitative criteria, non- statistical data and subjective investor preferences in order to provide a comprehensive and multifaceted assessment of investment attractiveness. In this article, the FAHP method is employed to determine the weights of the criteria, while the FTOPSIS method is applied to identify the most investment-attractive company. The study tested the model on Russian food retail companies, with Lenta emerging as the most attractive investment option. The results demonstrate the efficacy and applicability of the method for enhancing decision- making precision and objectivity across a spectrum of economic sectors