A Hybrid Model for Evaluating the Investment Attractiveness of Companies Using Fuzzy Multi-Criteria Methods

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