Adaptive scenario-based multi-criteria approach to assessing investment potential in agribusiness

This paper presents a comparative analysis of SAW, TOPSIS, and GDR multi-criteria aggregation methods, applied to the construction of a synthetic investment attractiveness index (SIAI). The relevance of this study is determined by the need to enhance the methods of integrated assessment of investment potential in the context of evolving financial reporting structures, particularly in strategically important sectors of the economy. Classical assessment methods of the investment potential of an enterprise assess loan capital as a negative factor; however, under subsidized financing, long-term liabilities can be treated as equity, necessitating the development of new aggregated assessment models and indices that can take into account capital’s changing structure. This study is aimed at finding the optimal ratio of classical and adapted coefficients using multi-criteria decision-making methods (MCDM). The purpose of the study is to perform a comparative analysis of three MCDM methods – SAW, TOPSIS, and GDR – based on a synthetically formed structure of input data reflecting possible configurations of classical and adapted stability coefficients. Empirical data are drawn from a three-year financial dataset of an agricultural enterprise (2022–2024). Additionally, the model was modified by introducing a trust coefficient (P/S) depending on the adaptation level. Each MCDM method was used to rank the scenarios, followed by a sensitivity and consistency analysis using Spearman’s rank correlation. The results indicate that GDR exhibited the greatest structural stability and lowest rank volatility after the inclusion of the external trust indicator. SAW remained robust, while TOPSIS showed significant sensitivity to the expansion of decision space. GDR, which combines inner structure of SAW and TOPSIS and is supplemented by MINMAX, demonstrated high degree of normalisation and correlation to SAW. An optimal scenario that balances classical and adapted indicators was determined, is recommended for further applied research. The practical value of the paper lies in application of the developed approach when constructing stable ratings of investment attractiveness in the context of transforming financial statements. Future research directions include weight optimization, expansion to cross-sectoral datasets and evaluation of alternative distance metrics within TOPSIS.