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Venture capital investing represents a specific segment of financial investments focused on providing capital to startups and young companies that demonstrate not only high growth potential but also a high level of risk. This form of investment is a vital component of the innovative economy, as it fosters the development of new technologies, products, and services. Venture capital has a significant impact on the economic development of startups by supporting innovation, creating new jobs, and enhancing the competitiveness of businesses. However, due to the high risks and specifics of this type of investment, an essential task is the adequate assessment of projects and a clear understanding of market trends. The aim of this research is to identify the risks associated with financing startups by external investors, as well as to create fuzzy models for a comprehensive risk assessment of venture funding. To achieve the research objectives, the expert evaluation method—fuzzy logic—was applied to determine the most critical risk factors in financing startups by external investors and to develop fuzzy models for comprehensive risk evaluation. The paper justifies the necessity of using a two-level structure of indicators in the fuzzy model as opposed to a linear (one-level) structure, allowing for the construction of fuzzy models with sufficient efficiency based on the number of indicators ranging from 7 to 25. In modeling, indicators are grouped into clusters of 3–5, and these group evaluations (3–5 in number) serve as input data for the second level of the model. During the research, a fuzzy model for investment risks in startups was developed and tested. The validation of this model confirmed its effectiveness and suitability for practical use. The model has two levels, with the same rules applied at each level. The two-level structure of indicators in the fuzzy model serves as an important tool for analyzing complex systems under uncertainty. The study identified the model's sensitivity, and experimental evidence confirmed that its stability improves with the addition of new variables affecting the investment risk of startups. Future developments of the model anticipate the inclusion of new parameters with varying weight coefficients.

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Modeling Venture Financing of Startups Using Fuzzy Logic Methods Based on a Hierarchical Structure of Indicators

  • 章节

  • 发表日期：

    2025年

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  • Kostyantyn Bozhko,
  • Nadiia Antonenko,
  • Oksana Korolyova,
  • Irina Karlova,
  • Valentyna Hoshovska,
  • Anna Dobrovolska

摘要

Venture capital investing represents a specific segment of financial investments focused on providing capital to startups and young companies that demonstrate not only high growth potential but also a high level of risk. This form of investment is a vital component of the innovative economy, as it fosters the development of new technologies, products, and services. Venture capital has a significant impact on the economic development of startups by supporting innovation, creating new jobs, and enhancing the competitiveness of businesses. However, due to the high risks and specifics of this type of investment, an essential task is the adequate assessment of projects and a clear understanding of market trends. The aim of this research is to identify the risks associated with financing startups by external investors, as well as to create fuzzy models for a comprehensive risk assessment of venture funding. To achieve the research objectives, the expert evaluation method—fuzzy logic—was applied to determine the most critical risk factors in financing startups by external investors and to develop fuzzy models for comprehensive risk evaluation. The paper justifies the necessity of using a two-level structure of indicators in the fuzzy model as opposed to a linear (one-level) structure, allowing for the construction of fuzzy models with sufficient efficiency based on the number of indicators ranging from 7 to 25. In modeling, indicators are grouped into clusters of 3–5, and these group evaluations (3–5 in number) serve as input data for the second level of the model. During the research, a fuzzy model for investment risks in startups was developed and tested. The validation of this model confirmed its effectiveness and suitability for practical use. The model has two levels, with the same rules applied at each level. The two-level structure of indicators in the fuzzy model serves as an important tool for analyzing complex systems under uncertainty. The study identified the model's sensitivity, and experimental evidence confirmed that its stability improves with the addition of new variables affecting the investment risk of startups. Future developments of the model anticipate the inclusion of new parameters with varying weight coefficients.