International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Accuracy and Interpretability in Hybrid Intelligent Algorithms in Rating the Effective Factors on Investment Firms in Various Crises

Document Type : Original Article

Authors
Sasan Pourbijan 1
Alireza Zamanpour 2
Seyed Yosef Ahadi Sarkani 3
1 Ph.D. Student, Department of Financial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
2 Assistant professor, Department of Financial Engineering, Faculty Member, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Associate professor, Department of Accounting, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
10.22034/ijfma.2025.78170.2215
Abstract
The purpose of this study is to rank the factors affecting investment companies based on risk criteria using fuzzy multi-criteria decision-making techniques. Given the importance of the topic, dimensions and criteria related to investment companies and their performance were extracted from theoretical foundations. These factors were categorized into dimensions through a decision-making team of 20 experts using semi-structured interviews and questionnaires. In the next phase, the approval or rejection of the factors was quantitatively evaluated. Subsequently, various techniques were applied to rank the factors influencing investment companies. The first technique used was Fuzzy Delphi, followed by the Fuzzy AHP approach. Later, the Fuzzy TOPSIS method and the Fuzzy VIKOR technique were applied in the final stage. The findings from the Fuzzy Delphi method indicated that out of 60 identified dimensions, 49 criteria were selected, and 11 were excluded. In the ranking process using the combined Fuzzy AHP-TOPSIS approach, financial risk, interest rate risk, business risk, liquidity risk, systematic risk, unsystematic risk, credit risk, exchange rate risk, inflation risk, country risk, and political risk were identified as significant factors. Similarly, in the AHP-VIKOR fuzzy ranking, financial risk, interest rate risk, business risk, liquidity risk, credit risk, exchange rate risk, inflation risk, country risk, and political risk were deemed critical. These findings suggest that investment companies exhibit adequate performance during risky and crisis situations when employing advanced fuzzy techniques.
Keywords
Crisis
Investment Firms
Genetic Algorithm
Artificial Neural Network
Fuzzy Logic
Adaptive Neuro-Fuzzy Network Technique
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