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<p>Effective stock selection is essential for investment decision-making, especially in uncertain markets. This study uses Interval Type-2 Fuzzy Sets (IT2 FSs) to handle uncertainty better than Type-1 Fuzzy Sets (IT1 FSs). It develops a Multi-Criteria Decision Making (MCDM) model by combining IT2-FAHP for weighting criteria and IT2-Fuzzy TOPSIS and IT2-Fuzzy GRA for ranking stocks. The model is tested on eight NASDAQ-listed companies using financial data from June 2014 to June 2024. It considers both accounting-based (AFM) and economic value-based (EFM) measures. Expert opinions from financial investors with 30 years of experience help build the decision matrix. Sensitivity analysis using IT2-Fuzzy GRA confirms the model’s accuracy. The results clearly indicate that, based on IT2-Fuzzy TOPSIS and IT2-Fuzzy GRA, AAPL secured the highest rank, while CTSH ranked the lowest. This analysis provides valuable insights for investors and trading managers, helping them select the right stocks with greater accuracy and confidence. The findings highlight the effectiveness of IT2 FS-based methods in improving stock ranking and investment decisions. This study enhances fuzzy MCDM techniques, providing a reliable and adaptive tool for financial decision-making.</p>

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Enhanced stock selection using a hybrid multi-criteria decision making approach with interval type-2 fuzzy framework

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • C. Veeramani,
  • R. Venugopal,
  • S. A. Edalatpanah

摘要

Effective stock selection is essential for investment decision-making, especially in uncertain markets. This study uses Interval Type-2 Fuzzy Sets (IT2 FSs) to handle uncertainty better than Type-1 Fuzzy Sets (IT1 FSs). It develops a Multi-Criteria Decision Making (MCDM) model by combining IT2-FAHP for weighting criteria and IT2-Fuzzy TOPSIS and IT2-Fuzzy GRA for ranking stocks. The model is tested on eight NASDAQ-listed companies using financial data from June 2014 to June 2024. It considers both accounting-based (AFM) and economic value-based (EFM) measures. Expert opinions from financial investors with 30 years of experience help build the decision matrix. Sensitivity analysis using IT2-Fuzzy GRA confirms the model’s accuracy. The results clearly indicate that, based on IT2-Fuzzy TOPSIS and IT2-Fuzzy GRA, AAPL secured the highest rank, while CTSH ranked the lowest. This analysis provides valuable insights for investors and trading managers, helping them select the right stocks with greater accuracy and confidence. The findings highlight the effectiveness of IT2 FS-based methods in improving stock ranking and investment decisions. This study enhances fuzzy MCDM techniques, providing a reliable and adaptive tool for financial decision-making.