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<p>Individual investors’ decisions regarding stock investment are largely shaped by biases, which often results in less-than-optimal outcome. The objective of this paper to identify and analyze prominent biases influencing investment decisions of HEI employees. Given their familiarity with financial concepts, it is presumed that these individuals possess certain level of financial literacy. Employing multifactor hierarchical approach with fuzzy ISM–MICMAC techniques, uncovering interdependencies among these factors, with fuzzy ISM mapping influences, and fuzzy MICMAC categorizing factors based on crisp value of driving and dependence power. Findings of this research reveal that biases are structured across six hierarchical levels shows F-ISM framework. Independent variables at the bottom influence dependent variables at the top. F-MICMAC analysis classifies biases into autonomous, dependent, independent, and linkage factors. This study’s limitations stem from its exclusive reliance on expert opinions. Future studies should explore new methods and additional influencing factors. This model helps stakeholders to navigate and manage bias-related complexities.</p>

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Biases at play: a strategic modeling of investor behavior using fuzzy ISM–MICMAC approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月12日

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  • Vandana Yadav,
  • Dr. Parveen Kumar,
  • Dr. Jyotsana Chawla

摘要

Individual investors’ decisions regarding stock investment are largely shaped by biases, which often results in less-than-optimal outcome. The objective of this paper to identify and analyze prominent biases influencing investment decisions of HEI employees. Given their familiarity with financial concepts, it is presumed that these individuals possess certain level of financial literacy. Employing multifactor hierarchical approach with fuzzy ISM–MICMAC techniques, uncovering interdependencies among these factors, with fuzzy ISM mapping influences, and fuzzy MICMAC categorizing factors based on crisp value of driving and dependence power. Findings of this research reveal that biases are structured across six hierarchical levels shows F-ISM framework. Independent variables at the bottom influence dependent variables at the top. F-MICMAC analysis classifies biases into autonomous, dependent, independent, and linkage factors. This study’s limitations stem from its exclusive reliance on expert opinions. Future studies should explore new methods and additional influencing factors. This model helps stakeholders to navigate and manage bias-related complexities.