Impact of behavioral biases on investment behavior: Mediating role of neuroticism among Indian retail investors

  • 18 Views
  • 2 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Behavioral theories, established in sociological and psychological models, provide intriguing descriptions and explanations of anomalies in the market and market inefficiencies. India represents itself as one of the fastest-growing economies on a global scale. The present article explores the role of nine behavioral biases in investment behavior, particularly by addressing the mediating effect of neuroticism among Indian investors. This research framework was developed by an in-depth literature analysis; hypotheses were tested experimentally using SPLS (smart partial least squares) and SEM (structural equation modeling) on a sample of 450 participants from October 1, 2024, to December 30, 2025, and utilized a structured adopted questionnaire to acquire data from retail investors. Anchoring β = 0.267, hindsight β = 0.088, mental accounting β = 0.249, and overconfidence β = 0.164 display a noticeably positive impact on investment behavior. Conversely, self-attribution β = -0.283 shows a significantly adverse impact. However, the disposition effect, emotional bias, herding behavior, and representativeness appear to exert an insignificant impact on investment behavior. The neuroticism trait β = 0.157 has a significantly positive impact on investment behavior. The findings show that anchoring β = -0.023, the disposition effect β = 0.030, emotional bias β = 0.023, herding β = 0.032, mental accounting β = -0.019, and overconfidence β = -0.031 in behavioral finance significantly impact investment behavior indirectly through neuroticism. This model explains 31.1% of the variance in biases; hence, it enhances the mediating role of neuroticism in shaping investment behavior.

view full abstract hide full abstract
    • Figure 1. Conceptual framework
    • Figure 2. Measurement model
    • Table 1. Loadings, CR, CA, AVE, VIF
    • Table 2. HTMT ratio
    • Table 3. Fornell and Larcker criterion
    • Table 4. Direct effect (H1a to H10)
    • Table 5. Specific effect (mediation effect H1c to H9c)
    • Conceptualization
      Ishrat Bashir, Sushil Mehta
    • Data curation
      Ishrat Bashir, Sushil Mehta
    • Formal Analysis
      Ishrat Bashir
    • Investigation
      Ishrat Bashir
    • Methodology
      Ishrat Bashir, Sushil Mehta
    • Resources
      Ishrat Bashir
    • Software
      Ishrat Bashir
    • Supervision
      Ishrat Bashir, Sushil Mehta
    • Validation
      Ishrat Bashir, Sushil Mehta
    • Visualization
      Ishrat Bashir, Sushil Mehta
    • Writing – original draft
      Ishrat Bashir
    • Writing – review & editing
      Ishrat Bashir, Sushil Mehta