Beyond bias: How behavioral biases shape investment decision-making and perceived financial well-being

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Type of the article: Research Article

Abstract
This study examines the associations among behavioral biases, Investment Decision-Making, and Perceived Financial Well-Being among individual investors in Indonesia. It distinguishes respondents’ involvement in the investment decision-making process from decision quality and investment performance.
A quantitative cross-sectional design was employed using survey data from 505 Indonesian investors. The relationships among Anchoring Bias, Confirmation Bias, Loss Aversion, Overconfidence, Investment Decision-Making, and Perceived Financial Well-Being were analyzed using partial least squares structural equation modeling. Permutation-based multi-group analysis was conducted to compare female and male respondents.
Anchoring Bias, Confirmation Bias, Loss Aversion, and Overconfidence were positively associated with Investment Decision-Making, with path coefficients ranging from 0.144 to 0.215 (p < 0.05). Investment Decision-Making was positively associated with Perceived Financial Well-Being (β = 0.527, p < 0.001). The significant specific indirect effects further indicated that Investment Decision-Making statistically mediated the associations between each behavioral bias and Perceived Financial Well-Being. The multi-group analysis showed that only the relationship between Investment Decision-Making and Perceived Financial Well-Being differed significantly by gender and was stronger among female respondents.
The findings contribute to behavioral finance research by showing that behavioral biases are associated with greater self-reported involvement in evaluating, selecting, and monitoring investments. These positive associations do not indicate that behavioral biases improve decision quality, investment performance, or objective financial conditions. Given the cross-sectional and self-reported nature of the data, the findings represent statistical associations rather than causal effects.

Acknowledgment
The authors gratefully acknowledge the support provided by the Institute for Research and Community Service (Lembaga Penelitian dan Pengabdian kepada Masyarakat, LPPM), Universitas Andalas, through the Penelitian Jalur Unggulan Kepakaran research grant under Contract No. 398/UN16.19/PT.01.03/PJUK/2025. This funding played an important role in supporting the completion of this study.

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    • Table 1. Respondent profiles
    • Table 2. Reflective measurement model
    • Table 3. Formative measurement model
    • Table 4. HTMT assessment of discriminant validity
    • Table 5. Model fit
    • Table 6. Structural model assessment
    • Table 7. Direct and specific indirect effects
    • Table 8. MICOM Results for Female and Male Groups: Compositional invariance
    • Table 9. Equality of composite means and variances
    • Table 10. Gender-based permutation MGA results
    • Conceptualization
      Rita Rahayu
    • Data curation
      Rita Rahayu, Verni Juita, Retnoningrum Hidayah, Ryanda Allysia Putri
    • Formal Analysis
      Rita Rahayu, Verni Juita, Retnoningrum Hidayah, Ryanda Allysia Putri
    • Funding acquisition
      Rita Rahayu
    • Investigation
      Rita Rahayu, Verni Juita, Retnoningrum Hidayah
    • Methodology
      Rita Rahayu, Verni Juita, Retnoningrum Hidayah
    • Validation
      Rita Rahayu, Ryanda Allysia Putri
    • Writing – original draft
      Rita Rahayu
    • Writing – review & editing
      Rita Rahayu, Verni Juita, Retnoningrum Hidayah, Ryanda Allysia Putri
    • Project administration
      Verni Juita
    • Resources
      Retnoningrum Hidayah, Ryanda Allysia Putri
    • Software
      Ryanda Allysia Putri
    • Visualization
      Ryanda Allysia Putri