A new conceptualization of investor sophistication and its impact on herding and overconfidence bias

  • Received February 25, 2022;
    Accepted April 7, 2022;
    Published April 20, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.19(2).2022.06
  • Article Info
    Volume 19 2022, Issue #2, pp. 67-80
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Despite the success of behavioral finance, the question of whether behavioral biases persist in the face of expertise is an oft-expressed concern. It becomes pertinent to explore if investor sophistication is associated with behavioral biases, as traders gain sophistication with experience and knowledge. The current study explores this relationship by proposing a new conceptualization of investors’ sophistication via the processes of learning and competition. The study empirically explores if herding and overconfidence biases are related to learning and competition, and thus, with investors’ sophistication via these aspects. Using data from equity investors from India (n = 257), the study employs ANOVA and multiple regression analysis through indicator function to form dummy variables for different categories. The results of the study conclude that diversification is significantly related to both the biases using ANOVA (F(3,253) = 3.081; p < 0.05) as well as multiple regression (p < 0.05). The other variables considered are found to be non-significant (p > 0.05) for both the biases. The study controls for all the other observed variables of the conceptual model to find out the effect of the change in the observed variables on the level of investor sophistication, making this study a novel and a distinct attempt.

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    • Figure 1. Authors’ conceptualization of Investor Sophistication
    • Table 1. Frequency distribution of investors in different categories
    • Table 2. Reliability of questionnaire statements
    • Table 3. Levene’s statistics: test of homogeneity of variances
    • Table 4. ANOVA results
    • Table 5. Results of the Welch and Brown-Forsythe tests
    • Table 6. Post hoc analysis (multiple comparisons: Hochberg)
    • Table 7. Multiple-regression analysis results
    • Conceptualization
      Ashutosh Yadav, Deepshikha Yadav, Ishan Kashyap Hazarika
    • Data curation
      Ashutosh Yadav, Deepshikha Yadav
    • Formal Analysis
      Ashutosh Yadav, Ishan Kashyap Hazarika
    • Investigation
      Ashutosh Yadav, Deepshikha Yadav
    • Methodology
      Ashutosh Yadav, Deepshikha Yadav, Ishan Kashyap Hazarika
    • Resources
      Ashutosh Yadav, Deepshikha Yadav, Ishan Kashyap Hazarika
    • Supervision
      Ashutosh Yadav, Deepshikha Yadav, Ishan Kashyap Hazarika
    • Visualization
      Ashutosh Yadav, Deepshikha Yadav
    • Writing – review & editing
      Ashutosh Yadav, Deepshikha Yadav
    • Project administration
      Ishan Kashyap Hazarika
    • Software
      Ishan Kashyap Hazarika
    • Validation
      Ishan Kashyap Hazarika
    • Writing – original draft
      Ishan Kashyap Hazarika