Behavioral biases affecting investment decisions of capital market investors in Bangladesh: A behavioral finance approach

  • Received January 11, 2023;
    Accepted April 20, 2023;
    Published May 5, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.20(2).2023.13
  • Article Info
    Volume 20 2023, Issue #2, pp. 149-159
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This work is licensed under a Creative Commons Attribution 4.0 International License

The aim of this paper is to identify the behavioral and psychologic biases that may affect the investment decisions of individual investors in Bangladesh. This study considered behavioral anomalies such as Cognitive Dissonance, Regret Aversion, Loss Aversion, Overconfidence, Hindsight, Illusion of Control, Herd instinct, Self-attribution and Representativeness, and analyzed how significantly each of these would prevail by preventing investors from making rational decisions when investing. The research has been developed through a structured questionnaire and analyzing the survey results collected from 196 individual investors involved in Dhaka Stock Exchange. Factor analysis on a behavioral approach was conducted to analyze the responses. The outcome reveals that investors are not rational, and that there is a significant impact of the different behavioral biases, particularly cognitive dissonance (0.8005), regret aversion (0.7793), loss aversion (0.7418) and illusion of control biases (0.7260) on the investment decisions of investors in Bangladesh. Moreover, the most influential of four factors extracted jointly can explain 55.63% of the variance of the variables. Finally, the factor loading values show that all nine hypotheses can be rejected, which makes it clear that all the designated psychological biases exist in the investment decision of DSE investors.

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    • Table 1. Summary of behavioral responses
    • Table 2. Descriptive statistics
    • Table 3. Correlation matrix
    • Table 4. Bartlett test and KMO test
    • Table 5. Principal component factor analysis
    • Table 6. Factor loadings and unique variance
    • Table 7. Principal component factor analysis (rotated)
    • Table 8. Rotated factor loadings
    • Table 9. Factor rotation matrix
    • Table 10. Rotated factor loadings (pattern matrix) and unique variance sorted
    • Conceptualization
      Farhana Yasmin, Jannatul Ferdaous
    • Data curation
      Farhana Yasmin, Jannatul Ferdaous
    • Formal Analysis
      Farhana Yasmin
    • Methodology
      Farhana Yasmin
    • Writing – original draft
      Farhana Yasmin
    • Writing – review & editing
      Farhana Yasmin
    • Project administration
      Jannatul Ferdaous
    • Supervision
      Jannatul Ferdaous
    • Validation
      Jannatul Ferdaous