Impact of psychological factors on investment decisions in Nepalese share market: A mediating role of financial literacy

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Psychological factors such as emotional reactions, cognitive biases, and herd behavior influence investment decisions because they shape investor behavior, drive market dynamics, and affect rational decision-making. Similarly, financial literacy improves investment decisions by facilitating informed choices, minimizing biases, enhancing risk management, and promoting long-term financial planning. This study aims to examine the influence of psychological factors on investment decisions in the Nepalese share market, emphasizing the mediating role of financial literacy. Smart PLS 4.0 was used to analyze the structural relationships within the proposed theoretical model. Data were collected from the primary source using a structured questionnaire administered through a random sampling technique. The respondents included 410 active individual investors from the Nepal Stock Exchange (NEPSE). The study's findings reveal that psychological factors have a positive and significant effect on investment decisions among investors in the Nepalese stock market. Furthermore, the study revealed that financial literacy mediates the relationship between psychological factors and investment decisions by enhancing individuals' understanding and confidence, leading to more informed and rational investment choices. The results highlight the critical role of financial literacy in investment decisions in the share market. The findings indicate that investors with higher financial literacy levels are better equipped to mitigate the adverse effects of psychological biases, leading to more rational and informed investment decisions. By understanding the interplay between psychological factors and financial literacy, policymakers and financial institutions can develop targeted strategies to foster a more robust and resilient financial market in developing economies such as Nepal.

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    • Figure 1. Conceptual framework
    • Figure 2. PLS-SEM showing positive relationships between variables
    • Table 1. Demographic profile of the respondents
    • Table 2. Measurement model
    • Table 3. Discriminant validity (latent variable correlation and square root of the AVE)
    • Table 4. Coefficients of determination (R2 and Q2) and model fit (SRMR-NFI)
    • Table 5. Hypotheses constructs
    • Conceptualization
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Data curation
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Formal Analysis
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Methodology
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Project administration
      Dhruba Prasad Subedi
    • Software
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Supervision
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Validation
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Writing – original draft
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Writing – review & editing
      Dhruba Prasad Subedi, Dilli Ram Bhandari
    • Investigation
      Dilli Ram Bhandari