Presenting a Structural Model of the Drivers Influencing Investor Behavior Considering Their Perception of Stock Returns

Abstract

Objective: The aim of this study is to present a structural model of the drivers influencing investor behavior, considering their perception of stock returns. Methodology: This research is applied in nature and adopts a descriptive-analytical approach. The methodology is mixed and carried out in both quantitative and qualitative phases. The statistical population for the qualitative phase consists of academic experts and managers of listed companies. Using snowball sampling and theoretical saturation, 11 participants were selected. The data collection tool for the qualitative phase was semi-structured interviews based on theoretical foundations. Data analysis was conducted using thematic analysis. For the quantitative phase, a researcher-made questionnaire, derived from the qualitative model and validated for reliability and validity, was distributed among a stratified sample of 213 respondents. The paths and causal relationships between the external and internal constructs in the structural model were confirmed using confirmatory factor analysis. Findings: The findings indicated that the drivers influencing investor behavior based on their perception of stock returns include "demographic factors, fundamental principles of the stock market, individual and psychological characteristics, risk tolerance capacity, and personal values and beliefs." In the quantitative section, the path coefficients were greater than 0.3, and the corresponding t-values exceeded 1.96, confirming all the paths. Conclusion: In investors' decision-making based on their perception of stock returns, behavioral factors and contextual variables such as demographic factors (e.g., age, gender, education, and individual perspectives) play a significant role. Additionally, proper and data-driven fundamental analysis in the stock market leads to more optimal investment decisions. Ultimately, risk acceptance and increasing the risk threshold significantly help individuals make profitable decisions based on technical analyses.