Identification and Modeling of Cognitive Biases Affecting Institutional Investors’ Preferences in Iran’s Financial Markets - Samii , Farzad; Farsad Amanollahi , Gholamreza; Keyghobadi , Amirreza (Business, Marketing, and Finance Open, 2024)
The present study aims to identify and model the cognitive biases affecting institutional investors' preferences in Iran's financial markets. This research is applied in terms of its objective and descriptive-analytical in nature. The research methodology is mixed and conducted in both qualitative and quantitative phases. The qualitative population consists of academic experts and managers of investment firms listed on the stock exchange, selected through the snowball sampling method until theoretical saturation was achieved, resulting in a sample size of 12 participants. The data collection tool for the qualitative phase was a semi-structured interview based on theoretical foundations. Data analysis in this phase was conducted using thematic analysis. The findings of the qualitative phase were categorized into four main themes and their subcategories. The identified cognitive and psychological factors include "overconfidence, disposition effect, herding behavior, information bias, and mental accounting." Intensifying factors include "macroeconomic decisions, information asymmetry, and information accumulation." Identified strategies include "reviewing and accepting mistakes, adhering to a trading plan, and avoiding bias toward specific stocks or characteristics." Identified outcomes include "better performance, greater investor attraction, and capital market efficiency." In the quantitative phase, a researcher-developed questionnaire derived from the qualitative findings was validated for reliability and distributed to a quantitative population, estimated to include 240 participants. The causal paths and relationships between exogenous and endogenous constructs in the structural model were confirmed using confirmatory factor analysis. Path coefficients greater than 0.3 and corresponding t-values exceeding 1.96 confirmed all hypothesized paths.