Iranian Journal of Finance

Abstract Iran’s capital market has witnessed substantial transformation over recent decades. However, the sharp downturn of the stock market in 2020 represented a critical juncture, extending beyond retail investors’ financial losses and culminating in a widespread public trust crisis. Addressing this issue, the present study proposes a neurofinance-based model aimed at strengthening public investor trust in Iran’s capital market. The model was developed through a mixed-methods design, integrating qualitative grounded theory exploration with quantitative validation via structural equation modeling (SEM). In the qualitative phase, data were collected in 2025 through semi-structured interviews with 17 capital market experts and analyzed using a three-stage coding procedure comprising: open, axial, and selective coding. In the quantitative phase, the proposed theoretical model was empirically tested using SEM on data obtained from 87 investors in the capital market. The qualitative findings reveal that the development of trust is shaped by causal conditions (e.g., information transparency and emotional responses), contextual conditions (e.g., economic stability and social capital), and intervening conditions (e.g., media, education, and supportive institutions). The quantitative results confirm that all model paths are statistically significant and that the model demonstrates an acceptable level of fit. Accordingly, strategies such as enhancing transparency, empowering retail investors, and promoting financial literacy were proposed, generating outcomes at the individual, market, and macro levels. The novelty of this research lies in integrating a neurofinance perspective with a mixed-methods approach to develop a context-specific model aligned with Iran’s institutional and cultural environment.

Abstract The primary objective of this study is to develop a predictive model for money laundering risk in Iranian listed firms. Initially, firm-level money laundering risk is measured using auditor assessments of anti-money laundering (AML) activities disclosed in annual audit reports. Subsequently, a quantitative modeling approach is employed, using financial and governance-related variables identified in prior research. To validate the quantitative findings, a qualitative approach based on grounded theory is also applied to identify additional explanatory factors. This research follows a mixed-methods design, incorporating both quantitative and qualitative phases. In the quantitative phase, a panel logit regression model is estimated using data from 1,680 firm-year observations covering the period 2012–2023. Independent variables include firm size, return on equity, leverage, investment opportunities, board independence, and board size. In the qualitative phase, semi-structured interviews were conducted with 10 experts to identify key risk factors, followed by the design and administration of an 18-item questionnaire distributed to 110 professionals. Exploratory factor analysis was then used to extract latent variables. The quantitative analysis reveals significant relationships between money laundering risk and several variables, such as firm size (positive), return on equity (negative), leverage (positive), and board independence (negative). The qualitative analysis identifies three core factors: (1) organizational culture and employee training, (2) corporate governance, and (3) a composite factor comprising compliance, organizational complexity, financial performance, firm size, and capital structure. Together, these factors explain over 50% of the variance in expert responses. The convergence of results from both methodological approaches confirms the robustness of the proposed model. Corporate governance indicators—particularly board size and independence—alongside financial attributes such as firm size, profitability, and capital structure, are found to be significant predictors of firm-level money laundering risk. The findings underscore the importance of strengthening internal control mechanisms and compliance structures in reducing money laundering risk.