Iranian Journal of Finance

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.

Abstract Investor sentiment is one of the non-fundamental factors that affect the financial markets, which itself is influenced by various factors, including oil price changes. This study aims to investigate the impact of oil price on investor sentiment in stock market industries in the Tehran Stock Exchange (TSE) using monthly data from April 2010 to June 2020. To investigate this issue, stock exchange industries were grouped into three categories: total industries, oil-related industries, and non-oil industries, and the effect of oil prices on investor sentiments in these three groups was examined using the pooled mean group (PMG) technique. The PMG approach considers both the short- and long-run relation between series and provides reliable results in the context of dynamic heterogeneous panel models. The implementation of PMG in all three models shows the impact of oil prices on investor sentiment over both the short and long run. Findings suggest also that oil price has positive and significant in all three models in the long run and the oil price coefficient is higher in oil-related industries than non-oil-related industries. These results are the opposite of the results obtained by similar studies, which can be due to the special features of countries, e.g. being oil exporters or oil importers