Accounting, Finance and Computational Intelligence

This study aims to develop a predictive model for investment efficiency using a meta-analysis approach and compare its predictive power with the model of Biddle et al. (2009). The study employed a meta-analysis approach to develop the predictive model. 33 articles out of 85 were selected to identify the factors influencing investment efficiency. The baseline model and the developed model were compared using neural network methods to evaluate their predictive accuracy. The Wilcoxon test was also used to assess the significance of the results. The results indicated that the developed model, using 13 input features, achieved higher predictive accuracy than the baseline model, which only used one feature. The developed model explained 75.75% of the variance in the target variable with an R² of 0.7575, while the baseline model explained only 3.7% of the variance. The Wilcoxon test confirmed a significant difference in the predictive accuracy between the two models. The developed model, incorporating diverse and complex variables, provided a more accurate prediction of investment efficiency in companies listed on the Tehran Stock Exchange. It is recommended that this model be used in investment decision-making processes.

This study aims to identify and classify organizational nodes that contribute to systemic risk propagation in the Tehran and Baghdad stock exchanges from a network perspective to inform regulatory and corporate risk management priorities. This applied qualitative study sampled domain experts (13 for Tehran, 13 for Iraq) drawn from senior firm managers, financial analysts, and industry specialists using theoretical/snowball sampling. Semi-structured interviews were conducted, transcribed, and coded in MAXQDA. Key risk attributes were inductively extracted via thematic content analysis and mapped to network roles (central, bridging/intermediary, peripheral) to identify high-risk nodes within selected industries. The qualitative inference identified 19 high-risk nodes across the two exchanges (6 in Tehran, 13 in Iraq), unevenly distributed across industries with petrochemicals and transport exhibiting the greatest concentration of risk nodes. In Iran, firms such as a major petrochemical producer and a leading automotive manufacturer emerged as central or intermediary nodes with high systemic spillover potential; in Iraq, major telecom and large service-sector firms were inferred as central risk transmitters. Financial leverage, high indebtedness, low liquidity, managerial weaknesses, dependency on foreign inputs/technology, and extensive contractor linkages were inferred as principal predictors of a node’s propensity to transmit risk. Cross-country differences were inferred: Iraqi nodes showed greater sensitivity to external shocks (oil price, exchange rates) while Iranian nodes reflected structural vulnerabilities tied to leverage and supply dependencies. The network role (central vs. intermediary vs. peripheral) was inferred to moderate the magnitude of systemic impact. Network analysis effectively pinpoints firms whose failure or distress could propagate systemic risk; regulators and firms should prioritize monitoring and interventions for central and bridging nodes—focusing on deleveraging, liquidity buffers, enhanced reporting, and contingency planning—to mitigate network-level contagion.

This study aims to develop and test an integrated credit validation model that incorporates environmental, social, and governance (ESG) indicators alongside climate risk into corporate credit rating assessments. A quantitative applied research design was employed using balanced panel data from 165 firms listed on the Tehran Stock Exchange from 2015 to 2023. The dependent variable was the firms’ credit score, while the independent variables included the three ESG dimensions and a composite climate risk index. Fixed-effects panel regressions estimated via generalized least squares (GLS) were used to analyze the relationships and interaction effects. Results revealed that environmental, social, and governance indicators each exert a positive and statistically significant influence on credit ratings, while climate risk has a significant negative impact. The interaction analysis further indicated that climate risk moderates the ESG–credit rating relationship: under higher climate risk conditions, the positive impact of ESG on creditworthiness weakens. The integrated model achieved the highest explanatory power (R² = 0.489), outperforming traditional credit assessment models. Integrating ESG indicators and climate risk into credit evaluation enhances model accuracy and provides a more comprehensive and realistic assessment of firms’ financial sustainability. The proposed model offers practical value for credit risk management and the development of sustainable financial systems.

The purpose of this study is to examine the relationship between the components of asset price bubbles and economic welfare indicators while considering the mediating role of monetary policies in Iran’s economy. This research employed a mixed-method design with a sequential exploratory strategy. In the qualitative phase, semi-structured interviews were conducted with academic and economic experts to identify the most influential factors in asset bubbles, monetary policy, and welfare. Data were analyzed through open, axial, and selective coding to develop the conceptual model and hypotheses. In the quantitative phase, a structured questionnaire was distributed among individual and institutional investors in selected markets. The collected data were analyzed using SPSS and LISREL software via structural equation modeling to test the relationships among variables. Results confirmed that asset price bubbles have a significant negative effect on economic welfare indicators (β=-0.62, t=-12.28). Asset bubbles also negatively affect monetary policies (β=-0.42, t=-7.17), while monetary policies positively influence welfare indicators (β=0.50, t=9.11). The Sobel test supported the mediating role of monetary policy between asset price bubbles and welfare. “Income distribution” and “poverty line” were the strongest welfare determinants, while “inflation expectations” and “liquidity” were key indicators of asset bubbles and monetary policy. The structural model showed good fit indices (CFI=0.97, RMSEA=0.062, χ²/df=2.77). Findings suggest that monetary policies significantly moderate the adverse effects of asset price bubbles on economic welfare. Implementing prudent monetary and liquidity management strategies can stabilize the financial system and enhance overall welfare.

This study aimed to identify and rank the factors influencing financial sustainability in the Iranian Premier Football League using a mixed-method sequential exploratory approach. This applied study was conducted in two phases. In the qualitative phase, a systematic literature review was performed, and thematic analysis was conducted using Nvivo 14 to identify the main themes related to financial sustainability. In the quantitative phase, a structured questionnaire derived from the qualitative findings was distributed among 20 experts in sports finance management based in Tehran. Data were analyzed using SPSS version 26, and the Friedman test was applied to determine the ranking of the identified factors. Results indicated six main categories affecting financial sustainability: financial and budgetary management, managerial structure and governance, sports marketing and branding, legal system and contract transparency, institutional and governmental support, and human resources and social capital. According to the Friedman test, financial and budgetary management ranked first (mean rank = 5.78), followed by managerial structure and governance (5.24) and sports marketing and branding (4.89). The findings highlight that financial transparency, diversification of revenue sources, and managerial independence are the key prerequisites for financial sustainability in Iranian football. Moreover, digital marketing development, legal standardization, and human resource training are essential for strengthening long-term financial stability. Implementing innovative financial policies and reforming governance frameworks can pave the way toward sustainable professional football in Iran.

The study aims to identify opportunities for achieving economic complexity in Iraq’s petrochemical industry and to determine effective strategies using Interpretive Structural Modeling (ISM). This applied research adopted a mixed quantitative–qualitative approach. In the quantitative phase, big data from the exports of 235 countries in 2021—including 218 petrochemical product codes based on the four-digit Harmonized System—were obtained from The Atlas of Economic Complexity. Data were analyzed using Economic Complexity Theory and Product Space modeling to identify products with revealed comparative advantage and to calculate activation probabilities for other products based on Alshamsi et al. (2018). In the qualitative phase, semi-structured in-depth interviews were conducted with 12 experts in industrial policy and petrochemical economics. Grounded Theory was used for coding and concept extraction, and Interpretive Structural Modeling (ISM) was applied to determine the hierarchical relationships among identified strategies. Results revealed that Iraq holds a revealed comparative advantage in 17 of the 218 petrochemical products and has a non-zero activation probability for 44 products, of which seven exhibit activation probabilities above 30% and higher complexity than Iraq’s average export basket. The qualitative analysis identified eight strategic drivers of economic complexity: redefinition of government’s role, benchmarking successful countries, developing an industrial strategy, foresight and future studies, spatial planning, human capital management, creating a government–industry–university interaction network, and adopting a systemic perspective. ISM analysis demonstrated that “industrial strategy development,” “government role redefinition,” and “human capital management” form the foundational layer influencing all other strategies. Achieving economic complexity in Iraq’s petrochemical sector requires a coherent combination of institutional reform, human capital development, and strategic industrial planning. Benchmarking advanced petrochemical economies and establishing interactive networks between government, industry, and academia can promote export diversification and strengthen Iraq’s position within the global product space.

This study aims to examine the impact of foreign direct investment (FDI) and human capital on export diversification in the Persian Gulf countries, with emphasis on the threshold effect of human capital. An applied econometric design was employed using annual panel data from eight Gulf countries (Iran, Iraq, Saudi Arabia, Oman, Qatar, Kuwait, Bahrain, and the UAE) covering 2000–2022. The Panel Smooth Transition Regression (PSTR) model was applied to detect nonlinearity and determine the human capital threshold influencing the FDI–export diversification relationship. Control variables included real per capita income, trade openness, infrastructure, institutional quality, financial development, and natural resource rents. The results revealed a nonlinear and threshold-dependent relationship between FDI, human capital, and export diversification. The estimated human capital threshold was 4.85. Below this level, FDI significantly stimulated export diversification; beyond it, the role of FDI diminished while human capital became a strong positive determinant. Real per capita income, trade openness, infrastructure, and institutional quality consistently had positive effects across regimes, whereas natural resource rents exerted a persistent negative impact. Achieving sustainable export diversification requires a phased policy approach: in early development stages, targeted attraction of FDI should modernize production capacity; at advanced stages, enhancing human capital quality enables economies to innovate and reduce reliance on external investment.

This study aimed to design and validate a comprehensive index to monitor systemic risk and market-wide stress in the Tehran Stock Exchange using advanced econometric models and machine learning algorithms. Daily time-series data of selected Tehran Stock Exchange indices from 2014 to 2024 were analyzed. Logarithmic returns were calculated, and the DCC-MGARCH model was applied to estimate the dynamic conditional correlation matrix and systemic risk metrics such as ΔCoVaR. To determine feature importance and optimal weighting of indices, three supervised learning algorithms (support vector regression, artificial neural networks, and random forest) were compared, with random forest selected due to superior predictive accuracy. The composite stress index was then constructed and validated using time stability analysis, stress (shock) testing, and logistic regression forecasting. The results revealed that automobile, real estate, paper products, and metal products sectors carried the highest systemic risk, while computer, coal, and textiles showed the lowest. The comprehensive stress index provided reliable early warning signals during market turbulence and achieved strong predictive performance, with an AUC of 0.801 and an accuracy of 89.9% in logistic regression analysis for shock detection. The developed composite stress index is a robust and dynamic tool for identifying vulnerability points and forecasting systemic crises in the Tehran Stock Exchange. It offers significant practical value for policymakers, market analysts, and regulatory authorities to strengthen market resilience and implement proactive risk management strategies. Incorporating macroeconomic variables and extending the historical dataset could further enhance its accuracy and generalizability.