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Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.

This study investigates the transmission channels of monetary policy effects on household consumption in Iran using a Threshold Structural Vector Autoregression (TSVAR) model and monthly data from 2006 to 2023. Empirical results demonstrate that the impact and symmetry of monetary policy on household spending are highly regime-dependent. In low-inflation periods, monetary easing mainly increases consumption through enhanced bank credit and exchange rate stability, resulting in significant and persistent effects. In contrast, under high inflation, these effects weaken, become short-lived or unstable, and the role of the stock market channel becomes more volatile and limited. Impulse response analysis also confirms asymmetric and heterogeneous channel behavior in response to monetary shocks. Overall, the findings underscore the need for regime-specific and channel-oriented monetary policy to support household welfare and macroeconomic stability in Iran.

in today's volatile business environment, selecting an optimal supply chain finance(SCF) strategy under credit risk uncertainty has become a cretical concern for firms-particularly for small and medium-sized enterprises(SMEs).This study aims to evluate and prioritize SCF sterategies by foucusing on credit risk mitigation using a multi-criteria decision-making(MCDM) approach. After identifying key evaluation criteria through expert consultation and Delphi methodology, the Analytic Hierarchy Process(AHP) was employed to assign weights to these criteria. Subsequently, the technique for order preference by similarity to ideal solution(TOPSIS) was used to rank five majore SCF strategies. Finding indicate that fintech-based strategies have highest effectiveness in mitigating credit risk due to features such as enhanced tranparency, felexibility and the ability to transfer risk to thirdparty financial institutions. in contrast, traditional receivable-based financing methods, such as factoring, ranked lowest in effectiveness. the study offers a structured and empirically validated framework for decision-makers aiming to improve financial resilience in supply chains, especially under credit uncertainty.

Central bank digital currencies (CBDC) as emerging tools in international monetary systems have a high potential to increase financial transparency, reduce transaction costs and promote monetary security. Considering the economic and political necessities, the implementation of the digital rial in Iran requires a detailed examination of its technical, economic and institutional dimensions. This research has been designed and implemented with the aim of systematically identifying and analyzing the key factors affecting the adoption, development and implementation of the digital rial in Iran, with an approach based on the principles of good governance. The research method is mixed methods. In the qualitative part, through semi-structured interviews with experts in the field of finance, technology and policy, the data were analyzed using thematic analysis. Then, the final model was obtained from the Interpretive Structural Modeling (ISM) method. In the quantitative part, using stratified sampling, the research data were collected through standard questionnaires and analyzed using the structural equation modeling (SEM) technique. The findings included 120 open codes, 24 basic themes, and 6 organizing themes. The results of the research showed that factors such as trust in good governance, advanced technology infrastructure, perception of security, system efficiency and social acceptance are important factors in the success of the adoption and development of the digital rial. Strengthening regulatory transparency, effective legal frameworks and coordinated macro-policy are critical requirements for the successful implementation of this project.

Despite the well-documented benefits of free trade, there has been a notable resurgence of protectionist policies, particularly in developing countries with low per capita incomes. Understanding how these protective measures are formed offers critical insights into the underlying motivations behind trade restrictions. The primary aim of this study is to test the hypothesis that components of the political economy, particularly macroeconomic indicators, play a significant role in shaping and driving trade protection policies. To evaluate this hypothesis, the study employs panel data from 18 lower-middle-income developing countries over the period 2002 to 2022. A system of simultaneous equations, estimated via Two-Stage Least Squares (2SLS), is used to identify the key determinants of trade protection. The empirical findings highlight inflation as a principal driver of protectionism in low-income contexts, largely triggered by currency depreciation and monetary expansion. In contrast, higher economic growth, greater import penetration, and improved government effectiveness are associated with lower levels of trade protection and increased support for liberalization. Interestingly, income inequality does not appear to exert a statistically significant influence on protectionist outcomes, suggesting the limited role of consumer advocacy and institutional representation in many developing countries. Overall, the results affirm that trade protection is not an exogenous policy instrument. Rather, it emerges as an endogenous response to the complex interaction of macroeconomic dynamics (including growth, inflation, and unemployment), structural features of the economy, and institutional shortcomings, particularly in the realms of governance and financial development.

This research delves into the application of Chaos Theory and non-linear analysis to the stock market, utilizing empirical data from international markets to scrutinize the presence of chaotic trends and non-linear processes within the time series of 20 international stock price indices, spanning from January 1984 to January 2024. Through the employment of predictability and non-linearity tests, the study found evidence of a non-linear process in the stock price index. Further, correlation dimension tests were conducted to evaluate the correlation between observations, uncovering a significant correlation between variables. Cumulative periodic tests were subsequently applied to refine the analysis, taking into account the chaotic nature of the stock price index variable, thereby affirming the chaotic nature of this process. Following the validation of the stock price index's predictability, ARFIMA, FIGARCH, LSTAR, and ESTAR models were applied for forecasting future periods. Among the models that incorporate long-term memory in the stock price index variable, the FIGARCH model exhibited superior forecasting power by accounting for both the long-term memory and the variance and changes of the variable. Among the nonlinear models, the ESTAR model demonstrated the highest prediction capabilities. The implications of this study are of considerable significance for investors aiming to comprehend and analyze stock market trends, offering a novel perspective on the stock market's dynamics.

This study tackles the challenge of customer churn in life insurance, which leads to substantial financial losses. It introduces a transparent, reproducible, and leakage-free machine learning framework designed to identify at-risk policyholders accurately and efficiently. Using 20,000 anonymized Iranian life insurance policies with a churn rate of 26%, the study develops a complete modeling pipeline that includes imputation, standardization, and encoding steps performed strictly within the training process to prevent data leakage. Three model types, logistic regression, random forest, and XGBoost, were trained and evaluated using stratified cross-validation, F1-optimized thresholds, and performance metrics such as ROC-AUC, F1, and precision. Results show that all models perform similarly (ROC-AUC ≈ 0.70), with logistic regression achieving the highest F1 score (0.66) and XGBoost offering the best precision (0.68). The top-ranked predictions captured about 69% of churners, demonstrating strong operational potential. Policyholders with major payment delays were identified as the most churn-prone and easily detectable group. Overall, the findings confirm that transparent and interpretable machine learning models can effectively balance accuracy, simplicity, and practicality, supporting data-driven, value-focused customer retention strategies in the life insurance industry