درخت حوزه‌های تخصصی

The main objective of this study is to compare the predictive accuracy of machine learning models, particularly Random Forest and Artificial Neural Networks, with classical statistical methods (such as Logistic Regression and Linear Discriminant Analysis) in forecasting the risk of Exchange-Traded Funds (ETFs) in member countries of the Federation of European and Asian Stock Exchanges. Furthermore, the study aims to identify the key performance and fundamental variables impacting the risk of these funds. This research adopts a quantitative approach based on secondary data analysis. Data were collected for the years 2015-2023 from the databases of the Federation of European and Asian Stock Exchanges and the Tehran Stock Exchange. After preprocessing, risk prediction models, including Random Forest, Artificial Neural Networks, Logistic Regression, and Linear Discriminant Analysis, were developed and validated for each country using unified evaluation metrics (such as accuracy and AUC). The statistical significance of differences in model performance was tested using non-parametric Mann-Whitney U tests, given the non-normal distribution of accuracy across countries. Sensitivity analysis was then conducted on the two superior machine learning models to determine the impact of independent variables (both performance indicators, such as Jensen's alpha and market return, and fundamental attributes, such as fund size and manager expertise) across different markets. Empirical results indicate that, across most countries and after harmonizing time and geographical dimensions, machine learning models, specifically Random Forest and Artificial Neural Networks, outperform classical statistical approaches in predicting ETF risk, with statistically significantly higher accuracy and AUC values (p<0.05 in Mann-Whitney U tests). The robustness of these findings is confirmed after controlling for heterogeneity among countries. Sensitivity analyses further reveal that both performance variables (e.g., Jensen's alpha, market return) and fundamental factors (e.g., fund size, manager expertise) have a significant impact on risk outcomes within these models. At the same time, machine learning methods exhibit a stronger ability to identify and quantify the importance of these variables compared to classical methods. The results highlight the practical advantage of adopting machine learning techniques for risk assessment and management in diverse international financial markets. Overall, the findings of this study reveal that employing machine learning models—especially Random Forest and Artificial Neural Networks—significantly improves the accuracy of ETF risk prediction and enables a more comprehensive identification of key risk factors compared to classical statistical approaches. These models demonstrate superior flexibility and the ability to capture complex, multidimensional data patterns, making them highly advantageous tools for financial risk management. The results suggest that integrating advanced machine learning techniques at both regional and international levels can enhance the responsiveness of investment systems to market changes, providing fund managers and investors with a more solid, data-driven basis for decision-making.

Monetary policy is one of the most important tools for policymakers to influence macroeconomic variables including production. However, implementing this policy sometimes yields unintended consequences. Understanding monetary policy transmission mechanisms is therefore critical for effective implementation. Research following the 2008-2009 financial crisis indicates that the shadow banking activity can disrupt the monetary policy transmission and weaken its effectiveness. An analysis of Iran’s financial system reveals increasing shadow banking activity. This paper therefore examines how shadow banking affects monetary policy transmission in Iran using a DSGE model that innovatively incorporates the shadow banking sector. We compare two scenarios: a financial system without shadow banking and one including shadow banking. The effects of two contractionary monetary policies—interest rate increase and reductions in money supply growth—on GDP, investment, and inflation were analyzed under each scenario. The findings indicate that shadow banking diminishes monetary policy’s impact on all three variables by weakening the credit channel of monetary policy transmission. In the scenario without shadow banking, In the scenario without shadow banking, all three variables will decline in response to the monetary shock of decreasing money supply growth. However, in the scenario with shadow banking, investment levels are not declining but rising. The impact of monetary policy on output and inflation is diminished in the presence of shadow banking. In the case of interest rate shocks, the results also indicate a negative impact of shadow banking on the effectiveness of monetary policy.

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.

Embedded options are virtually new instruments identical to options in many aspects except their non-tradable nature. Testing the efficiency of the Variance Gamma and Black-Scholes-Merton model on these instruments would provide a vision of transitioning from the classical model with its deficiency to more intricate models. Considering the complicated nature of the Variance Gamma stochastic process to price options, the Fast Fourier Transform (FFT) method is used in conjunction with the Nelder-Mead Simplex method to calibrate models. This research uses the Fast Fourier Transform (FFT) to price four embedded options with the ticker symbols Hefars912, Heghadir912, Heksho208, and Hetrol911 under the two models. The result approves that the Variance Gamma process is more efficient than the Black-Scholes-Merton model in pricing embedded options. Consequently, the variance gamma process would generate fewer errors in pricing those options that can be used in a practical sense.