Advances in Mathematical Finance and Application (AMFA)

The financial markets are encountering uncertain conditions that are heightening their tail risk. This study analyzed eight stock market indices employing a neural network quantile regression methodology from 24 July 2017 to 22 August 2023. The findings demonstrated that the proposed model effectively estimated the tail risk by VaR and CoVaR of the sample indices of the Iranian stock market while considering oil and gold price fluctuations as risk factors. The results showed that the global crisis of the COVID-19 pandemic, which began in China in 2020, had significant impacts on global indices. However, the shock was relatively worse in the Iranian stock market, particularly in some industries such as Metals, Metal ores, and Chemicals, and the Overall indices had greater vulnerability than the rest of the indices. During the global crisis in 2022, which was triggered by the war in Ukraine, the Iranian capital market experienced a significant shock.

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.

The Neighborhood Rough Set (NRST) method is a valuable approach for selecting a subset of features from a complete dataset, enabling us to preserve the essential information that the entire feature set provides. In financial datasets, which often contain high-dimensional input features, effective feature selection techniques are crucial to identify the features that yield the most predictable results. In this work, we use neighborhood concepts to discover data dependencies and reduce the number of features in a financial dataset based solely on the data itself, without relying on additional information. This process also includes removing extra features. To facilitate a simple algorithm, we use the properties of neighbourhood rough sets to formulate a Binary Integer Linear Programming (BILP) model. Optimal solutions to these problems are obtained using genetic algorithms. Our approach allows for feature reduction from minimum to maximum cardinality. We demonstrate the efficiency of our proposed method compared to other techniques through various tables showing the results on several benchmark datasets characterized by unbalanced class distributions. The financial dataset used in the present study is taken from the UCI Machine Learning Repository.

In this research, first the financial criteria used in capital decision-making were identified and refined, then the most effective criteria were selected based on the deep learning algorithms including: RF, XGBoost, and LightGBM. In this stage, 11 factors were selected from the 35 factors found in previous research. In the next stage, based on the Forensic-Based Investigation algorithm (FBI), feasible investment options were identified and the internal rate of return was calculated over a 5-year period, and 42 companies that had an internal rate of return higher than the risk-free investment were selected as feasible investment options. During the next stage, different random combinations were used as investment portfolios using three methods: equal weight allocation, mean-variance model, and hierarchical risk preference model. Investment weights were determined for each invested share (combination) and investment returns were evaluated using different metrics. Finally, in order to validate the findings, the feasible investment options were divided into two categories of companies active in the financial industry and others, and the superiority of decision-making (higher returns) in a dynamic process was accepted.

This research aims to investigate the effect of risk management on the speed of adjustment of commercial credit by considering the role of structural characteristics of companies' management. The statistical population is all the companies listed on the Tehran Stock Exchange, and using the systematic elimination sampling method, 124 companies were selected as the research sample. They were examined in the ten years between 2014 and 2023. The results of the research hypotheses test showed that risk management has a direct and significant effect on the adjustment speed of trade receivables and payables. Also, the interaction of management history with risk management directly affects the speed of commercial credit payable and receivable adjustment. The interaction of management independence and risk management has a direct and significant effect on the speed of adjustment of trade credit payable, the interaction of these two variables has an inverse impact on the speed of adjustment of trade credit receivable, and finally, the interaction of the position of management and risk management influences the speed of adjustment of trade credit payable and is not receivable. In summary, this research indicates that risk management plays a significant role in the speed of trade credit adjustment, and this relationship is influenced by the company’s managerial structural characteristics. The findings emphasize the importance of risk management strategies and managerial structure in enhancing financial transparency and efficiency.

The motivations driving cash holdings have a profound influence on corporate decision-making and performance. Exploring the dynamics between risk-taking behaviour, cash reserves, and their adjustment pace provides valuable insights into effective financial resource management. This study examines the impact of corporate risk-taking on the adjustment speed of cash holdings, with a focus on the moderating effect of CEO tenure. A sample of 151 firms listed on the Tehran Stock Exchange from 2011 to 2023 (1,963 firm-year observations) was analysed using multiple regression and the Generalized Method of Moments (GMM) estimator. Results indicate that the adjustment speed of cash holdings is 49.5%. A significant negative relationship exists between corporate risk-taking and the speed of cash holdings adjustment, suggesting that elevated risk-taking decelerates the alignment of cash reserves with optimal levels. Moreover, the findings highlight the moderating role of CEO tenure in the relationship between corporate risk-taking and the speed of cash holdings adjustment; in other words, in firms with longer-tenured CEOs, the negative association between corporate risk-taking and cash holdings adjustment speed is weaker than in firms with shorter-tenured CEOs. These findings suggest that risk-taking hinders swift cash adjustment, necessitating a precise determination of optimal cash levels to prevent liquidity shortages in high-risk scenarios. Additionally, the experience of long-tenured CEOs appears to facilitate better liquidity management, aligning corporate interests with strategic financial goals.