درخت حوزه‌های تخصصی

One of the biggest risks in the economies of countries is the bankruptcy of industries and companies. Predicting the bankruptcy of companies and industries provides the opportunity for the government, investors, and shareholders to take strategic actions to deal withcrises and prevent potential losses. From an economic perspective, financial distress can be interpreted as the company being loss-making, in which case the company has failed to succeed. In fact, in this case, the company's rate of return is less than the cost of capital. Another form of financial distress occurs when the company fails to comply with one or more of the debt covenant terms, such as maintaining the current ratio or the special value ratio to total assets according to the contract. Therefore, there is no general agreement on the definition of financial distress, and the reasons that lead companies towards failure are very different. One possible reason for failure is the wrong direction ofthe company, which can be analyzed through the financial ratios of the companies. Since the 1960s, many studies have only examined financial ratios as indicators of failure. Therefore, the analysis is the basis for improving and developing failureprediction models. In this study, the influential variables in predicting bankruptcy were first selected using backgrounds. The most important factors were identified with the opinions of experts, and the weight of each criterion was obtained using the AHP technique. Then, these criteria were ranked using the TOPSIStechnique to identify the most important factors. After completing these steps, by entering these variables into an artificial neural network of the multilayer perceptron type as input and using the backpropagation algorithm to train the network, the prediction accuracy increased, and accurate and reliable results were obtained

This research aims to study agency costs and tax planning, emphasizing the moderating role of government ownership. Taxation, as the primary means of raising revenue, can help governments achieve their macroeconomic objectives. Paying taxes is one of the most important tasks citizens can perform to provide financial resources for social welfare. In addition, politically connected companies have an opaque operating environment due to government support, resulting in more significant conflicts of interest and higher agency costs. To achieve this purpose, two hypotheses were formulated. To test these hypotheses, a sample of 142 companies was selected from the companies listed in the Tehran Stock Exchange during 2014-2023. A multivariate regression model was applied using the panel data method. The results of the research hypotheses indicate that agency costs and tax planning have a positive and significant relationship. The results also suggest that government ownership does not affect the relationship between agency costs and tax planning

Transparency can be defined as the provision of sufficient, relevant, and reliable information for stakeholders. Due to numerous limitations, transparency and disclosure remain a challenge in the Iranian banking sector. Therefore, the aim of this study is to examine the impact of ownership structure on the transparency and disclosure of banks. To assess ownership structure, three variables are used: the percentage of shares held by the largest shareholder, the percentage of shares held by institutional shareholders, and the percentage of shares held by individual investors. The research method is of a correlational type in terms of content and nature. This study is applied in terms of its goal and is quantitative in nature based on the data. Statistical analysis was conducted on the information of 13 banks listed on the Tehran Stock Exchange during the years 2014 to 2022. An ordinary least squares (OLS) estimator with fixed-year effects and Stata software version 17 were used to test the research hypotheses. The results of this study show that the variables of individual investors' shares, the largest shareholder's shares, and institutional shareholders' shares have the most significant impact on the transparency and disclosure of banks, respectively. These findings indicate that the presence and influence of individual investors, large shareholders, and institutional shareholders each play a crucial role in improving the transparency and disclosure of banks.

Companies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable companies to gain more efficiency, as well as apply more optimal policies to keep cash. The purpose of this research is to investigate the effect of financial flexibility value and financial constraint on the speed of adjustment of net working capital, as well as the effect of financial constraint on the relationship between the value of financial flexibility and the speed of adjustment of net working capital in companies listed on the Tehran Stock Exchange. The appropriate pattern recognition test in combined data indicates the use of the regression model of the research using the panel data method with the fixed and random effects approach for the panel and pooled data patterns to estimate the regression model. The statistical sample includes 100 companies accepted to the Tehran Stock Exchange during the period from 2005 to 2020. The findings indicate that the value of financial flexibility has a positive and significant effect on the speed of adjustment of net working capital in the models of partial adjustments and error correction. Financial constraint has a positive and significant effect on the speed of net working capital adjustment, and it also has a positive and significant effect on the relationship between the value of financial flexibility and the speed of net working capital adjustment.

This study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power.

Although it is unclear whether these services are reaching their full potential, mobile technology may assist smallholder farmers in receiving better financial services. This article fills the lacuna by providing an exploratory overview of the research on the effects of financial services offered by mobile phones on farmers, with a slight focus on Africa and other developing countries. The review emphasizes how little empirical data there is in this field. Results imply that farmers profited from advances in risk management, input management, post-harvest sales and loan disbursement ease. The evaluation also identifies several methodological flaws, most notably the use of lack of creditworthiness data and the diversion of resources by smallholder farmers