درخت حوزه‌های تخصصی

Financing decisions such as capital structure have gained much attention in literature of financial development over the last decade. Capital structure deviations from its optimal level can occur for various reasons. According to the dynamic trade-off theory, continuous adjustment of capital structure to maximize company value is essential. If companies adjust their capital structure quickly towards the target leverage, past financing activities and historical market conditions will only have short-term effects on the current capital structure. Conversely, if companies adjust their capital structure slowly, the opposite is true. We examine the relationship between organizational and performance characteristics and capital structure, as well as the speed of adjustment, in financial and non-financial firms listed on the Tehran Stock Exchange and compare these effects between the two categories of companies. The statistical population consists of companies listed on the Tehran Stock Exchange from 2017 to 2022. The results show that the speed of capital structure adjustment is lower in the financial sector compared to the non-financial sector. Additionally, three performance variables—profitability, growth opportunities, and liquidity—are statistically significant and impact capital structure and its adjustment speed in both financial and non-financial sectors. However, the growth opportunities variable has a different effect direction in the financial sector compared to the non-financial sector, while the age variable does not have a significant effect. Regarding organizational characteristics—complexity, institutional ownership, and size—only organizational complexity in the financial sector is significant at a 95% confidence level, while all organizational characteristics are significant in the non-financial sector

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .

This research aims to identify the effective factors on sustainable supply chain in the National Iranian South Oil Company (NISOC). The method of data collection was mixed methods. In the qualitative section, data were collected through reviewing 32 previous studies as well as in-depth interviews with 10 faculty members and experts in the field of sustainable supply chain of crude oil and oil products of the NISOC using a Non-Probability Judgmental Sampling. The main dimensions were identified from interviews, and the dimensions were grouped in 13 main categories and 78 sub-categories. 13 main components included organizational factors, production products, sustainable supply chain, industry supply chain, macro policies, technology, customer, economic factors, deficits of supply chain, social factors, political factors, organizational performance, and organizational productivity. Second, through reviewing literature and previous studies, 72 open codes in 9 categories were identified, including strategies, blockchain technology, improving the economic environment, improving the social and environmental policies and laws, internal organizational solutions, external organizational solutions, environmental management, market and capital factors. In the quantitative part, the researcher-made questionnaire was applied in a preliminary study on 10 experts in the field of management, and its relative content validity is examined. Then the final and modified version was provided to 384 employees of the NISOC. SmartPLS software were used for quantitative evaluation of the model. The presented model helps industry managers and decision makers to significantly improve the performance of the supply chain by re-designing the supply chain and taking appropriate measures.

Cash flow forecasting has significantly increased since 2000 due to more attention paid by investors and financial analysts than before. If cash flows can be predicted appropriately, a significant part of the informational needs associated with cash flows will be provided. In this regard, this study aims to examine the impact of firm characteristics on predictable future cash flows from operating activities by employing present operating cash flow and profitability. Eight hypotheses were developed, and the information was analyzed for 127 firms listed on the Tehran Stock Exchange between 2011 and 2020. The regression model was tested with a fixed effect model using panel data. The study's findings showed that firm characteristics like size, level of competition, and level of supervision positively impact the predicting power of present operating cash flow and profitability in anticipating future operating cash flow. By contrast, the outcomes disclose that characteristics such as the company's life will not significantly affect the predicted strength of present operating cash flow and present profitability to forecast future cash flow from operating activities.

The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.