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

This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the smooth transition regression (STR) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first.

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 study investigates the role of digital financial services in enhancing innovation within Iranian sports companies. The research follows a descriptive survey method with a quantitative approach and employs Structural Equation Modeling (SEM) for data analysis. The statistical population includes senior managers, financial managers, and IT managers of sports companies, as well as financial and technology experts in sports clubs and startups. The sampling method was stratified random sampling, and the sample size was determined as 200 participants using Cohen’s method, considering 10 latent variables in the model. Data collection was carried out through two standardized questionnaires with validated content and face validity confirmed by experts. Reliability was assessed using Cronbach’s alpha. The first questionnaire, digital financial services assessment, consists of 15 items across five components (digital financial technology infrastructure, digital payment services, digital banking, digital investment, and digital financial innovation).The second questionnaire, Innovation Assessment in Sports Companies, includes 15 items across five components (product innovation, process innovation, organizational innovation, marketing innovation, and strategic innovation). The Cronbach’s alpha scores were 0.899 for innovation and 0.794 for digital financial services. The findings reveal that digital financial services have a significant impact on various dimensions of innovation, including product, process, organizational, marketing, and strategic innovation. These services enhance transparency, reduce costs, and improve access to financial resources, thereby alleviating financial constraints and fostering innovation capabilities within sports companies