درخت حوزه‌های تخصصی

Objectives: This study aims to examine the impact of blockchain technology on transparency, security, fraud reduction, and stakeholder trust in financial reporting. It is grounded in the view that emerging technologies like blockchain can transform financial processes by enhancing accuracy, reliability, and efficiency. Methodology/Design/Approach: A quantitative approach and Partial Least Squares Structural Equation Modeling (PLS-SEM) were employed to analyze data collected from 312 accounting and financial professionals working in companies listed on the Tehran Stock Exchange. Findings: The results indicate that the adoption of blockchain technology significantly enhances financial reporting transparency, improves information security, reduces fraud and financial errors, and increases stakeholder trust and technology acceptance. Innovation: This research contributes to the literature by empirically demonstrating the strategic importance of blockchain technology in transforming financial and auditing processes. It highlights its role as an effective tool for improving the accuracy, reliability, and efficiency of financial reporting in emerging markets.

In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well. In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well.