درخت حوزه‌های تخصصی

The collapse of large corporations such as Enron and WorldCom caused much concern about the ethical behavior of accountants among users of accounting service. Ethical behavior in accounting is one of the distinguishing features of accountants, which enable them to make the best decisions for stakeholders. Professional ethics learning plays a major role in the development of ethical behavior. Also, TOPSIS method is a well-known methodology that is applied in a various decision making problems. On the other hand, in order to determine the importance of criteria, the Entropy method is known as a powerful tools. Therefore, the purpose of this study was to investigate the effect of ethics teaching methods on the development of ethical behavior in accounting. This is done by means of an integrated Entropy/TOPSIS based Model for Financial prioritization of professional ethics teaching methods in accounting. The findings of the study showed that the variables of ethics education, style of teaching ethics and ethics textbook content respectively had a 1, 24 and 55 percent impact on the development of ethical behavior in accounting. The results of this study indicate that an ethical training program in accounting is needed to provide accountants with guidance on ethical issues and the importance of ethical standards.

This research was conducted with the aim of explain and prioritize information disclosure factors related to sustainable development accounting with fuzzy approach" in 2019 of companies active on the Iranian Stock Exchange. Qualitative data were obtained through the study of research and credible sources in the field of sustainable development accounting and using coding through content analysis, the initial variables of the model were identified; 61 indicators were extracted from the initial codes in 4 dimensions including: environment, social factors. Economic and governance factors were categorized. In the quantitative section, the statistical population of the study included knowledgeable and professional and academic experts in the field of ac-counting. Using the targeted sampling method, 25 experts were selected as statistical samples. In the quantitative part, using fuzzy Delphi technique in one step, the indicators were screened. In the next step, prioritization of criteria and sub-criteria was done by hierarchical analysis method with fuzzy approach; among the main criteria, environmental dimension with weight 0.405 in rank 1 The social dimension with a weight of 0.296 was ranked 2nd, the economic dimension with a weight of 0.186 was ranked 3rd and the leadership dimension with a weight of 0.113 was ranked 4th. Finally, based on the calculated final weight, the strategic approach to environmental impacts with a weight of 0.955 in the first place, management and efficiency in consumption in the second place, social development and humanity in the third place and management Waste and waste came in fourth.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.