جستجوی مقالات مرتبط با کلیدواژه « m41 » در نشریات گروه « مالی »

New financial technologies strengthen the financial system to provide smarter, more agile and broader services. The widespread influence of new financial technologies has changed people's lifestyles as well as banking rules. Leading companies in the field of financial technology are covering more and more parts of the value chain of banks every day. One of the main conditions in the discussions of economic growth is the discussion of public access to financial services (financial inclusion), which is in line with the principles of justice and equality in the Islamic financial system. Another important benefit of fintechs is financial transparency, which is emphasized by most stakeholders. The present study has been conducted with regard to the importance of the financial services sector and the transformative capacity of new technologies in promoting banking services in the direction of economic development of the country and the role they play in their survival and competitiveness. In terms of purpose-based classification, this research is of applied research type and in terms of data collection, it is of survey type. For data analysis, heuristic factor analysis method and SPSS and Amos software were used. The results of the questionnaires have been approved for model design. The findings show that the independent variable called new financial technologies, through indicators of cost reduction, improvement of financial services, revenue growth, financial security, financial transparency, asset management, increase in cash flow, reduction of information asymmetry and time dependent auditing on the variable (Banks' ecosystem), is especially effective in interest-free banking in Iran.

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one of the ways to help investors is to provide investment risk forecasting patterns. The more predictions are closer to reality, the decisions made on the basis of such predictions will be correct. In this research, the goal of predicting the systematic risk of companies admitted to Tehran Stock Exchange using artificial neural network software and three night-worm algorithms, decision tree algorithm and backup vector machine regression algorithm. For this research, a sample of 92 companies from listed companies in Tehran Stock Exchange during the period 2013 to 2018 has been used. The results obtained from the research hypothesis test showed that the predictive power of systematic risk in the night cream algorithm is more than the decision tree algorithm and the support vector machine regression algorithm, as well as the predictive power of the decision tree algorithm in relation to the backup vector machine regression algorithm It is higher for systematic risk prediction