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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Petroleum Business Review
- نوع مقاله: Journal Article
- کلمات کلیدی: Information Asymmetry,Thematic analysis,intellectual capital,Financial Reporting,Oil industry companies
- چکیده:
- چکیده انگلیسی: Due to the importance of technology and innovation in the oil industry, it is necessary to look more closely at the intellectual property of this industry. Intellectual capital is a concept that can classify and report the technology capabilities and knowledge spillover in a comparative format. The present research is aimed at providing an appropriate framework for reporting intellectual capital in oil industry companies. For this research, semi-structured interviews have been done with 15 experts and people from petrochemical and petroleum companies with intellectual and experiential thinking space. After the interview, the relevant texts were analyzed by the thematic analysis method. Finally, the intellectual capital reporting framework was extracted as a qualitative research product. Then, a questionnaire was designed to assess the acceptance of the qualitative model, which was distributed among the statistical community consisting of professors and PhD students and experts of different universities and companies. The results of the distributed questionnaire showed that the components of the framework were approved by the respondents.
- انتشار مقاله: 18-05-1397
- نویسندگان: Iman ِِDadashi,Hamid Reza Gholamnia Roshan,Amir Firooznia
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: stock price,accounting variables,artificial intelligence algorithm,backup vector regression
- چکیده:
- چکیده انگلیسی: The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode.
- انتشار مقاله: 06-03-1398
- نویسندگان: Aliasgar Davoodi Kasbi,Iman Dadashi,Kaveh Azinfar
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: stock price,particle swarm optimization algorithm,Chaid rule-based algoritm
- چکیده:
- چکیده انگلیسی: Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.
- انتشار مقاله: 22-11-1397
- نویسندگان: Aliasghar Davoodi Kasbi,Iman Dadashi
- مشاهده