درخت حوزه‌های تخصصی

Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential.

The purpose of this research was to identify the skills and abilities learners need in a digital learning environment during the Corona crisis. The research approach was applied in terms of objective and the data collected using the mixed-method approach. In the first part, using a qualitative approach and a document search method, 33 components (related to the skills and abilities of e-learners) were identified, and then19 components out of 33 with the highest frequencies were selected. The target population for this study included all MA students of the University of Tehran who had the experience of studying in an online learning environment, then 374 individuals were selected as a sample using the random sampling method. In the second part, using a quantitative approach and a descriptive-analytical research method, the questionnaire developed by the researcher relies on 19 components identified in the previous step. The experts' opinions proved the validity of the questionnaire—moreover, its reliability in 0.94 (Cronbach alpha). The data was analyzed by SPSS and LISREL, and the results of the data analysis demonstrate that the proposed model is a good fit. Among the 19 components surveyed, all are above average. Finally, the results indicated that the most critical components from e-learners’ view which more critical in digital learning were effective communication, use of the computer, active learning, learning skills, resilience, motivation toward e-learning, emotional intelligence, interaction with e-learning system, self-direct learning, time management, flexibility and adaptability, critical thinking, creativity, asking someone for help, leadership skills, stress management, self-organized, cognitive skills and self-assessment