درخت حوزه‌های تخصصی

The unpredictability of the stock market makes it a serious area of study and analysis. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a great focus on using these capabilities to design algorithms that can learn stock market trends and successfully predict stock prices. The main goal is to create an intelligent system that provides these features for predicting short-term stock price trends to facilitate the investment decision process. To increase the accuracy and productivity of these systems and facilitate the routine of using common-sense knowledge in machine learning systems, developing or enriching knowledge bases and ontology for market modeling will be one of the effective measures in this field. In this research, an attempt has been made to strengthen and enrich the basic ontology created by the authors by using other global ontologies related to the subject of the stock market, and parts of the target space that were not addressed have been added to the ontology. By combining reference ontologies, a level of standardization is also created for the ontology and stability in the representation of concepts and relationships is ensured. In the next step, it has been tried to test the impact of the concepts and relations of the ontology in predicting stock price movements. For this purpose, news in the field of economy is considered as input and a model is created that first filters the textual inputs related to the desired stock symbol and then observes their effect on the price changes of the related stock. After improving the performance and comprehensiveness of the ontology, the study conducted in this report presented a model to measure and prove the effect of the relationships in this ontology on price changes. In practice, according to human limitations and the tools used, this effect was observed and confirmed with a proper level of certainty by checking the economic news.

This research synthesized the methodological issues of studies published in English for Specific Purposes from its debut in 1980 up to 2020, from both an overall perspective and also periodically in four decades. To this aim, we examined 617 empirical articles with regard to two themes: (a) research methodology, and (b) data sources. More specifically, we probed into the methodological designs, how they were integrated and the points of departure in qualitative research. Our results brought to the fore that the majority of ESP articles were framed as combined methods studies, most frequently concurrent designs, dominated by qualitative data and data collection. As well, about half of qualitative studies did not specify their approach and the majority of quantitative works utilized descriptive statistics and combinations of different tests. Pertaining to data sources, text samples as well as an amalgamation of different data collection techniques were the most prominent ones. Based on these results and discussion, we provided empirically–grounded recommendations and suggestions for promoting methodological transparency and rigor for the prospective studies for the field of ESP and applied linguistics in general.