Constructing the ontology of the Persian stock and financial markets

It is very difficult to predict stocks and commodity price index due to the presence of many and influential uncertainties. With the help of the accumulated information available in the current digital age and the power of high-performance computing machines, there is a lot of focus on designing algorithms that can learn stock market trends and successfully predict stock prices. Therefore, it will be very useful to create appropriate knowledge bases in order to increase the accuracy and efficiency of these systems and to facilitate the routine of using conventional knowledge in machine learning systems. The purpose of this research is to develop a Persian ontology for modeling the stock market and identifying factors affecting the stock market. The created ontology will lead to the enrichment and completion of the capacities of the existing knowledge bases in this field. For this purpose, in this research, a domain-specific ontology has been developed in the field of stock market and financial markets, which was prepared in Persian language by the authors of this research. After introducing this ontology, the details of the steps required to collect relevant data, semi-automated development and evaluation of this knowledge resource are described. The constructed ontology includes 565 concepts, 496 hierarchical relationships, 137 non-hierarchical relationships, and 937 samples that have been evaluated with various criteria and have a favorable status. It seems that this ontology in the current conditions and according to the evaluated volume and quality, is quite suitable to be used as a source of knowledge to improve the performance of machine learning systems for stock forecasting, and it can also be used to training stock market analysts and creating a knowledge base for brokerages, improving the process of retrieving semantic information and helping to determine the investment strategies of individuals in investment funds.