An Effective Model for Ontology Relations Efficacy on Stock prices: A Case Study of the Persian Stock Market

Document Type : Research Paper

Authors

1 PhD Candidate, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

2 Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

Abstract

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.

Keywords

Main Subjects

References

Batet, M., & Sánchez, D. (2014). A semantic approach for ontology evaluation. 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, 138–145.
Bennett. (2015). Financial Instrument Global Identifier®.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. doi: 10.1016/j.eswa.2016.02.006
Chen, J., & Yang, L. (2021a). A Bibliometric Review of Volatility Spillovers in Financial Markets: Knowledge Bases and Research Fronts. Emerging Markets Finance and Trade, 57(5), 1358–1379. doi: 10.1080/1540496X.2019.1695119
Chen, J., & Yang, L. (2021b). A Bibliometric Review of Volatility Spillovers in Financial Markets: Knowledge Bases and Research Fronts. Emerging Markets Finance and Trade, 57(5), 1358–1379. doi: 10.1080/1540496X.2019.1695119
Fernández, M., Overbeeke, C., Sabou, M., & Motta, E. (2009). What makes a good ontology? A case-study in fine-grained knowledge reuse. The Semantic Web: Fourth Asian Conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings 4, 61–75.
Gopinathan, R., & Durai, S. R. S. (2019). Stock market and macroeconomic variables: new evidence from India. Financial Innovation, 5(1). doi: 10.1186/s40854-019-0145-1
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. doi: 10.1006/knac.1993.1008
Houstoun, K. J., Milne, A., & Parboteeah, P. (2014). Preliminary Report on Standards in Global Financial Markets. SSRN Electronic Journal, May, 1–88. doi: 10.2139/ssrn.2531210
Lamparter, S., & Schnizler, B. (2006). Trading services in ontology-driven markets. Proceedings of the 2006 ACM Symposium on Applied Computing, 1679–1683.
Metz, C. E. (n.d.). Basic Principles of ROC Analysis.
Mishra, A. K., Anand, S., Debnath, N. C., & Patel, A. (2023). An Ontological Framework for Risk Mitigation in Stock Market. In Intelligent Systems and Applications: Select Proceedings of ICISA 2022 (pp. 517–527). Springer.
Mohti, W., Dionísio, A., Ferreira, P., & Vieira, I. (2019). Contagion of the subprime financial crisis on frontier stock markets: A copula analysis. Economies, 7(1). doi: 10.3390/economies7010015
Odilon Tilly Sardou. (2021). Regulatory sanctions and their effects on the stock market.
Omar, A. Bin, Ali, A., Mouneer, S., Kouser, R., & Al-Faryan, M. A. S. (2022). Is stock market development sensitive to macroeconomic indicators? A fresh evidence using ARDL bounds testing approach. PLoS ONE, 17(10 October). doi: 10.1371/journal.pone.0275708
Raad, J., & Cruz, C. (2015). A survey on ontology evaluation methods. Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Part of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.
Samani, M., & Albadvi, A. (2023). Constructing the ontology of the Persian stock market. Iranian Journal of Information Processing & Management.
Sharma, N., Soni, M., Kumar, S., Kumar, R., Deb, N., & Shrivastava, A. (2023). Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(5), 1–24.
Wang, S., Zhe, Z., Kang, Y., Wang, H., & Chen, X. (2008). An ontology for causal relationships between news and financial instruments. Expert Systems with Applications, 35(3), 569–580. doi: 10.1016/j.eswa.2007.07.022
Zaki, M., Theodoulidis, B., & Diaz, D. (2019). Ontology-driven framework for stock market monitoring and surveillance. In HANDBOOK OF GLOBAL FINANCIAL MARKETS: Transformations, Dependence, and Risk Spillovers (pp. 75–103). World Scientific.
Zdraveski, V., Jovanoski, M., & Franke, U. (2017). Stock Market Ontology. ICT Innovations, 1–7.
Zhang, L., Zhao, M., & Feng, Z. (2019). Research on Knowledge Discovery and Stock Forecasting of Financial News Based on Domain Ontology. International Journal of Information Technology and Decision Making, 18(3), 953–979. doi: 10.1142/S0219622019500160
Zhao, C., Hu, P., Liu, X., Lan, X., & Zhang, H. (2023). Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction. Mathematics, 11(5). doi: 10.3390/math11051130
 
Journal of Information Technology Management
Volume 16, Issue 3
2024
Pages 1-20

Files

Share

How to cite

Statistics