ساخت هستان‌نگاری بورس و بازارهای مالی فارسی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده صنایع و سیستم‌ها؛ دانشگاه تربیت مدرس؛ تهران، ایران

چکیده

پیش‌بینی سهام و شاخص قیمت سهام و کالا به‌دلیل وجود عدم قطعیت‌های فراوان و تأثیرگذار بسیار دشوار است. با کمک اطلاعات انباشته موجود در عصر دیجیتال فعلی و قدرت ماشین‌های محاسباتی قوی، تمرکز زیادی بر طراحی الگوریتم‌هایی وجود دارد که می‌توانند روند بازار سهام را بیاموزند و قیمت‌های سهام را با موفقیت پیش‌بینی کنند. بنابراین، ایجاد پایگاه‌های دانش مناسب در راستای افزایش دقت و بهره‌وری این سیستم‌ها و تسهیل روال استفاده از دانش عرفی موجود در سیستم‌های یادگیری ماشینی بسیار مفید خواهد بود. هدف از این پژوهش توسعه هستان‌نگاری فارسی برای مدل‌سازی حوزه بورس و تشخیص عوامل تأثیرگذار بر بازار سهام است. هستان‌نگاری ایجادشده منجر به غنی‌سازی و تکمیل ظرفیت‌های پایگاه‌های دانش موجود در این حوزه خواهد شد. بدین ‌منظور در این پژوهش یک هستان‌نگاری خاص ‌دامنه در حوزه بورس و بازارهای مالی توسعه داده‌شده که به ‌زبان فارسی توسط نویسندگان این پژوهش تهیه شده است. پس از معرفی این هستان‌نگاری، جزئیات گام‌های مورد نیاز برای جمع‌آوری دادگان مرتبط، توسعه نیمه‌خودکار و ارزیابی این منبع دانش بیان می‌گردد. هستان‌نگاری ساخته‌شده شامل 565 مفهوم، 496 رابطه ‌سلسله‌مراتبی، 137 رابطه غیر‌سلسله‌مراتبی و 937 نمونه است که با معیارهای مختلفی ارزیابی شده و وضعیت مطلوبی دارد. به نظر می‌رسد این هستان‌نگاری در شرایط فعلی و با توجه به حجم و کیفیت ارزیابی‌شده، برای استفاده به‌عنوان منبع دانشی برای بهبود عملکرد سیستم‌های یادگیری ماشین برای پیش‌بینی سهام به‌طور کامل مناسب بوده و همچنین، می‌توان از آن در جهت آموزش تحلیلگران بورس و ایجاد پایگاه دانش برای کارگزاری‌ها، ‌بهبود فرایند بازیابی اطلاعات معنایی و کمک به تعیین استراتژی‌های سرمایه‌گذاری افراد در صندوق‌های سرمایه‌گذاری استفاده نمود. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Constructing an Ontology of the Persian Stock Market and Financial Markets

نویسندگان [English]

  • Mohammad Hossein Samani
  • Amir Albadvi
Faculty of Industries and Systems; Tarbiat Modares University; Tehran, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Ontology
  • Ontology Learning
  • Stock Market
  • Financial Markets
  • Stock Forecasting
 
References:
Asim, Muhammad Nabeel, Muhammad Wasim, Muhammad Usman Ghani Khan, Waqar Mahmood, and Hafiza Mahnoor Abbasi. 2018. A Survey of Ontology Learning Techniques and Applications. Database: The Journal of Biological Databases and Curation. Oxford University Press, Volume 2018, bay101, 1–24.
Batet, Montserrat, and David Sánchez. 2014. “A Semantic Approach for Ontology Evaluation.” In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, 138–45. Cyprus: IEEE.
Buitelaar, Paul, Philipp Cimiano, and Bernardo Magnini. 2005. Ontology Learning from Text: An Overview. Ontology Learning from Text: Methods, Evaluation and Applications. Amsterdam: IOS Press. 3–12.
Chandrasekaran, B., John R. Josephson, and V. Richard Benjamins. 1999. “What Aro Ontologies, and Why Do We Need Them?” IEEE Intelligent Systems and Their Applications 14 (1): 20–26. doi:10.1109/5254.747902.
Chou, Tung Hsiang, John A. Vassar, and Binshan Lin. 2008. Knowledge Management via Ontology Development in Accounting. Kybernetes 37 (1): 36–48. doi:10.1108/03684920810850970.
Ding, Ying, and Schubert Foo. 2002. Ontology Research and Development. Part 1 - a Review of Ontology Generation. Journal of Information Science 28 (2): 123–36. doi:10.1177/016555150202800204.
Fernández, Miriam, Chwhynny Overbeeke, Marta Sabou, and Enrico Motta. 2009. “What Makes a Good Ontology? A Case-Study in Fine-Grained Knowledge Reuse.” In The Semantic Web: Fourth Asian Conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings 4, 61–75. Springer.
Gruber, Thomas R. 1993. A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5 (2): 199–220. doi:10.1006/knac.1993.1008.
Hazman, Maryam, Samhaa R El-Beltagy, and Ahmed Rafea. 2011. “A Survey of Ontology Learning Approaches.” International Journal of Computer Applications 22 (9): 36–43.
Hearst, Marti A. 1992. “Automatic Acquisition of Hyponyms from Large Text Corpora.” In COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics. Nantes, France.
Heijst, Gertjan van, Sabina Falasconi, Ameen Abu-Hanna, Guus Schreiber, and Mario Stefanelli. 1995. A Case Study in Ontology Library Construction. Artificial Intelligence In Medicine 7 (3): 227–55. doi:10.1016/0933-3657(95)00005-Q.
Lamparter, Steffen, and Björn Schnizler. 2006. Trading Services in Ontology-Driven Markets. In Proceedings of the 2006 ACM Symposium on Applied Computing, 1679–83. Dijon, France.
Mishra, Ambrish Kumar, Shweta Anand, Narayan C Debnath, and Archana Patel. 2023. “An Ontological Framework for Risk Mitigation in Stock Market.” In Intelligent Systems and Applications: Select Proceedings of ICISA 2022, 517–27. Springer.
Ostani, Morteza Mohammadi, Maryam Azargoon, and Mozaffar Cheshmesohrabi. 2018. Methodology of Construction and Design of Ontologies: A Case Study of Scientometrics Field. Iranian Journal of Information Processing Management 33 (4): 1793–1822.
Raad, Joe, and Christophe Cruz. 2015. “A Survey on Ontology Evaluation Methods.” In 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. Lisbon, Portugal.
Schreiber, Guus, Bob J Wielinga, and Joost Breuker. 1993. KADS : A Principled Approach to Knowledge-Based System Development. Vol. 11. Academic Press, April 21, 1993. In https://api.semanticscholar.org/CorpusID:57045801.
Shamsfard, Mehrnoush, and Ahmad Abdollahzadeh Barforoush. 2003. “The State of the Art in Ontology Learning: A Framework for Comparison.” The Knowledge Engineering Review 18 (4). Cambridge University Press: 293–316. doi:DOI: 10.1017/S0269888903000687.
Sharma, Neha, Mukesh Soni, Sumit Kumar, Rajeev Kumar, Nabamita Deb, and Anurag Shrivastava. 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). ACM New York, NY: 1–24.
Uschold, Michael, and Michael Gruninger. 2004. Ontologies and Semantics for Seamless Connectivity. SIGMOD Record 33 (4): 58–64. doi:10.1145/1041410.1041420.
Wang, Shanshan, Zhang Zhe, Ye Kang, Huaiqing Wang, and Xiaojian Chen. 2008. An Ontology for Causal Relationships between News and Financial Instruments. Expert Systems with Applications 35 (3): 569–80.
Zaki, Mohamed, Babis Theodoulidis, and David Diaz. 2019. “Ontology-Driven Framework for Stock Market Monitoring and Surveillance.” In HANDBOOK OF GLOBAL FINANCIAL MARKETS: Transformations, Dependence, and Risk Spillovers, 75–103. Singapore :World Scientific.
Zdraveski, Vladimir, Mihail Jovanoski, and Uwe Franke. 2017. “Stock Market Ontology.” Data-Driven Innovation. 9th International Conference, ICT Innovations 2017, Macedonia 2017, Web proceedings ISSN 1865-0937, 1–7.
Zhang, Lingling, Minghui Zhao, and Zili Feng. 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): 953–79. doi:10.1142/S0219622019500160.
Zhou, Lina. 2007. Ontology Learning: State of the Art and Open Issues. Information Technology and Management 8: 241–52.