Abstract
Traders make their investment based on stock trends or price directions to get more profit. Many researchers have tried to retrieve the interesting features for trends on large financial data such as news. Data obtained from stock market is highly volatile and correlated. It is characterized with high dimensionality to make prediction of stock trends a challenging. Feature extraction is an important part in developing a fully automated stock market prediction system. Features in stock news have Multiple Interdependent Nature (MIN) which are the relationship between two or more features. Rule and Syntactic Feature based relation extraction have already proposed to get trend related features. Our previous work is still required to solve MIN for stock trend prediction. This paper proposes the additional criteria to improve the trend extraction on MIN. These criteria are date conversion, main verb identification, stock reference and advanced trend extraction. According to the experiment, the trend extraction with additional criteria on stock news gets more extracted trends than the extraction without criteria. The stock trend extraction results with additional criteria become more consistent with the real stock price movement.
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Acknowledgement
I would like to thank supervisors, professors and rectors at University of Information Technology. I would like to regard with participating as a member in Data Analysis and Management (DAM) Lab. Our research supports in part by the DAM lab at the University of Information Technology.
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Khaing, E.T., Thein, M.M., Lwin, M.M. (2020). Enhance Trend Extraction Results by Refining with Additional Criteria. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_63
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DOI: https://doi.org/10.1007/978-3-030-63119-2_63
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