A Proposed Model for Stock Price Prediction Based on Financial News
Mubarek Selimi and
A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, 2019, pp 100-107 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb
In this paper we will propose a model and needed steps that one should undertake in order to try and predict potential stock price fluctuation solely based on financial news from relevant sources. The paper will start with providing background information on the problem and text mining in general, furthermore supporting the idea with relevant research papers needed to focus on the problem we are researching. Our model relies on existing text-mining techniques used for sentiment analysis, combined with historical data from relevant news sources as well as stock data.
Keywords: text mining; finance; news; crawling; stock; prices; prediction; naïve bayes (search for similar items in EconPapers)
JEL-codes: C89 (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www.econstor.eu/bitstream/10419/207669/1/1 ... mi-et-al-100-107.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:entr19:207669
Access Statistics for this chapter
More chapters in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().