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  • 标题:Applied Text-Mining Algorithms for Stock Price Prediction Based on Financial News Articles
  • 本地全文:下载
  • 作者:Adrian Besimi ; Zamir Dika ; Visar Shehu
  • 期刊名称:Managing Global Transitions
  • 印刷版ISSN:1581-6311
  • 出版年度:2019
  • 卷号:17
  • 期号:4
  • 页码:335-351
  • DOI:10.26493/1854-6935.17.335-351
  • 语种:English
  • 出版社:University of Primorska, Faculty of Management Koper
  • 摘要:This article includes a developed model and well-defined process that one should undertake in order to contribute in the prediction of the potential stock price fluctuation solely based on financial news from relevant sources. We are providing background information on this topic adding the role of text mining in general, furthermore supporting the idea with the study of relevant research articles to narrow the focus on the problemwe are researching.Our proposedmodel relies on existing text-mining techniques used for sentiment analysis, combinedwith historical data from relevant news sources as well as stock data. In confirming the model, after the experiment we have provided the results of the simulation, which are opening the ground for further explorations in this sensitive area of prediction.
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