Applied Text-Mining Algorithms for Stock Price Prediction Based on Financial News Articles

• Main Authors: Adrian Besimi, Zamir Dika, Visar Shehu, Mubarek Selimi
• Format: Article
• Language: English
• Published: University of Primorska 2019-12-01
• Series: Managing Global Transitions
• Subjects: text mining; finance; news; crawling; stock; prices; prediction; naive bayes
• Online Access: http://www.hippocampus.si/ISSN/1854-6935/17.335-351.pdf

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.