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

Adrian Besimi, Zamir Dika, Visar Shehu and Mubarek Selimi
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Adrian Besimi: South East European University, North Macedonia
Zamir Dika: South East European University, North Macedonia
Visar Shehu: South East European University, North Macedonia
Mubarek Selimi: South East European University, North Macedonia

Managing Global Transitions, 2019, vol. 17, issue 4 (Winter), 335-351

Abstract: 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.

Keywords: text mining; finance; news; crawling; stock; prices; prediction; naive bayes (search for similar items in EconPapers)
JEL-codes: C89 G17 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.26493/1854-6935.17.335-351

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