A Proposed Model for Stock Price Prediction Based on Financial News

Autor:	Adrian Besimi, Mubarek Selimi
Rok vydání:	2019
Předmět:	Background information Computer science Sentiment analysis finance Financial news text mining prediction naïve bayes Data science Stock price Naive Bayes classifier stock crawling ddc:330 news prices Stock (geology) C89
Zdroj:	ENTRENOVA-ENTerprise REsearch InNOVAtion Volume 5 Issue 1
ISSN:	1556-5068 2706-4735
DOI:	10.2139/ssrn.3490159
Popis:	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. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d262a3c610b951a4692ffe2787cceaed https://doi.org/10.2139/ssrn.3490159 Zobrazit plný text záznamu