A Proposed Model for Stock Price Prediction Based on Financial News

Autori

  • Mubarek Selimi South East European University, Republic of North Macedonia
  • Adrian Besimi South East European University, Republic of North Macedonia

Ključne reči:

text mining, finance, news, crawling, stock, prices, prediction, naïve bayes

Apstrakt

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.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Reference

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Yip, J. (2018), “Algorithmic Trading using Sentiment Analysis on News Articles”, available at: https://towardsdatascience.com/https-towardsdatascience-com-algorithmic-trading-using-sentiment-analysis-on-news-articles-83db77966704 (15 May 2019).

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Objavljeno

2019-10-31

Broj časopisa

Sekcija

Mathematical and Quantitative Methods