Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 2, 20 February 2023
Open
Access | Article
Predict FTSE100 Stock Movements Using Business News Sentiment and Machine Learning
* Author to whom correspondence should be addressed.
Abstract
In order for investors to maximize their benefit by having better forecasts of the complex dynamics of the stock market, there are many factors that affect the stock market, from a company's financial ratios to investor sentiment and reactions to financial news. This project aims to collect UK business news from the Guardian and uses NLP techniques to transform unstructured text data into usable structured sentiment data to predict the movement of the FTSE100 index. The program uses two different libraries TEXTBLOB and VADER to extract sentiments from both the headlines and main bodies of the business news articles. Four machine learning algorithms including Logistic Regression, Naive Bayes, K-Nearest Neighbours and Support Vector Machines and a voting classifier were used to predict FTSE100 index movement given the business news sentiments of the previous day.
Keywords
sentiment analysis, NLP, Stock price prediction
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)
- ISBN (Print)
- 978-1-915371-13-3
- ISBN (Online)
- 978-1-915371-14-0
- Published Date
- 20 February 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/2/20220148
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited