Theoretical and Natural Science

Theoretical and Natural Science

TNS Vol.2 (CIAP 2022), 02 February 2023

Open Access | Article

Predict FTSE100 Stock Movements Using Business News Sentiment and Machine Learning

Congjun Jin * 1 , Rongzheng Liu 2 , Bangfeng Tang 3 , Bokun Cai 4
1 The School of Environmental and Civil Engineering, University of New South Wales, Sydney 2052, Australia
2 University of St Andrews, Scotland
3 Perth College, University of the Highland and Islands, Scotland
4 Limai Chinese American (International) School, Beijing, China

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, TNS Vol.2 (CIAP 2022), 155-160
Published 02 February 2023. © 2023 The Author(s). Published by EWA Publishing
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.
Citation Congjun Jin, Rongzheng Liu, Bangfeng Tang, Bokun Cai. Predict FTSE100 Stock Movements Using Business News Sentiment and Machine Learning. TNS (2023) TNS Vol.2 (CIAP 2022): 155-160.


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.


sentiment analysis, NLP, Stock price prediction


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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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