Predicting UK stock market short-term activity and returns from Daily Mail Online

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Journal Title
Journal ISSN
Volume Title
School of Business | Bachelor's thesis
Date
2017
Major/Subject
Mcode
Degree programme
Rahoitus
Language
en
Pages
31
Series
Abstract
In this paper, I’m deriving a direct measure of investor sentiment from Daily Mail Online news articles using SentiWordNet lexical resource for opinion mining, and I find that it has the ability to predict London Stock Exchange market activity and returns. More precisely the results state that all news – not only financial news – can offer a significant input in predicting stock market activity and that the direction of the effects differs between news categories. The dataset used in this paper is unique; it consists of 1,139,243 Daily Mail Online news articles published between years 2008 and 2017.
Description
Thesis advisor
Lof, Matthijs
Keywords
opinion mining, finance, sentiment analysis, online news, investor sentiment, big data, computational linguistics, Daily Mail
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Citation