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Politics, Artificial Intelligence, Twitter and Stock Return: An Interdisciplinary Test for Stock Price Prediction Based on Political Tweets
Jönköping University, Jönköping International Business School, JIBS, Business Administration.
Jönköping University, Jönköping International Business School, JIBS, Business Administration.
2020 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twitter account. This investigation will look into the relationship between political tweets' sentiment and market movement and the subsequent longevity of such an effect. The classified sentiments are positive or negative. The presence of artificial intelligence is vital for a data-driven investigation; in the context of this investigation, artificial intelligence will be used to classify the sentiment of the political tweet. The methods chose to assess the impact of a political tweet and market movement is event-study. The impact is expressed in either a positive or a negative cumulative abnormal return subsequent to the political tweet. The findings of the investigation indicate that on average, there is no statistical evidence that a political tweets' sentiment leads to an abnormal return. However, in specific cases, political tweet leads to abnormal return. Moreover, it has been determined that the longevity of the effect is rather short. This is an interdisciplinary approach that can be applied by individual and institutional investors and financial institutions.

Place, publisher, year, edition, pages
2020. , p. 82
Keywords [en]
Stock Price Prediction, Politics, Efficient Market Hypothesis, Twitter, Artificial Intelligence, Sentiment Analysis, Event Study
National Category
Business Administration
Identifiers
URN: urn:nbn:se:hj:diva-48436ISRN: JU-IHH-FÖA-2-20201118OAI: oai:DiVA.org:hj-48436DiVA, id: diva2:1430899
Subject / course
JIBS, Business Administration
Examiners
Available from: 2020-06-24 Created: 2020-05-18 Last updated: 2025-10-13Bibliographically approved

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CiteExportLink to record
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