The power of words: predicting stock market returns with fine-grained sentiment analysis and XGBoost

Balaneji, Farshid, Maringer, Dietmar and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2024. The power of words: predicting stock market returns with fine-grained sentiment analysis and XGBoost. Presented at: Intelligent Systems Conference (IntelliSys), Amsterdam, 7-8 September 2023. Published in: Arai, Kohei ed. Intelligent Systems and Applications. Lecture Notes in Networks and Systems , vol.1 (822) Springer Nature, 577–596. 10.1007/978-3-031-47721-8_39

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Abstract

This study investigates the relationship between news sentiment and the stock market’s return. The sentiment was automatically analyzed using four methods, including lexicon-based and deep learning-based approaches, at three levels of granularity, i.e., sentence, paragraph, and full text. The sentiment was combined with features from the calendar year, lagged returns, and news publishers, which were fed into the XGBoost algorithm trained to classify the direction of market return for the following business day. The performance was maximized using Bayesian hyperparameter optimization and evaluated using nested cross-validation. The proof of concept was demonstrated using ten companies in the Dow Jones Index, which were grouped into five sectors. The findings indicate an asymmetric power of sentiment measures in different sectors, with the petroleum industry being the most responsive to the sentiment expressed in the news. The study highlights the significance of targeted sentiment measures in making informed decisions about the market direction, particularly for the petroleum industry.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Springer Nature
Last Modified:	02 Feb 2024 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/165751

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