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The Impact of Sentiment in the News Media on Daily and Monthly Stock Market Returns

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Data Mining (AusDM 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1504))

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Abstract

Sentiment analysis allows for the subjective information contained within a piece of media to be classified. This subjective information is particularly relevant in the finance domain as opinions and speculation may influence investment decisions and, consequently, affect asset prices. State-of-the-art sentiment classification methods in machine learning use contextual word representations from pre-trained BERT language models and several papers have fine-tuned BERT for the sentiment classification of financial texts. However, these preceding studies have not considered extensive real-world data sets or provided a robust assessment of whether the sentiment scores derived from BERT predict asset prices. This paper addresses these limitations by fine-tuning BERT to analyse the sentiment across an extensive set of news articles published by the Wall Street Journal. The analysis also extends beyond financial news stories, assessing the sentiment in economic and national news articles. An econometric evaluation of the sentiment scores shows that financial news sentiment is statistically significant in predicting daily market returns, while longer-term economic news sentiment predicts monthly returns.

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Notes

  1. 1.

    For reviews of textual analysis in financial economics see [1, 19, 30, 44].

  2. 2.

    The LDA analysis was performed with a batchsize of 1000, an asymmetric alpha hyperparameter, and 10 passes over the corpus. These parameters were selected as they generate the most interpretable word clusters. The pre-processing steps taken for the LDA included; tokenization; the removal of stopwords; and lemmetization.

  3. 3.

    The Transformer [45] is a deep learning model that, like recurrent neural networks, is designed to handle sequential data. However, the Transformer does not feature the same network structure as an RNN and therefore does not require that the sequential data be processed in order.

  4. 4.

    The economic and national news items were labelled with regard to the tone or emotion–namely positive, negative or neutral–expressed by the article. The subject matter of the article did not effect the sentiment label that was given to it.

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Author information

Authors and Affiliations

  1. School of Economics and Finance, Queensland University of Technology, 2 George Street, Brisbane City, QLD, 4000, Australia

    Justin Case & Adam Clements

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  1. Justin Case
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  2. Adam Clements
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Correspondence to Justin Case .

Editor information

Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Yue Xu

  2. Western Sydney University, Parramatta, NSW, Australia

    Rosalind Wang

  3. University of Queensland, Herston, Australia

    Anton Lord

  4. RMIT University, Melbourne, VIC, Australia

    Yee Ling Boo

  5. Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

  6. Data61, CSIRO, Canberra, ACT, Australia

    Yanchang Zhao

  7. Australian National University, Canberra, ACT, Australia

    Graham Williams

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Case, J., Clements, A. (2021). The Impact of Sentiment in the News Media on Daily and Monthly Stock Market Returns. In: Xu, Y., et al. Data Mining. AusDM 2021. Communications in Computer and Information Science, vol 1504. Springer, Singapore. https://doi.org/10.1007/978-981-16-8531-6_13

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  • DOI: https://doi.org/10.1007/978-981-16-8531-6_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8530-9

  • Online ISBN: 978-981-16-8531-6

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