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Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Regression Techniques

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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

Abstract

In recent years, the public opinion is swayed by online social, media and news platforms, such as Twitter, podcasts, and streaming news broadcasts. The public opinion can alter the outcome of various social-economic events, e.g., the volatility of the stock market. This paper presents an overview of forecasting the volatility of the indices of several companies in the U.S. stock market while considering the sentiment and features extracted from the metadata of a tweet and its author’s social activity and network. The daily changes in the prices of an index in the U.S. stock market were estimated by applying several regression techniques. The results indicate a strong correlation between the approximated closing prices of the stocks in the U.S. stock market, the sentiment along with the features extracted from a tweet, and its author’s activity and network. Finally, the obtained results indicate that the number of attributes did not impact the performance of the applied regression techniques.

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Acknowledgement

This research was co-financed by the European Union and Greek national funds through the “Competitiveness, Entrepreneurship and Innovation” Operational Programme 2014–2020, under the Call “Support for regional excellence”; project title: “Intelligent Research Infrastructure for Shipping, Transport and Supply Chain - ENIRISST+”; MIS code: 5047041.

Author information

Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Christina Saravanos

  2. Department of Informatics, Ionian University, Corfu, Greece

    Andreas Kanavos

Authors
  1. Christina Saravanos
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  2. Andreas Kanavos
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Corresponding author

Correspondence to Andreas Kanavos .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

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Saravanos, C., Kanavos, A. (2023). Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Regression Techniques. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-34171-7_27

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-34171-7

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