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The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends

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Abstract

The study of the stock market with the attraction of machine learning approaches is a major direction for revealing hidden market regularities. This knowledge contributes to a profound understanding of financial market dynamics and getting behavioural insights, which could hardly be discovered with traditional analytical methods. Stock prices are inherently interrelated with world events and social perception. Thus, in constructing the model for stock price prediction, the critical stage is to incorporate such information on the outside world, reflected through news and social media posts. To accommodate this, researchers leverage the implicit or explicit knowledge representations: (1) sentiments extracted from the texts or (2) raw text embeddings. However, there is too little research attention to the direct comparison of these approaches in terms of the influence on the predictive power of financial models. In this paper, we aim to close this gap and figure out whether the semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends. We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices. To start, we demonstrate the connection of tweet sentiments with the volatility of companies’ stock prices. Convinced of the existing relationship, we train Temporal Fusion Transformer models for price prediction supplemented with either tweet sentiments or tweet embeddings. Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics. Noteworthy, the conclusions are justifiable within the considered scenario involving Twitter posts and stocks of the biggest tech companies.

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Notes

  1. 1.

    Source code foreco2AI is available athttps://github.com/sb-ai-lab/Eco2AI.

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Author information

Authors and Affiliations

  1. Higher School of Economics University, Moscow, Russia

    Andrei Zaichenko

  2. Sber AI Lab, Moscow, Russia

    Aleksei Kazakov, Elizaveta Kovtun & Semen Budennyy

  3. Artificial Intelligence Research Institute (AIRI), Moscow, Russia

    Semen Budennyy

Authors
  1. Andrei Zaichenko

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  2. Aleksei Kazakov

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  3. Elizaveta Kovtun

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  4. Semen Budennyy

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Corresponding author

Correspondence toSemen Budennyy.

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. Universität Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence and Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Nordrhein-Westfalen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University and HSE University, Moscow, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Zaichenko, A., Kazakov, A., Kovtun, E., Budennyy, S. (2024). The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends. In: Ignatov, D.I.,et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science, vol 1905. Springer, Cham. https://doi.org/10.1007/978-3-031-67008-4_12

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