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An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

Author

Listed:
  • Traianos-Ioannis Theodorou

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Alexandros Zamichos

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Michalis Skoumperdis

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Anna Kougioumtzidou

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Kalliopi Tsolaki

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Dimitris Papadopoulos

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Thanasis Patsios

    (Media2Day Publishing S.A., 15232 Athens, Greece)

  • George Papanikolaou

    (Media2Day Publishing S.A., 15232 Athens, Greece)

  • Athanasios Konstantinidis

    (Department of Electrical Engineering, Imperial College London, London SW7 2AZ, UK)

  • Anastasios Drosou

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Dimitrios Tzovaras

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

Abstract

In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors.

Suggested Citation

  • Traianos-Ioannis Theodorou & Alexandros Zamichos & Michalis Skoumperdis & Anna Kougioumtzidou & Kalliopi Tsolaki & Dimitris Papadopoulos & Thanasis Patsios & George Papanikolaou & Athanasios Konstanti, 2021. "An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements," Future Internet, MDPI, vol. 13(6), pages 1-22, May.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:138-:d:559210
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    References listed on IDEAS

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    Cited by:

    1. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages," Papers 2404.08665, arXiv.org.
    2. Cristescu Marian Pompiliu & Nerişanu Raluca Andreea & Mara Dumitru Alexandru, 2022. "Using Data Mining in the Sentiment Analysis Process on the Financial Market," Journal of Social and Economic Statistics, Sciendo, vol. 11(1-2), pages 36-58, December.
    3. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    4. Marian Pompiliu Cristescu & Raluca Andreea Nerisanu & Dumitru Alexandru Mara & Simona-Vasilica Oprea, 2022. "Using Market News Sentiment Analysis for Stock Market Prediction," Mathematics, MDPI, vol. 10(22), pages 1-12, November.

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