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Game Theory for Predicting Stocks’ Closing Prices

Author

Listed:
  • João Costa Freitas

    (Faculty of Sciences, University of Porto, R Campo Alegre, 4169-007 Porto, Portugal)

  • Alberto Adrego Pinto

    (Faculty of Sciences, University of Porto, R Campo Alegre, 4169-007 Porto, Portugal
    Artificial Intelligence and Decision Support (LIAAD)—Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), University of Porto, R Campo Alegre, 4169-007 Porto, Portugal)

  • Óscar Felgueiras

    (Faculty of Sciences, University of Porto, R Campo Alegre, 4169-007 Porto, Portugal
    Centro de Matemática da Universidade do Porto (CMUP), University of Porto, R Campo Alegre, 4169-007 Porto, Portugal)

Abstract

We model the financial markets as a game and make predictions using Markov chain estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/her risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then, we estimate the market’s mixed probabilities by defining Markov chains and utilizing its transition matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. From this, we obtained a model for the financial markets that has a good performance in terms of accuracy and profitability.

Suggested Citation

  • João Costa Freitas & Alberto Adrego Pinto & Óscar Felgueiras, 2024. "Game Theory for Predicting Stocks’ Closing Prices," Mathematics, MDPI, vol. 12(17), pages 1-49, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2676-:d:1466107
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    References listed on IDEAS

    as
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