Article
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This version is not peer-reviewed
Game Theory for predicting stocks’ closing prices
Version 1
: Received: 19 July 2024 / Approved: 21 July 2024 / Online: 22 July 2024 (09:45:31 CEST)
How to cite: Freitas, J. C.; Pinto, A. A.; Felgueiras, Ó. Game Theory for predicting stocks’ closing prices. Preprints 2024, 2024071668. https://doi.org/10.20944/preprints202407.1668.v1 Freitas, J. C.; Pinto, A. A.; Felgueiras, Ó. Game Theory for predicting stocks’ closing prices. Preprints 2024, 2024071668. https://doi.org/10.20944/preprints202407.1668.v1
Abstract
We model the financial markets as a game and make predictions using Markov chains 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/hers 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 transitions 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. After all, we obtained a model for the financial markets that has an outstanding performance in terms of accuracy and profitability.
Keywords
Financial Markets; Games Against Nature; Markov Chains
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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