Optimizing financial portfolio management using integrated fuzzy logic, game theory, and reinforcement learning: Python
Ndateba Mandela, Eddy (A.A. 2023/2024) Optimizing financial portfolio management using integrated fuzzy logic, game theory, and reinforcement learning: Python. Tesi di Laurea in Data-driven models for investment, Luiss Guido Carli, relatore Antonio Simeone, pp. 59. [Master's Degree Thesis]
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Abstract/Index
Literature review. Introduction to fuzzy logic. Fuzzy logic in finance. Game theory in financial markets. Reinforcement learning in portfolio management. Attempted integrated approaches in finance and other fields. Theoretical framework. Fuzzy rule. Game theory concepts applicable to modeling investors’ interactions. Reinforcement learning algorithms suitable for dynamic investment strategy. Theoretical foundation for integrating fuzzy logic, game theory, and reinforcement learning in portfolio management. Portfolio optimization. Methodology. Game theory framework for portfolio management. Key game theory models related to portfolio management. Data collection and selection criteria. Algorithm development. Algorithm to be used. Results and discussion. Analysis of the optimized portfolio. Compare with benchmark. Game theory.
References
Bibliografia: pp. 45-48.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data-driven models for investment |
| Thesis Supervisor: | Simeone, Antonio |
| Thesis Co-Supervisor: | Stocchi, Elio |
| Academic Year: | 2023/2024 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 09 Jan 2025 15:17 |
| Last Modified: | 09 Jan 2025 15:17 |
| URI: | https://tesi.luiss.it/id/eprint/40875 |
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