Optimal execution using reinforcement learning
Quaranta, Andrea (A.A. 2024/2025) Optimal execution using reinforcement learning. Tesi di Laurea in Financial economics, Luiss Guido Carli, relatore Nicola Borri, pp. 57. [Master's Degree Thesis]
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Abstract/Index
Background and literature review. Market microstructure. Limit order book. Optimal execution. Optimal execution: reinforcement learning approaches. Simulation environment and methodology. Reinforcement learning framework. MDP representation of the execution problem. Simulation environment: ABIDES. Benchmarks. Hyperparameter tuning for RL agents: Optuna integration. Experimental results. Performance and convergence. Behavioral analysis of the RL agent. Performance comparison with baseline strategies. Cumulative reward analysis.
References
Bibliografia: pp. 53-56.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56) |
| Chair: | Financial economics |
| Thesis Supervisor: | Borri, Nicola |
| Thesis Co-Supervisor: | Patnaik, Megha |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 20 Feb 2026 14:32 |
| Last Modified: | 20 Feb 2026 14:32 |
| URI: | https://tesi.luiss.it/id/eprint/44919 |
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