Marcinko, Spencer Patrick Gengis (A.A. 2023/2024) Optimising financial decision-making: an integrated approach to automated investment models through machine learning and advanced financial indicators. Tesi di Laurea in Data-driven models for investment, Luiss Guido Carli, relatore Elio Stocchi, pp. 70. [Master's Degree Thesis]

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Abstract/Index

Theoretical framework. Reinforcement learning fundamentals. Supervised learning fundamentals. Financial technical indicators. Integration of techniques. Application on financial markets. Literature review. The use of financial technical indicators. Deep reinforcement learning in portfolio management. Proximal policy optimisation algorithms. Materials and methods. Implementation details. Data sources and preprocessing. Algorithm implementation. Evaluation metrics and performance analysis. Experiment design and validation. Implementation challenges and solutions. Ethical considerations and regulatory compliance. Deep RL method. Pure technical indicator method. Strategic technical indicator method. Discussion. Evaluation of reinforcement learning algorithm. Impact of technical indicators. Effectiveness of strategic technical indicators. Comparison of algorithms. Addressing research questions. Market conditions, algorithmic design and data quality.

References

Bibliografia: pp. 61-62.

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:	Stocchi, Elio
Thesis Co-Supervisor:	Simeone, Antonio
Academic Year:	2023/2024
Session:	Summer
Deposited by:	Alessandro Perfetti
Date Deposited:	17 Jan 2025 15:10
Last Modified:	17 Jan 2025 15:10
URI:	https://tesi.luiss.it/id/eprint/40915

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