Empirical stock market returns forecasting: machine learning in modern portfolio theory
Feroce, Luca (A.A. 2020/2021) Empirical stock market returns forecasting: machine learning in modern portfolio theory. Tesi di Laurea in Asset pricing, Luiss Guido Carli, relatore Paolo Porchia, pp. 128. [Master's Degree Thesis]
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Abstract/Index
Machine learning: a real-world perspective. FinTech revolution. Stock market prediction. Research objective. Boosted regression trees forecasting framework. Modern portfolio theory. Decision tree learning. Conditioning information. Predicting optimal portfolio allocations. Out-of-sample empirical application's results. Data. Two step BRT model. One step BRT model. Portfolio allocation performance.
References
Bibliografia: pp. 58-65.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree program in Corporate Finance, English language (LM-77) |
| Chair: | Asset pricing |
| Thesis Supervisor: | Porchia, Paolo |
| Thesis Co-Supervisor: | Pirra, Marco |
| Academic Year: | 2020/2021 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 10 May 2022 07:26 |
| Last Modified: | 10 May 2022 07:26 |
| URI: | https://tesi.luiss.it/id/eprint/32204 |
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