Testing performances of machine learning models in the predictioin of excess return using financial statement related predictors
Calvani, Alessio (A.A. 2019/2020) Testing performances of machine learning models in the predictioin of excess return using financial statement related predictors. Tesi di Laurea in Asset pricing, Luiss Guido Carli, relatore Paolo Porchia, pp. 82. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Machine learning overview. What is machine learning. Why use machine learning. Types of machine learning systems. Main challenges of machine learning. A more detailed definition of machine learning. Related works. Two strands of empirical literature on stock returns. Empirical applications of machine learning. Statistical description of machine learning models. Linear regression models. Principal component regression and partial least square. Neural network. Predicting excess return via machine learning. Model training. Feature importance.
References
Bibliografia: pp. 60-63.
| 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: | 2019/2020 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 06 May 2021 14:07 |
| Last Modified: | 06 May 2021 14:07 |
| URI: | https://tesi.luiss.it/id/eprint/29385 |
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