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Analytical versus machine learning calibration: which one works best in portfolio optimization?

Rigal, Arthur
(2023)

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RIGAL_62511800_2023.pdf
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Details

Supervisors
Lassance, Nathan
Faculty
Louvain School of Management
Degree label
Master [120] : ingénieur de gestion, à finalité spécialisée
Abstract
This master's thesis conducts empirical research on the estimation via machine learning of the decision parameters of different financial portfolio optimization strategies. Using the cross-validation method, the aim of the dissertation is to see whether the out-of-sample performance of portfolios estimated via machine learning is superior to that of conventional, analytically estimated, with strong statistical assumptions, portfolios.