Ensemble learning-based approach for the global minimum variance portfolio ; Approche basée sur l'apprentissage d'ensemble pour le portefeuille global minimum variance

Ensemble Learning has a simple idea that combining several learning algorithms tend to yield a better result than any single learning algorithm. Empirically, the ensemble method is better if its base models are diversified even if they are non-intuitively random algorithms such as random decision trees. Because of its advantages, Ensemble Learning is used in various applications such as fraud detection problems. In more detail, the advantages of Ensemble Learning are because of two points: i) combines the strengths of its base models then each model is complementary to one another and ii) neutralizes the noise and outliers among all base models then reduces their impacts on the final predictions. We use these two ideas of Ensemble Learning for different applications in the Machine Learning and the Finance industry. Our main contributions in this thesis are: i) efficiently deal with a hard scenario of imbalance data problem in the Machine Learning which is extremely imbalance big data problem by using undersampling technique and the Ensemble Learning, ii) appropriately apply time-series Cross-Validation and the Ensemble Learning to resolve a covariance matrix estimator selection problem in Quantitative Trading and iii) reduce the impact of outliers in covariance matrix estimations in order to increase the stability of portfolios by using the undersampling and the Ensemble Learning. ; Ensemble Learning a une idée simple selon laquelle la combinaison de plusieurs algorithmes d'apprentissage a tendance à donner un meilleur résultat que n'importe quel algorithme d'apprentissage unique. Empiriquement, la méthode d'ensemble est meilleure si ses modèles de base sont diversifiés même s'il s'agit d'algorithmes aléatoires non intuitifs tels que des arbres de décision aléatoires. En raison de ses avantages, Ensemble Learning est utilisé dans diverses applications telles que les problèmes de détection de fraude. Plus en détail, les avantages d'Ensemble Learning tiennent à deux points : i) combine les points forts de ses ...