Machine Learning techniques for equity markets: from custom loss functions in predictive modelling, to dynamic neural network ensemble learning for capital deployment

This thesis proposes the adoption of machine learning methods for equity market forecasting, expanding the toolbox available to market participants. Machine learning techniques, while effective in extracting patterns, capturing non-linear relationships among different variables of different nature