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Applications Of Machine Learning And Deep Learning In Macroeconomic And Financial Forecasting, Andi Cupallari

Dissertations, Theses, and Capstone Projects

This dissertation consists of three chapters.

In the first chapter I propose a novel approach to forecast risk premia selecting relevant predictors among hundreds of correlated stock characteristics. I adapt a recently developed method from the deep learning literature, Deep Neural Networks with Group Lasso Regular- ization. This method achieves high out of sample R2, and at the same time yields a sparse representation of the characteristics space that allows for interpretability of the otherwise black box deep learning model. For each period, the model chooses a subset of characteris- tics to be relevant for the risk premia forecast. Our …