Thesis title:
Deep Learning architectures in macroeconomics and finance
Abstract:
Felix’s research focuses on now-casting and forecasting macroeconomic variables such as GDP, inflation or rate of unemployment in the United Kingdom and other developed economies using deep neural networks (DNN). The thesis will make three key contributions to current research: First, in the context of macroeconomic forecasting, the application of deep neural networks is not fully explored yet. Secondly, a data-driven algorithm is presented to derive an optimal network structure for a multi-layer radial basis function network. Thirdly, to the best of our knowledge, no research has yet been conducted in the field of macroeconomic and financial forecasting using datasets with up to 50,000 variables. The thesis therefore presents an unprecedented empirical study which also includes state-of-the-art innovations in methodology. It aims to not only impact the academic discourse but also to make relevant contributions to the non-academic world. The research combines large amounts of data including (macro-) economic and financial variables as well as “Big Data” such as Twitter sentiment or Google Trends data. To the best of our knowledge, such extensive datasets have not yet been applied in the macroeconomic forecasting literature partly due to the problem of overfitting.
First supervisor:
George Kapetanios
Pathway:
4 – Economics, Finance & the World Economy
Cohort:
2018-19