Faculty of Science, School of Mathematics and Statistics
Abstract
Financial markets are difficult learning environments. The data generation process is time-varying,
returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge
of applying sophisticated, high capacity learning models in financial markets. Driven by recent
advances of deep learning in other fields, we focus on applying deep learning in a portfolio
management context. This thesis contains three distinct but related contributions to literature. First,
we consider the problem of neural network training in a time-varying context. This results in a neural
network that can adapt to a data generation process that changes over time. Second, we consider
the problem of learning in noisy environments. We propose to regularise the neural network using a
supervised autoencoder and show that this improves the generalisation performance of the neural
network. Third, we consider the problem of quantifying forecast uncertainty in time-series with
volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies
and U.S. stocks