  Apart from assessing individual asset performance, investors in financial
markets also need to consider how a set of firms performs collectively as a
portfolio. Whereas traditional Markowitz-based mean-variance portfolios are
widespread, network-based optimisation techniques have built upon these
developments. However, most studies do not contain firms at risk of default and
remove any firms that drop off indices over a certain time. This is the first
study to incorporate risky firms and use all the firms in portfolio
optimisation. We propose and empirically test a novel method that leverages
Graph Attention networks (GATs), a subclass of Graph Neural Networks (GNNs).
GNNs, as deep learning-based models, can exploit network data to uncover
nonlinear relationships. Their ability to handle high-dimensional features and
accommodate customised layers for specific purposes makes them particularly
appealing for large-scale problems such as mid- and small-cap portfolio
optimization. This study utilises 30 years of data on mid-cap firms, creating
graphs of firms using distance correlation and the Triangulated Maximally
Filtered Graph approach. These graphs are the inputs to a GAT model that we
train using custom layers which impose weight and allocation constraints and a
loss function derived from the Sharpe ratio, thus directly maximising portfolio
risk-adjusted returns. This new model is benchmarked against a network
characteristic-based portfolio, a mean variance-based portfolio, and an
equal-weighted portfolio. The results show that the portfolio produced by the
GAT-based model outperforms all benchmarks and is consistently superior to
other strategies over a long period while also being informative of market
dynamics.
