Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks
<|reference_start|>Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks: 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.<|reference_end|>
@article{korangi2024large-scale,
    title={Large-scale Time-Varying Portfolio Optimisation using Graph Attention
  Networks},
    author={Kamesh Korangi, Christophe Mues and Cristi'an Bravo},
    journal={arXiv preprint arXiv:2407.15532},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15532},
    primaryClass={q-fin.PM cs.AI cs.SI q-fin.RM stat.ML}
}