Learning to Learn Financial Networks for Optimising Momentum Strategies
Xingyue Pu Stefan Zohren Stephen Roberts Xiaowen Dong
Network momentum provides a novel type of risk premium, which exploits the
interconnections among assets in a financial network to predict future returns.
However, the current process of constructing financial networks relies heavily
on expensive databases and financial expertise, limiting accessibility for
small-sized and academic institutions. Furthermore, the traditional approach
treats network construction and portfolio optimisation as separate tasks,
potentially hindering optimal portfolio performance. To address these
challenges, we propose L2GMOM, an end-to-end machine learning framework that
simultaneously learns financial networks and optimises trading signals for
network momentum strategies. The model of L2GMOM is a neural network with a
highly interpretable forward propagation architecture, which is derived from
algorithm unrolling. The L2GMOM is flexible and can be trained with diverse
loss functions for portfolio performance, e.g. the negative Sharpe ratio.
Backtesting on 64 continuous future contracts demonstrates a significant
improvement in portfolio profitability and risk control, with a Sharpe ratio of
1.74 across a 20-year period.