  Cross-sectional strategies are a classical and popular trading style, with
recent high performing variants incorporating sophisticated neural
architectures. While these strategies have been applied successfully to
data-rich settings involving mature assets with long histories, deploying them
on instruments with limited samples generally produce over-fitted models with
degraded performance. In this paper, we introduce Fused Encoder Networks -- a
novel and hybrid parameter-sharing transfer ranking model. The model fuses
information extracted using an encoder-attention module operated on a source
dataset with a similar but separate module focused on a smaller target dataset
of interest. This mitigates the issue of models with poor generalisability that
are a consequence of training on scarce target data. Additionally, the
self-attention mechanism enables interactions among instruments to be accounted
for, not just at the loss level during model training, but also at inference
time. Focusing on momentum applied to the top ten cryptocurrencies by market
capitalisation as a demonstrative use-case, the Fused Encoder Networks
outperforms the reference benchmarks on most performance measures, delivering a
three-fold boost in the Sharpe ratio over classical momentum as well as an
improvement of approximately 50% against the best benchmark model without
transaction costs. It continues outperforming baselines even after accounting
for the high transaction costs associated with trading cryptocurrencies.
