Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity

Abstract

Modern cross-sectional strategies incorporating sophisticated neural architectures outperform their traditional counterparts when applied to mature assets with long histories. However, deploying them on instruments with limited samples generally produces over-fitted models with degraded performance. In this paper, we introduce Fused Encoder Networks -- a hybrid parameter-sharing transfer ranking model which fuses information extracted using an encoder-attention module from a source dataset with a similar but separate module operating on a smaller target dataset of interest. This mitigates the issue of models with poor generalisability. Additionally, the self-attention mechanism enables interactions among instruments to be accounted for, both at the loss-level during model training and at inference time. Focusing on momentum applied to the top ten cryptocurrencies by market capitalisation as a demonstrative use-case, our model outperforms state-of-the-art benchmarks on most measures and significantly boosts the Sharpe ratio. It continues outperforming baselines even after accounting for the high transaction costs associated with trading cryptocurrencies.