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

TL;DR

This paper introduces Fused Encoder Networks, a transfer learning model that improves cross-sectional momentum trading strategies in data-scarce settings, especially for cryptocurrencies, by leveraging source data and self-attention mechanisms.

Contribution

The paper proposes a novel hybrid transfer ranking model that fuses information from source and target datasets using encoder-attention modules to enhance generalization in limited data scenarios.

Findings

Outperforms benchmarks with a three-fold increase in Sharpe ratio.

Achieves approximately 50% improvement over the best benchmark without transaction costs.

Remains effective even after accounting for high cryptocurrency transaction costs.

Abstract

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.