Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention

Abstract

The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. Although this ranking step is traditionally performed using heuristics or by sorting the outputs produced by pointwise regression or classification techniques, strategies using learning-to-rank algorithms have recently presented themselves as competitive and viable alternatives. Although the rankers at the core of these strategies are learned globally and improve ranking accuracy on average, they ignore the differences between the distributions of asset features over the times when the portfolio is rebalanced. This flaw renders them susceptible to producing suboptimal rankings, possibly at important periods when accuracy is actually needed the most. For example, this might happen during critical risk-off episodes, which consequently exposes the portfolio to substantial, unwanted drawdowns. The authors tackle this shortcoming with an analogous idea from information retrieval: that a query’s top retrieved documents or the local ranking context provide vital information about the query’s own characteristics, which can then be used to refine the initial ranked list. In this work, the authors use a context-aware learning-to-rank model that is based on the transformer architecture to encode top/bottom-ranked assets, learn the context and exploit this information to rerank the initial results. Back testing on a slate of 31 currencies, the authors’ proposed methodology increases the Sharpe ratio by around 30% and significantly enhances various performance metrics. Additionally, this approach also improves the Sharpe ratio when separately conditioning on normal and risk-off market states.