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

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  • Daniel Poh
  • Bryan Lim
  • Stefan Zohren
  • Stephen Roberts

Abstract

The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. While 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 sub-optimal 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. We 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, we 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 re-rank the initial results. Backtesting on a slate of 31 currencies, our 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.

Suggested Citation

  • Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
  • Handle: RePEc:arx:papers:2105.10019
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Kieran Wood & Sven Giegerich & Stephen Roberts & Stefan Zohren, 2021. "Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture," Papers 2112.08534, arXiv.org, revised Nov 2022.
    2. Daniel Poh & Stephen Roberts & Stefan Zohren, 2022. "Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity," Papers 2208.09968, arXiv.org, revised Feb 2023.
    3. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2024. "Deep Learning for Options Trading: An End-To-End Approach," Papers 2407.21791, arXiv.org.
    4. Daiya Mita & Akihiko Takahashi, 2022. "Multi-Agent Model Based Proactive Risk Management For Equity Investment," CIRJE F-Series CIRJE-F-1207, CIRJE, Faculty of Economics, University of Tokyo.
    5. Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
    6. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2023. "Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies," Papers 2302.10175, arXiv.org.

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