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Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention. (arXiv:2105.10019v2 [q-fin.PM] UPDATED)
Jan. 31, 2022, 2:11 a.m. | Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
cs.LG updates on arXiv.org arxiv.org
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 …
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