  The success of a cross-sectional systematic strategy depends critically on
accurately ranking assets prior to portfolio construction. Contemporary
techniques perform this ranking step either with simple heuristics or by
sorting outputs from standard regression or classification models, which have
been demonstrated to be sub-optimal for ranking in other domains (e.g.
information retrieval). To address this deficiency, we propose a framework to
enhance cross-sectional portfolios by incorporating learning-to-rank
algorithms, which lead to improvements of ranking accuracy by learning pairwise
and listwise structures across instruments. Using cross-sectional momentum as a
demonstrative case study, we show that the use of modern machine learning
ranking algorithms can substantially improve the trading performance of
cross-sectional strategies -- providing approximately threefold boosting of
Sharpe Ratios compared to traditional approaches.
