Building Cross-Sectional Systematic Strategies by Learning to Rank

Abstract

The success of a cross-sectional systematic strategy depends critically on accurately ranking assets before 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 suboptimal for ranking in other domains (e.g., information retrieval). To address this deficiency, the authors propose a framework to enhance cross-sectional portfolios by incorporating learning-to-rank algorithms, which lead to improvements in ranking accuracy by learning pairwise and listwise structures across instruments. Using cross-sectional momentum as a demonstrative case study, the authors 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 with traditional approaches.