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.

TOPICS: Big data/machine learning, portfolio construction, performance measurement

Key Findings

• ▪ Contemporary approaches (e.g., simple heuristics) used to score and rank assets in portfolio construction are sub optimal as they do not learn the broader pairwise and listwise relationships across instruments.

• ▪ Learning to rank algorithms can be used to address this shortcoming, learning the broader links across assets, which consequently allow them to be ranked more accurately.

• ▪ Using Cross-sectional Momentum as a demonstrative use-case, we show that more precise rankings produce long/short portfolios that significantly outperform traditional approaches across various financial and ranking-based measures.