Building Cross-Sectional Systematic Strategies By Learning to Rank
The Journal of Financial Data Science Spring 2021, jfds.2021.1.060; DOI: https://doi.org/10.3905/jfds.2021.1.060
12 Pages Posted: 19 Feb 2021 Last revised: 23 Aug 2022
Date Written: December 12, 2020
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.
Keywords: Momentum Strategies, Systematic Trading, Portfolio Construction, Machine Learning, Learning to Rank, Information Retrieval, Deep Neural Networks
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