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Building Cross-Sectional Systematic Strategies By Learning to Rank (2012.07149v1)

Published 13 Dec 2020 in q-fin.TR, cs.IR, cs.LG, and q-fin.PM

Abstract: 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.

Citations (18)

Summary

  • The paper presents a novel method integrating learning-to-rank algorithms into asset ranking, leading to substantial performance gains in cross-sectional trading strategies.
  • The authors compare various LTR models, including RankNet, LambdaMART, ListNet, and ListMLE, against traditional heuristics, showing improved ranking metrics and profitability.
  • The study demonstrates practical implications for portfolio optimization and risk management, paving the way for innovative approaches in quantitative finance.

Enhancing Cross-Sectional Systematic Strategies with Learning-to-Rank Algorithms

This paper explores a novel approach to improving cross-sectional systematic trading strategies by integrating learning-to-rank (LTR) algorithms to enhance the asset ranking process. By concentrating on cross-sectional momentum strategies as a case paper, the authors present a compelling argument for the application of LTR techniques in this domain, demonstrating marked improvements in performance metrics compared to traditional approaches.

Cross-sectional strategies have been a staple in systematic trading, where the central task involves ranking assets to construct portfolios by buying assets with the highest expected returns and selling those with the lowest. Traditionally, this ranking has been achieved using basic heuristics or regression outputs. However, these methods have demonstrated limitations in other ranking domains, such as information retrieval, which could similarly detract from the effectiveness of financial strategies.

The authors propose leveraging LTR algorithms to address these limitations, creating a framework that allows for more accurate ranking by capturing pairwise and listwise relationships between assets. Their case paper on cross-sectional momentum strategies illustrates that using modern machine learning ranking algorithms can significantly boost trading performance, specifically noting improvements in Sharpe Ratios by approximately threefold over conventional methods.

The paper draws attention to various LTR methods, including RankNet, LambdaMART, ListNet, and ListMLE, which employ both pairwise and listwise approaches for ranking. These methods are compared against traditional heuristics and machine learning regression models. The LTR models show superior performance, particularly in terms of ranking metrics like Normalised Discounted Cumulative Gain (NDCG) and profitability metrics including Sharpe Ratios.

From a practical standpoint, the integration of LTR techniques into cross-sectional strategies could substantially enhance portfolio performance, enabling more precise asset selection and better risk-adjusted returns. The modular nature of these algorithms also suggests potential applicability across a broader spectrum of financial strategies, accommodating diverse feature inputs for varied types of market conditions.

The theoretical implications of this research are equally significant, offering a new lens through which to consider cross-sectional trading problems. By focusing on the ranking capabilities of models rather than solely on predictive accuracy, this work aligns closely with the core objectives of the strategies it seeks to enhance.

In speculating on future developments, the paper opens pathways for further exploration into more sophisticated LTR architectures and their implications on different asset classes and higher frequency data. The adaptability and demonstrated success of LTR in this financial context could encourage adoption across a wide range of systematic trading strategies, reshaping the landscape of quantitative finance.

This research presents a methodical and well-argued case for the infusion of LTR methods into the design of cross-sectional strategies, establishing a foundation for future explorations and potential real-world applications in quantitative trading environments.