aiqbench analysis
This paper introduces RegimeRank, a machine learning model designed to improve sector rotation strategies by incorporating macroeconomic regimes and cross-sector dependencies. The study demonstrates that using interpretable attention mechanisms to condition signals on latent macro states yields superior performance over traditional ranking baselines in predicting risk-adjusted active returns. While the application is financial, the core contribution lies in advancing interpretable machine learning techniques for sequential decision-making under changing market conditions.
Regime-Aware Interpretable Ranking for Sector Rotation
arXiv:2610.26456v1 Announce Type: cross Abstract: Sector rotation is a common decision in benchmark-relative portfolio management, where portfolio managers rank sectors for overweight and underweight positions. We formulate this task as ranking sector actions by expected future risk-adjusted active performance, measured with forward sector-benchmark Sharpe ratios. Such rankings should preserve temporal information, express model outputs through economically familiar signal characteristics, account for changes in signal relevance across macroeconomic environments, and capture dependencies among sectors. We propose RegimeRank, an interpretable sector-ranking model that represents fundamental and technical signals through expert-defined temporal characteristics, conditions them on learned macro factors via cross-attention, and models cross-sector dependencies through self-attention. Expected-gradient attributions identify the contributions of individual signal-transformation pairs, while macroeconomic and cross-sector attention maps provide diagnostics of the learned conditioning and relational structure. We evaluate RegimeRank on weekly S&P 500 data from 2002 to 2024 under combinatorial purged cross-validation, where it outperforms representative ranking and machine-learning baselines across multiple ranking metrics. Ablation studies support the contributions of latent macro states, regime conditioning, and cross-sector modelling, while attribution and attention analyses illustrate the temporal characteristics, macro states, and sector relationships underlying its rankings.
Abstract:Sector rotation is a common decision in benchmark-relative portfolio management, where portfolio managers rank sectors for overweight and underweight positions. We formulate this task as ranking sector actions by expected future risk-adjusted active performance, measured with forward sector-benchmark Sharpe ratios. Such rankings should preserve temporal information, express model outputs through economically familiar signal characteristics, account for changes in signal relevance across macroeconomic environments, and capture dependencies among sectors. We propose RegimeRank, an interpretable sector-ranking model that represents fundamental and technical signals through expert-defined temporal characteristics, conditions them on learned macro factors via cross-attention, and models cross-sector dependencies through self-attention. Expected-gradient attributions identify the contributions of individual signal-transformation pairs, while macroeconomic and cross-sector attention maps provide diagnostics of the learned conditioning and relational structure. We evaluate RegimeRank on weekly S&P 500 data from 2002 to 2024 under combinatorial purged cross-validation, where it outperforms representative ranking and machine-learning baselines across multiple ranking metrics. Ablation studies support the contributions of latent macro states, regime conditioning, and cross-sector modelling, while attribution and attention analyses illustrate the temporal characteristics, macro states, and sector relationships underlying its rankings.
Portfolio Management (q-fin.PM); Machine Learning (cs.LG); Computational Finance (q-fin.CP)
arXiv:2610.26456 [q-fin.PM]
(or arXiv:2610.26456v1 [q-fin.PM] for this version)
https://doi.org/10.48550/arXiv.2610.26456 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3858063.3858823 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 290 words