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From Economic Explanation to Performance Improvement in Machine-Learning Portfolios

Yunqi Liang, Stefan Zohren, Mihai Cucuringu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.15144 · cited by 0 works (OpenAlex) · open-access PDF

Korean

전통적인 모델 수준의 특징의 중요성을 경제적으로 의미있는 포트폴리오 결과 및 결정을 확대하기 위해, 우리는 할당 기능 특징 노출을 순차적 포트폴리오 성능 측정의 지역 민감성에 매핑하는 로컬 첫 번째 계층의 할당 확산 프레임 워크를 제안합니다. 우리는 배포 된 할당 값을 포트폴리오 목표에 직접 정의된 Shapley 값에 연결하고 Sharpe 비율의 할당에 대한 통계적 추론을 추출합니다. 그 결과의 할당 지시된 특징 노출 개입은 다른 모델 클래스에 걸쳐 샤프 비율에 대한 통계적으로 중요한 개선을 제공합니다.

Machine translation (Meta M2M100); the English original follows.

Abstract

To extend conventional model-level feature importance to economically meaningful portfolio outcomes and decisions, we propose a local first-order attribution-propagation framework that maps allocation-function feature exposures to local sensitivities of sequential portfolio performance metrics. We relate the propagated attributions to Shapley values defined directly for portfolio objectives and derive statistical inference for Sharpe-ratio attributions. The resulting attribution-guided feature-exposure intervention enables post-training adaptation, translating portfolio-metric explanations into out-of-sample decisions without retraining the underlying allocation model. We further introduce temporal stabilisation and cross-model averaging as complementary regularisation mechanisms. Empirically, the interventions yield statistically significant improvements in net Sharpe ratios across different model classes.

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Topics: Portfolio, Econometrics, Sharpe ratio, Inference, Feature (linguistics), Computer science

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