Yunqi Liang, Stefan Zohren, Mihai Cucuringu · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.15144 · cited by 0 works (OpenAlex) · open-access PDF
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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