Generating Input Distributions for Explaining Portfolio Optimization Pipelines
We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes. Unlike traditional feature-importance methods, this approach directly probes decision pipelines (predictive models coupled with portfolio optimization) by constructing economically meaningful what-if questions. We focus on four such questions: under what macroeconomic conditions a predict-then-optimize pipeline closes or reverses its return gap with a predict-and-optimize pipeline; what conditions lead a pipeline to diversify rather than concentrate its allocation; when a pipeline ...
| Authors | Batuhan Ataş, Nurşen Aydın, E. Mehmet Kıral et al. |
|---|---|
| Primary category | math.OC |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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