🤖 AI Summary
Traditional feature importance methods struggle to interpret combinatorial investment decisions where prediction and optimization are tightly coupled, and they fail to elucidate how macroeconomic conditions influence investment outcomes. This work proposes a novel prediction–optimization–explanation framework that, for the first time, integrates gradient-guided counterfactual sample generation with portfolio optimization to construct economically meaningful “what-if” scenarios. By jointly modeling the prediction and optimization processes, the method flexibly generates macroeconomic scenarios tailored to specific investment objectives, effectively identifying critical conditions—such as those that narrow strategy return gaps, trigger diversification, or enable excess returns. Empirical results demonstrate that the proposed framework substantially enhances both the interpretability and robustness of portfolio strategies.
📝 Abstract
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 trained on calm markets overtakes one trained through crises; and what conditions would let a pipeline match a benchmark return. These examples illustrate how our framework uncovers key behavioral differences between various decision pipelines. Beyond these cases, the proposed framework is flexible and can support a wide range of probing questions tailored to specific portfolio objectives. Our findings highlight the value of integrating prediction, optimization, and explanation to produce more robust and transparent portfolio strategies.