When Coordinated Knowledge Distillation Meets Mixture of Expert Inference: Insights from Portfolio Optimization

To achieve stable profits in uncertain financial environments characterized by pervasive noise signals, unavoidable transaction costs and zero-sum dynamics, it is crucial to construct optimized portfolios based on comprehensive data pro-cessing. However, existing methods often overlook the importance of learning hedge financial knowledge from data and leveraging mixture-of-expert MoE in-ferences to maximize agent profitability. To address this issue, we propose the Coordinated Knowledge Distillation and Inference Framework CKDIF . CKDIF introduces a three-dimensional discrete coordinate system to train deep rein-forcement learning agents with hedge trading behaviors, enabling the effective distillation of underlying micro-financial knowledge directly from noisy financial data. Furthermore, CKDIF constructs a novel ensemble of MoE networks by harnessing these pretrained agents and uses the ensemble to make final portfolio selection across any asset dimension. Notably, with transaction costs set at a real-istic rate of 0.1 , CKDIF outperforms eight representative algorithms on five out of six real-world financial datasets. It achieves an average cumulative wealth and Calmar ratio that are 1.66 and 3.70 times higher, respectively, compared to the buy-and-hold strategy. These results underscore the potency of coordinated knowledge distillation and MoE inference in enhancing agent performance in competitive environments.