This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture. In the first stage, next-period returns...

In the rapidly evolving landscape of manufacturing, the ability to make accurate predictions is crucial for optimizing processes. This study introduces a novel framework that combines predictive uncertainty with explanatory mechanisms to enhance decision-making in complex systems. The approach...

We synthesize the extended linear beta regression with a neural network structure to model and predict the mean and precision of market-based loss rates. We can incorporate non-linearity in mean and precision in a flexible way and resolve the problem of specifying the underlying form in advance....