Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction

ArXi:2603.19288v1 Announce Type: cross Portfolio construction traditionally relies on separately estimating expected returns and covariance matrices using historical statistics, often leading to suboptimal allocation under time-varying market conditions. This paper proposes a joint return and risk modeling framework based on deep neural networks that enables end-to-end learning of dynamic expected returns and risk structures from sequential financial data.