Risk-Aware Stock Recommendation via Functional Causal Modeling

Stock recommendation aims to rank stocks by their expected returns based on historical market data. Most existing methods model inter-stock correlations, which may fail to capture the directed information flow induced by asymmetric relations such as supplier-consumer dependencies. They also often prioritize profitability without explicitly accounting for stock-specific risk. To address these limitations, we propose DISCO, a unified risk-aware stock recommendation framework that combines temporal causal discovery with functional causal modeling. DISCO learns directed relations among stocks and models dynamic risk as a stochastic noise variable, enabling it to jointly estimate future returns and stock-level uncertainty. An optional multi-situation inference strategy further accounts for uncertainty in both the learned causal graph and stock risk. Experiments on three real-world stock markets demonstrate that DISCO consistently outperforms strong baselines in profitability and risk-adjusted performance. Code is available at https://anonymous.4open.science/r/DISCO-D088.