HiMarket: A HIERARCHICAL MARKET WORLD MODEL FOR STOCK SELECTION

Stock selection plays a pivotal role in quantitative investment, requiring models to identify stocks likely to outperform under changing market conditions. Recently, representation learning and pretraining methods have shown promise for this task. However, market states are implicit, hierarchical, and time-varying, making it difficult to separate shared dynamics from stock-specific variation. Moreover, predicting shared dynamics alone does not identify which stocks will outperform. To address these challenges, we propose HiMarket, a Hierarchical Market world model for stock selection. Specifically, a hierarchical market-state construction module decomposes encoded stock representations into global, dynamic-group, and residual components. Then, during pretraining, a market-conditioned residual prediction objective uses forecast shared market states to predict future stock residual representations. Finally, a stock-conditioned factor-query readout combines encoded stock representations with their residual components to form stock-specific queries that retrieve context from historical global and group memories. Experiments on three real-world stock-market datasets demonstrate that HiMarket achieves state-of-the-art (SOTA) performance.