Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM-VQ (PRior-Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates expert prior factors, vector-quantized discrete latent factors learned from cross-sectional structure, and a structure-conditioned Mixture-of-Experts to generate time-varying factor loadings. Vector quantization acts as an information bottleneck that suppresses noise while capturing robust market structure, with discrete codes serving both as latent factors and as routing signals for temporal expert specialization. Experiments on CSI 300 and S&P 500 show consistent improvements in cross-sectional return prediction and portfolio performance over strong baselines while preserving interpretability. Our code is available at https://github.com/finxlab/PRISM-VQ.

翻译：截面股票收益预测因信噪比低和市场状态演变而极具挑战性。经典因子模型具有可解释性但灵活性有限，而深度学习模型虽性能优异却常未充分利用金融先验知识。为弥合这一差距，我们提出PRISM-VQ（融合向量量化的先验知识股票模型），该动态因子框架整合专家先验因子、从截面结构习得的向量量化离散潜在因子，以及结构条件化的混合专家网络，用于生成时变因子载荷。向量量化作为信息瓶颈，在抑制噪声的同时捕捉稳健的市场结构，离散编码既充当潜在因子又作为时序专家专业化的路由信号。在沪深300和标普500指数上的实验表明，该方法在截面收益预测和投资组合绩效方面较强劲基线取得一致提升，同时保持可解释性。代码已开源：https://github.com/finxlab/PRISM-VQ