In the pursuit of accurate and scalable quantitative methods for financial
market analysis, the focus has shifted from individual stock models to those
capturing interrelations between companies and their stocks. However, current
relational stock methods are limited by their reliance on predefined stock
relationships and the exclusive consideration of immediate effects. To address
these limitations, we present a groundbreaking framework for financial market
analysis. This approach, to our knowledge, is the first to jointly model
investor expectations and automatically mine latent stock relationships.
Comprehensive experiments conducted on China's CSI 300, one of the world's
largest markets, demonstrate that our model consistently achieves an annual
return exceeding 10%. This performance surpasses existing benchmarks, setting a
new state-of-the-art standard in stock return prediction and multiyear trading
simulations (i.e., backtesting).

该研究提出了一种金融市场分析的新方法，首次联合建模投资者预期和自动挖掘潜在的股票关系，超过其他指标成为股票收益预测和多年交易模拟的最新标准。