Joint Latent Topic Discovery and Expectation Modeling for Financial
  Markets
Lili Wang Chenghan Huang Chongyang Gao Weicheng Ma Soroush Vosoughi
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).