Joint Latent Topic Discovery and Expectation Modeling for Financial Markets

TL;DR

This paper introduces a novel framework for financial market analysis that jointly models investor expectations and automatically discovers latent stock relationships, leading to improved return predictions and surpassing existing benchmarks.

Contribution

It is the first to simultaneously model investor expectations and mine latent stock relationships without relying on predefined connections.

Findings

Achieves over 10% annual return on China's CSI 300.

Outperforms existing stock prediction benchmarks.

Sets new state-of-the-art in multiyear trading simulations.

Abstract

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).