# Choosing News Topics to Explain Stock Market Returns

arXiv: https://arxiv.org/abs/2010.07289 · HTML: https://mlsanity.com/paper/2010.07289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach](https://mlsanity.com/paper/2112.02095/) (2021)
- [Does Overnight News Explain Overnight Returns?](https://mlsanity.com/paper/2507.04481/) (2025)

## Abstract (arXiv)

We analyze methods for selecting topics in news articles to explain stock returns. We find, through empirical and theoretical results, that supervised Latent Dirichlet Allocation (sLDA) implemented through Gibbs sampling in a stochastic EM algorithm will often overfit returns to the detriment of the topic model. We obtain better out-of-sample performance through a random search of plain LDA models. A branching procedure that reinforces effective topic assignments often performs best. We test methods on an archive of over 90,000 news articles about S&P 500 firms.