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.
@article{arxiv.2010.07289,
title = {Choosing News Topics to Explain Stock Market Returns},
author = {Paul Glasserman and Kriste Krstovski and Paul Laliberte and Harry Mamaysky},
journal= {arXiv preprint arXiv:2010.07289},
year = {2020}
}