  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.
