Choosing News Topics to Explain Stock Market Returns

We find, through empirical and theoretical drops are created equal, and being able to associate a particular results, that supervised Latent Dirichlet Allocation (sLDA) implemented move to an underlying reason may yield important insights about through Gibbs sampling in a stochastic EM algorithm will what comes next. This paper fits into a broader research agenda that often overfit returns to the detriment of the topic model. We obtain aims to first explain contemporaneous price moves, and only then to better out-of-sample performance through a random search of plain think about forecasting future ones. See [11] and references there LDA models. A branching procedure that reinforces effective topic for background on news and text analysis in financial economics.