Choosing News Topics to Explain Stock Market Returns

Paul Glasserman, Kriste Krstovski, Paul Laliberte and Harry Mamaysky

Papers from arXiv.org

Abstract: 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.

Date: 2020-10
New Economics Papers: this item is included in nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2010.07289

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