Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3383455.3422557 | ICAIF |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Glasserman | 1 | 496 | 95.86 |
| Kriste Krstovski | 2 | 0 | 0.68 |
| Paul Laliberte | 3 | 0 | 0.34 |
| Harry Mamaysky | 4 | 0 | 0.34 |