论文标题
选择新闻主题来解释股票市场收益
Choosing News Topics to Explain Stock Market Returns
论文作者
论文摘要
我们分析了选择新闻文章中的主题来解释股票收益的方法。我们通过经验和理论结果发现，通过随机EM算法中的Gibbs采样实施的监督潜在的Dirichlet分配（SLDA）通常会过分地恢复到对主题模型的损害。我们通过随机搜索普通LDA模型获得更好的样本外部性能。加强有效主题分配的分支程序通常表现最好。我们测试了有关标准普尔500家公司的90,000篇新闻文章的档案档案。
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.
