Financial Analyst Journal, Volume 73, Issue 3, Third Quarter 2017

新闻与情绪测度：从新闻中预测股票收益

作者：Steven L. Heston (University of Maryland),

Nitish Ranjan Sinha (Federal Reserve System)

摘要：本文通过使用超过90万个新闻故事的数据集对新闻能否预测股票收益进行检验。本文利用汤普森路透神经网络衡量市场情绪，发现日度新闻仅能预测未来1到2天的股票回报率，该结果证实了前人研究。然而，周度新闻则能预测一个季度的股票收益。进一步发现，正面的新闻报道会迅速增加股票的收益，而市场对负面消息的反应则存在延迟。对新闻的延迟反应大部分在随后的收益公告中表现。

News vs. Sentiment: Predicting Stock Returns from News Stories

Steven L. Heston (University of Maryland), Nitish Ranjan Sinha (Federal Reserve System)

ABSTRACT

The authors used a dataset of more than 900,000 news stories to test whether news can predict stock returns. They measured sentiment with a proprietary Thomson Reuters neural network and found that daily news predicts stock returns for only one to two days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories receive a long-delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.

原文链接：

http://www.cfapubs.org/doi/pdf/10.2469/faj.v73.n3.3

翻译：秦秀婷