News Versus Sentiment: Predicting Stock Returns from News Stories

36 Pages Posted: 9 Jun 2016

See all articles by Steven L. Heston

Steven L. Heston

University of Maryland - Department of Finance

Nitish Ranjan Sinha

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: 2016-06

Abstract

This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.

Keywords: News, Text Analysis

JEL Classification: G12, G14

Suggested Citation

Heston, Steven L. and Sinha, Nitish Ranjan, News Versus Sentiment: Predicting Stock Returns from News Stories (2016-06). FEDS Working Paper No. 2016-048. Available at SSRN: https://ssrn.com/abstract=2792559 or http://dx.doi.org/10.17016/FEDS.2016.048

Steven L. Heston (Contact Author)

University of Maryland - Department of Finance ( email )

Robert H. Smith School of Business
Van Munching Hall
College Park, MD 20742
United States

Nitish Ranjan Sinha

Board of Governors of the Federal Reserve System ( email )

20th & C. St., N.W.
Washington, DC 20551
United States

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