News versus Sentiment: Predicting Stock Returns from News Stories

38 Pages Posted: 18 Aug 2013 Last revised: 4 Aug 2015

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: August 3, 2015

Abstract

This paper uses a dataset of more than 900,000 news stories to test whether news predicts stock returns. We measure sentiment with the Harvard psychosocial dictionary used by Tetlock, Saar-Tsechansky, and Macskassy (2008), the financial dictionary of Loughran and McDonald (2011), and a proprietary Thomson-Reuters neural network. Simpler processing techniques predict short-term returns that are quickly reversed, while more sophisticated techniques predict larger and more persistent returns. 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 returns quickly, but negative stories have a long-delayed reaction.

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 (August 3, 2015). Robert H. Smith School Research Paper, Available at SSRN: https://ssrn.com/abstract=2311310 or http://dx.doi.org/10.2139/ssrn.2311310

Steven L. Heston

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 (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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