News versus Sentiment: Predicting Stock Returns from News Stories

Steven Heston () and Nitish R. Sinha
Additional contact information
Steven Heston: https://www.rhsmith.umd.edu/directory/steve-heston
Nitish R. Sinha: https://www.federalreserve.gov/econres/nitish-r-sinha.htm

No 2016-048, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 (search for similar items in EconPapers)
JEL-codes: G12 G14 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2016-06
New Economics Papers: this item is included in nep-cfn, nep-cmp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2016-48

DOI: 10.17016/FEDS.2016.048

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