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Investor sentiment from internet message postings and the predictability of stock returns

Authors
Kim, Soon-HoKim, Dongcheol
Issue Date
11월-2014
Publisher
ELSEVIER
Keywords
Investor sentiment; Return predictability; Internet posting messages; Text classification; Volatility; Trading volume
Citation
JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION, v.107, pp.708 - 729
Indexed
SSCI
SCOPUS
Journal Title
JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION
Volume
107
Start Page
708
End Page
729
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/96865
DOI
10.1016/j.jebo.2014.04.015
ISSN
0167-2681
Abstract
By using an extensive dataset of more than 32 million messages on 91 firms posted on the Yahoo! Finance message board over the period January 2005 to December 2010, we examine whether investor sentiment as expressed in posted messages has predictive power for stock returns, volatility, and trading volume. In intertemporal and cross-sectional regression analyses, we find no evidence that investor sentiment forecasts future stock returns either at the aggregate or at the individual firm level. Rather, we find evidence that investor sentiment is positively affected by prior stock price performance. We also find no significant evidence that investor sentiment from Internet postings has predictive power for volatility and trading volume. A distinctive feature of our study is the use of sentiment information explicitly revealed by retail investors as well as classified by a machine learning classification algorithm and a much longer sample period relative to prior studies. (C) 2014 Elsevier B.V. All rights reserved.
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