Analyzing Microblogging Activity and Stock Market Behavior through Artificial Neural Networks
2020 ◽
Vol 2
(2)
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pp. 1
Keyword(s):
This paper attempts to analyze the relationship between social network activity (message sentiment) and stock market (trading volume and risk premium). We used Artificial Neural Networks to analyze 87,511 stock-related microblogging messages related to S&P500 Index posted between October 2009 and October 2014. The results obtained suggest that there is a direct relationship between trading volume and negative sentiment, and between risk premium and negative sentiment. The paper concludes with several directions for future research.
2005 ◽
Vol 32
(10)
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pp. 2499-2512
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Keyword(s):
2011 ◽
pp. 47-79
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2018 ◽
Vol 41
(1)
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pp. 233-253
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Keyword(s):
2004 ◽
Vol 03
(01)
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pp. 145-165
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2012 ◽
Vol 4
(4)
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pp. 398
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