Abstract
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.
Highlights
The recent advances in technology have made it possible to access more information almost instantaneously
We analyzed the relationship between market variables and social network variables
The results suggest that social media can reflect what happens in the market
Summary
The recent advances in technology have made it possible to access more information almost instantaneously. In a world where information has a great importance, as in the case of the financial world and especially in financial markets, these advances have reached a great relevance. When we talk about information, it is necessary to talk about social media, and about social networks. The number of social network users is constantly growing. The amount of information shared through social networks is increasing, and financial markets are not indifferent to this phenomenon. When the new language analysis software appeared, it
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