  In this paper, we investigate the impact of the social media data in
predicting the Tehran Stock Exchange (TSE) variables for the first time. We
consider the closing price and daily return of three different stocks for this
investigation. We collected our social media data from Sahamyab.com/stocktwits
for about three months. To extract information from online comments, we propose
a hybrid sentiment analysis approach that combines lexicon-based and
learning-based methods. Since lexicons that are available for the Persian
language are not practical for sentiment analysis in the stock market domain,
we built a particular sentiment lexicon for this domain. After designing and
calculating daily sentiment indices using the sentiment of the comments, we
examine their impact on the baseline models that only use historical market
data and propose new predictor models using multi regression analysis. In
addition to the sentiments, we also examine the comments volume and the users'
reliabilities. We conclude that the predictability of various stocks in TSE is
different depending on their attributes. Moreover, we indicate that for
predicting the closing price only comments volume and for predicting the daily
return both the volume and the sentiment of the comments could be useful. We
demonstrate that Users' Trust coefficients have different behaviors toward the
three stocks.

Statistical Finance;Computation and Language;Machine Learning