  The gargantuan plethora of opinions, facts and tweets on financial business
offers the opportunity to test and analyze the influence of such text sources
on future directions of stocks. It also creates though the necessity to distill
via statistical technology the informative elements of this prodigious and
indeed colossal data source. Using mixed text sources from professional
platforms, blog fora and stock message boards we distill via different lexica
sentiment variables. These are employed for an analysis of stock reactions:
volatility, volume and returns. An increased sentiment, especially for those
with negative prospection, will influence volatility as well as volume. This
influence is contingent on the lexical projection and different across Global
Industry Classification Standard (GICS) sectors. Based on review articles on
100 S&P 500 constituents for the period of October 20, 2009, to October 13,
2014, we project into BL, MPQA, LM lexica and use the distilled sentiment
variables to forecast individual stock indicators in a panel context.
Exploiting different lexical projections to test different stock reaction
indicators we aim at answering the following research questions: (i) Are the
lexica consistent in their analytic ability? (ii) To which degree is there an
asymmetric response given the sentiment scales (positive v.s. negative)? (iii)
Are the news of high attention firms diffusing faster and result in more timely
and efficient stock reaction? (iv) Is there a sector-specific reaction from the
distilled sentiment measures? We find there is significant incremental
information in the distilled news flow and the sentiment effect is
characterized as an asymmetric, attention-specific and sector-specific response
of stock reactions.
