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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: Are the lexica consistent in their analytic ability?To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)?Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction?Is there a sector specific reaction from the distilled sentiment measures? We find that 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.
Zhang et al. (Tue,) studied this question.