Abstract
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.
Highlights
Xi,t is a vector of control variables that includes a set of market variables to control for systematic risk such as (1) S&P 500 index return (RM,t) to control for general market returns; (2) the CBOE VIX index on date t to measure the generalized risk aversion (V IXt); and a set of firm idiosyncratic variables such as (3) the lagged log volatility; (4) the lagged return (Ri,t); (5) the lagged detrended log trading volume (Vi,t), where the lagged dependent variable is used to capture the persistence and omitted variables
To analyze the reaction of stocks’ future log volatility, future detrended log trading volume and future returns to social media news, we distill sentiment measures from news using two general-purpose lexica (BL and MPQA) and a lexicon designed for financial applications (LM)
We demonstrate that these sentiment measures carry incremental information for future stock reactions
Summary
We define pessimistic (optimistic) sentiment with specific meaning as the average proportions of negative (positive) words in articles published in specific time windows before the focal trading day, and examine their impacts on stock trading volume, volatility and return We analyze those effects in a panel data context and study their influence on stock reactions. Groß-Klußmann and Hautsch (2011) analyze in a high frequency context market reactions to the intraday stock specific “Reuters NewsScope Sentiment” engine Their findings support the hypothesis of news influence on volatility and trading volume, but are in contrast to our study since they are based on a single news source and confined to a limited number of assets for which high frequency data are available.
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