# Distillation of News Flow into Analysis of Stock Reactions*††footnotetext: *
This research was supported by the Deutsche Forschungsgemeinschaft through the
SFB 649 ‘Economic Risk’, Humbold-Universität zu Berlin. We like to thank the
Research Data Center (RDC) for the data used in this study. We would also like
to thank the International Research Training Group (IRTG) 1792.
This is a post-peer-review, pre-copyedit version of an article published in
the Journal of Business and Economic Statistics. The final authenticated
version is available online at:
http://dx.doi.org/10.1080/07350015.2015.1110525

Junni L. Zhang
Guanghua School of Management and Center for Statistical Science
Peking University
Beijing, 100871, China

Wolfgang K. Härdle
Humboldt-Universität zu Berlin
Unter den Linden 6, Berlin 10099, Germany
and
Sim Kee Boon Institute for Financial Economics
Singapore Management University
Administration Building, 81 Victoria Street, Singapore 188065

Cathy Y. Chen
Chung Hua University
707, Sec.2, WuFu Rd., Hsinchu, Taiwan 30012
and
Humboldt-Universität zu Berlin
Unter den Linden 6, Berlin 10099, Germany

Elisabeth Bommes
Humboldt-Universität zu Berlin
Unter den Linden 6, Berlin 10099, Germany

###### 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:
(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.

Keywords: Investor Sentiment, Attention Analysis, Sector Analysis, Volatility
Simulation, Trading Volume, Returns
JEL Classifications: C81, G14, G17

##  1 Introduction

News are driving financial markets. News are nowadays massively available on a
variety of modern digital platforms with a wide spectrum of granularity
scales. It is exactly this combination of granularity and massiveness that
makes it virtually impossible to process all the news relevant to certain
financial assets. How to distinguish between “noise” and “signal” is also here
the relevant question. With a few exceptions the majority of empirical studies
on news impact work has therefore been concentrated on specific identifiable
events like scheduled macroeconomic announcements, political decisions, or
asset specific news. Recent studies have looked at continuous news flow from
an automated sentiment machine and it has been discovered to be relevant to
high frequency return, volatility and trading volume. Both approaches have
limitations since they concentrate on identifiable indicators (events) or use
specific automated linguistic algorithms.

This paper uses text data of different granularity from blog fora, news
platforms and stock message boards. Using several lexical projections, 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. These reactions
might be interesting since large institutions, more sophisticated investors,
usually express their views on stock prospective or prediction through
published analyst forecasts. However, analysts’ recommendations may be
contaminated by their career concerns and compensation scheme; they may also
be in alliance with other financial institutions such as investment banks,
brokerage houses or target companies (Hong and Kubik,, 2003; Liu,, 2012). Due
to the possible conflicts of interest from analysts and their powerful
influence on naive small investors, the opinions from individual small
investors may be trustworthy since their personal opinions hardly create any
manipulation that governs stock reactions. The advent of social media such as
Seeking Alpha enables small investors to share and express their opinions
frequently, real time and responsively.

We show that small investors’ opinions contribute to stock markets and create
a “news-driven” stock reaction. The conversation in the internet or social
media is valuable since the introduction of conversation among a subset of
market participants may have large effects on the stock price equilibrium (Cao
et al.,, 2001). Other literature such as Antweiler and Frank, (2004), Das and
Chen, (2007), Chen et al., (2014) demonstrate the value of individual opinions
on financial market. They show that small investor opinions predict future
stock returns and earnings surprises even after controlling the financial
analyst recommendation.

The projections (of a text into sentiment variables) we employ are based on
three sentiment lexica: the BL, LM and MPQA lexica. They are used to construct
sentiment variables that feed into the stock reaction analysis. Exploiting
different lexical projections, and using different stock reaction indicators
we aim at answering the following research questions:
(i) Are the lexica consistent in their analytic ability to produce stock
reaction indicators, including volatility, detrended log trading volume and
return?
(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?

Question (i) addresses the variation of news content across different
granularity and lexica. Whereas earlier literature focuses on numerisized
input indices like ReutersNewsContent or Google Search Volume Index, we would
like to investigate the usefulness of automated news inputs for e.g.
statistical arbitrage algorithms. Question (ii) examines the effect of
different sentiment scales on stock reactions like volatility, trading volume
and returns. Three lexica are employed that are producing different numerical
values and thus raise the concern of how much structure is captured in the
resulting sentiment measure. An answer to this question will give us insight
into whether the well known asymmetric response (bad vs. good news) is
appropriately reflected in the lexical projections. Question (iii) and (iv)
finally analyze whether stylized facts play a role in our study. This is
answered via a panel data scheme using GICS sector indicators and attention
ratios.

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.

Antweiler and Frank, (2004) analyze text contributions from stock message
boards and find that the amount and bullishness of messages have predictive
value for trading volume and volatility. On message boards, the self-disclosed
sentiment to hold a stock position is not bias free, as indicated in Zhang and
Swanson, (2010). Tetlock, (2007) concludes that negative sentiment in a Wall
Street Journal column has explanatory power for downward movement of the Dow
Jones. Bollen et al., (2011) classify messages from the micro-blogging
platform Twitter in six different mood states and find that public mood helps
to predict changes in daily Dow Jones values. Zhang et al., (2012) extends
this by filtering the Twitter messages (tweets) for keywords indicating a
financial context and they consider different markets such as commodities and
currencies. Si et al., (2013) use a refined filtering process to obtain stock
specific tweets and conclude that topic based Twitter sentiment improves day-
to-day stock forecast accuracy. Sprenger et al., (2014) also use tweets on
stock level and conclude that the number of retweets and followers may be used
to assess the quality of investment advice. Chen et al., (2014) use articles
and corresponding comments on Seeking Alpha, a social media platform for
investment research, and show predictive value of negative sentiment for stock
returns and earnings surprises. According to Wang et al., (2014), the
correlation of Seeking Alpha sentiment and returns is higher than between
returns and sentiment in Stocktwits, messages from a micro-blogging platform
specialized in finance.

Using either individual lexical projections or a sentiment index comprising
the common component of the three lexical projections, we find that the text
sentiment shows an incremental influence on the stocks collected from S&P 500
constituents. An asymmetric response of the stock reaction indicators to the
negative and positive sentiments is confirmed and supports the leverage
effect, that is, the stocks react to negative sentiment more. The reaction to
the distilled sentiment measures is attention-specific and sector-specific as
well. Due to the advent of social media, the opinions of small traders that
have been ignored from past till now, do shed some light on stock market
activity. The rest of the paper is organized as follows. Section 2 describes
the data gathering process, summarizes definitions of variables and introduces
the different sentiment lexica. In Section 3, we present the regression and
simulation results using the entire sample and samples grouped by attention
ratio and sectors. The conclusion follows in Section 4.

##  2 Data

###  2.1 Text Sources and Stock Data

While there are many possible sources of financial articles on the web, there
are also legal and practical obstacles to clear before obtaining the data. The
text source Seeking Alpha, as used in Chen et al., (2014), prohibits any
application of automatic programs to download parts of the website (web
scraper) in their Terms of Use (TOS). While the usage of web scrapers for non-
commerical academic research is principally legal, these TOS are still binding
as stated in Truyens and Eecke, (2014). For messages on Yahoo! Finance,
another popular source of financial text data used in Antweiler and Frank,
(2004); Zhang and Swanson, (2010), the TOS are not a hindrance but only
limited message history is provided. As of December 2014, only the last 10,000
messages are shown in each stock specific message board and this roughly
corresponds to a two-month-period for stocks that people talk frequently about
like Apple. In opposition to these two examples, NASDAQ offers a platform for
financial articles by selected contributors including social media websites
such as Seeking Alpha and Motley Fool, investment research firms such as
Zacks. Neither do the TOS prohibit web scraping nor is the history of shown
articles limited. We have collected 116,691 articles and corresponding stock
symbols, spanning roughly five years from October 20, 2009 to October 13,
2014. The data is downloaded by using a self-written web scraper to automate
the downloading process.

Figure 1: Flowchart of data gathering process

The process of gathering and processing the article data and producing the
sentiment scores can be seen in Figure 1. Firstly, the URLs of all articles on
NASDAQ are gathered and every webpage containing an article is downloaded.
Each URL can be used in the next steps as unique identifier of individual
articles to ensure that one article is not used twice due to real-time updates
of the NASDAQ webpage. In the pre-processing step, the page navigation and
design elements of NASDAQ are removed. The specifics of each article, namely
contributor, publication date, mentioned stock symbols, title and article
text, are identified and read out. In case of the article text, the results
are stored in individual text files. This database is available for research
purposes at RDC, CRC 649, Humboldt-Universität zu Berlin.

Furthermore, we collected stock specific financial data. Daily prices and
trading volume, defined as number of shares traded, of all stock symbols that
are S&P 500 constituents are collected from Datastream while Compustat is used
to gather the GICS sector for these stocks.

We consider three stock reaction indicators: log volatility, detrended log
trading volume and return. For stock symbol $i$ and trading day $t$, we first
compute the Garman and Klass, (1980) range-based measure of volatility defined
as:

$\displaystyle\sigma_{i,t}^{2}$
$\displaystyle=0.511(u-d)^{2}-0.019\left\\{c(u+d)-2ud\right\\}-0.383c^{2}$ (1)
$\displaystyle\mbox{with}\ u$
$\displaystyle=\log(P_{i,t}^{H})-\log(P_{i,t}^{O}),$ $\displaystyle d$
$\displaystyle=\log(P_{i,t}^{L})-\log(P_{i,t}^{O}),$ $\displaystyle\ c$
$\displaystyle=\log(P_{i,t}^{C})-\log(P_{i,t}^{O}),$

with $P_{i,t}^{H}$, $P_{i,t}^{L}$, $P_{i,t}^{O}$, $P_{i,t}^{C}$ as the daily
highest, lowest, opening and closing stock prices, respectively. Chen et al.,
(2006) and Shu and Zhang, (2006) show that the Garman and Klass range-based
measure of volatility essentially provides equivalent results to high-
frequency realized volatility. For example, Shu and Zhang, (2006) find that an
empirical test with S&P 500 index return data shows that the range-based
variances are quite close to the high-frequency realized variance computed
using the sum of 15-minute squared returns. Andersen and Bollerslev, (1997)
show that the high-frequency realized volatility is very sensitive to the
selected interval. In addition, it is also affected by the bid/ask spread. The
range-based measure of volatility, on the other hand, avoids the problems
caused by microstructure effects. However, Alizadeh et al., (2002) argue that
range based measures such as the Garman-Klass estimator do not make use of the
log-normality of volatility. As shown by Andersen et al., (2001), log realized
volatility is less skewed and less leptokurtic in comparison to raw realized
volatility. Therefore, we use $\log\sigma_{i,t}$ instead, which also avoids
regressing on a strictly positive variable in the subsequent analysis.

Following Girard and Biswas, (2007), we estimate the detrended log trading
volume for each stock by using a quadratic time trend equation:

$V^{*}_{i,t}=\alpha+\beta_{1}(t-t_{0})+\beta_{2}(t-t_{0})^{2}+V_{i,t},$ (2)

where $t_{0}$ is the starting point of the time window in consideration,
$V^{*}_{i,t}$ is the raw daily log trading volume and the residual $V_{i,t}$
is the detrended log trading volume. In order to avoid imposing a look-ahead
bias, for each trading day $t$, we use a rolling window of past 120
observations, $V^{*}_{i,t-120},\ldots,V^{*}_{i,t-1}$ with $t_{0}=t-120$, to
estimate the coefficients and get a one-step ahead out-of-sample forecast
$\hat{V}^{*}_{i,t}$, and then calculate
$V_{i,t}=V^{*}_{i,t}-\hat{V}^{*}_{i,t}$. Furthermore, we calculate the returns
as $R_{i,t}=\log P_{i,t}^{C}-\log P_{i,t-1}^{C}$.

We focus on 100 stock symbols that are S&P 500 constituents on all 1,255
trading days between October 20, 2009 and October 14, 2014, that belong to one
of nine major GICS sectors for stock symbols that are S&P 500 constituents on
at least one trading day during this period, and that have the most trading
days with articles. The distribution of GICS sectors among these 100 symbols
are given in Table 1. Out of the 116,691 articles collected, there are 43,459
articles associated with these 100 stock symbols; the number of articles for
these stocks range from 340 to 5,435, and the number of trading days with
articles ranges from 271 to 1,039. Most of the articles are not about one
single symbol but contain references to several stocks.

GICS Sector | No. Stocks
---|---
Consumer Discretionary | $21$
Consumer Staples | $9$
Energy | $6$
Financials | $12$
Health Care | $15$
Industrials | $10$
Information Technology | $21$
Materials | $4$
Telecommunication Services | $2$
Table 1: Distribution of GICS sectors among the 100 stock symbols

###  2.2 Sentiment Lexica and Sentiment Variables

To distill sentiment variables from each article, we use and compare three
sentiment lexica. The first lexicon (BL) is a list of 6,789 sentiment words
(2,006 positive and 4,783 negative) compiled over many years starting from Hu
and Liu, (2004) and maintained by Bing Liu at University of Chicago, Illinois.
We filter each article with this lexicon and calculate the proportions of
positive and negative words. The second lexicon (LM) is based on Loughran and
McDonald, (2011) which is specifically designed for financial applications,
and contains 354 positive words, 2,329 negative words, 297 uncertainty words,
886 litigious words, 19 strong modal words and 26 weak modal words. To be
consistent with the usage of the other lexica, we only consider the list of
positive and negative words and calculate the proportions of positive and
negative words for each article.

The third lexicon is the MPQA (Multi-Perspective Question Answering)
Subjectivity Lexicon by Wilson et al., (2005) which we later refer to as the
MPQA lexicon. This lexicon contains 8,222 entries. In order to show the rather
tedious distillation process let us look at six example entries:

⬇

type=weaksubj len=1 word1=abandoned pos1=adj stemmed1=n priorpolarity=negative

type=weaksubj len=1 word1=abandonment pos1=noun stemmed1=n
priorpolarity=negative

type=weaksubj len=1 word1=abandon pos1=verb stemmed1=y priorpolarity=negative

type=strongsubj len=1 word1=abase pos1=verb stemmed1=y priorpolarity=negative

type=strongsubj len=1 word1=abasement pos1=anypos stemmed1=y
priorpolarity=negative

type=strongsubj len=1 word1=abash pos1=verb stemmed1=y priorpolarity=negative

Here `type` refers to whether the word is classified as strongly subjective,
indicating that the word is subjective in most contexts, or weakly subjective,
indicating that the word only has certain subjective usages; `len` denotes the
length of the word; `word1` is the spelling of the word; `pos1` is part-of-
speech tag of the word, which could take values adj (adjective), noun, verb,
adverb, or anypos (any part-of-speech tag); `stemmed1` is an indicator for
whether this word is stemmed, where stemming refers to the process of reducing
inflected (or sometimes derived) words to their word stem, base or root form;
and `priorpolarity` refers to polarity of the word, which could take values
negative, positive, neutral, or both (both negative and positive). The MPQA
lexicon contains 4913 entries with negative polarity, 2718 entries with
positive polarity, 570 entries with neutral polarity, and 21 entries with both
polarity. To be consistent with the usage of the other two lexica, we only
consider positive and negative polarity.

We first use the NLTK package in Python to tokenize sentences and (un-stemmed)
words in each article, and derive the part-of-speech tagging for each word. We
filter each tokenized article with the list of entries with `stemmed1=n` in
the MPQA lexicon to count the number of positive and negative word. We then
use the Porter Stemmer in the NLTK package to stem each word and filter each
article with the list of entries with `stemmed1=y` in the MPQA lexicon. If a
word has been assigned polarity in the first filtering step, it will no longer
be counted in the second filtering step. For each article, we can thus count
the numbers of negative and positive words, and divide them by the length of
the article to get the proportions of negative and positive words.

Regardless of which lexicon is used, we use a variation of the approach in Hu
and Liu, (2004) to account for sentiment negation. If the word distance
between a negation word (“not”, “never”, “no”, “neither”, “nor”, “none”,
“n’t”) and the sentiment word is no larger than 5, the positive or negative
polarity of the word is changed to be the opposite of its original polarity.

Among the words that appear at least three times in our list of articles,
there are 470 positive and 918 negative words that are unique to the BL
lexicon, 267 positive and 916 negative words that are unique to the LM
lexicon, and 512 positive and 181 negative words that are unique to the MPQA
lexicon. The LM lexicon contains less unique positive words than the other two
lexica, and the MPQA lexicon contains less unique negative words than the
other two lexica. Table 2 presents the lists of ten most frequent positive
words and ten most frequent negative words that are unique to these three
lexica. Since the BL and MPQA lexica are designed for general purpose and the
LM lexicon is designed specifically for financial applications, the unique
words under the BL and MPQA lexica indeed look more general.

Words in the general-purpose lexica may be misclassified for financial
applications; for example, the word “proprietary” in the negative list of the
BL lexicon may refer to things like “a secure proprietary operating system
that no other competitor can breach” and hence have a positive tone in
financial applications, and the word “division” in the negative list of the
MPQA lexicon may only refer to divisions of companies. However, financial
analysis using textual information is unavoidably noisy, and words in the LM
lexicon can also be misclassified; for example, the word “closing” in the
negative list of the LM lexicon may actually refer to a positive event of
closing a profitable deal. Also, the LM lexicon does not take into account
financial words such as “debt” and “risks” in the BL lexicon.

BL | LM | MPQA
---|---|---
Positive (470) | Negative (918) | Positive (267) | Negative (916) | Positive (512) | Negative (181)
Available | Debt | Opportunities | Declined | Just | Low
(5,836) | (12,540) | (4,720) | (9,809) | (17,769) | (12,739)
Led | Fell | Strength | Dropped | Help | Division
(5,774) | (9,274) | (4,393) | (4,894) | (17,334) | (5,594)
Lead | Fool | Profitability | Late | Profit | Least
(4,711) | (5,473) | (4,174) | (4,565) | (15,253) | (5,568)
Recovery | Issues | Highest | Claims | Even | Stake
(4,357) | (3,945) | (3,409) | (3,785) | (13,780) | (4,445)
Work | Risks | Greater | Closing | Deal | Slightly
(3,808) | (2,850) | (3,321) | (3,604) | (13,032) | (3,628)
Helped | Issue | Surpassed | Closed | Interest | Close
(3,631) | (2,821) | (2,464) | (3,378) | (12,237) | (3,105)
Enough | Falling | Enable | Challenges | Above | Trial
(3,380) | (2,768) | (2,199) | (2,574) | (12,203) | (2,544)
Pros | Aggressive | Strengthen | Force | Accord | Decrease
(2,841) | (1,796) | (2,157) | (2,157) | (11,760) | (2,205)
Integrated | Hedge | Alliance | Unemployment | Natural | Disease
(2,652) | (1,640) | (1,842) | (2,062) | (10,135) | (2,001)
Savings | Proprietary | Boosted | Question | Potential | Little
(2,517) | (1,560) | (1,831) | (1,891) | (9,905) | (1,775)

Table 2: Lists of ten most frequent positive words and ten most frequent
negative words that are unique to the BL, MPQA or LM lexica, along with their
frequencies given in parentheses.

We next investigate the pairwise relationship among the above three lexica.
Among the words that appear at least three times in our list of articles,
there are 131 positive and 322 negative words that are shared only by the BL
and LM lexica, 971 positive and 1,164 negative words that are shared only by
the BL and MPQA lexica, and 32 positive and 30 negative words that are shared
only by the LM and MPQA lexica. It is not surprising that the two general-
purpose lexica, BL and MPQA, share the most positive and negative words. Out
of the two general-purpose lexica, BL lexicon shares more positive and
negative words with the special-purpose LM lexicon. Table 3 presents the lists
of ten most frequent positive words and ten most frequent negative words that
are shared only by two of these three lexica. Words shared by the two general-
purpose lexica (BL and MPQA) may be misclassified for financial applications;
for example, the word “gross” shared by the negative lists of these two lexica
may refer to “the annual gross domestic product” and have a neutral tone.
However, words shared by the LM lexicon and one of the general-purpose lexica
may also be misclassified; for example, the word “critical” shared by the
negative lists of the BL and LM lexica may appear in sentences such as “mobile
devices are becoming critical tools in the worlds of advertising and market
research” and have a positive tone.

BL and LM | BL and MPQA | LM and MPQA
---|---|---
Positive (131) | Negative (322) | Positive (971) | Negative (1164) | Positive (32) | Negative (30)
Gains | Losses | Free | Gross | Despite | Against
(7,604) | (5,938) | (133,395) | (8,228) | (7,413) | (8,877)
Gained | Missed | Well | Risk | Able | Cut
(7,493) | (3,165) | (3,0270) | (7,471) | (5,246) | (3,401)
Improved | Declining | Like | Limited | Opportunity | Challenge
(7,407) | (3,053) | (24,617) | (5,884) | (4,398) | (1,042)
Improve | Failed | Top | Motley | Profitable | Serious
(5,726) | (2,421) | (14,899) | (5,165) | (3,580) | (1,022)
Restructuring | Concerned | Guidance | Crude | Efficiency | Contrary
(3,210) | (1,991) | (11,715) | (5,109) | (2,615) | (401)
Gaining | Declines | Significant | Cloud | Popularity | Severely
(3,150) | (1,654) | (10,576) | (4,906) | (1,588) | (348)
Enhance | Suffered | Worth | Fall | Exclusive | Despite
(2,753) | (1,435) | (10,503) | (4,732) | (1,225) | (342)
Outperform | Weaker | Gold | Mar | Tremendous | Argument
(2,518) | (1,288) | (9,303) | (3,190) | (611) | (324)
Stronger | Critical | Support | Hard | Dream | Seriously
(1,657) | (1,131) | (9,120) | (2,957) | (581) | (240)
Win | Drag | Recommendation | Cancer | Satisfaction | Staggering
(1,491) | (1,095) | (8,993) | (2,521) | (410) | (209)

Table 3: Lists of ten most frequent positive words and ten most frequent
negative words that are shared only by BL and LM lexica, only by BL and MPQA
lexica, or only by LM and MPQA lexica, along with their frequencies given in
parentheses.

The above discussion shows that projections using the three lexica are all
noisy, therefore it is worthwhile to compare results from these projections.
For each stock symbol $i$ and each trading day $t$, we derive the sentiment
variables listed in Table 4 based on articles associated with symbol $i$ and
published on or after trading day $t$ and before trading day $t+1$.

Sentiment Variable | Description
---|---
$I_{i,t}$ | Indicator for whether there is an article.
$Pos_{i,t}$ (BL) | The average proportion of positive words using the BL lexicon.
$Neg_{i,t}$ (BL) | The average proportion of negative words using the BL lexicon.
$Pos_{i,t}$ (LM) | The average proportion of positive words using the LM lexicon.
$Neg_{i,t}$ (LM) | The average proportion of negative words using the LM lexicon.
$Pos_{i,t}$ (MPQA) | The average proportion of positive words using the MPQA lexicon.
$Neg_{i,t}$ (MPQA) | The average proportion of negative words using the MPQA lexicon.

Table 4: Sentiment variables for articles published on or after trading day
$t$ and before trading day $t+1$.

##  3 Empirical Results

###  3.1 Entire Sample Results

####  3.1.1 Descriptive Statistics and Comparison of the Lexical Projections

Table 5 presents summary statistics of the sentiment variables derived using
the BL, LM and MPQA lexical projections for 43,569 symbol-day combinations
with $I_{i,t}=1$, where $I_{i,t}$ is defined in Table 4 and indicates whether
there is an article associated with symbol $i$ and published on or after
trading day $t$ and before trading day $t+1$. This number is slightly
different from the number of articles associated with the 100 selected symbols
(43,459), since an article can be associated with multiple symbols. The
positive proportion is the largest under the MPQA projection, and the smallest
under the LM projection. The negative proportions under the three projections
are similar. Polarity in Table 5 measures the relative dominance between
positive sentiment and negative sentiment. For example, the situation,
$Pos_{i,t}$ (BL)$>Neg_{i,t}$ (BL), accounts for 88.04% of the 43,569
observations. Note that under each projection, there are a small percentage of
the observations for which $Pos_{i,t}=Neg_{i,t}$. Under both the BL and MPQA
projections, positive sentiment is more dominant and widespread than negative
sentiment. The LM projection, however, results in a relative balance between
positive and negative sentiment.

Variable | $\widehat{\mu}$ | $\widehat{\sigma}$ | Max | Q1 | Q2 | Q3 | Polarity
---|---|---|---|---|---|---|---
$Pos_{i,t}$ (BL) | $0.033$ | $0.012$ | $0.134$ | $0.025$ | $0.032$ | $0.040$ |  $88.04$%
$Neg_{i,t}$ (BL) | $0.015$ | $0.010$ | $0.091$ | $0.008$ | $0.014$ | $0.020$ |  $10.51$%
$Pos_{i,t}$ (LM) | $0.014$ | $0.007$ | $0.074$ | $0.009$ | $0.013$ | $0.018$ |  $55.70$%
$Neg_{i,t}$ (LM) | $0.012$ | $0.009$ | $0.085$ | $0.006$ | $0.011$ | $0.016$ |  $40.17$%
$Pos_{i,t}$ (MPQA) | $0.038$ | $0.012$ | $0.134$ | $0.031$ | $0.038$ | $0.045$ |  $96.26$%
$Neg_{i,t}$ (MPQA) | $0.013$ | $0.008$ | $0.133$ | $0.007$ | $0.012$ | $0.017$ |  $2.87$%
Note: Sample mean, sample standard deviation, maximum value, 1st, 2nd and 3rd
quartiles, and polarity. These descriptive statistics are conditional on
$I_{i,t}=1$.
Table 5: Summary Statistics for Text Sentiment Variables

To check whether the sentiment polarity actually reflects the sentiment of the
articles, we actually carefully checked and read the contents of 100 randomly
selected articles and manually classified their polarity (positive, negative
and neutral), and also use the lexical projections to automatically classify
these articles as follows. If the proportion of positive words for an article
is larger than (or small than, or equal to) the proportion of negative words
for the same article, then this article is automatically classified as
positive (or negative, or neutral). Table 6 reports the results. It appears
that the BL and MPQA projections put too much weight on positive sentiment,
and are not powerful in detecting negative sentiment. In contrast, the LM
sentiment is powerful in detecting negative sentiment, but is not so good in
detecting positive sentiment.

Manual | BL Label | LM Label | MPQA Label |
---|---|---|---|---
Label | Pos | Neg | Neu | Pos | Neg | Neu | Pos | Neg | Neu | Total
Pos | 56 | 4 | 1 | 41 | 12 | 8 | 61 | 0 | 0 | 61
Neg | 9 | 2 | 1 | 0 | 9 | 3 | 9 | 2 | 1 | 12
Neu | 22 | 5 | 0 | 10 | 15 | 2 | 26 | 0 | 1 | 27
Total | 87 | 11 | 2 | 51 | 36 | 13 | 96 | 2 | 2 | 100
Table 6: Sentiment Classification Results for 100 Randomly Selected Articles

Figure 2 and 3 respectively show the monthly correlation between positive and
negative proportions under two of the three projections. In general, the
negative proportions are more correlated than positive proportions. Also, the
correlation between the BL and LM projections and that between the BL and MPQA
projections are larger than the correlation between the LM and MPQA
projections, which is consistent with the discussion about the list of words
shared by two of the three projections (see Table 3).

Figure 2: Monthly Correlation between Positive Sentiment: BL and LM (solid),
BL and MPQA (dashed), LM and MPQA (dotted) Figure 3: Monthly Correlation
between Negative Sentiment: BL and LM (solid), BL and MPQA (dashed), LM and
MPQA (dotted)

####  3.1.2 Main Results

Recall from Section 2.1 that we focus on three stock reaction indicators: log
volatility $\log\sigma_{i,t}$, where $\sigma_{i,t}^{2}$ is defined in (1),
detrended log trading volume $V_{i,t}$ as in (2) and returns $R_{i,t}$. We
first consider analyzing these three indicators with one trading day into the
future, and use the following (separate) panel regressions.

$\displaystyle\log\sigma_{i,t+1}$ $\displaystyle=$
$\displaystyle\alpha+\beta_{1}I_{i,t}+\beta_{2}Pos_{i,t}+\beta_{3}Neg_{i,t}+\beta_{4}^{\top}X_{i,t}+\gamma_{i}+\varepsilon_{i,t},$
(3) $\displaystyle V_{i,t+1}$ $\displaystyle=$
$\displaystyle\alpha+\beta_{1}I_{i,t}+\beta_{2}Pos_{i,t}+\beta_{3}Neg_{i,t}+\beta_{4}^{\top}X_{i,t}+\gamma_{i}+\varepsilon_{i,t},$
(4) $\displaystyle R_{i,t+1}$ $\displaystyle=$
$\displaystyle\alpha+\beta_{1}I_{i,t}+\beta_{2}Pos_{i,t}+\beta_{3}Neg_{i,t}+\beta_{4}^{\top}X_{i,t}+\gamma_{i}+\varepsilon_{i,t}.$
(5)

where $\gamma_{i}$ is the fixed effect for stock symbol $i$ satisfying
$\sum_{i}\gamma_{i}=0$. $X_{i,t}$ 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 ($R_{M,t}$) to control for general market returns; (2)
the CBOE VIX index on date $t$ to measure the generalized risk aversion
($VIX_{t}$); and a set of firm idiosyncratic variables such as (3) the lagged
log volatility ($\log\sigma_{i,t}$); (4) the lagged return ($R_{i,t}$); (5)
the lagged detrended log trading volume ($V_{i,t}$), where the lagged
dependent variable is used to capture the persistence and omitted variables.
These three indicators essentially have a triple dynamic correlation, and they
have been modeled as a trivariate vector autoregressive (VAR) model, see Chen
et al., (2001) and Chen et al., (2002). Our indicators in Eqs.(4) to (5) not
only have themselves dynamic relationship with their lagged values, but also
are impacted by the other lagged indicators. We incorporate clustered standard
errors by Arellano, (1987) as they allow for both time and cross-sectional
dependence in the residuals. Petersen, (2009) concludes that standard errors
clustered on both dimensions are unbiased and achieve correctly sized
confidence intervals while ordinary least squares standard errors might be
biased in a panel data setting.

Variable | BL | LM | MPQA | PCA
---|---|---|---|---
| Panel A: Future Log Volatility $\log\sigma_{i,t+1}$
$I_{i,t}$ | $-0.005$ | $(0.009)$ |  $-0.019$∗∗∗ | $(0.007)$ | $-0.004$ | $(0.010)$ | $-0.014$ | $(0.010)$
$Pos_{i,t}$ |  $-0.396$∗ | $(0.228)$ | $0.156$ | $(0.378)$ |  $-0.517$∗∗ | $(0.217)$ | $-0.210$ | $(0.201)$
$Neg_{i,t}$ |  $0.905$∗∗∗ | $(0.257)$ |  $0.942$∗∗∗ | $(0.271)$ |  $1.464$∗∗∗ | $(0.325)$ |  $1.041$∗∗∗ | $(0.247)$
$R_{M,t}$ |  $-1.507$∗∗∗ | $(0.217)$ |  $-1.501$∗∗∗ | $(0.216)$ |  $-1.500$∗∗∗ | $(0.216)$ |  $-1.505$∗∗∗ | $(0.216)$
$VIX_{t}$ |  $2.329$∗∗∗ | $(0.085)$ |  $2.335$∗∗∗ | $(0.085)$ |  $2.331$∗∗∗ | $(0.086)$ |  $2.330$∗∗∗ | $(0.085)$
$\log\sigma_{i,t}$ |  $0.242$∗∗∗ | $(0.010)$ |  $0.242$∗∗∗ | $(0.010)$ |  $0.242$∗∗∗ | $(0.010)$ |  $0.242$∗∗∗ | $(0.010)$
$R_{i,t}$ |  $1.652$∗∗∗ | $(0.196)$ |  $1.653$∗∗∗ | $(0.196)$ |  $1.651$∗∗∗ | $(0.196)$ |  $1.653$∗∗∗ | $(0.196)$
$V_{i,t}$ |  $0.065$∗∗∗ | $(0.006)$ |  $0.065$∗∗∗ | $(0.006)$ |  $0.065$∗∗∗ | $(0.006)$ |  $0.065$∗∗∗ | $(0.006)$
| Panel B: Future Detrended Log Trading Volume $V_{i,t+1}$
$I_{i,t}$ |  $0.040$∗∗∗ | $(0.008)$ |  $0.027$∗∗∗ | $(0.005)$ |  $0.046$∗∗∗ | $(0.009)$ |  $0.035$∗∗∗ | $(0.008)$
$Pos_{i,t}$ |  $-0.496$∗∗∗ | $(0.188)$ | $0.051$ | $(0.275)$ |  $-0.483$∗∗ | $(0.194)$ |  $-0.274$∗ | $(0.166)$
$Neg_{i,t}$ |  $0.726$∗∗∗ | $(0.257)$ |  $0.563$∗∗ | $(0.251)$ |  $0.548$∗ | $(0.290)$ |  $0.590$∗∗ | $(0.232)$
$R_{M,t}$ |  $-3.625$∗∗∗ | $(0.181)$ |  $-3.620$∗∗∗ | $(0.181)$ |  $-3.617$∗∗∗ | $(0.181)$ |  $-3.622$∗∗∗ | $(0.181)$
$VIX_{t}$ |  $-0.492$∗∗∗ | $(0.027)$ |  $-0.487$∗∗∗ | $(0.027)$ |  $-0.487$∗∗∗ | $(0.027)$ |  $-0.489$∗∗∗ | $(0.027)$
$\log\sigma_{i,t}$ |  $0.132$∗∗∗ | $(0.004)$ |  $0.132$∗∗∗ | $(0.004)$ |  $0.132$∗∗∗ | $(0.004)$ |  $0.132$∗∗∗ | $(0.004)$
$R_{i,t}$ |  $1.164$∗∗∗ | $(0.126)$ |  $1.166$∗∗∗ | $(0.126)$ |  $1.164$∗∗∗ | $(0.126)$ |  $1.166$∗∗∗ | $(0.126)$
| Panel C: Future Returns $R_{i,t+1}$
$I_{i,t}$ | $0.000$ | $(0.000)$ | $0.000$ | $(0.000)$ | $0.000$ | $(0.000)$ | $-0.000$ | $(0.000)$
$Pos_{i,t}$ |  $0.019$∗∗∗ | $(0.007)$ |  $0.030$∗∗∗ | $(0.011)$ |  $0.014$∗ | $(0.008)$ |  $0.018$∗∗∗ | $(0.006)$
$Neg_{i,t}$ | $-0.004$ | $(0.008)$ | $-0.000$ | $(0.010)$ | $-0.009$ | $(0.010)$ | $-0.003$ | $(0.008)$
$R_{M,t}$ |  $-0.050$∗∗∗ | $(0.006)$ |  $-0.050$∗∗∗ | $(0.006)$ |  $-0.050$∗∗∗ | $(0.006)$ |  $-0.050$∗∗∗ | $(0.006)$
$VIX_{t}$ |  $0.011$∗∗∗ | $(0.001)$ |  $0.011$∗∗∗ | $(0.001)$ |  $0.011$∗∗∗ | $(0.001)$ |  $0.011$∗∗∗ | $(0.001)$
$\log\sigma_{i,t}$ |  $-0.001$∗∗∗ | $(0.000)$ |  $-0.001$∗∗∗ | $(0.000)$ |  $-0.001$∗∗∗ | $(0.000)$ |  $-0.001$∗∗∗ | $(0.000)$
$R_{i,t}$ |  $-0.018$∗∗∗ | $(0.007)$ |  $-0.018$∗∗∗ | $(0.007)$ |  $-0.018$∗∗∗ | $(0.007)$ |  $-0.018$∗∗∗ | $(0.007)$
$V_{i,t}$ | $0.000$ | $(0.000)$ | $0.000$ | $(0.000)$ | $0.000$ | $(0.000)$ | $0.000$ | $(0.000)$
∗∗∗ refers to a $p$ value less than 0.01, ∗∗ refers to a $p$ value more than
or equal to 0.01 and smaller than 0.05, and ∗ refers to a $p$ value more than
or equal to 0.05 and less than 0.1. Values in parentheses are clustered
standard errors.

Table 7: Entire Panel Regression Results

To answer our research question (i), if the three lexica are not consistent in
their analytic ability to produce stock reaction indicators, we would expect
that the value and the significance of $\beta_{1}$, $\beta_{2}$ or $\beta_{3}$
varies across three lexical projections. For question (ii), if the positive
and negative sentiments have asymmetric impacts, we would expect that
$\beta_{2}$ and $\beta_{3}$ have different signs or significance. To address
question (iii), we would expect that the value and the significance of
$\beta_{1}$, $\beta_{2}$ or $\beta_{3}$ varies with different attention levels
and in particular that the coefficient size is larger for higher attention
firms. As to question (iv), we would expect that the coefficients of sentiment
variables are sector-specific.

We will discuss the analysis of different attention levels and different
sectors respectively in Sections 3.2 and 3.3, and focus now on the entire
sample. The regression results are given in Table 7. Results in Panel A
indicate that the arrival of articles ($I_{i,t}$) distilled using the LM
method is strongly negatively related to future log volatility, and that
contingent on arriving articles, the negative sentiment distilled using the
three methods is significantly positively related to future log volatility,
whereas the positive sentiment distilled using the BL and MPQA methods is
significantly negatived related to future log volatility. Results in Panel B
show that contingent on arriving articles, the positive and negative sentiment
have asymmetric strong impacts on future detrended log trading volume: the
negative sentiment across three lexica strongly drives up future detrended log
trading volume, whereas the positive sentiment distilled using the BL and MPQA
methods is strongly negatively related to future detrended log trading volume.
The arrival of articles also strongly drives up future detrended log trading
volume across three lexica. These findings support the mixture of distribution
hypothesis originated by Clark, (1973). As to future returns in Panel C,
across three lexica and contingent on arriving articles, the positive
sentiments are strongly positively related to future returns whereas the
negative sentiment is unrelated to future returns. This finding sheds light on
case against one unpleasant finding from Antweiler and Frank, (2004) in which
bullishness is not statistically significant for future return. It is
interesting to note that the coefficients for the control variables do not
vary much across lexical projections, which indicates that the sentiment
measures are not so much correlated with the control variables and indeed
provide incremental information.

It is difficult to diagnose a consensual performance from Table 7 because each
lexicon may not fully reflect the complete sentiment and may have its own
idiosyncratic nature as being evident from Table 2. To overcome this problem
that none of the lexica is perfectly complete, we design an artificial
sentiment index: the first principal component, to capture a common component
of three lexica and to take into account the fact from Figures 2 and 3 that
they reveal the shared sentiment. The positive (negative) sentiment index
explains 94.14% (92.33%) of the total sample variance. As seen in the last
column of Table 7, these general positive and negative sentiment indices are
beneficial to achieve more consistent and interpretable results. The negative
sentiment index spurs the future stock volatility and trading volume. However,
the positive sentiment index has very restrictive influence on future
volatility, and suppresses the trading volume but increases stock returns.

####  3.1.3 Sentiment Effect with Larger Lags and Neutral Sentiment

Based on the sequential arrival of information hypothesis (hereafter SAIH,
Copeland,, 1976, 1977), information arrives to traders at different times and
hence relationship with lags larger than one can exist. Hence, we extend the
length of lag under investigation to be two to five trading days and run
regressions using the entire sample. From Table 8, volatility still reacts to
the news in lagged two days but no more earlier than it: lagged two day
negative sentiment extracted by BL and LM are influential, indicating that the
SAIH has been observed here but lagged relationship is restricted to past one
and two day while article was posting. In this sense, the market seems
efficient to incorporate information no longer than two days. Likewise, we
find the negative sentiment in lagged two day still has an influence on future
return. The coefficients across three lexicon projections are significant but
positive. The coefficients of negative sentiment projected by the BL and LM
methods are significant but positive. The negative sign, even insignificant,
in lagged one day turns positive in lagged two day to reflect that stock
returns revert to mean value, which is consistent with Antweiler and Frank,
(2004). Although not significant, the coefficients’ sign for lag one indicates
a slight negative influence on tomorrow’s stock returns, but return will
revert to its mean value in two days later shown by positive sign as negative
news vanish. The sooner reversion is the more efficient market is. For the
detrended log trading volume, the lagged effect is relatively insignificant.

Financial market is characterized by the clustering of information (news)
arrival, so that we will see the volatility clustering (Engle,, 2004). The
clustering of arrival of sentimental information motivates us to accumulate
the sentiment variables from past trading days. Let $I_{i,t:(t+h-1)}$,
$Pos_{i,t:(t+h-1)}$ and $Neg_{i,t:(t+h-1)}$ denote the indicator of arrival of
articles, the average proportion of positive words and the average proportion
of negative words based on articles published on or after trading day $t$ and
before trading day $t+h$. Strikingly, the accumulated sentiment effect
projected by BL and LM method on future volatility shown in Table 9 is very
clear and keeps asymmetric, that is, only reacts to negative not to positive
sentiment. Sometimes the sentiment news arrive consecutively and its
accumulated influence lasts up to five trading days (one week). The
accumulative sentiment effect can be also observed on the detrended log
trading volume while accumulating to lagged four and five days, and on the
future return while accumulating to lagged two days.

We also tried to consider the proportion of neutral words and examine its
impact. Based on the neutral proportion defined by MPQA method, in general we
find the neutral words have no influence in stock indicators. The results can
be provided upon the request.

| BL | LM | MPQA
---|---|---|---
Lag $h$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$
| Panel A: Future Volatility $\log\sigma_{i,t+h}$
$h=2$ | $-0.000$ | $0.000$ |  $0.005$∗ | $-0.000$ | $0.001$ |  $0.005$∗ | $-0.000$ | $0.001$ | $0.004$
| $(0.000)$ | $(0.002)$ | $(0.003)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=3$ | $-0.000$ | $-0.001$ | $0.003$ | $-0.000$ | $0.001$ | $0.004$ | $-0.000$ | $0.000$ | $0.003$
| $(0.000)$ | $(0.002)$ | $(0.003)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=4$ | $-0.000$ | $0.000$ | $0.002$ | $-0.000$ | $0.002$ | $0.004$ | $-0.000$ | $0.001$ | $0.000$
| $(0.000)$ | $(0.002)$ | $(0.003)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=5$ | $0.000$ | $-0.002$ | $0.002$ | $0.000$ | $-0.002$ | $0.003$ | $-0.000$ | $-0.000$ | $0.001$
| $(0.000)$ | $(0.002)$ | $(0.003)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
| Panel B: Future Detrended Log Trading Volume $V_{i,t+h}$
$h=2$ | $0.003$ | $0.112$ | $-0.198$ | $0.004$ | $0.079$ | $-0.158$ | $0.003$ | $0.006$ | $-0.414$
| $(0.006)$ | $(0.140)$ | $(0.174)$ | $(0.005)$ | $(0.227)$ | $(0.183)$ | $(0.007)$ | $(0.140)$ | $(0.219)$
$h=3$ | $0.001$ | $-0.011$ | $-0.082$ | $0.001$ | $-0.003$ | $-0.125$ | $0.002$ | $-0.170$ | $-0.188$
| $(0.006)$ | $(0.140)$ | $(0.174)$ | $(0.005)$ | $(0.227)$ | $(0.183)$ | $(0.007)$ | $(0.140)$ | $(0.219)$
$h=4$ | $-0.001$ | $0.064$ | $-0.539$ | $0.004$ | $-0.324$ | $-0.556$ | $0.001$ | $-0.020$ | $-0.811$
| $(0.006)$ | $(0.140)$ | $(0.488)$ | $(0.005)$ | $(0.227)$ | $(0.536)$ | $(0.007)$ | $(0.140)$ | $(0.479)$
$h=5$ | $0.008$ | $-0.208$ | $-0.410$ | $-0.004$ | $-0.022$ | $-0.096$ | $0.001$ | $-0.069$ | $-0.416$
| $(0.006)$ | $(0.140)$ | $(0.301)$ | $(0.005)$ | $(0.227)$ | $(0.183)$ | $(0.007)$ | $(0.140)$ | $(0.278)$
| Panel C: Future Returns $R_{i,t+h}$
$h=2$ | $-0.000$ | $0.000$ |  $0.016$∗ | $-0.000$ | $-0.003$ |  $0.024$∗∗ | $-0.000$ | $0.001$ |  $0.026$∗∗
| $(0.000)$ | $(0.007)$ | $(0.009)$ | $(0.000)$ | $(0.012)$ | $(0.010)$ | $(0.000)$ | $(0.008)$ | $(0.012)$
$h=3$ | $0.000$ | $-0.001$ | $-0.001$ | $0.000$ | $-0.010$ | $0.005$ | $0.001$ | $-0.011$ | $0.003$
| $(0.000)$ | $(0.008)$ | $(0.009)$ | $(0.000)$ | $(0.012)$ | $(0.010)$ | $(0.000)$ | $(0.008)$ | $(0.012)$
$h=4$ | $-0.000$ | $0.001$ |  $0.016$∗ | $-0.000$ | $0.010$ | $0.006$ | $-0.000$ | $-0.003$ | $0.011$
| $(0.000)$ | $(0.007)$ | $(0.009)$ | $(0.000)$ | $(0.012)$ | $(0.010)$ | $(0.000)$ | $(0.008)$ | $(0.012)$
$h=5$ | $0.000$ | $-0.011$ | $0.009$ | $0.000$ | $-0.018$ | $0.002$ | $0.000$ | $-0.013$ | $0.014$
| $(0.000)$ | $(0.007)$ | $(0.009)$ | $(0.000)$ | $(0.012)$ | $(0.010)$ | $(0.000)$ | $(0.009)$ | $(0.012)$
∗∗∗ refers to a $p$ value less than 0.01, ∗∗ refers to a $p$ value more than
or equal to 0.01 and smaller than 0.05, and ∗ refers to a $p$ value more than
or equal to 0.05 and less than 0.1. Values in parentheses are standard errors.

Table 8: Entire Panel Regression Results with Larger Lags (Noncumulative
Articles)

| BL | LM | MPQA
---|---|---|---
Lag $h$ | $I_{i,t:(t+h-1)}$ | $Pos_{i,t:(t+h-1)}$ | $Neg_{i,t:(t+h-1)}$ | $I_{i,t:(t+h-1)}$ | $Pos_{i,t:(t+h-1)}$ | $Neg_{i,t:(t+h-1)}$ | $I_{i,t:(t+h-1)}$ | $Pos_{i,t:(t+h-1)}$ | $Neg_{i,t:(t+h-1)}$
| Panel A: Future Volatility $\log\sigma_{i,t+h}$
$h=2$ | $-0.000$ | $-0.001$ |  $0.006$∗∗ | $-0.000$ | $-0.001$ |  $0.007$∗∗ | $-0.000$ | $-0.001$ | $0.004$
| $(0.000)$ | $(0.002)$ | $(0.002)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=3$ | $-0.000$ | $-0.002$ |  $0.006$∗∗∗ |  $-0.000$∗ | $-0.001$ |  $0.008$∗∗∗ | $-0.000$ | $-0.001$ | $0.005$
| $(0.000)$ | $(0.002)$ | $(0.002)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=4$ | $-0.000$ | $-0.001$ |  $0.006$∗∗∗ |  $-0.000$∗∗ | $-0.000$ |  $0.008$∗∗∗ | $-0.000$ | $-0.001$ | $0.003$
| $(0.000)$ | $(0.002)$ | $(0.002)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
$h=5$ | $0.000$ | $-0.003$ |  $0.006$∗∗ | $-0.000$ | $-0.002$ |  $0.008$∗∗∗ | $-0.000$ | $-0.002$ | $0.003$
| $(0.000)$ | $(0.002)$ | $(0.002)$ | $(0.000)$ | $(0.003)$ | $(0.003)$ | $(0.000)$ | $(0.002)$ | $(0.003)$
| Panel B: Future Detrended Log Trading Volume $V_{i,t+h}$
$h=2$ | $0.006$ | $-0.016$ | $-0.133$ |  $0.008$∗ | $-0.148$ | $-0.187$ | $0.002$ | $0.006$ | $-0.253$
| $(0.006)$ | $(0.125)$ | $(0.156)$ | $(0.004)$ | $(0.203)$ | $(0.169)$ | $(0.006)$ | $(0.126)$ | $(0.198)$
$h=3$ | $0.006$ | $-0.072$ | $-0.111$ | $0.005$ | $-0.189$ | $-0.078$ | $0.001$ | $-0.063$ | $-0.174$
| $(0.005)$ | $(0.123)$ | $(0.153)$ | $(0.004)$ | $(0.198)$ | $(0.167)$ | $(0.006)$ | $(0.124)$ | $(0.193)$
$h=4$ | $0.008$ |  $-0.310$∗∗ | $-0.096$ |  $0.010$∗∗ |  $-0.486$∗∗ |  $-0.293$∗ | $0.004$ | $-0.138$ | $-0.473$
| $(0.005)$ | $(0.152)$ | $(0.124)$ | $(0.004)$ | $(0.200)$ | $(0.168)$ | $(0.006)$ | $(0.125)$ | $(0.327)$
$h=5$ |  $0.014$${**}$ |  $-0.242$∗ |  $-0.408$∗ |  $0.008$∗ |  $-0.428$∗∗ | $-0.228$ | $0.009$ | $-0.193$ | $-0.646$
| $(0.006)$ | $(0.126)$ | $(0.246)$ | $(0.004)$ | $(0.202)$ | $(0.171)$ | $(0.007)$ | $(0.126)$ | $(0.493)$
| Panel C: Future Returns $R_{i,t+h}$
$h=2$ |  $-0.001$∗ |  $0.013$∗∗ | $0.009$ | $-0.000$ |  $0.019$∗ | $0.010$ |  $-0.001$∗∗ |  $0.013$∗ | $0.009$
| $(0.000)$ | $(0.007)$ | $(0.008)$ | $(0.000)$ | $(0.011)$ | $(0.009)$ | $(0.000)$ | $(0.007)$ | $(0.010)$
$h=3$ | $-0.000$ | $0.009$ | $0.004$ | $-0.000$ | $0.013$ | $0.007$ | $-0.000$ | $0.004$ | $0.007$
| $(0.000)$ | $(0.007)$ | $(0.008)$ | $(0.000)$ | $(0.011)$ | $(0.009)$ | $(0.000)$ | $(0.007)$ | $(0.010)$
$h=4$ | $-0.000$ | $0.008$ | $0.012$ | $-0.000$ | $0.017$ | $0.009$ | $-0.001$ | $0.005$ | $0.016$
| $(0.000)$ | $(0.007)$ | $(0.008)$ | $(0.000)$ | $(0.011)$ | $(0.009)$ | $(0.000)$ | $(0.007)$ | $(0.010)$
$h=5$ | $-0.000$ | $0.000$ | $0.010$ | $-0.000$ | $0.004$ | $0.006$ | $-0.000$ | $-0.003$ | $0.019$
| $(0.000)$ | $(0.007)$ | $(0.008)$ | $(0.000)$ | $(0.011)$ | $(0.009)$ | $(0.000)$ | $(0.007)$ | $(0.010)$
∗∗∗ refers to a $p$ value less than 0.01, ∗∗ refers to a $p$ value more than
or equal to 0.01 and smaller than 0.05, and ∗ refers to a $p$ value more than
or equal to 0.05 and less than 0.1. Values in parentheses are standard errors.

Table 9: Entire Panel Regression Results with Larger Lags (Cumulative
Articles)

####  3.1.4 Monte Carlo Simulation based on Entire Sample Results

The text sentiment effects, as reported in Table 7, allow us deeper insights
and analysis. More precisely we may address the important question of
asymmetric reactions to the given sentiment scales. In order to do so we
employ Monte Carlo techniques to investigate different facets of the sentiment
effects. The components of this Monte Carlo study are: (1) to simulate the
appearance of articles with presumed probabilities; (2) to provide a realistic
set of scenarios regarding the frequency and content (positive v.s. negative)
of articles; (3) to obtain volatility induced by the generated article (using
Table 7); (4) to demonstrate the impact of synthetic text on future
volatility; (5) to visualize and test an asymmetry effect as formulated in
research question (ii).

The simulation scenarios (for each variable involved) are summarized briefly
as follows. We employ a Bernoulli random variable $I_{i,t}$ indicating that
articles arrive at a specific frequency $p_{i}$, where for each individual
stock symbol $i$, $p_{i}$ is estimated by the fraction of days with at least
one relevant article. Given the outcome of this article indicator, we generate
the corresponding positive and negative proportions through a copula approach
using the conditional inversion method as described in Frees and Valdez,
(1998). We follow the two-step approach that is widely mentioned in literature
such as Patton, (2006), Hotta et al., (2006) and Di Clemente and Romano,
(2004). In the first step, the marginal distributions are modeled by their
corresponding empirical distribution function (edf) to avoid imposing a
parametric distribution; in the second step, a Gaussian copula is estimated to
take the inherent dependence among variables into account. For the sentiment
variables, this approach is applied to each firm separately since each firm
has a different $p_{i}$ and only days with at least one article relevant to
the firm are included in the estimation. To simulate market returns $R_{M,t}$
and individual returns $R_{i,t}$ for all 100 symbols, we first filter these
variables by estimated MA(1)-GARCH(1,1) processes and standardize the
residuals by dividing them by estimated standard deviations. We then apply the
copula approach to the standardized residuals, and the simulated standardized
residuals are transformed into simulated values of $R_{M,t}$ or $R_{i,t}$ by
multiplying them by the median of the priorly estimated standard deviations
for the market or the specific firm $i$. The company specific fixed effects
$\gamma_{i}$ are not incorporated as the simulated volatility for different
firms is otherwise not graphically comparable. For the other control
variables, CBOE VIX index $VIX_{t}$ is fixed at its mean value over the sample
period, and past log volatility and past detrended log trading volume are not
used in the simulation.

Figure 4 demonstrates, for one simulation, the association between the
negative and positive proportions as distilled via our three projection
methods and their simulated future volatility outcomes. We estimate a local
linear regression model (solid line) and corresponding 95% uniform confidence
bands based on Sun and Loader, (1994). Both are estimated using Locfit by
Loader, (1999) in the R environment. Loader and Sun, (1997) discuss the
robustness of this approach and conclude that the results are conservative but
reasonable for heavy tailed error distributions. The bandwidth is
automatically chosen by using the plug-in selector according to Ruppert et
al., (1995). We limit the the visible area to sentiment values between $0$ and
$0.04$ as well as volatility values between $1.45$ and $1.65$ to make the
different lexica visually comparable. Nevertheless, all simulated values are
utilized in the estimation of the regression curve and confidence bands. The
clustered points lying on the vertical axis indicate that there is absence of
articles. The range of this cluster from $0.77$ to $2.57$ is caused by the
impact from the simulated control variables as well as the idiosyncratic
impact captured by the residual term.

Apparently, an asymmetry effect becomes visible. One observes that the slopes
of the volatility curves given negative sentiment is mainly positive while the
curves for positive sentiment seem to be rather flat and even go down in the
case of BL and MPQA methods. One can also compare the confidence bands to
address the question whether negative sentiment has a significantly higher
effect on the volatility than positive sentiment. The confidence bands of
$Pos$ and $Neg$ do not overlap for sentiment values between $0.023$ and
$0.056$ for BL, between $0.017$ and $0.039$ for LM and between $0.023$ and
$0.05$ for MPQA.

This asymmetry effect parallels the well known imbalance of future volatility
given good v.s. bad news. The leverage effect depicts a negative relation
between the lagged return and the risk resulting from bad news that causes
higher volatility. Black, (1976) and Christie, (1982) find that bad news in
the financial market produce such an asymmetric effect on future volatility
relative to good news. This leverage effect has also been shown by Bekaert and
Wu, (2000) and Feunou and Tédongap, (2012). In the same vein, Glosten et al.,
(1993) introduce GARCH with differing effects of negative and positive shocks
taking into account the leverage effect.

Figure 4: Monte Carlo Simulation based on Entire Sample Results

###  3.2 Does Attention Ratio matter?

While people post their text to express their opinions, or the comments to
other articles, they are undoubtedly paying attention to the firm mentioned by
their articles. In this respect article posting is a revealed attention
measure. In fact, in our collected 43,459 articles across 100 stocks, it is
obvious that not every firm shares the attention equivalently. To reflect
these differences, we define the attention ratio for a symbol as the number of
days with articles divided by the total number of days in the sample period,
1,255. The symbol “AAPL” (Apple Computer Inc.) attracts the most attention
with an attention ratio of 0.818. Articles involving AAPL arrive in social
media almost every day (81.8 days over 100 days). However, the symbol “TRV”
(Travelers Companies, Inc.) has the lowest attention ratio, 0.204, which means
that one finds a related article every five trading days, i.e. one week.
Different from the “indirect” attention measures from stock indicators such as
trading volumes, extreme returns or price limits, this attention measure is a
kind of “direct” measure of investor attention, and shares the same idea as
the Search Volume Index (SVI) constructed by Google. Beyond the SVI, our
attention can be further projected to “Positive” or “Negative” attention. In
our main research question (ii), we are interested in whether the well known
asymmetric response (bad vs. good news) is appropriately reflected in the
lexical projections. Assuming that investors are more risk-averse, they should
be more aware of negative articles and pay more attention to them.

Attention is one of the basic elements in traditional asset pricing models.
The conventional asset pricing models assume that information is
instantaneously incorporated into asset prices when it arrives. The basic
assumption behind this argument is that investors pay “sufficient” attention
to the asset. Under this condition, the market price of asset should be very
efficient in incorporating any relevant news. In this aspect, the high
attention firms should be more responsive to the text sentiment distilled from
the articles, and their market prices should reflect this efficiency. As such,
the high attention samples stand on the side of the traditional asset pricing
models, and the findings from them are expected to support the efficient
market hypothesis. However, attention in reality is a scarce cognitive
resource, and investors have limited attention instead (Kahneman,, 1973).
Further research on this topic from Merton, (1987), Sims, (2003) and Peng and
Xiong, (2006) confirms that the limited attention can affect asset pricing.
The low attention firms with very limited attention may ineffectively or
insufficiently reflect the text sentiment information, so that their
corresponding stock reactions could be greatly bounded. This argument is in
accordance with the fact that the limited attention causes stock prices to
deviate from the fundamental values (Hong and Stein,, 1999), implying a
potential arbitrage opportunity.

####  3.2.1 Descriptive Statistics for the Firms with different Attention
Ratios

Grouping the samples by their attention ratios and examining the responses
from different attention groups may offer a clue to the aforementioned
conjectures. The criterion used to group the sample firms is based on the
quantiles of the attention ratio. Firms whose attention ratios are above the
75% quantile (0.3693) are grouped as “extremely high”, between 50% (0.3026)
and 75% quantiles as “high”, between 25% (0.2455) and 50% quantiles as
“median”, and lower than 25% quantile as “low”. For each attention group,
Table 10 reports across lexical projections the mean values of positive
($\mu_{Pos}$) and negative ($\mu_{Neg}$) sentiment proportions, calculated by
averaging $Pos_{i,t}$ or $Neg_{i,t}$ over all relevant symbol-day
combinations, the proportion of relevant symbol-day combinations with
$Neg_{i,t}>Pos_{i,t}$, the average attention ratio, and the average number of
days with articles, calculated by averaging the number of days with articles
over all relevant symbols. The “extreme high” groups receive an average
attention ratio of 55.14%, indicating on average these firms have been looked
at every two days. By contrast, the low attention group with an average
attention ratio of 21.97% receives attention at weekly frequency (5 trading
days). By comparing the magnitude of $\mu_{Neg}$, one observes that investors
are inclined to express negative sentiments in the “extreme high” group. One
may conclude therefore that higher attention is coming with a “negative text”,
or inversely speaking: the negative article creates higher attention. This is
evident for example in the case of the LM method, where the proportion of
symbol-day combinations with dominance of negative sentiment is 46% in the
“extremely high” group. For the constituents in this particular attention
group, we find on average 691 days with articles observed over a total of 1255
sample days (5 years), which is almost three times the average number of days
with articles for the low attention group.

| BL | LM | MPQA | Attention | Number of Days
---|---|---|---|---|---
Attention | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | Ratio | with Articles
Extremely high | $0.032$ | $0.016$ | $0.119$ | $0.013$ | $0.014$ | $0.460$ | $0.038$ | $0.013$ | $0.027$ | $0.551$ | 691
High | $0.032$ | $0.015$ | $0.113$ | $0.013$ | $0.012$ | $0.403$ | $0.038$ | $0.013$ | $0.031$ | $0.343$ | 430
Median | $0.035$ | $0.014$ | $0.083$ | $0.014$ | $0.011$ | $0.339$ | $0.039$ | $0.012$ | $0.027$ | $0.273$ | 356
Low | $0.036$ | $0.014$ | $0.086$ | $0.015$ | $0.011$ | $0.333$ | $0.040$ | $0.012$ | $0.031$ | $0.220$ | 264

Table 10: The Summary Statistics for different Attention Ratio Groups

####  3.2.2 The Results of Attention Analysis

The central interest of this research focuses on understanding to which extent
distilled news flow and its derived parameters (like attention) impacts the
relation between text sentiment and stock reactions. We employ panel
regression designed for the given attention groups, and therefore each panel
regression equally comprises of 25 sample firms. The results are displayed in
Table 11. For the “extremely high” group, the text sentiment carries a major
and highly significant influence on future volatility consistently across the
three lexical projections. As a caveat though please note that the sentiment
effect on volatility shown in Panel A is exclusive for negative news
contingent on arriving articles, the stock volatility rarely reacts to
positive or optimistic news. Panel B summarizes the attention analysis on the
detrended log trading volume. For the “extremely high” group, in the LM and
MPQA projection methods, arrival of articles ($I_{i,t}$) brings relevant
information, and creates a growing trading volume, especially when it comes
with negative news. The corresponding analysis for stock returns are also
reasonable. The stock returns of “high” group react clearly to the sentiments,
contingent on arriving articles, they rise for optimistic news and decline for
pessimistic consensus. In the case of LM method, the significant positive
coefficient of $Neg_{i,t}$ for the “extremely high” group suggests that the
market participants act according to the uncertain market hypothesis developed
by Brown et al., (1988) and based on the overreaction hypothesis by Bondt and
Thaler, (1985). Here, the market participants set new prices before the full
range of the news content is resolved. In case of unfavorable news, the
investors set stock prices significantly below their conditional expected
values and thus, react risk-averse. On the subsequent day, the mispriced stock
price will revert to its true value.

The collected empirical evidence so far suggests that the distilled news of
high attention firms effectively drive their stock volatilities, trading
volumes and returns. They are highly responsive to the sentiment across
lexical projections.

| BL | LM | MPQA
---|---|---|---
Attention | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$
| Panel A: Future Volatility $\log\sigma_{i,t+1}$
Low | $0.020$ | $-0.736$ | $-0.074$ | $0.010$ | $-1.027$ | $-0.195$ | $0.016$ | $-0.655$ | $0.275$
| $(0.025)$ | $(0.666)$ | $(0.766)$ | $(0.016)$ | $(1.027)$ | $(0.788)$ | $(0.029)$ | $(0.633)$ | $(0.866)$
Median | $0.004$ | $-0.690$ |  $1.107$∗∗ | $-0.012$ | $-0.308$ |  $1.126$∗ | $0.008$ |  $-0.872$∗ |  $1.767$∗∗
| $(0.016)$ | $(0.449)$ | $(0.446)$ | $(0.016)$ | $(0.778)$ | $(0.630)$ | $(0.019)$ | $(0.515)$ | $(0.707)$
High | $-0.016$ | $-0.460$ |  $1.324$∗∗∗ |  $-0.046$∗∗∗ | $0.967$ |  $1.806$∗∗∗ | $-0.019$ |  $-0.636$∗∗ |  $2.548$∗∗∗
| $(0.017)$ | $(0.442)$ | $(0.475)$ | $(0.013)$ | $(0.724)$ | $(0.615)$ | $(0.016)$ | $(0.315)$ | $(0.662)$
Extremely | $-0.010$ | $0.027$ |  $0.784$∗∗ | $-0.013$ | $0.483$ |  $0.747$∗∗ | $-0.002$ | $-0.182$ |  $0.909$∗∗
High | $(0.014)$ | $(0.257)$ | $(0.371)$ | $(0.013)$ | $(0.457)$ | $(0.300)$ | $(0.017)$ | $(0.284)$ | $(0.433)$
| Panel B: Future Detrended Log Trading Volume $V_{i,t+1}$
Low |  $0.054$∗∗ | $-0.817$ | $0.312$ |  $0.044$∗∗∗ | $-0.923$ | $-0.109$ |  $0.049$∗ | $-0.433$ | $-0.197$
| $(0.024)$ | $(0.502)$ | $(0.665)$ | $(0.014)$ | $(0.657)$ | $(0.556)$ | $(0.029)$ | $(0.567)$ | $(0.796)$
Median |  $0.052$∗∗∗ |  $-0.851$∗∗ |  $1.116$∗ |  $0.032$∗∗∗ | $-0.199$ | $0.861$ |  $0.062$∗∗∗ |  $-0.754$∗∗ | $0.449$
| $(0.014)$ | $(0.398)$ | $(0.600)$ | $(0.010)$ | $(0.535)$ | $(0.601)$ | $(0.013)$ | $(0.342)$ | $(0.689)$
High |  $0.036$∗∗∗ | $-0.198$ | $0.554$ |  $0.021$∗ |  $0.815$∗ | $0.447$ |  $0.046$∗∗∗ | $-0.358$ | $0.419$
| $(0.009)$ | $(0.299)$ | $(0.459)$ | $(0.011)$ | $(0.487)$ | $(0.451)$ | $(0.016)$ | $(0.385)$ | $(0.559)$
Extremely | $0.023$ | $-0.242$ |  $0.958$∗∗ |  $0.017$∗ | $0.299$ |  $0.796$∗ |  $0.032$∗∗ | $-0.408$ |  $1.084$∗∗
High | $(0.014)$ | $(0.336)$ | $(0.416)$ | $(0.008)$ | $(0.521)$ | $(0.429)$ | $(0.014)$ | $(0.299)$ | $(0.427)$
| Panel C: Future Returns $R_{i,t+1}$
Low | $0.000$ | $0.012$ | $0.009$ | $0.000$ | $0.021$ | $-0.001$ | $0.000$ | $0.010$ | $-0.016$
| $(0.001)$ | $(0.022)$ | $(0.023)$ | $(0.000)$ | $(0.030)$ | $(0.023)$ | $(0.001)$ | $(0.021)$ | $(0.032)$
Median | $-0.001$ |  $0.024$∗ | $0.009$ | $0.000$ |  $0.035$∗ | $-0.022$ | $-0.001$ |  $0.034$∗ | $0.007$
| $(0.001)$ | $(0.012)$ | $(0.018)$ | $(0.000)$ | $(0.019)$ | $(0.024)$ | $(0.001)$ | $(0.018)$ | $(0.024)$
High | $0.000$ |  $0.028$∗∗ |  $-0.034$∗∗∗ |  $0.001$∗∗ |  $0.038$∗ |  $-0.046$∗∗ | $0.000$ |  $0.024$∗∗ |  $-0.044$∗∗∗
| $(0.000)$ | $(0.012)$ | $(0.011)$ | $(0.000)$ | $(0.022)$ | $(0.018)$ | $(0.001)$ | $(0.011)$ | $(0.016)$
Extremely | $0.000$ | $0.017$ | $0.004$ | $-0.000$ | $0.031$ |  $0.033$∗∗ |  $0.001$∗ | $-0.006$ | $0.009$
High | $(0.000)$ | $(0.012)$ | $(0.012)$ | $(0.000)$ | $(0.021)$ | $(0.013)$ | $(0.000)$ | $(0.011)$ | $(0.016)$
∗∗∗ refers to a $p$ value less than 0.01, ∗∗ refers to a $p$ value more than
or equal to 0.01 and smaller than 0.05, and ∗ refers to a $p$ value more than
or equal to 0.05 and less than 0.1. Values in parentheses are clustered
standard errors.

Table 11: Attention Analysis: The Impact on future Volatility, Trading Volume
and Returns

Given the high attention received, any relevant information including the
articles made by individual traders has been fully incorporated into their
asset prices and dynamics. Due to their efficiency, the article posting and
discussing today can predict stock reactions tomorrow. For lower attention
firms, one cannot make such a strong claim. Investors may think those firms
are negligible and may therefore underreact to the available information. The
underreaction from limited attention is likely to cause stock prices to
deviate from the fundamental values, and an arbitrage opportunity may emerge.
Our evidence is in line with Da et al., (2011) in which they support the
attention-induced price pressure hypothesis. By using the SVI from Google as
attention measure, they find stronger attention-induced price pressure among
stocks in which individual investor attention matters most. Beyond their
study, we find that high attention is usually accompanied with negative
articles, and negative articles contribute more to attention and cause more
stock reactions, supporting an asymmetric response.

It is interesting to note that the coefficients for the control variables do
not vary much across lexical projections in each attention group (results not
shown here), which indicates that for each attention group, the sentiment
measures are not so much correlated with the control variables and provide
incremental information.

####  3.2.3 Monte Carlo Simulation based on Attention Analysis

Like Section 3.1.4, we present a realistic Monte Carlo scenario for different
attention groups using the results from Table 11. We keep the parameter
settings of the data generation and the calculation of confidence bands as
before. Figure 5 summarizes the associations between the negative proportions
and the simulated future volatilities across different attention groups. The
scatter plots of the high attention panel are quite dense, whereas those of
the low attention group are sparser due to its lower frequency of articles.
Interestingly, the higher volatilities of high attention firms are prominently
driven by negative text sentiment, but have an inverse relationship with
positive sentiment. Through comparison of the confidence bands we can conclude
for all three lexica that the effect of negative sentiment significantly
differs from that of positive sentiment. The regions where the bands do not
overlap are quite large for BL ($0.022$ \- $0.056$) and MPQA ($0.020$ \-
$0.053$) but much smaller for LM ($0.019$ \- $0.024$). The associations in the
low attention panel are somewhat ambiguous. Indeed, we can note that the
confidence bands for positive and negative sentiment overlap over the whole
range of sentiment value and across all three lexica. These simulations
support the estimations in Table 11 with a strong link found in the “extremely
high” and “high” attention groups and a preeminent asymmetric response. The
firms that have been paid high attentions are more sensitive to the text
sentiment than negligible firms. The sentiment effect together with the
observable asymmetry are highly influential on stock returns, volatilities and
trading volumes. In this sense, their stock reactions are more responsive to
the opinions in social media. In other words, they are also more vulnerable to
signals from small investors.

Figure 5: Monte Carlo Simulation based on Attention Analysis Results

###  3.3 Sector Analysis

The stock reactions that we analyze in relation to text sentiment can be
further segmented into sector specific responses. Given a growing body of
literature that has suggested that industry plays a role in stock reactions
(see Fama and French, (1997), Chen et al., (2007), Hong et al., (2007)), we
investigate whether this relation is industry-specific in nature. A detailed
analysis of sector specific reactions would go far beyond the scope of this
paper and is in fact the subject of research by Chen et al., (2015). We
therefore only highlight a few insights from lexical sentiment for the
business sectors. We ignore the “Telecommuication Services” sector since it
only contains two stock symbols. Descriptive statistics for the other 8
sectors are displayed in Table 12 across the three lexical projections. It is
of interest to study the variation of the proportion of negative over positive
sentiments across the 8 sectors. One observes that consistently over all
lexical projections the financial sector has the highest average discrepancy
in negative and positive proportion. By contrast the health care sector has
(except for MPQA) the lowest average discrepancy. Investors show their
discrepant opinions or disagreement in a very extreme case of $Neg>Pos=0.5$,
implying that 50% of investors stand on one side and the rest of 50% stand on
the opposite side. Table 12 indicates that the financial sector related texts
are more divergent in opinions than others and that apparently the health care
sector does not receive such adverse opinion positions as the other sectors
do. The investors who invest the stocks in health care sector are more likely
to reach their shared concensus or convergent agreement.

| BL | LM | MPQA | Attention
---|---|---|---|---
Sector | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | $\mu_{Pos}$ | $\mu_{Neg}$ | $Neg>Pos$ | Ratio
Consumer Discretionary | $0.034$ | $0.014$ | $0.088$ | $0.014$ | $0.011$ | $0.346$ | $0.038$ | $0.012$ | $0.030$ | $0.332$
Consumer Staples | $0.034$ | $0.014$ | $0.099$ | $0.014$ | $0.012$ | $0.365$ | $0.037$ | $0.013$ | $0.025$ | $0.324$
Energy | $0.028$ | $0.015$ | $0.152$ | $0.011$ | $0.011$ | $0.467$ | $0.038$ | $0.014$ | $0.033$ | $0.370$
Financials | $0.032$ | $0.019$ | $0.195$ | $0.013$ | $0.018$ | $0.594$ | $0.038$ | $0.015$ | $0.045$ | $0.413$
Health Care | $0.035$ | $0.014$ | $0.059$ | $0.014$ | $0.011$ | $0.344$ | $0.039$ | $0.014$ | $0.031$ | $0.287$
Industrials | $0.035$ | $0.012$ | $0.069$ | $0.013$ | $0.011$ | $0.355$ | $0.041$ | $0.011$ | $0.018$ | $0.336$
Information Technology | $0.033$ | $0.015$ | $0.101$ | $0.014$ | $0.012$ | $0.373$ | $0.038$ | $0.023$ | $0.012$ | $0.364$
Materials | $0.034$ | $0.014$ | $0.097$ | $0.013$ | $0.013$ | $0.498$ | $0.039$ | $0.031$ | $0.013$ | $0.287$
Note: This table reports, for the BL, LM and MPQA methods, the mean values of
positive ($\mu_{Pos}$) and ($\mu_{Neg}$) negative sentiment proportions as
well as the proportion of relevant symbol-day combinations with dominance of
negative sentiment. For each sector, an article is accumulated only if a firm
appeared in this article belongs to this sector. The attention ratio for each
sector is calculated as the number of days with articles related to this
sector divided by the total number of days in the sample period.

Table 12: Summary statistics in each sector

The attention also vary with the sectors. The evidence that financials sector
has attracted the highest attention with an attention ratio of 0.413 may be
attributed to (1) the investors’ widespread involvement in this industry
because we all need to keep a relationship with banks to deposit our money,
trade for securities or some financial reasons; (2) the outbreak of the US
subprime crisis and the European sovereign debt crisis have brought the
highest attention to this sector; (3) their sensitivity on changes in the
economy, monetary policy and regulatory policy. The health care sector,
however, is much less attractive and this could be explained by a stable
demand and reduced sensitivity to economic cycles. Given these observations we
will now continue our analysis of stock reactions for these two sectors only,
and leave a bundle of interesting issues to further research.

To address the important question of whether there is a sector dependent stock
reactions, we further analyze how the text sentiment affects, as reported in
Table 13, the future volatility, trading volume and return. In order to do so
we employ the panel regression (as described in (4)-(5)) and report the
results in Table 13. The variable $I_{i,t}$ was used to indicate arrival of
articles on this sector. Contingent on arriving articles, the three sentiment
projections in financial sectors yielded significant and positive effects on
future log volatility from negative proportions, meaning that increasing the
negative text sentiments will result in higher volatility. The exclusive
response to negative sentiment in financial sector indeed is in line with our
entire panel evidence. However, the finding in the health care sector is too
insignificant to claim it. Potentially, investor inattention for the health
care sector may cause a significant mispricing on the stocks. Investors
possibly neglect the news of this sector posted on social media, or this
sector has a slow information diffusion that could lead to a delayed reaction.

| BL | LM | MPQA
---|---|---|---
Sector | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$ | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$
| Panel A: Future Volatility $\log\sigma_{i,t+1}$
Financials | $-0.023$ | $-0.052$ |  $1.075$∗∗ | $-0.025$ | $0.275$ |  $1.027$∗∗∗ | $-0.025$ | $-0.143$ |  $1.816$∗∗∗
| $(0.026)$ | $(0.319)$ | $(0.435)$ | $(0.027)$ | $(0.924)$ | $(0.259)$ | $(0.029)$ | $(0.503)$ | $(0.586)$
Health Care | $0.031$ | $-0.426$ | $-0.509$ | $0.009$ | $0.052$ | $-0.130$ | $0.001$ | $-0.118$ | $0.854$
| $(0.026)$ | $(0.522)$ | $(0.891)$ | $(0.023)$ | $(1.138)$ | $(0.921)$ | $(0.024)$ | $(0.595)$ | $(0.783)$
| Panel B: Future Detrended Log Trading Volume $V_{i,t+1}$
Financials |  $0.037$∗ | $-0.334$ | $0.015$ | $0.017$ | $1.110$ | $-0.313$ |  $0.054$∗∗∗ |  $-0.747$∗∗ | $0.049$
| $(0.020)$ | $(0.494)$ | $(0.527)$ | $(0.015)$ | $(0.766)$ | $(0.476)$ | $(0.015)$ | $(0.305)$ | $(0.536)$
Health Care | $0.031$ | $0.110$ | $-0.314$ | $0.022$ | $0.603$ | $-0.042$ | $0.037$ | $-0.104$ | $-0.211$
| $(0.023)$ | $(0.436)$ | $(0.846)$ | $(0.018)$ | $(0.863)$ | $(0.837)$ | $(0.025)$ | $(0.443)$ | $(0.873)$
| Panel C: Future Returns $R_{i,t+1}$
Financials | $-0.001$ |  $0.034$∗ |  $0.028$∗∗ | $-0.000$ | $0.030$ |  $0.042$∗∗ | $0.001$ | $0.003$ | $0.013$
| $(0.001)$ | $(0.017)$ | $(0.014)$ | $(0.001)$ | $(0.033)$ | $(0.016)$ | $(0.001)$ | $(0.020)$ | $(0.019)$
Health Care | $0.000$ | $-0.000$ | $0.008$ | $0.000$ | $0.006$ | $0.015$ | $0.000$ | $0.006$ | $-0.011$
| $(0.000)$ | $(0.008)$ | $(0.018)$ | $(0.000)$ | $(0.019)$ | $(0.018)$ | $(0.001)$ | $(0.012)$ | $(0.022)$
∗∗∗ refers to a $p$ value less than 0.01, ∗∗ refers to a $p$ value more than
or equal to 0.01 and smaller than 0.05, and ∗ refers to a $p$ value more than
or equal to 0.05 and less than 0.1. Values in parentheses are standard errors.

Table 13: Sector analysis: The Impact on future Volatility, Trading Volume and
Returns

The trading volume is another stock reaction we may attribute to text
sentiments. Using the BL and the MPQA projection method, we find that the
arrival of article brings relevant information and therefore stimulates the
trading volume. It is interesting to note that contingent on arriving
articles, the negative sentiment distilled using the BL and LM methods is
significantly positively related to stock returns on the next trading day. To
investigate the reason for this, we also run a contemporaneous regression for
the financials sector (results not shown) and found a significantly negative
impact of the negative sentiment distilled using the BL and MPQA methods on
contemporaneous returns $R_{i,t}$, and the size of the coefficients is about
twice of that in lagged regression in Table 13. This might suggest that the
market participants monitor financial companies quite carefully and overreact
in case of bad news. On the next day, the participants fully recognize the
scope of the news and reverse part of their prior decisions, and hence the
negative sentiment on trading day $t$ has positive impact on returns on
trading day $t+1$. This is also in line with the finding in Kuhnen, (2015)
which suggests that that being in a negative domain leads people to form
overly pessimistic beliefs about stocks. After the 2008 financial crisis and
the bankruptcy of some major financial companies, this might be the case for
the financials sector.

From these analysis, we know that investors indeed pay different attentions to
sectors they are of interest, and their attentions effectively govern the
equity’s variation. Attention constraints in some sectors may affect
investors’ trading decisions and the speed of price adjustments.

##  4 Conclusion

In this paper, 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 specifically designed for financial applications (LM).
We demonstrate that these sentiment measures carry incremental information for
future stock reactions. Such information varies across lexical projections,
across groups of stocks that attract different level of attention, and across
different sectors. The positive and negative sentiments also have asymmetric
impact on future stock reaction indicators. A detailed summary of the results
is given in Table 14 in the Supplementary Material. There is no definite
picture for which lexicon is the best. This is an important contribution of
our paper to the line of research on textual analysis for financial market.
Besides, the advanced statistical tools that we have utilized, including panel
regression and confidence bands, are novel contributions to this line of
research.

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##  5 Supplementary Material

Table 14 summarizes all the results from entire panel sample analysis,
attention analysis and sector analysis. Take the “BL” row in Panel A as an
example. Arrival of articles ($I_{i,t}$) and the positive sentiment distilled
using the BL method ($Pos_{i,t}$) has no significant impact on future
volatility $\log\sigma_{i,t+1}$ in entire sample analysis, attention analysis
or sector analysis; the negative sentiment distilled using the BL method
($Neg_{i,t}$) is significantly positively related to future volatility in
entire sample analysis and for the “Extremely High” group in attention
analysis, and is significantly negatively related to future volatility for the
“Health Care” sector in sector analysis.

Lexicon | Type of Analysis | $I_{i,t}$ | $Pos_{i,t}$ | $Neg_{i,t}$
---|---|---|---|---
Panel A: Future Volatility $\sigma_{i,t+1}$
BL | Entire Sample | / | Negative | Positive
Attention Analysis | / | / | Positive for “Median”, “High” and “Extremely High”
Sector Analysis | / | / | Positive for “Financials”
LM | Entire Sample | Negative | / | Positive
Attention Analysis | Negative for “High” | / | Positive for “Median”, “High” and “Extremely High”
Sector Analysis | / | / | Positive for “Financials”
MPQA | Entire Sample | / | Negative | Positive
Attention Analysis | / | Negative for “Median” and “High” | Positive for “Median”, “High” and “Extremely High”
Sector Analysis | / | / | Positive for “Financials”
Panel B: Future Detrended Log Trading Volume $V_{i,t+1}$
BL | Entire Sample | Positive | Negative | Positive
Attention Analysis | Positive for “low”, “Median” and “High” | Negative for “Median” | Positive for “Median” and “Extremely High”
Sector Analysis | Positive for “Financials” | / | /
LM | Entire Sample | Positive | / | Positive
Attention Analysis | Positive for all groups | Positive for “High” | Positive for “Extremely High”
Sector Analysis | / | / | /
MPQA | Entire Sample | Positive | Negative | Positive
Attention Analysis | Positive for all groups | Negative for “Median” | Positive for “Extremely High”
Sector Analysis | Positive for “Financials” | Negative for “Financials” | /
Panel C: Future Returns $R_{i,t+1}$
BL | Entire Sample | / | Positive | /
Attention Analysis | / | Positive for “Median” and “High” | Negative for “High”
Sector Analysis | / | Positive for “Financials” | Positive for “Financials”
LM | Entire Sample | / | Positive | /
Attention Analysis | Positive for “High” | Positive for “Median” and “High” | Negative for “High”, positive for “Extremely High”
Sector Analysis | / | / | Positive for “Financials”
MPQA | Entire Sample | / | Positive | /
Attention Analysis | Positive for “Extremely High” | Positive for “Median” and “High” | Negative for “High”
Sector Analysis | / | / | /
The signs of the significant coefficients are given, with a significance level
of 0.1.

Table 14: Summary of the Results