Informational Content of CEO Postings and Stock
Market Predictability
Kang-Pyo Lee∗Suyong Song†

Abstract
We show that CEO postings contain informational content on the U.S. stock markets.
We create a unique sample of CEO users on Twitter, extract hashtags and sentimentsthat can be used as features for prediction from large, unstructured tweet text, andconstruct time series data. To prove their stock market predictability using machinelearning, we predict three numeric stock market indicators as a regression problem
and the direction of stock prices as a classification problem. Findings confirm thatthe select list of hashtags and sentiments have predictive power on the stock return,
trading volume, volatility, and stock price direction.

JEL classification: C45, C53, C55, G12, G17
Keywords: text analysis, machine learning, CEO tweets, stock market predictability∗†

Department of Analytics, Fairfield University, klee1@fairfield.edu
Department of Economics and Department of Finance, University of Iowa, suyong-song@uiowa.edu

1.

Introduction
Elon Musk, the founder and CEO of Tesla Inc. and SpaceX as well as the new ownerof Twitter, or now X, is undoubtedly the most followed CEO user on Twitter with morethan 100 million followers as of August 2022. He is known to be very popular, influential,
and occasionally controversial on Twitter, and what he mentions online draws substantial
attention from the public and investors. He posts numerous tweets on Twitter covering avariety of subjects, most of which are related to the companies he is currently involved in.
Through what he posts on Twitter, his followers can not only get to know better about
him but also obtain unique information about his businesses, which otherwise could not be
accessible through the other channels.
Musk is not the only CEO actively using social media. Many of the incumbent or former
star CEOs are taking advantage of social media to communicate with people in the world
who are interested in the CEOs themselves or the companies they represent. As mostCEOs are using their own personal accounts, their tweets can cover a wide range of topics,
whether they are business related or unrelated, including their personal lives, interests, andopinions. Malhotra and Malhotra (2016) classify CEOs based on their use of Twitter into
four different groups: Generalist CEOs (sharing a wide range of content), Expressionist
CEOs (not trying to share business-related content but sharing their opinions and giving
their followers insights), Information Maven CEOs (sharing links to information, news, andother happenings), and Business Maven CEOs (sharing business-related content).
The main idea of this study is that what CEOs post on Twitter can be a powerful signalof the economy, especially the stock market movement, based on our beliefs that CEOs are
the ones who would be more attentive to business factors and environments than anyone
else and that they are believed to be insightful leaders in their industries. While each CEO
may have a different opinion on an economic situation, their collective voice can predict how
the stock markets move over time as a whole. Their voice can be expressed in their tweets
in different forms such as thoughts, opinions, questions, concerns, criticisms, etc. The most1

recent example of this is their reactions to the ongoing COVID-19 pandemic. They haveexpressed deep concerns about the worldwide pandemic in their tweets, focusing on how the
pandemic could impact their businesses and also the entire economy. They have also shared
their prospects for the future at each turning point and breakthrough during the pandemic,
whether it is positive or negative.
To be specific, we show that select keywords from CEO tweets have predictive powerof stock market indicators such as stock return, trading volume, volatility, and stock pricedirection. To that end, we identify 4,714 CEO users in Twitter from our Big Data poolof unstructured text (approximately 4.7 billion tweets) and collect their tweets posted from
June 2009 to December 2021 (approximately 6.3 million CEO tweets). Based on the ideathat the hashtags and sentiment words used in CEO tweets serve as keywords that can beused as predictors in our models, we construct hashtag and sentiment time series data from
CEO tweets using tweet count and users count, and utilize them as features to predict stock
market indicators from four major stock indexes: S&P 500, Dow 30, Nasdaq, and Russell
2000. We find that hashtags and sentiment words do have predictive power of the stock
market indicators in all stock indexes. We also find that aggregated sentiment features built
on CEOs’ tweet text provide informational content on the stock market indicators beyond
what macroeconomic and financial variables considered in the literature do (e.g., Fama andFrench, 1988, 1989; Cochrane, 1991, 2007; Pesaran and Timmermann, 1995, 2000; Welch and
Goyal, 2008; Van Binsbergen and Koijen, 2010; Golez and Koudijs, 2018).
We utilize the 1,000 most popular hashtags and seven predefined sentiment categories(Negative, Positive, Uncertainty, Litigious, Strong Modal, Weak Modal, and Constraining)as features for regression and classification, respectively, in our daily and weekly predictivemodels. Each of the three numeric stock market indicators – close, volume, and volatility– is used as a dependent variable to predict in regression, whereas the stock price direction
is used as a target to predict in classification. For the regression task, as the number ofhashtag regressors in our model is much larger than the number of time series observations,2the ordinary least squares (OLS) estimator is inconsistent. As a means of relevant variable selection, therefore, we employ one of the commonly-used machine learning methods
for feature selection, the least absolute shrinkage and selection operator (lasso) approach
introduced by Tibshirani (1996), and its variant adaptive group lasso (Yuan and Lin, 2006;
Zou, 2006), which considers the hashtags from the same group. For the adaptive group lasso
that considers the group information of regressors, we develop our own hashtag clustering
method. In general, both the lasso and the adaptive group lasso based on hashtag clustering
have predictive power of stock market indicators. We believe that the results are promisingconsidering the widely-known difficulty of stock market prediction.
To evaluate the out-of-sample performances of our predictive models, we build models on
the training set and predict the target variable with the models learned on the test set. We
employ the rolling windows scheme for both regression and classification. We compare thesample loss function by calculating the mean squared errors (MSE) for regression and theprediction accuracy for classification, respectively, over different sizes of models (0.1, 0.2, ...,
0.9) and different horizons (from 1 to 5). The results present high predictive power of themodels and interesting heterogeneous patterns in forecasting stock market indicators acrossdifferent stock indexes.
For the classification task, we build models on the training set by applying six classification algorithms such as k-Nearest Neighbors, Logistic Regression, Decision Trees, Random
Forest, Support Vector Machines, and Deep Neural Network. We then predict the directionof stock prices in the test set and compare prediction performances using classification accuracy. We find surprisingly high accuracy of the classification performances, which confirmsthat a variety of hashtags are informative of the direction of stock prices.
A large number of studies in finance literature have shown that a variety of macroeconomic and financial variables can predict stock returns (e.g., Welch and Goyal, 2008). We
check whether the information from the CEO sentiment features is subsumed by macroeconomic and financial variables by controlling for each macroeconomic or financial variable3

in the regression of stock indicators on CEO sentiment features using monthly data. The
results show that the CEO sentiments still provide predictive power of the stock indicators
beyond what the existing macroeconomic and financial variables do. Therefore, we argue
that there is evidence of stock market predictability.
Recent studies have investigated the importance of CEOs’ characteristics and roles incorporate decisions and outcomes. It is found that the managerial styles, influences, andpersonality traits of top executives can affect a wide range of corporate decisions, policies, andoutcomes (Bertrand and Schoar, 2003; Adams, Almeida, and Ferreira, 2005; Malmendier andTate, 2005; Kaplan, Klebanov, and Sorensen, 2012; Gow, Kaplan, Larcker, and Zakolyukina,2016). More recent studies focus on the active use of social media not only by firms but
also by CEOs. For example, Chen, Hwang, and Liu (2018) report the emergence of “social
executives” directly communicating with investors through social media. Elliott, Grant, andHodge (2018) discuss the benefits of direct communication of managers on social media. Men
and Tsai (2016) take a public relation standpoint on why the public wants to engage withCEOs on social media and why that matters.
Another strand of the literature which is closely related to our paper is the studies that
employed textual analysis to examine whether text messages on social media, stock message
boards, analyst reports, firm financial statement, and conference calls have informational
content on stock market. The literature shows that stock messages are predictive of market
volatility and trading volume (Antweiler and Frank, 2004) and stock index levels (Das andChen, 2007) and also that negative words in media reports serve as a proxy for investor
sentiment (Tetlock, 2007; Chen, De, Hu, and Hwang, 2014) and convey negative information
about firm earnings (Tetlock, Saar-Tsechansky, and Macskassy, 2008). It is also shown thatinvestor sentiment predicts returns in the cross-section (Baker and Wurgler, 2006; Huang,
Jiang, Tu, and Zhou, 2015) and that manager sentiment predicts future aggregate stock
market returns and cross-section of stock returns (Jiang, Lee, Martin, and Zhou, 2019). Most
recently, Wolfskeil (2023) explores the link between firms’ voluntary disclosure strategies on

4

Twitter and their equity returns.
This paper makes several contributions to the extant literature. First, through our datadriven automated approach to CEO discovery followed by a thorough manual investigation,
we create a large, unique sample of CEOs by identifying 4,714 actual CEO users in Twitter
who have never been targeted at in the previous research.1 Second, to translate large,
unstructured text data from social media into structured machine-readable data, we apply
text analysis techniques such as natural language processing (NLP), regular expressions,word clustering, and sentiment analysis. Third, we apply state-of-the-art machine learningtechniques to high-dimensional social media data and show that they are useful in predicting
stock market indicators. Fourth, we conduct comprehensive time series prediction analyses
with the hashtags and sentiment features for various stock market indicators (return, trading
volume, volatility, and stock price direction), stock indexes (S&P 500, Dow 30, Nasdaq, andRussell 2000), and time frequencies (daily, weekly, and monthly) optimizing the prediction
performance for different model sizes and horizons. We also find that the predictive power ofCEO sentiments still stands after controlling for well-known, macroeconomic and financial
variables. The work done by Wolfskeil (2023) is very similar to ours in that the three aspects,
timing, tone, and content, that she analyzes can translate to the horizon, sentiments, andhashtags in our approach, while there is a significant distinction between the two approaches
in terms of textual data used: tweets posted by the firms’ official Twitter accounts versus
by the CEOs.
The rest of this paper proceeds as follows. Section 2 outlines the background of this study.
Section 3 provides the details on the data we collect and analyze. Section 4 presents thefindings from the stock market prediction. Section 5 contains conclusions of this study anddiscussion of possible extensions in the future. Lastly, the Supplementary Online Appendix
contains detailed or additional information.
1

We make all data used in this paper publicly available at the following website: https://ceoattention.herokuapp.com/. This website also provides a dashboard for CEO hashtags and sentiments thatare updated on a daily basis by automated NLP.

5

2.

Background and Related Literature

2.1.

CEOs and Social MediaWith the growing popularity of some best-performing CEOs, extensive work has been
done primarily in the economics and finance literature over the last two decades to investigate the importance of CEOs’ characteristics and roles in critical corporate decisions andoutcomes. Bertrand and Schoar (2003) argue that manager fixed effects or managerial styles
affect a wide range of corporate decisions. They utilize the Forbes 800 files and ExecuComp
data to identify approximately 600 firms and 500 managers including CEOs, CFOs, and
COOs. Adams et al. (2005) focus on the firms run by powerful CEOs, based on the hypothesis that top executives can impact firm outcomes only if they have influence over critical
corporate decisions. They identify 336 firms from the Fortune 500 and their CEOs fromthe ExecuComp data and find that firm performance is more variable for the firms run bypowerful CEOs. Malmendier and Tate (2005) focus on the negative effects of overconfident
managers, arguing that managerial overconfidence can lead to distortions in corporate investment policies. They analyze a sample of 477 large publicly traded U.S. firms and identify
their CEOs from the COMPUSTAT database. Kaplan et al. (2012) pay attention to CEOs’
30 specific individual characteristics in five general categories including Leadership, Personal,Intellectual, Motivational, and Interpersonal. They identify 316 CEO candidates considered
for positions in 224 companies funded by private equity investors and find that subsequent
performance is positively related to general ability and execution skills. The work done by
Gow et al. (2016) is another study that focuses on CEO personality. They show that the
Big Five personality traits (agreeableness, conscientiousness, extraversion, neuroticism, andopenness to experience) of 119 CEOs identified from U.S. technology and public firms areassociated with crucial financial choices and firm operating performance. Pan, Siegel, andWang (2019) investigate the role of cultural heritage in shaping the attitudes of CEOs towards uncertainty. They take a unique approach to cultural origin inference by comparing

6

CEO’s last name with the same last name of the passengers arriving in the port of New Yorkbetween 1820 and 1957.
Research on CEOs relying on social media is relatively new compared to research on firms
taking advantage of social media. It has been generally accepted that firms can benefit fromsocial media in a number of respects, as presented by Blankespoor, Miller, and White (2014),
Miller and Skinner (2015), Jung, Naughton, Tahoun, and Wang (2018), and Lee, Hosanagar,
and Nair (2018). On the other hand, there have been conflicting views among top executives
over the use of social media directly run by themselves, primarily due to the CEO users’
indifference to its positive effects or misconceptions about its negative effects, i.e., the costs
and risks of using social media. Porter, Anderson, and Nhotsavang (2015) report results froma qualitative content analysis of Fortune 500 CEOs’ use of Twitter, which present that senior
mangers using Twitter tend to engage in one-sided conversation even though Twitter is a
two-way medium and that most of them are using more formal language than general Twitterusers. They argue that this naturally results in low credibility and value of social media bysenior managers. Capriotti and Ruesja (2018) report that the presence of Fortune Global
500 CEOs in five major social media platforms including Facebook, Instagram, Linkedin,
Twitter, and YouTube is as low as 26.0% and that most of them are not adequately using
Twitter as 25% of the accounts are inactive and 90% of the active accounts have a low
activity.
Recent studies consistently emphasize the importance of social media use by executives.
Chen et al. (2018) analyze a sample of 155 S&P 1500 CEOs and CFOs and argue that the
emergence of “social executives” who directly communicate with investors through social
media and grant investors access to value-relevant corporate information, which can increase
investor participation and improve stock market liquidity. They also find that the tone intheir personal tweets is useful in predicting future earnings surprises. Elliott et al. (2018)
discuss the benefits of managers’ communicating via their personal Twitter account, arguingthat they can utilize social media to mitigate investors’ loss of trust and negative reaction to7

negative earnings surprises. Gao (2019) analyze a sample of 226 CEOs from the ExecuComp
database and show that positive words in CEO tweets can be used to predict positive futureabnormal returns. From a public relations standpoint, Men and Tsai (2016) discusses why
the public wants to engage with corporate CEOs on social media and why such engagement
matters in terms of organizational reputation. Chatterji and Toffel (2016), presenting theexample of Apple CEO Tim Cook, provide evidence that CEO activism on social media can
influence public opinion and consumer attitudes, even if the issues are unrelated to their corebusiness.

2.2.

Informational content

There have been a number of studies employing textual analysis to examine whether
messages on social media, stock message boards, analyst reports, firm financial statements,
and conference calls have informational content on stock market. Bollen, Mao, and Zeng
(2011) is known as one of the early papers that present promising results. They investigate
whether large-scale, collective mood states are correlated to Dow Jones Industiral Average(DJIA) over time. Xing, Cambria, and Welsch (2018) review the recent efforts on natural
language-based financial forecasting. The work done by Gjerstad, Meyn, Molnár, and Næss(2021) is a recent work that utilizes tweets in financial market prediction. In order to study
how financial markets respond to the live statements posted by the former U.S. president
Donald Trump on Twitter, they take an interesting approach by relating the precise timestamp information of each tweet to high-frequency financial data. Antweiler and Frank (2004)
examine the level of message activity on Internet stock message boards, and find that thebullishness of the stock messages are predictive of market volatility and disagreement among
the messages is associated with more trading volume. Baker and Wurgler (2006) propose anovel investor sentiment index that aggregates the information from six proxies using principal components analysis, and find that high investor sentiment predicts strongly low returns
in the cross-section. Das and Chen (2007) develop algorithms to extract sentiment from8

stock message boards, and find that the net of positive and negative opinion is related tostock index levels, volumes, and volatility. Tetlock (2007) study the relationship between
the content of media reports and daily stock market activity using principal components
analysis, and find that the fraction of negative words in media content serve as a proxy forinvestor sentiment. Tetlock et al. (2008) extend Tetlock (2007)’s analysis to examine theimpact of negative words on individual firm’s performances, and find that negative words
convey negative information about firm earnings and aspects of firm’s fundamentals are embedded in linguistic media content. Similarly, Chen et al. (2014) analyze the negative words
in the articles and comments posted on Seeking Alpha2 to find the fraction of those negative
words negatively predict stock returns. To study the information content of analyst report
text, Huang, Zang, and Zheng (2014) use a naive Bayes machine learning approach to extract textual opinions from analyst reports, and show that these provide useful information
to investors. Huang et al. (2015) use the partial least squares method to exploit the information of Baker and Wurgler (2006)’s six sentiment proxies in a more efficient manner, andfind that the sentiment index can predict the aggregate stock market well. Wolfskeil (2023)
investigates the link between firms’ voluntary disclosure strategies on social media in termsof timing, tone, and content and their equity returns. This work is close to ours in that thetiming aspect is reflected as the horizon in our approach, the tone aspect as sentiments, andthe content aspect as hashtags. Her work is different from ours, however, as it is interested
in tweets posted by firms’ official Twitter accounts rather than those by CEOs.
A growing body of research in economics and finance underscores the importance of developing word classification categories which gauge tone in economic and financial applications.
Loughran and McDonald (2010) provide evidence that a commonly-used Harvard-IV-4 TagNeg list substantially misclassifies words when gauging tone in financial applications. They
expand the word classification categories to six word lists (negative, positive, uncertainty,
litigious, strong modal, and weak modal). They find that their word lists have significant
2

See https://seekingalpha.com/.

9

relations with file date returns, trading volume, subsequent return volatility, standardized
unexpected earnings, and two separate samples of fraud and material weakness. Bodnaruk,
Loughran, and McDonald (2015) create a constraining word list to measure the level of financial constraints of publicly-traded companies, in addition to Loughran and McDonald(2010)’s six-word lists. They provide the word list by examining tens of thousands of words
that appear in at least 5% of all annual reports and selecting only words that would be
most likely considered constraining by other researchers. Jiang et al. (2019) construct amanager sentiment index based on the aggregated textual tone in firm financial statements
and conference calls. They measure textual tone as the difference between the number of
positive and negative words in the disclosure scaled by the total word count of the disclosure.
They find that the proposed manager sentiment significantly and negatively predicts future
aggregate stock market returns and cross-section of stock returns. Ke, Kelly, and Xiu (2019)
propose a supervised learning framework which measures a sentiment score that is adapted
to the return prediction and study the extent to which business news predicts observed variation of asset price. We also refer to Gentzkow, Kelly, and Taddy (2019) and Loughran and
McDonald (2020) for excellent reviews on textual analysis in economics and finance.

3.

Data
We begin this section by presenting formal modeling of the data space that we analyze

throughout this study. Our Twitter data space is noted as U × P × W, where U is a setof CEO users on Twitter, P is a set of tweets, or postings, created by CEO users, and W
is a set of words used in CEO tweets. Accordingly, for a certain time t ∈ T where T isa set of timestamps such as days, weeks, and months, the snapshot of the data space canbe noted as Ut × Pt × Wt , where Ut ⊂ U is a set of CEO users in Twitter existing at timet, Pt ⊂ P is a set of tweets created at time t, and Wt ⊂ W is a set of words used in thetweets at time t. This implies that, at time t, a CEO user ui,t ∈ Ut creates a tweet pj,t ∈ Pt

10

using a set of words Wi,j,t ⊂ Wt , where user i ∈ {1, ..., |U|} and tweet j ∈ {1, ..., |P|}. Someof the words in W can take the form of hashtags by using a hash symbol (#) as a prefix,
which are denoted as H ⊂ W, where H is a set of hashtags. Replacing W and Wt with H
and Ht , respectively, the aforementioned data space can be transformed to U × P × H and
Ut × Pt × Ht at time t, respectively, when we are only interested in hashtags instead of allwords.

3.1.

Discovering CEO Users in TwitterAs the first step to identify CEO users U on Twitter, we adopt the approach proposedby Lee and Song (2022). We begin by collecting a large pool of random users. Twitter opens
part of its user-created data to the public via Application Programming Interface (API),
called Twitter API.3 As the Twitter API does not allow users to search for Twitter users
by querying over their bio text, we instead use Twitter Streaming API4 to collect random
tweets written in English, each of which contains the author information. In other words, wecollect a set of random users from a set of random tweets. The Streaming API allow users
to retrieve real-time tweets from Twitter and is known to provide at most 1% sample of all
the tweets created on Twitter at a given time. The 1% sample at a given time may soundtoo small to be used in a study, but it could form a large volume of tweets when collected
over a long period of time. The Streaming API allows users to filter real-time tweets on
a set of keywords of interest to the user. As a means to collect random tweets written in
English, we use a set of extremely general words such as ‘a’, ‘and’, and ‘the’, instead of
actual keywords, which is a commonly-used trick to collect random or general tweets. Table
1 presents the monthly statistics of the data collected for 3 years from January 2019 to
December 2021, which totals approximately 4.7 billion (specifically 4,704,883,067) uniqueEnglish tweets created by approximately 177 million (specifically 176,850,729) unique users.
In order to discover CEO users from the pool of random users, we examine the bio text of3
4

See https://developer.twitter.com/en/docs/twitter-api.
See https://developer.twitter.com/en/docs/twitter-api/v1/tweets/filter-realtime/overview.

11each user, which can be found in the Description field of each User object in the tweet data.
This is based on our observation that many CEO users on Twitter tend to indicate their roleas CEO in their bio. For example, Apple CEO Tim Cook (@tim cook) describes himself in
his Twitter bio as “Apple CEO” and Google CEO Sundar Pichai (@sundarpichai) describes
himself as “CEO, Google and Alphabet”. Some CEO users link the Twitter account of
the company they work for to their bio. For example, Arianna Huffington (@ariannahuff)
describes herself as “@HuffPost Founder. Founder & CEO of @Thrive Global”, which gives
us an idea of how to identify the account of their company as additional information to
extract.
As there are many different ways to indicate their CEO role and those expressions can
exist with some irrelevant text, we employ a text pattern matching technique called regularexpressions that are designed to catch only the CEO-indicating expressions in the bio text.
In addition to leveraging regular expressions to find CEO users, we also check if the foundaccount is verified by Twitter. A verified account indicates that Twitter has verified thatthe account of public interest is authentic.5 Verified accounts have a blue verified badge
on Twitter. We utilize this unique feature of Twitter as a means to guarantee the user’s
popularity or authority.
From the 177 million users in our pool, 5,226 users are identified by the aforementioned
process. We filter out 512 accounts that are suspended or closed for some reason using theTwitter API6 or that prove to be not CEO accounts after thorough manual investigation,
which results in the set U of 4,714 users as the final sample of CEO users, i.e., |U| = 4714.
Many of the false positives are attributed to ambiguous descriptions of CEO roles, e.g.,“Follow our CEO: @TonyPorterACTM” or untrue statements, e.g., “CEO of coffee.” Table
2 presents the top-20 users identified, sorted by the number of followers they have. Elon
Musk (@elonmusk) tops the ranking, followed by Paris Hilton (@parishilton) and Dwayne5

See https://help.twitter.com/en/managing-your-account/about-twitter-verified-accounts.
See
https://developer.twitter.com/en/docs/accounts-and-users/follow-search-get-users/apireference/get-users-lookup.
6

12

Johnson (@therock). The late Kobe Bryant (@kobebryant), who was once an NBA superstar and later the CEO of a production company called Granity Studios, ranks number four,
as his Twitter account is still active as of August 2022, although there have been no new
tweets posted since his death.
What is unique about this sample of CEO users is that it covers a wide range of CEOs
from famous star CEOs of large firms to lesser-known CEOs of smaller businesses. Table 3
presents the bottom-20 users in the ranking who have only several hundreds or even dozens
of followers but nevertheless are Twitter verified users. Previous literature on CEO heavily
relies on proprietary data sets such as Standard and Poor’s ExecuComp database that focuses
on executives of large firms. For example, Chen et al. (2018) and Gao (2019) identify 155
and 226 Twitter accounts, respectively, of CEOs and/or CFOs from ExecuComp. They
start with a list of executives in the data set to search Twitter for any matching accounts
run by the executives. In contrast, we employee a data-driven, automated approach byexamining a large amount of historical tweet data to discover CEO users. Our data set
covers a unique and wide range of 4,714 CEOs who have never been identified and analyzed
in the previous work but are expected to represent the collective voice of different levels of
CEOs. We believe that we have significantly overcame the limitation from the low rate of
CEO presence on social media reported in Capriotti and Ruesja (2018), which allows us tominimize the sampling bias.
One issue to note regarding finding CEOs from their bios is that Twitter users can update
their bios at any time they would want. For that reason, it is common that our CEO users
indicate their CEO role in their bio at some point, but they do not at other points fortheir own reasons. In other words, there is no guarantee that the users who we previously
identified as CEO are currently still holding their CEO position. For example, John Legere(@johnlegere) was identified as CEO in our data set, but he stepped down as T-Mobile CEO
in April 2020 and updated his bio accordingly, so he is no longer a CEO. To address thisissue, we assume that, once a user is identified as CEO in our data set, the user is considered13

to be a CEO whether or not their current bio actually indicates their CEO role, unlike theapproach adopted by Gao (2019) who exclude the tweets posted outside CEO tenure. This
assumption arises from the fact that our data set does not allows us to track their term as
CEO. We believe, however, that our assumption is acceptable as the users are still importantand influential on social media, even if they currently no longer act as official CEO.
Another issue is that some of the CEO users do not use their Twitter accounts solely for
their companies. In other words, they also use Twitter for their own good, not necessarily
for the company they represent. Chen et al. (2018) reports that top executives use their own
personal Twitter accounts not only for breaking company news and describing their work
related day-to-day activities but also for sharing their unrelated-to-work personal interests.
We, again, do not exclude the users from our data set simply because they do not talk about
their companies and, for the same reason, do not exclude any of their tweets. We assume
that everything they mention on Twitter matters in one way or another, even if what they
say on Twitter appears irrelevant, personal, or trivial. Unlike our approach, Gao (2019)
exclude tweets about firm operations because their study focuses on the information content
of CEOs’ personal activities.
The last issue to note is that we do not consider the locations of CEO users. We only
consider 1) whether their bio text has any expression indicating CEO and 2) whether theaccount in question is verified by Twitter. This means that CEOs located outside the UnitedStates can be included in our data. We do not attempt to identify and exclude those non-U.S.
CEO users because, according to a web report from a market and consumer data firm called
Statista,7 U.S. Twitter users represent approximately 75% of all Twitter users in English
speaking countries such as United Kingdom and Canada, so we believe that our data set
mostly captures the voice of U.S.-based CEOs. We also believe that even the voice from nonU.S.-based CEOs can be helpful in our stock market prediction, given the interconnectedworld economy.
7

See https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/.

14

3.2.

Collecting and Processing CEO Tweets

With the 4,714 CEO users found, we collect the set P of 62,754,212 tweets posted by
the users, i.e., |P| = 62754212. The Twitter API8 allows users to retrieve up to 3,200 mostrecent tweets of a user in a structured manner, as long as the account is set to public. As
CEO users typically post much more than 3,200 tweets, however, we use web scraping to
retrieve up to 200,000 tweets from each account. The mean and the standard deviation of
tweet count per user are 13,272 and 19,582, respectively. Figure 1 presents the histogram oftweet count per user. The histogram is skewed right, and tweet count of up to 15K accountfor approximately 75% of the total count.
Figure 2 demonstrates the change of CEO tweet count over time, represented with a solidline on the left primary Y-axis, and the change of CEO user count over time, represented
with a dotted line on the right secondary Y-axis. The earliest tweet found in our data wasposted on June 29, 2006 by Kevin Systrom (@kevin), which accordingly becomes the startdate of our data set, and the last date is December 31, 2021, which we determine for thisstudy. As presented in the figure, both the tweet and user counts increase at a fast rate until
2012, after which the user count continues to grow gradually, while the tweet count plateaus
until 2020 but increases exponentially in 2020 and 2021 during the COVID-19 pandemic.

3.2.1.

Mining Hashtags

Now that we have collected necessary data, the next step is to mine keywords from tweet
text that will be used as predictors in our predictive models. Here, we focus on hashtags,
rather than on all words in the text, as hashtags are users’ active expressions of interest
that facilitate search and aggregation of messages related to the same topic (Laniado andMika (2010)). In other words, a hashtag can serve as an indicator of something popular or
wide-spread on social media. Although not all tweets have hashtags, hashtag sparsity can
8

See https://developer.twitter.com/en/docs/twitter-api/v1/tweets/timelines/api-reference/get-statusesuser timeline.

15

Figure 1. Histogram of tweet count per user
The histogram of tweet count per user is plotted.

Figure 2. CEO tweet count and user count over time
The change of CEO tweet count over time (a solid line on the left, primary Y-axis) and the change of CEO
user count over time (a dotted line on the right, secondary Y-axis) are plotted.

16

be overcome when a large number of tweets and their hashtags are aggregated.
We first identify all 1,861,649 English hashtags used in the CEO tweets and then calculate
the frequency for each of all the hashtags. When calculating hashtag frequencies, there
are two approaches. The first approach calculates how many ‘tweets’ contain the hashtag.
Formally, for a hashtag h ∈ H, the tweet count function T C(h) = |{(pj , h)|j ∈ {1, ..., |P |}}|.
The other approach calculates how many ‘users’ mention the hashtag. Formally, the user
count function U C(h) = |{(ui , h)|i ∈ {1, ..., |U |}}|. As there are 4,714 users in total in ourdata set, the user count cannot exceed 4714. The first tweet count metric focuses on the
resource aspect of social media data, whereas the second user count metric emphasizes thesocial aspect. While we equally utilize both metrics, we present mostly with the user countmetric throughout this paper, as it is novel and significantly reduces the numbers in ourdata. A performance comparison of the two metrics is presented later in Section 4.4.
As not all hashtags in H have frequencies higher enough to be used as regressors in
our predictive models, we select only the 1,000 most popular hashtags, which leads to the
reduced final set H of 1,000 hashtags, i.e., |H| = 1000. In terms of user count, the number one
hashtag #covid19 has frequency of 2,568, while the 1000th hashtag #ethics has frequencyof 283, which we believe is still high enough to avoid the data sparsity problem. In terms oftweet count, the number one hashtag is #quote, which has frequency of 121,410, while the
1000th hashtag is #payments, which has frequency of 1989.
Table 4 lists the top-100 popular hashtags, out of 1,000 hashtags, sorted by user count(the complete list of all 1,000 most popular hashtags is available on the Supplementary Online Appendix). The #covid19 hashtag tops the ranking used by more than 2,500 CEO users,
along with #coronavirus (number four) and #covid (number 19), which is a clear indicationof CEOs’ significant interest in the virus and the pandemic. The ranking includes manybusiness-related hashtags such as #business, #startup (#startups), #jobs, #entrepreneur,#marketing, and #work. It also has the hashtags that represent technologies, tech services,or tech companies, such as #tech, #twitter, #facebook, #podcast, #socialmedia, #tech-17

nology, #periscope, #apple, #ai, #google, #instagram, #iphone, #bitcoin, #digital, and#youtube.
The ranking also includes city names such as #nyc, #london, #newyork, and #paris,
most of which indicate where they tweeted, and the names of countries or internationalorganizations such as #usa, #china, #brexit, and #india, which indicate CEOs’ interest
in international affairs. The ranking also shows CEOs’ interest in sports, represented by#superbowl, #worldcup, #olympics, and #nfl, and in politics represented by #vote and#trump. CEOs also show interest in social issues using such hashtags as #blacklivesmatter,#neverforget, #women, climatechange, #metoo, and #diversity. It is also interesting to seethat the CEO users like to follow the trend on Twitter that people use specific hashtags on different days of the week or to celebrate something, e.g., #tbt (meaning Throwback Thursday),#ff (meaning Follow Friday), #internationalwomensday, #mondaymotivation, #christmas,#halloween, #thanksgiving, #happynewyear, #mothersday, #throwbackthursday, #valentinesday, #blackfriday, #followfriday, #fathersday, #merrychristmas, #givingtuesday, and#earthday.
Table 5 lists the top-100 popular hashtags sorted by tweet count, as opposed to user count(the complete list of all 1,000 most popular hashtags is available on the Supplementary Online
Appendix). This tweet count-based ranking provides slightly different perspectives from theprevious user count-based ranking, mainly because there can be biases when some users
create a large number of tweets using a specific set of hashtags. For example, the number
one hashtag #quote, which has frequency of more than 121,000, does not appear in theprevious ranking. It turns out that approximately 34% of the tweet count is attributed to asingle user named Tim Fargo (@tim fargo).

3.2.2.

Mining Sentiments

In addition to the hashtags used by the CEO users, we believe that the sentiments expressed in their tweets can also be useful for our stock prediction for the following reasons.

18First, we consider all sentiment words in tweets, so that the word coverage of sentiments
is more comprehensive than that of hashtags. Second, we use discipline-specific word lists
focusing on financial contexts to construct sentiment time series. It is, therefore, expected
to predict financial events well. Third, the analysis based on sentiments improves the interpretability of the regressors, as only a small number of sentiment features are included as
predictors. In order to identify sentiments in CEO tweets, we employ the Sentiment Word
Lists defined in Loughran and McDonald (2010) and Bodnaruk et al. (2015) (available fromBill McDonald’s website). The sentiment lexicon covers seven sentiment categories: Negative, Positive, Uncertainty, Litigious, Strong Modal, Weak Modal, and Constraining, eachof which has a comprehensive list of sentiment words specifically designed for the language
used in the business domain.
Specifically, the seven sentiment categories above have 2,355, 354, 297, 904, 19, 27,
and 184 words, respectively. The Negative and Positive lists include words that appear innegative and positive contexts, respectively. For example, the Negative category starts withABANDON, ABANDONED, and ABANDONING, while the Positive category starts with
ABLE, ABUNDANCE, and ABUNDANT. The Uncertainty list categorizes words reflecting
uncertainty or imprecision, such as CONTINGENCY and INDEFINITE. The litigious list
includes words denoting a propensity for legal contest, such as CLAIMANT, TESTIMONY,
and LEGISLATION. The Strong Modal and Weak Modal lists categorize words expressing
levels of confidence, such as ALWAYS and STRONGLY for Strong Modal and ALMOST
and COULD for Weak Modal. Lastly, the Constraining list developed in Bodnaruk et al.
(2015) includes words related to financial constraints and starts with ABIDE, ABIDING,
and BOUND. The seven word lists were originally developed specifically in the context of
10-K filings, not of social media data, so the application of the lexicon to tweet text may
not be ideal. As modifying the foundational words in each list is beyond the scope of thispaper, however, we follow their recommendation on the word lists.
Since any of the words listed in each of the seven sentiment categories can match a word19

in W defined earlier, we denote Sc ⊂ W as a set of sentiment words in category c ∈ C,
where C is a set of the seven sentiment categories, and thus |C| = 7. Accordingly, our data
space U × P × W and Ut × Pt × Wt at time t ∈ T can be transformed to U × P × Sc andUt × Pt × Sc,t with respect to c ∈ C, respectively, when we are only interested in sentimentwords instead of all words.

3.3.

Creating Time Series Data

3.3.1.

CEO hashtag features

Due to the small number of CEO users and their tweets in the early period of data, weneed to trim the data set to avoid the data sparsity problem, which otherwise could have anegative impact on prediction. We remove all the tweets posted before Monday, June 29,
2009 (represented with a vertical dashed line in Figure 2), based on the two facts that 1) June2009 was determined by the Business Cycle Dating Committee of the National Bureau of
Economic Research (NBER)9 to be the end of the recession that had begun in December 2007
and 2) the number of CEO users had increased exponentially until that month as presented
in Figure 2, which provides a significant amount of tweets to be used for prediction. The
last date in our data set is December 31, 2021. This results in the reduced final set T oftimestamps: 3,152 days for daily analysis, i.e., |T | = 3152, and 653 weeks for weekly analysis,i.e., |T | = 653.
We next construct time series data for each hashtag, such that the number of tweets
containing each hashtag is counted with the tweet count metric and also the number ofusers mentioning each hashtag is counted with the user count metric. When calculating thecount, two different time frequencies, day and week, are applied to make daily and weekly
predictions of the stock market indicators. Here, daily counts are calculated first, basedon which weekly counts are aggregated by week. Formally, for a hashtag h ∈ H at a time9

We refer to the Business Cycle Dating Committee Announcement on September 20, 2010,https://www.nber.org/news/business-cycle-dating-committee-announcement-september-20-2010.

20

t ∈ T , the tweet count function T C(h, t) = |{(pj,t , h)|j ∈ {1, ..., |P |}}| and the user countfunction U C(h, t) = |{(ui,t , h)|i ∈ {1, ..., |U |}}|. For daily time series data, as there would be
no corresponding stock market data for weekends and holidays when the stock markets wereclosed, the counts for those closed days are aggregated with the count for the last week day
when the market was open. For example, the counts for Saturday and Sunday are aggregatedwith the count for Friday. This issue does not apply to the weekly time series data, as all
the daily counts are simply aggregated by week.
Another issue with these raw hashtag counts is that, as more people use Twitter overtime, the numbers of CEO users and their tweets and hashtags grow as well, as alreadyshown in Figure 2. To address this bias, we divide the raw tweet count by the total numberof tweets for that day or week with the tweet count metric and, likewise, divide the raw user
count by the total number of users for that day or week with the user count metric. EachUser object from Twitter API provides information of when the account was created, whichallows us to calculate the number of CEO users existing at a certain point. As there would
be many zeros in the raw counts, we add one to all values before dividing them by tweet or
user count. We define hashtag features as these normalized counts, which can be formallydenoted as follows:N ormT C(h, t) =T C(h, t) + 1
,
|Pt |

(1)

N ormU C(h, t) =U C(h, t) + 1
,
|Ut |

(2)

for hashtag h ∈ H and time t ∈ T .

3.3.2.

CEO sentiment features

Using the sentiment lexicon introduced in Section 3.2.2, we count the number of tweets
containing any of the matching words in each sentiment category with the tweet count metric21

and also the number of users mentioning the matching words with the user count metric,
and then create time series data for the seven categories by day, week, and month in thesame way as with hashtags. Formally, for a sentiment category c ∈ C at a time t ∈ T , thetweet count function T C(c, t) = |{(pj,t , sc,t )|j ∈ {1, ..., |P |}}| and the user count function
U C(c, t) = |{(ui,t , sc,t )|i ∈ {1, ..., |U |}}|. We finally normalize the raw counts by dividingthem by the total number of tweets for that timestamp with the tweet count metric anddivide them by total number of users for that timestamp with the user count metric. We
define sentiment features as these normalized counts, which can be formally denoted asfollows:

N ormT C(c, t) =T C(c, t) + 1
,
|Pt |

(3)

N ormU C(c, t) =U C(c, t) + 1
,
|Ut |

(4)

for sentiment category c ∈ C and time t ∈ T .

3.3.3.

Stock market indicators

In order to combine the hashtag and sentiment time series data described above with stock
market data, we retrieve the historical data of the four major stock indexes including S&P
500 (called SP500 hereafter), Dow Jones Industrial Average (Dow30), NASDAQ Composite(Nasdaq), and Russell 2000 (Russell2000) from the Yahoo! Finance web site10 with threedifferent frequency options, daily, weekly, and monthly. Each of the historical stock market
data sets includes six columns of values including Open, High, Low, Close, Adj Close, andVolume. We use Close and Volume as they are to refer to return and trading volume,
respectively, while deriving a new variable called Volatility being the difference between
High and Low to capture the uncertainty ranging from High to Low. In addition, we derive10

See https://finance.yahoo.com/.

22

another variable called Direction, which has one when the previous Close value goes up at
the current timestamp or zero when the Close value goes down or stay. Those four variables
Close, Volume, Volatility, and Direction will be used as stock market indicators to predictin the following section.

4.

Stock Market Prediction
In this section, we present the results and findings from predicting the three numericstock market indicators Close, Volume, and Volatility, which can be classified as a regression
problem, and also the Direction of stock prices, which is a binary classification problem. We
also compare the performance of our approaches with that of other baselines.

4.1.

Predicting Numeric Stock Market Indicators

We predict various stock market indicators using hashtags used by CEOs on Twitterand their sentiment words. This is a linear regression problem, as we aim to forecast thestock market indicators as numeric values. In particular, we consider the following predictivemodel for the stock market indicators:
0

yt = α + xt−1 β + εt , t = 2, ..., |T |(5)

where yt is the stock market indicator, xt−1 is m × 1 vector of regressors at t − 1, εt is theidiosyncratic error term, α is a constant, β is m × 1 vector of parameters of interest, andt is a timestamp of either daily or weekly frequency. For daily regression, the number ofobservations is |T | = 3152, and for weekly regression, |T | = 653.
The hashtag time series data for the 1,000 hashtags in H and the sentiment time series
data for the seven sentiment categories in S are used as regressors xt−1 in our predictive models, while the stock market time series data for the three stock market indicators (Close, Volume, and Volatility) from the four stock indexes (SP500, Dow30, Nasdaq, and Russell2000)
23

are used as dependent variables to forecast. We therefore define 12 dependent variablesfrom the combinations of the three stock indexes and the four stock market indicators andname them SP500 Close, Dow30 Close, Nasdaq Close, Russell2000 Close, SP500 Volume,
Dow30 Volume, Nasdaq Volume, Russell2000 Volume, SP500 Volatility, Dow30 Volatility,
Nasdaq Volatility, and Russell2000 Volatility. In addition, we add the first-order lagged values of the dependent variables to the set of regressors for both the daily and weekly data inorder to take into account possible serial correlation in the error term. We put ‘ L1’ after
each stock market indicator name as a suffix, e.g., ‘SP500 Close L1’. Note that we do not
combine the hashtag features and sentiment features into a single model, as the two sets offeatures are heterogeneous and thus should be treated separately, as supported by Cookson,
Lu, Mullins, and Niessner (2024).
In predictive models for stock returns, there have been concerns on the spurious regressions or regressions with persistent lagged regressors (e.g., Granger and Newbold, 1974;
Stambaugh, 1999; Ferson, Sarkissian, and Simin, 2003). To address the issue, we proceed
to unit-root test to check whether the time series for each regressor and dependent variable
is either stationary or non-stationary and transform the variable by taking the percentagechange if it is non-stationary. Then, as the last step, we standardize all variables by removing
the mean and scaling to unit variance in order to facilitate comparison and interpretation
across regressors. Figures 3 and 4 present the standardized percentage changes of the twelve
business-related hashtag time series as examples and the standardized percentage changesof seven sentiment time series, respectively.
It is well-known that the OLS estimator is inconsistent when the number of regressors mis larger than the number of observations |T | in time series (note that this only applies toour 1,000 hashtag features, not to the seven sentiment features). Thus, when 1,000 hashtag
features are used as the regressors, we estimate the model using the least absolute shrinkage
and selection operator (lasso) approach with L1 penalty introduced by Tibshirani (1996)
and select relevant regressors. As a comparison, we have estimated the model by the OLS

24

Figure 3. Standardized percentage changes of the twelve business-related hashtag time series
We standardize the percentage changes of the hashtag series by removing the mean and scaling to unit
variance. The standardized percentage changes of the twelve business-related hashtag time series over time
are plotted.

Figure 4. Standardized percentage changes of the seven sentiment time series
We standardize the percentage changes of the sentiment time series by removing the mean and scaling tounit variance. The standardized percentage changes of the sentiment time series over time are plotted.

25

with the most-frequently mentioned regressors of which the number is smaller than |T |and confirmed that the lasso regression outperforms the OLS. The lasso approach estimatescoefficients of the linear model by minimizing the sum of squared residuals subject to ab is defined bypenalty term. The lasso estimate (bα, β)b = arg min
(b
α, β)


|T | 
X0yt − α − xt−1 β

2

+λ

m
X

 t=1

i



|βi | ,


(6)

where λ ≥ 0 is a tuning parameter and βi is a scalar coefficient for i-th regressor in xt−1 .
Because of the penalty function the lasso tends to generate some coefficients that are exactly
zero, so that we can select the non-zero coefficients as relevant regressors.
It has been shown in the recent studies that the lasso could be inconsistent for variableselection (Zou, 2006). Furthermore, if variables have a grouped structure, it is more desirable
to proceed prediction based on a subset of important groups (Yuan and Lin, 2006). Thus,
we also utilize the adaptive group lasso method to overcome drawbacks of the original0

0

0

lasso. We assume that xt can be grouped into l factors as xt = (xt1 , ..., xtl ) , where xti =0b is defined(xti1 , ..., xtidi ) is a group of di variables. The adaptive group lasso estimate (b
α, β)

by
b = arg min
(b
α, β)


|T | 
X0yt − α − xt−1 β

 t=1

2

+λ

l
X
i



wi ||βi || ,


(7)

0

where wi ≥ 0 is a weight for i-th group in xt−1 and where ||βi || = (βi βi )1/2 . Below weprovide a detailed procedure for grouping hashtags. Note that if the number of variables
in each group is one (i.e., di = 1 for all groups) and the weight is one (i.e., wi = 1 for
all groups), the equation (7) essentially goes back to the lasso equation (6). In both lasso
and adaptive group lasso, users are required to select the tuning parameter λ. We find the
optimal parameter by searching over λ = [0.001, 0.003, 0.01, 0.03, 0.1, 0.3]. Throughout the
paper, we estimate the standard errors using heteroskedasticity– and autocorrelation–robust
standard errors (Newey and West, 1987).

26

We perform out-of-sample forecasting by splitting the entire time series data set into
an training set and a test set. We estimate models using the lasso and adaptive grouplasso on the training set and predict the dependent variable at the very next timestamp in
the test set based on the estimated coefficients when hashtags are used as the regressors.
Similarly, OLS is applied to the model when sentiment features are used in place of hashtags.
Two dimensions are considered for evaluation: model size and horizon. For the model size
dimension, we evaluate how the training set size affects the forecasting performance bytrying nine different sizes ranging from 0.1 (i.e., 10% of the entire time series) to 0.9 (i.e.,
90% of the entire time series). Following the literature on forecasting, we consider one of
the most commonly-used schemes, rolling windows scheme. As well described in Elliott
and Timmermann (2016), the rolling windows scheme for forecasting refers to the practiceof adopting an equal-weighted window of the most recent ω̄ observations to estimate theparameters of the model and dropping older observations as new observations are added.
Specifically, we use the following estimator based on the rolling windows for OLS whensentiment features are of interest:(b t ) = arg min(bαt , βtX0ys − α − xs−1 β

2

)
.

(8)

s=t−ω̄+1

On the other hand, we have the following estimators for lasso:(b t ) = arg min(bαt , β

)
t

2
X
X
0
|βi | .
ys − α − xs−1 β + λs=t−ω̄+1(9)i

The rolling windows scheme can be similarly applied to the adaptive group lasso.
b)
Let ft+q|t be q-step-ahead forecast of the dependent variable given the estimates (bαt , βtat time t with the forecast horizon q based on the rolling windows scheme. Then the mean27

squared errors is calculated as the sample loss function as follows:|T |−q−1

M SE = (|T | − q)X

e2t+q|t ,

(10)

t=1

with q-step-ahead forecast errors, et+q|t = yt+q − ft+q|t , over a sample t ∈ {1, ..., |T | − q}. Wethen find the best model that yields the smallest MSE in each stock indicator.
For the horizon dimension, we define horizon as the time length of the future to bepredicted by the model. For example, if the horizon is set to 1 for weekly analysis, a model
is built with the data of up to a certain week and it attempts to predict the target value ofthe very next week; if the horizon is set to 2, the model attempts to predict the target valueof the week after next week. We evaluate how far the model predicts well for the future
by considering different horizons ranging from 1 to 5 for both weekly and daily analysis.
Formally, horizon q ∈ Q = {1, 2, 3, 4, 5}.
Regarding the feature selection algorithms for the hashtag features, specifically, we compare the simple lasso technique with the adaptive group lasso technique, based on the ideathat grouping of similar hashtags could help to improve the model performance. As there
is no controlled vocabulary for hashtags on Twitter, some hashtags can refer to the samething. For example, the four hashtags, #covid, #covid19, #covid2019, #covid 19, and#coronavirus share exactly the same meaning and thus can be used interchangeably. Examples include different names of an entity as with #coronavirus and #covid19, different
representations of an entity as with #covid19 and #covid 19, singular and plural nouns aswith #startup and #startups, variations of a word as with #happy (adjective) and #happiness (noun), or two words that substantially overlap in their meanings as with #quarantine
and #stayhome. Based on our belief that it would make more sense to consider those hashtags to be one rather than separated, we group the hashtags that are so similar that they
can be used interchangeably.
In order to find those very similar hashtags, we first apply some heuristics: a) find the28pairs of hashtags with and without an underscore, e.g., #covid 19 and #covid19, b) findthe pairs of hashtags with and without trailing numbers or with different trailing numbers,e.g., #iwd and #iwd2021 or #iwd2021 and #iwd2020, c) find the pairs of hashtags thathave the same word stem in common using the word stemming technique, e.g., #inspiration
and #inspiring. In addition to the simple heuristics, we also consider correlation between
hashtags in the time series data. Specifically, we include the pairs of hashtags if the correlation coefficient between the hashtags is greater than 0.95 or less than -0.95, which means
extremely highly positive/negative correlation, e.g., #thanksgiving and #blackfriday (correlation coefficient of 0.987). As the weekly and daily time series differ, there are both weekly
and daily versions of highly correlated hashtags.
Once all the pairs of similar hashtags have been identified, we build an undirected graphin which nodes represent hashtags and edges represent that the two hashtags are similar.
We then discover all of the connected components in the graph, in which any two nodes areconnected to each other by paths. Figure 5 demonstrates three examples of the connectedcomponents found. In the first hashtag cluster, #stayathome can be connected to #quarantine via #stayhome, which results in all those three hashtags being in the same cluster.
The second hashtag cluster in the figure comprises three hashtags, #blackfriday, #thanksgiving, and #happythanksgiving, all directly connected to each other. The third hashtag
cluster demonstrates a larger cluster of six hashtags that are partially interconnected to eachother. Table 6 presents all of the 56 identified clusters of similar hashtags from the weekly
time series data. We manually examined the clustering results and found no errors at leastfrom the precision perspective. This hashtag clustering information is finally passed to theadaptive group lasso algorithm as the group index information.
Figure 6 demonstrates how the MSE from weekly forecasting changes with different model
sizes for each of the hashtag time series and sentiment time series and for each of the four
stock markets. Here, the lower MSE, the better performances. The cross (x) on each line
indicates the minimum MSE achieved. The figure shows that the MSE tend to go up as the29Figure 5. Three examples of clusters of similar hashtags
Three examples of clusters of similar hashtags are plotted in a graph. The middle cluster comprises three
hashtags that are fully-connected, while the other two comprise hashtags that are partially-connected.

model size increases until it drops when the model size is the largest, i.e., 0.9. Most of theminimum MSE are achieved when the model size is either small or large. The forecasting
based on sentiment features always outperforms two lasso approaches with hashtag features.
The hashtag feature selection by the adaptive group lasso performs better than the one by
the simple lasso in terms of MSE, which indicates that our hashtag clustering works well inweekly forecasting. Figure 7 presents the MSE from daily forecasting with different model
sizes. The sentiment features perform better than hashtags features based on both lasso
approaches. The MSE from the simple lasso are more stable than those from the adaptive
group lasso in daily forecasting. Overall, the minimum MSE from all three methods are close
to each other.
Figure 8 provides a slightly different perspective: how the MSE change with different
horizons in weekly forecasting. The sentiment-based forecast again performs the best in mostcases over different horizons except for Russell2000 Close where the adaptive group lasso
with hashtag features slightly perform better than the forecasting with sentiment features.
Among the figures for sentiment features, the results for Volume and Volatility show thatthe lowest MSE are mostly achieved when the horizons are small, which is in line with our
expectation. On the other hand, results for Close indicate that MSEs are relatively flat overdifferent horizons. The MSE from the adaptive group lasso are more stable than those fromthe simple lasso over different horizons in weekly forecasting. Figure 9 presents MSE change

30

Figure 6. Mean Square Errors by different model sizes from weekly forecasting
For weekly forecasting, we calculate the Mean Squared Errors by different model sizes ranging from 0.1 to
0.9. The lower MSE, the better.

31

Figure 7. Mean Square Errors by different model sizes from daily forecasting
For daily forecasting, we calculate the Mean Squared Errors by different model sizes ranging from 0.1 to 0.9.
The lower MSE, the better.

32

Figure 8. Mean Square Errors by different horizons from weekly forecasting
For weekly forecasting, we calculate the Mean Squared Errors by different horizons ranging from 1 to 5. The
lower MSE, the better.

33

Figure 9. Mean Square Errors by different horizons from daily forecasting
For daily forecasting, we calculate the Mean Squared Errors by different horizons ranging from 1 to 5. The
lower MSE, the better.

34

with different horizons in daily forecasting. Similarly, the forecasting based on sentimentfeatures outperform two lasso approaches.
Tables 7 and 8 present the parameter setting that yields the best forecasting performancefor each of the combinations of two frequencies, four stock markets, three targets, three
feature types, nine model sizes, and five horizons. The lowest MSE achieved from thehashtag and sentiment features are 0.7 and 0.548, respectively, both from SP500 Volume.
We believe that these forecasting performances are promising, considering the widely-known
difficulty of stock market prediction.
It also shows that the optimal forecasting model is varying across different target variables. This finding confirms that which factors matter in explaining stock market movement
varies by stock market indicators and stock indexes of interest. SP500 index tracks 500 large
U.S. companies and the stocks in the index represents roughly 75% of all stocks on the New
York Stock Exchange (NYSE). Dow30 index follows the 30 largest U.S. companies which
represents approximately 25% of all publicly traded stocks and it gauges the overall healthof financial markets for some of biggest “blue chip” companies. Nasdaq index tracks about
3,000 companies traded on the Nasdaq Exchange and measures the performances of innovative and tech-sector companies. Russell2000 index tracks 2,000 of the smallest-capitalizationcompanies in the stock market and it is frequently used as a benchmark for small-cap investors. Since these indexes track different types of stocks and indicators measures different
characteristics of stock movement, it makes sense to see differential predictive power of hashtags and sentiment features across different stock indexes and indicators.
In order to learn which hashtags are informative of predicting stock markets, we estimate
the equation (5) using the full sample period. For brevity, Table 9 only reports the top5 most significant hashtag features from lasso regression for weekly frequency if there are
more than five significant hashtag features identified, sorted by absolute coefficient. It turns
out that there are lots of hashtags which are statistically significant. This confirms thatmany hashtags have significant predictive power on the stock market indicators and that35

the estimation methods are capable of capturing such relationships in the model where thenumber of regressors is larger than the number of observations.
We find that various CEO hashtags predict stock returns, which is in line with thosepapers on stock return predictability such as Baker and Wurgler (2006) who find that highinvestor sentiment predicts strongly low returns in the cross-section. Interestingly, trading
volume and volatility in addition to stock returns are also predicted by many hashtags. The
result is similar to that in Das and Chen (2007) who find that the net of positive and negative
opinion from stock message boards predicts stock index levels, volumes, and volatility andthat in Antweiler and Frank (2004) who find that the bullishness of the stock messages are
predictive of market volatility and disagreement among the messages is associated with more
trading volume.

4.2.

Predicting Stock Price Directions

The hashtag time series data for the 1,000 hashtags in H and the sentiment time series
data for the seven sentiment categories in S are again be used as features in our predictivemodels, while the stock market time series data for the stock price directions from the four
stock indexes are used as targets to predict. We therefore define four target variables named
SP500 Direction, Dow30 Direction, Nasdaq Direction, and Russell2000 Direction. This is abinary classification problem, as opposed to the regression problem that we dealt with in
Section 4.1, as we attempt to classify the stock price direction as either 0 (going down or
staying) or 1 (going up). In order to make the original time series data simple enough to
fit into classification algorithms, we do not add the first-order lagged values of the target
variables for this classification task, unlike the previous forecasting. We also keep the original
features as predictors without transforming them (Park and Phillips, 2000). We, again, do
not combine the hashtag features and sentiment features into a single model.
The rest of the process is basically the same as the previous forecasting except that it
is a classification task. We perform out-of-sample prediction by splitting the entire time36

series data set into a training set and a test set. We build models on the training set by
applying six classification algorithms ranging from traditional classification algorithms suchas k-Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and Support
Vector Machines to state-of-the-art Deep Neural Network. These algorithms are listed in asurvey by Jiang (2021) as widely-used classification algorithms for stock market prediction.
We then predict the Direction variable at the timestamp in the test set that is horizon-steps
away. Formally, we consider the following predictive model for the stock price direction:

yt+q =
1 if p(xt ) > 0.5

(11)



0 otherwise
where yt+q is the q-step-ahead stock price direction over a sample t ∈ {1, ..., |T | − q} andq ∈ Q, and p(xt ) is the estimated probability at time t ∈ T that the stock price would go
up at t + q with the given features. Different classification algorithms determine different pfunctions depending on their underlying ideas. When building a model based on a selectedclassification algorithm, we find the optimal set of hyper-parameter values whenever possible
by cross validation, i.e., further splitting the training set into a training set and a validationset.
As with the previous forecasting, two dimensions are considered for evaluation: model
size and horizon. For the model size dimension, we evaluate how the training set size affects
the prediction performance by taking nine different sizes ranging from 0.1 to 0.9. We again
employ the rolling windows scheme for prediction. For the horizon dimension, we consider
five different horizons ranging from 1 to 5 for both weekly and daily analyses. To compare
prediction performances model by model, we employ classification accuracy, which is one of
the most commonly-used and intuitive classification metrics, ranging from 0 to 1. Formally,
accuracy is defined as the number of correct predictions divided by the number of all samples

37

Figure 10. Prediction accuracy by different model sizes from weekly prediction
For weekly prediction, we calculate the prediction accuracy by different model sizes ranging from 0.1 to 0.9.
The higher accuracy, the better.

as follows:|T |−q−1

Accuracy = (|T | − q)X

rt+q|t ,

(12)

t=1

with q-step-ahead prediction results,

rt+q|t =
1 if yt+q = ft+q|t

(13)



0 otherwise
over a sample t ∈ {1, ..., |T | − q} and q ∈ Q, where yt+q is the true target value (either 0or 1) at time t + q from the training set and ft+q|t is q-step-ahead prediction (0 or 1) of thetarget variable at time t with the prediction horizon q from the test set based on the rolling
windows scheme. We then find the best model that yields the highest accuracy.
Figures 10 and 11 demonstrate how the weekly and daily prediction accuracy, respectively,changes with different model sizes for each of the hashtag time series and sentiment time series
and for each of the four stock markets. Here, in contrast to the MSE previously, the higher
classification accuracy, the better. The cross (x) indicates the maximum accuracy scoreachieved. The figures show that the accuracy tends to go up as the model size increases and38

Figure 11. Prediction accuracy by different model sizes from daily prediction
For daily prediction, we calculate the prediction accuracy by different model sizes ranging from 0.1 to 0.9.
The higher accuracy, the better.

Figure 12. Prediction accuracy by different horizons from weekly prediction
For weekly prediction, we calculate the prediction accuracy by different horizons ranging from 1 to 5. The
higher accuracy, the better.

39

Figure 13. Prediction accuracy by different horizons from daily prediction
For daily prediction, we calculate the prediction accuracy by different horizons ranging from 1 to 5. The
higher accuracy, the better.

thus most of the maximum accuracy scores are achieved when the model size is the largest,i.e., 0.9. The sentiment features generally outperform the hashtag features, although which
type outperforms with respect to the highest accuracy varies case by case. Figures 12 and13 present how the weekly and daily accuracy, respectively, changes with different horizons.
Contrary to our expectation that the farther the model predicts, the worse the prediction
would be, the accuracy does not always go down as the horizon increases, and the highest
accuracy scores are achieved when the horizons are larger than one in most cases. Thisimplies that it takes some time for what CEO mentions on Twitter to get reflected in thestock markets in terms of stock price direction.
Table 10 presents the parameter setting that yields the best prediction performance alongwith the contributing classification algorithm for each of the combinations of frequency, stock
market, feature type, model size, horizon, and classification algorithm. The highest accuracy
scores achieved from the hashtag and sentiment features are 70.3% and 64.5%, respectively,
both from weekly Dow30, while the lowest accuracy scores achieved are 55.4% from daily
Russell2000 and 56.4% from daily SP500, respectively. We believe that these prediction
performances are surprisingly good, considering the widely-known difficulty of stock market
prediction. As stated by Nguyen and Shirai (2015), the classification accuracy of 56% orhigher is generally reported as satisfying results for stock predictions. Interestingly, weekly40

predictions clearly outperform daily predictions, which reflects the difficulty of short-term
stock market prediction with high variability. A variety of classification algorithms prove
to contribute the best performances, ranging from the simplest k-Nearest Neighbors to thestate-of-the-art Deep Neural Network.
As with the previous forecasting, we, again, identify the hashtags that are informative ofstock price direction prediction over the full sample period. To that end, we calculate theimportance score of each hashtag feature, which indicates how much a feature contributes tothe classification outcome. For Logistic Regression, we can simply use the coefficients fromthe model as the importance scores; The Decision Trees algorithm supports native featureimportance scores; for k-Nearest Neighbors, Support Vector Machines, and Deep Neural Network, as they do not offer native feature importance scores, we instead use the permutation
feature importance scores, which can be acquired by calculating relative importance scoresthat are independent of the model used (Altmann, Toloşi, Sander, and Lengauer, 2010).
When selecting the classification algorithm out of five for each of the target, we select thealgorithm that yields the best performance, i.e., the highest accuracy, when the model size
and the horizon are set to 0.9 and 1, respectively. Table 11 reports the top-5 most important
hashtags features from each of the algorithm selected for weekly frequency only, sorted byabsolute importance score. It turns out that a variety of hashtags have predictive powerof the direction of stock prices. Some hashtags have positive importance scores for SP500
Direction, e.g., #brexit and #superbowl, whereas other hashtags have negative scores, e.g.,#ff, #internationalwomensday, and #fb.

4.3.

Comparison of Hashtag/Sentiment Features with Full Text Features

As elaborated earlier, we select the 1,000 most popular hashtags and predefined sentimentwords for stock market prediction, as opposed to all words in the CEO tweet text. In order
to compare our hashtag and sentiment features with full text features, we apply the Bidirectional Encoder Representations from Transformers, or BERT (Devlin, Chang, Lee, and41

Figure 14. Prediction accuracy comparison: hashtag/sentiment features versus full text
features
The figure compares the prediction accuracy from the models with the hashtag/sentiment features and theaccuracy from the model with the full text features. The higher accuracy, the better.

Toutanova, 2019). Everything is the same as the previous stock price direction classificationtask, except that full text is used as features for classification. We perform this classification
only for SP500 due to the considerable computation cost for the BERT algorithm. As shownin Figure 14, our hashtag and sentiment features outperform the full text features in termsof not only general performance but also the highest accuracy, i.e., 64.1% from the hashtag
features versus 62.5% from the sentiment features versus 60.5% from the full text features.
We believe that it is a better idea to utilize a select list of hashtags and sentiment words
than considering all words in terms of both performance and computational cost.

4.4.

Comparison of User Count with Tweet CountAs described in Section 3.2, we rely on both the user count and tweet count metrics whencreating time series data but mostly present the user count-driven outcome in this paper.
Recall that both metrics have different sets of top-1,000 hashtags as shown in Tables 4 and5, whereas they have the same set of predefined sentiment words for the sentiment features.
Figure 15 presents the pairwise lowest MSE (the lower MSE, the better) from the user countand tweet count metrics for each of the three weekly stock market indicators and for each
42

Figure 15. Mean Squared Errors comparison: user count-based versus tweet count-based
The figure compares the Mean Squared Errors from the user count-based models with those from the tweet
count-based models. The lower MSE, the better.

43

Figure 16. Prediction accuracy comparison: user count-based versus tweet count-based
The figure compares the prediction accuracy from the user count-based models with that from the tweet
count-based models. The higher accuracy, the better.

of the four stock markets, and Figure 16 exhibits the pairwise highest accuracy scores (thehigher accuracy, the better) from the two metrics for the weekly stock price direction. While
it is hard to tell from Figure 15 alone which metric is better for regression, the overall
classification performance of the user count metric slightly outweighs that of the tweet countmetric as shown in Figure 16. This implies that our novel user count metric works betterfor classification or at least performs as well as the common tweet count metric. The tweet
count-driven results are available on the Supplementary Online Appendix.

4.5.

Comparison with Macroeconomic Variables

A large number of studies in finance literature have shown that a variety of macroeconomic and financial variables can predict stock returns (Fama and French, 1988, 1989;
Cochrane, 1991, 2007; Pesaran and Timmermann, 1995, 2000; Welch and Goyal, 2008;
Van Binsbergen and Koijen, 2010; Golez and Koudijs, 2018). If these variables are sufficient statistics for the relationship with stock indicators, our proposed CEO sentimentfeatures would not have predictive power after controlling for them. In order to check such a
possibility, we now include macroeconomic and financial variables considered by Welch and
Goyal (2008) in the models for the stock market indicators (available from Amit Goyal’s

44website) and estimate the relationship between stock indicators and sentiment features. In
that way, we can test if the information from the CEO sentiment features is subsumed by
the macroeconomic and financial variables. As these macroeconomic and financial variables
are available at most as monthly data, we reconstruct our sentiments on a monthly basis.
We first present the list of dependent variables and control variables.
• Dependent variables: Four variables are considered. The first three variables are thesame as before; Close, Volume, and Volatility. The fourth dependent variable is theequity premium (Excess Return), that is, the total rate of return on the stock market
SP500 minus the prevailing short-term interest rate given as

rt =Pt + Dt − Pt−1
− T BtPt−1

where Pt is the stock price (SP500 index), Dt is dividends, T Bt is the return fromholding the risk-free Treasury-bill.
• Control variables: Total 13 variables are included.
1. Dividend Price Ratio (d/p): is the difference between the log of dividends paidon the SP 500 index and the log of prices.
2. Dividend Yield (d/y): is the difference between the log of dividends and the logof lagged prices.
3. Earnings Price Ratio (e/p): is the difference between the log of earnings on the
SP 500 index and the log of prices.
4. Dividend Payout Ratio (d/e): is the difference between the log of dividends andthe log of earnings on the SP 500 index.
5. Stock Variance (svar): is computed as sum of squared daily returns on the SP
500 index.
6. Book-to-Market Ratio (b/m): is the ratio of book value to market value for the
Dow Jones Industrial Average.
45

7. Net Equity Expansion (ntis): is the ratio of 12-month moving sums of net issues
by NYSE listed stocks divided by the total end-of-year market capitalization of
NYSE stocks.
8. Treasury-bill rates (tbl): are interest rate on the 3-Month Treasury Bill; Secondary Market Rate from the economic research data base at the Federal Reserve
Bank at St. Louis (FRED).
9. Long Term Rate of Returns (ltr): are returns on long-term government bonds.
10. Term Spread (tms): is the difference between the long-term yield on government
bonds and the Treasury-bill.
11. Default Yield Spread (dfy): is the difference between BAA- and AAA-rated corporate bond yields.
12. Default Return Spread (dfr): is the difference between long-term corporate bondand long-term government bond returns.
13. Inflation (infl): is the Consumer Price Index (all urban consumers) from the
Bureau of Labor Statistics.
As before, we perform unit-root tests on each control variable and transform the variable by calculating the percentage change if it is non-stationary. Before estimating controlaugmented models, we regress each dependent variable on a constant and the first-order lagsof seven sentiment time series on a monthly basis and find significant sentiment indexes ineach stock indexes. Table 12 summarizes the set of sentiment features which are statisticallysignificant at 10% significance level. It also reports the estimated coefficients and Newey–
West heteroskedasticity– and autocorrelation–robust standard errors inside parentheses.
Interestingly, we find different set of significant sentiment features in the monthly analyses. ‘Constraining’ is significant predictors for stock return (i.e., Close) or excess return inall stock indexes. ‘StrongModal’ and ‘Litigious’ are also commonly detected as a significant
predictor for stock return. ‘Positive’ is statistically significant predictors of trading volume
in SP500, Nasdaq, and Russell2000, while ‘Negative’ is significant for trading volume in46

Dow30. ‘Uncertainty’ is the most significant predictor of stock volatility in SP500. On theother hand, no sentiment index explains stock volatility in Nasdaq.
We next estimate control-augmented models by adding one of the macroeconomic or
financial variables as a control variable to the basic model described above. For this, we only
consider the models that obtain at least one significant sentiment feature in Table 12 andestimate them by regressing the stock indicator on all significant sentiments and one of thecontrol variables, as follows:0

yt = φ + z t−1 θ + wt−1 π + t , t = 1, ..., |T |(14)

where yt is a stock market indicator, z t−1 is r × 1 vector of significant sentiment features att − 1 from Table 12, wt−1 is a scalar macroeconomic or financial variable at t − 1 and t is
the error term; where φ is a constant, θ is r × 1 vector of parameters of interest, and π isthe parameter of the control variable; and where t is monthly frequency ranging from June2009 to December 2019.
Tables 13-23 report the estimation results for each stock market indicator. The findings
confirm that the proposed CEO sentiment features still provide predictive power of the stock
indicators such as stock returns beyond what the existing macroeconomic and financial variables do. We observe that including sentiment features significantly improves the adjusted
R2 in all models. For example, Table 13 shows that the multivariate regression of the excess
return on ‘Constraining’ and ‘Litigious’ in addition to a macroeconomic/financial variable
significantly improves R2 , compared to the univariate regression of the excess return on amacroeconomic/financial variable.
Next, we consider a “Kitchen Sink” regression that includes all the macroeconomic/financial
variables in the equation 14. Thus, wt−1 in the equation 14 is now a vector of all the macroeconomic/financial variables. Table 24 reports the estimation results. Interestingly, in all
stock indicators, we observe that the regression model with the sentiment features as well47

as the macroeconomic/financial variables significantly improves the adjusted R2 , comparedto the regression model with only macroeconomic/financial variables. These findings confirm that the sentiment features from the CEO tweets contain predictive power on the stock
movement.

4.6.

Possible structural break since 2020

The world economy has been experiencing an unprecedented challenges imposed by the
pandemic since early 2020. The Business Cycle Dating Committee of the NBER has announced that a peak in monthly economic activity occurred in the U.S. economy in February2020 which marks the end of the expansion that began in June 2009 and the beginning of arecession. Although it is not straightforward to claim a structural break and it is generally
challenging to detect breaks in financial forecasting models (Kim, Morley, and Nelson, 2005;
Timmermann, 2018), it would be reasonable to be concerned about a structural break in
the relation between stock market indicators and CEO hashtag (or sentiment) features since2020 during our sample period. To address this concern, we re-estimate the weekly regressions in the previous sections (Tables 9 and 11) using the same algorithms and settings as
before, but the data only from June 2009 to December 2019.
Tables 25-26 report the most significant hashtag features from the regression model fornumeric stock market indicators in Section 4.1 and the predictive model for the stock pricedirections in Section 4.2, respectively. As expected, there are changes in the ranking of themost popular hashtag features. For example, the hashtags related to COVID-19 no loner
show up, since these begin to appear after January 2020. However, we observe that manyhashtag features are still statistically significant and that some of them are the same as
those from the full-sample estimation in Tables 9 and 11. These results confirm that our
main findings are robust to the possible structural break in that CEO tweets still contain
informational content on the U.S. stock market.

48

5.

Concluding Remarks
In this study, we demonstrate how the collective voice of CEOs on Twitter can be an-alyzed in predicting the economy, especially the most prominent stock indexes in the U.S.
Specifically, we create a large, unique sample of CEOs by identifying 4,714 CEO users on
Twitter and translate the unstructured tweet text data into structured time series by applying text analysis techniques. We perform comprehensive time series prediction analyses
in a wide range of settings and show that the select list of hashtags and sentiment words in
CEO tweets have significant predictive power of the stock market indicators such as return,
trading volume, volatility, and stock price direction for each of the four stock market indices– S&P 500, Dow 30, Nasdaq, and Russell 2000. We provide experimental evidence that
CEOs’ language expressed in their tweets has informational content on various stock market
indicators.
We believe this study could be a stepping stone for promising extensions as future research. First, we do not take user weighting into account in this study when constructing
time series data. In other words, all CEO users and their tweets are equally weighted. Considering the different amounts of influence of CEOs, however, it would make more sense tomeasure each CEO’s influence and weigh their tweets accordingly. For example, we may
simply take the number of followers as the influence score of a user and use that measure toweigh the tweets posted by the user, rather than counting every tweet as just one as done inthis study. Second, we do not consider the network of CEO users in our study. Each CEO
user can have follow relationships on Twitter not only with the other CEOs in our sample but
also with any other general users on Twitter. By looking at the network structure of CEO
users, we can advance user weighting schemes mentioned above by calculating CEO network
centrality (El-Khatib, Fogel, and Jandik, 2015) or explore how executive peer networks canaffect corporate decisions (Shue, 2013). Third, in addition to being able to identify the 4,714
CEO users who describe themselves as CEOs in their bios, we are also able to identify theTwitter accounts of the firms they represent for approximately one thirds of the CEOs, as
49

discussed earlier in Section 3.1. This unique information on CEO-firm matching provides us
a great opportunity to investigate how CEOs are interacting with or taking advantage of theTwitter accounts of the firms they currently work for. Lastly, the aggregate-level analysis of
this study can be extended further to a firm-level analysis. In order to do this, we need tomap the CEO users and their firms virtually existing on social media to the corresponding
real world entities of which characteristics are publicly available.

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57

Table 1: Monthly data statistics
This table reports the monthly statistics of the data collected for 36 months from January 2019 to December 2021 with respect
to user count and tweet count.

Month
User Count Tweet Count
12/2021
22,569,110
133,387,546
11/2021
21,876,935
129,462,997
10/2021
22,175,272
133,334,050
09/2021
21,446,941
127,009,377
08/2021
21,708,191
133,447,209
07/2021
21,979,242
133,358,039
06/2021
21,611,226
128,414,906
05/2021
22,651,068
133,741,215
04/2021
22,138,958
129,235,713
03/2021
22,441,309
133,544,952
02/2021
21,529,017
120,703,913
01/2021
22,317,570
133,754,300
12/2020
21,107,115
120,627,976
11/2020
21,950,691
129,635,445
10/2020
21,889,317
133,221,211
09/2020
22,344,474
128,867,950
08/2020
22,643,060
133,302,754
07/2020
22,930,209
133,609,303
06/2020
22,419,694
128,885,150
05/2020
23,554,291
133,389,857
04/2020
23,420,878
129,330,138
03/2020
23,400,803
133,474,640
02/2020
21,260,800
125,029,995
01/2020
22,275,681
133,666,464
12/2019
22,176,797
133,683,030
11/2019
21,905,451
129,514,576
10/2019
22,236,154
132,998,513
09/2019
22,070,159
129,350,458
08/2019
22,349,152
133,653,777
07/2019
22,218,823
133,510,690
06/2019
22,046,523
129,321,270
05/2019
22,474,111
133,517,533
04/2019
21,961,307
129,453,370
03/2019
22,276,114
133,600,069
02/2019
21,103,458
120,796,157
01/2019
22,060,698
133,048,524
Total (Unique) 176,850,729 4,704,883,067

58

Table 2: Top-20 CEO users sorted by follower count
The user screen names, follower counts, and descriptions of the top-20 users are presented, sorted by follower count.

Rank User Screen Name1
elonmusk
2
parishilton

Followers
72,497,051
16,879,195

3
4
5

therock
kobebryant
tim cook

15,752,600
15,023,019
13,232,112

650cent12,535,470

7

werevertumorro

8,687,262

8
9jacksepticeye
sudhirchaudhary

7,499,889
6,941,980

10

jeffreestar

6,651,465

11

johnlegere

5,995,821

12billsimmons

5,674,187

13

souljaboy

5,479,229

14
15
16

cz binancellcoolj
tonyhawk

5,100,429
5,003,555
4,555,521

17
18

sundarpichai
peterpsquare

4,203,860
3,730,922

19

nelly mo

3,727,025

20

tommyinnit

3,557,222

Description
Former CEO of Dogecoin
Text PH to 833-240-3728 for updates Hear my new spinoff
series #Dominated, hosted by @CindyPinkCEO on @ThisIsParisPod
CEO Seven Bucks Companies@Granity Studios - CEO, Writer, Producer
Apple CEO Auburn Duke National Parks “Life’s most
persistent and urgent question is, ’What are you doing forothers?’” - MLK. he/him
CEO G-UNIT FILM & TV, SIRE SPIRITS, G-UNIT
RECORDS & THE G-UNITY FOUNDATION, 2X NY Times
Best Selling Author, Award Winning Director & Rapper
CEO @Timbersesports y @ace1 gg Somos uno, para siempre.
Negocios: contactowerever@gmail.com
Artist, Entrepreneur, CEO http://topofthemornincoffee.com
Editor In Chief & CEO , Zee News, WION, Zee Business. Hosts India’s No.1 News Show DNA.Ramnath Goenka
Awardee https://t.co/mR8AzsmCw2
Self-Made. Makeup Magician. CEO. Owner of Jeffree Star
Cosmetics Mom of 7 Pomeranians Living in Wyoming raisingyaks & writing my autobiography.
Lover of all things slow cookers, golf, NFTs, Batman, andmagenta! Ex-CEO @TMobile@ringer (CEO) + The BS Podcast + @BookoBasketball 2.0
pod + @therewatchables ... Past Life: @grantland33 @30for30
CEO of #SouljaBoyApparel — Platinum Recording Artist —Bitcoin Investor #BTC — http://SouljaBoyApparel.com#BNB, #bitcoin hodler CEO @binanceCEO of @rockthebells (917)540-5512
Pro skater, husband, child rearer, videogame character, CEO,food/spirit glutton and public skatepark advocate. Old AF
and still skating. Have ramp, will travel
CEO, Google and Alphabet
Musician/Entertainer/Businessman/Dr&CEO@zoomupyourlife @pclassicgroup @ziprepublic @theokoyes @aphrospirit
Management info@one1mgt.com +2348121110000
https://t.co/l8dkNt6I4H New single #FreakyWithYou
NELLY’s OFFICIAL TWITTER...CEO of Nelly Inc, Derrty
Ent, NELLYVILLE on BET ...!!!
CEO @realmeIndia & @realmeeurope

59

Table 3: Bottom-20 CEO users sorted by follower count
The user screen names, follower counts, and descriptions of the bottom-20 users are presented, sorted by follower count.

Rank User Screen Name
4695 secchambersin

4696

zacharymoses

4697

swayanchaudhuri

4698

fairtrade ceo

4699
4700

aumaldivesdavidawilliams

4701
4702

sbirdabrdn
erikmunderwood

4703

goshantanu

4704
4705

mhedengren
jsrung4706

davidhillok

4707

davidsilveroak

4708

yiehsin4709

karenjhanrahan

4710
4711

kellycoffeycnb
kevin makely

4712

drvinaynair

4713
4714

brianpocrassmihaelhp

Followers Description
400
Secretary of Commerce for the State of #Indiana and CEO of
the Indiana Economic Development Corporation (@Indiana EDC)
#AStateThatWorks
399
CEO of https://t.co/Nkyju6l9jA, https://t.co/9USbdSC9a2, &https://t.co/EVrdclmpUi, Comedian, Travel Guru, former candidate for UT Governor
396
Urban Enthusiast —— Trying to make use of Technology for the
Common Good —— Formerly Mission Director & MD & CEO Smart City & AMRUT, Goa
359
Interim @Fairtrade Global CEO — Born and raised in Kenya to a
farmer, I am dedicated to improving the livelihoods of small-scale
farmers around the world.
357
Entrepreneur Founder & CEO of @FaseyhaRecharge
346
President + CEO @GenesysWorks. Love to play #basketball and#tennis. New grandfather. #nonprofitleadership273
CEO @abrdn plc
256
Tech Innovator. Colorado. CEO of My24. Dog lover, , buff, runner, GQ, & Netflix. @My24Erica.Com @KillerPolitics @illmaticv1. All of my views are my own.
256
IAS 2004 — CEO & Principal Secretary, BTC, Assam— Princeton— Passionate about Sustainability, Green Growth and Energy.
253
CEO @Readly — Bringing the magic of magazines into the future
252
President/CEO of Shaw Media (Illinois and Iowa). Avid reader,lover of history, family guy, Bears fan, former triathlete.
231
Husband. Father of 6. CEO. Business Owner. Manufacturer.
Proud Oklahoman
225
Proprietor and Chairman/CEO of @SilverOak, @Twomey, OVID
Napa Valley and Timeless Napa Valley
219
CEO for New York Life Investment Management, the asset management business of @NewYorkLife Insurance Company. Securities
distributed by NYLIFE Distributors LLC.
214
Pres. & CEO of @GLIDEsf. Global human rights lawyer. Social
innovator. Former @StateDept for Democracy + Security. Passionate about equity for all. Proud mom.
207
CEO @CityNational
198
Actor / Producer / CEO Papa Octopus Productions
https://t.co/gT2GR9NuDj https://t.co/KZ2CfpCgGE
196
A serial entrepreneur, investor and academic. Founder, CEO andChairman of @TIFINfintech: @investpositivly • @DiscoverMagnifi• @content clout • @TotumRisk
159
CEO/Founder/Producer, 22 Vision
51
Genetics and Medicine, CEO @vandapharma, tweets are my own
ideas and opinions

60

Table 4: Top-100 popular hashtags and their user counts sorted by user count
The top-100 popular hashtags and their user counts are presented, sorted by user count.

Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Hashtag#covid19#tbt#ff#coronavirus#nyc#superbowl#internationalwomensday
#love
#tech
#rip
#twitter#facebook#innovation#respect#leadership#usa#winning#blacklivesmatter#covid#business#fail#truth#mondaymotivation#oscars#podcast#family#christmas#london#newprofilepic#socialmedia#music#breaking#halloween#education#sxsw#worldcup#startup#olympics#thanksgiving#technology#vote#periscope#apple#inspiration#neverforget#jobs#happynewyear#ai#blessed#thankyou

Frequency
2,568
2,113
2,079
1,835
1,707
1,681
1,677
1,602
1,524
1,478
1,477
1,455
1,454
1,453
1,445
1,442
1,398
1,391
1,384
1,376
1,344
1,330
1,306
1,290
1,280
1,261
1,258
1,258
1,256
1,255
1,247
1,237
1,229
1,228
1,222
1,222
1,219
1,215
1,203
1,198
1,197
1,193
1,190
1,185
1,176
1,161
1,154
1,146
1,139
1,133

61

Rank
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

Hashtag#proud#startups#health#entrepreneur#google#women#mothersday#china#throwbackthursday#art#brexit#awesome#marketing#instagram#trump#valentinesday#blackfriday#fb#video#success#legend#life#iphone#followfriday#fathersday#nowplaying#merrychristmas#live#bitcoin#climatechange#newyork#givingtuesday#selfie#happy#justsaying#nfl#india#icymi#news#paris#digital#amazing#win#travel#youtube#work#metoo#grateful#diversity#earthday

Frequency
1,127
1,113
1,112
1,105
1,091
1,091
1,089
1,084
1,084
1,066
1,066
1,065
1,046
1,035
1,032
1,016
1,012
1,011
1,000
997
995
991
987
987
985
985
984
983
983
977
966
964
958
952
951
950
949
948
947
946
945
945
943
941
939
921
917
917
916
916

Table 5: Top-100 popular hashtags and their tweet counts sorted by tweet count
The top-100 popular hashtags and their tweet counts, as opposed to user counts, are presented, sorted by tweet count.

Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Hashtag#quote#ff#socialmedia#marketing#startup#covid19#leadership#bitcoin#entrepreneur
#ai
#tech
#business#np#askfft#sales#fashion#travel#nowplaying#success#fb#tbt#periscope#getrealchat
#cio
#nyc
#tcot
#syria#repost#contentmarketing#twitter#blockchain#facebook#inspiration#motivation#healthcare#soundcloud#lifestyle#innovation#cloud#iot#rt#trump#sxsw#egypt#podcast
#hyc
#seo
#love
#coronavirus#mambomseto

Frequency
121,41080,51877,461
77,06462,086
61,491
61,05544,99444,251
41,97541,922
41,59439,94736,91336,63535,74333,32531,98130,972
28,46727,93926,764
26,37325,90025,76325,52525,23825,114
24,12323,80323,32323,22823,17623,06722,293
22,25422,221
21,956
21,837
21,67021,36921,252
21,167
20,91820,811
20,19419,92819,826
19,512
19,507

Rank
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

62

Hashtag#mobile#music#israel#mondaymotivation#instagram#justhaves#portraitdestartuper#startups#quotes#breaking#nfl#education#celtics#salute#quoteoftheday#digital#fintech#cdnpoli
#iran
#news
#cleantech#entrepreneurship
#ad
#yeg
#nba
#mufc
#video
#smm
#maga
#ux
#crypto#china#artdesign#london#coast2coast#fail#entrepreneurs
#stem
#jobs
#climate#india#women#superbowl#technology#truth#brexit#txlege#wednesdaywisdom#oscars#healthFrequency
19,397
18,60918,482
18,236
17,872
17,447
17,351
17,144
17,045
16,768
16,59916,364
16,273
15,815
15,813
15,791
15,653
14,693
14,64914,18014,171
14,152
14,13814,121
13,84013,827
13,79913,721
13,632
13,501
13,49813,48913,42013,091
13,04012,90912,816
12,65812,651
12,646
12,586
12,525
12,405
12,24011,984
11,928
11,91011,898
11,84911,833

Table 6: List of 58 clusters of similar hashtags among the top-1,000 popular hashtags
All the 58 clusters of similar hashtags found among the top-1,000 popular hashtags are listed.
#wfh, #covid, #covid19, #covid2019, #covid 19, #socialdistancing#innovation, #innovative#winning, #win
#oscar, #oscars#podcasts, #podcast#london, #london2012#worldcup2014, #worldcup#startups, #startup#olympic, #olympics#thanksgiving, #blackfriday, #happythanksgiving#inspirational, #inspiration, #inspire, #inspired, #inspiring#jobs, #job
#happynewyear, #newyearseve#blessed, #blessings#entrepreneur, #entrepreneurs#mothersday, #happymothersday#valentinesday, #happyvalentinesday#legend, #legends#fathersday, #happyfathersday#happy, #happiness#facts, #fact#debate, #debates, #debates2020#quotes, #quote#thanks, #thankful#beauty, #beautiful#power, #powerful#goodfriday, #easter, #happyeaster#quarantine, #stayathome, #stayhome#sports, #sport#sustainability, #sustainable#election, #election2020, #election2016#hero, #heroes#books, #book#creativity, #creative#leader, #leaders#hashtags, #hashtag#holidays, #holiday#brand, #branding, #brands#apps, #app
#nft, #nfts#learn, #learning#tokyo, #tokyo2020#icebucketchallenge, #alsicebucketchallenge#gaming, #game#dream, #dreams#iwd2016, #iwd2021, #iwd2020, #iwd2019, #iwd2018, #iwd2017, #iwd
#investment, #investing#movie, #movies#dogs, #dog
#trending, #trends#parents, #parenting#champions, #champion#drones, #drone#positive, #positivity#robotics, #robots#euro2020, #euro2016#mandela, #nelsonmandela#event, #events

63Table 7: The parameter settings that yield the best weekly forecasting performancesThis table reports the parameter settings that yield the best weekly forecasting performances, i.e., the lowest Mean Squared
Errors, for the weekly frequency, four markets, three targets, three feature types, nine model sizes, and five horizons.

Frequency
WeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyMarket
SP500
SP500
SP500
SP500
SP500
SP500
SP500
SP500
SP500
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
NasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqRussell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000

Target
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility

Feature Type
Model Size
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.2
Hashtags (AGLasso)
0.1
Sentiments
0.2
Hashtags (Lasso)
0.2
Hashtags (AGLasso)
0.1
Sentiments
0.2
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.2
Hashtags (AGLasso)
0.1
Sentiments
0.2
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.3
Sentiments
0.2
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.3
Sentiments
0.9
Hashtags (Lasso)
0.7
Hashtags (AGLasso)
0.1
Sentiments
0.7
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.2
Sentiments
0.2
Hashtags (Lasso)
0.1
Hashtags (AGLasso)
0.2
Sentiments
0.2
Hashtags (Lasso)
0.1
Hashtags (AGLasso)
0.1
Sentiments
0.1

64

Horizon
2
2
4
1
1
1
1
2
1
2
2
4
2
5
1
1
1
1
5
1
5
2
2
1
1
4
1
2
1
5
1
1
1
1
1
1

MSE
0.767
0.836
0.769
0.841
0.7
0.548
0.938
0.842
0.624
0.746
0.843
0.784
0.892
0.809
0.619
0.897
0.861
0.676
1.055
1.003
0.953
0.919
0.899
0.73
1.09
1.056
0.966
1.032
0.939
0.98
0.813
0.785
0.55
0.941
0.637
0.564

Table 8: The parameter settings that yield the best daily forecasting performancesThis table reports the parameter settings that yield the best daily forecasting performances, i.e., the lowest Mean Squared
Errors, for the daily frequency, four markets, three targets, three feature types, nine model sizes, and five horizons.

Frequency
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily

Market
SP500
SP500
SP500
SP500
SP500
SP500
SP500
SP500
SP500
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
Dow30
NasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqNasdaqRussell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000
Russell2000

Target
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility
Close
Close
Close
VolumeVolumeVolumeVolatility
Volatility
Volatility

Feature Type
Model Size
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.3
Hashtags (AGLasso)
0.1
Sentiments
0.2
Hashtags (Lasso)
0.4
Hashtags (AGLasso)
0.1
Sentiments
0.1
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.6
Sentiments
0.6
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.1
Sentiments
0.1
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.9
Hashtags (Lasso)
0.3
Hashtags (AGLasso)
0.1
Sentiments
0.1
Hashtags (Lasso)
0.9
Hashtags (AGLasso)
0.9
Sentiments
0.2
Hashtags (Lasso)
0.3
Hashtags (AGLasso)
0.1
Sentiments
0.2
Hashtags (Lasso)
0.3
Hashtags (AGLasso)
0.1
Sentiments
0.1

65

Horizon
5
3
1
1
3
1
1
4
1
4
2
5
3
2
5
1
2
1
5
5
1
3
2
4
1
5
1
4
4
5
1
3
1
1
4
1

MSE
0.701
0.658
0.642
0.611
0.982
0.472
0.961
1.128
0.533
0.68
0.652
0.621
0.456
0.387
0.321
0.884
1.149
0.534
0.937
0.92
0.909
0.504
0.416
0.352
0.729
0.765
0.447
0.995
0.976
0.87
0.658
1.0
0.489
0.822
0.854
0.523

Table 9: Top-5 most significant hashtag features from weekly forecastingThis table summarizes the set of top-5 hashtag features from lasso that are statistically significant at 10% significance level
and reports the estimated coefficients and Newey–West heteroskedasticity– and autocorrelation–robust standard errors inside
parentheses.
TargetMarketSignificant Features
Close
SP500
#iwd2020 -0.245(0.016) #pandemic 0.191(0.064) #womensday -0.076(0.043)
Close
Dow30
#iwd2020 -0.278(0.017) #pandemic 0.240(0.072) #womensday -0.077(0.039)
Close
Nasdaq#corona 1.109(0.421) #covid -0.646(0.304) #stayhome 0.455(0.269)#covid 19 0.367(0.218) #breonnataylor 0.257(0.058)
Close
Russell2000#iwd2020 -0.185(0.030)
VolumeSP500#coronavirus 0.291(0.089) #clubhouse 0.250(0.043) #holiday -0.221(0.058)#iwd2020 0.196(0.034) #finalfour -0.184(0.076)
VolumeDow30#merrychristmas 0.175(0.062) #random 0.133(0.031) #cybersecurity 0.128(0.038)#happyvalentinesday 0.123(0.040) #sorrynotsorry 0.121(0.037)
VolumeNasdaq#merrychristmas 0.191(0.082) #happyvalentinesday 0.164(0.032) #veteransday -0.137(0.020)#game 0.122(0.033) #vmas 0.118(0.022)
VolumeRussell2000#coronavirus 0.341(0.112) #followfriday 0.187(0.038) #clubhouse 0.170(0.045)#holiday -0.143(0.058) #iwd2020 0.141(0.031)
Volatility
SP500
SP500 Volatility L1 0.285(0.058) #coronavirus 0.229(0.131) #georgefloyd 0.120(0.018)#safety 0.092(0.032) #car -0.086(0.025)
Volatility
Dow30#boston -0.757(0.090) #resist 0.646(0.079) #valentinesday 0.626(0.111)#inauguration -0.609(0.128) #nelsonmandela 0.580(0.105)
Volatility
NasdaqVolatility
Russell2000
Russell2000 Volatility L1 0.225(0.060) #iwd2020 0.136(0.018) #georgefloyd 0.109(0.028)#stem 0.093(0.028) #blackhistorymonth 0.090(0.045)

Table 10: The parameter settings that yield the best prediction performancesThis table reports the parameter settings that yield the best weekly prediction performances, i.e., the highest accuracy, for eachof the two frequencies, four markets, two feature types, nine model sizes, five horizons, and five classification algorithms.

Frequency
WeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeeklyWeekly
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily

Market
SP500
SP500
Dow30
Dow30
NasdaqNasdaqRussell2000
Russell2000
SP500
SP500
Dow30
Dow30
Dow30
Dow30
NasdaqNasdaqRussell2000
Russell2000

TargetFeature Type Model Size
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9
Direction
Sentiments
0.9
Direction
Hashtags
0.8
Direction
Sentiments
0.9
Direction
Hashtags
0.9
Direction
Sentiments
0.9

66

Horizon Algorithm Accuracy
3
Logit
0.641
3
Logit
0.625
3
DTrees
0.703
5
Logit
0.645
5
k-NNs
0.581
5
SVC
0.597
2
Logit
0.615
3
DeepNeural
0.641
2
SVC
0.57
2
SVC
0.564
3
DTrees
0.572
3
Logit
0.572
3
DeepNeural
0.572
3
Logit
0.572
1
Logit
0.583
1
Logit
0.587
4
SVC
0.554
1
k-NNs
0.613

Table 11: Top-5 most important hashtag features from weekly forecastingThis table summarizes the set of the most important hashtag features along with their importance scores.
TargetMarketAlgorithm
Significant Features
Direction
SP500
Logit
#ff -0.0034 #brexit 0.0024 #internationalwomensday -0.002 #superbowl 0.0018 #fb -0.0017
Direction
Dow30
DTrees#good 0.0322 #siliconvalley 0.0307 #culture 0.0298 #newprofilepic 0.0296 #cheers 0.0273
Direction
NasdaqSVC#coronavirus 0.0101 #superbowl 0.009 #syria 0.009 #sxsw 0.0078 #grammys 0.0075
Direction
Russell2000
SVC#ai -0.0207 #blockchain -0.0181 #covid19 0.0145 #fintech -0.0123 #iot -0.0083

Table 12: Significant sentiment regressors from monthly forecastingThis table summarizes the set of sentiment features which are statistically significant at 10% significance level and reports theestimated coefficients and Newey–West heteroskedasticity– and autocorrelation–robust standard errors inside parentheses.
TargetMarketSignificant Sentiment Features
Excess Return
SP500
Constraining 0.365(0.133) Litigious 0.209(0.107)
Close
SP500
Constraining 0.378(0.122) StrongModal 0.223(0.130) Litigious 0.205(0.106)
Close
Dow30
Constraining 0.303(0.130) Litigious 0.218(0.107)
Close
NasdaqConstraining 0.380(0.128) StrongModal 0.311(0.163)
Close
Russell2000
Constraining 0.219(0.124) Litigious 0.201(0.110)
Volume
SP500
Positive -0.920(0.226) StrongModal 0.899(0.231)
Volume
Dow30
Negative -0.336(0.198)
VolumeNasdaqWeakModal 0.808(0.423) Positive -0.350(0.151) StrongModal 0.322(0.157) Constraining -0.286(0.109)
VolumeRussell2000
Positive -0.919(0.227) StrongModal 0.894(0.231)
Volatility
SP500
Uncertainty 0.969(0.577) WeakModal -0.704(0.410)
Volatility
Dow30
StrongModal 1.185(0.186) Positive -1.125(0.208) Negative -0.264(0.141)
Volatility
Nasdaq–Volatility
Russell2000
Constraining -0.258(0.106)

Table 13: Estimation results for sentiments, monthly frequency, and SP500 Excess Return
We estimate control-augmented models for monthly Excess Return by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining: Litigious
d/p-0.111 (0.078)
0.006-0.042 (0.101)
0.176 (0.103)
0.130 (0.082)
0.061
d/y
-0.105 (0.090)
0.004-0.112 (0.080)
0.175 (0.081)
0.145 (0.081)
0.072
e/p
-0.061 (0.078)-0.003-0.056 (0.078)
0.195 (0.089)
0.121 (0.080)
0.063
d/e
0.146 (0.056)
0.014
0.132 (0.055)
0.197 (0.087)
0.111 (0.077)
0.077
svar
0.235 (0.051)
0.049
0.156 (0.069)
0.100 (0.096)
0.146 (0.084)
0.078
b/m
0.085 (0.069)
0.000
0.001 (0.059)
0.191 (0.089)
0.126 (0.085)
0.060
ntis
-0.115 (0.017)
0.006-0.102 (0.016)
0.191 (0.089)
0.120 (0.082)
0.070
tbl
-0.114 (0.068)
0.006-0.084 (0.055)
0.175 (0.087)
0.132 (0.081)
0.067
lty
-0.120 (0.085)
0.008-0.102 (0.067)
0.174 (0.079)
0.136 (0.080)
0.070
ltr
0.063 (0.091)-0.0030.044 (0.092)
0.186 (0.086)
0.128 (0.080)
0.062
tms
-0.224 (0.029)
0.044-0.139 (0.051)
0.106 (0.099)
0.145 (0.085)
0.074
dfy
0.090 (0.120)
0.001
0.057 (0.086)
0.176 (0.080)
0.133 (0.083)
0.063
dfr
0.036 (0.102)-0.0050.156 (0.079)
0.257 (0.115)
0.116 (0.080)
0.081
infl
-0.062 (0.071)-0.003-0.107 (0.068)
0.188 (0.086)
0.149 (0.087)
0.071

67

Table 14: Estimation results for sentiments, monthly frequency, and SP500 Close
We estimate control-augmented models for monthly SP500 Close by adding one of the macroeconomic and financial variables
as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– and autocorrelation–robust
standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining: StrongModal: Litigious
d/p-0.094 (0.078)
0.002-0.025 (0.101)
0.166 (0.097)
0.071 (0.078)
0.112 (0.084)
0.054
d/y
-0.092 (0.088)
0.002-0.092 (0.075)
0.165 (0.077)
0.062 (0.076)
0.126 (0.082)
0.062
e/p
-0.083 (0.063)
0.000-0.095 (0.064)
0.177 (0.081)
0.087 (0.072)
0.101 (0.081)
0.063
d/e
0.123 (0.055)
0.008
0.140 (0.057)
0.170 (0.081)
0.111 (0.073)
0.093 (0.080)
0.072
svar
0.221 (0.055)
0.042
0.126 (0.070)
0.108 (0.096)
0.047 (0.072)
0.127 (0.086)
0.065
b/m
0.074 (0.066)-0.001-0.004 (0.057)
0.176 (0.081)
0.070 (0.077)
0.111 (0.087)
0.054
ntis
-0.111 (0.020)
0.006-0.105 (0.017)
0.173 (0.081)
0.079 (0.077)
0.104 (0.084)
0.065
tbl
-0.122 (0.068)
0.008-0.088 (0.052)
0.161 (0.080)
0.063 (0.076)
0.117 (0.082)
0.062
lty
-0.090 (0.092)
0.001-0.073 (0.069)
0.163 (0.077)
0.072 (0.077)
0.118 (0.083)
0.059
ltr
0.047 (0.094)-0.0050.031 (0.095)
0.172 (0.080)
0.071 (0.078)
0.111 (0.082)
0.055
tms
-0.216 (0.029)
0.040-0.119 (0.051)
0.106 (0.100)
0.057 (0.074)
0.127 (0.088)
0.064
dfy
0.088 (0.116)
0.001
0.049 (0.078)
0.165 (0.078)
0.066 (0.075)
0.117 (0.084)
0.056
dfr
0.044 (0.101)-0.0050.166 (0.079)
0.246 (0.105)
0.069 (0.072)
0.100 (0.080)
0.078
infl
-0.005 (0.072)-0.007-0.057 (0.072)
0.171 (0.079)
0.078 (0.077)
0.122 (0.087)
0.057

Table 15: Estimation results for sentiments, monthly frequency, and Dow30 Close
We estimate control-augmented models for monthly Dow30 Close by adding one of the macroeconomic and financial variables
as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– and autocorrelation–robust
standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining: Litigious
d/p-0.082 (0.079)
0.000-0.019 (0.103)
0.151 (0.099)
0.151 (0.088)
0.054
d/y
-0.106 (0.087)
0.005-0.116 (0.076)
0.142 (0.076)
0.170 (0.084)
0.068
e/p
-0.101 (0.064)
0.004-0.095 (0.062)
0.164 (0.084)
0.140 (0.084)
0.063
d/e
0.151 (0.051)
0.016
0.135 (0.049)
0.164 (0.083)
0.134 (0.083)
0.073
svar
0.198 (0.044)
0.033
0.122 (0.060)
0.086 (0.092)
0.166 (0.089)
0.065
b/m
0.076 (0.063)-0.001-0.008 (0.059)
0.158 (0.084)
0.152 (0.091)
0.054
ntis
-0.139 (0.028)
0.013-0.126 (0.022)
0.158 (0.083)
0.142 (0.087)
0.070
tbl
-0.128 (0.059)
0.010-0.103 (0.050)
0.139 (0.081)
0.157 (0.085)
0.065
lty
-0.076 (0.089)-0.001-0.061 (0.075)
0.147 (0.077)
0.156 (0.085)
0.058
ltr
0.024 (0.096)-0.0060.007 (0.098)
0.157 (0.083)
0.150 (0.085)
0.054
tms
-0.186 (0.027)
0.028-0.103 (0.050)
0.095 (0.096)
0.164 (0.089)
0.062
dfy
0.090 (0.106)
0.001
0.063 (0.080)
0.142 (0.079)
0.158 (0.088)
0.058
dfr
0.045 (0.091)-0.0050.157 (0.079)
0.224 (0.113)
0.139 (0.085)
0.075
infl
-0.035 (0.074)-0.006-0.081 (0.075)
0.155 (0.082)
0.167 (0.094)
0.061

68

Table 16: Estimation results for sentiments, monthly frequency, and Nasdaq Close
We estimate control-augmented models for monthly Nasdaq Close by adding one of the macroeconomic and financial variables
as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– and autocorrelation–robust
standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining: StrongModald/p-0.086 (0.075)
0.001-0.015 (0.093)
0.213 (0.080)
0.077 (0.091)
0.044
d/y
-0.075 (0.086)-0.001-0.060 (0.074)
0.216 (0.068)
0.072 (0.089)
0.047
e/p
-0.072 (0.059)-0.002-0.091 (0.064)
0.214 (0.070)
0.093 (0.088)
0.052
d/e
0.116 (0.058)
0.007
0.143 (0.064)
0.202 (0.069)
0.118 (0.089)
0.063
svar
0.234 (0.055)
0.049
0.139 (0.070)
0.153 (0.080)
0.052 (0.081)
0.058
b/m
0.069 (0.055)-0.0020.016 (0.046)
0.213 (0.073)
0.077 (0.090)
0.044
ntis
-0.062 (0.011)-0.003-0.060 (0.016)
0.214 (0.070)
0.082 (0.091)
0.048
tbl
-0.126 (0.073)
0.009-0.089 (0.058)
0.206 (0.068)
0.070 (0.089)
0.052
lty
-0.145 (0.090)
0.014-0.122 (0.067)
0.203 (0.064)
0.080 (0.089)
0.059
ltr
0.095 (0.087)
0.002
0.079 (0.089)
0.210 (0.068)
0.080 (0.092)
0.050
tms
-0.239 (0.019)
0.051-0.146 (0.040)
0.144 (0.079)
0.061 (0.086)
0.059
dfy
0.098 (0.117)
0.003
0.052 (0.077)
0.209 (0.066)
0.072 (0.088)
0.046
dfr
0.035 (0.106)-0.0060.152 (0.080)
0.277 (0.089)
0.076 (0.086)
0.064
infl
-0.041 (0.073)-0.005-0.075 (0.071)
0.221 (0.069)
0.088 (0.091)
0.049

Table 17: Estimation results for sentiments, monthly frequency, and Russell2000 Close
We estimate control-augmented models for monthly Russell2000 Close by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining: Litigious
d/p-0.047 (0.087)-0.0050.012 (0.111)
0.139 (0.088)
0.139 (0.079)
0.040
d/y
-0.049 (0.080)-0.004-0.058 (0.073)
0.127 (0.067)
0.151 (0.082)
0.043
e/p
-0.075 (0.079)-0.001-0.069 (0.078)
0.140 (0.069)
0.134 (0.079)
0.044
d/e
0.124 (0.072)
0.009
0.109 (0.069)
0.140 (0.068)
0.128 (0.078)
0.052
svar
0.172 (0.053)
0.023
0.104 (0.070)
0.074 (0.080)
0.154 (0.081)
0.048
b/m
0.089 (0.048)
0.001
0.015 (0.065)
0.134 (0.068)
0.137 (0.085)
0.040
ntis
-0.055 (0.029)-0.004-0.044 (0.019)
0.135 (0.069)
0.138 (0.080)
0.041
tbl
-0.162 (0.058)
0.020-0.142 (0.049)
0.109 (0.065)
0.150 (0.080)
0.060
lty
-0.037 (0.092)-0.005-0.024 (0.083)
0.131 (0.067)
0.143 (0.079)
0.040
ltr
0.004 (0.090)-0.007-0.010 (0.093)
0.136 (0.070)
0.140 (0.079)
0.040
tms
-0.165 (0.028)
0.021-0.093 (0.049)
0.079 (0.083)
0.153 (0.082)
0.046
dfy
0.045 (0.109)-0.0050.020 (0.083)
0.130 (0.067)
0.143 (0.082)
0.040
dfr
0.042 (0.087)-0.0050.141 (0.086)
0.195 (0.097)
0.131 (0.077)
0.057
infl
-0.067 (0.068)-0.002-0.110 (0.068)
0.132 (0.067)
0.164 (0.086)
0.051

69

Table 18: Estimation results for sentiments, monthly frequency, and SP500 Volume
We estimate control-augmented models for monthly SP500 Volume by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Positive: StrongModald/p-0.020 (0.142)-0.006-0.043 (0.109) -0.901 (0.227)
0.878 (0.224)
0.174
d/y
0.056 (0.107)-0.0030.034 (0.080)-0.889 (0.226)
0.874 (0.229)
0.173
e/p
-0.336 (0.089)
0.110-0.201 (0.127) -0.688 (0.259)
0.728 (0.248)
0.203
d/e
0.375 (0.074)
0.138
0.302 (0.090)-0.652 (0.222)
0.741 (0.206)
0.247
svar
0.338 (0.072)
0.111
0.219 (0.103)-0.743 (0.227)
0.673 (0.234)
0.212
b/m
0.213 (0.078)
0.040
0.203 (0.056)-0.887 (0.218)
0.863 (0.214)
0.215
ntis
-0.051 (0.037)-0.004-0.034 (0.037) -0.891 (0.227)
0.875 (0.225)
0.173
tbl
-0.077 (0.081)-0.0010.008 (0.046)-0.898 (0.227)
0.879 (0.226)
0.172
lty
-0.123 (0.149)
0.009-0.071 (0.092) -0.876 (0.212)
0.859 (0.207)
0.177
ltr
0.103 (0.119)
0.004
0.078 (0.094)-0.884 (0.219)
0.868 (0.218)
0.178
tms
-0.220 (0.019)
0.043-0.110 (0.043) -0.838 (0.216)
0.797 (0.215)
0.183
dfy
0.052 (0.142)-0.0040.012 (0.091)-0.893 (0.227)
0.873 (0.223)
0.172
dfr
-0.056 (0.135)-0.004-0.038 (0.091) -0.893 (0.223)
0.870 (0.218)
0.173
infl
-0.058 (0.073)-0.003-0.086 (0.058) -0.900 (0.219)
0.894 (0.211)
0.179

Table 19: Estimation results for sentiments, monthly frequency, and Dow30 Volume
We estimate control-augmented models for monthly Dow30 Volume by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Negative
d/p
0.048 (0.076)-0.004-0.014 (0.103) -0.258 (0.042)
0.053
d/y
0.052 (0.063)-0.0040.044 (0.056)-0.253 (0.046)
0.054
e/p
-0.017 (0.051)-0.006-0.020 (0.048) -0.255 (0.043)
0.053
d/e
-0.008 (0.047)-0.0070.004 (0.048)-0.255 (0.044)
0.052
svar
-0.203 (0.067)
0.035-0.096 (0.077) -0.205 (0.056)
0.059
b/m -0.065 (0.057)-0.0020.018 (0.066)-0.261 (0.040)
0.053
ntis
0.069 (0.034)-0.0020.062 (0.041)-0.253 (0.043)
0.056
tbl
0.049 (0.041)-0.0040.028 (0.035)-0.253 (0.044)
0.053
lty
0.191 (0.123)
0.030
0.148 (0.131)-0.227 (0.048)
0.074
ltr
-0.174 (0.123)
0.024-0.143 (0.129) -0.236 (0.040)
0.073
tms
0.104 (0.027)
0.004-0.050 (0.032) -0.282 (0.052)
0.054
dfy
-0.122 (0.061)
0.008-0.075 (0.051) -0.240 (0.053)
0.058
dfr
0.089 (0.067)
0.001-0.001 (0.090) -0.255 (0.044)
0.052
infl
-0.031 (0.068)-0.006-0.008 (0.062) -0.254 (0.040)
0.053

70

Table 20: Estimation results for sentiments, monthly frequency, and Nasdaq Volume
We estimate control-augmented models for monthly Nasdaq Volume by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: WeakModal: Positive: StrongModal : Constraining
d/p0.058 (0.083)-0.003-0.059 (0.109)
0.163 (0.090)-0.321 (0.158)
0.289 (0.160)-0.439 (0.103)
0.091
d/y
0.084 (0.061)
0.000
0.055 (0.064)
0.154 (0.089)-0.300 (0.153)
0.273 (0.157)-0.412 (0.085)
0.091
e/p
-0.027 (0.063)-0.006
0.052 (0.073)
0.156 (0.090)-0.361 (0.173)
0.315 (0.157)-0.415 (0.087)
0.089
d/e
-0.004 (0.050)-0.007-0.057 (0.060)
0.156 (0.090)-0.353 (0.158)
0.300 (0.152)-0.411 (0.087)
0.090
svar
-0.181 (0.075)
0.026-0.134 (0.085)
0.173 (0.092)-0.391 (0.150)
0.373 (0.158)-0.368 (0.088)
0.099
b/m -0.053 (0.054)-0.004-0.004 (0.067)
0.160 (0.091)-0.308 (0.148)
0.276 (0.154)-0.416 (0.086)
0.087
ntis
0.101 (0.044)
0.003
0.103 (0.055)
0.165 (0.089)-0.319 (0.148)
0.277 (0.152)-0.416 (0.086)
0.098
tbl
0.031 (0.048)-0.0060.019 (0.051)
0.160 (0.090)-0.315 (0.151)
0.284 (0.156)-0.415 (0.085)
0.088
lty
0.083 (0.084)
0.000
0.087 (0.077)
0.177 (0.092)-0.330 (0.146)
0.292 (0.152)-0.420 (0.084)
0.095
ltr
-0.096 (0.083)
0.002-0.092 (0.081)
0.172 (0.090)-0.319 (0.144)
0.281 (0.151)-0.417 (0.083)
0.096
tms
0.095 (0.021)
0.002-0.029 (0.045)
0.155 (0.090)-0.296 (0.146)
0.263 (0.152)-0.428 (0.095)
0.088
dfy
-0.116 (0.050)
0.007-0.082 (0.052)
0.165 (0.091)-0.318 (0.159)
0.292 (0.165)-0.408 (0.085)
0.094
dfr
0.175 (0.080)
0.024
0.076 (0.103)
0.164 (0.090)-0.306 (0.152)
0.274 (0.155)-0.390 (0.088)
0.093
infl
-0.058 (0.054)-0.003-0.042 (0.054)
0.160 (0.089)-0.311 (0.144)
0.285 (0.145)-0.416 (0.085)
0.089

Table 21: Estimation results for sentiments, monthly frequency, and Russell2000 Volume
We estimate control-augmented models for monthly Russell2000 Volume by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Positive: StrongModald/p-0.021 (0.143)-0.006-0.044 (0.110) -0.902 (0.229)
0.875 (0.225)
0.174
d/y
0.056 (0.107)-0.0040.033 (0.080)-0.890 (0.228)
0.871 (0.231)
0.173
e/p
-0.334 (0.090)
0.109-0.197 (0.130) -0.693 (0.262)
0.728 (0.250)
0.202
d/e
0.376 (0.074)
0.139
0.302 (0.090)-0.653 (0.224)
0.737 (0.207)
0.247
svar
0.339 (0.073)
0.112
0.222 (0.103)-0.741 (0.227)
0.667 (0.233)
0.213
b/m
0.216 (0.078)
0.041
0.206 (0.055)-0.887 (0.219)
0.859 (0.215)
0.216
ntis
-0.051 (0.037)-0.004-0.035 (0.037) -0.891 (0.229)
0.871 (0.227)
0.173
tbl
-0.076 (0.080)-0.0010.009 (0.046)-0.899 (0.228)
0.877 (0.228)
0.172
lty
-0.131 (0.146)
0.011-0.079 (0.090) -0.874 (0.212)
0.854 (0.207)
0.178
ltr
0.109 (0.117)
0.005
0.084 (0.093)-0.884 (0.220)
0.864 (0.219)
0.179
tms
-0.221 (0.019)
0.044-0.112 (0.043) -0.837 (0.217)
0.792 (0.215)
0.183
dfy
0.053 (0.143)-0.0040.013 (0.091)-0.893 (0.228)
0.869 (0.224)
0.172
dfr
-0.057 (0.136)-0.003-0.039 (0.091) -0.894 (0.225)
0.867 (0.219)
0.173
infl
-0.064 (0.072)-0.002-0.092 (0.057) -0.901 (0.220)
0.892 (0.211)
0.180

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Table 22: Estimation results for sentiments, monthly frequency, and Dow30 Volatility
We estimate control-augmented models for monthly Dow30 Volatility by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: StrongModal: Positive: Negatived/p-0.265 (0.137)
0.064-0.321 (0.119)
1.243 (0.207)-1.175 (0.219) -0.130 (0.059)
0.377
d/y
-0.096 (0.084)
0.003-0.119 (0.060)
1.181 (0.184)-1.121 (0.209) -0.048 (0.049)
0.293
e/p
-0.178 (0.144)
0.025
0.049 (0.100)
1.210 (0.230)-1.152 (0.264) -0.046 (0.052)
0.280
d/e
0.099 (0.126)
0.003-0.054 (0.093)
1.195 (0.208)-1.144 (0.238) -0.041 (0.053)
0.281
svar
0.290 (0.031)
0.078
0.129 (0.050)
1.086 (0.194)-1.030 (0.214) -0.101 (0.056)
0.290
b/m
0.114 (0.165)
0.006
0.126 (0.165)
1.191 (0.205)-1.111 (0.220) -0.090 (0.056)
0.293
ntis
0.018 (0.020)-0.006
0.031 (0.020)
1.173 (0.198)-1.105 (0.219) -0.044 (0.052)
0.280
tbl
-0.119 (0.067)
0.008-0.008 (0.029)
1.170 (0.200)-1.099 (0.219) -0.046 (0.053)
0.279
lty
-0.181 (0.167)
0.026-0.130 (0.091)
1.157 (0.173)-1.075 (0.192) -0.071 (0.049)
0.295
ltr
0.187 (0.159)
0.028
0.167 (0.121)
1.170 (0.187)-1.087 (0.202) -0.070 (0.048)
0.307
tms
-0.236 (0.026)
0.049-0.126 (0.041)
1.120 (0.182)-1.056 (0.205) -0.107 (0.060)
0.290
dfy
0.215 (0.082)
0.040
0.169 (0.051)
1.146 (0.170)-1.086 (0.194) -0.073 (0.048)
0.307
dfr
-0.257 (0.135)
0.060-0.273 (0.113)
1.189 (0.180)-1.121 (0.200) -0.142 (0.062)
0.346
infl
-0.009 (0.082)-0.007-0.054 (0.056)
1.184 (0.188)-1.105 (0.212) -0.044 (0.052)
0.282

Table 23: Estimation results for sentiments, monthly frequency, and Russell2000 Volatility
We estimate control-augmented models for monthly Russell2000 Volatility by adding one of the macroeconomic and financial
variables as a control variable and report the estimated coefficients and Newey–West heteroskedasticity– andautocorrelation–robust standard errors inside parentheses.
Univariate
Multivariate
Coef. (S.E.)
Adj. R2
Coef. (S.E.)
Coef. (S.E.)
Adj. R2: Constraining
d/p-0.087 (0.060)
0.001-0.143 (0.072)-0.180 (0.050)
0.024
d/y
0.094 (0.061)
0.002
0.089 (0.062)-0.132 (0.053)
0.013
e/p
-0.050 (0.044)-0.004-0.048 (0.044)-0.135 (0.051)
0.007
d/e
0.031 (0.045)-0.006
0.033 (0.045)-0.136 (0.051)
0.006
svar
-0.147 (0.059)
0.015-0.105 (0.062)-0.081 (0.069)
0.013
b/m
0.032 (0.052)-0.006
0.068 (0.069)-0.151 (0.049)
0.009
ntis
0.031 (0.025)-0.0060.027 (0.029)-0.135 (0.051)
0.006
tbl
0.072 (0.038)-0.002
0.053 (0.037)-0.128 (0.051)
0.008
lty
0.045 (0.081)-0.0050.031 (0.078)-0.132 (0.052)
0.006
ltr
0.017 (0.094)-0.0060.028 (0.096)-0.138 (0.049)
0.006
tms
0.077 (0.014)-0.0010.006 (0.037)-0.132 (0.069)
0.005
dfy
-0.043 (0.056)-0.005-0.020 (0.052)-0.132 (0.055)
0.005
dfr
0.056 (0.086)-0.0040.003 (0.100)-0.134 (0.055)
0.005
infl
0.048 (0.066)-0.0040.061 (0.062)-0.141 (0.050)
0.009

Table 24: Estimation results for sentiments, monthly frequency, and all indexes
We estimate control-augmented models for all stock market indexes by adding all macroeconomic and financial variables ascontrol variables and report the adjusted R2 .
TargetMarketAdj. R2 − EconV ars Adj. R2 − EconV ars + Sentiments
Excess Return
SP500
0.098
0.135
Close
SP500
0.055
0.091
Close
Dow30
0.049
0.092
Close
Nasdaq
0.058
0.083
Close
Russell2000
0.018
0.045
Volume
SP500
0.263
0.315
Volume
Dow30
0.015
0.059
VolumeNasdaq0.016
0.111
VolumeRussell2000
0.266
0.319
Volatility
SP500
0.067
0.070
Volatility
Dow30
0.244
0.389
Volatility
Nasdaq––Volatility
Russell2000-0.0010.007

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Table 25: Top-5 most significant hashtag features from the weekly forecasting for the time
period ending with December 2019
This table summarizes the set of hashtag features that are statistically significant at 10% significance level from the weekly
prediction for the time period ending with December 2019 and reports the estimated coefficients and Newey–Westheteroskedasticity– and autocorrelation–robust standard errors inside parentheses.
TargetMarketSignificant Features
Close
SP500
#iwd2018 -0.109(0.003)
Close
Dow30
#iwd2018 -0.102(0.003)
Close
Nasdaq#iwd2018 -0.108(0.003)
Close
Russell2000
VolumeSP500#followfriday 0.261(0.037) #goodfriday -0.224(0.119) #holiday -0.177(0.061)#cybermonday 0.167(0.053) #easter 0.164(0.081)
VolumeDow30#xmas 0.239(0.046) #random 0.135(0.040) #vmas 0.100(0.039) #skills 0.098(0.042)
VolumeNasdaq#merrychristmas 0.204(0.099) #happyvalentinesday 0.154(0.026)#vmas 0.122(0.025) #transparency -0.094(0.037)
VolumeRussell2000#followfriday 0.233(0.033) #cybermonday 0.148(0.036) #holiday -0.144(0.032)#egypt -0.143(0.030) const -0.132(0.029)
Volatility
SP500
SP500 Volatility L1 0.290(0.053) const -0.150(0.032) #climatechange 0.074(0.027)#climateaction 0.071(0.030) #orlando 0.069(0.006)
Volatility
Dow30
Dow30 Volatility L1 0.261(0.060) const -0.119(0.034) #climateaction 0.082(0.031)#blackpanther 0.071(0.013) #parisagreement -0.062(0.016)
Volatility
Nasdaq#beyonce 0.127(0.075) #skills 0.113(0.044)
Volatility
Russell2000
const -0.288(0.034)

Table 26: Top-5 most important hashtag features from the weekly prediction for the time
period ending with December 2019
This table summarizes the set of the most important hashtag features along with their importance scores from the weekly
prediction for the time period ending with December 2019.
TargetMarketAlgorithm
Significant Features
Direction
SP500
Logit
#ff -0.0029 #internationalwomensday -0.0023 #superbowl 0.0022 #brexit 0.0022 #fb -0.0014
Direction
Dow30
DTrees#obama 0.0566 #event 0.0381 #barcelona 0.0367 #please 0.0364 #mentor 0.0349
Direction
NasdaqSVC#sxsw 0.0166 #brexit 0.0121 #ff 0.0103 #sotu 0.0072 #blockchain 0.0072
Direction
Russell2000
SVC#fb -0.0204 #ff -0.0143 #winning 0.0122 #libya 0.01 #sxsw 0.0096

73