stock market reactions to presidential social media usage...
TRANSCRIPT
Stock Market Reactions to Presidential Social Media
Usage: Evidence from Company-Specific Tweets∗
Qi Ge † Alexander Kurov ‡ Marketa Halova Wolfe §
**** PRELIMINARY DRAFT: DO NOT CITE OR CIRCULATE ****
First Draft: March 20, 2017This Draft: May 4, 2017
Abstract
Recent political developments in the United States offer a unique opportunity to
examine the role of social media in the stock market. Specifically, we analyze the impact
of tweets from President Trump’s official Twitter accounts from November 9, 2016 to
February 28, 2017 that include a name of a publicly traded company. We find that
these tweets move company stock prices, increase trading volume, and affect Bloomberg
institutional investor attention and company-specific sentiment, with a stronger impact
before the presidential inauguration.
Keywords: Twitter, company-specific tweets, President Trump, stock price, tradingvolume, Bloomberg institutional investor attention, Bloomberg sentiment, event studyJEL classification: G12, G14
∗We thank seminar participants at Skidmore College for helpful comments. We also thank Chen Gu forresearch assistance. The opinions in this paper are those of the authors and do not necessarily reflect theviews of Skidmore College or West Virginia University.†Assistant Professor, Department of Economics, Skidmore College, Saratoga Springs, NY 12866, Phone:
+1-518-580-8302, Email: [email protected]‡Associate Professor, Department of Finance, College of Business and Economics, West Virginia Univer-
sity, P.O. Box 6025, Morgantown, WV 26506, Phone: +1-304-293-7892, Email: [email protected]§Assistant Professor, Department of Economics, Skidmore College, Saratoga Springs, NY 12866, Phone:
+1-518-580-8374, Email: [email protected]
1 Introduction
Media coverage helps disseminate news by alleviating informational frictions. Past research
has extensively studied the role of traditional mass media in financial markets. Recent
topics include the role of local media (Engelberg & Parsons, 2011), the impact of newspaper
reports on the momentum of stock returns (Hillert, Jacobs, & Muller, 2014), sources of
news stories (Dougal, Engelberg, Garcia, & Parsons, 2012; Ahern & Sosyura, 2014), cross-
sectional evidence on media coverage and stock returns (Fang & Peress, 2009), newspaper
coverage and mutual fund investment decisions (Kaniel & Parham, 2017), and the role of
investor relations firms (Solomon, 2012). The rise and popularity of social media, such
as Facebook and Twitter, as alternative media building on real-time information delivery
and social networking, have understandably attracted scholarly attention and allowed us
to broaden our understanding of the impact of media on the financial markets. Exploring
a rich body of social media messages, many existing studies within this growing strand
of research apply linguistic content analysis to measure investor sentiment and consider
its impact in the financial markets (Bollen, Mao, & Zeng, 2011; Siganos, Vagenas-Nanos,
& Verwijmeren, 2014; Sprenger, Sandner, Tumasjan, & Welpe, 2014; Bartov, Faurel, &
Mohanram, 2016; Azar & Lo, 2016). Recent political developments in the United States
offer a unique opportunity to further advance this literature by examining the stock market
responses to presidential social media messages that target specific companies.1
Donald J. Trump, the 45th President of the United States, was elected on November
8, 2016. President Trump has broken with the long-standing tradition of presidents and
other high-ranking government officials to abstain from making comments about specific
companies. In particular, President Trump has utilized Twitter to target specific companies
in his tweets about industrial policy. To motivate our study, Figure 1 shows an example
of the impact on the price and trading volume of Toyota’s American depositary receipts
1Researchers have also recently examined other aspects of social media’s role in the 2016 presidentialelection. For example, Allcott and Gentzkow (2017) study the importance of social media in disseminatingfake news during the 2016 presidential election.
1
(ADRs) in the 60-minute window around 13:14 EST on January 5, 2017 when President
Trump tweeted: “Toyota Motor said will build a new plant in Baja, Mexico, to build Corolla
cars for U.S. NO WAY! Build plant in U.S. or pay big border tax.” The figure suggests that
the trading volume spiked, and price dropped by more than one dollar immediately following
President Trump’s tweet. While no systematic inference can be drawn based on this figure
alone, it is plausible that investors respond to these firm-specific tweets.
Figure 1: Toyota ADR (TM) on January 5, 2017
Stock price Trading volume
120.
412
0.6
120.
812
1.0
121.
2S
tock
pric
e ($
)
12:45 13:14 13:44
020
,000
40,0
0060
,000
80,0
00Vo
lum
e (N
umbe
r of s
hare
s)
12:45 13:14 13:44
Toyota (TM) on January 5, 2017
The figure shows the price and trading volume of Toyota ADRs (TM) in the 60-minute window around 13:14on January 5, 2017 when President Trump tweeted: “Toyota Motor said will build a new plant in Baja,Mexico, to build Corolla cars for U.S. NO WAY! Build plant in U.S. or pay big border tax.” The figure isconstructed using minute-by-minute transaction data from Genesis Financial Technologies.
In this study, we analyze the impact of all tweets from @realDonaldTrump and @POTUS
Twitter accounts used by President Trump that include a name of a publicly-traded company
from November 9, 2016 to February 28, 2017. We find that the tweets move company stock
prices, increase trading volume, and affect Bloomberg institutional investor attention and
company-specific sentiment. We also find that the impact was stronger before the President-
elect was inaugurated on January 20, 2017. During the pre-inauguration period, the tweets
on average move the company stock price by approximately 1.16% and increase trading
volume by approximately 48% on the day of the tweet.
2
Our study makes the following contributions. First, to our knowledge, it is the first
to document the stock market reactions to a government official’s direct, unscheduled, and
non-neutral comments targeting specific companies. The paper thus contributes to our
understanding of the role of social media in the financial markets and to the broader literature
on the impact of media coverage and news announcements. Second, within the growing
strand of social media studies, the novelty of our study lies in the fact that our events are
defined by presidential social media messages which often came as surprises. This stands
in contrast to prior studies that derive investor sentiment from social media reactions to
pre-scheduled events from the financial markets or the overall economy, such as quarterly
earnings announcements (Bartov et al., 2016) and monetary policy announcements (Azar &
Lo, 2016).
The paper is organized as follows. Section 2 surveys the related literature. Sections 3
and 4 describe the data and methodology, respectively. Section 5 presents the empirical
results. Section 6 concludes with a discussion of future research questions.
2 Related Literature
Our study is related to a number of research channels that consider the impact of news
announcements and media attention in financial markets and is most akin to the growing
strand of literature on the role of social media in financial markets.2 Previous studies tend to
focus on measuring Twitter message volumes as proxies for news arrivals and investors pro-
cessing relevant information. Linguistic textual analysis of related social media messages has
often been applied to quantify investor sentiment. Much of the existing literature considers
the (causal) relation between investor sentiment and stock returns at market or individual
stock level, where the sentiment can be derived from opinions posted on online investment
forums (Chen, De, Hu, & Hwang, 2014), Facebook posts (Karabulut, 2013; Siganos et al.,
2A closely related but non social media based stream of literature studies Google search queries as a proxyfor investor attention, for example, Da, Engelberg, and Gao (2011) and Joseph, Wintoki, and Zhang (2011).
3
2014), and Twitter feeds (Bollen et al., 2011; Sprenger et al., 2014; Mao, Counts, Bollen, et
al., 2015).
News announcements provide the foundation to event studies in economics and finance.
Despite a voluminous literature on firm-specific news events (see MacKinlay (1997) for a
survey of event study literature) and macro announcements (for example, Balduzzi, Elton,
& Green, 2001; Andersen, Bollerslev, Diebold, & Vega, 2003; Kurov, Sancetta, Strasser, &
Wolfe, 2017), existing social media studies rarely seek to examine the role of social media
around specific news announcements. Notable exceptions include Bartov et al. (2016) that
investigate the impact of pre-earnings announcement opinions expressed via tweets on post-
announcements returns, and Azar and Lo (2016) that study how tweets related to the Federal
Open Market Committee (FOMC) meetings can predict future returns. It is worth noting
that both studies consider pre-scheduled news events. On the other hand, a considerable
number of news announcement studies concerns the impact of unanticipated news events (for
example, Brooks, Patel, & Su, 2003; Knittel & Stango, 2013). Our paper thus differs from
and contributes to existing social media studies by investigating the stock market impact of
the unexpected firm-specific comments made by the President via social media.
More broadly, to our knowledge, no prior studies have documented the financial market
impact of government officials’ comments, whether through traditional media or social me-
dia, targeting particular companies. Such vacuum is understandable given that government
leaders typically refrain from targeting specific companies in their public communications.
The popularity of social media, combined with the newest political developments in the
United States, gives us a unique opportunity to bridge this gap in the literature by con-
sidering President Trump’s tweets as a platform for firm-specific news and examining their
impact on the stock market.
4
3 Data
Section 3.1 describes the presidential tweets data followed by a description of the dependent
variables in Section 3.2.
3.1 Twitter Data
Table A1 lists all tweets from @realDonaldTrump and @POTUS Twitter accounts3 that include
a name of a publicly-traded company from November 9, 2016 to February 28, 2017. We use
November 9, 2016 as the beginning of the sample period because the presidential election
took place on November 8, 2016.4 The first such tweet appears on November 17, 2016. The
last such tweet appears on February 17, 2017.
Most of the tweets are posted outside of the U.S. stock market open hours from 9:30 to
16:00 EST on business days – in the early morning, in the evening, on weekends or holidays
– such as a tweet about Rexnord on December 2, 2016 at 22:06. Therefore, to analyze the
impact of the tweets on the stock market, we use daily (rather than intraday) stock prices,
trading volume, institutional investor attention, and company-specific sentiment.
When there are multiple tweets on the same day, the daily prices, trading volume, institu-
tional investor attention, and company-specific sentiment combine the effects of the multiple
tweets. These multiple tweets can take place over several hours (for example, tweets about
Carrier on November 29 and 30, 2016) or within a couple of minutes when a longer message
is split up into multiple tweets (for example, tweets about SoftBank on December 6, 2016
at 14:09 and 14:10), which arises from Twitter restricting the length of each tweet to 140
characters. The last column of Table A1 shows how multiple tweets combine into a single
3@POTUS with approximately 16 million followers is the official Twitter account of the President of theUnited States that became available to President Trump after inauguration on January 20, 2017. Tweetscreated by President Obama were archived into @POTUS44 account. @realDonaldTrump with approximately27 million followers is President Trump’s personal account that continues to be used. As indicated inTable A1, majority of the tweets in our sample were posted on @realDonaldTrump with only four tweetsposted on @POTUS.
4We exclude tweets about media companies such as CNN (owned by Time Warner Inc) and New YorkTimes (owned by the New York Times Company) because their impact on the stock market is complicatedby President Trump’s ongoing feud with media.
5
event.
The column Code in Table A1 classifies the tweets as negative (-1) if the tweet is a
“threat” and positive (1) if the tweet is a “praise”. For example, we interpret the tweet
about Rexnord on December 2, 2016 “Rexnord of Indiana is moving to Mexico and rather
viciously firing all of its 300 workers. This is happening all over our country. No more!” as
negative (a “threat”) and the tweet about Ford on November 17, 2016 “Just got a call from
my friend Bill Ford, Chairman of Ford, who advised me that he will be keeping the Lincoln
plant in Kentucky - no Mexico” as positive (a “praise”).5
If a tweet mentions more than one company such as a tweet about General Motors and
Walmart on January 17, 2017 “Thank you to General Motors and Walmart for starting the
big jobs push back into the U.S.!, the tweet is listed twice to capture the impact on both
companies. This is important especially when a tweet is positive about one company and
negative about another company such as a tweet about Lockheed Martin and Boeing on
December 22, 2016 “Based on the tremendous cost and cost overruns of the Lockheed Martin
F-35, I have asked Boeing to price-out a comparable F-18 Super Hornet! that is negative
about Lockheed Martin but positive about Boeing. Our sample does not contain any days
with both positive and negative tweets about the same company. In two tweets about
Ford, General Motors and Lockheed Martin on January 18, 2017, the tone of the tweets is
ambiguous, so we exclude them. Our data set then includes 27 events. Six are classified as
a threat, and 21 are classified as a praise.
3.2 Market Data
We analyze the impact of the presidential tweets on company stock price, trading volume,
institutional investor attention, and company-specific sentiment. The price and trading
volume are obtained from Yahoo Finance. We employ the Bloomberg institutional investor
5This classification focuses on the tone of the tweet rather than potential economic impacts that are likelyto be complex. For example, a decision to keep a plant in the United States may be advantageous for acompany if it is able to negotiate incentives such as tax breaks or reduced regulation or disadvantageous ifit forgoes cost savings from relocating to a country with lower production costs.
6
attention (IIA) measure described by Ben-Rephael, Da, and Israelsen (2016). Bloomberg
tracks how many times Bloomberg users read articles about a given company and search for
information about the company. Bloomberg records hourly counts, compares the counts in
the previous eight hours to previous 30 days and assigns a score of 0, 1, 2, 3 and 4 if the
average over the last eight hours is less than 80%, between 80% and 90%, between 90% and
94%, between 94% and 96%, or higher than 96%, respectively, compared to the previous 30
days. Following Ben-Rephael et al. (2016), we construct a binary measure of abnormal IIA
that equals 1 if IIA equals 3 or 4, and 0 otherwise, so that the abnormal IIA captures the
right tail of the IIA distribution, and the value of 1 represents an IIA shock.
The company-specific sentiment is constructed by Bloomberg for each stock based on
messages posted about the company on Twitter and StockTwits, a social media platform for
sharing information about stocks and markets. Bloomberg separates messages with company
tickers into three categories (positive, negative and neutral) and assigns confidence scores
using a proprietary algorithm. These scores are then aggregated to derive a measure of
sentiment for each company. This computation is performed every 30 minutes. The values
are averaged over 24 hours (from 9:30 a.m. on the previous day to 9:29 a.m. on the current
day) to compute daily sentiment which is then made available to Bloomberg users rescaled
to a range from -1 (the most negative sentiment) to +1 (the most positive sentiment).
4 Methodology
To analyze the impact of the presidential tweets on company stock prices, we use daily
closing stock prices, Pi,t, for each company i. We compute the holding period return for
each company as Ri,t =Pi,t−Pi,t−1
Pi,t−1. We compute excess return as the return for that company
in excess of the risk-free return, RFt, obtained from Kenneth French’s website, ERi,t =
Ri,t −RFt. Table 1 shows the summary statistics.
We estimate the standard Fama-French three-factor model (Fama & French, 1993) using
7
Table 1: Summary Statistics
Absolute AbnormalAbsolute Value Abnormal Company- Institutional
Value Abnormal Abnormal Trading Specific InvestorReturn Return Return Return Volume Sentiment Attention
Median 0.108 0.726 -0.005 0.617 -0.061 0.000 0.000Mean 0.144 1.068 -0.006 0.930 0.080 -0.023 0.249Minimum -10.280 0.000 -9.617 0.001 -0.814 -0.987 0.000Maximum 9.682 10.280 6.881 9.617 10.858 0.980 1.000Std Dev 1.574 1.165 1.397 1.041 0.726 0.193 0.433
This table shows the summary statistics for return computed as Ri,t = (Pi,t − Pi,t−1)/Pi,t−1, the absolutevalue of the return, abnormal return computed as residuals from equation (1), the absolute value of abnormalreturn, abnormal volume computed as AVi,t = (Vi,t−VAvrg,t)/VAvrg,t, and Bloomberg measures of sentimentand abnormal institutional investor attention. Returns are expressed in percentage terms. The sample periodis from November 9, 2016 to February 28, 2017. The number of days is 75. The number of companies is15 for all variables except for the Bloomberg sentiment measure where data is not available for Rexnordcompany. The resulting number of panel observations is 1,125 except for the Bloomberg company-specificsentiment measure where the number of observations is 1,045.
OLS that regresses the excess return on the stock market return, RMt, minus the risk-free
return factor, the small-minus-big market capitalization factor, SMBt, and the high-minus-
low book-to-market ratio factor, HMLt:6
ERi,t = β0 + β1(RMt −RFt) + β2SMBt + β3HMLt + εi,t. (1)
Controlling for the stock market return is especially important since the overall market
increased during our sample period. We obtain residuals, ui,t, or abnormal returns, from
equation (1) for each company. We then estimate a fixed effects panel model:
ui,t = γ0 + γ1Ti,t + γi + vi,t, (2)
where γi accounts for the fixed effects and Ti,t is the Twitter variable described in Section 3.7
6This regression uses data from January 1, 2016 to February 28, 2017 to properly estimate the effect ofthe Fama-French factors on the excess return.
7In contrast to studies that analyze the impact of scheduled announcements (for example, Balduzzi etal. (2001), Andersen et al. (2003) and Kurov et al. (2017) on macroeconomic announcements) that have tosubtract market’s expectations from the actual released announcement values to compute the unexpectedcomponent of the announcement, our study does not have to subtract market’s expectations because the
8
The number of days is 75. The number of companies is 15. The resulting number of panel
observations is 1,125. We use panel-corrected standard errors to account for cross-correlation
across stocks.
To analyze the impact of the presidential tweets on company stock trading volume, we
compute abnormal trading volume, AVi,t, for each company i as the difference between
trading volume Vi,t and mean trading volume of the previous five days divided by the mean
trading volume of the previous five days similarly to Joseph et al. (2011): AVi,t =Vi,t−VAvrg,t
VAvrg,t
where VAvrg,t =ΣJ
1 Vi,t−j
Jand J = 5. We then estimate a fixed effects panel model:
AVi,t = δ0 + δ1|Ti,t|+ δi + εi,t, (3)
where δi accounts for the fixed effects, and the absolute value of the Twitter variable is used
because both positive and negative tweets may increase the trading volume. We again use
panel-corrected standard errors to account for cross-correlation across stocks.
To analyze the impact of the presidential tweets on the Bloomberg measure of sentiment
about the company, we estimate the fixed effects panel model specified in equation (2) with
the sentiment variable in place of the residual, ui,t.
Lastly, to analyze the impact of the presidential tweets on the abnormal IIA measure
described in Section 3.2, we follow Ben-Rephael et al. (2016) and estimate a panel probit
model of the abnormal IIA measure on the absolute value of the Twitter variable |Ti,t| with
dummies for individual stocks.
5 Empirical Results
Section 5.1 reports the overall impact of the presidential tweets on company stock returns,
trading volume, and IIA. Section 5.2 discusses how the impact varies over time including the
impact on company-specific sentiment.
presidential tweets are unscheduled and unexpected.
9
5.1 Stock Market Reactions to Presidential Tweets
Column (1) of Table 2 reports the impact of the presidential tweets on abnormal returns
in the full sample period from November 9, 2016 to February 28, 2017. The coefficient on
the Twitter variable indicates that presidential tweets on average move the company stock
price by approximately 0.78%: If the tweet is positive, the stock price tends to rise; if the
tweet is negative, the stock price tends to fall. This is an economically meaningful effect on
shareholder value because the median values of the absolute daily return and absolute daily
abnormal return are approximately 0.73% and 0.62%, respectively, per Table 1.
Table 2: Impact of Presidential Tweets
(1) (2) (3) (4)Abnormal Abnormal Abnormal Institutional Company-Specific
Return Trading Volume Investor Attention Sentiment
Full SampleTwitter variable 0.776*** 0.408*** 0.462*** 0.056
(0.257) (0.129) (0.082) (0.038)
Pre-InaugurationTwitter variable 1.157*** 0.476*** 0.521*** 0.078*
(0.306) (0.173) (0.327) (0.100)
Column (1) reports results for the fixed effects panel model specified in equation (2). Column (2) reportsresults for the fixed effects panel model specified in equation (3). Column (3) reports the marginal effectsof the probit model of the Bloomberg abnormal IIA discussed in Section 4. Column (4) reports resultsfor the fixed effects panel model specified in equation (2) with the market sentiment variable in placeof the residual, ui,t. Panel-corrected standard errors are shown in parentheses. *, **, and *** indicatestatistical significance at 10%, 5% and 1% levels, respectively. The full sample period is from November9, 2016 to February 28, 2017. The number of days is 75. The number of companies is 15. The resultingnumber of panel observations is 1,125. This includes 27 observations with tweets listed in Table A1. Thepre-inauguration sample period is from November 9, 2016 to January 19, 2017. The number of days is48. The number of companies is 12. The resulting number of panel observations is 576. This includes20 observations with tweets listed in Table A1. The market sentiment data is not available for Rexnordcompany, so the number of companies used in the analysis of Bloomberg company-specific sentiment is 14and 11 in the full sample period and pre-inauguration sample sub-period, respectively.
Column (2) reports the impact on abnormal trading volume. The coefficient on the
Twitter variable indicates that the presidential tweets (both positive and negative) on average
increase trading volume by approximately 41% compared to the average trading volume on
the previous five days.
10
Column (3) reports the marginal effects of the probit model of the abnormal IIA. The es-
timate indicates that the presidential tweets (both positive and negative) on average increase
the probability of abnormal IIA by 46%. This suggests that President Trump’s company-
specific tweets capture attention of institutional investors.
5.2 Pre-Inauguration Period
Our sample period comprises two distinct sub-periods. The first sub-period is from the
presidential election to inauguration (November 9, 2016 to January 19, 2017). The second
sub-period is from the inauguration to the end of our sample period (January 20, 2017 to
February 28, 2017). We analyze whether the impact of the tweets is stronger during the pre-
inauguration period than during the full sample period. Table 2 presents the results for the
pre-inauguration period. There is a clear pattern of the coefficients on the Twitter variable
being higher than in the full sample period. The presidential tweets on average move the
company stock price by approximately 1.16% compared to 0.78% in the full sample period.
The tweets on average increase trading volume by approximately 48% compared to 41% in
the full sample period. Moreover, the tweets increase the probability of abnormal IIA by
52% compared to 46% in the full sample period.
The company-specific sentiment results seem to support this finding: The sentiment vari-
able is not significant in the full sample period but shows significance in the pre-inauguration
sub-period, with the tweets on average moving the Bloomberg sentiment measure by approx-
imately 0.08. Positive tweets have a positive effect on the sentiment whereas negative tweets
have a negative effect. The magnitude is economically meaningful given that the scale of the
sentiment variable is from -1 to 1.
It will be interesting to formally extend this sub-sample analysis to the post-inauguration
period when more data becomes available to determine whether the market reaction is indeed
lessening. Three potential explanations for this trend exist. First, the initial presidential
communications about specific companies took the markets by surprise, but the markets may
11
have become accustomed to this new industrial policy targeting specific companies and do
not react as strongly any more. Second, Twitter was the primary channel of communicating
with the markets for the President-elect before inauguration. Other channels have been in
effect since the inauguration such as presidential executive orders, press releases, and press
briefings. These other channels could lessen the Twitter impact if investors consider them
more influential in setting the tone of the presidential industrial policy. Third, some of the
post-inauguration tweets were posted on the @POTUS account, which may differ in impact
from the @realDonaldTrump account since the two accounts differ in the number and perhaps
even characteristics of followers.
6 Conclusion
We analyze the impact of presidential tweets about specific companies on stock prices, trad-
ing volume, institutional investor attention, and company-specific sentiment. We find that
the tweets move company stock prices, increase trading volume and affect the institutional
investor attention and company-specific sentiment. These findings raise the question of
whether it is optimal for high-ranking government officials to communicate industrial policy
via Twitter where unexpected announcements can potentially instantly create or wipe out
millions of dollars in shareholder value. If this communication channel does become the
mainstay of presidential industrial policy, it will be important to implement procedures pre-
venting premature dissemination of the information similar to procedures utilized by other
government institutions that release market-moving announcements.
While our study documents the stock market impact of presidential social media usage,
this topic lends itself to further questions regarding the mechanisms when a larger sample
of presidential tweets becomes available. One avenue of future research could investigate
whether certain industry or firm-level attributes make the tweets particularly influential. For
example, some industries may be more influenced by the tweets due to their dependence on
12
government contracts (for example, defense industry) or bailouts (for example, automobile
industry). A tweet about Nordstrom on February 8, 2017 “My daughter Ivanka has been
treated so unfairly by @Nordstrom. She is a great person – always pushing me to do the right
thing! Terrible!” provides anecdotal evidence that this may the case. Figure 2 shows that
the trading volume spiked, but after an initial dip the stock price increased in spite of the
tweet being negative about the company. This may be due to the company operating in the
retail industry that does not depend on government contracts or bailouts. Likewise, size of
the targeted company could play an important role in explaining the stock market impact
of the President’s social media messages.
Figure 2: Nordstrom (JWN) Stock on February 8, 2017
Stock price Trading volume
42.1
42.2
42.3
42.4
42.5
42.6
Sto
ck p
rice
($)
10:22 10:51 11:21
050
,000
100,
000
150,
000
200,
000
Volu
me
(Num
ber o
f sha
res)
10:22 10:51 11:21
Nordstrom (JWN) on February 8, 2017
The figure shows Nordstrom (JWN) stock price and trading volume reaction in the 60-minute window around10:51 when President Trump tweeted “My daughter Ivanka has been treated so unfairly by @Nordstrom. Sheis a great person – always pushing me to do the right thing! Terrible!” The figure is constructed usingminute-by-minute transaction data from Genesis Financial Technologies.
Another possibility for future research could focus on the nature of the tweets. For
instance, one could investigate whether the impact is stronger when the President tweets
about a company for the first time. If there are multiple tweets about a given company, the
impact could also differ depending on whether the tweets rapidly follow each other or are
more distributed over time. If more tweets are posted on the @POTUS account, it will also be
13
possible to test whether the two Twitter accounts differ in their impact. Linguistic textual
analysis could also be utilized to carefully analyze the tone of President Trump’s tweets.
Finally, if more tweets occur during the stock market open hours, a comprehensive analysis
of intraday data will reveal high-frequency price and trading volume moves that are likely
to be interesting based on anecdotal evidence in Figures 1 and 2.
14
Table
A1:ListofTweets
Company
Ticker
Date
Tim
eTweet
Code
Event#
For
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7/16
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1610
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1622
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.We
wil
lkee
pou
rco
mp
an
ies
an
djo
bs
inth
eU
.S.
Th
an
ks
Carr
ier
13
Car
rier
aU
TX
11/3
0/16
13:2
1G
reat
inte
rvie
won
foxan
dfr
ien
ds
by
Ste
veD
oocy
w/
Carr
ier
emp
loye
e-w
ho
has
am
essa
ge
for
#P
EO
TU
Sre
alD
on
ald
Tru
mp
&#
VP
EO
TU
Sm
ike_
pen
ce.
13
Car
rier
aU
TX
11/3
0/16
15:0
0It
snot
un
com
mon
for
aR
epu
bli
can
tob
ep
ro-b
usi
nes
s.B
ut
Pre
sid
ent-
elec
tD
on
ald
Tru
mp
show
edT
ues
day
nig
ht
hes
pro
-work
er,
too,
by
savin
g1,0
00
job
sat
the
Carr
ier
pla
nt
inIn
dia
na.
13
Car
rier
UT
X11
/30/
1622
:48
Look
forw
ard
togoin
gto
Ind
ian
ato
morr
owin
ord
erto
be
wit
hth
egre
at
work
ers
of
Carr
ier.
Th
eyw
ill
sell
many
air
con
dit
ion
ers!
14
Car
rier
aU
TX
12/0
1/16
9:3
8G
etti
ng
read
yto
leav
efo
rth
eG
reat
Sta
teofIn
dia
na
an
dm
eet
the
hard
work
ing
an
dw
ond
erfu
lp
eop
leof
Carr
ier
A.C
.1
4
Rex
nor
dR
XN
12/0
2/16
22:0
6R
exn
ord
of
Ind
ian
ais
mov
ing
toM
exic
oan
dra
ther
vic
iou
sly
firi
ng
all
of
its
300
work
ers.
Th
isis
hap
pen
ing
all
over
ou
rco
untr
y.N
om
ore
!-1
5
Boei
ng
BA
12/0
6/16
8:5
2B
oei
ng
isb
uil
din
ga
bra
nd
new
747
Air
Forc
eO
ne
for
futu
rep
resi
den
ts,
bu
tco
sts
are
ou
tof
contr
ol,
more
than
$4
bil
lion
.C
an
cel
ord
er!
-16
Sof
tBan
ka,b
SF
TB
Y12
/06/
1614
:09
Masa
(Soft
Ban
k)
of
Jap
an
has
agre
edto
inves
t$50
bil
lion
inth
eU
.S.
tow
ard
bu
sin
esse
san
d50,0
00
new
job
s...
.1
7
Sof
tBan
ka,b
SF
TB
Y12
/06/
1614
:10
Masa
said
he
wou
ldnev
erd
oth
ish
ad
we
(Tru
mp
)not
won
the
elec
tion
!1
7
Exxon
Mob
ilX
OM
12/1
1/16
10:2
9W
het
her
Ich
oose
him
or
not
for
”Sta
te”-
Rex
Til
lers
on
,th
eC
hair
man
&C
EO
of
Exxon
Mob
il,
isa
worl
dcl
ass
pla
yer
an
dd
ealm
ake
r.S
tay
tun
ed!
18
Exxon
Mob
ilX
OM
12/1
3/16
6:4
3I
hav
ech
ose
non
eof
the
tru
lygre
at
busi
nes
sle
ad
ers
of
the
worl
d,R
exT
ille
rson
,C
hair
man
an
dC
EO
of
Exxon
Mob
il,
tob
eS
ecre
tary
of
Sta
te.
19
15
Boei
ng
BA
12/2
2/16
17:2
6B
ase
don
the
trem
end
ou
sco
stan
dco
stov
erru
ns
of
the
Lock
hee
dM
art
inF
-35,
Ih
ave
ask
edB
oei
ng
top
rice
-ou
ta
com
para
ble
F-1
8S
up
erH
orn
et!
110
Lock
hee
dM
arti
nL
MT
12/2
2/16
17:2
6B
ase
don
the
trem
end
ou
sco
stan
dco
stov
erru
ns
of
the
Lock
hee
dM
art
inF
-35,
Ih
ave
ask
edB
oei
ng
top
rice
-ou
ta
com
para
ble
F-1
8S
up
erH
orn
et!
-111
Gen
eral
Mot
ors
GM
01/0
3/17
7:3
0G
ener
al
Moto
rsis
sen
din
gM
exic
an
mad
em
od
elof
Ch
evy
Cru
zeto
U.S
.ca
rd
eale
rs-t
ax
free
acr
oss
bord
er.
Make
inU
.S.A
.or
pay
big
bord
erta
x!
-112
For
da
F01
/03/
1711
:44
”@
Dan
Sca
vin
o:
Ford
tosc
rap
Mex
ico
pla
nt,
inve
stin
Mic
hig
an
du
eto
Tru
mp
poli
cies
”1
13
For
dF
01/0
4/17
8:1
9T
han
kyou
toF
ord
for
scra
pp
ing
an
ewp
lant
inM
exic
oan
dcr
eati
ng
700
new
job
sin
the
U.S
.T
his
isju
stth
eb
egin
nin
g-
mu
chm
ore
tofo
llow
114
Toy
otaa
TM
01/0
5/17
13:1
4T
oyota
Moto
rsa
idw
illb
uil
da
new
pla
nt
inB
aja
,M
exic
o,
tob
uil
dC
oro
lla
cars
for
U.S
.N
OW
AY
!B
uil
dp
lant
inU
.S.
or
pay
big
bord
erta
x.
-115
Fia
tC
hry
sler
FC
AU
01/0
9/17
9:1
4It
’sfi
nall
yh
app
enin
g-
Fia
tC
hry
sler
just
an
nou
nce
dp
lan
sto
inve
st$1B
IL-
LIO
Nin
Mic
hig
an
an
dO
hio
pla
nts
,ad
din
g2000
job
s.T
his
aft
er..
.1
16
Fia
tC
hry
sler
FC
AU
01/0
9/17
9:1
6F
ord
said
last
wee
kth
at
itw
illex
pan
din
Mic
hig
an
and
U.S
.in
stea
dof
bu
ild
ing
aB
ILL
ION
doll
ar
pla
nt
inM
exic
o.
Th
an
kyou
Ford
&F
iat
C!
116
For
dF
01/0
9/17
9:1
6F
ord
said
last
wee
kth
at
itw
illex
pan
din
Mic
hig
an
and
U.S
.in
stea
dof
bu
ild
ing
aB
ILL
ION
doll
ar
pla
nt
inM
exic
o.
Th
an
kyou
Ford
&F
iat
C!
117
Gen
eral
Mot
orsa
GM
01/1
7/17
12:5
5T
han
kyo
uto
Gen
eral
Moto
rsan
dW
alm
art
for
start
ing
the
big
job
sp
ush
back
into
the
U.S
.!1
18
Wal
mar
taW
MT
01/1
7/17
12:5
5T
han
kyo
uto
Gen
eral
Moto
rsan
dW
alm
art
for
start
ing
the
big
job
sp
ush
back
into
the
U.S
.!1
19
For
dF
01/1
8/17
7:3
4T
ota
lly
bia
sed
@N
BC
New
sw
ent
out
of
its
way
tosa
yth
at
the
big
an
nou
nce
-m
ent
from
Ford
,G
.M.,
Lock
hee
d&
oth
ers
that
job
sare
com
ing
back
0n
a
For
dF
01/1
8/17
7:4
4to
the
U.S
.,b
ut
had
noth
ing
tod
ow
ith
TR
UM
P,
ism
ore
FA
KE
NE
WS
.A
skto
pC
EO
’sof
those
com
pan
ies
for
real
fact
s.C
am
eb
ack
bec
au
seof
me!
0n
a
Gen
eral
Mot
ors
GM
01/1
8/17
7:3
4T
ota
lly
bia
sed
@N
BC
New
sw
ent
out
of
its
way
tosa
yth
at
the
big
an
nou
nce
-m
ent
from
Ford
,G
.M.,
Lock
hee
d&
oth
ers
that
job
sare
com
ing
back
0n
a
Gen
eral
Mot
ors
GM
01/1
8/17
7:4
4to
the
U.S
.,b
ut
had
noth
ing
tod
ow
ith
TR
UM
P,
ism
ore
FA
KE
NE
WS
.A
skto
pC
EO
’sof
those
com
pan
ies
for
real
fact
s.C
am
eb
ack
bec
au
seof
me!
0n
a
Lock
hee
dM
arti
nL
MT
01/1
8/17
7:3
4T
ota
lly
bia
sed
@N
BC
New
sw
ent
out
of
its
way
tosa
yth
at
the
big
an
nou
nce
-m
ent
from
Ford
,G
.M.,
Lock
hee
d&
oth
ers
that
job
sare
com
ing
back
0n
a
16
Lock
hee
dM
arti
nL
MT
01/1
8/17
7:4
4to
the
U.S
.,b
ut
had
noth
ing
tod
ow
ith
TR
UM
P,
ism
ore
FA
KE
NE
WS
.A
skto
pC
EO
’sof
those
com
pan
ies
for
real
fact
s.C
am
eb
ack
bec
au
seof
me!
0n
a
Bay
erA
Gb
BA
YN
01/1
8/17
8:0
0”B
ayer
AG
has
ple
dged
toad
dU
.S.
jobs
an
din
vest
men
tsaft
erm
eeti
ng
wit
hP
resi
den
t-el
ect
Don
ald
Tru
mp
,th
ela
test
ina
stri
ng..
.”W
SJ
120
For
dF
01/2
4/17
19:4
6G
reat
mee
tin
gw
ith
Ford
CE
OM
ark
Fie
lds
an
dG
ener
alM
oto
rsC
EO
Mary
Barr
aat
the
Wh
iteH
ou
seto
day
.1
21
Gen
eral
Mot
ors
GM
01/2
4/17
19:4
6G
reat
mee
tin
gw
ith
Ford
CE
OM
ark
Fie
lds
an
dG
ener
alM
oto
rsC
EO
Mary
Barr
aat
the
Wh
iteH
ou
seto
day
.1
22
Har
ley-D
avid
sona,c
HO
G02
/02/
1712
:56
Gre
at
mee
tin
gw
ith
@h
arl
eyd
avid
son
exec
uti
ves
from
Mil
wau
kee
,W
isco
nsi
nat
the
@W
hit
eHou
se.
123
Har
ley-D
avid
sona,c
HO
G02
/03/
1713
:26
#IC
YM
I-R
emark
sby
Pre
sid
ent
Tru
mp
Bef
ore
Mee
tin
gw
ith
Harl
ey-D
avid
son
Exec
uti
ves
an
dU
nio
nR
epre
senta
tives
:1
24
Nor
dst
rom
a,c
JW
N02
/08/
1710
:51
My
dau
ghte
rIv
an
kah
as
bee
ntr
eate
dso
un
fair
lyby
@N
ord
stro
m.
Sh
eis
agre
at
per
son
–alw
ays
pu
shin
gm
eto
do
the
right
thin
g!
Ter
rib
le!
-125
Inte
laIN
TC
02/0
8/17
14:2
2T
han
kyo
uB
rian
Krz
an
ich
,C
EO
of
@In
tel.
Agre
at
inve
stm
ent
($7
BIL
LIO
N)
inA
mer
ican
INN
OV
AT
ION
an
dJO
BS
!#
Am
eric
aF
irst
126
Boei
ngc
BA
02/1
7/17
6:3
8G
oin
gto
Ch
arl
esto
n,
Sou
thC
aro
lin
a,
inord
erto
spen
dti
me
wit
hB
oei
ng
an
dta
lkjo
bs!
Look
forw
ard
toit
.1
27
Th
ista
ble
list
stw
eets
from
@realDonaldTrump
an
d@POTUS
Tw
itte
racc
ou
nts
use
dby
Pre
sid
ent
Tru
mp
that
incl
ud
ea
nam
eof
ap
ub
licl
ytr
ad
edco
mp
any
from
Nov
emb
er9,
2016
toF
ebru
ary
28,
2017.
Tim
eis
show
nin
East
ern
Tim
e.C
od
ecl
ass
ifies
the
twee
tsas
neg
ati
ve(-
1)
ifth
etw
eet
isa
“th
reat
”an
dp
osit
ive
(1)
ifth
etw
eet
isa
“pra
ise”
.E
vent
#sh
ows
how
mu
ltip
letw
eets
com
bin
ein
toa
sin
gle
even
tw
hen
twee
tsocc
ur
wit
hin
the
sam
ed
ay.
Th
eto
tal
nu
mb
erof
even
tsis
27.
aT
his
twee
tw
asp
oste
dd
uri
ng
the
Un
ited
Sta
tes
stock
mark
etop
enh
ou
rson
bu
sin
ess
day
sfr
om
9:3
0to
16:0
0.
All
oth
ertw
eets
wer
ep
ost
edou
tsid
eof
the
open
hou
rsin
the
earl
ym
orn
ing,
inth
eev
enin
g,
on
wee
ken
ds
or
holi
day
s.b
Th
isst
ock
istr
aded
asan
Am
eric
anD
epos
itar
yR
ecei
pt.
cT
his
twee
tw
asp
oste
don
the@POTUS
Tw
itte
racc
ount.
All
oth
ertw
eets
wer
ep
ost
edon
the@realDonaldTrump
acc
ou
nt.
Tw
eet
#25
was
post
edon
the@realDonaldTrump
acco
unt;
itw
asla
ter
retw
eete
dfr
om
the@POTUS
acc
ou
nt.
17
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19