Trump’s Tweets: The Effect on Target Firms

Author: J.P. Neggers ANR: 665952 Date: 22-08-2017 Supervisor: prof. dr. H.M. Prast

Master Thesis submitted to the Department of Finance, Tilburg University Master Thesis Finance: Tilburg University, School of Economics and Management, Department of Finance.

Trump’s Tweets: The Effect on Target Firms

Author: J.P. Neggers ANR: 665952 Date: 22-08-2017 Supervisor: prof. dr. H.M. Prast Chairperson/second reader dr. K.K. Nazliben

The current thesis is written as part of the graduation process for a Master in Finance at Tilburg University. I would like to use this opportunity to say special thanks to my supervisor, prof. dr. H.M. Prast, for taking the time to respond to my questions and to give feedback and overall guidance during the writing process. Furthermore I would like to thank all my teachers, colleagues and friends that directly or indirectly helped me to write this thesis.

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Contents 1. Introduction and problem statement ...... 3

1.1. Problem indication ...... 3 1.2. Problem statement ...... 4 1.3. Research questions...... 4 1.4. Thesis structure ...... 5 2. Literature and Theory ...... 6

2.1. Information models ...... 6 2.2. Trump’s tweets and the information models...... 8 2.3. Market attention and trading volume ...... 10 2.4. Effect on firm value ...... 11 3. Data and methodology ...... 14

3.1. data and selection...... 14 3.2. Financial data ...... 15 3.3. Abnormal trading volume methodology ...... 16 3.4. Abnormal returns methodology ...... 17 3.5. Event day and trading hours ...... 18 3.6. Contamination ...... 19 4. Results ...... 20

4.1. Abnormal trading volume ...... 20 4.2. Event-window abnormal returns ...... 20 4.3. Cumulative (average) abnormal returns ...... 23 4.4. Using contamination controls ...... 23 5. Conclusion, limitations and further research ...... 25

5.1. Conclusion ...... 25 5.2. Discussion ...... 26 5.3. Limitations ...... 26 5.4. Suggestions for further research ...... 26 6. References ...... 28 7. Appendix ...... 32

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1. Introduction and problem statement

The current chapter introduces the problem that is studied in this Master Thesis. First of all, it provides a problem indication to introduce the topic. Secondly, it provides a problem statement. Thirdly, it states the corresponding research questions used to solve this problem statement. Finally, the current chapter discusses the structure of the remainder of the current study, including a short overview of the following chapters.

1.1. Problem indication

Does Trump's Twitter account have the power to move share prices? The current President has made it a habit to directly attack - or praise – firms using his Twitter account. What effect these addresses have on the value of targeted firms however remains an intriguing question. With President Donald J. Trump's erratic Twitter behavior becoming almost legendary, this question has received a lot of attention in recent news coverage (e.g. Cohan, 2016; Revesz, 2017; Rupp, 2017). It is an intriguing issue, as Trump's Twitter account represents a direct channel of the most powerful man of the US - and arguably, the world - to the people. The current study aims to investigate the effect of a Twitter message of Trump on the firm value of the mentioned firm(s). For the remainder of the current study, these firms will be referred to as target firms1. Twitter is an online platform that allows users to send Twitter messages - or "tweets" up to 140 characters per message. The platform can be accessed through either an online website or a mobile application. Furthermore, it allows users to "follow" another user - such as Trump – which means you subscribe to that person’s (or organization) tweets. Furthermore, it means that you automatically receive any message sent by this user. The Twitter account of Donald J. Trump has over 28 million followers (@realDonaldTrump, 2017). However, these followers make up only a portion of the audience that Trump reaches through his Twitter account, due to both “retweeting” (sharing another user’s tweet with your own followers) and the messages being publically available. The effect of microblogging forums – such as Twitter – is not new in academic literature. For example, Bollen, Mao, and Zeng (2011) find that the “Twitter mood” has the ability to predict the stock market. More specifically, Sprenger, Tumasjan, Sandner and Welpe (2014), as well as Oh and Sheng (2011), find that the sentiment - or bullishness - of tweets is associated with abnormal stock returns. Furthermore, evaluating a similar microblogging forum, Antweiler and Frank (2004) find that stock message boards have a small but significant effect on stock returns. In addition, past academic

1 This term is generally used in M&A literature to denote the acquired firm in a takeover 3 research (e.g. Wysocki, 1998 & Sprenger et al, 2014) has found that the message volume of microblogging forums is able to predict the next-day trading volume. Most of the past academic literature in this field (such as mentioned in the previous paragraph) focuses on the aggregate data of large groups of Twitter users. Research on the effect of individual users is more scarce. A study on the subject by Oranburg (2015) notes that famous activist investor, Carl Icahn, has moved stock markets via tweets mentioning publicly listed firms. Notably, Trump’s rival candidate during the presidential campaign, , sent down stocks of biotech companies by tweeting negatively about drug-pricing (Egan, 2015). It should be noted, however, that this is anecdotal evidence of the influence of a single user at best and that the Twitter message followed a high-profile price-gouging scandal (Egan, 2015). The current study focuses more systemically on the effects of the tweets of a single user - the tweets of President Donald J. Trump. As the most powerful man of the US his influence on stock markets is undeniable. For example, after Trump was elected in November, stocks associated with heavy industry and banking as well as companies with high tax burdens received favorable market reactions (Wagner, Zeckhauser and Ziegler, 2017). On the flipside, stocks of firms associated with e.g. healthcare, textiles and apparel were relative losers. Furthermore, Wagner et al. (2017) found that investors favored domestically oriented companies following Trump’s election. Naturally, these price reactions were a result of expectations of the consequences of new regulations under the new Trump administration. Sporadic evidence, however, seems to suggest that something as simple as a tweet of President Trump can have an effect on the stock markets, even though findings are anecdotal and sometimes contradicting (e.g. Cohan, 2016; Revesz, 2017; Rupp, 2017). Sprenger et al (2014) note that the voice of an individual or account in microblogging forums is amplified by the amount of followers. With President Trump having over 28 million followers he has a great potential influence even without considering his presidency.

1.2. Problem statement

Following the problem indication as discussed above, the current study aims to provide an answer to the following research question:

“What is the effect of a tweet by President Donald J. Trump on the value of a target firm?”

1.3. Research questions

To place the research question in a broader context, two additional sub questions are posed:

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“How does a tweet by President Donald J. Trump influence the value of a target firm?” “What is the effect of a tweet by President Donald J. Trump on the trading volume of a target firm’s stock?”

1.4. Thesis structure

To answer the research questions, the remainder of the study is structured as follows. Chapter 2 discusses the existing academic literature and theories. Chapter 3 describes the data and methodology used for the empirical analysis. Chapter 4 describes the results. Finally, Chapter 5 provides a conclusion, limitations, and recommendations for further research.

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2. Literature and Theory

There are multiple theories that could explain why Trump’s tweets have the potential to influence the value of targeted firms. The current chapter firstly describes three of these theories. Secondly, it provides a discussion of the respective theories in regard to the research questions of the current study. Thirdly, it discusses the signs that markets are paying attention to Trump’s tweets. Finally, it discusses the expected effect direction of a tweet by Trump. In some cases, investor trading strategies are discussed. It should be noted however that the current study does not aim to find a profitable trading strategy. These strategies are discussed solely to understand investor rationale and underlying market mechanisms.

2.1. Information models

According to the Efficient Market Hypothesis (EMH) (Fama, 1970) all information about a company is reflected in current prices. In its strongest form, this includes insider information. Relaxed slightly, this covers all “obviously publicly available” information (Fama, 1970). Therefore, according to the EMH, stock market prices cannot be predicted and should follow a random walk pattern as news is unpredictable (Bollen et al., 2011). As a consequence, it would be impossible for an investor to gain an excess return using trading strategies which involve information that is publically available (Fama, 1991). Even if the EMH is relaxed slightly the marginal benefits of information will not exceed the marginal costs, which makes the net value of such trading strategies equal to zero, implying efficient markets (Jensen, 1978). However, Bollen et al. (2011) find two problems with the EMH. Firstly they note that past academic research has shown that stock market prices in fact do not follow a random walk and can indeed be predicted to a degree. Secondly, they note that “news may be unpredictable but […] very early indicators can be extracted from online social media (blogs, Twitter feeds, etc) to predict changes in various economic and commercial indicators” (Bollen et al., 2011). The stock market could be an example of such indicators. In short, the EMH may be unsatisfactory. The Gradual-Information-Diffusion (GID) model (Hong and Stein, 1999) presents an alternative to the EMH. It also represents a shift from strictly rational models – such as the EMH – to a (more) “behavioral” theory. Hong and Stein (1999) define “behavioral” as “some departure from the classical assumptions of strict rationality and unlimited computational capacity on the part of investors”. In other words, it takes into account the concept of “bounded rationality” (Simon, 1955), which acknowledges that humans have limitations that prevent them from making fully rational decisions. The GID model distinguishes between two types of investors which are both characterized by bounded rationality. Hong and Stein (1999) term these two types of investors “newswatchers”

6 and “momentum traders”. Following the model, newswatchers make forecasts based on privately observed signals about future fundamentals. This private information is a subset of all publically available information. Momentum traders, on the other hand, trade on past price changes. Furthermore, the GID model assumes that information – as implied by the name of the model - diffuses gradually among the population. Therefore, there is a potential (initial) underreaction to the arrival of new information on a company’s future fundamentals. As a consequence, investors are able to gain an excess return as new information has yet to disseminate among the investing population (Hong and Stein, 1999). This provides them with a potential trading strategy that capitalizes on an information lead. Recall that according to the EMH this would be impossible (Fama, 1991). In the longer run, there is the potential for an overreaction as momentum traders get involved with trend-chasing (Hong and Stein, 1999). Due to trading on privately observed signals and an initial underreaction, the potential for a profitable trading strategy (and price reaction) holds even when newswatchers observe publically available information that is in fact not truly new. This relates back to the concept of bounded rationality. Furthermore, it takes into account “salience theory”. As humans have only limited processing ability, investors may focus their time and attention on “highly visible, easy to process information” (Palomino, Renneboog and Zhang, 2009). Palomino et al (2009) note that as a consequence, reactions to public news are dependent on its relative salience: if information salience is higher, public information is processed and incorporated into share prices faster by the investing population. Huberman and Regev (2001) describe a notable example of this concept. In their paper they describe how EntreMed’s stock experienced a substantial and long-term positive price reaction after mentioned the company in a high-publicity Sunday article that reported on a breakthrough in cancer research. The article, however, did not contain any genuinely new information as the breakthrough had already been reported on in both scientific and popular press five months earlier (Huberman and Regev, 2001). In fact, even the New York Times itself had previously reported on the news story (Wade, 1997). This example showcases how there can be an underreaction to news even though it’s both publically available and relevant if salience is low. Therefore, a news story or event can make non-salient existing information salient if it makes the information highly visible (e.g. due to large media coverage). The current study explores a third alternative to the models we have thus far described. For the sake of the current study we will call this the Noise Trading-model. The term “noise trading” was introduced by Black (1986). On the subject, he states the following: “Noise trading is trading on noise as if it were information. People who trade on noise are willing to trade even though from an objective point of view they would be better off not trading. Perhaps they think the noise they are

7 trading on is information. Or perhaps they just like to trade” (Black, 1986). In other words, these noise traders may trade on events or news stories that are in fact irrelevant. Whereas according to salience theory investors may disregard relevant news, in noise-trading investors react to news that is actually irrelevant. There is an exception however; if one considers a piece of news irrelevant, but expects that other traders will react regardless, he could earn a profit by exploiting these noise traders. This notion is further discussed in Section 5.2 (Discussion). According to Oh and Sheng (2011) people that engage in noise trading frequently engage in investing discussions or conversations. As noted by Zhang and Swanson (2010), past research (e.g. Komaromi, 2002) suggests that these noise traders are able to affect stock prices whenever information can be shared and disseminated through web channels among investors. According to Oh and Sheng (2011) “microblogging is one such channel where sentiments of irrational investors spread quickly with the continuous streaming of information”. Furthermore, they note that specifically sentiment, represented by opinions, plays a vital role in this regard (Oh and Sheng, 2011). Moving from the GID model to the Noise Trading-model we move even further away from a strict rational model and into behavioral theory. In the domain of finance, behavioral theory has shown that financial decisions are significantly driven by factors that are non-rational such as emotion and mood (Nofsinger, 2005). Bollen et al. (2011) find that as a consequence, public mood and sentiment have the potential to influence stock market values as much as news. As decisions based on these concepts are by definition not (fully) rational, the impact of these moods and sentiments could be considered a type of noise. Noise trading seems to be a troubling concept for financial markets as it makes them inefficient. However Black (1986) identifies a number of advantages of noise trading. For example, it makes trading in financial markets possible and is essential to the existence of liquid markets. Furthermore, due to the existence of a substantial amount of noise traders it is beneficial for individual investors with relevant information to trade. This is even the case when investors seek out costly information for their trading strategies (Black, 1986). So, in contrast to the EMH – as well as Jensen’s (1978) slightly relaxed version of the EMH – the marginal benefits of information can exceed the associated marginal costs. As a consequence, similar to the GID model but in contrast to the EMH, the noise trading model allows for profitable trading strategies.

2.2. Trump’s tweets and the information models

The previous paragraph discusses three financial information models. The current paragraph discusses these three models and their associated expectations in light of the study’s research

8 questions. If the (relaxed version of the) Efficient Market Hypothesis were to be valid, we would expect a stock reaction only if a Twitter message by Trump contains genuine news on the target company’s (future) fundamentals. If this is the case, there should be an immediate and permanent price shock that reflects the information contained in the tweet. Accordingly, if the tweet contains no genuine news we expect no stock price movement. In light of the Gradual-Information-Diffusion model Trump’s tweets may contain new information which can influence stock prices. For instance, investors could identify Trump’s tweets as early signals in regard to future policy changes. Put differently, Trump’s tweets may be an example of those early indicators which according to Bollen et al. (2011) are able to predict changes in economic and commercial indicators such as the stock market, as mentioned earlier. For example, consider the following tweet that Trump sent in December of 2016 – at the time U.S. President-elect - concerning aerospace companies Lockheed Martin and Boeing:

“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!” (Donald J. Trump (@realDonaldTrump), 2016).

Based on this message, investors could infer negative prospects for Lockheed Martin if they believe that Trump is to follow through on this threat. This in turn could drive Lockheed Martin’s stock prices down. Simultaneously, it could have a similar but reverse effect on Boeing. However, it remains a question whether Trump’s tweets provide investors with new information that can be used to predict future company’s fundamentals. In this case, one could argue that it does, as the Twitter message could be an early indicator of changes in government aerospace policy and contracts. However, naturally such changes in policy are more complex than the message as quoted above would suggest. Moreover, in other cases, it is unlikely that Trump’s Twitter messages provide investors with relevant information about future company fundamentals at all. If Trump’s tweets have the potential to serve as early predictive indicators, then according to the GID model there would be a price reaction in the period following a Twitter message by Trump. During this period, the new information is disseminated and incorporated into stock prices by investors. After a certain time, the stock price will reach a new longer-run equilibrium. In practice, we would therefore expect a significant abnormal return after a tweet. Furthermore, this effect is expected to remain significant in the longer run. In other words, a buy-and-hold strategy would provide an early investor with a significant abnormal return in line with the information contained in the Twitter message. According to the Noise Trading-model Trump’s tweets have the potential to influence the stock market if the message is able to affect the noise trading part of the investing population. This could be the case if it contains irrelevant news that nevertheless provokes a reaction from noise traders for

9 whatever reason, e.g. because these traders incorrectly believe the information they’re acting on is actually relevant. Furthermore, sentiments and moods may play an important role in this regard. For example, consider the following tweet that President Trump sent in February of 2017 concerning luxury warehouse Nordstrom:

“My daughter Ivanka has been treated so unfairly by @Nordstrom. She is a great person -- always pushing me to do the right thing! Terrible!” (Donald J. Trump (@realDonaldTrump), 2017)

If the message above is able to influence the mood and sentiments of a part of the investing population in regard to Nordstrom, noise traders may be able to move its stock market price despite the fact that no new information on Nordstrom’s (future) fundamentals is released. If the Noise Trading-model were to hold true, we expect that a Twitter message by Trump is followed by a short-term price reaction in the period shortly following a tweet by Trump. However, we also expect that the effect will be only short-lived as the market will adjust afterwards. In other words, we expect a significant abnormal return only in the period just after a tweet by Trump.

2.3. Market attention and trading volume

In short, there are different ways in which Trump’s tweets could potentially move stock prices. Kaissar (2017) however doubts if the markets are even watching Trump, stating that trading volume sometimes surges after a stock tweet by Trump, but at other times the volume drops. However, the fact that trading volume does not always rise following Trump’s Twitter messages does not mean markets are disregarding them completely. Moreover, there are realistic signs that markets in fact are paying attention. For example, according to Peltz (2017) so-called “high-frequency traders” (HFT’s) have developed algorithms that allow them to instantly trade on Trump’s Twitter remarks. The attention to Trump’s twitter remarks however is not limited to institutional investors. For example, online applications such as “Trigger Finance Inc.” and “IFTTT Inc.” alert users if Trump tweet’s about a stock that they own. A similar service provided by Bloomberg is “one of the fastest- growing alerts for a news product that we have ever launched,” according to Bloomberg’s global head of news product, Ted Merz (Peltz, 2017). These services allow small-time retail investors, or “armchair investors”, to trade on the remarks of (potential) online influencers, such as Trump. Apart from these direct channels, there are alternative pathways through which Trump’s tweets reach the investors that are not directly watching Trump’s Twitter channel. Firstly, a tweet by Trump generally garners large numbers of retweets, and in doing so increases overall Twitter activity (in terms of posting volume) with regard to target firms. Furthermore, the information contained in Trump’s tweet is usually disseminated quickly through news sources in various media forms. Specifically the messages pertinent to the current study – mentioning publicly listed firms - are widely covered in

10 financial news. Firms seem to be aware of the potential impact of Trump’s tweets as well, with the term “tweet risk” becoming part of the Wall Street lexicon (Garrison, 2017; Rodionova, 2017). For example, in its annual report, Lockheed Martin addressed a critique by Trump voiced through his Twitter channel on the cost of the aerospace manufacturer’s F-35 program (Lockheed Martin, 2016). Similarly, following Trump’s Nordberg critique, the company explicitly stated that it was not a political decision to drop Ivanka Trump’s clothing line (Abrams, 2017). Notably, two former staff members of Trump offer consulting services to companies that appear on Trump’s Twitter feed (Rodionova, 2017). In short, there are realistic reasons to assume that markets are watching Trump’s Twitter activity both directly and indirectly. Therefore, we expect that a tweet by Trump increases trading volume of the target firm(s). In the same vein, past research has found that both microblogging posting volume (e.g. Wysocki, 1999 & Sprenger et al, 2014) and media coverage (e.g. Engelberg and Parsons, 2011) increase trading volume. This leads to the following hypothesis:

H1) A twitter message by President Trump has a positive effect on target firm’s trading volume

2.4. Effect on firm value

While there are a number of realistic signs that markets are watching Trump’s Twitter remarks, the effect on company value is not directly clear. At first thought one might expect that a positive tweet by Trump would have a positive effect on the stock market, if any, and vice versa. However, in some cases the opposite may be true. For example, consider the EMH and the GID-model. In both models, the movement of a stock is in line with the new (EMH) or recent (GID) information. However, the informational content of Trump’s statements regarding target firm’s (future) fundamentals are not necessarily in line with the sentiment expressed in the statement. More specifically, a decision by a target company could be beneficial for the firm, while provoking a negative reaction from Trump, and vice versa. There are additional factors to consider. For example, Fang and Peress (2009) find that stocks with no media coverage tend to outperform stocks with high media coverage. As a consequence, a high- profile tweet could drive down stock market prices for positive (as well as negative) tweets. Furthermore, it is important to consider that high-profile people have both proponents and opponents among their audience. In fact, even among the followers of popular Twitter users (in terms of followers), there is a divide between positive and negative audiences (Bae and Lee, 2012). Therefore, a positive tweet could elicit a negative response and vice versa. Especially when a statement is of a personal nature, opposing sentiments in regard to the issuer of the statement may

11 play an important role in the reaction. As a case in point, consider the tweet in which Trump blasts Nordstrom for dropping his daughter Ivanka’s clothing line. The criticism aimed at Nordstrom led some Trump-supporters to call for a boycott of Nordstrom’s products (Pérez-Peña and Abrams, 2017). At the same time however, the backlash against Trump following the tweet seemed to favor Nordstrom as Trump-opponents got involved. For example, a number of celebrities decided to show off their Nordstrom purchases via social media in an attempt to mock ’s criticism following his Twitter remark (Ledbetter, 2017). Moreover, according to Rachel Mayer, co-founder of Trigger Finance (mentioned in Section 2.3), there were even signs that Trump-opponents bought Nordstrom stock as a form of protest (Popper, 2017). A lot has been said of the polarizing effect that Trump seems to have on the population. For example, Eisler (2016) notes that Trump won the election with the lowest minority vote in decades. There is also the influence of the American political system. Newport (2017) notes that “the highly polarized way Americans are looking at Trump is not at all unusual for recent presidents”. Whether Trump is responsible for the polarization or not is irrelevant to the current study. What’s relevant is the fact that there is divisiveness regarding opinions on him. As financial behavioral theory suggests that these sentiments can translate to stock market movements, the divide between positive and negative audiences needs to be taken into account when considering the effect of his tweets. As Shleifer and Summers (1990) note, market trading can bring together different noise traders who cancel each other out (in terms of returns). In the current case, this could hold true for positive and negative audiences. However, when trading strategies based on noise are correlated, this could result in an aggregate shift in demand (Shleifer and Summers, 1990). Regarding audiences, trading strategies could be correlated if there is an aggregate disposition among the audience towards either a positive or negative view. In any case, the effect of either a positive or negative tweet is in reality not as straightforward as one might expect at first thought. However, past research (e.g. Oh and Sheng, 2011) finds that abnormal returns are in line with the sentiment expressed in microblogging messages. Furthermore, it is likely that the original message (by Trump) is the most effective, as the number of retweets and amount of followers amplify one’s voice in the microblogging sphere. Similarly, the original message is disseminated through other media forms while responses (in most cases) are not. Finally, due to the nature of investing it seems unlikely that investors will structurally buy stock as a form of protest. These arguments lead to the following hypotheses:

H2a) A positive twitter message by President Trump has a positive effect on firm value H2b) A negative twitter message by President Trump has a negative effect on firm value

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3. Data and methodology

To analyze the effect of Trump’s tweets, the current study adopts an “event study” methodology. To conduct the empirical analysis both Twitter data and financial data are gathered. The current chapter firstly describes the retrieval and selection of Twitter messages. Secondly, it describes the retrieval of financial data and variables of interest. Thirdly, it describes the event study design that is used to test our hypotheses.

3.1. Twitter data and selection

To identify the events that are appropriate to the research question, Twitter data is downloaded from the Trump Twitter Archive, which collects Trump’s Twitter messages into a searchable database with accurate (EST2) time stamps. The current study considers only the tweets sent by President Trump from his personal account (@realDonaldTrump) rather than the POTUS (@POTUS) account, which is the official Twitter account of the in-office U.S. president and has been used by in the past. The former account is used as it’s Trump’s main social media outlet, it’s most popular one (e.g. in terms of followers) and because it includes the period during which Trump was still president-elect, rather than only his Presidential term (following his inauguration on January 20, 2017). Following the data retrieval, the tweets that explicitly mention publicly listed firms are manually selected from the database. News outlets such as the New York Times and CNN are eliminated due to the high frequency of mentions3. Furthermore, we only consider companies listed on either the NYSE or the NASDAQ (Antweiler and Frank, 2004). Companies listed on the NYSE or the NASDAQ were selected for three reasons. Firstly, a practical reason, daily stock data of these companies were easier to gather into a single database. Secondly, as both the NYSE and NASDAQ are U.S. stock exchanges it is easier to find an appropriate market proxy. Thirdly, only a very small number of target companies are eliminated by this method, as most of Trump’s tweets target companies listed on one of these two stock markets. Subsequently, these messages are analyzed (again manually) in order to assess the sentiment (or bullishness) of the statement regarding the target firm(s). A binary variable (P) is introduced that takes on the value of 1 if the remark aimed at a target firm is positive or the value of 0 if the remark

2 Eastern Standard Time (also used for NYSE and NASDAQ trading hours) 3 The Twitter sample period includes at least 28 mentions of the New York Times (@nytimes) and 20 mentions of CNN (@CNN) 14 aimed at a target firm is negative. Appendix A contains a complete overview of the Twitter statements considered in the current study. Twitter sampling starts from Trump’s election, on November 8, 2016. The final tweet in the sample was sent on May 7, 2017. Thus, in total, the Twitter sample period covers just under six months and includes 18 messages and 21 distinct events. Sometimes, messages are conveyed over multiple tweets (due to the character limit of tweets). If this is the case, these tweets are grouped together. While most news articles seem to focus on tweets criticizing companies, the final Twitter sample contains more positive than negative tweets. The 18 tweets in the final sample contain 13 positive remarks and 8 negative remarks targeting publicly listed firms. However, the negative tweets seem to gain more attention in the Twittersphere itself as well, with negative tweets on average gaining 21% more “retweets” and 6% more “likes”.4 The Twitter messages can be broadly categorized into the following categories; I. Criticizing a firm for moving business and/or jobs to Mexico; II. Praising a firm for investing in U.S. business and/or jobs; III. Praising a CEO of a company or commenting positively on a past or upcoming CEO meeting; IV. Expressing concern over government contracts; V. Miscellaneous (e.g. a personal issue).

3.2. Financial data

In addition to Twitter Data, the current study draws financial data at daily intervals from Google Finance. Firstly, this includes stock data of the target firms contained in the final Twitter sample. Secondly, it includes stock index data of the S&P500. Both data on target firms’ stock and the market proxy include opening prices, closing prices and trading volume. The S&P500 is a market capitalization-weighted index comprised of the largest 500 U.S. firms compiled by credit-rating agency Standard and Poor’s (US S&P Indices, 2017). The event study used this index as a market proxy. In practice, equally or value-weighted CRSP returns are often used as a proxy for the market portfolio returns. Unfortunately however, these returns are unavailable for the current study, as this data is updated annually (for the previous calendar year) and thus does not include the necessary data for 2017. As an alternative the S&P500 returns are used. Antweiler and Frank (2004) use the same market proxy for NYSE and NASDAQ companies.

4 Data on “retweets” and “likes” are retrieved on 30 July, 2017. Tweets that contain both a positive and negative remark are excluded from the calculation. If a message is spread out over multiple tweets, only the first one is considered. 15

3.3. Abnormal trading volume methodology

As there is little consensus on how to compute abnormal trading volume (in contrast to abnormal returns), abnormal trading volume is calculated in four different ways, which are explained below. For all four methods, daily abnormal trading volumes are calculated in a 5-trading day event window centered around the event date (from T-2 + T+2). Method I calculates abnormal trading volume using a relatively small estimation window. The remaining methods use a large(r) estimation window.

I. First, abnormal trading volume is computed as the (unexpected) change in trading volume on the event day compared to the two-day average trading volume prior to the event. This allows us – among other things - to compare the event-day trading volume to the average from the start of the event-window to the event day.

AV(I)it = (Vit – V̅i(t-2;t-1)) / V̅i(t-2;t-1))

In the equation above, AV(I)it is abnormal trading volume (in terms of unexpected change in

volume), Vit is trading volume for firm i on time t, and V̅i(t-2;t-1) is average trading volume for firm i from time t-2 to t-1. A t-test shows that AV(I) has a significant positive coefficient (0.0686) for the entire sample. This implies that trading volume on average increases over time. To control for this, an adjusted abnormal trading volume variable is created by multiplying the two-day average trading volume prior to the event with a constant 1.0686 (total sample coefficient + 1):

AAV(I)it = (Vit – 1.0686 * V̅i(t-2;t-1)) / 1.0686 * V̅i(t-2;t-1))

By construction, AAV(I) for the total sample is equal to zero on average. This method biases against finding support for Hypothesis 1 (that event-day trading volume increases) by increasing the threshold.

Past research (e.g. Ajinkya and Jain (1989) has suggested to take the natural logarithm of raw trading volume. This results in an appropriate normal distribution (Ajinkya and Jain, 1989). Therefore, for the remaining methods, a new variable V(ln) is introduced that takes the natural logarithm of trading volume.

II. For Method II, expected trading volume is computed as the average of the logged trading volume over the same estimation window (T-110;T-10) used to compute abnormal returns (see Section 3.4). Subsequently, abnormal trading volume is computed as the difference

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between actual (logged) volume and the expected trading volume

E(Vit)II = V̅(ln)i(t-110;t-10)

AV(II) = V(ln)it - E(Vit)II

In the first equation above E(Vit)II is the expected trading volume and V̅(ln)i(t-110;t-10) is the average logged trading volume during the estimation window. In the second equation, AV(II)

is the abnormal trading volume and V(ln)it is the logged trading volume of firm i on time t. III. The third method is similar to Method II, except that expected trading volume is computed as the average of the logged trading volume of the entire sample (from 2015-06-29 to 2017- 06-23).

E(Vit)III = V̅(ln)i

AV(III) = V(ln)it - E(Vit)III

The equation above uses the same variables as Method II, except for V̅(ln)I , which is calculated over the total sample period (Joseph, Wintoki and Zhang (2011). An advantage of this method is that it includes post-event day trading volumes to control for changes in trading volume over time, regardless of the event.

Some past research (e.g. Campbell and Wasley, 1996) suggests using a market model to compute abnormal trading volume. Therefore, we compute a final alternative for abnormal trading volume using a market model. However, the trading volume correlation with the S&P proxy used in the current study is very low (0.0522). Therefore, the sum of the daily trading volumes of all firms included in the sample is taken to create an alternative trading volume market proxy (resulting in a correlation of 0.2313). A similar approach is used by Ajinkya and Jain (1989).

IV. The market model methodology used to compute abnormal trading volume for Method IV is similar to the one that is used to compute abnormal returns (See Section 3.4 for an explanation). The only difference is that in this case logged trading volume is used instead of raw data.

3.4. Abnormal returns methodology

The event study to test our research questions is conducted using market model residuals. As a first step, returns are calculated at daily intervals for both the target firms and the market proxy. In addition to the total daily return (from previous day closing price to closing price), returns are

17 calculated for both overnight trading (from previous day close to open price) and intraday trading (from open to close price). Slope and intercept estimates are obtained using an OLS regression as proxies for risk factors in the period leading up to the event:

Rit = αi + βiRmt + εit

In the equation above, Rit is the stock return of firm i at time t, ai and Bi are intercept and slope estimates respectively, Rmt is the return of the market portfolio at time t, and eit represents an error term. Slope and intercept estimates are obtained during a 120-trading day estimation window starting 130 days prior to the first tweet. A similar estimation period is used by e.g. Sprenger et al. 2014) to assess the impact of Twitter messages. Secondly, estimated returns are calculated in the 5-trading day event window centered around the event date (from T-2 to T+2) using the slope estimates and market proxy, as follows:

E(Rit) = αi + βiRmt

Subsequently, abnormal returns are defined as the residual of the actual return over the estimated return:

ARit = Rit - (âi + B̂iRmt) (in which ARit is the abnormal return of firm I at time t)

Furthermore, cumulative abnormal returns are calculated by summing the abnormal returns within the calculated event window:

CARi = ∑(ARit) (in which CARi is the cumulative abnormal return of firm I)

Finally, cumulative average abnormal returns are calculated by dividing the cumulative abnormal returns by the amount of days:

CARi = ∑(ARit)/n (in which n is the number of trading days considered)

Finally, t-tests are used to asses whether the abnormal returns are significantly different from zero during event-windows.

3.5. Event day and trading hours

All but five tweets in the final sample were sent outside of US trading hours (9:30 am to 4:00 pm EST), one of which containing two target firms (events). If a tweet is sent outside of trading hours (after 4:00 pm), the associated event or events are assigned to the following trading day. A similar approach is used by Antweiler and Frank (2004) and Sprenger et al. (2014). Sometimes, event-windows for a target firm overlap if multiple events happen in a short time-period, but on different trading days. If this is the case, only the first event is considered.

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As a result of these proceedings, event days are spread out over the week as follows; 6 both on Monday and Tuesday; 4 on Wednesday; 1 on Thursday; 4 on Friday. The fact that most tweets are sent outside of trading hours might imply that it is Trump himself that is sending out these messages, rather than e.g. a staff member. It is noteworthy however that some tweets are sent from an Android phone, while others are sent from an iPhone (Twitter includes this information between brackets). This suggests that multiple people (and thus, people other than Trump) are sending out messages through the President’s personal account5. Notably, according to text analysis “the Android and iPhone tweets are clearly from different people, posting during different times of day and using hashtags, links and retweets in distinct ways” (Robinson, 2017). Furthermore, Robinson (2016) notes that the Android tweets are angrier, more negative and include more emotionally charged words than the iPhone tweets, which tend to contain announcements and pictures. According to Kang (2017), Trump uses an (old) Android phone. This suggests that the more hyperbolic tweets are from Trump, while the more official tweets are from someone else (e.g. a staff member). McGill (2016) also notes that Twitter message that were angrier and posted later at night (outside of trading hours) were generally sent from an Android, while more generic tweets (e.g. event announcements) were usually sent from an iPhone. The Twitter message that are supposedly from Trump (based on this assumption) also closely suit the rhetorical style he displays in other media forms (Hopper, 2016).

3.6. Contamination

As can be seen from Appendix A, some Twitter messages are responses to - or mentions of - firm news events. Therefore, the results in Chapter 4 may be contaminated by other events than the tweets sent by Trump. To control for this, a collection of tweets that do not contain references (either explicit or implicit) to highly recent news are manually selected. This way we can assure that the event is the tweet itself, rather than the news it responds to. Appendix A2 shows these events (14 in total) and shortly explains why we can assure that the tweet – and not a news story – is the event.

5 Tweets from March 25 on are sent exclusively from iPhone, which suggests that Trump might have stopped writing Tweets or, perhaps more likely, switched to an iPhone 19

4. Results

The current chapter discusses the results that are found using the data and methodology as discussed in Chapter 3.

4.1. Abnormal trading volume

Table 4.1 shows the abnormal trading volume results. As can be seen, event-day abnormal trading volumes are significant for all four methods. According to Method 1, trading volume increases with 38,47% on average following a tweet by Trump (29,58% using the adjusted abnormal trading volume). For Method II, Method III and Method IV, the results are harder to interpret directly. As abnormal trading volume is taken as the difference between two logs, the average change in volume can be calculated using the following mathematical definition:

log(a)−log(b)=c ↔ a=ecb (in which e stands for Euler’s number)

For simplicity’s sake, the average of the three coefficients 0,2624 is taken as c (as the coefficients have similar values). Event-day trading volume therefore is “ec” times expected trading volume: e0,2624 = 1.30. In other words, event-day trading volume is on average 30% higher than expected trading volume. This is similar to the finding using the adjusted abnormal trading volume following Method I. Thus, Hypothesis 1 is supported.

Table 4.1. Abnormal trading volume

I II III IV Event day AV (%) t-value AV t-value AV t-value AV t-value -2 17.3512 0.57 -0.0158 -0.12 -0.0543 -0.42 0.0158 0.14 -1 12.9316 1.35 0.0775 0.58 0.0390 0.31 0.0621 0.48 0 38.4731 3.00 *** 0.2791 2.99 *** 0.2407 2.56 ** 0.2456 2.93 *** +1 12.3640 0.88 0.1701 1.52 0.1317 1.27 0.1050 1.31 +2 12.9308 -1.85 0.0621 0.59 0.0237 0.24 -0.0110 -0.13 I* AAV (%) t-value -2 9.8144 0.34 -1 5.6787 0.63 0 29.5798 2.46** +1 5.1475 0.39 +2 -18.5227 -2.83* * * p < 10%, ** p < 5%, *** p < 1%

4.2. Event-window abnormal returns

First of all, the daily event-window effects are examined. As mentioned in Chapter 3, in addition to the total daily return, returns are calculated for both overnight and intraday trading. The event-day

20 effect of an event that occurs outside trading hours can take place during both overnight trading and (next-day) intraday trading. Any event-day effect of an event that occurs during trading hours, however, can only take place during intraday trading. Therefore, event-day tests involving total daily return and return from overnight trading include only those events that occur outside of trading hours. Otherwise, the tests would take into consideration trading effects that in fact cannot be attributed to the event, as it hasn’t happened yet. Tests involving intraday trading include all events in the final sample. Thus, tests involving intraday trading includes tweets sent during trading hours together with outside-trading hours tweets. First, to test if the tweets have a general effect regardless of sentiment, we examine the tweet effects without distinguishing between positive and negative tweets. The results of these tests are in Appendix B. As can be seen, abnormal returns are for the most part insignificant, with a few exceptions. Considering total daily returns there is only one significant finding (other significant findings are insignificant within a full trading day). There is a significant negative abnormal total daily return two days after a Trump tweet. This could be the result of the high media coverage following Trump’s tweets, as discussed in Section 2.4. However, the significance levels (and effect sizes) for these significant findings are relatively low (small) and do not occur on event-days. The results when distinguishing between positive and negative tweets are shown in Table 4.2 and Table 4.3 for positive and negative tweets respectively.

Table 4.2. Abnormal returns positive tweets

Opening Intraday Total Event day AR (%) t-value AR (%) t-value AR (%) t-value -2 0.2955 1.92 * -0.1941 -1.15 0.0158 0.08 -1 0.4464 1.42 -0.1892 -0.75 0.2033 0.42 0 0.5395 2.05 * 0.4879 1.91* 1.0754 2.92 ** +1 0.1067 0.56 -0.2358 -0.50 -0.1178 -0.20 1 +2 -0.0063 -0.05 -0.4018 -1.08 -0.3895 -1.15 * p < 10%, ** p < 5%, *** p < 1%

Table 4.3. Abnormal returns negative tweets

Opening Intraday Total Event day AR (%) t-value AR (%) t-value AR (%) t-value -2 -0.4531 -0.85 -0.3815 -1.54 -0.7366 -1.31 -1 0.0816 0.46 -0.9806 -2.24* -0.8103 -1.48 0 -1.0197 -4.50 *** 0.0764 0.09 -1.4785 -3.13 ** +1 0.0563 0.26 0.3151 0.55 0.3315 0.48 +2 -0.0219 -0.23 -0.4470 -1.34 -0.5488 -1.60 * p < 10%, ** p < 5%, *** p < 1%

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We will first consider the event-day effects. As can be seen from Table 4.2, positive tweets are associated with positive abnormal total daily returns significant at the 5% level. Thus, Hypothesis 2a is supported. Furthermore, it can be seen that both abnormal returns from overnight and intraday trading are positive and significant at the 10% level. Similarly, as can be seen from Table 4.3, negative tweets are associated with negative abnormal total daily returns significant at the 5% level. Thus, Hypothesis 2b is also supported. Furthermore, Table 4.3 shows that abnormal opening returns are positive and highly significant (at the 1% level), while the abnormal trading-day returns are highly insignificant and have the reverse sign6. In general, the event-day results show that abnormal returns are in line with the sentiment underlying the tweet. In other words, a positive tweet has a positive impact on target firm’s stock price, and vice versa on the event day. Interestingly, the effect of negative tweets is larger (approximately 37% for event-day total return) than the opposite effect of positive tweets. In GID-models, research finds that bad news in general travels slowly (e.g. Hong, Lim and Stein, 2000; Frazzini, 2006). However, as Naveed, Gottron, Kunegis and Alhadi (2011) note, bad news travels fast on Twitter. Similarly, we have seen in Section 3.1 that negative tweets on average gain more attention than positive ones both on Twitter (in terms of “likes” and “retweets”) and in the media. In the case of the current study, this could also be because the negative tweets are in general stated in a more hyperbolic rhetoric. Looking at abnormal returns centered around the event date, we find two noteworthy results. First of all, there is a positive and significant (at the 10% level) positive abnormal opening return two trading days preceding a positive tweet. However, the abnormal total daily return is insignificant. Secondly, there is a negative and significant (at the 10% level positive abnormal trading return in the trading day just before a negative tweet. However, again, the abnormal total daily return is insignificant. Apart from these two findings, there are significant abnormal returns on the event date exclusively. Two things are important to consider in regard to the significant findings before the event date. Firstly, the significance level is relatively low in both cases (compared to event-day results). Secondly, in both cases, the abnormal total daily return is insignificant. So, whatever the reasons for these abnormal returns may be, the market has adjusted within a full, single day.

6 These results are striking, as there is a huge difference in significance levels, while sample size is larger for the highly insignificant finding. 22

4.3. Cumulative (average) abnormal returns

In the previous paragraph the daily event-window effects are examined. To examine the long-term effects, the current paragraph examines the cumulative (average) abnormal returns following the event day. Table 4.4 and Table 4.5 show both the cumulative abnormal returns and cumulative average abnormal returns. For simplicity’s sake, in this case only the events that occur outside of trading hours are considered (this way total daily returns can be used). As can be seen, abnormal returns do not remain significant over time. In fact, the market on average adjusts immediately after the event day.

Table 4.4. Cumulative (average) abnormal returns positive tweets

Event window CAR (%) t-value ACAR (%) t-value [0] 1.0754 2.92 ** 1.0754 2.92** [0,1] 1.1681 1.25 0.5841 1.25 [0.2] 0.9057 1.09 0.3019 1.09 * p < 10%, ** p < 5%, *** p < 1%

Table 4.5. Cumulative (average) abnormal returns negative tweets

Event window CAR (%) t-value ACAR (%) t-value [0] -1.4785 -3.13 ** -1.4785 -3.13** [0,1] -1.1705 -0.94 -0.5852 -0.94 [0,2] -1.6010 -1.48 -0.5337 -1.48 * p < 10%, ** p < 5%, *** p < 1%

4.4. Using contamination controls

Finally, we consider the effect of exclusively those tweets from which we can assure that the event is the statement itself, rather than the news it responds to (see Section 3.6). The results of the tests regarding both volume and abnormal returns are in Appendix C. As can be seen from the tables, the effect direction and significance remain the same for the most part. The positive effect on trading volume remains significant within all four proxies. This finding confirms that markets are in fact watching Trump’s tweets rather than only the news events he responds to. Similarly, the effect of a negative tweet remains significant while maintaining the expected sign. However, notably, the effect of a positive tweet becomes insignificant (while maintaining the expected sign). This could imply that positive tweets from Trump do not have an effect on the stock market. A possible explanation is that the positive tweets are not as strongly worded as the negative tweets. As discussed in Section 4.2, the effect size of a positive tweet was also smaller than the effect on a negative tweet in the original sample. It could also be a statistical issue. The latter seems more likely because firstly, markets are

23 paying attention (as implied from abnormal trading volume). Secondly, the effect of both positive and negative tweets in the original sample are in line with the sentiment of the tweet, rather than the implications for the firm (further discussed in the Conclusion). Finally, in general Trump’s tweets have a higher salience level than the firm news (due to immediate notifications through Twitter or financial applications and high media coverage), which makes them more likely to have an impact.

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5. Conclusion, limitations and further research

The current and final chapter firstly describes the conclusion based on the previous chapters. Secondly, it discusses the limitations of the study. Finally, it provides suggestions for further research related to the current topic.

5.1. Conclusion

The primary aim of the current study was to find out what effect a tweet by President Donald J. Trump has on the firm value of a target firm. The results show that there are abnormal returns in line with the sentiment contained within a Twitter message by Trump. In other words, firm value increases following a positive tweet by Trump, while it decreases following a negative tweet by Trump. However, concerning positive tweets it is not exactly clear how much of the effect is due to the tweets themselves, rather than the news events to which these tweets are in response to. Furthermore, the study finds (using a number of different methods) that target firm trading volume increases on the trading day that follows a tweet by Trump aimed at the company. This confirms that markets are watching Trump’s Twitter activity, which enables the effects on firm value. The stock market effects of Trump’s tweets are most likely primarily a result of noise trading. There are a number of reasons for this assumption. Firstly, the abnormal returns do not remain significant over a long time-window. In fact, on average, the stock markets adjust in the day directly following the event day on which the stock market movement occurs. This, in combination with the increase in trading volume, implies the impact of noise traders. Secondly, the stock market movements are in line with the sentiment contained within Trump’s tweets. From a rational investing perspective, however, this is not necessarily what one would expect. For example, firm value may fall after a tweet that criticizes a company for moving its business abroad. Yet, from a business perspective this may be a sound decision. Vice versa, a company could see its stock price increase following a tweet praising its investment in the U.S., while business-wise this hurts a company’s (future) fundamentals. Moreover, when assessing the information content of the tweets that are considered in the current study, the informational content on (future) company fundamentals in most cases seems low at best. In any case, the findings suggests that while Trump’s tweets may be interesting for day-traders, if you are an average long-term investor you should not worry about these (noisy) signals that only influence stocks on a very short term.

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5.2. Discussion

In the previous paragraph, it is concluded that the current study’s findings are most likely a result of noise trading. However, there are a large number of players on the stock market. Naturally, not all of these players are noise-traders. In fact, some of these traders may capitalize on the noise trading- part of the investing population. For example, HFT’s could benefit from noise trading by taking positions in target firm shares before (part of) the noise-trading impact takes place. Alternatively, rational investors could buy or sell a position in target firm shares after stocks are underpriced or overpriced respectively, as a result of the noise trading spurred on by a Trump tweet.

5.3. Limitations

As with any study, the current study is subject to a number of limitations. The most important limitation relates to a statistical issue; namely the small sample size. This small sample size prohibits one to investigate different aspects of the Twitter messages. For example, the current study does not distinguish between the informational content of tweets. Rather, it only distinguishes tweets based on sentiment (in terms of positive/negative). Interesting distinctions and the effect of these differences which were made practically impossible due to the small sample size furthermore include the categories in which Trump’s tweets can be grouped (as mentioned in Section 3.1), or the tone of the tweets (e.g. in terms of hyperbolic or non-hyperbolic). Furthermore, the insignificant findings regarding the effects of positive tweets using the second sample (which rules out contamination issues) could be due to the statistical issue of sample size.

5.4. Suggestions for further research

First of all, future research could investigate the effect of tweets by Trump other than those aimed at specific firms. Topics that Trump often tweets about include protectionism (e.g. criticizing the North American Free Trade Agreement, or NAFTA), tax cuts, Mexico and healthcare legislation (Obamacare). Further research in the field could investigate the effects of these tweets on certain market indicators. For example, tweets favoring protectionism could have a negative effect on importing firms. Tweets announcing - or expressing willingness to roll out - tax cuts could have a positive impact on domestic and high-tax firms. Negative tweets about Mexico could have a negative effect on the Mexican peso (Mexico’s domestic currency). Tweets criticizing Obamacare and propagating reforms could have an impact on companies within the healthcare industry. Such studies would elaborate on the current one by including different asset classes and investigating if Trump’s tweets can move markets rather than specific firms’ value only. As discussed in Section 5.3, the small sample size of the current study imposes some restrictions on

26 the empirical research. Further research could investigate more aspects of the Twitter messages (see Section 5.3) as sample size grows in the future. Furthermore, it could shed more light onto the effects of positive tweets. Furthermore, further research on the topic could analyze the effect of Trump’s tweets using high- frequency data in a structural manner to investigate if there are patterns in the way stock prices move following a relevant tweet. Such research could – among other things - elaborate on the types of investors that are involved in trading on Trump’s tweets. Finally, in addition to abnormal returns and trading volume, further research could focus on the effect of influential individuals’ tweets regarding target firms on volatility. By doing so, the noise trading-theory can be tested in more detail.

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7. Appendix

Appendix A1

Date and time Company Tweet # P

Nov 17, 2016 Ford Just got a call from my friend Bill Ford, Chairman of Ford, who advised me 1 1 09:01:52 PM that he will be keeping the Lincoln plant in Kentucky - no Mexico AN

Nov 17, 2016 I worked hard with Bill Ford to keep the Lincoln plant in Kentucky. I owed 1 09:15:28 PM it to the great State of Kentucky for their confidence in me! AN

Dec 2, 2016 Rexnord Rexnord of Indiana is moving to Mexico and rather viciously firing all of its 2 0 10:06:41 PM 300 workers. This is happening all over our country. No more! AN

Dec 6, 2016 Boeing Boeing is building a brand new 747 Air Force One for future presidents, 3 0 08:52:35 AM but costs are out of control, more than $4 billion. Cancel order! AN

Dec 11, 2016 Exxon Whether I choose him or not for "State"- Rex Tillerson, the Chairman & 4 1 10:29:10 AM Mobil CEO of ExxonMobil, is a world class player and dealmaker. Stay tuned! AN

Dec 13, 2016 I have chosen one of the truly great business leaders of the world, Rex 06:43:38 AM Tillerson, Chairman and CEO of ExxonMobil, to be Secretary of State. AN

Dec 22, 2016 Lockheed Based on the tremendous cost and cost overruns of the Lockheed Martin 5 0 05:26:05 PM Martin F-35, I have asked Boeing to price-out a comparable F-18 Super Hornet! AN

See above Boeing See above 6 1

Jan 3, 2017 General General Motors is sending Mexican made model of Chevy Cruze to U.S. 7 0 07:30:05 AM Motors car dealers-tax free across border. Make in U.S.A.or pay big border tax! AN

Jan 3, 2017 Ford "@DanScavino: Ford to scrap Mexico plant, invest in Michigan due to 8 1 11:44:13 AM Trump policies" https://t.co/137nUo03Gl iP

Jan 4, 2017 Thank you to Ford for scrapping a new plant in Mexico and creating 700 08:19:09 AM new jobs in the U.S. This is just the beginning - much more to follow AN

Jan 5, 2017 Toyota Toyota Motor said will build a new plant in Baja, Mexico, to build Corolla 9 0 01:14:30 PM cars for U.S. NO WAY! Build plant in U.S. or pay big border tax. AN

Jan 9, 2017 Fiat It's finally happening - Fiat Chrysler just announced plans to invest 10 1 09:14:10 AM Chrysler $1BILLION in Michigan and Ohio plants, adding 2000 jobs. This after... AN

Jan 9, 2017 Ford said last week that it will expand in Michigan and U.S. instead of 09:16:34 AM building a BILLION dollar plant in Mexico. Thank you Ford & Fiat C! AN 7

Jan 17, 2017 General Thank you to General Motors and Walmart for starting the big jobs push 11 1 12:55:38 PM Motors back into the U.S.! WC

See above Walmart See above 12 1

7 Event is not included for Ford due to overlapping event windows (January 4 tweet) 32

Appendix A1 (continued)

Date and time Company Tweet # P

Jan 24, 2017 Ford Great meeting with Ford CEO Mark Fields and General Motors CEO Mary 13 1 07:46:57 PM Barra at the @WhiteHouse today. https://t.co/T0eIgO6LP8 iP

See above General See above 14 1 Motors

Jan 30, 2017 Delta Only 109 people out of 325,000 were detained and held for questioning. 15 0 07:16:30 AM Big problems at airports were caused by Delta computer outage,..... AN

Jan 30, 2017 protesters and the tears of Senator Schumer. Secretary Kelly said that all 07:20:49 AM is going well with very few problems. MAKE AMERICA SAFE AGAIN! AN

Feb 8, 2017 Nordstrom My daughter Ivanka has been treated so unfairly by @Nordstrom. She is a 16 0 10:51:01 AM great person -- always pushing me to do the right thing! Terrible! iP

Feb 8, 2017 Intel Thank you Brian Krzanich, CEO of @Intel. A great investment ($7 BILLION) 17 1 02:22:33 PM in American INNOVATION and JOBS!… https://t.co/oicfDsPKHQ iP

Feb 17, 2017 Boeing Going to Charleston, South Carolina, in order to spend time with Boeing 18 1 06:38:20 AM and talk jobs! Look forward to it. AN

Mar 6, 2017 Exxon 'President Trump Congratulates Exxon Mobil for Job-Creating Investment 19 1 04:19:04 PM Mobil Program' https://t.co/adBzWhtq8S iP

Mar 6, 2017 Buy American & hire American are the principles at the core of my 10:49:54 PM agenda, which is: JOBS, JOBS, JOBS! Thank you @exxonmobil. iP

Mar 6, 2017 Thank you to @exxonmobil for your $20 billion investment that is 10:50:49 PM creating more than 45,000 manufacturing & construction jobs in the USA! iP

Mar 28, 2017 Ford Big announcement by Ford today. Major investment to be made in three 20 1 05:36:02 AM Michigan plants. Car companies coming back to U.S. JOBS! JOBS! JOBS! iP

May 7, 2017 Rexnord Rexnord of Indiana made a deal during the Obama Administration to 21 0 05:58:47 PM move to Mexico. Fired their employees. Tax product big that's sold in U.S. iP iP: tweet sent from iPhone AN: tweet sent from Android Phone

33

Appendix A2

# Company Tweet

1 Ford “Ford never announced plans to move either of its plants in Kentucky to Mexico”8

2 Rexnord Plans to move a plant and 300 jobs were previously announced (in October) 9

3 Boeing Critique first voiced via Twitter10

5 Lockheed Announced via Twitter11 Martin

6 Boeing See above

7 General Production in Mexico (of which a small number is sold in U.S.) started in 201612 Motors

9 Toyota Plans to open a factory in Guanajuato (which is actually in the Bajio region, not Baja) were previously announced (in 2015)13

11 General Plans to invest in U.S. were previously announced (in December)14 Motors

12 Walmart Plans to invest in U.S. were previously announced (in October)14

13 Ford Comment on meeting announced via Twitter

14 General See above Motors

16 Nordstrom Nordstrom removed Ivanka Trump’s brand from its stores a week before the tweet15

8 https://www.washingtonpost.com/news/fact-checker/wp/2016/11/18/trumps-claim-taking-credit-for-ford- keeping-the-lincoln-plant-in-kentucky/?utm_term=.53254656b9e8 9 http://www.reuters.com/article/usa-trump-rexnord-idUSL1N1DY0E2 10 http://www.politifact.com/truth-o-meter/statements/2016/dec/06/donald-trump/fact-checking-donald- trumps-tweet-air-force-one-bo/ 11 http://www.reuters.com/article/usa-trump-defense-idUSW1N1DJ01R 12 http://www.politifact.com/truth-o-meter/statements/2017/jan/04/donald-trump/mostly-true-trump-tweet- about-gm-chevy-cruze-manuf/ 13 http://money.cnn.com/2017/01/05/news/trump-toyota-mexico/index.html 14 http://www.reuters.com/article/us-walmart-employment-idUSKBN1511JE 15 https://www.nytimes.com/2017/02/02/business/nordstrom-ivanka-trump.html 34

# Company Tweet

18 Boeing Announced via Twitter16

21 Rexnord Announced during Obama Administration (see #3)

16 https://www.businessinsider.nl/trump-boeing-meeting-2017-2/?international=true&r=US 35

Appendix B

Table B1. Abnormal returns tweets (both positive and negative)

Opening Intraday Total Event day AR (%) t-value AR (%) t-value AR (%) t-value -2 0.0103 0.04 -.02655 -1.92* -0.2708 -1.08 -1 0.3074 1.49 -.04907 -2.06* -0.1828 -0.49 0 -0.0842 -0.31 .01021 0.30 0.0538 0.12 1 0.0875 0.62 -.00259 -0.07 0.0534 0.13 2 -0.0122 -0.14 -.04190 -1.63 -0.4501 -1.86 * * p < 10%, ** p < 5%, *** p < 1%

36

Appendix C

Table C1. Abnormal trading volume without contamination

I II III IV Event day AV (%) t-value AV t-value AV t-value AV t-value -2 33.3111 0.73 -0.0439 -0.22 -0.0623 -0.33 -0.0187 -0.11 -1 13.1992 1.36 0.0333 0.18 0.0150 0.09 0.0317 0.18 0 44.7686 2.58** 0.2718 2.00 * 0.2535 1.91 * 0.2750 2.23 ** +1 -5.5077 -0.36 -0.0223 -0.17 -0.0407 -0.34 -0.0329 -0.34 +2 -22.0528 -2.94** -0.1342 -1.24 -0.1526 -1.56 -0.1553 -1.75 I* AAV (%) t-value -2 24.7493 0.58 -1 5.921 0.65 0 35.4710 2.18** +1 -11.5764 -0.80 +2 -27.0589 -3.85*** * p < 10%, ** p < 5%, *** p < 1%

Table C2. Abnormal returns positive tweets without contamination

Opening Intraday Total Event day AR (%) t-value AR (%) t-value AR (%) t-value -2 0.2707 3.20 ** -0.3596 -1.42 -0.2245 -0.92 -1 0.0288 0.14 -0.1293 -0.34 -0.1262 -0.28 0 0.4059 2.05 0.1958 0.76 0.6381 1.32 1 -0.2586 -1.32 -0.5975 -1.63 -0.8276 -1.68 2 0.0510 0.30 -0.1459 -0.52 -0.0028 -0.01 * p < 10%, ** p < 5%, *** p < 1%

Table C3. Abnormal returns negative tweets without contamination

Opening Intraday Total Event day AR (%) t-value AR (%) t-value AR (%) t-value -2 -0.6571 -1.16 -0.4913 -1.91 -1.0338 -1.88 -1 0.0951 0.47 -0.7776 -1.73 -0.5820 -1.01 0 -1.0798 -4.04 ** 0.4314 0.52 -1.1011 -3.17 ** 1 -0.0617 -0.30 0.5998 1.03 0.5145 0.67 2 -0.0103 -0.10 -0.4839 -1.26 -0.6010 -1.54 * p < 10%, ** p < 5%, *** p < 1%

37