“Do Trump-tweets about a specific company affect stock returns and trading volumes of the corresponding industry?”

Master Thesis Department Finance

Furkan Ekinci ANR: 546675

Supervisor: David Hollanders Second reader: Dr. Peter de Goeij

Date of completion: 22-10-2017

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“Do Trump-tweets about a specific company affect stock returns and trading volumes of the corresponding industry?”

Furkan Ekinci Tilburg University

Abstract

This research examines whether tweets from US-President Trump about a specific company in the time period 8th November 2016 to 30th April 2017 affect its’ and competitors’ stock prices and trading volumes. This study states that these news-events can be related to companies in the same industry. Thus, the hypothesis that “Trump-tweets” cause abnormal stock prices and trading volumes for mentioned companies and its corresponding industry is tested. The findings demonstrate that when Trump tweets about a specific company, stock prices increase while trading volumes decrease.

Keywords: President Trump, tweets, industry, stock prices, trading volumes

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Preface

This research paper is the last step to achieve my MSc. Finance degree. The last three months I was busy conducting this study. Because of the actuality of the topic, it was really interesting to figure out if the tweeting activity of Trump really affects the stock market.

I would like to thank a number of people. Firstly, I would like to thank my supervisor David, without whose feedback my thesis would not be as it is now. Furthermore, I am grateful to my brother, my girlfriend, family, friends and God for always being there for me whenever I needed them, and providing me with the strength I needed to make this study the best possible.

Furkan Ekinci

Tilburg, October 2017

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TABLE OF CONTENTS

INTRODUCTION...... 1

I. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT ...... 3 Web Data and Financial Data ...... 3 Twitter and Trading ...... 7 Trump, and Twitter as an Information Intermediary ...... 9 Why do Investors React on Tweets: Psychological and Behavioral Approach ...... 10 Intra-industry Information Transfer: Are News Events about one Specific Firm Applicable to Whole Industry? ...... 12

II. RESEARCH METHOD ...... 16 Sample and Industry Definitions ...... 16 Event Study Methodology ...... 18

III. EMPIRICAL RESULTS ...... 28 Descriptive Statistics ...... 28 Event Study for Returns ...... 29 Event Study for Volumes ...... 32 Cross Sectional Analysis ...... 35

IV. CONCLUSION ...... 38

REFERENCES ...... 39 Appendix A……………………………………………………………………………… 41 Appendix B……………………………………………………………………………….42 Appendix C……………………………………………………………………………….44 Appendix D……………………………………………………………………………….44 Appendix E………………………………………………………………………………. 46 Appendix F……………………………………………………………………………….47

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INTRODUCTION

Twitter is one of the largest microblogging service providers and has shown steady growth over the past few years. Currently, it has more than 317 million active users and it is among the most popular online media services.1 Individuals, including politicians and celebrities, and businesses can communicate and share information via this platform. Since a few years, Twitter has also become an increasingly popular platform used for financial markets. Already known as “Twitter-president” of the United States, Donald J. Trump attracts strong attention in this online platform. In his tweets (messages), he particularly highlights certain companies or industries either in a negative or positive manner, a fact which seems to have an impact on the stock market. According to the Financial Times, a fake tweet about the injury of Obama caused the S&P 500 to decrease within seconds.2 Apparently, Twitter content can have an effect on stock returns. There seems to be a tendency among professional investors to use Twitter sentiment to react on trades. The impact of Trump’s tweets are thought to be that strong that even now an advertising company, named T3, in the United States developed the “Trump and Dump Bot” which analyzes his tweets and trades based on the content of these tweets. There is only limited financial research that examines the relationship between “Trump- tweets” and stock markets. Ge, Kurov and Wolfe (2017) analyze the impact of tweets from President Trump’s official Twitter account that include the name of a publicly traded company. They find that tweets have an impact on stock returns and increase trading volume, volatility and investor attention. However, a deeper explanation whether “Trump-tweets” affect stock returns and trading volumes of the industry as a whole still lacks evidence. Hence, there is a necessity to further investigate this issue. This research focuses on the relationship between tweets from President Trump, and its impact on stock returns and trading volumes of the whole industry. More specifically, it will analyze publicly traded companies appearing in a “Trump-tweet” and corresponding competitors in the same industry. Investors are inclined to associate news about a specific company with competitors. Due to asymmetric information in the market, market participants use the release of new public information about one firm to make valuation inferences about corresponding rival firms in the same industry. First to introduce this issue to financial research is Foster (1981) who examines the impact that a firm’s earnings releases have on the stock

1 https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/ 2 http://business.time.com/2013/04/24/how-does-one-fake-tweet-cause-a-stock-market-crash/ 1 prices of other firms in its industry. He finds that investors relate news event about a company to the companies’ in the same industry. To my knowledge, this study is first to examine this particular issue. Question to be answered will be whether Trump-tweets’ impact reach not only mentioned companies, but also competitors in the same industry. Results will provide valuable information for investors. Whether or not paying attention and caring about Trump tweets is important for investors to figure out since profit-making strategies can be established. For example, selling the mentioned companies stock, and buying competitors’ stocks could be a possible scenario. Based on prior literature, my expectation is that “Trump-tweets” about a specific publicly traded company will move its and competitors’ stock returns and trading volumes. The main data for this are tweets from Trump, stock prices and trading volumes. Data for “Trump-tweets” are obtained from a tweet archive database for tweets by Trump.3 Data for stock prices and trading volumes are obtained from the Bloomberg Terminal.4 This also counts for data for control variables. Two samples are established based on specific industry definition. The full sample (small sample) is composed of 54 (30) companies.5 The data for tweets, stock prices and trading volumes are collected for beginning of Trump’s presidency (8th November 2016) until 30th April 2017.6 Obtained results of this research indicate that stock prices and trading volumes of companies mentioned in a “Trump-tweet” and competitors are significantly affected. While abnormal trading volumes are significantly negative for both samples, only the small sample (larger companies) shows significantly positive abnormal stock returns. This study is divided into four main chapters: Chapter I gives an overview of the relevant literature and hypothesis. The research method is explained in Chapter II. Chapter III discusses the results of the study and Chapter IV provides the conclusion.

3 http://www.trumptwitterarchive.com/archive 4 Access to Bloomberg is provided by IBS Capital Management BV. 5 All companies are listed in the United States. 6 The ending date 30th April 2017 has been chosen since it was the most current time when this research was written. 2

I. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

Web Data and Financial Data

The examination of news-based data and the effect on stock prices is longstanding. There are several different studies investigating the relation between news and stock price movements. Especially the focus on web data and its impact on stock prices has been studied in the past. Through the increasing usage of online media services like Twitter, researchers more and more focus on research in the area of sentiment analysis and its relationship to financial markets. Previous research seeks to highlight the relationship between web data and financial markets. In the past twenty to thirty years, some research has been done on this interesting field. Eventually, three main areas of research can be identified: the relationship between financial markets and either 1.) web news, 2.) search engine queries or 3.) social media data. Regarding web news, researchers distinguish between endogenous and exogenous news which can affect stock prices. In the stock market setting, exogenous news constitute any news derived from an external source such as web news (e.g. online articles, online newspapers), search engine queries (e.g. Google, Yahoo) or social media data (e.g. Twitter, Facebook) or any other news event. By contrast, endogenous news represent changes in fundamental values such as financial ratios, volatility, return etcetera. Both sources of news are on the agenda of traders to identify value trading opportunities, however the focus on exogenous news seems to be more important based on the huge prevailing literature in this area. The standard view is that movements in stock prices are explained by changes in fundamental values. However, researchers are one at the fact that asset prices react to arrival of exogenous information flux. For example, announcements to corporate control, regulatory policy and macroeconomic conditions can have an effect on fundamentals. The pioneering work of Cutler, Poterba and Summers (1989) initiated a whole line of research in this field. Their work examines if exogenous macroeconomic news have an effect on price movements. They develop several news proxies and look at how much the variation in aggregate stock returns can be assigned to these proxies. The proxies try to capture both real and financial conditions such as real money supply and dividend payments. Their findings highlight that the news proxies explain only approximately one-third of the variance in stock returns. Other factors seem to outweigh the news-effect. Another research by Ederington and Lee (1993) emphasizes that macroeconomic news announcements (such as e.g. employment report) on interest rate and foreign exchange 3 futures markets are responsible for most of the intra-day and day-of-the week volatility in these markets. They further show that especially during the first minute after the release the volatility in prices is really high and that the subsequent direction of price movements is independent of the initial price change. Beginning the new century, a lot of studies have been performed in order to further analyze the effect of news on stock prices. Among others, different studies aim to demonstrate the effect of news on the post-announcement drift (Chan 2003; Vega, 2006). A post-announcement drift (or earnings momentum) basically explains the tendency of stock prices to keep moving in the direction of the respective news surprise. For example, when earnings of a certain stock are announced, according to this theory, that stock can follow the initial reaction on an earnings surprise for several weeks or even months. Chan (2003) finds that stocks with news exhibit momentum, while stocks without news do not. He illustrates that stocks with bad public news display a negative drift for up to 12 months, whereas stocks with good news have less drift. In addition to the post-announcement drift Vega (2006) tries to link media coverage to the arrival rate of uninformed traders. The theory behind is that a post- announcement drift exists and persists since the drift may be a function of the type of information agents receive. This may be due to underreaction to public information and an overreaction to private information (behavioral theory) or, on the other hand, due to the distribution of information in the economy (structural uncertainty theory). The latter one states if there are more (less) informed traders, there will be low (high) structural uncertainty and hence low (high) drifts. Above mentioned researches focus both on the very short-term effect (intraday+-2 days), but also on the post-announcement period up to including 12 months. This shows that news can have a quite long-standing effect on future stock returns. Traders thus are quite keen to figure out which news events have the most impact on stock returns. This way, profit-making strategy can be developed and applied. Finally, as opposed to previous literature which only produced weak evidence to support the hypothesis that real economic news affects stock returns, Birz and Lott (2011) conclude that news on macroeconomic factors like GDP and unemployment does have a statistically significant effect on stock prices. According to them, previous research struggled to find stock market effects of macroeconomic factors mainly due to two reasons. Firstly, it is quite hard to measure the portion of statistical release that is relevant for stock prices. The efficient market hypotheses state that stock prices reflect all past and future (to be expected) public information. Researchers attempted to determine new information in economic releases by measuring the difference between the release and financial market participants’ previous expectations of the release, as demonstrated by surveys. This difference represents the 4 economic surprise of the releases and can be seen as “new” information in the market. Secondly, investors’ expectations are formed by the interpretation of the release. Investors base investment decision based on their interpretation of the release of macroeconomic variables. This impacts their demand for stocks and hence leads to changes in the stock prices. An example to simplify this is following: When the unemployment rate is lower than expected it could be interpreted as good news for stock prices in recessions whereas it could bad news in expansions. Investors might associate different implications in different states of the economy. In this example, investors might think that good news in recessions are a sign for an improving economy leading to higher stock prices. On the other hand, in expansions, good news might interpreted by investors as a reason for the government to intervene with contractionary monetary policy. This, in turn, might result in higher interest rates leading to lower stock prices. This is why the authors chose a different approach than previous research to determine the relationship between macroeconomic news and stock prices. Namely, they inspect newspaper article headlines which are classified as positive, negative, mixed or neutral. This way the classification captures whether the release is good or bad news which is what matters for investors’ expectations about future economic conditions. Next to this, research has also put emphasis on relations between mentions of a company in financial news and stock prices and/or trading volume. Alanyali, Moat and Preis (2013) who examine daily print issues of the Financial Times for a five-year period (2007- 2012) find a positive correlation between the daily number of mentions of a company in the Financial Times and the daily transaction volume and daily absolute return of a company’s stock both on the day before the news is released, and on the same day as the news is released. However, their results provide evidence that there is no relationship between the number of mentions of a company in the Financial Times and the change in company’s stock price if direction of stock price movement is taken into account (id est, daily return). While research mentioned above was busy with the news itself, another new line of research evolved trying to measure the overall attitude of investors that is caused by these news. The sentiment forwarded by news has been first examined by Tetlock (2007). He investigates the relation between the media and U.S. stock market by using daily content from a Wall Street Journal column from 1984-1999 (16 years). After developing a proxy for media pessimism, his key finding is that high levels of media pessimism predict downward pressure on stock returns. However, the prices revert to fundamentals afterwards. Additionally, he finds that very high or low values of media pessimism forecast high market trading volume and that low stock returns can cause high media pessimism. Moreover, Tetlock, Saar-Tschechansky and Macskassy (2008) point 5 out that especially negative words in firm-specific news stories forecast low earnings. Furthermore, they demonstrate that while there is an underreaction on firms’ stock prices to the information embedded in negative words, the earnings and return predictability from negative words increases when stories focus on fundamentals. The second area of interest in literature is web search queries (2.) which seem to have an impact on the financial market. Researchers mainly investigated the relationship between the daily number of queries in search engines (for example Google) for a specific stock, and its daily trading volume. Here, Preis, Reith and Stanley (2010) examine weekly search volume data for certain terms in the period 2004-2010. They highlight that there is a clear correlation between weekly search volume of company stocks and the corresponding transaction volume of the S&P 500 and vice versa. As an illustrative example, they shed light on the famous crash of the Lehman Brothers in the financial crisis. Here, the peak of the Lehman Brothers in the search engine (Google) coincides with the bankruptcy of this institution. Finally, the authors mention that one might think that search volume reflects the present attractiveness of trading a particular stock. However, this is not the case since there is not an increase in buying/selling transactions when search volume increases. Thus, financial market movements should be influenced by some other variable such as news which is linked to search volume since news should be the reason for searching company names in search engines. Bordino et al. (2012) even go one step further and show that web search queries (in Yahoo! search engine) of companies can predict corresponding trading volumes in the stock market. More specifically, daily volumes of queries of stocks traded in the NASDAQ-100 show a distinct positive correlation with daily trading volume. This direction of the correlation is robust and confirmed by several statistical tests. Most interestingly, they highlight that in many cases, search query volumes are able to foresee trading peaks by approximately one day. Finally, there is even an attempt by one paper to link search queries to stock prices. Da, Engelberg and Gao (2011) show that these kind of queries can predict higher stock prices within a two-week interval. Their main reason for using search queries is that it represents an investor attention measure. If investors google a stock, they do so as they are interested in it. Their paper is based on the Search Volume Index (SVI) provided by Google. The weekly SVI for a search term is the number of searches for that term scaled by its time-series average. The authors’ chain of thought is following: First, they show that the SVI is positively correlated with existing attention measures such as extreme returns, news etc. Simultaneously, they prove that the variation in their main variable abnormal SVI remains unexplained by using alternative existing attention measures. Their main variable, abnormal SVI (ASVI), is defined as the SVI 6 during the current week minus the median SVI during the previous eight weeks. Secondly, they highlight that SVI captures the attention of less sophisticated individual investors by examining retail order execution from Securities Exchange Commission (SEC) reports. In a last step, they demonstrate that in accordance with the attention theory of Barber and Odean (2008) stocks with an increase in ASVI this week are associated with an outperformance of around 0.3% during the subsequent two weeks. The attention theory of Barber and Odean (2008) implies that individuals are net buyers of attention-grabbing stocks. Examples for attention-grabbing stocks are e.g. stock in the news, stocks with high trading volume or high search queries. The reasoning behind their main argument is that investors tend to buy attention-grabbing stocks since it is otherwise difficult to choose from the thousands of stocks they can potentially buy. By contrast, when selling a stock, individual investors tend to sell stocks they already own. On basis of this theory, Da et al. (2011) find that a positive ASVI (higher attention) predict higher stock prices in the short term and price reversals in the long term.

While 1.) and 2.) focus on data either retrieved from search engines or web news, my research puts emphasis on analyzing the relationship between financial markets and social media data. Literature regarding the impact of social media, in this case Twitter, on financial markets is prevalent. This will be discussed in the upcoming chapters.

Twitter and Trading

Since its launch in October 2006, Twitter has become one of the most used social networking service for sharing real-time information with people all around the world. The unique feature of this social medium are the so called “tweets” which are short messages with a maximum of 140 characters. To reach more people, Twitter also allows unregistered users to access and read tweets posted by registered users. According to an online statistics portal, there are approximately more than 328 million monthly active users all around the world.7 To give an idea about the number, this is more than the entire population in the United States. An overview of monthly active users in Twitter, Figure 1 is shown below. One can see that in seven years (2010-2017) the amount of monthly active users has increased more than tenfold.

7 https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/

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Average number of monthly active users per year (in millions) 350 328 304,5 314,75 300 274,5 250 223,675 200 160,25 150 92,75 100 43,25 50 0 2010 2011 2012 2013 2014 2015 2016 2017

Figure 1: Overview of monthly active users per year from 2010-2017

Over Twitter, easy form of communication for users is made possible to broadcast and share information about their activities, opinions and status. Another very appealing feature for Twitter users is the ability to follow any other user with a public profile, enabling them to interact with celebrities like singers or politicians. For many traders, Twitter has become a daily part of their work such as watching, for example, Reuters. Though, in some countries, professional traders in the financial services industry, like the United Kingdom, are banned access to Twitter due to matters concerning risks such as financial promotion (of certain products) or market abuse (e.g. putting out false rumors). In 2013, the Securities and Exchange Commission, recognized the impact of Twitter as an information provider and allowed companies to tweet corporate activity. Other financial data companies, such as Bloomberg, even integrated certain accounts into their terminal. For some years, hedge funds and traders have been focusing on Twitter based content. Many even started developing algorithms that can automatically recognize news’ sentiment and trade on it. This mostly happens by analyzing the content of a tweet for negative or positive words, and hence grading the news as neutral, negative or positive. For example, the company Cayman Atlantic (Sentiment Analysis, n.d.), an early pioneer in the use of social media sentiment analysis to trade financial products, created an automated algorithm which evaluates a tweet about a company as positive (negative), and automatically trades by buying (selling) the certain company’s stock. One can recognize the value that the financial industry gives to Twitter.

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Trump, and Twitter as an Information Intermediary

Currently, one of the most followed person on Twitter is Donald J. Trump, the president of the United States, with about 36 million followers. Trump is known to be very active in the Twitter scene as he comments on recent news or highlights strengths or weaknesses of certain companies or industries. For example, after Trump’s negative posting about “Boeing” on December 6, 2016 to cancel a contract due to rising costs, the stock market reacted immediately. According to Nanex, a market analytics firm, Boeing’s shares price dropped by 1.6% only after 10 seconds of Trump’s tweet. This might imply that market participants follow and react on tweets’ contents. Traders seem to anticipate tweets posted by Trump. According to Efrem Hoffman, founder of Running Alpha, Trump’s tweets are a new source of market information. The analytics firm which looks for investment opportunities before they arrive in the market, works on a strategy that analyzes sentiment-related words in Trump’s tweets and tries to correlate this emotional volatility to key term of certain policy areas. Based on Trump’s Tweets who regularly posts over the stock market, the natural question that arises is whether Trump can be used as the oracle of stock exchange. Considering the impact of Twitter in the financial markets nowadays, researchers put emphasis on this area of literature. The relation between social media data (Twitter) and financial data is prevalent. As one of first papers to investigating this issue, Bollen Mao and Zeng (2011) analyze the content of tweets. They find two types of mood regarding tweets: polarity (positive vs. negative) and emotions (calm, alert, sure, vital, kind and happy). This textual analysis approach to tweets, the researchers highlight that there is a clear relation between the mood indicators and Dow Jones Industrial Average (DJIA). Their results show that the accuracy of DIJA predictions are significantly improved, namely there is an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA. As a contribution to the field of event study methodology, Sprenger et al. (2014) examine a dataset of more than 400,000 S&P 500 stock-related tweets, and illustrate that stock market impact of news events differs substantially across different categories. Additionally, they show that negative news are only incorporated into stock prices the event day itself whereas positive news mostly leak and are incorporated into stock price before the information is officially announced. To put it another way, while there is a surprise-effect with negative news in the stock market, there is none with positive news. Based on previous research which focuses on sentiment analysis of Twitter, my research will put emphasis on the informational analysis of tweets by treating every single tweet from Trump as the same (no negative/positive polarity)

9 since positive tweets by Trump might incorporate information into stock prices differently than negative tweets.

Why do Investors React on Tweets: Psychological and Behavioral Approach

There are aspects of behavioral psychology of traders which mainly cause traders’ over- or underreaction to news events. This holds true for not only individual traders, but also to the collective trading environment as a whole. Psychology in trading is far more than only emotional responses by market participants. It is deeply entrenched into the systems of human beings and continuously in their psyches. Where some areas of behavioral psychology can be very beneficial in one’s normal life, in trading they can be most damaging since they affect the actions, responses and interpretation of information by traders. One of the behavioral aspects is overoptimism. This feature mainly exists due to two underlying reasons: the illusion of control and the self-attribution bias. The former is defined as the tendency for people to overestimate their ability to control events. The latter one states that people attribute positive outcomes to their own skill, while negative outcomes to bad luck. For example, a trader who figured out a profit-making strategy regarding Trump’s tweet will credit his own strategy every time he wins, but attribute occurring losses based on his strategy to unforeseen events. This results in an increase of likelihood that traders will not learn from mistakes. To support this empirically, Cao and Ou-Yang (2009) point out that differences in investors’ opinion which are generated by optimism of public information cause high trading volume and price volatility. The second aspect of behavioral psychology related to trading which caught far more attention in literature is overconfidence. Investors tend to overestimate the precision of their private information and underestimate the risk of assets. This results in an overreaction to their private information. Researchers are at one that traders’ overconfidence help explaining matters in the stock market such as excessive trading, price volatility and return momentum. One general finding in literature is that the more overconfident a trader is, the more aggressively he will trade meaning higher trading volume. Odean (1998), for example, demonstrates that traders’ overconfidence in private information can lead to aggressive trading and enormous price volatility. Finally, and most relevant for this research, the attention theory of Barber and Odean (2008) implies that individuals are net buyers of attention-grabbing stocks. They argue that due to time-limitation, investors only consider a limited number of stocks from the entire universe of stocks. When it then comes to making a decision out of the many alternatives, options that attract attention are more likely to be considered, hence more likely to be chosen. On the other

10 hand, options that do not attract attention will be ignored. Examples for attention-grabbing stocks are e.g. stock in the news, or with high search queries. This behavior is based on the bounded rationality human beings have, and there are cognitive and temporal limits to how much information one can process. In this context, bounded rationality is defined as the limitation of an investor’s rationality due to his cognitive abilities. As mentioned above, these cognitive abilities are limited by overoptimism and overconfidence. This will significantly affect the decision-making process of investors, and can lead to sub-optimal decisions. Investors will look for attention-grabbing stocks. This does not mean that they will buy all stocks that catch their attention, but when they buy a stock it is quite likely that that particular stock arouses their attention. When it comes to selling, following two factors lower the search problem for individual investors. Firstly, this kind of investors hold relatively few stock in their portfolio. Secondly, the large part of individual investors does not sell short, and only sell stock that they already own, id est are part of their portfolio. The reason for this is simply that individual investors focus on future returns when buying a stock, but concentrate on past returns when selling a stock. Of course, there is a different type of investors where this phenomenon is less applicable, namely institutional investors. The fact that individual investors are affected by attention when buying, however not when selling a stock, is not true for institutional investors. Firstly, for institutional investors buying and selling a stock have the same searching problem efforts. For example, hedge funds usually short sell8 which expands their set of choice in the stock universe. Also, when not allowed to short sell, institutional investors simply own more stocks, which makes it harder to choose which ones to sell. Secondly, attention is not an important determinant, since institutional investors have more time to spend on choosing the right stocks based on certain criteria such as strong momentum or high return on equity. The authors propose that a sign of attention-driven buying can be proxied by news, unusual trading volume and extreme returns. When news about a company reaches enough investors, it is quite likely that the company stock’s trading volume will be greater than usual. However, this must not be the case since investors could perceive the news as being not relevant to the firm’s future earnings and not trade. But if news are considered as significant, investors’ belief will result in more trading. High abnormal volumes might also be due to the liquidity or information-based trades of a few large investors, and not news. However, this is less true for large capitalization stocks since the effect on trading volume is only minimal. Next to trading volumes, also returns play a role. While it is known that

8 Sell a stock that you do not own (which is borrowed). 11 important news can significantly affect returns positively or negatively, there might also be news which are more difficult to interpret. This results in high abnormal trading volume, but no price change. While individual investors are keen to buy on high-attention days, institutional investors are more likely to sell on high-attention days. Based on above mentioned behavioral aspects, one can infer how and why traders perceive information as they do. Here, psychological research can clarify more deeply. Psychological research shows that, when making judgements and decisions, human beings overweight salient information and at the same time give too much attention to how extreme information is and not enough to its validity (Kahneman and Tversky, 1973). To reinforce the argument by Kahneman and Tversky (1973), Fiske and Taylor (as cited in Odean, 1998) state that people “often behave as though information is to be trusted regardless of its source, and make equally strong or confident inferences, regardless of the information’s predictive value […] Whether the information is accurate fully reliable or alternatively out of date, inaccurate, and based on hearsay may […] matter little”. To sum it up, market participants underreact to abstract, important and highly relevant information, while they overreact to less relevant information, more attention-grabbing information (e.g. an extreme event, prominent news article, rumor). Applying this to Trump’s tweets about certain companies, this might mean that traders might not act rational shortly after the release of the tweet, and overreact although the tweet itself might be less relevant for the company. However, traders might consider the tweet as highly salient, since the president of the United States, Donald J. Trump is the one posting. One can clearly identify that investors are overconfident and that overconfidence can affect financial markets.

Intra-industry Information Transfer: Are News Events about one Specific Firm

Applicable to Whole Industry?

Previous research has showed how information about a single firm could have an impact on the stock prices of other firms in the industry. Due to asymmetric information in the market, market participants use the release of new public information about one firm to make valuation inferences about corresponding rival firms in the same industry. One of the first studies to investigate this issue is by Foster (1981). He examined the impact that a firm’s earnings releases have on the stock prices of other firms in its industry. Generally speaking, if there are positive (negative) news, such as an earnings release, about a company, then the company’s stock price will increase (decrease) as investors respond by buying (selling) more stocks of the firm.

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Depending on investors’ interpretation of the news for competitors, there might be some effect on these as well. There are two effects which can take place within this context: contagion effect or competition effect. In the case of earnings release of a firm, there can be good or bad news. If there are good news for the announcing firm, and this is interpreted by investors as good news for firms in the same industry, then there is a contagion effect. Thus, it implies that competitors’ stock price will change in the same direction as the announcing firm. By contrast, the competition effect is the exact opposite, namely that competitors’ stock prices will move in the opposite direction as the announcing firm. If a firm in a highly-competitive industry announces a positive earnings release, investors might perceive this as negative information for competitors since higher earnings presented by the announcing firm could indicate lower earnings for competitors. Thus, good news for the announcing firm being interpreted by investors as bad news for competitors, will lead to a decrease in competitors’ stock prices. Hence, according to Laux, Starks and Yoon (1998) here the competition effects may overweigh the contagion effects. Many other studies followed to investigate intra-industry information transfers (Clinch and Sinclair, 1987); Fenn and Cole, 1994;)Aharony and Swary, 1996) The general finding is that there is a positive correlation between a firm’s earnings announcement and the stock prices of other firms in its industry. Regarding my research, the event of interest will be a ‘Trump-tweet about a specific company’ instead of earnings announcement. Within the context of tweets, there could be either of the abovementioned effects in place. Considering Trump’s tweets about a specific company, investors evaluate the tweet as positive, negative or neutral. Consequently, there will most likely be a positive, negative or no effect on the company’s stock price. For companies’ stock prices in the same industry (competitors), there might be either a positive, negative or no effect depending whether the contagion effect outweighs the competition effect or not. If investors assess the contagion effect derived by news to be stronger than the competition effect, then the competitors’ stock prices will move in the same direction as the company’s stock price which the news is about. If the competition effect is stronger, then competitors’ stock prices will move in the opposite direction. Finally, if both effects cancel each other, then there will be no effect on competitors’ stock prices. To illustrate this with an example consider Trump’s tweet on December 6, 2016 about the airplane manufacturer Boeing.9 Before Trump’s tweet, which was sent just before 9. a.m. Eastern time, ahead of the stock market’s open, the stock had traded

9 Tweet (6.12.2016): “Boeing is building a brand new 747 Air Force One for future presidents, but costs are out of control, more than $4 billion. Cancel order!”. 13 unchanged at $152.16 (closing price on December 5). Within a matter of seconds after Trump’s tweet, the company’s share price fell by approximately 1.6% during pre-market trading from $152.16 to $149.75. However, then it jumped back up to $151.25 shortly after 9 a.m. Eastern time and kept trading around this value until 12 p.m.. Then, after Boeing’s and the White House’s response to Trump’s tweet which contradicted the high cost number, the stock price recovered, and traded within a range of $150.02 to $152.64. By this example, one can see the general course of stock prices after a Trump tweet. Within the first seconds, there is a huge overreaction, after which the stock price recovers a little, though stays quite volatile. Most of the times, in the end of the day, the stock price recovers from the initial overreaction, however can still trade lower than the closing price of the prior day. Investors assessed the tweet about Boeing as clearly negative. This lead to a selloff by investors, whereby the stock price decreased enormously within seconds. It is obvious that a single “Trump-tweet” can cause huge stock price volatility and high trading volume of the corresponding company. However, the question whether a “Trump-tweet” also has an impact on stock prices of companies in the corresponding industry remains open. When investors consider tweets about a specific company to possibly have any effects on competitors’ firms, then competitors’ stock prices and trading volumes could either increase or decrease after the tweet is released. To what extent it could have an effect depends on investors and further details regarding the tweet. As already mentioned, based on Ederington and Lee (1995), some investors might disagree with the market consensus. This could have an opposing effect on the competitors’ stock price. In addition to this, according to Laux et al. (1998) investors might take the two opposing effects (contagion and competition) into consideration, and to what extent these can affect an industry. On the other hand, further details from the release of the tweet could become available which can have subsequent effect on stock prices and trading volume of companies in the corresponding industry. Based on abovementioned reasons, one can expect a reaction of competitors’ stock prices or stock trading volume around the news (tweet) is released. Based on this and other previous literature in this chapter, I propose following hypotheses:

Hypothesis 1: When Trump tweets about a specific company, stock returns of that company and its corresponding industry will significantly be affected in the short-term (in either direction).

14

Hypothesis 2: When Trump tweets about a specific company, trading volume of that company and its corresponding industry will significantly be affected in the short-term (in either direction).

15

II. RESEARCH METHOD

Sample and Industry Definitions

This research uses tweets posted by Donald J.Trump mentioning publicly traded companies which are listed on a US-exchange.10 The data are collected for the period 8th November 2016 to 30th April 2017.11 On the 8th November 2016 Trump has been elected the president of the United States. Thus, this research puts emphasis on the presidential period of Trump. The reason for this simply is that investors might consider this situation to a non- presidential period of Trump way different, as after the election he is in power to directly influence laws and politics which in turn might affect companies. To establish the sample of tweets, Table 1 is shown below. All “Trump-tweets” (911 tweets) in the mentioned period have been accurately read and only tweets where Trump directly mentions or refers to a company’s name kept (29 tweets).12 While all companies have been directly mentioned by their company name in corresponding tweets, only United Technologies has been referred to as “Carrier”. The companies Trump mentioned amount to a total number of 11 companies. However, to more than half of the companies he refers more than once. For example, Boeing Co. is brought up exactly 3 times. Furthermore, one can see that there are 8 industries mentioned in total. Especially tweets about companies belonging to industries such as “Motor Vehicles and Passenger Car Bodies” (SIC:3711) and industries related to Aircraft (SIC: 3721;3724) are more present. Table 1 Overview of Mentioned Companies and Number of Tweets

SIC- Ticker Companies mentioned Industry CODE Tweets BA Boeing Co. Aircraft 3721 3 FCAU Fiat Chrysler Automobiles N.V. Motor Vehicles and Passenger Car Bodies 3711 2 F Ford Motor Co. Motor Vehicles and Passenger Car Bodies 3711 7 GM General Motors Co. Motor Vehicles and Passenger Car Bodies 3711 4 LMT Lockheed Martin Co. Guided Missiles and Space Vehicles 3761 2 JWN Nordstrom Inc. Department Stores 5311 1 RXN Rexnord Co. Mechanical Power Transmission Equipment 3568 1 TM Toyota Motor Vehicles and Passenger Car Bodies 3711 1

10 Mentioned companies in this time period are solely listed on either New York Stock Exchange (NYSE) or National Association of Securities Dealers Automated Quotation System (NASDAQ). 11 The ending date 30th April 2017 has been chosen since it was the most current time when this research was written. 12 All the 29 tweets in the mentioned period can be found in Appendix A. 16

Table 1 (continued) Overview of Mentioned Companies and Number of Tweets

SIC- Ticker Companies mentioned Industry CODE Tweets TWTR Twitter Inc. Information Retrieval Services 7375 1 UTX United Technologies Co. (Carrier) Aircraft Engines and Engines Parts 3724 6 WMT Walmart Stores Inc. Variety Stores 5331 1 TOTAL 29

To determine competitors of the mentioned 11 companies, this research will apply two different industry definitions based on Foster (1981). Both methods classify competitors based on the Standard Industrial Classification (SIC) (Table 2 (1) and (2)). For the first method, the databases of Compustat and Center for Research and Security Prices (CRSP) have been employed. Since this method could have some limitations due to the level of aggregation at which some SIC industries are defined, and the diversification strategies adopted by many firms, a second method is also introduced. This method is similar to the “Dominant Firm industry definition” applied by (Foster, 1981). It also classifies competitors based on the SIC- Code, however only retains larger companies in an industry.13 All mentioned companies by Trump rank in the top half of its corresponding industry.14 Table 2 shows the sample size consisting of 54 (30) for method 1 (2).15 The reason for introducing a second method is since it assumes that Trump tweeting about larger firms can cause a greater shift in market share in an industry relative to smaller firms. Hence, larger firms may react much more sensitive to “Trump-tweets”. Consequently, this method should increase the magnitude of information transfers between the mentioned company and its competitors. Finally, for both industry definitions, it is true that non-US-listed competitors have been excluded.16 Below, Table 2 highlights further details to all industries.

13 In each industry, the average of each firm’s total revenues for the period 2010-2016 is calculated. In a next step, these averages are ranked for each industry. Finally, those firms which rank in the top half in each industry are determined. When there are only 2 firms in an industry, both are included in the sample. If there are industries with an odd number of firms, then [(N/2)+1] firms are taken into consideration. 14 Also in method (1), the mentioned company is part of its corresponding industry. 15 In Appendix B the full sample with company names and the average revenue can be found. 16 For the first, and hence also second method, 6 companies have been excluded since this will help decreasing the distortions in the event study. 17

Table 2 Overview of Industry Constituents

Ticker Companies mentioned SIC-CODE (1) Number of firms (2) Number of firms RXN Rexnord 3568 2 2 FCAU Fiat Chrysler 3711 F Ford Motor 3711 16 8 GM General Motors 3711 TM Toyota 3711 BA Boeing 3721 6 3 UTX United Technologies (Carrier) 3724 4 2 LMT Lockheed Martin 3761 2 2 JWN Nordstrom 5311 7 4 WMT Walmart 5331 11 6 TWTR Twitter 7375 6 3 TOTAL 54 30

Event Study Methodology

Event Study of Returns

This section describes the event study approach that will be used to conduct this research. An event study is a statistical method to determine the impacts of an event (e.g. announcement of a takeover) on the value of a firm. The basic underlying assumption for an event study is that capital markets are efficient, stock prices incorporate all relevant information accessible to market participants and that any new relevant information made public to market participants are immediately incorporated into stock prices (Fama and French, 1998). Efficiency mentioned here is defined as semi-strong efficiency. This implies that market participants incorporate all public information into stock prices. Consequently, they will trade until all public information is priced correctly. This assumption made here is only reasonable when such public information has simultaneous release for all market participants, and moreover are free of charge. This is given within the context of this study. Posted tweets by Trump are simultaneously accessible for all market participants, and likewise creating a twitter account is free of charge. In case of absence of the semi-strong efficiency assumption, it would not be able to properly conduct this study. This would mean that not all market participants would have the same information, hence obtained results could not be generalized to the market since returns would be affected differently. One might wonder how the stock market efficiency

18 assumption could hold given the bounded rationality investors have. Fama (1970) argues that the variations in investor decision making will tend to cancel out, which will allow prices to follow a type of a random walk. Even if an investor will make an irrational decision, there will most likely be another one with an opposite view. The main idea behind the event study approach is to detect abnormal returns which are stock returns around the announcement compared with the returns had there been no event. In this research, the event is defined as “Trump tweeting about a specific company”. This way, the relation between “Trump-tweets” (events) and stock returns of the industries can be examined. There will be two separate event study analyses conducted for the two different industry definition methods. On basis of the event study approach, one can determine if “Trump-tweets” about mentioned companies affect the share prices of the corresponding industry positively or negatively. This will be measured by means of the abnormal returns (ARs) and cumulative abnormal returns (CARs) of the industry during the event window. Obviously, positive (negative) CARs of companies mean abnormal increase (decrease) in share prices, hence create (destroy) value for investors owning the stock. To calculate the (cumulative) abnormal returns, event dates (events), estimation window and event window have to be determined.

Event Dates

Exact event dates can be easily identified via the Twitter platform where exact date and time can be found. It is important to choose the first trading day on which the event became public information. Thus, when Trump tweets after market closing time and on weekends or holidays, the next successive trading day is taken as an event date. After determining the event dates, the amount of events can be easily calculated per industry.17 In Table 3 one can tell that, for example, regarding Boeing’s industry (SIC:3721) there are 18 (9) events in total.18 Table 3 Overview of Total Number of Events per Industry

SIC-Code (1) Events Total (2) Events Total 3568 2 2 3711 144 72

17 One simply has to multiply the number of industry constituents with the number of corresponding tweets. 18 Calculation: Method 1: (3 related tweets) X (6 firms); Method 2: (3 related tweets) X (3 firms). 19

Table 3 (continued) Overview of Total Number of Events per Industry

SIC-Code (1) Events Total (2) Events Total 3721 18 9 3724 12 6 3761 4 4 5311 7 4 5331 11 6 7375 6 3 TOTAL 204 106

Estimation Window and Event Window

Abnormal returns (퐴푅푖푡) are simply calculated as realized return (푅푖푡) minus the normal return (푁푅푖푡) in the event window. There are several methods proposed in previous research to estimate normal returns. In this research, the most common model used in other literature will be used, namely the Capital Asset Pricing Model (CAPM) which is a market- adjusted model based on market risk.19 To calculate the normal returns an estimation window is needed. This estimation window does not overlap with the event window and mostly there is a gap between the two windows to account for information which could leak before the actual event date. As in this research, there is no or very little likelihood of information leakage (since is Trump the one tweeting), following estimation window is chosen [-200,-10].20 Below in Figure 2 abovementioned estimation and event window are shown.

Estimation window Event window

푇−200 푇−10 푇−1 푇0 푇2

Due to using the CAPM-method, normal returns might lose some explanatory power since Fama and French (1992) find that there are two other risk factors influencing expected

19 Other models that are used in literature are Mean Adjusted Return Method and Market Adjusted Return Method. They have disadvantages compared to CAPM. The former does not account for stock price movements caused by the market. The latter assumes that all stocks are equally risky. That’s why the CAPM is used. 20 There is no consensus in literature with respect to the length of the estimation window. The most common used estimation window is the given one. Regressions are also run for estimation windows of [-200,-5] and [- 180,-10]. 20 returns, namely size and book to market. The consequence would be that normal returns might be biased which in turn would distort results. However, in this research a short-term event window is chosen for which the accurate choice of a normal return model seems to be less important. Kothari and Warner (2007) demonstrate that adjusting risk is less important in short- term event studies since errors in abnormal returns calculation caused by errors or inaccuracy in the adjustment of risk tend to be small.21 Finally, according to several studies stock returns on different days might differ significantly. For example, Gibbons and Hess (1981) find that stock returns on Mondays are significantly lower than stock returns on other days in the week. Also, Dubois and Louvet (1996) demonstrate in an international sample of firms that this phenomena is prevalent. Since some “Trump-tweets” fall on different days within the week, additional dummy control variables are included. Normal returns of the CAPM-model are calculated as follows:

푁푅푖푡 = 훼푖 + 훽푖푅푚푡+훿1푇푢푒푠푑푎푦푖 + 훿2푊푒푑푛푒푠푑푎푦푖 + 훿3푇ℎ푢푟푠푑푎푦푖 +

훿4퐹푟𝑖푑푎푦푖 + ɛ푖푡

Where 푁푅푖푡= normal return on day 푡 of firm 𝑖’s common stock. Alpha and beta are parameters of the market model estimated using an OLS regression analysis. 푅푚 is the market return or benchmark return based on the broad market index of the firm’s home stock market.22 The other control variables (푇푢푒푠푑푎푦, 푊푒푑푛푒푠푑푎푦, 푇ℎ푢푟푠푑푎푦 and 퐹푟𝑖푑푎푦) are dummies. ɛ푖푡 is a parameter of a residual term with an expected value of zero.

The event window has to be determined in order to calculate abnormal returns. 퐴푅푖푡 are calculated for all firms over short event window for one day prior to the event, the event date itself and one day after the event date. [−1,0,1]. Then, these are cumulated (퐶퐴푅푖 =

∑퐴푅푖푡). The reason to choose short-term event windows is to maximize the capturing-effect of the event on stock prices without the influence from other external factors. In longer-term event windows it is simply more likely that stock prices will be influenced by other factors (Ryngaert and Netter, 1990).23 Further, to get accurate and interpretable results, all events that are

21 Daily expected returns are about 0.05% (i.e., annualized about 12–13%). Therefore, even if the event firm portfolio’s beta risk is misestimated by 50% (e.g., estimated beta risk of 1.0 when true beta risk is 1.5), the error in the estimated abnormal error is small relative to the abnormal return of 1% or more that is typically documented in short-window event studies. 22 The S&P-500 index is used as benchmark return for all firms in the sample since all are US-listed firms. 23 One potential problem in long-term event studies is serial correlation. CAR’s from events in different months can be correlated. This is due to the event window being long and overlapping with returns of events in 21 announced together with other price relevant information must be excluded. For this, all events have been checked for earnings warnings/releases, announcement of share repurchases/dividends and (reverse) stock split.24

(Cumulative) Abnormal Returns

For each firm in the sample, the abnormal returns over the mentioned event period are calculated as follows: ̂ ̂ 퐴푅푖푡 = 푅푖푡 − 푁푅푖푡 = 푅푖푡- (훼̂푖 + 훽푖푅푚푡 + 훿1푇푢푒푠푑푎푦푖 + ̂ ̂ ̂ 훿2푊푒푑푛푒푠푑푎푦푖 + 훿3푇ℎ푢푟푠푑푎푦푖 + 훿4퐹푟𝑖푑푎푦푖)

Where: 퐴푅푖푡 is the abnormal return on day 푡 of firm 𝑖’s common stock 푅푖푡 is the actual return on day 푡 of firm 𝑖’s common stock. Instead of considering each firm’s stock price behavior around events separately, one should focus on the average of the whole sample. The reasoning behind this is that by averaging the abnormal returns around one particular event, information which is unrelated to the event cancels out on average. For each day in the event window, the average abnormal return is calculated by averaging the abnormal returns across the sample. For this following formula is applied: 푁 1 퐴퐴푅 = ∑ 퐴푅 푡 푁 푖푡 푖=1

If at 푡 = 0 (event date), average abnormal return clearly differs from 0, this indicates abnormal performance at the event date. However, the main interest in event studies lies in the stock performance on the period surrounding the event date which also includes the event date itself. This is simply done by summing the abnormal returns over the event window 푡1 to 푡2 for a firm (𝑖), and getting cumulative abnormal returns (CARs). The formula used is following:

푡2

퐶퐴푅푖 = ∑ 퐴푅푖푡

푡=푡1

neighbouring months. The consequence is that due to the correlation t-statistics are too high, rejecting the hypothesis too often. 24 No companies were found that could have been excluded here. 22

Also here the interest lies in the effect of tweets for all firms’ stock prices as a whole. The average cumulative abnormal return (CAAR) is calculated by averaging the CARs across the whole sample of firms. Formula used is:

푁 1 퐶퐴퐴푅 = ∑ 퐶퐴푅 푁 푖 푖=1

A positive CAAR indicates that the stock market reacts favorably to a “Trump-tweet about a specific company”, hence companies’ stock prices in corresponding industry increase on average. A negative CAAR, on the other hand, indicates a negative market reaction to a “Trump-tweet about a specific company”, hence companies’ stock prices in the corresponding industry decrease on average. For (cumulative) abnormal returns to be reliable, statistical tests are conducted to test the given hypotheses, consequently reinforcing the validity of results. To test whether (cumulative) abnormal returns are statistically different from zero the following hypotheses are tested:

퐻0 = 퐸(퐴퐴푅푡) = 0 (1)

퐻0 = 퐸(퐶퐴퐴푅) = 0 (2)

A simple t-test will be used to test the hypotheses. For this, some restrictive assumptions apply. Abnormal returns (퐴푅푖푡) are independently, identically and normally distributed implying that abnormal returns are cross-sectionally uncorrelated. In this study, these assumptions hold since using a short-time event window reduces significantly the problem of cross-sectional correlation problem which would otherwise effect the independency assumption. If these assumptions would not hold, obtained t-statistics would be inflated, hence rejecting the hypothesis too often. This would bias the results. Assuming that the first two assumptions hold, it can be shown that in a large sample also the normal distribution assumption holds. This is based on the Central Limit Theorem, which defines that by multiplying √푁 with the average, and dividing by the standard deviation, the test statistic converges to a standard normal random variable. Generally, in event studies, it is sufficient for N to be larger than 30 (N>30), so that test statistics follow a standard normal distribution for both (1) and (2):

23

퐴퐴푅푡 퐶퐴퐴푅 푇푆1 = √푁 ≈ 푁(0,1) 푇푆2 = √푁 ≈ 푁(0,1) 푠푡 푠

N= number of events AAR= Average of each firm’s abnormal return on event day (t=0) CAAR= Average of each firm’s CAR over the event window [-1,0,1]

1 S= standard deviation25 calculated as: 푠 = √ ∑푁 (퐶퐴푅 − 퐶퐴퐴푅)2 푁−1 푖=1 푖

After performing the t-tests one can say if (cumulative) abnormal returns are positively or negatively statistically different from zero by looking at critical values.

Event Study of Trading Volumes

Next, a second event study is conducted to check whether there is abnormal trading volume of stock prices of mentioned companies and its corresponding industry constituents. The methodology for this event study is mainly the same as for returns. Estimation window and event windows are the same. The main differences of this event study from abnormal return event study is that instead of returns, the log-transformed relative volume per firm is used (Campbell and Wasley, 1996). The results of Cready and Ramanan (1991) highlight that it is of importance to use a log-transformation of raw trading volume. Further, a small constant of 0.000255 is added to the formula to not take the logarithm of zero in case of zero trading volume (Cready and Ramanan, 1991). Finally, also here dummy control variables for weekdays are included since several studies find that trading volumes differ significantly along weekdays (Jain and Joh, 1988; Dellavigna and Pollet, 2009). Following formula represents the fraction of outstanding shares for firm 𝑖 at time 푡.

푛푖푡 푉푖푡 = 퐿푁( + 0.000255) 푠푖푡

25 The standard deviation calculated here is an estimator of the true variance sigma which is unknown. This is why in place of sigma a constructed estimator based on the cross-sectional variance as shown above is used. 1 Standard deviation for AAR is calculated in a similar way by using the formula: 푠 = √ ∑푁 (퐴퐴푅 − 퐴퐴푅)2 푁−1 푖=1 푖

24

Where 푛푖푡 is the number of shares traded for firm 𝑖 on day 푡 and 푠푖푡 is the firm’s outstanding shared on day 푡. Following Cready and Ramanan (1991) and adding dummy control variables, the normal trading volume is calculated as follows:

푉푖푡 = 훼푖 + 훽푖푉푚푡 + 훿1푇푢푒푠푑푎푦푖 + 훿2푊푒푑푛푒푠푑푎푦푖 + 훿3푇ℎ푢푟푠푑푎푦푖 + 훿4퐹푟𝑖푑푎푦푖 + 1 ɛ with 푉 = ∑푁 푉 푖푡 푚푡 푁 푖=1 푖푡 Where N is the number of securities in the market index (here S&P 500, NASDAQ). Then, the log-transformed abnormal relative volumes for each company i and each trading day t in the event window, can be calculated and these abnormal volumes are averaged across the sample 1 (2) to obtain average abnormal volumes using following formulas

̂ ̂ ̂ ̂ ̂ 퐴푉푖푡 = 푉푖푡 − (훼̂푖 + 훽푖푉푚푡 + 훿1푇푢푒푠푑푎푦푖 + 훿2푊푒푑푛푒푠푑푎푦푖 + 훿3푇ℎ푢푟푠푑푎푦푖 + 훿4퐹푟𝑖푑푎푦푖)

푁 1 퐴퐴푉 = ∑ 퐴푉 푡 푁 푖푡 푖=1 When these abnormal volumes are aggregated over the event window, one obtains cumulative abnormal volumes. Following formulas are applied:

푡2

퐶퐴푉푖 = ∑ 퐴푉푖푡

푖=푡1

푁 1 퐶퐴퐴푉 = ∑ 퐶퐴푉 푁 푖 푖=1 To test whether (cumulative) abnormal volumes are statistically different from zero following hypotheses are tested:

퐻0 = 퐸(퐴퐴푉푡) = 0 (1)

퐻0 = 퐸(퐶퐴퐴푉) = 0 (2)

A simple t-test will be used to test the hypotheses. For this, some restrictive assumptions apply. Abnormal volumes (퐴푉푖푡) are independently, identically and normally

25 distributed implying that abnormal volumes are cross-sectionally uncorrelated. If the number of events is large enough (N>30), the tests statistics follow a standard normal distribution for both (1) and (2):26

퐴퐴푉푡 퐶퐴퐴푉 푇푆1 = √푁 ≈ 푁(0,1) 푇푆2 = √푁 ≈ 푁(0,1) 푠푡 푠

N= number of events AAV= Average of each firm’s abnormal volume on event day (t=0) CAAV= Average of each firm’s CAV over the event window [-1,0,1]

1 S= standard deviation27 calculated as: 푠 = √ ∑푁 (퐶퐴푉 − 퐶퐴퐴푉)2 푁−1 푖=1 푖

After performing the t-tests one can say if (cumulative) abnormal volumes are statistically different from zero. A statistically significant CAAV indicates that there are abnormal trading volumes surrounding a “Trump-tweet”.

Cross Sectional Analysis

Finally, it will be investigated whether specific control variables influence abnormal returns or volumes around “Trump-tweets”. The reason for this is that abnormal return and volumes might be affected though some other variables. To overcome this problem, a cross- sectional analysis with included control variables will be applied. As dependent variable, abnormal returns (volumes) at event date (t=0) will be used, while independent variables will be SIZE, MARKET_TO_BOOK, RETWEETS and LIKES. Fama and French (1998) find returns are significantly affected by two additional factors next to the risk factor 푟푚-푟푓 assumed by the CAPM-model. They demonstrate that on average small stocks outperform large ones; and that stocks with a high book-to-market ratio (value stocks) do better than stocks with low book-to-market ratio (growth stocks). There are also two additional control variables included to account for the fact that single tweets might have a

26 Further details to why certain assumptions hold, were discussed in the previous paragraph “Event study for Returns” 27 The standard deviation calculated here is an estimator of the true variance sigma which is unknown. This is why in place of sigma a constructed estimator based on the cross-sectional variance as shown above is used. 1 Standard deviation for AAV is calculated in a similar way by using the formula: 푠 = √ ∑푁 (퐴퐴푉 − 퐴퐴푉)2. 푁−1 푖=1 푖

26 different impact. The third control variable is RETWEETS which describes number of retweets of a tweet. The last control variable (LIKES) is the number of people who liked the tweet. Following Barbear and Odean (2008) “attention-grabbing” stocks are popular amongst investors. This means that the more retweets and likes a “Trump-tweet” has, the higher the chance is that certain tweets could address a larger audience. This might affect the trading behavior of investors on these stocks. Following regression models will be conducted for both samples in separate regressions:

(1)퐴푅푖푡 = 훼 + 훽1 ∗ 푆퐼푍퐸푖푡 + 훽2 ∗ 푀퐴푅퐾퐸푇_푇푂_퐵푂푂퐾푖푡 + 훽3 ∗ 푅퐸푇푊퐸퐸푇푆푖푡 + 훽4 ∗

퐿퐼퐾퐸푆푖푡 + 휀푖푡

(2)퐴푉푖푡 = 훼 + 훽1 ∗ 푆퐼푍퐸푖푡 + 훽2 ∗ 푀퐴푅퐾퐸푇_푇푂_퐵푂푂퐾푖푡 + 훽3 ∗ 푅퐸푇푊퐸퐸푇푆푖푡 + 훽4 ∗

퐿퐼퐾퐸푆푖푡 + 휀푖푡

Databases used for above shown variables and returns, volumes and tweets are shown in Appendix C. When collecting data for RETWEETS and LIKES, on the 5th and 9th January two tweets (events) occur respectively. For this reason the average of both tweets’ retweets and likes is taken.28

28 Amount of retweets and likes per tweet can be found in Appendix D. 27

III. EMPIRICAL RESULTS

This section is structured as following: Descriptive statistics, empirical results for event study for returns and volumes, and finally cross sectional analysis will be presented for both samples, respectively.

Descriptive Statistics

Below in Table 4 and 5 descriptive statistics for both samples are shown.29 Descriptive statistics are reported for returns, market returns, volume, market trading volume, market capitalization, price-to-book., retweets and likes. Means, standard deviations, median, 10% and 90 % quintiles are presented which all are daily measures. Comparing the two tables one can see that on average returns and market returns are almost the same for both samples (0.5% vs 0.6% and 0.15% vs 0.13%). Also, average daily trading volumes for both samples are about the same (0.8% vs 0.7%) and daily market trading volumes (0.14% vs 0.14%).30 Market capitalization and price-to-book are both higher in the small sample ($30.2 billion vs $53.3 billion and 3.3 vs 4.2). This makes sense as the small sample is composed of companies which generate more revenues. ______Table 4 Descriptive Statistics Full sample

Variable Mean SD P10 P50 P90

RETURN 0.005 0.021 -0.018 0.003 0.030 MARKET_RETURN 0.001 0.004 -0.003 0.001 0.008 Vit 0.008 0.010 0.000 0.006 0.019 MARKET_VOLUME 0.0014 0.000 0.001 0.001 0.001 SIZE 30,162 58,410 231 5861 57,259 MARKET_TO_BOOK 3.225 13.493 0.010 1.795 7.481

29 The full sample (Method 1) contains 204 observations; the small sample (Method 2) contains 106 observations. 30 The metric for trading volume is the percentage of outstanding shares traded on a given day. Vit is not the shown as log, but normal. 28

Table 4 (continued) Descriptive Statistics Full sample

Variable Mean SD P10 P50 P90 RETWEETS 21,977 9,777 10,103 21,332 41,825 LIKES 95,142 31,926 52,266 100,060 141,014

The full sample contains 204 observations (54 firms). The descriptive statistics present the mean, standard deviation (SD), 10th, 50th and 90th percentile.

Table 5 Descriptive Statistics Small sample

Variable Mean SD P10 P50 P90

RETURN 0.006 0.019 -0.014 0.003 0.032 MARKET_RETURN 0.001 0.004 -0.003 0.001 0.008 Vit 0.007 0.008 0.000 0.006 0.015 MARKET_VOLUME 0.0014 0.000 0.001 0.001 0.001 SIZE 53,046 73,445 2,221 24,557 185,501 MARKET_TO_BOOK 4.199 13.997 0.007 1.287 4.716 RETWEETS 21,434 9,826 10,103 18,733 41,825 LIKES 93,934 32,107 52,266 98,042 141,014

The small sample contains 106 observations (30 firms). The descriptive statistics present the mean, standard deviation (SD), 10th, 50th and 90th percentile.

Event Study for Returns

In this section, the results of the event study for returns are highlighted.31 This event study is conducted over a short-term event window (3 days) since the effect of a “Trump-tweet” can best be captured around its release since market participants incorporate all relevant information into stock prices in a highly fast pace manner.

31 Results for other estimation windows show the same results and can be found in Appendix E. 29

Figure 3 (full sample) and Figure 4 (small sample) present the graphical representation of average abnormal returns around a “Trump-tweet”. In both graphs, there is a clear stock price reaction at t=0. Returns in Figure 4 seem to have a much stronger spike than in Figure 3 (at t=0). This suggests that for larger firms there might be significant stock price reaction to “Trump-tweets”.

0,40%

0,30%

0,20%

0,10%

0,00% -5 -4 -3 -2 -1 0 1 2 3 4 5

-0,10%

-0,20%

Figure 3: Full sample: Average abnormal returns plotted over 10 days (t=0 is the day of a “Trump-tweet”)

0,40%

0,30%

0,20%

0,10%

0,00% -5 -4 -3 -2 -1 0 1 2 3 4 5

-0,10%

-0,20%

-0,30%

-0,40%

Figure 4: Small sample: Average abnormal returns plotted over 10 days (t=0 is the day of a “Trump-tweet”)

30

Table 6 shows particular average abnormal returns for the three-days in the event window and the cumulative average abnormal return over the three-day event window. While there is no statistically significant effect for the full sample, the small sample yields a significant effect on the event date (t=0). The average abnormal return of 0.36% is statistically significant on the 5%-level and positive. However, the cumulative average abnormal return is also not significant. These results are also supported by Figure 4, where there is clear strong spike on the event date, however an unsteady movement around it. The results confirm the Hypothesis 1. When Trump tweets about a specific company, stock returns of that company and its corresponding industry are significantly positively affected on an intraday-basis. However, this is only true for the companies in the small sample. The theory of Barber and Odean (2008) is in play here. Investors become aware of mentioned stocks and the corresponding competitors after a “Trump-tweet” and buy these “attention-grabbing” stocks. This effect is only true on the day of the tweet itself and not over the event window. The fact that the hypothesis is only confirmed for the small sample might imply that investors only care about larger competitors of mentioned stocks by Trump. These are more known to market participants, and hence attract more attention than smaller competitors. ______Table 6 Event Study for Returns (Full and small sample)

Event (Cumulative) average (Cumulative) average Days abnormal returns p- abnormal returns p- (Full sample) value (Small sample) value -1 -0.16% 0.21 0.00% 0.97 0 0.17% 0.22 0.36%** 0.03 1 0.09% 0.51 0.00% 0.97 [-1,0,1] 0.10% 0.63 0.37% 0.20 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-200,-10]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively. ______

31

Event Study for Volumes

In this section, the results of the event study for trading volumes are discussed.32 This way, the trading volume behavior around a “Trump-tweet” can be closely examined. Both Figure 5 (full sample) and Figure 6 (small sample) show a clear decrease from average abnormal volumes at the event date (t=0), one day after and two days after. After day two, the average abnormal volumes normalize again to the initial level of three days before the event date. Both graphs imply a significant negative effect of “Trump-tweets” on trading volumes.

0,00% -5 -4 -3 -2 -1 0 1 2 3 4 5 -0,02%

-0,04%

-0,06%

-0,08%

-0,10%

-0,12%

-0,14%

-0,16%

-0,18%

-0,20%

Figure 5: Full sample: Average abnormal volumes plotted over 10 days (t=0 is the day of a “Trump-tweet”) 0,05%

0,00% -5 -4 -3 -2 -1 0 1 2 3 4 5

-0,05%

-0,10%

-0,15%

-0,20%

-0,25%

Figure 6: Small sample: Average abnormal volumes plotted over 10 days (t=0 is the day of a “Trump-tweet”)

32 Results for other estimation windows show the same result and can be found in Appendix F. 32

Based on Table 7, the abnormal trading volumes show a clear negative movement on the day of a “Trump-tweet” (t=0). Days after the event date, the abnormal trading volumes clearly decrease. For both samples, cumulative average abnormal volumes are negative and significant at the 1%-level. The largest effect is on the day after the event date. Results confirm Hypothesis 2, that there is a significant effect of trading volumes when Trump tweets. The results of both samples are in line, clearly indicating that after a “Trump-tweet” investors seem to trade less on mentioned stock and competitors. ______Table 7 Event Study for Volumes (Full and small sample)

Event (Cumulative) average (Cumulative) average Days abnormal volume p- abnormal volume p- (Full sample) value (Small sample) value -1 -0.05% 0.27 -0.05% 0.38 0 -0.09** 0.02 -0.07% 0.11 1 -0.15%*** 0.01 -0.13%** 0.03

[-1,0,1] -0.27%***33 0.00 -0.24%***34 0.04 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-200,-10]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively. ______The main takeaway from both results for stock returns and trading volume is that the both results show significant results, confirming the initially proposed hypotheses. Results of trading volumes seem to be more convincing than stock returns, since they show more significant variables. When Trump tweets about a specific company, stock returns and trading volumes of that company and its corresponding industry are significantly affected in the short-

33 The sum of single average abnormal volumes does not add up to -0.29% since some observations are missing for day -1 and day 1. 34 The sum of single average abnormal volumes does not add up to -0.24% since some observations are missing for day -1 and day 1. 33 term. In this research it is anticipated that a highly-attentive stock in the news, also affects the competitors of the corresponding industry.35 The main theory suggested in this research is that investors prefer to buy “attention-grabbing stocks” (Barber and Odean, 2008). This behavior is definitely shown in the positive significant average abnormal return on the event date itself. Investors have the specific “Trump-tweet” in contemplation. They buy more of the mentioned company’s stocks, and also competitors’ stock in the same industry. However, the corresponding trading volumes do not increase, but in fact decrease in size. This implies that investors seem to trade less in these “attention-grabbing stocks” and corresponding industries. This in turn means that investors might stand off stocks, and industries Trump is referring to in his tweet. This is clearly a sign for investors having trouble to figure out what Trump is trying to convey to its audience with his tweet. Since investors can not accurately figure out to what extent “Trump-tweets” could possibly have an effect, disparity between stock returns and trading volumes are the result. While results are significant for stock returns and trading volumes, it is important to mention that the size of the coefficients is relatively small. Thus, results should be interpreted cautiously. Furthermore, within the scope of this research it is not answered, however, whether a “Trump-tweet” is negative or positive. By assessing the polarity of the tweets, more detailed information can be collected about the direction of stock returns and trading volumes. One could make inferences, for example, about a negative tweet implying negative stock returns or trading volumes. The main reason for not including the polarity of tweets is that within the scope of this research it would be hard to draw any conclusions about the relationship between polarity of tweets and stock prices or trading volumes. There are two reasons for this. Firstly, the assessment of tweets seems to be not always accurate. Especially, regarding “Trump-tweets” where often irony or sarcasm are used, a tweet’s polarity might get biased. Secondly, a negative or positive tweet could also imply inverted reaction of stock prices or trading volumes. For example, if a “Trump-tweet” would be assessed as negative, investors might trade less in the stock of the mentioned company and more in stocks within that industry. This would most likely result in an overall positive effect on stock prices and trading volumes although the tweet was negative. Because of above mentioned reasons, in this research it is not answered whether “Trump-tweets” are negative or positive but rather whether they have any absolute effect on stock price and trading volumes.

35 This has been discussed in detail in Chapter II, “Intra-industry information transfer: Are news events over one specific firm applicable to whole industry?” 34

Cross Sectional Analysis

This part of the research summarizes results for the cross-sectional analysis of average abnormal stock returns and average abnormal trading volumes. For this, the control variables SIZE, MARKET_TO_BOOK, RETWEETS and LIKES are used and presented. For both samples, same regression will be run separately. In Table 8, results are presented for abnormal stock returns. There are 612 (318) observations for the full sample (small sample).36 While in the small sample there is no variable significant, in the full sample only the dependent variable SIZE is significant at the 5%-level, and positive. This means that if the market capitalization of a company increases by 1%, average abnormal returns increase by 0.001%. This found result is opposed to the theory of Fama and French (1992), that smaller firms actually outperform larger firms. This is thus not the case, when there is an event such as a “Trump-tweet”. Then larger firms actually increase more abnormally than smaller firms. However, due to the very small size of the coefficient SIZE, this effect is less meaningful. MARKET_TO_BOOK is negative as proposed by Fama and French model, however not significant. Fama and French (1992) demonstrate that firms with higher book-to-market ratio (lower market-to-book ratio) outperform stock with lower book-to-market ratio (higher market-to-book ratio). The control variables RETWEETS and LIKES are not significant, and very small in size. They both do not have any significant effect on average abnormal stock returns. Based on the full sample, the finding suggests that larger firms actually increase more in stock prices than small firms around the time of a Trump-tweet. ______Table 8 Regression of Abnormal Returns on Control Variables

Variables Abnormal return Abnormal return (Full sample) (Small sample)

Constant -0.010* -0.017 (0.096) (0.334) SIZE 0.001** 0.013 (0.016) (0.211) MARKET_TO_BOOK -0.106e-3 -0.252e-4 (0.130) (0.616) RETWEETS 0.222e-8 -0.178e-6 (0.944) (0.559)

36 All average abnormal returns and average abnormal volumes on single days are combined. 35

Table 8 (continued) Regression of Abnormal Returns on Control Variables

Variables Abnormal return Abnormal return (Full sample) (Small sample)

LIKES -0.183e-8 0.506e-7 (0.830) (0.633)

No. of observations 612 318 Adj. R2 0.008 0.002 The full sample (small sample) consists of 612 (318) observations and 54 (30) firms. */**/*** represent statistical significance at 10%/5%/1% levels two-tailed. p-values are in parentheses. ______Table 9 shows results for abnormal average volumes. The cross-sectional analysis is conducted in two separate regressions with same control variables as in the prior table. For the full sample, SIZE and MARKET_TO_BOOK are positive and significant at 1%level. Thus, when SIZE increases by 1%, average abnormal volume increases by 0.0000000107%. If a firm has higher MARKET_TO_BOOK ratio (by one unit), average abnormal volume increases by 0.000065%. While the first finding is line with Fama and French (1992), the second proposes the opposite effect. For the small sample SIZE is positive and significant at the 1%-level, whereas MARKET_TO_BOOK is positive and significant at the 10%-level. Here, both the sign of both significant variables does not support findings of Fama and French. For both samples, RETWEETS and LIKES are not significant, and thus do not have impact on average abnormal volumes. When there is a “Trump-tweet” apparently, abnormal trading volumes of larger firms move more compared to small firms’ trading volumes. This finding has to be interpreted cautiously since the coefficient sizes are very small, barely above 0. ______Table 9 Regression of Abnormal Volumes on Control Variables

Variables Abnormal volume Abnormal volume (Full sample) (Small sample)

Constant -0.003*** -0.001 (0.005) (0.273) SIZE 0.107e-7*** 0.103e-7*** (0.000) (0.001)

36

Table 9 (continued) Regression of Abnormal Volumes on Control Variables

Variables Abnormal volume Abnormal volume (Full sample) (Small sample)

MARKET_TO_BOOK 0.650e-4*** 0.252e-4* (0.004) (0.071) RETWEETS 0.481e-7 -0.263e-7 (0.462) (0.720) LIKES 0.286e-8 0.761e-8 (0.893) (0.762)

No. of observations 612 318 Adj. R2 0.004 0.003 The full sample (small sample) consists of 612 (318) observations and 54 (30) firms. */**/*** represent statistical significance at 10%/5%/1% levels twotailed. p-values are in parentheses.

37

IV. CONCLUSION

This research examines the impact of “Trump-tweets” on stock returns and trading volumes. In particular, when Trump mentions a specific company in his tweet, that company and its corresponding industry is examined. To determine the appropriate industry, the SIC- codes have been used. Additionally, a second sample has been created to more closely identify the main competitors of mentioned companies. The results found in this study support the findings of Barber and Odean (2008). Investors prefer buying “attention-grabbing” stocks. After a “Trump-tweet” about a specific company, its and competitors’ abnormal stock returns are affected positively. Opposed to that, abnormal trading volumes show a negative sign. This implies that investors actually trade less stocks of the referred company and its corresponding industry. Market participants seem to have troubles to figure out exactly, if tweets posted by Trump actually are relevant for these stocks. In conclusion, there is an impact on stock returns and trading volumes around the time when Trump tweets about a specific company. Saying this, the two proposed hypotheses are confirmed in this study. This study adds new insights to existing research. Apparently, investors associate “Trump- tweets” about a specific company with its corresponding industry. The firm’s and competitors’ experience positive abnormal stock return and negative trading volume movements in the short-term. While trading volume results were both significant for smaller firms (full sample) and larger firms (small sample), stock return results were only significant for larger firms (small sample). This suggests that investors only buy larger competitors’ stock instead of concentrating on the whole industry. The reason for this simply might be that larger firms are also mostly more-known, hence more “attention-grabbing”. This study also has some limitations. The first and most important problem is very hard to address and more incumbent in financial literature regarding news events. It is possible that stock returns and trading volume movement are actually caused by some other unobservable factors, and not by the “Trump-tweet” itself. By adding some variables in the cross-sectional analysis, this problem was partly addressed. There might be still some other non-included control variables which have a systematic effect on the relationship between the dependent variable and independent variables. Additionally, there might be a selection bias effect when choosing competitors of mentioned companies in the tweet. This problem has been addressed by forming another smaller sample, which only concentrates on largest competitors. Secondly, this research does not elaborate on the text content of the tweet and rates all tweets as the same. However, some “Trump-tweets” might be assessed by investors differently due to their content. 38

For example, when Trump tweets about a company without using negative words, the market might react differently (affecting stock returns and trading volumes) than when Trump would tweet about a company by using very negative words. Thus, there exists a “tweet-content” bias. Finally, this research uses only daily data due to limited data access, however intrahourly data might be more appropriate to use since the market reacts on “Trump-tweets” most vigorously within matter of seconds. The movement of stock returns and trading volumes might be much larger in the very short-term. In conclusion, the results presented within this research might only describe a correlation instead of causality between stock returns and trading volumes and “Trump-tweets”. These tweets and movements in stock returns and trading volumes may exert a mutual influence upon each other. Further research building on this study will seek to provide further insight into the direction and causality of the relationship between “Trump-tweets” and market movements. Furthermore, by using the polarity of tweets, intrahourly data and more observations new insights to whether “Trump-tweets” cause significant market movements can be concluded.

______

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Event Date Tweet 18.11.2016 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. 25.11.2016 I am working hard, even on Thanksgiving, trying to get Carrier A.C. Company to stay in the U.S. (Indiana). MAKING PROGRESS - Will know soon! 30.11.2016 Big day on Thursday for Indiana and the great workers of that wonderful state.We will keep our companies and jobs in the U.S. Thanks Carrier. 01.12.2016 text: Getting ready to leave for the Great State of Indiana and meet the hard working and wonderful people of Carrier A.C.. 05.12.2016 text: 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! 06.12.2016 text: Boeing is building a brand new 747 Air Force One for future presidents, but costs are out of control, more than $4 billion. Cancel order!

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Appendix A (continued) Selected “Trump-tweets”

Event Date Tweet 23.12.2016 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! 03.01.2017 @DanScavino: Ford to scrap Mexico plant, invest in Michigan due to Trump policies\http://www.foxnews.com/politics/2017/01/03/ford-to-scrap- mexico-plant-invest-in-michigan-due-to-trump-policies.html … 03.01.2017 General Motors is sending Mexican made model of Chevy Cruze to U.S. car dealers-tax free across border. Make in U.S.A.or pay big border tax! 04.01.2017 Thank you to Ford for scrapping a new plant in Mexico and creating 700 new jobs in the U.S. This is just the beginning - much more to follow. 05.01.2017 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. 09.01.2017 Ford said last week that it will expand in Michigan and U.S. instead of building a BILLION dollar plant in Mexico. Thank you Ford & Fiat C! 09.01.2017 It's finally happening - Fiat Chrysler just announced plans to invest $1BILLION in Michigan and Ohio plants, adding 2000 jobs. This after... 17.01.2017 @levisteveholt: @realDonaldTrump I appreciate your use of Twitter to keep us informed and maintain transparency.\ Very dishonest media! 17.01.2017 Thank you to General Motors and Walmart for starting the big jobs push back into the U.S.! 18.01.2017 Totally biased @NBCNews went out of its way to say that the big announcement from Ford, G.M., Lockheed & others that jobs are coming back... 25.01.2017 Great meeting with Ford CEO Mark Fields and General Motors CEO Mary Barra at the @WhiteHouse today.pic.twitter.com/T0eIgO6LP8. 08.02.2017 My daughter Ivanka has been treated so unfairly by @Nordstrom. She is a great person -- always pushing me to do the right thing! Terrible! 17.02.2017 Going to Charleston, South Carolina, in order to spend time with Boeing and talk jobs! Look forward to it. 28.03.2017 Big announcement by Ford today. Major investment to be made in three Michigan plants. Car companies coming back to U.S. JOBS! JOBS! JOBS!

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Appendix B Full sample with Average Revenues (in million USD)

Company Name Ticker SIC-Code Average Revenue (2010-2016)

ALTRA INDUSTRIAL MOTION CORP AIMC 3568 704 REXNORD CORP NEW RXN 3568 1,895 TOYOTA MOTOR CORP TM 3711 233,638 GENERAL MOTORS CO GM 3711 152,602 FORD MOTOR CO F 3711 141,689 HONDA MOTOR CO LTD HMC 3711 110,354 42

Appendix B (continued) Full sample with Average Revenues (in million USD)

Company Name Ticker SIC-Code Average Revenue (2010-2016) FIAT CHRYSLER AUTOMOBILES NV FCAU 3711 101,194 TATA MOTORS LTD TTM 3711 33,932 PACCAR INC PCAR 3711 16,567 NAVISTAR INTERNATIONAL CORP NAV 3711 11,269 OSHKOSH CORP OSK 3711 7,495 WABCO HOLDINGS INC WBC 3711 2,637 TESLA INC TSLA 3711 2,427 LCI INDUSTRIES LCII 3711 1,063 FEDERAL SIGNAL CORP FSS 3711 796 SPARTAN MOTORS INC SPAR 3711 499 CHINA AUTOMOTIVE SYSTEMS INC CAAS 3711 402 KANDI TECHNOLOGIES GROUP INC KNDI 3711 106 BOEING CO BA 3721 83,258 GENERAL DYNAMICS CORP GD 3721 31,674 TEXTRON INC TXT 3721 12,461 EMBRAER S A ERJ 3721 6,002 WESCO AIRCRAFT HOLDINGS INC WAIR 3721 1,054 AEROVIRONMENT INC AVAV 3721 268 UNITED TECHNOLOGIES CORP UTX 3724 58,714 A A R CORP AIR 3724 1,804 HEICO CORP NEW HEI 3724 998 SIFCO INDUSTRIES INC SIF 3724 111 ASTROTECH CORP ASTC 3761 17 LOCKHEED MARTIN CORP LMT 3761 46,260 MACYS INC M 5311 26,435 KOHLS CORP KSS 5311 18,700 PENNEY J C CO INC JCP 5311 14,356 NORDSTROM INC JWN 5311 12,074 DILLARDS INC -CL A DDS 5311 6,533 BURLINGTON STORES INC BURL 5311 4,681 STEIN MART INC SMRT 5311 1,265 WAL MART STORES INC WMT 5331 457,274 TARGET CORP TGT 5331 70,551 SEARS HOLDINGS CORP SHLD 5331 35,433 T J X COMPANIES INC NEW TJX 5331 26,491 DOLLAR GENERAL CORP DG 5331 16,804

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Appendix B (continued) Full sample with Average Revenues (in million USD)

Company Name Ticker SIC-Code Average Revenue (2010-2016) DOLLAR TREE INC DLTR 5331 9,725 CASEYS GENERAL STORES INC CASY 5331 6,414 BIG LOTS INC BIG 5331 5,144 FREDS INC FRED 5331 1,956 OLLIE'S BARGAIN OUTLET HLDGS OLLI 5331 708 FIVE BELOW INC FIVE 5331 566 ALPHABET INC GOOGL 7375 58,355 TWITTER INC TWTR 7375 1,206 WEBMD HEALTH CORP WBMD 7375 571 YELP INC YELP 7375 306 BOX INC BOX 7375 220 TUCOWS INC TCX 7375 134

Appendix C Databases Used for Research

Variables Databases

RETURN Bloomberg VOLUME Bloomberg TWEETS Twitter Archive (http://www.trumptwitterarchive.com/archive) SIZE Bloomberg MARKET_TO_BOOK Bloomberg RETWEETS Twitter Archive (http://www.trumptwitterarchive.com/archive) LIKES Twitter Archive (http://www.trumptwitterarchive.com/archive)

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Appendix D Retweets and likes

Event Date Retweets Likes

18.11.2016 47,944 162,376

25.11.2016 30,064 135,546

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Appendix D (continued) Retweets and likes

Event Date Retweets Likes

30.11.2016 18,303 81,799

01.12.2016 9,602 55,760

05.12.2016 17,091 62,626

06.12.2016 41,825 138,424

23.12.2016 14,598 60,845

03.01.2017 15,437 52,266

03.01.2017 18,694 72,118

04.01.2017 18,733 72,302

05.01.2017 19,371 85,644

09.01.2017 23,182 108,443

09.01.2017 23,062 99,560

17.01.2017 21,332 102,918

18.01.2017 10,103 47,572

25.01.2017 18,571 100,060

08.02.2017 26,933 141,014

17.02.2017 14,237 98,042

28.03.2017 23,124 108,163

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Appendix E Results for different estimation windows (Returns)

Table 10 Event Study for Returns (Full and small sample)

Event (Cumulative) average (Cumulative) average Days abnormal returns p- abnormal returns p- (Full sample) value (Small sample) value -1 -0.16% 0.20 -0.00% 0.97 0 0.17% 0.24 0.36%** 0.03 1 0.09% 0.72 0.00% 0.94

[-1,0,1] 0.10% 0.48 0.37% 0.20 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-200,-5]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively.

Table 11 Event Study for Returns (Full and small sample)

Event (Cumulative) average (Cumulative) average Days abnormal returns p- abnormal returns p- (Full sample) value (Small sample) value -1 -0.17% 0.17 -0.00% 0.89 0 0.18% 0.21 0.36%** 0.03 1 0.11% 0.81 0.00% 0.90

[-1,0,1] 0.11% 0.63 0.36% 0.20 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-180,-10]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively.

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Appendix F Results for different estimation windows (Volumes)

Table 12 Event Study for Volumes (Full and small sample)

Event Days (Cumulative) average (Cumulative) average abnormal volume p- abnormal volume p- (Full sample) value (Small sample) value -1 -0.04% 0.33 -0.05% 0.38 0 -0.08** 0.03 -0.06% 0.11 1 -0.14%*** 0.01 -0.12%** 0.03

[-1,0,1] -0.24%*** 0.01 -0.20%* 0.04 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-200,-5]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively.

Table 13 Event Study for Volumes (Full and small sample)

Event (Cumulative) average (Cumulative) average Days abnormal volume p- abnormal volume p- (Full sample) value (Small sample) value -1 -0.04% 0.38 -0.05% 0.42 0 -0.09** 0.03 -0.07% 0.14 1 -0.13%*** 0.01 -0.11%* 0.06

[-1,0,1] -0.24%*** 0.00 -0.21%* 0.06 N 204 106

This table presents results from the event study for a three-day event window surrounding the ”Trump-tweet”. The event study consists of 204 (106) observations for the full sample (small sample). The market model with day dummies is used as a benchmark with estimation window [-180,-10]. The event date is at t=0. *, ** and ** represent significance levels of 10%, 5% and 1%, respectively.

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