Trump’s Tweets: The Effect on Target Firms Author: J.P. Neggers ANR: 665952 Date: 22-08-2017 Supervisor: prof. dr. H.M. Prast Master Thesis submitted to the Department of Finance, Tilburg University Master Thesis Finance: Tilburg University, School of Economics and Management, Department of Finance. Trump’s Tweets: The Effect on Target Firms Author: J.P. Neggers ANR: 665952 Date: 22-08-2017 Supervisor: prof. dr. H.M. Prast Chairperson/second reader dr. K.K. Nazliben The current thesis is written as part of the graduation process for a Master in Finance at Tilburg University. I would like to use this opportunity to say special thanks to my supervisor, prof. dr. H.M. Prast, for taking the time to respond to my questions and to give feedback and overall guidance during the writing process. Furthermore I would like to thank all my teachers, colleagues and friends that directly or indirectly helped me to write this thesis. 1 Contents 1. Introduction and problem statement ............................................................................................. 3 1.1. Problem indication ................................................................................................................... 3 1.2. Problem statement ................................................................................................................... 4 1.3. Research questions................................................................................................................... 4 1.4. Thesis structure ........................................................................................................................ 5 2. Literature and Theory ...................................................................................................................... 6 2.1. Information models .................................................................................................................. 6 2.2. Trump’s tweets and the information models........................................................................... 8 2.3. Market attention and trading volume .................................................................................... 10 2.4. Effect on firm value ................................................................................................................ 11 3. Data and methodology .................................................................................................................. 14 3.1. Twitter data and selection...................................................................................................... 14 3.2. Financial data ......................................................................................................................... 15 3.3. Abnormal trading volume methodology ................................................................................ 16 3.4. Abnormal returns methodology ............................................................................................. 17 3.5. Event day and trading hours .................................................................................................. 18 3.6. Contamination ........................................................................................................................ 19 4. Results ........................................................................................................................................... 20 4.1. Abnormal trading volume ...................................................................................................... 20 4.2. Event-window abnormal returns ........................................................................................... 20 4.3. Cumulative (average) abnormal returns ................................................................................ 23 4.4. Using contamination controls ................................................................................................ 23 5. Conclusion, limitations and further research ................................................................................ 25 5.1. Conclusion .............................................................................................................................. 25 5.2. Discussion ............................................................................................................................... 26 5.3. Limitations .............................................................................................................................. 26 5.4. Suggestions for further research ............................................................................................ 26 6. References ..................................................................................................................................... 28 7. Appendix ........................................................................................................................................ 32 2 1. Introduction and problem statement The current chapter introduces the problem that is studied in this Master Thesis. First of all, it provides a problem indication to introduce the topic. Secondly, it provides a problem statement. Thirdly, it states the corresponding research questions used to solve this problem statement. Finally, the current chapter discusses the structure of the remainder of the current study, including a short overview of the following chapters. 1.1. Problem indication Does Trump's Twitter account have the power to move share prices? The current President has made it a habit to directly attack - or praise – firms using his Twitter account. What effect these addresses have on the value of targeted firms however remains an intriguing question. With President Donald J. Trump's erratic Twitter behavior becoming almost legendary, this question has received a lot of attention in recent news coverage (e.g. Cohan, 2016; Revesz, 2017; Rupp, 2017). It is an intriguing issue, as Trump's Twitter account represents a direct channel of the most powerful man of the US - and arguably, the world - to the people. The current study aims to investigate the effect of a Twitter message of Trump on the firm value of the mentioned firm(s). For the remainder of the current study, these firms will be referred to as target firms1. Twitter is an online platform that allows users to send Twitter messages - or "tweets" up to 140 characters per message. The platform can be accessed through either an online website or a mobile application. Furthermore, it allows users to "follow" another user - such as Trump – which means you subscribe to that person’s (or organization) tweets. Furthermore, it means that you automatically receive any message sent by this user. The Twitter account of Donald J. Trump has over 28 million followers (@realDonaldTrump, 2017). However, these followers make up only a portion of the audience that Trump reaches through his Twitter account, due to both “retweeting” (sharing another user’s tweet with your own followers) and the messages being publically available. The effect of microblogging forums – such as Twitter – is not new in academic literature. For example, Bollen, Mao, and Zeng (2011) find that the “Twitter mood” has the ability to predict the stock market. More specifically, Sprenger, Tumasjan, Sandner and Welpe (2014), as well as Oh and Sheng (2011), find that the sentiment - or bullishness - of tweets is associated with abnormal stock returns. Furthermore, evaluating a similar microblogging forum, Antweiler and Frank (2004) find that stock message boards have a small but significant effect on stock returns. In addition, past academic 1 This term is generally used in M&A literature to denote the acquired firm in a takeover 3 research (e.g. Wysocki, 1998 & Sprenger et al, 2014) has found that the message volume of microblogging forums is able to predict the next-day trading volume. Most of the past academic literature in this field (such as mentioned in the previous paragraph) focuses on the aggregate data of large groups of Twitter users. Research on the effect of individual users is more scarce. A study on the subject by Oranburg (2015) notes that famous activist investor, Carl Icahn, has moved stock markets via tweets mentioning publicly listed firms. Notably, Trump’s rival candidate during the presidential campaign, Hillary Clinton, sent down stocks of biotech companies by tweeting negatively about drug-pricing (Egan, 2015). It should be noted, however, that this is anecdotal evidence of the influence of a single user at best and that the Twitter message followed a high-profile price-gouging scandal (Egan, 2015). The current study focuses more systemically on the effects of the tweets of a single user - the tweets of President Donald J. Trump. As the most powerful man of the US his influence on stock markets is undeniable. For example, after Trump was elected in November, stocks associated with heavy industry and banking as well as companies with high tax burdens received favorable market reactions (Wagner, Zeckhauser and Ziegler, 2017). On the flipside, stocks of firms associated with e.g. healthcare, textiles and apparel were relative losers. Furthermore, Wagner et al. (2017) found that investors favored domestically oriented companies following Trump’s election. Naturally, these price reactions were a result of expectations of the consequences of new regulations under the new Trump administration. Sporadic evidence, however, seems to suggest that something as simple as a tweet of President Trump can have an