Rapid technological development has allowed more and more people to gain access to the internet, reshaping connectivity and the dispersal of information. It is estimated that from 2018 to 2019, the number of internet users around the world grew about 9.1%, or about 367 million new users, due to improved accessibility brought on by new developments in mobile phone technology (Kemp, 2019). In effect, increased digital connectivity has also ushered in the development of social media platforms such as Twitter, Facebook, Instagram, and Snapchat, among others. These websites and applications have not only revolutionized social networking; they have also provided a means for users to create and share content with a much wider audience. Social media usage, like internet usage, has likewise grown considerably, rising by about 9.0% or by 288 million new users from 2018 to 2019 (Kemp, 2019). And recently, social media has shown itself to be an extremely popular communication tool not just for the average user, but for politicians and highly influential figures as well.
With regards to social media and politics, one particular social media platform stands out
— Twitter. With a large and growing user base, Twitter has become an indispensable tool for real-time political communication. Users communicate through “tweets” which are 280-character messages which can include text, images, videos, or links. The service is free to use and users are free to follow other Twitter users to subscribe to more updates. In addition, Twitter features a “retweet” function which allows users to repost another user’s tweet. And despite Twitter’s relatively restrictive character limit, politicians have learned how to fully utilize Twitter to broadcast their political views, boost their campaigns, and engage with the public in real time, essentially extending a politicians’ reach or influence across the public sphere. Indeed, this seems to be the case with individuals such as U.S.
President Donald Trump, who has used Twitter as both a key campaigning tool during the electoral race, as well as a major communication tool during his presidency.
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Since formally announcing his presidential campaign in 2015, all eyes seem to be on Trump and at the time of writing this, Trump’s follower count has breached past 61 million people (Trump, n.d.). His rather bombastic statements regarding race, immigration, trade, and politics, have placed him in the media spotlight for quite some time. And this focus on Trump has perhaps, been further amplified by the frequency at which he tweets. Brueninger (2018) noted that in 2018, Trump published over 3,400 tweets, which puts his frequency at about 10 tweets per day and effectively making Twitter the president’s main means of communication.
In addition, a study by Clemson University’s Social Media Listening Center found that slightly more than half of Trump’s tweets were negative, while about 40% were positive and about 9-10% were neutral (Mak, 2019). In the same study, researchers found that Trump’s negative tweets were more likely to be recirculated (through the retweet function on Twitter), which entails that Trump’s negative tweets do indeed get more attention (Mak, 2019). It is not surprising then, that the American media portrays Trump in a more negative light, often emphasizing his more accusatory and negative tweets (Kurtzleben, 2017).
To some extent, these tweets can be interpreted as official presidential statements, which would make them points of interest for traders, in the same way, they would pay attention to central bank announcements, for instance. Moreover, the media often tends to highlight the market-moving nature of Trump’s tweets, and have released headlines such as:
“Trump’s tweets swing stock market amid trade deal uncertainty” (DiChristopher, 2019);
“With Two Tweets, Trump Shatters Historic Calm in Global Markets” (Ossinger &
Patterson, 2019), and “Trump’s latest tweet takes down Amazon stock and the Nasdaq”
(Goldman, 2018), to name a few. But how influential are these tweets and are these tweets sufficient enough to trade on? Does the media tend to overplay this effect?
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1.2 Objectives and significance
To answer these questions, this research has selected six negative tweets pertaining to companies and countries to examine whether or not these tweets affect financial markets, particularly equity markets and foreign exchange markets. This research adds to the growing literature in the field of social media research by analyzing the causal relationship between Trump’s tweets and stock prices, as well as exchange rates. This thesis specifically focuses on Trump’s more negative tweets pertaining to trade and business — themes that tend align with Trump’s political credo about “Making America Great Again”. Generally, we would expect these tweets to have a negative impact on stock prices and exchange rates, although as seen in the summary of results, this may not always seem to be the case.
With the way the media emphasizes the market-moving aspect of Trump’s tweets, this could possibly lead to more attention-based trading, as opposed to trading based on all available information (Rayarel, 2018). This in effect, could create more volatility and inefficiency in financial markets. This thesis posits that the effects of Trump’s tweets tend to be overplayed and that the causal relationship between Trump’s tweets and financial markets must be further explored. In doing so, this should provide a better understanding of these linkages and eventually help traders and investors make more informed and rational decisions, as opposed to immediately reacting to Trump’s tweets.
1.3 Methods
To test the causal relationship between Trump’s tweets and financial markets, this thesis utilizes the synthetic control (SC) method, which is a statistical method that evaluates the effect of an event or intervention. The SC method has been most commonly used for state policy evaluation to analyze the effects of reforms or the implementation of laws and policies (Abadie, Diamond, & Hainmueller, 2010; Abadie, Diamond, & Hainmueller, 2015). However, use cases have expanded to other fields such as advertising (Tirunillai &
Tellis, 2017), finance (Opatrny, 2017), educational assessment (Johnson, 2013), and health
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policies (Kreif et al., 2016), among others. In contrast, the use of the SC method in social media analytics has not been as thoroughly explored yet.
Moreover, the existing body of research on this particular topic -- politicians’ tweets and financial markets -- is primarily dominated by machine-learning studies and sentiment analysis. These studies often focus more on or topic-specific tweets, as opposed to user-specific tweets (Mittal & Goel, 2011; Ozturk & Ciftci, 2014; Ranco, Aleksovski, Caldarelli, Grčar, & Mozetič, 2015; Pagolu, Reddy, Panda, & Majhi, 2016; Nisar & Yeung, 2018).
And while these studies are particularly helpful for those interested in the predictive capabilities of social media data, these studies often gloss over the causal relationship between these tweets and movements in stock prices or exchange rates. Though limited, there are also papers that do in fact analyze the impact of Trump’s tweets on financial markets, although the methodology used in these studies differ from the one presented in this thesis (Born, Myers, & Clark, 2017; Colonescu, 2018; Ge, Kurov, & Wolfe, 2019;
Rayarel, 2018). The existing literature in this particular topic is further discussed in the methodology chapter of this thesis.
1.4 Structure
The arrangement of chapters in this study is as follows. The literature review forms the second chapter of this thesis and follows right after this introduction. It provides a brief explanation of the theoretical framework of this study and discusses the role of social media in politics, as well as the research that has emerged from this. More importantly, the literature review touches on relevant existing literature, particularly studies that focus on how Twitter impacts financial markets. It then provides more details on the use of Twitter among politicians, particularly U.S. President Donald Trump, and how this might affect financial markets as well. In the third chapter, the methodology of this study elaborates on the main statistical procedure used in this study -- the synthetic control method. This chapter also describes the data collection procedure, including the sources used and variables chosen. The fourth chapter presents the results for each event or tweet studied and the interpretation of these results. Finally, the conclusion ties everything together and
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summarizes the main points and findings of this thesis. The final chapter also discusses limitations and suggestions to improve on this study.
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國立 政 治 大 學