The Trump Effect

A Case-Study of Immediate Stock Market Reactions to the President’s Company-specific Twitter Mentions

Andreas E. Palmlöv

Spring 2018

Department of Political Science

Bachelor’s Thesis, 15 Credits

Thesis Advisor: Pär Nyman

Abstract

This thesis investigates how the U.S President’s Twitter mentions of individual companies’ investment announcements influence the short-term price of their stock. By assuming that the President’s comments on a company’s plans should be incorporated by markets as new information, testing the Efficient Market Hypothesis assumption that the markets incorporate all new information, the thesis seeks to contribute to a new, unexplored and growing, research field. This thesis utilizes a qualitative analysis method, studying Twitter mentions on the topic of Trump’s Tax Reform. The data in this thesis is derived from the President’s personal Twitter-account, company announcements, stock price charts, and the Standard & Poor’s S&P500 Index. To conclude, this study finds that although the President’s Twitter comments may signal his public approval of a company and its plans, it appears that any market reaction is only short- term, and that as the market incorporates additional information it returns to an informed state in terms of stock valuations. This study suggests that there are few observable indicators that Trump’s positive mentions on Twitter causes any significant market reaction.

Keywords: President Trump, Efficient Market Hypothesis, Twitter, tweets, S&P 500, Market reaction, individual stocks.

Word count: 12 268 Pages: 38

Table of Contents

1. BACKGROUND ...... 4 From candidate to President ...... 4 Selling the Trump Agenda on Twitter ...... 5 Why Twitter Matters ...... 6 1.1 RESEARCH QUESTION AND PROBLEM STATEMENT ...... 7 1.2 THESIS STRUCTURE ...... 8 2. LITERATURE AND THEORY ...... 8 2.1 THEORY ...... 8 Why Trump’s Twitter mentions of companies should affect stock price levels ...... 9 Efficient Market Hypothesis (EMH) ...... 10 2.2 LITERATURE ...... 11 Contribution ...... 13 3. DATA AND METHODOLOGY...... 14 3.1 TWITTER DATA SEARCH METHOD ...... 14 3.2 TWITTER DATA COLLECTION METHOD ...... 16 List of Twitter posts (tweets) positively mentioning 10 individual S&P 500 companies: ...... 16 3.3 FINANCIAL DATA COLLECTION METHOD ...... 17 3.4 FINANCIAL DATA CONTENT ...... 18 Analysis of Graphs ...... 20 3.5 CONTAMINATION ...... 20 4. RESULTS ...... 22 4.1 MARKET REACTIONS ...... 22 4.2 ANALYSIS OF RESULTS ...... 31 5. CONCLUSIONS ...... 33 6. DISCUSSION ...... 33 6.1 LIMITATIONS AND FURTHER RESEARCH ...... 34 7. REFERENCES ...... 36

1. Background

From candidate to President

On November 8, 2016, billionaire businessman and Presidential candidate Donald J. Trump won what his opponents would come to call the biggest political upset in American history. In an epic battle for the United States’ presidency, Donald Trump triumphed over his Democrat opponent, and longtime establishment figure, Hillary Clinton. But winning over “Hillary” was only part of the battle – at least according to Trump. With the campaign’s unconventional, deemed populist by some, conservative promises, Trump accused mainstream media for being dishonest and biased in its reporting of key events. This belief, that the media was on Trump’s opponents’ side, a liberal bias, led to Trump successfully establishing the term “Fake News”, spurring popular following of the candidate’s personal communications channel – Twitter.

When only a candidate, Trump took to Twitter to get his campaign message across. Communicating directly with the public, the campaign no longer had to rely on the “Fake News” media industry that Trump thought refused to provide honest news reporting on the success of his campaign. Voters bought it, which shifted the election’s narrative to focus on what Trump said on his personal Twitter. Spurred by the claim that the mainstream media had a liberal bias, and that Trump for that reason did not like when the media controlled the narrative, Trump used Twitter to discredit popular news outlets such as the New York Times, Washington Post, and CNN. The shift from traditional media dependence, which until recently was the standard, towards a world where a candidate can control the sole channels for his political communications, has revealed new opportunities for democratic interaction between voters and public officials. Trump gaining and energizing a large following on social media, traditional media has a new competitor.

President Trump’s use of Twitter has changed the way the political power and the public interact. It is a display of information sharing and communicative revolution where the 45th President of the United States has sought to communicate directly, not often without being questioned for it, with the public, on the issues important to the public. Of the various characteristics unique for the Trump presidency, his personal use of social media on Twitter can be said to have been of key importance for his 2016 U.S election campaign, as well as for the Trump Administration’s communicative efforts (Cillizza 2017).

President Trump’s use of Twitter should be seen in the light of the past decade’s technological advancements allowing public officials and the public to interact directly over the internet, without

4 third party channels such as the media, leading to greater interaction between the public and elected officials.

Even after the election, President Trump communicates the message against what he referred to as “Fake News” and the “Fake News Media”, referring to several mainstream media outlets he believed where biased in their reporting of the Trump campaign (Grynbaum et al., 2017). Twitter became a key channel for the Trump campaign’s communications towards not only its supporters but for anyone who needed to know the President’s opinion.

Selling the Trump Agenda on Twitter

In 2016, Trump campaigned on a wide platform of political promises, many times addressing the faults of individual political opponents and even businesses’ actions such as investments or job cuts via Twitter (Berenson, 2018). An outspoken candidate, Trump’s mentioning of individual businesses has continued to be common occurrence in the Administration’s efforts to promote and defend its policies, most notably on the topic of Tax Cuts and Jobs Act of 2017, often referred to as “Tax Reform”. The Republican Tax Reform was signed into law by President Trump on December 22, 2017, less than a year after President Trump’s inauguration, and two days after the bill had passed the Senate vote on December 20th, 2017. From the administration’s and the Republican Party’s perspective, the passage of Tax Reform is one of its proudest achievements.

Not only the Republican Party and the administration were satisfied with the passage of Tax Reform – so were also major stakeholders within American business. Many major companies, large enough to be part of the Standard & Poor’s 500 Index (S&P 500), have since the election of President Trump signaled continuous support for the President’s pro-business agenda (Crooks et al., 2017). The S&P 500 index is considered one of the most accurate indicators of the U.S economy and stock market. The index consists of a diverse variation of publicly listed companies and because it is made up of over 500 companies, it is perceived as a highly representative index. American companies of the S&P 500 have not only signaled support for the President’s agenda but also for the expected political action in the form of pro-business policies. For that reason, when the Trump administration successfully managed to deliver on its campaign promises of reforming the U.S Tax Code, many American businesses responded positively.

What followed the passage of Tax Reform, after December 20th , 2017, was a stream of public announcements by various businesses stating their support for Tax Reform and, more importantly, how they planned to respond to it. There were hundreds of companies that publicly announced they would either increase investments in the U.S, hire more American workers, increase the minimum

5 wages paid to employees, increase employee benefits, provide employee bonuses, support new work- force initiatives, and contribute to charitable and educational causes. In many of these announcements, President Trump used Twitter to positively comment on several select companies who had announced their plans to invest again in their American operations and workforces.

On the one-year anniversary of President Trump’s inauguration day, the S&P 500 Index had climbed 23%, “the best performance for the index during a Republican president’s first year in office” (Otani, 2018). With President Trump’s Twitter being a constant key factor in the administration’s information sharing, it is of scientific value to understand how and if Trump’s use of Twitter has influenced individual target firms stock value. For example, how did Trump’s positive mentioning of individual firms in the context of Tax Reform affect their stock value? Is it possible that an individual Twitter post by President Trump influence markets?

Why Twitter Matters

Trump’s Twitter posts, referred to as “Tweets”, have an increasingly relevant effect on the intercourse of politics and business. The second chapter of this thesis is dedicated to the theory of the efficient market hypothesis in previous literature. There, it is discussed how publicly available information affects market valuation of individual companies such as those mentioned by Trump, and of aggregates, such as the aggregated market value of the S&P 500 index.

The study of social media and specifically of Twitter as a tool for public officials’ communications is not new, but in a field where the development of political communications has grown at an exponential pace for the past few years thanks to new technology there are ever-renewed insights to be drawn from new, specific cases – such as this, the case of President Trump on Tax Reform. What is unique about the case of President Trump is the contemporary relevance of such research. In the context of Trump’s constantly increasing number of followers on Twitter, which suggests that his activity on Twitter is gaining attention, it is important to understand that social media is the new standard in political communications.

With the transition from traditional communications channels between public officials towards social media, the U.S National Archives and Records Administration now categorize President Trump’s Twitter posts as official presidential records, like any other electronic communications created or received by the White House (Turner-Lee, 2017). This is of importance to the study because it implies that any post on Twitter by President Trump is an official statement. As this study aims to highlight what effect Trump’s tweets may have on individual businesses, and in a greater context markets, it is vital to understand why Trump’s use of Twitter is of key importance.

The social media platform Twitter has gained an increasingly central role in politics, media and information sharing and is considered one of the most effective communications tools for public officials to communicate their messages to public audiences. Twitter is based on “Tweets” and “Followers”, referring to the posts one can make and the people who can subscribe to receive instant updates of the posts you make. Twitter was previously known for limiting all “tweets” to a maximum of 140 characters, but in November 2017 doubled the character count to 280 characters to meet user preference. Many organizations such as private companies, Non-Governmental Organizations (NGOs), and media outlets today use Twitter as one of their primary tools for sharing information with the public.

With 51.6 million followers as of writing (May 2018), President Trump uses Twitter (Schwartz, 2017) to communicate independently of the media, often setting the narrative on issues by including hashtags, which are words or phrases preceded by a hash sign (#) frequently used on social media, to identify messages on specific topics. Yet, President Trump still communicates via his personal Twitter account @realDonaldTrump, that he also used prior to winning the 2016 presidential election, just as anyone else can setup a personal Twitter account. The official Presidential Twitter account, @POTUS, was previously used by President Barack Obama and although it is now used by President Trump, he more actively uses his personal @realDonaldTrump account.

As a Twitter user, you may follow or unfollow another account as well as post or repost others’ “Tweets”, but without an account you may only read other’s contents. Many people do not necessarily have a Twitter account since they have little need to post anything or personally follow another user. However, in the case of President Trump, his following of 51.6 million other user accounts, generates a much larger reach because anyone, even without Twitter, can read his tweets. Additionally, the opportunity for other Twitter users to re-post, referred to as “retweet”, results in Trump reaching more people than his following base alone.

1.1 Research question and problem statement

Research question: “What effect does President Trump’s positive Twitter mentions of companies have on the mentioned company’s stock price?“

Problem statement: Assuming markets pay attention to the President’s comments on individual companies’ stocks, considering the President has unique insider information on policy, this study aims to analyze what effect the President’s Twitter comments have on mentioned stock’s price.

1.2 Thesis structure

The goal of this thesis is to provide an answer to the research questions and to do so the thesis is structured into five main chapters. The introduction is meant to equip the reader with an overview of the context in which the research question is to be answered.

The Theory & Literature chapter is meant to provide an overview of the theoretical framework this thesis aims to provide answers to the research questions. This chapter will discuss past academic literature within the field of Twitter and market reactions.

The Methods chapter will describe how this study in an empirical manner aims to provide answers through analysis of data. This chapter describes how data has been collected, analyzed, and chosen to provide answers to the research question.

The Results chapter describes the findings of this study and aims to provide a case-by-case presentation of a limited selection of market reactions in the context of tax reform.

The Conclusions chapter concludes the study with a discussion, the limitations for this study, and recommendations for further research to be done in this field.

The Discussion chapter discusses the results found and conclusions made, including suggested further research.

2. Literature and Theory

2.1 Theory

The purpose of the theory section is to provide an understanding of the underlying mechanisms that would be applicable to other Twitter accounts with similar characteristics, such as celebrities and accounts with many millions of followers. Because a President’s direct information sharing with the public can be described as a new phenomenon for stakeholders in politics and the markets, a scientific goal of this study is to build on the aggregated knowledge regarding social media, publicly available information, and market reactions caused by political announcements.

With the purpose of this section being to explain why Trump’s tweets may be able to move markets, the theories described in this chapter aim to tie the research questions to a few fundamental theoretical understandings. The theoretical framework will allow us to explain why markets react to publicly

8 available information, and why it is rational to assume markets are interested in following President Trump’s Twitter posts. While this study aims to analyze the effect of one specific Twitter user’s tweets, many past studies have focused on larger data sets, referring to the study of large amounts of Twitter accounts and tweets. This study is theoretically bound to the Efficient Market Hypothesis, which is described and motivated below.

Why Trump’s Twitter mentions of companies should affect stock price levels

The President’s access to insider information on the U.S government’s policy plans indicates that when he tweets, he instantly provides high-value, new, political information to the markets, even in the case where a company has made a previous announcement. When the President publicly approves of a company’s decisions and investments, it is reasonable to believe the markets incorporate that information in the context of the government’s policy agenda.

One reason why this is the case is because the President has incentives to further promote that company, and other companies making decisions that confirm the success of the President’s agenda, through various pro-business policies. This is especially true for his mentions of individual companies, signaling his direct support or discontent for their individual plans – information that was previously unknown to the markets. If the President indicates he intends to implement policies that would benefit the mentioned company, it is likely that the market reacts positively on the President’s public approval if those policies were to have an expected impact on company fundamentals.

A second reason the President’s mentioning of individual companies, for example Apple Inc., matters is because of the undeniable public relations effect it may have on the company’s brand. Trump’s tweets can be seen as free advertising, to many millions of consumers, that confirms that the company has the President’s approval, which may lead to increased sales, and thus increase stock value, as found in a study by Endres & Panagopoulos (2017).

A third reason, of obvious political nature, is the partisan consumerism from a political mentioning of a company. It is reasonable to believe that a company receiving the President’s approval sees a stronger consumer approval rating among the consumers supporting President Trump politically. In fact, as found the study by Endres & Panagopoulos (2017) a mention by a Republican President may be understood by supporters as an encouragement to buy that company’s products or services (Endres & Panagopoulos 2017). The results of the President’s public approval would thus have a positive effect on the consumer approval among his supporters., information that could have a positive effect on stock value.

These three reasons support the thesis that Trump’s mentioning would influence stock price levels, however, another result of a Trump tweet is the possibility for increased trading volume. However, this study does not analyze the trading volume of each stock mentioned.

Efficient Market Hypothesis (EMH)

The theory behind the Efficient Market Hypothesis (EMH) is used to explain markets’ predictability in relation to public information sharing (Fama, 1965, 1991). Research on the topic of market predictability is questioned by theories such as the EMH that states that markets are informationally efficient, and that price prediction is impossible because the price development of a stock is random.

The EMH describes stock prices on a market as fully reflective of all information publicly available. Markets are fully effective and all information publicly available is automatically reflected in the market’s value (Fama, 1965, 1991). Because financial markets are efficient, predicting the future value of a stock to yield greater return on investment would, according to the EMH, be impossible. Because there is no way to predict stock value, a buyer or seller on the market can only act on the intrinsic value of a stock, not the future predicted value.

The EMH assumes that it is impossible for an investing party on the market, that acts only on publicly available information, to achieve excessive returns (Fama, 1991). The unpredictability in a stock or security’s future value is referred to as a random walk pattern, since the release of new information such as news cannot be predicted (Bollen et al., 2011). Bollen et al. (2011) finds that the random walk pattern is not entirely correct since the price level of individual stocks can, to a certain extent, be predicted according to previous research on the topic.

The EMH in turn can be categorized into three different grades, each grade defining the amount of information incorporated in a stock’s market value. Weak, semi-strong, and strong, the “Strong” version of EMH states that all information including insider information, which is not publicly available and available to only insiders, is reflected in the current price level of a stock. The “Semi- strong” version of the EMH states that a stock reflects all information publicly available and that any new information (such as a tweet by the President) is reflected instantaneously in the price level of a stock (during trading day hours). Since the incorporation of new information is instant, an investor can de facto not predict the future value of a stock. The “Weak” version of the EMH simply states that a stock reflects all information available to the public. To conclude, the strong version of EMH can be described as the most comprehensive version, including even insider information.

With the three versions described, this study seeks to analyze the Twitter and financial data from a “semi-strong” version of EMH. The motivation for such choice is that this study seeks to analyze the instant effect of information released on Twitter, the President’s commentary.

The aim of this study is to test the semi-strong EMH thesis that any new information is instantaneously incorporated in stock prices, to see if the President’s tweets can influence price levels or if it is other previously made available information the market reacts on. If the semi-strong version can be confirmed, meaning that the markets react significantly on the President’s tweets alone, that could allow us to show there may be a predictable market reaction pattern to President Trump’s public mentioning of individual companies. By seeking to test the EMH’s clause that markets are fully unpredictable, this thesis aims to answer if and how the President’s tweets can be used to predict instant market behavior of individual stocks. Using the strong version of EMH, that includes insider information, is for this study irrelevant since the President’s tweets are obviously publicly available information – even if the sentiment of his commentary is influenced by the insider info he has.

The semi-strong EMH states that there would be no change in stock value should a tweet mention a company but not announce any previously unknown information. However, the President’s commentary is new information. The application of this is that the semi-strong version of EMH asserts a price reaction will take place should the market consider Trump’s tweets be of news value. In a case where the market incorporates the President’s tweet as new information, even after an original company announcement was made, an immediate price reaction answering to the tweet’s sentiment should be expected.

2.2 Literature

Previous research has in many cases focused on big data on Twitter, such as a higher number of accounts or tweets analyzed, the effect of negative sentiment tweets, traditional media coverage effects, and effects on trading volumes. This study, however, analyzes a limited number (10) company announcements and tweets, coming from one specific account with intrinsic characteristics, making the President’s Twitter account more relevant to markets than most other accounts.

In a study conducted by Bollen et al. (2011) it is found that information on social media can be used to predict market reactions, meaning that the EMH’s rationale may not fully explain the predictive behaviors of stock price levels. The study refers to what they call the “Twitter-mood”, which according to them can be used as an indicator in predicting stock market behavior. Other than the price level prediction, various studies have shown that social media message volume (the amount of posts

11 published on various social media platforms) can be used as an indicator to predict trading volumes (number of stocks being bought and sold) the upcoming trading day (Wysocki, 1998).

Additionally, Bollen et al. (2011) states that the new information made available is incorporated into stock price levels and that Trump’s tweets may indicate policy shifts relevant to the market. In a study, Bollen et al. (2011) discusses indicators of interest to predict changes in economic circumstances, and Trump’s tweets may qualify as such an indicator, even if the information in the tweet was previously known to the public.

For this study, the samples analyzed are selected manually depending on sentiment, which is limited to positive mentions of individual companies. A study conducted by Tumasjan et al. (2010) presented scientific proof that the sentiment of a tweet can yield what they refer to as abnormal stock returns, especially if the tweet content is “bullish”. For this study, where the data samples are limited to positive mentions on Twitter, these Tumasjan et al. (2010) conclusions may be less compatible, yet relevant as background.

There has been extensive research conducted on Twitter and its mechanisms. A study by Tumasjan et al. (2010) found that the number of followers, in Trump’s case over 50 million, amplifies the impact a Twitter post may have. For that reason, celebrities and other individuals with large following, should expect an amplified impact than an account with less followers. Since the President is political, he is likely to have a follower base consisting of both supporters and opponents, which means that his tweets may reach both his worst enemies and best friends. Can the diverse following base lead to unexpected responses? A study by Bae and Lee (2012) finds that may be the case, and that a follower base can be divided into positive (supporting) and negative (opposing) audiences. A consequence of this finding is that the followers’ response may be more difficult to predict, a circumstance that may have implications for how Trump’s tweets translates into price level reactions.

When the President tweets about an individual company, his message reaches many millions of people. A study conducted by Engelberg and Parsons (2011) finds that media coverage, for example because of Trump’s mentioning, can increase a stock’s trading volume. One question that arises in this context is if media coverage is good for a stock’s value. A study by Fang and Peress (2009) found that it may not be. According to their study, stocks with little media coverage outperformed stocks with high levels of media coverage and attention. They found that a tweet from an account with large Twitter following may decrease stock price levels, independent of tweet sentiment, dependent on coverage. However, this study does not analyze individual stocks’ trading volume.

Another study on market reactions to Trump’s economic policies in general, by Wagner et al. (2017), finds that investors preferred American companies with relatively large domestic market shares. Additionally, companies within industries under extensive regulatory oversight, such as finance and manufacturing, also experienced larger investments after Trump won the 2016 election according to Wagner et al. (2017). This study’s result suggests investors assumed a more promising future for these industries under the Trump administration.

Contribution

With the background on previous research that has been conducted, this study aims to build on the aggregated knowledge by studying a limited, specific, field within a continuously growing and undiscovered area. This study contributes insight on how the market reacts on a very specific category of tweets which is the President’s positive mentions of individual companies in the context of tax reform, during his first year in presidency. This is a field where data is recent, and understandings are narrow, since there are continuously tweets being added. Understanding the effect of the President’s tweets, which most often seem to be comments on previously announced information, is vital to understanding the ever-developing digital world in which political figures can have an instant effect on markets.

While previous research has focused largely on larger data sets of tweets and Twitter accounts, negative sentiment tweets, partisan consumerism and predictability, this study seeks to contribute insights in regard to what effect the most powerful politician’s commenting on individual company announcement has on their stock value. Since the data base of tweets is continuously growing, and because the President’s Twitter use seems to be a continuing activity, it has previously not been analyzed what patterns can be concluded.

Key to the value of this study is how recent the data collected and analyzed was published, which helps to build on the aggregate knowledge in the field. The fact that the political use of digital media such as Twitter has grown over time is a methodological issue for previous research focusing on social media. In this study, it holds especially true because the phenomena of a President personally using Twitter for political commentary has previously not been the standard (Turner-Lee, 2017). Additionally, a President using his personal Twitter account to applaud individual S&P 500 companies’ decisions has previously not been a common sight. This study finds the market effects on such behavior.

3. Data and methodology

3.1 Twitter Data Search Method

The study of President Trump’s Twitter is a study of correlating events. Simplified, Trump’s tweet is an event, and by studying data retrieved from the market, we seek to understand what, if any, effect Trump’s tweet may have on the market.

The time-period for this study is centered around the passing of tax reform, which has been a Trump Campaign promise and a lengthy process. This study only studies the time-period from one week before Trump’s inauguration on January 20, 2017, and one year forward plus one week, focusing on his first year as President. From Inauguration Day on January 20, 2017, to the One-Year Anniversary of the Trump Administration on January 20, 2018 Trump has tweeted about individual companies, both positive and negative mentions. This study has included all positive, S&P 500 company-specific tweets from January 17, 2017 to January 24th, 2018, totaling ten S&P 500 companies. This is a study of the positive mentions and whether these positive mentions have an immediate effect on these companies’ stock value.

The reasoning for limiting the time-frame to the first year as president is motivated by several factors. The first factor is that it is reasonable to assume there is an intrinsic value in studying the President’s tweets from the week he became President, and not before. One alternative method would be an extended time-period that includes the period Donald Trump was the president-elect, referring to the whole time-period after he had won the election but was transitioning into the White House between November 2016 and January 2017. This study seeks to study the President’s Twitter communications and the study is thus limited to the time since the week Trump was inaugurated.

The time-period of the tweets studied in this paper is motivated by several factors. Firstly, because Trump has been the President only for one full-year at the time this study is being conducted, it is reasonable to want to study the Twitter communications during that first year. Secondly, the Tax Cuts and Jobs Act of 2017 passed the Senate on December 20th 2017 and was signed into law by the President on December 22nd, 2017 (Dye, 2017). This Trump Administration policy goal was achieved within a year from his swearing in. Additionally, the anniversary on January 20, 2018, provides a reasonable time limit to study the tweets of the President that were sent out after the passing of Tax Reform. However, the most recent tweet studied in this paper was posted on January 24, 2018. The reasoning for this is that it took companies a few weeks to respond to tax reform, since the Republican majority passed the bill within a matter of days by parliamentarian procedure in the U.S House of

Representatives and then the U.S Senate. More importantly, it was not certain tax reform would be signed into law by the President before Christmas 2017, but so it was. For these reasons, this study seeks to study the President’s positive mentions of companies on Twitter during his first year in the White House, including tweets posted one week prior and one week after.

Finding individual tweets by going on Trump’s Twitter account, in this case @realDonaldTrump, is inefficient since he has posted 37.500 tweets (May, 2018). This study collected several tweets on a specific topic during a specific time-frame, and because Twitter itself does not provide a comprehensive, searchable, database that is user-friendly, a search-method was established.

To search the President’s tweets, a database called the Trump Twitter Archive (Trump Twitter Archive, 2018), which has compiled all the President’s tweets into a searchable register, was used. The Trump Twitter Archive allows you to not only search and find any tweet coming from the President’s personal account in real-time (Eastern Time, ET), but it also has a link to all the tweets, which allows us to go directly on Twitter and see the first-hand source. The search function allows you to select between time periods and on specific topics or individual words. The Trump Twitter Archive is an effective online tool to search the President’s tweets by topic within a specific time-period while at the same time being able to find the first-hand source on Twitter.

In this study, the database is used to search topic-specific tweets on the @realDonaldTrump account, within the chosen time-frame. Retrieving the same tweets without effectively being able to search the database would pose a time-consuming challenge to the study because the separate tweets would be harder to find. Additionally, the search-function of the Trump Twitter Archives is not the source used in this study, but rather one of several search tools.

Secondly, this study has searched a number of reputable, mainstream, media outlets to find mentions of the President’s tweets. There has been extensive reporting on Trump’s use of Twitter and several media outlets have thus reported extensively on various tweets by the president. Often, the media has reported on the President mentioning companies, and this study seeks to study only the positive mentions of the President, on the topic of tax reform. This study has searched and found news media reports on the President’s tweets, and then collected those tweets by finding the same tweets directly on the @realDonaldTrump account. This means that the source used is exclusively the President’s personal Twitter account, but that various mainstream media outlets are used as search-tools.

3.2 Twitter Data Collection Method

The Trump Twitter Archives is used as a database search-tool and is followed by collecting the relevant tweets. In this study, I have chosen a number of tweets that highlights and positively mentions a specific business in the context of Tax Reform.

Each tweet should be seen as a separate event and in the case that two or more tweets have been posted in direct continuation of the previous those tweets will be compiled and presented together, if it is obvious that this was the intent of the President. In those cases, as a general rule, the President often ends the former and continues the following tweet with three full-stops (…). In this study, none of the tweet mentions were posted in continuing order.

Because the President’s tweets are posted at random times correlating with separate political events, there is no standard time interval between the separate tweets. For that reason, it should be said there has been varying activity on the topic of Tax Reform throughout various months of the President’s first year in power. As long as the tweet was posted within the time-frame described above, this study assigns no internal valuation of the events other than the chronological order of the tweets.

After using the Trump Twitter Archive and Google Search functions, the tweets that positively mentions individual companies in the context of tax reform are selected individually. The selection of companies is based on three principles: firstly, the company is positively mentioned by @realDonaldTrump on Twitter, secondly, the company is a S&P 500 Company, and thirdly it is mentioned in the context of tax policy.

List of Twitter posts (tweets) positively mentioning 10 individual S&P 500 companies:

1. Trump’s tweet: 2:22 PM - 8 Feb 2017, “Thank you Brian Krzanich, CEO of @Intel. A great investment ($7 BILLION) in American INNOVATION and JOBS! #AmericaFirst1 “ 2. Trump’s tweet: 06:36:02 AM, 28 March 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!2“ 3. Trump’s tweet: 3:58 PM, November 2, 2017, “Today, we are thrilled to welcome @Broadcom CEO Hock Tan to the WH to announce he is moving their HQ’s from Singapore back to the U.S.A..... (video content)3“

1 Twitter post by @realDonaldTrump, retrieved on May 12, 2018 https://twitter.com/realdonaldtrump/status/829410107406614534 2 Twitter post by @realDonaldTrump, retrieved on May 1, 2018, https://twitter.com/realdonaldtrump/status/846672219073863681 3 Twitter post by @realDonaldTrump, retrieved on May 1, 2018, https://twitter.com/realdonaldtrump/status/926176823117074433

4. Trump’s tweet: 12:55 PM, 17 January 2017, “Thank you to General Motors and Walmart for starting the big jobs push back into the U.S.!4“ 5. Trump’s tweet: 12:55 PM, 17 January 2017, “Thank you to General Motors and Walmart for starting the big jobs push back into the U.S.!5“ 6. Trump’s tweet: 6:37 PM, 10 January 2018, “Cutting taxes and simplifying regulations makes America the place to invest! Great news as Toyota and Mazda announce they are bringing 4,000 JOBS and investing $1.6 BILLION in Alabama, helping to further grow our economy! 6“ 7. Trump’s tweet: 9:49 PM, 11 January 2018, “More great news as a result of historical Tax Cuts and Reform: Fiat Chrysler announces plan to invest more than $1 BILLION in Michigan plant, relocating their heavy-truck production from Mexico to Michigan, adding 2,500 new jobs and paying $2,000 bonus to U.S. employees!7“ 8. Trump tweet: 6:28 PM, January 18 2018, “I promised that my policies would allow companies like Apple to bring massive amounts of money back to the United States. Great to see Apple follow through as a result of TAX CUTS. Huge win for American workers and the USA! 8“ 9. Trump tweet: 6:58 AM, January 24 2018, “Tremendous investment by companies from all over the world being made in America. There has never been anything like it. Now Disney, J.P. Morgan Chase and many others. Massive Regulation Reduction and Tax Cuts are making us a powerhouse again. Long way to go! Jobs, Jobs, Jobs!9“ 10. Trump tweet: 6:58 AM, January 24 2018, “Tremendous investment by companies from all over the world being made in America. There has never been anything like it. Now Disney, J.P. Morgan Chase and many others. Massive Regulation Reduction and Tax Cuts are making us a powerhouse again. Long way to go! Jobs, Jobs, Jobs!10“

3.3 Financial Data Collection Method

The abbreviation S&P 500 stands for Standard & Poor’s 500, a stock market index (Standard & Poor’s, 2018) based on market capitalizations of 500 large companies listed on NYSE, the New York Stock Exchange (including NYSE Arca or NYSE MKT), or NASDAQ ( NASDAQ Global Select Market, NASDAQ Select Market or the NASDAQ Capital Market). Standard & Poor’s is a credit- rating agency that offers a variety of services and products, including indexes like the S&P 500.

The index uses what is called a market capitalization methodology, which means that larger companies are given a higher weighting. The market cap weighting is in many cases perceived to more accurately represent the real market structure. Other market indexes, such as the Dow Jones Industrial Average (DJIA) is made up of only 30 companies total and has a weighting method that gives more expensive

4 Twitter post by @realDonaldTrump, retrieved on May 2, 2017, https://twitter.com/realdonaldtrump/status/821415698278875137 5 Twitter post by @realDonaldTrump, retrieved on May 1, 2018, https://twitter.com/realdonaldtrump/status/821415698278875137 6 Twitter post by @realDonaldTrump, retrieved on May 2, 2018, https://twitter.com/realDonaldTrump/status/951236634095308800 7 Twitter post by @realDonaldTrump, retrieved May 1, 2018, https://twitter.com/realDonaldTrump/status/951647402599026689 8 Twitter post by @realDonaldTrump, retrieved May 2, 2018, https://twitter.com/realdonaldtrump/status/953771038114045954 9 Twitter post by @realDonaldTrump, retrieved May 1, 2018, https://twitter.com/realdonaldtrump/status/956134228726558720 10 Twitter post by @realDonaldTrump, retrieved May 1, 2018, https://twitter.com/realdonaldtrump/status/956134228726558720

(rather than larger) stocks a higher weighting. Because of this, the S&P 500 is considered more representative.

The S&P 500 index consists of companies that are selected by a committee on a strict merit based eligibility criteria, using eight primary criteria: market capitalization, liquidity, domicile, public float, sector classification, financial viability, and length of time the stock has been traded as well as on what stock exchange. A company that is added to the index needs to meet requirements on each of these eight primary criteria. A committee at Standard & Poor selects companies that are representative of the industries in the U.S economy. Additionally, the index incorporates non-U.S companies, both formerly U.S -incorporated companies reincorporated outside of the U.S and companies that was never incorporated in the U.S. These factors combined make the S&P 500 index one of the most accurate indicators of the U.S economy and stock market.

Since this study only analyzes mentions of S&P 500 companies that are listed on either NYSE or NASDAQ, the study largely eliminates the risk that companies mentioned by Trump are not publicly listed. Because all companies in the S&P 500 are publicly listed, access to real-time financial data of the companies mentioned is available. We know that we can search and find financial data such as stock value for the specific time frame which correlates with the event of President Trump’s tweets. S&P 500 is commonly used as an index for comparison of investment strategy result. Since the companies analyzed in this study are part of the 500 companies who’s aggregated value development is shown as an index, we can compare individual stocks to the S&P 500 market index.

In the results section this study presents the stock value development as well as the S&P 500 market index as reference in comparison. For example, if a company is mentioned by Trump and is a S&P 500 company, it is of value to this study to understand if the stock value differs from the index or if the stock follows the index.

Since this study aims to study only those companies praised or positively mentioned by Trump there is less categorization needed than for a study that seeks to analyze all the President’s tweets. However, this is a result of the study specifically studying positive mentions of individual companies on the topic of tax reform. Because the research question provides a relatively specific category of tweets to be analyzed and compared to the S&P 500, the tweets will only be categorized into specific sub-topics as events (general topic being tax reform) and time.

3.4 Financial Data Content

Analyzing S&P 500 companies, against the S&P 500 market index, mentioned by Trump, the study will use real-time market data from the dates of the tweets. The time-period for analyzing each event is

24 hours. The limit to 24 hours can be discussed and other studies would perhaps suggest extending the time-period to 48 hours or even on a monthly basis post-mentioned by Trump. The time-frame can be seen as a limitation or strength when studying the market’s reaction, and this study seeks to analyze if there is an immediate effect on the stock value responding to the President’s tweets. Immediate in this case is defined as market reaction within 24 hours from the President’s tweet.

In addition to allowing for the measurement of an immediate effect is the fact that the markets open and close and that the President may tweet at any hour of the day. The time between the market opens in the morning and closes in the afternoon is in this study referred to as a “trading day”. The market can only react in real-time during the trading day hours, which is from 9:30 AM to 4:00 PM Eastern Standard Time (EST), which is 5 hours behind the Coordinated Universal Time (UTC-5). The financial data retrieved is controlled against the Eastern Standard Time, and so are the President’s tweets. By controlling for EST on both the market data and Twitter data we can accurately analyze the immediate effect Trump’s tweets may have.

The President’s tweets can be seen as comments on already previously known information, such as a company announcement or news reporting preceding the tweet. This means the market will react on the original new information made available. However, this study seeks to find if the President’s tweet itself has an effect on the stock’s price level.

Naturally, there are cases where the President tweets outside of the trading day hours, post 4:00 PM, EST. For the study, the implication is that the market reaction to such mentioning cannot be measured until the upcoming trading day. For that reason, a tweet mentioning a company sent after trading day hours will be analyzed in the context of the upcoming trading day’s market value.

This study analyzes ten (10) cases of market reactions. The limit to ten cases is a natural result of President Trump’s tweets, only positively mentioning these S&P 500 companies.

The financial data is retrieved using a standardized market graph, in this study Barchart’s (Barchart, 2018) visualization of individual stocks’ graphs against the S&P 500 index. Barchart allows you to compare the course of two or more graphs to each other, in this case the individual stock of the companies mentioned and the S&P 500 index. This allows us to effectively study the effects of Trump’s tweets, comparing the individual stocks with the market index. The financial data retrieved on each company mentioned can be categorized into three parts: the trading day opening price, price reaction after the time of tweet, and trading day closing price. Each case presented in the results chapter will include the visualized graphs.

Analysis of Graphs

With the methodology described above, there are several reasons motivating the use of visualized graphs. Firstly, since it is possible to track and pin-point the time of the President’s tweets in real-time, compared to the market’s reaction, any significant reaction is observable on the chart. As the time of the President’s tweet, the companies’ announcements and the market’s reaction, is trackable, the charts provide the reader with a results presentation that is visually easy to understand. Theoretically, this study assumes that a market reaction to the President’s tweets would be immediate, as it is new information to be incorporated in a stock’s price, and having the financial data collected, a chart would allow us to analyze market activity at the exact moment of each tweet. In previous research, little attention has been paid to what immediate effect the President’s positive mentions may have on stock intra-day value.

Alternatively, a regression analysis model could have been used for studies analyzing a larger data-set. However, based on the data being studied, a regression analysis approach would merely allow us to study the average value of a dependent variable against a larger number of tweets. Since the tweet sample in this study is limited to ten (10) positive tweets, a regression analysis would not provide any satisfactory additional value to the study’s overall contribution.

Objectively, the chart methodology visualizing market reactions on tweets are highly transparent. It transparently allows visualization of the study’s result since the researcher and reader independently can observe the same price level changes on the chart. Additionally, the comparatively limited number of specific tweets make it possible to visually analyze their effects. In a study analyzing extensive amount of Twitter data it would be less suitable to use the same methodology.

3.5 Contamination

The study is specified to analyze the companies positively mentioned by @realDonaldTrump on the topic of tax reform. Independent of what effect the President may have on a company’s value, other events in the real world may have had an effect too.

The tweets in the study sample are chosen to contain the President’s own comments on an event. There is an important difference between tweets that respond to news stories and tweets that makes the news stories. There is a contamination risk on the results in the case where a tweet would be responding to an existing news story, since the market had already gained access to that publicly available information. However, because all the President’s tweets in this study are responding to an existing company announcement this study seeks to find what, if any, additional effect on the stock price the President’s tweet may have.

This study has not controlled the timing of each tweet to other new outside information made available to the markets such as world events, important political and business announcements, and other market trends. This means there is a risk that outside events and information, apart from the company announcement and Trump’s tweet, may have influenced the stock valuation throughout trading day.

4. Results

The findings of this study are presented case-by-case, showing the market reaction to a new announcement made by the company or White House prior to Trump’s tweet. Secondly, it shows the market reaction (or absence thereof) at the time of Trump’s tweet. Details regarding time of new information announced and Trump tweet is found below each chart. 4.1 Market Reactions

Company 1 - Intel (INTC)

Background: Intel Corporation plans on major investment in manufacturing facilities and employment of 3000 workers in Arizona, USA.

First Announcement: At 12:52 PM, Trump and Intel Corp. CEO Brian Krzanich announced plans for a major manufacturing facility in Arizona. The plant would employ 3,000 people, officials said. At 2:22 PM, Trump tweeted that it was a “great investment” in American innovation and jobs (The White House, 2017).

Trump’s tweet: At 2:22 PM - 8 Feb 2017. “Thank you Brian Krzanich, CEO of @Intel. A great investment ($7 BILLION) in American INNOVATION and JOBS! #AmericaFirst11 “

Figure 1.1 Line chart showing stock market price for Intel Corporation (INTC), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, February 8, 2017. Initial company announcement at 12:52 PM ET. Trump’s tweet posted at 2:22 PM, ET. No observable market reaction at initial company announcement or immediately following Trump’s tweet.

11 Twitter post by @realDonaldTrump, retrieved on May 12, 2018 https://twitter.com/realdonaldtrump/status/829410107406614534

Figure 1.2 Same as previous figure. Two-day Time range: 9:30 AM – 5:00 PM, February 8-9, 2017. Initial company announcement at 12:52 PM ET. Trump’s tweet posted at 2:22 PM, February 8, ET, 2017.

Company 2 – Ford (F)

Background: Ford plans on investment in three production facilities in Michigan, USA.

Trump’s tweet: At 06:36:02 AM, 28 March, 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!12“

Company announcement: Ford announced investments in three Michigan plants by market opening on March 28, 2017, and combined with the President’s tweet, the company’s stocks were up about 2 percent later the same trading day (Carey and Heavey, 2017). However, the information on the company’s investment was previously known, since 2015, when the company announced for the first time (Durbin, 2017).

Figure 2.1 Line chart showing stock market price for Ford Motor Company (F), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, March 28, 2017. Trump announced company investment before official company announcement was made.

12 Twitter post by @realDonaldTrump, retrieved on May 12, 2018, https://twitter.com/realdonaldtrump/status/846672219073863681

Trump’s tweet posted at 6:36 AM, ET, before trading day hours. Initial company announcement before 9:30 AM, ET. Observable positive market reaction at market opening. Not possible to distinguish if market reacted on Trump’s tweet or the official company announcement.

Figure 2.2 Same as figure 2.1, but two-day line chart March 28-29, 2017. Trump announced company investment before official company announcement was made. Trump’s tweet posted at 6:36 AM, ET, before trading day hours on March 28, 2017. Initial company announcement before 9:30 AM, ET.

Company 3 – Broadcom

Background: Broadcom plans on relocating headquarters from Singapore to the U.S.

Company announcement: Released by company representative during press conference at the White House with President Trump, at 12:20 PM (The White House, 2017).

Trump’s tweet: At 3:58 PM, “Today, we are thrilled to welcome @Broadcom CEO Hock Tan to the WH to announce he is moving their HQ’s from Singapore back to the U.S.A..... (video content)13“

Figure 3.1 Line chart showing stock market price for Broadcom Ltd (AVGO), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, November 2, 2017. At a White House press conference at 12:20 PM, ET, Trump announces company decision

13 Twitter post by @realDonaldTrump, retrieved on May 12, 2018, https://twitter.com/realdonaldtrump/status/926176823117074433

24 before official company announcement has been made. First new information at 12:20 PM, Trump tweet at 3:58 PM, ET. Trump’s tweet posted at 3:58 PM, ET, during trading day hours. Observable positive market reaction after White House announcement and at 3:58 PM Trump tweet. Possible to distinguish between first announcement and Trump tweet reaction.

Figure 3.2 Line chart showing stock market price for Broadcom Ltd (AVGO), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, November 2-3, 2017. At a White House press conference at 12:20 PM, ET, on Nov. 2, Trump announces company decision before official company announcement has been made.

Company 4 - General Motors (GM)

Background: General Motors plans for $1 billion in investment that would create 1,500 U.S. jobs.

Trump’s tweet: “Thank you to General Motors and Walmart for starting the big jobs push back into the U.S.!14“

Company Announcement: General Motors’ announcement came before market opening, and market reaction on news reporting was not significant, though negative. One reason for the insignificant market reaction may have been that the announcement was already previously known information, since October 2017 (Bose, 2017).

Figure 4.1 Line chart showing stock market price for General Motors Company (GM), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 17, 2017. First new information released by company before market opening. Positive market

14 Twitter post by @realDonaldTrump, retrieved on May 12, 2017, https://twitter.com/realdonaldtrump/status/821415698278875137

25 reaction on company announcement. Trump tweet at 12:55 PM, ET, during trading day hours. Observable modest negative market reaction after Trump’s tweet. Possible to distinguish between company announcement (positive) reaction and Trump tweet (negative) reaction.

Figure 4.2 Line chart showing stock market price for General Motors Company (GM), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 16-20, 2017. First new information released by company before market opening on January 17. Positive market reaction on company announcement. Trump tweet at 12:55 PM, ET. Observable modest negative market reaction after Trump’s tweet.

Company 5- Wal-Mart Stores (WMT)

Background: Wal-Mart plans to invest $6.8 Billion in the U.S economy, creating 10,000 jobs. Trump’s tweet: “Thank you to General Motors and Walmart for starting the big jobs push back into the U.S.!15“ Company Announcement: Wal-Mart company announcement prior to market opening, indicating significant immediate, short-term, positive market reaction.

Figure 5.1 Line chart showing stock market price for Wal-Mart Stores (WMT), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 17, 2017. First new information released by company before market opening. Positive market reaction at market

15 Twitter post by @realDonaldTrump, retrieved on May 12, 2018, https://twitter.com/realdonaldtrump/status/821415698278875137

26 opening, on company announcement. Trump tweet at 12:55 PM, ET. Trump’s tweet posted at 12:55 PM, ET, during trading day hours. Weak observable market reaction after Trump’s tweet, but negative trend continued. Possible to distinguish between company announcement (positive) reaction by market opening and Trump tweet (negative) reaction.

Figure 5.2 Line chart showing stock market price for Wal-Mart Stores (WMT), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 13-19, 2017.

Company 6 – TOYOTA (TM)

Background: Toyota-Mazda plans for major investments and job creation in Alabama, USA

Company announcement: Released prior to Trump’s tweet, on January 9, 2018.

Trump’s tweet: “Cutting taxes and simplifying regulations makes America the place to invest! Great news as Toyota and Mazda announce they are bringing 4,000 JOBS and investing $1.6 BILLION in Alabama, helping to further grow our economy!16“

Figure 6 Line chart showing stock market price for Toyota Motor Corp Ltd Ord (TM), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM,

16 Twitter post by @realDonaldTrump, retrieved on May 12, 2018, https://twitter.com/realDonaldTrump/status/951236634095308800

January 9-11, 2018. First new information released by company on January 9. Positive market reaction, on company announcement. Trump tweet after trading day hours, at 6:37 PM, ET, on January 10. Observable immediate negative market reaction at market opening on January 11, after Trump’s tweet. Possible to distinguish between company announcement (positive) reaction by market opening and Trump tweet (negative) reaction.

Company 7 - FIAT CHRYSLER AUTOMOBILES (FCAU)

Background: Fiat Chrysler plans to invest more than $1 Billion in Michigan, USA, plant, relocating production from Mexico to the U.S (Lienert and White, 2018).

Company announcement: Before market opening on January 11, 2018.

Trump’s tweet: “More great news as a result of historical Tax Cuts and Reform: Fiat Chrysler announces plan to invest more than $1 BILLION in Michigan plant, relocating their heavy-truck production from Mexico to Michigan, adding 2,500 new jobs and paying $2,000 bonus to U.S. employees!17“

Figure 7. Line chart showing stock market price for Fiat Chrysler Automobiles N.V. (FCAU), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 10-12, 2017. First new information released by company on January 11. Positive market reaction at market opening, following company announcement. Trump’s tweet posted at 9:49 PM, ET, after trading day hours. Observable negative market reaction at market opening the day after Trump’s tweet. Possible to distinguish between company announcement (positive) reaction by market opening and Trump tweet (negative) reaction.

17 Twitter post by @realDonaldTrump, retrieved May 12, 2018, https://twitter.com/realDonaldTrump/status/951647402599026689

Company 8 – Apple (AAPL)

Background: Apple plans to repatriate billions of dollars from overseas, contributing $350 billion, adding 20,00 jobs, to U.S economy, due to tax reform.

Company Announcement: First release before 1:00 PM on January 17, 2018, during trading day (Apple Inc., 2018).

Trump tweet: At 6:28 PM, January 18, 2018, “I promised that my policies would allow companies like Apple to bring massive amounts of money back to the United States. Great to see Apple follow through as a result of TAX CUTS. Huge win for American workers and the USA!18“

Figure 8. Line chart showing stock market price for Apple Inc. (AAPL), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 16-19, 2018. First new information released by company on January 17 before 1:00 PM, ET. Positive market reaction following company announcement throughout trading day. Trump’s tweet posted at 6:28 PM, ET, after trading day hours. Observable negative market reaction at market opening the day after Trump’s tweet. Not possible to distinguish if Trump’s tweet caused the negative market reaction, possibly due to other information.

18 Twitter post by @realDonaldTrump, retrieved May 5, 2018, https://twitter.com/realdonaldtrump/status/953771038114045954

Company 9– Walt Disney Company (DIS)

Background: As a result of the tax reform being signed in to law, Disney (DIS) announced that it will give bonuses to 125,000 employees and plans to invest $50 million to help cover tuition costs for employees.

Trump tweet: On January 24, 2018, at 6:58 AM, “Tremendous investment by companies from all over the world being made in America. There has never been anything like it. Now Disney, J.P. Morgan Chase and many others. Massive Regulation Reduction and Tax Cuts are making us a powerhouse again. Long way to go! Jobs, Jobs, Jobs!19“

Company Announcement: Released by company on January 23, after 1:00 PM.

Figure 9. Line chart showing stock market price for Walt Disney Company (DIS), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 23-24, 2018. First new information released by company on January 23 after 1:00 PM, ET. Positive market reaction following company announcement throughout trading day. Trump’s tweet posted at 6:58 AM, ET, before market opening on January 24. Observable positive market reaction at market opening after Trump’s tweet. Positive market opening possibly due to other information, tweet outside trading hours increases risk for lower correlation due to reduced trackability.

19 Twitter post by @realDonaldTrump, retrieved May 5, 2018, https://twitter.com/realdonaldtrump/status/956134228726558720

Company 10 – JP Morgan Chase & Co (JPM)

Background: JP Morgan Chase plans to increase wages for its lowest-paid workers and reverse its recent trend of branch closures, as part of 5-year plan worth $20bn, as result of Trump’s tax reform (Moyer, 2018).

Figure 10. Line chart showing stock market price for JP Morgan Chase & Co (JPM), black line, right hand scale, and S&P 500 index, blue line, left hand scale. Time range: 9:30 AM – 5:00 PM, January 23-24, 2018. First new information released by company on January 23 before trading hours, ET. Positive market reaction following company announcement throughout trading day. Trump’s tweet posted at 6:58 AM, ET, before market opening on January 24, day after announcement. Observable positive market reaction at market opening after Trump’s tweet. Positive market opening possibly due to other information, tweet outside trading hours increases risk for lower correlation due to reduced trackability.

4.2 Analysis of Results

A market reaction following the President’s tweet may theoretically indicate the stock price was influenced by other, negative, information following Trump’s tweet. It is likely that Trump’s tweets lead to increased media coverage of mentioned company which according to a study by Fang and Peress (2009) found that stocks with little media coverage outperformed stocks with high levels of

20 Twitter post by @realDonaldTrump, retrieved May 5, 2018, https://twitter.com/realdonaldtrump/status/956134228726558720

31 media coverage and attention. A tweet from an account with large Twitter following may decrease stock price levels, independent of tweet sentiment, dependent on what coverage that follows.

In several of the cases above, the company’s announcement was met with various critical news reporting that stated investment plans were previously announced and that the company and the President used each other for media purposes. For example, Intel’s announcement was originally made as early as in 2011, under President Barack Obama (Solon, 2017).

In the cases where it is possible to observe a positive market reaction following the President’s tweets, it is likely that the market reacted in fact on the President’s tweets, even in the cases where the company’s announcement also was made prior to trading day opening. For example Trump’s tweet on Broadcom indicates a significant positive market reaction during trading day hours, separate from the company’s announcement.

In the cases where it was revealed that the company’s investments were already planned, the effect of the President’s tweet was more limited. In fact, negative market reactions followed several of those Tweets, which may be a result of increased media coverage and critique of companies for using Trump’s tax reform as a publicity generating event. The wide public knowledge of the announced information may have a limiting effect on any potential Trump tweet reactions, as in the case with General Motors.

Stock Market Reaction on Tweet Comment Intel (0) 퐼푛푑푖푓푓푒푟푒푛푡 Ford (+) 푃표푠푖푡푖푣푒 Broadcom (+) 푃표푠푖푡푖푣푒 General Motors (-) 푁푒푔푎푡푖푣푒 Wal-Mart (-) 푁푒푔푎푡푖푣푒 Toyota (-) 푁푒푔푎푡푖푣푒 Fiat Chrysler (-) 푁푒푔푎푡푖푣푒 Apple (-) 푁푒푔푎푡푖푣푒 Walt Disney (+) 푃표푠푖푡푖푣푒 JP Morgan (+) 푃표푠푖푡푖푣푒

Table showing market reactions on Trump’s tweets. Total of 4 positive market reactions after tweet. Total of 5 negative market reactions after tweet. Total of 1 indifferent market reaction after tweet. Note, all company announcements yielded positive market reactions, except Intel’s announcement.

5. Conclusions

This study finds that the President’s tweets has no major impact on individual stock price levels throughout trading day hours. It can be concluded that the market reactions on individual stocks is reflected at the time of when the first new information is announced, which in all these cases is released prior to Trump’s tweets. There is no separate, significant, immediate market reaction following the President’s tweet alone. In the case the President’s tweet has correlated with an observable market reaction, it indicates only an immediate short-term market reaction. Additionally, the new information released on the companies’ announcements lies not in the President’s tweets, even in the cases where Trump makes the announcement from an official White House press conference. Trump’s tweets should thus be seen as a comment on an announcement made prior to the tweet, even in the cases where Trump, in a live press conference at the White House, announces the company’s decision.

Theoretically, the semi-strong EMH states that an immediate price reaction reflecting the tweet’s sentiment should be expected if the markets are in fact paying attention to Trump’s comments. However, this study finds that since the Trump tweet itself does not contain any previously unknown information on the companies’ fundamentals, the market does not seem to automatically incorporate the President’s approval itself as any significant new market indicator, even if the President has insider information on future policies that might have a significant effect on those stocks. Additionally, there are no significant indicators that Trump’s positive Twitter mentions are causing a positive market reaction. In fact, this study finds it may in varying cases have the opposite effect on stock price.

6. Discussion

In the few cases the market reacts on Trump’s tweets a pattern of reaction cannot be found, at least not throughout the trading day. It is not possible to conclude any trend of positive or negative immediate market reactions on tweets. This means there is no obvious pattern of how the market reacts, and that the market reactions to the tweets cannot be predicted from this study’s results. While some would assume a positive mention on Twitter by President Trump would yield a positive market reaction, this study finds there is little evidence that is the case. As discussed in the literature chapter, it is possible the President’s tweets lead to increased media coverage, which in turn can have a negative effect on stock price.

The purpose of this case study was to provide an answer to the question: “What effect does President Trump’s positive Twitter mentions of companies have on the mentioned company’s stock price? “.

With the study results concluded, it is clear how the markets do not significantly value the President’s approval of companies in the immediate short-term during trading day hours. By testing the Efficient Market Hypothesis, assuming that the President’s comments would be incorporated by the markets as a valuable indicator for future government policy, the study found the markets are perhaps more rational than reacting on the comment on one single actor on the market. Further, it suggests the market does not react on opinions, such as a positive mentioning by the President, no matter how influential the President might be in implementing policy with real market effects.

6.1 Limitations and Further Research

Since this study observes mentions and their respective market reactions in an immediate short-term market, it may provide conclusions that are not in line with a study that would measure the long-term effects on a stock of the President’s tweet. In this study, this is limited to the immediate effect of a tweet, of positive sentiment, on the topic of Tax Reform.

Since this study includes only Trump’s Twitter mentions with positive sentiments, often thanking or praising the company mentioned, and since the study finds there is little obvious causation, one suggestion for further research would be to study the market reactions of negative sentiment tweets.

The time frame of this study is the President’s first year in office, however, including more recent tweets would be beneficial for the accuracy of the results found. It is possible that a greater sample of positive tweets would allow us to make more specific conclusions on how the market reacts. As the data-base is continuously growing since the President is active on Twitter, there are more insights to be gained in this field.

One area that would be valuable to build on is the topic-specific tweets. This study is limited to tweets on the topic of tax reform and economic policy, but focuses on the individual mentions. However, with different policy areas affecting different industries, such as healthcare, banking, and manufacturing, it would be valuable to provide insight specifically on how stocks within those industries react to tweets relating specifically to those policy areas.

While no one can ignore the political influence of President Trump, the tweets studied have not commented on the value of specific stocks. Referring to the Efficient Market Hypothesis, it is possible that a Trump tweet that announces the President’s projection of a stock’s value would have a more significant effect, since it would provide the market with previously unannounced, new information, and not only his approval.

Since this study has not controlled the timing of each tweet against other new outside information made available to the markets such as world events, important political and business announcements, and general trends, further research could incorporate outside factors in order to more precisely specify the effect and causation of events. Specifically, world events within a specific policy area, for example on free trade, could be used to control for the effects the President’s tweets may have alone.

Finally, it would be interesting to analyze the effect the President’s foreign policy related tweets have on markets. Two recent examples of such could be to understand how the markets reacts when President Trump announces new policy-related information on the topic of North Korea’s and Iran’s nuclear programs via his Twitter. Trump being the Commander-in-Chief, these, more geo-political research fields, are uniquely interconnected to the President’s opinions and it is possible his comments on these issues may have an effect on market behaviors at a greater scale.

7. References

Apple Inc., (2018-01-17). Apple accelerates US investment and job creation. Apple Newsroom. Retrieved: 2018-04-23. https://www.apple.com/newsroom/2018/01/apple-accelerates-us-investment- and-job-creation/. Html-document.

Bae, Y., and Lee, H., 2012: Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the Association for Information Science and Technology, V 63, Issue 12, PP 2521-2535. Retrieved: 2018-04-12. https://doi.org/10.1002/asi.22768. Html-document.

Barchart, 2018. Retrieved: 2018-04-03. https://www.barchart.com/about. Html-document.

Berenson, T., (2018-01-19). Here Are the Promises President Trump Kept and Broke in His First Year. TIME Magazine. Retrieved: 2018-04-23. http://time.com/5106302/donald-trump-first-year- promises/. Html-document.

Blake, A., (2017-11-14). DOJ: Trump’s tweets are ‘official statements of the President’. The Washington Times. Retrieved: 2018-04-21. https://www.washingtontimes.com/news/2017/nov/14/doj- donald-trump-tweets-are-official-statements-of/. Html-document.

Bollen, J., Mao, H., Zeng, X., 2011: Twitter mood predicts the stock market, Journal of Computational Science, V 2, Issue 1, PP 1-8, Elsevier

Bose, N., (2017-01-17). Wal-Mart, General Motors reaffirm jobs long planned, Trump says thanks. Reuters. Retrieved: 2018-04-23. https://www.reuters.com/article/us-walmart-employment- idUSKBN1511JE. Html-document.

Carey, N., and Heavey, S., (2017-03-28). Trump applauds Ford's previous planned investment in Michigan plants. Reuters. Retrieved: 2018-04-19. https://www.reuters.com/article/us-autos-ford- jobs/trump-applauds-fords-previous-planned-investment-in-michigan-plants-idUSKBN16Z19M. Html-document.

Cillizza, C., (2017-06-05). Why we should never, ever ignore Donald Trump's tweets. CNN Politics. Retrieved: 2018-04-24. https://edition.cnn.com/2017/06/05/politics/trump-twitter-conway. Html- document.

Crooks, E., and Manson, K., (2017-12-17). US companies set to be big winners from Trump’s tax reform. Financial Times. Retrieved: 2018-04-25. https://www.ft.com/content/9eef31ba-e13d-11e7- 8f9f-de1c2175f5ce. Html-document.

Durbin, D-A., (2017-03-28). Ford investing $1.2B in 3 Michigan plants, adding 130 jobs. Chicago Tribune. Retrieved: 2018-05-12. http://www.chicagotribune.com/business/ct-trump-ford-investment- michigan-20170328-story.html. Html-document.

Dye, J., (2017-12-22). Tax reform bill signed into law by Trump. The Financial Times. Retrieved: 2018-04-12. https://www.ft.com/content/4bd4441d-08eb-3288-803d-5057af75fb40. Html-document.

Endres, K., and Panagopoulos, C., 2017: Boycotts, buycotts, and political consumerism in America, Research and Politics, V 4, Issue 4, PP 1-9. Retrieved: 2018-05-03. http://journals.sagepub.com/doi/10.1177/2053168017738632. Html-document.

Engelberg, J., E., and Parsons, C., A., 2011: The Causal Impact of Media in Financial Markets, Journal of Finance, V 66, Issue 1, PP 67-97. Retrieved: 2018-04-15. https://EconPapers.repec.org/RePEc:bla:jfinan:v:66:y:2011:i:1:p:67-97. Html-document.

Fama, F., E. 1965: The Behavior of Stock-Market Prices, The Journal of Business, V 38, No. 1, PP 34- 105, The University of Chicago Press.

Fama, F., E. 1991: Efficient Capital Markets: II, The Journal of Finance, V 46, No. 5, PP 1575-1617, American Finance Association. Published by Wiley.

Fang, L., and Peress, J., 2009: Media Coverage and the Cross‐section of Stock Returns, The Journal of Finance, V 64, Issue 5, PP 2023-2052. Retrieved: 2018-04-16. https://doi.org/10.1111/j.1540- 6261.2009.01493.x. Html-document.

Grynbaum, M., M., and Ember, S., (2017-12-08). CNN Corrects a Trump Story, Fueling Claims of ‘Fake News’. The New York Times. Retrieved: 2018-04-26. https://www.nytimes.com/2017/12/08/business/media/cnn-correction-donald-trump-jr.html. Html- document.

Lienert, P., and White, J., (2018-01-12). Fiat Chrysler to invest $1 billion in Michigan plant, add 2,500 jobs. Reuters. Retrieved: 2018-04-21. https://www.reuters.com/article/us-fiat-chrysler- investment/fiat-chrysler-to-invest-1-billion-in-michigan-plant-add-2500-jobs-idUSKBN1F034F. Html-document.

Megav, N., (2018-01-23). JPMorgan promises $20bn investment after US tax cuts. Financial Times. Retrieved: 2018-04-22. https://www.ft.com/content/e64b15a2-0036-11e8-9650-9c0ad2d7c5b5. Html- document.

Moyer, L., (2018-01-23). JP Morgan Chase to build 400 new branches, raise wages because of the tax cut. CNBC. Retrieved: 2018-04-26. https://www.cnbc.com/2018/01/23/j-p-morgan-to-spend-20- billion-after-tax-cuts-on-jobs-branches.html. Html-document.

Otani, A., (2018-01-16). Stock Market Roared During Donald Trump’s First Year, Boosted by Earnings and Tax Cut. The Wall Street Journal. Retrieved: 2018-04-17. https://www.wsj.com/articles/stock-market-roared-during-donald-trumps-first-year-boosted-by- earnings-and-tax-cut-1516098600. Html-document.

Schwarz, H., (2017-12-05). The top tweeted political account in 2017 was @realDonaldTrump and the top hashtag was #resist. CNN’s Cover/Line. Retrieved: 2018-04-19. https://edition.cnn.com/2017/12/05/politics/top-tweeted-political-account-trump/index.html. Html- document.

Solon, O., (2017-02-09). Intel gives Trump credit for $7bn US factory it announced under Obama. The Guardian. Retrieved: 2018-04-22. https://www.theguardian.com/technology/2017/feb/09/intel- fab-42-arizona-plant-trump-obama. Html-document.

Standard & Poor’s, 2018. Retrieved: 2018-05-03. https://www.standardandpoors.com. Html- document.

The White House, (2017-02-08). Remarks by President Trump and Intel CEO Brian Krzanich on U.S. Jobs. The White House. Retrieved on 2018-04-20. https://www.whitehouse.gov/briefings- statements/remarks-president-trump-intel-ceo-brian-krzanich-u-s-jobs/. Html-document.

The White House, (2017-11-02). Remarks by President Trump in Jobs Announcement. The White House. Retrieved: 2018-04-17. https://www.whitehouse.gov/briefings-statements/remarks-president- trump-jobs-announcement/. Html-document.

Trump Twitter Archive, 2018. Retrieved: 2018-04-03. http://www.trumptwitterarchive.com/archive. Html-document.

Dye, J., (2017-12-22). Tax reform bill signed into law by Trump. The Financial Times. Retrieved: 2018-04-12. https://www.ft.com/content/4bd4441d-08eb-3288-803d-5057af75fb40. Html-document.

TUMASJAN, A., SPRENGER, T., SANDNER, P., WELPE, I., 2010: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment, International AAAI Conference on Web and Social Media. Retrieved: 2018-04-13. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1441/1852. Html-document.

Turner-Lee, N., (2017-02-16). How the president’s Twitter account affects civil society. Brookings Institution. Retrieved: 2018-04-16. https://www.brookings.edu/blog/techtank/2017/02/16/how-the- presidents-twitter-account-affects-civil-society/ Html-documents.

Wagner, F., A., and Zeckhauser, J., R., and Ziegler, A., 2017: Company Stock Price Reactions to the 2016 Election Shock: Trump, Taxes and Trade, Journal of Financial Economics (JFE), Forthcoming; Swiss Finance Institute Research Paper No. 17-06; HKS Working Paper No. 17-005. Retrieved: 2018- 04-19. https://ssrn.com/abstract=2909835. Html-document.

Wysocki, D., P., 1998: Cheap Talk on the Web: The Determinants of Postings on Stock Message Boards, University of Michigan Business School Working Paper No. 98025. Retrieved: 2018-04-20. https://ssrn.com/abstract=160170. Html-document.