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The Effects of Donald Trump's Tweets on the Stock Exchange Munich Personal RePEc Archive The Effects of Donald Trump’s Tweets on The Stock Exchange Yardley, Ben 20 March 2020 Online at https://mpra.ub.uni-muenchen.de/102578/ MPRA Paper No. 102578, posted 26 Aug 2020 11:41 UTC Dissertation The Effects of Donald Trump’s Tweets on The Stock Exchange Ben Yardley - 16008349 20th March 2020 BSc (Hons) Economics Word Count: 8201 This dissertation is submitted in part fulfilment of the requirements of the BSc (HONS) Economics in the Department of Accounting, Finance and Economics at Manchester Metropolitan University. 1 DECLARATION/ COPYRIGHT: This dissertation is an original and authentic piece of work produced in fulfilment of my degree regulations. I have fully acknowledged and referenced all secondary sources. The dissertation has not been submitted in whole or part of assessment in another unit at this or any other university. Signature: B Yardley Date: 20/03/20 2 ABSTRACT Donald Trump is a huge personality in a unique social, political and financial situation. Through twitter he is able to influence his following instantly with no regulation, often with large unforeseen repercussions. The aim of this report is to identify Trump’s company specific tweets, quantify the mood and feeling of the tweets through sentiment analysis and then investigate the correlation between this and the effect on the stock prices of these companies. This will be done through a regression of the sentiment and the returns of the company over serval time periods. Further investigation will the take place in order to analyse how neutrally classified tweets are impacted, as well as the effect of the number likes and retweets a tweet has on the stock price of a company. 3 ACKNOWLEDGEMENTS Firstly, I would like to thank my dissertation supervisor Lefteris Giovanis for all his help throughout the year on this project. We have had many endless conversations on this topic which I believe we have both become very passionate about. Without his guidance and support, this report would not have been produced to the same standard, of which I am very proud. I would also like to thank Gavin Brown, though not my dissertation supervisor, he also provided amazing and useful insight that helped to shape this paper. Finally, I would like to thank my family, friends and girlfriend. Their help and support both direct and indirect made my work possible; and for that I am very grateful. 4 TABLE OF CONTENTS DECLARATION/ COPYRIGHT: ......................................................................................................................... 2 ABSTRACT ..................................................................................................................................................... 3 ACKNOWLEDGEMENTS ................................................................................................................................. 4 1. INTRODUCTION ......................................................................................................................................... 6 1.1 RESEARCH QUESTION ................................................................................................................................ 8 1.2 RESEARCH AIM .......................................................................................................................................... 8 1.3 RESEARCH OBJECTIVES ............................................................................................................................ 8 1.4 RATIONAL ................................................................................................................................................... 8 2. LITERATURE REVIEW ................................................................................................................................. 9 2.1 CONTRIBUTION ......................................................................................................................................... 13 3. DATA COLLECTION & METHODOLOGY ...................................................................................................... 14 3.1 DATA COLLECTION ................................................................................................................................... 14 3.2 METHODOLOGY ........................................................................................................................................ 18 4. RESULTS AND DISCUSSION ....................................................................................................................... 22 4.1 THE EFFECT OF TWITTER SENTIMENT ON 1-DAY AND 5-DAY RETURNS ............................................... 22 4.2 THE IMPACT OF POSITIVE AND NEGATIVE SENTIMENT TWEETS ON STOCK PRICES .............................. 25 4.3 A COMPARISON OF TWEET SENTIMENT AND TWEET CLASSIFICATION .................................................. 28 4.4 THE IMPACT OF NEUTRAL SENTIMENT TWEETS ON STOCK PRICES ...................................................... 30 4.5 THE IMPACT OF LIKES AND RETWEETS ON STOCK PRICES .................................................................... 32 5. CONCLUSION ............................................................................................................................................ 34 5.1 CALLS FOR FUTURE RESEARCH .............................................................................................................. 35 6. APPENDIX ................................................................................................................................................. 37 6.1 FORMULAS ................................................................................................................................................ 37 6.2 TABLES ..................................................................................................................................................... 37 7. REFERENCES ............................................................................................................................................. 38 5 1. INTRODUCTION On the 20th of January 2017 Donald Trump was inaugurated as the 45th President of the United States. He is a highly outspoken and vocal president; and although he divides opinion, his influence over both his admirers and critics is undeniable. The extent of this influence was highlighted when he was published in the Time100: Most Influential People in the World 2019 shortlist (TIME, 2019). Twitter is a social media platform that began in 2006, the platform has over 330 million active monthly users, this number has grown consistently since its launch with a nearly 30% increase in the last 5 years (Statista, 2019). Trump is an avid Twitter user, using the site daily for everything from expressing his personal beliefs, to trying to block international trade deals and intimidate American corporation owners. By cross-referencing the Time100: Most Influential People in the World 2019 shortlist (TIME, 2019), with a list with a list of the most followed users on twitter (FriendorFollow, 2019); there are five individuals on both lists, see below. Name Twitter Followers Total Tweets Taylor Swift 86 million 434 Lady Gaga 81 million 9,110 Donald Trump 74 million 49,910 Ariana Grande 72 million 46,106 LeBron James 45 million 6,911 Table 1: The Most Followed and Influential Individuals 2019 Of these individuals, Donald Trump and Ariana Grande are the only ones comparable in terms of followers and total tweets. Also, Trump is the by far the most financially and politically motivated, and most likely to be able influence traders in the opinion in the author; for these reasons Trump will be investigated in this report. There is however scope for investigation of the other individuals highlighted above. 6 Trump is able to impact beliefs directly through the platform, without any mandatory regulation and instantly connect to his 67 million followers (Twitter, 2019). His current twitter position is unique, he uses twitter daily and despite his influence, position and power, he does not separate the subjective from fact in his postings online. Here is an example of how one tweet can have an instant and significant impact in both the short and long term. Source: (Rayarel, 2018) Due to the above tweet the share price of Lockheed Martin fell 2% by the start of the next trading day, a total Market Cap depreciation of $1.2 billion. (Rayarel, 2018) JPMorgan Chase have highlighted the significance of these movements through their creation of the VOLFEFE Index. This index is a measure of the volatility of US treasury bond yields, as affected by Trumps tweets (Bloomberg, 2019). Similarly, Citigroup stated that around 10% of the president’s tweets since his November 2016 election refer to subjects of importance to U.S. markets (Citigroup, 2019). 7 1.1 Research Question What effects do Donald Trump’s Tweets have on the stock exchange? 1.2 Research Aim To analyse both the tweets pertaining to large corporations, and the effect this has on the share prices of these corporations. 1.3 Research Objectives - To understand the possible influence of tweets on financial markets; consolidating an understanding of how this occurs in line with EMH assumptions. - To appropriately quantify these effects in order to set a framework by which tweet related financial analysis can be carried out. - To analyse the effects on stock prices, using the tweets and identifying the indicated significance of a relationship between a positive tweet and a positive increase
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