President Donald J. Trump’s Tweets on Publicly Traded Companies – Impact on Stock Price and Competitor Stock Price
Cole Flynn, Bentley University
Abstract: This research study takes a deep dive into the polarizing tweets by President Donald J. Trump, specifically the ones that mention publicly traded companies. Stock price data was pulled for companies that were mentioned in President Trump’s tweets, as well as data for the direct competitor of the mentioned company. Tweets were categorized into firm specific and industry specific tweets so that proper analysis can done to see impacts of his tweets on the companies. The range of prices ranged from a week prior to the tweet, to five weeks after the tweet. The data was run through Stata in regression analysis after pulling the prices for the companies and their competitors. After the regression was run, the resulting data showed a conclusion that the tweets did not have a statistically significant impact. The conclusion was that Trump’s tweets about companies do not impact the mentioned company, or their main competitor.
The President of the United States frequently uses Twitter as a mode of communication to the American people. The major use of the account began when Trump began to run for office, and he still uses the account to this day. His tweets range from all matters of the presidency, and even things that do not have to do with his agenda. Few presidencies have had access to such a wide-reaching platform, and none have utilized the platform as extensively as President Trump. In turn, the magnitude of the impact has yet to be seen.
Another important facet to Trump’s twitter usage is the type of language and statements that are made. Trump makes very loud and ambitious statements, and many media outlets follow his account and show them on live television. The main news outlets show the Trump’s media tweets, but Trump’s tweets on individual companies are often overlooked. They are overshadowed because Trump’s political tweets garner major attention by his political opponents and rival political party.
Trumps financial tweets range from tweets about the market to tweets about specific industries and companies (Exhibits 1 & 2). President Trump’s take on financial industry and companies is interesting because it is such a unique topic. It is different than many other topics because of the implications that it has. With new information on the economy, industries, and individual companies, the stock market reacts. If the price of a stock fluctuates, people can gain or lose money. As a result, any fluctuation has severe ramifications.
Stock traders look at many different things when they analyze securities and place trades. Although looking into facts regarding the company is crucial, some even follow public opinion. It is hard to deny that a statement made by a President does not impact many people’s viewpoint. The oval office brings a certain level of knowledge and experience that allows the President to have a very overarching viewpoint of the country and many facets of it. It should also be mentioned that Trump’s supporters and those who believe in him as a leader will hold his comments and opinions very heavily.
Given the nature of the stock market, Trump’s tweets, and his influence as President, an incredibly unique combination is formed that the country has not seen. If an elderly citizen has invested in a company that Trump tweets negatively about, there is a possibility of major loss and implications that can be very important for that citizen. Obviously, the stock market is a risk, but this situation brings an unnatural order. Traders use new and public information, and speculation by a public figure may cloud judgement and provide disarray to the natural order of the market.
It is very important to see the affects that Trump’s tweets have on certain companies and their stock price. Trading volume is not very important in this case. If the volume of trades is high, but the price is merely impacted, then the impact of the tweet is low. Therefore, price is the most important variable. There are thousands of trades on both sides when it comes to price. People may gain or lose money based on fluctuations in price. So, the first goal in analyzing this nationwide problem is to look at the company that is directly mentioned in the tweet.
Another important aspect of this problem is the ripple effect that may occur after the company is mentioned. When news about companies, industries, or the economy are released, there are many sides of the resulting action. More specifically, when one company is impacted by something, their main competitors have a resulting impact, and it sometimes can affect the whole industry. For example, if a JetBlue flight crashes, American Airlines and Spirit Airline may increase because people do not want to invest in JetBlue’s stock anymore.
Trump’s tweets that mention individual companies have a significant and far-reaching impact. I think looking to the individual company and their main competitors can give an accurate read on how the tweet impacted the stock market. It is unrealistic to think that the tweet will affect the entire market, and stock indexes. They are composed of too many companies, so there will be a very small change if a couple stocks change in price. Also, if the one company decreases, and its competitors increase, it balances out to create such a small change.
Therefore, it is important to find out what this problem can impact. The implications of this problem extend to all Americans that have any amount of money in the stock market. It is hard to tell the circumstances that the country will be in in the coming years. There may be different problems and experiences that the United States will face that will cause President Trump to speak on certain companies. There is no way to tell which companies will be mentioned and impacted by a Trump tweet, so having a further understanding of all risks of investing is crucial. It is crazy to think that one individual’s twitter account is now considered a trading tool and one that can impact the fortune and livelihood of American citizens, but the time has come. It is time to be more knowledgeable on this matter.
Importance of Study
A study into Trump’s tweets regarding publicly traded companies will bring valuable contribution to the economic field. Many people invest in the stock market and are impacted heavily with market losses. Thus, it is important to have a full grasp as to what can be influencing the market. Even a slight percentage point change can lose investors thousands of well-earned dollars, so knowing the extent of this problem is important.
Donald Trump’s major twitter usage dates to the 2016 Presidential Election. A study done a year later focuses on the election, and how social media is being used more and more in politics and elections. The author, Davis, focuses the research at Facebook and Twitter usage by both candidates, and how they were utilizing the media platforms. The results yielded that both candidates used the platforms frequently, but Twitter was utilized slightly more (Davis, 2017). Also, the results yielded that Clinton used the platforms slightly more than Trump. The study is useful because it portrays the increase in use of social media for politics and elections, and it gives an origin to the Twitter use of current President, Donald Trump.
Trump’s tweets have the most notable influence on trading volatility, but volatility does not directly mean uncertainty. Looking into the daily data of the S&P 500 and VIX (Volatility Index), Simpson classified the tweets (negative, neutral, or positive) to see how different tweets affected volatility and uncertainty in the market. In turn, the goal was to see the impact the tweets have on volatility and market uncertainty. The study showed that “negative” tweets increased VIX, “positive tweets” decreased VIX, and that trading volume heavily increased after 15 minutes of the tweet. The results show that Trump’s tweets are heavily followed and can be considered a player in the daily volatility of the market (Simpson, 2018). Although he is influencing markets in terms of volatility, there would need to be more evidence to show that there is a pattern of this volatility bringing uncertainty in people’s minds. Just because the market is fluctuating does not exactly mean that uncertainty is inherent.
An important financial aspect of a nation is currency. Currency exchanges fluctuate and affect how valuable a certain currency is. A 2017 study looks at the Mexican peso and the United States dollar exchange rate (Manolo, 2017). It uses daily exchange rate data and President Trump’s tweets to research the affect that the tweets have. A volatility in the exchange rate affects the financial world and can create opportunities for people to capitalize on and affects countries monetary influence due to the value of their currency. The study looks at tweets that are “negative” and uses an econometric model to see how the tweet affects the exchange rate as a result. The study yielded results that the exchange rate was affected by President Trump’s negative tweets regarding Mexico and any other topics related to the country (Manolo, 2017). This study is important because this shows the reach of the tweets and how they can have real influence globally. So, the fact that President Trump can change something like that with the click of a button is very intriguing.
An Athens Journal of Business and Economics also studies the broad range of affects that Trump’s tweets can have. The subjects of focus are US and foreign exchange markets. More specifically, it is the DJIA index of several exchange rates. The method of the study is to create a time series of the sentiment in the tweets. From there, econometric methods are used to connect the sentiment to market indexes and exchange rates. The tweet data came from a Trump Twitter Archive and filters it from the time of his inauguration to show how his Presidency has also played a role. From the study, there is evidence that the tweets have short term (daily) effects on the Dow Jones, the US-Canadian exchange rate, and on an aggregate index for exchange rates of major currencies (Colonescu, 2018).
The efficient market hypothesis states that prices of stocks should respond quickly to the release of public and new information. Also, with a lack of new information, prices should not move drastically. Two Northeastern University professors and their student looked at the hypothesis and whether Trump’s tweets followed the method. The research spanned for 10 publicly traded companies and 15 tweets. The period was when he was the President elect. The study yielded results that showed Trump’s tweets, although not containing new information, did have effects on stock prices. They also found that the reason for this contradiction of the efficient market hypothesis was due to “irrational and noise traders” (Born, Meyers, Clark 2017). These are traders who do not follow common principle and follow exotic practices in their trading habits. This study is important because it shows the extremity of Trump’s tweets to the financial industry and the market. He has the ability to fluctuate prices of stocks because of his public presence. Although it may be due to irrational traders, there are certainly enough of them out there to influence the market daily.
Although Trump is a major voice on a social platform, his tweets do not directly influence the market. A study in 2018 by Palmlov concluded from research into the tweets and S&P 500 returns, that the efficient market hypothesis is very present in the markets and causes price fluctuation. Because the hypothesis states that new information creates price changes, Palmov concludes from his research that there is very little evidence that Trump’s tweets have a trend of influencing stock prices (Palmlov, 2018). The aim is more toward the hypothesis, as the EMH is the true director of the prices of stocks. The stock prices fluctuate because of new information such as earnings reports and news articles, and Trump’s tweets are not the driver.
The two main market variables that Trump can influence are volume and price. An honor college study in 2019 analyzed both. Statistical analysis was run, and the conclusion was that there was a very small and short-lasting effect on the company’s returns. After looking at normal and abnormal volumes, the researcher found that the volumes are increased for the two trading days after the tweet. For the first trading day after the tweet, the volume was up 32%, and the second day after was 21% (Fenn, 2019). The study concludes that Trump’s tweets heavily affect trader sentiment, which is a key factor in trading volume.
If Trump has any tangible influence over the market through Twitter, it begs the question of whether investors can foresee the market movement by monitoring his tweets. According to Hynek, this may be possible. His study in 2017 strays away from Trump and looks more generally at Twitter and its correlation to the market. It looked at a sample of SNAP tweets and measured the sentiment of the tweet. After that, an analysis of the DJIA was done to see if there was any way the tweet could precursor a market reaction. The researcher believes there may be some truth to the Twitter having an ability to foresee market movement, but there needs to be more accessibility to data on Twitter to make a proper case (Hynek, 2017). With his tweets affecting certain companies, it opens the door to the idea that his tweets, and tweets of others, can be used as a resource in security analysis and impact a trader’s mindset. If tweets impact public sentiment in a meaningful way, it could possibly be used to find opportunities to predict market movement and execute certain trades as a result.
The current research on the matter mainly covers trading volume, immediate price reaction, and short-term effect of the tweet. The research has only gotten this far, but there is certainly more to be found. My research is aimed at short- and long-term price movements as a result of the tweet, and also looks at the impacts on the companies’ closest competitor. This an important addition to the current state of research, as many companies can be heavily impacted when even just one is mentioned in the news
The data for the study was collected in a thorough and extensive manner with the end goal of inputting enough data to run regression analysis. The regression analysis would be done in order to find the change in gap between stock prices of the mentioned company and the competitor. Regressions would be run for firm specific and industry specific tweets and for positive and negative tweets. The regressions would also be used to find the gaps of prices from the time before the tweet to 1 day, 3 days, 7 days, and 30 days after the tweet. This is done to see how the gap in prices changed from before the tweet to those time points, in order to see how the tweets impacted the stock prices of the two companies, and to see how long this possible impact may last. The collection began by first identifying the relevant tweets of the dataset. In order to identify tweets with positive or negative sentiment toward publicly traded companies, Trump’s Twitter Archive was dissected (Trump’s Twitter Archive 2020). Searching and filtering his entire Twitter database for keywords and companies yielded many relevant tweets but scrolling through his Twitter feed on the actual platform yielded many more. Through those two strategies, the tweets needed for the study were identified.
The next step in the collection was to identify the seven trading days before the tweet, the seven trading days following the tweet, and the trading day a month past that date. After identifying those dates, all tweets were labeled as positive or negative in terms of tweet sentiment.
Another key element of the data was in identifying whether the tweet was specific to the company or had implications for the entire industry. For example, a firm specific tweet would be one that mentions JetBlue and has to do with only that company. An industry tweet would refer to the entire airline industry, while also mentioning JetBlue.
The next stage of data collection was the most extensive. Using MarketWatch, closing prices were found for every data identified previously. Part of this step also included an identification of the main competitor of the identified companies and pulling the closing prices for this company as well (MarketWatch 2020).
After all important data points were found, it was time to prepare the data to be formatted to be properly inputting into the regression.
To format and prepare the data set, labeling the dates was completed with a variable of -1 for the trading days before tweet, 0 for the day of the tweet, 1 for the seven days following the tweet, and 2 for the month following that. Sentiment was labeled as 1 for a negative tweet, and 0 for a positive one. It was also important to have a distinction between firm and industry tweets, so firm tweets were labeled with 1 and industry tweets were labeled with 0. This aspect of the research was important in the operations of the regression analysis and crucial in determining the equation that allows analysis of this study.
To analyze the impact of Trump’s tweets on the stock prices of a mentioned company and a competitor, I utilized the equation below using Ordinary Least Squares (OLS).
The resulting data above shows the results of the analysis of the data. Breaking down the study into first firm and industry specific tweets and then positive or negative allowed proper analysis so that conclusions could be made about both types of tweets. The average price difference portion provides context to show the relative change in price difference that is shown for the different time periods. For the firm specific tweets that were positive, the gap between prices decreased. The gap grew larger for the first few days after the tweet, but then began to regress (Figure 3).
For the firm specific negative tweets, a different trend occurred. The gap also decreased as the prices moved slightly closer together, but there is a longer lasting impact than with the positive tweets. The gap ebbed and flowed for the first several days and ended up having the largest noted impact a month later. Next, Trump’s industry tweets were analyzed for their impact in order to see any difference between them and the firm specific ones (Figure 3).
For the data above, which shows the industry specific tweets, there is a different result than the firm specific tweets. As seen in the second column of Table 2, the average price difference is much lower. The changes in price differences for all periods is a much larger portion of the average starting price and shows more impact. If the average starting price difference is 14.72 like it is for the negative tweets, and after one day the change in price difference is 17.73, the gap between stock prices changes heavily. This is different than the firm tweets, where the average starting price difference was much higher and the change in price difference is much less impactful (ie: 364 price difference, 27.6 change).
For the positive industry tweets, the price gap grew in an interesting way. There was an initial spike in price movement, but it slowed as the week progressed. Then, another wave came, and the impact grew for the rest of the month and the highest movement occurred one-month post-tweet (Figure 4).
The negative tweets had a similar trend, one that was also positive in the price gap movement. There was also an initial significant change in the gap, but it slowed as the days went, and by Day 30, the impact was minimal (Figure 4).
After analyzing the data, the conclusion is that there is no major trend that shows impact from Trump’s tweets to the change in price difference between the mentioned company and the competitor. Although there seems to be a larger change in price gap for firm specific tweets, there is no evidence of correlation between this and Trump’s tweets. It may have been a result of other factors that impact stock price, or it could have been due to the fact that the firm specific tweets mentioned companies that had large gaps of price between their competitor.
After collecting the data and putting it through regression analysis to solve the equation mentioned above, it was found that Trump’s tweets do not have a statistically significant impact on the mentioned company or their competitor. This conclusion applies for both firm specific and industry specific tweets, and furthermore for both positive and negative tweets.
The conclusion can be made since the p value for the analysis was not close to the statistically significant level of 0.05. Many regressions that were run to help solve the equation showed p values around 0.5 to 0.9. This shows that there is not sufficient evidence to reject the null hypothesis that his tweets do not have an impact.
After determining the conclusion of this research and considering other previous research on the matter, much has been done to research and analysis this topic. There have been many studies on President Trump’s tweets and their impact, but his Presidency is finished. Famous and influential people will continue to have a large Twitter platform as technology is growing, so it possible this research takes a slight turn. Research may be done on other influential figures and analyzing the impacts of their tweets. The impact of Trump’s tweets will be less impactful due to his loss in the 2020 election, but there will certainly be many more figures that rise and have impactful reactions from their tweets.
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