The digital age and social media have undoubtedly shaped the way we create, consume, and access information. For politicians, social media platforms such as Twitter and Facebook have become a key part of their communication arsenal. As such, it is not too surprising to see prominent leaders, such as U.S. President Donald Trump, post on platforms such as Twitter. With Trump in the limelight, albeit not in the best light, admittedly, we find that there is growing interest in what the president has to say, perhaps because Trump tends to be more openly critical and more openly accusatory of companies, individuals, and companies that he feels go against his economic credo of “making America great again”. The media has been drawn to these statements, and numerous articles have stated that Trump’s tweets have had an impact on stock markets or exchange rates. At first glance, this almost seems believable, after all, the U.S. President is a rather influential person on the global stage. However, one may also argue that these effects may happen simply by chance and that perhaps that the effect may be overstated. In understanding this relationship better, this could help traders make a more informed decision and avoid the uncertainty that often follows Trump’s more negative statements on Twitter.
To explore this, we utilized the SC method to analyze the cause-and-effect relationship between Trump’s negative tweets and financial markets, particularly the stock market and the foreign exchange market. Daily closing stock price data and daily closing exchange rate data were used as dependent variables. For each event, we produced two models with different covariates, but with the same number of outcome lags. Upon analyzing the results, we found that the effects of each tweet varied depending on the ‘target’ of said tweet. It would appear that some companies or countries are more likely to be affected and that there would appear to be a causal link between Trump’s tweets with movement in some companies’ stock prices and some country’s exchange rates. This, however, does not hold for all countries nor companies selected in this study. We found that Trump’s tweet about imposing tariffs on Mexico, as well as his tweet about Amazon taking advantage of the U.S. Postal Service, had the most significant effects on exchange rates and stock prices.
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Different results may indicate that traders or investors react differently to Trump’s tweets, basing their decisions perhaps, on trade ties between countries, for instance, or the type of industry a certain company is in. We find that larger states, such as the European Union, which is a collection of states, are not as affected by Trump’s tweets compared to neighboring countries such as Mexico. This is likely because of geographical proximity, but also, as is more likely the case, the fact that trade with the U.S. contributes to a rather large part of Mexico’s total trade activity.
Notwithstanding, of course, is to acknowledge the limitations of this thesis. Because of the nature of the SC method, the selection of events is rather small. Thus, it could be rather difficult to make large scale inferences based on this, as each country or company is different. Still, the effects of this study still provide some insight into how these tweets may move (or not move) markets. Moving forward, it could be more insightful to expand this perhaps to include positive tweets as well. That said, integrating more sentiment analysis methods to a study like this could provide a more thorough, multifaceted understanding of the causal effects of politicians’ tweets on financial markets. We also found that the currencies and stock prices that were significantly affected by Trump’s tweets, were also more frequently tweeted about compared to the other treatment units that saw no significant effects. Although this was not as thoroughly examined, for future studies, it could also be interesting to assess whether this holds true.
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List of Companies used as Control Units and Ticker Symbols 1 AAP Advance Auto Parts, Inc.
2 AAPL Apple
3 AAXN Axon Enterprise, Inc.
4 AJRD Aerojet Rocketdyne Holdings, Inc.
5 ANF Abercrombie & Fitch Company 6 AVAV AeroVironment, Inc.
7 AYR Aircastle Limited
8 AZO AutoZone, Inc.
9 BBY Best Buy Co., Inc.
10 BKNG Booking Holdings, Inc.
11 BURL Burlington Stores, Inc.
12 BWA BorgWarner, Inc.
13 COST Costco
14 CRI Carter's, Inc.
15 CW Curtiss-Wright Corporation 16 DDS Dillard's, Inc.
17 DKS Dicks Sporting Goods, Inc.
18 EBAY eBay, Inc.
19 ESE ESCO Technologies, Inc.
20 ETSY Etsy, Inc.
21 EXPE Expedia Group, Inc.
22 F Ford Motor Company
23 FB Facebook, Inc.
24 FDX FedEx Corporation
25 FOXF Fox Factory Holding Corporation 26 GD General Dynamics Corporation
27 GM General Motors
28 GOOG Google
29 GPC Genuine Parts Company
30 HD Home Depot, Inc.
31 HEI Heico Corporation
32 HII Huntington Ingalls Industries, Inc.
33 HON Honeywell International, Inc.
34 IBM IBM Common Stock