A Trumpish Market
Nicholas Murray, Dexter Norris
Templeton Secondary
Floor Location : S 088 F

In this project, we were identifying the effect of Donald Trumps tweets on market index’s (S&P 500, NASDAQ, Russell 3000, Wilshire, DJIA) and sector index’s (Industrial, Consumer Discretionary and Health Care). Our project was a statistics and computer science based project, so we used both a null and alternative hypothesis. Our alternative hypothesis was: on days where Donald Trump tweets about certain industries or the economy there will be more change on market and sector indices than on days where he does not tweet. To perform our study, we constructed a Twitter API to download all of Donald Trumps tweets since November 8th. We performed our data analysis using Python and Microsoft Excel, along with the T-Test for statistical significance. The answers we obtained proved our alternative hypothesis correct. While only 6/8 index’s tested produced statistically significant results, all of them produced results where the average change on days where Donald Trump tweeted about the economy/sector was greater than the average change on days where he did not tweet. For example, on days where the POTUS tweeted about the Health Care sector, the average point change was 6.4, versus only 3.2 on days where he did not tweet about the sector. Overall, we believe that our project produced results that can and will be shared to the public as a whole, so they can better understand how much influence Trump actually has. We also believe that our project will continue to expand, and we are currently developing a trading algorithm based on the data analysis that we performed.