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Risk Insight: Trump’s Tweets: Presidential Drivers Of Credit Risk

February 6, 2017   //   By James Elder

President Donald Trump has on several occasions used Twitter to both criticize and praise specific companies that either align or go against his policy positions, primarily around government spending and the outsourcing of jobs to other nations like Mexico. Trump’s willingness to directly call out firms over Twitter has introduced a never before seen dynamic to the markets, and, correspondingly, it has affected the market implied credit risk for individual companies. Since his election, and based on market reactions, the calculations of our Probability of Default Market Signals (PDMS) model have shown a short-term impact on the credit quality of individual firms that have entered his crosshairs, for better or worse.

Watch Jim’s 3 minute rundown on this topic in our Risk Insight Video.

Risk Insight

For example, within the Industrial Machinery industry, President Trump tweeted on November 24 that he was negotiating with Carrier, a subsidiary of United Technologies (NYSE:UTX), to keep 1,400 jobs in Indiana. From November 23-25, United Technologies’ one-year forward-looking probability of default, or PD, dropped 17.5%. Just a few days later on December 3, Rexnord (NYSE:RXN ) drew Trump’s criticism for plans to close a factory in Indiana and, in turn, saw their PD increase by 11.9% between December 2-5.

Relative Change in Market Perceived

Similarly, the Automobile Manufacturers have been a hot topic for President Trump. On January 5, Trump threatened Toyota (TSE:7203) with a border tax for building Corollas in Mexico and subsequently saw its PD increase by 26.2% between January 4-6. Seemingly learning from Toyota’s experiences, Fiat Chrysler (BIT:FCA) drew Trump’s praise on January 9 for planning to build a $1 billion plant in the U.S. and saw its PD drop by 15.3% between January 6-10.

Within the Aerospace and Defense industry, Lockheed Martin (NYSE:LMT) was also the subject of a negative tweet regarding the sticker price of the F-35 stealth fighter jet on December 22, and we observed Lockheed’s PD rise by 10.9% between December 21-23.

However, there were three other notable “@realdonaldtrump” tweets that we analyzed that did not seem to increase or decrease the risk of the mentioned firm in a way that corresponded with the negative or positive sentiment that Trump projected. This includes a highly publicized negative tweet directed at Boeing (NYSE: BA) on December 6 regarding an Air Force One contract.  In this case, while Boeing’s stock dipped 1% immediately after the tweet, it was back in the black by the end of the day, in large part because of Boeing’s statement explaining to the market that the company is “currently on contracts totaling approximately $170 million for the initial design and engineering work” needed before production – not the $4bn dollar figure President Trump quoted. This number is less than 0.2% of 2016 revenues and, as such, was not taken as a material increase in credit risk, which decreased by nearly 12% over that period.

Second, on January 3, General Motors (NYSE: GM) was the target of a negative tweet for the production of the Chevy Cruze in Mexico, but this again was overshadowed by GM’s announcement of a significant December sales jump on January 4. Finally, on January 9, Trump tweeted positively about Ford (NYSE: F) bringing 700 jobs to Michigan after canceling a factory in Mexico, but separately Ford reiterated earnings guidance on January 10 predicting EPS declines in 2017 relative to 2016.  This resulted in a modest increase in risk (the only single digit change) that overshadowed Trump’s praise.

Industrial Machinery

In summary, this latest dynamic in presidential communication introduces a new factor to the monitoring of daily credit risk. Our Probability of Default model shows that President Trump does impact the market perceived credit quality of firms, but traditional fundamental drivers of the firms can still hold greater sway. We will continue to watch these indicators to see whether short term risk dislocation leads to longer term fundamental shifts in risk for firms and industries due to new regulations.


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