President-elect Donald Trump’s Cabinet picks have included both predictable choices and complete surprises. Some of those surprises have been lucrative for prediction market traders, who have made as much as five times their investment on sites like Kalshi and Polymarket.
Other picks have been so surprising that they weren’t even available to bet on prior to their announcement. Kalshi had to add over half of Trump’s nominees after their respective markets launched. They weren’t even on an initial list of possible nominees.
Here’s a breakdown of Trump’s Cabinet picks in terms of their current, initial, and lowest odds, according to the prediction market platform Kalshi.
UPDATE: Former Rep. Matt Gaetz withdrew his nomination for attorney general on Thursday, Nov. 21, after this story was originally published.
| Position | Nominee | Current Odds | Opening Odds | Lowest Odds | Available at Market’s Opening? |
| Secretary of State | Marco Rubio | 95% | 30% | 19.4% | Yes |
| Attorney General | Matt Gaetz | 40% | 56% | 28.5% | No |
| Secretary of Defense | Pete Hegseth | 67% | 87.6% | 69.6% | No |
| Secretary of Homeland Security | Kristi Noem | 93% | 94% | 86.3% | No |
| CIA Director | John Ratcliffe | 92% | 94.1% | 83.2% | Yes |
| Director of National Intelligence | Tulsi Gabbard | 73% | 80.6% | 71% | Yes |
| EPA Administrator | Lee Zeldin | 95% | 96% | 86.1% | No |
| UN Ambassador | Elise Stefanik | 97% | 89.4% | 89.4% | Yes |
| Secretary of Health and Human Services | Robert Kennedy Jr. | 73% | 71.2% | 18.2% | Yes |
| Secretary of the Interior | Doug Burgum | 95% | 44.7% | 3% | Yes |
| Secretary of Veterans Affairs | Doug Collins | 97% | 93% | 92% | No |
| Energy Secretary | Chris Wright | 93% | 45.9% | 41.3% | No |
| Secretary of Transportation | Sean Duffy | 92% | 87.4% | 87.4% | No |
| Commerce Secretary | Howard Lutnick | 94% | 5.3% | 5.3% | No |
| Secretary of Education | Linda McMahon | 96% | 82.7% | 46.3% | No |
Remember, the nominees still must receive confirmation by the Senate before beginning their new role in the Trump administration.
The last and only time a Cabinet nominee was rejected by a Senate vote happened in 1989 when George H.W. Bush nominated John Tower to be his Secretary of Defense. According to prediction markets, it may happen again as President-elect Trump begins his second term.
Matt Gaetz and Pete Hegseth, in particular, have rocky paths before actually serving, while Robert F. Kennedy Jr. and Tulsi Gabbard are not shoe-ins, either.
In any case, some of the bizarre market movements in the table above are a testament to Trump’s unpredictability.
Unpredictability and trading
People often tout unpredictability as a way to make money in volatile markets like the derivatives found across prediction markets. New information can change contract prices instantly. Underdogs at 3% can suddenly become frontrunners like Doug Burgum did when he was nominated for Secretary of the Interior instead of Secretary of Energy.
However, some surprises can be so off the radar that traders don’t even get the chance to profit from them. Hegseth, a combat veteran and Fox News host, was not originally in the running for Secretary of Defense on Kalshi or Polymarket. Kalshi listed Hegseth’s odds only after Trump announced Hegseth’s nomination. Polymarket didn’t list him at all.
Hegseth’s odds opened at 87.6% and although that forecast has dropped, the extreme unpredictability reduced the potential profit for traders.
Some nominees were introduced late, and their odds remained high rather than falling. Doug Collins wasn’t added to Kalshi’s Secretary of Veterans Affairs market until eight days after the market opened. Collins started at 93% and has hovered around that figure ever since.
Prediction markets have their strongest claim to predictive power in markets with an abundance of publicly available information. Markets can only aggregate information that is available to their traders, and several of Trump’s Cabinet decisions, like Matt Gaetz, have surprised even his Chief of Staff.
Prediction markets may reflect the state of public knowledge about a topic at a certain time, but prediction markets aren’t immune to surprises. Going into Election Night, prediction markets only gave Trump a little over a 20% chance of winning the popular vote.
Even with the diverse information aggregated in prediction markets, public knowledge still has blind spots that prediction markets react to rather than proactively overcome.
