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Rep. Don Davis Bill Would Fine Candidates at Least $10,000 for Trading on Their Own Races
The proposal would fine violators at least $10,000 and extend the ban to spouses, children and campaign committees.
- On Monday, Rep. Don Davis, D-N.C., introduced the 'No Betting on Your Own Race Act,' seeking to ban federal candidates from trading on prediction market contracts related to their own elections.
- The legislation follows a controversy involving Republican opponent Laurie Buckhout, who settled with prediction market platform Kalshi in August after trading on contracts tied to her North Carolina 1st Congressional District race.
- Buckhout paid a penalty of just under $2,600 and received a three-year suspension from Kalshi, later calling the trades 'a dumb mistake' and stating she worked to make it right.
- Under the proposal, violators would face a fine of $10,000 or three times the net financial gain, as Davis argued candidates should face restrictions similar to those applied to athletes.
- With Congress not scheduled to meet until after the midterm elections, the proposal currently lacks a clear path to implementation, though the Senate previously approved a similar ban for its members.
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US lawmaker moves to ban candidates from betting on their own elections
A U.S. lawmaker has introduced legislation that would impose penalties of at least $10,000 on federal candidates who trade prediction market contracts tied to their own elections. According to an Oct. 5 announcement from Rep. Don Davis, D-N.C., the No…
Bill Aims to Stop U.S. Lawmakers From Betting on Their Own Elections Ahead of Midterms
Rep. Don Davis (D-NC) has introduced legislation that would bar federal candidates and their close family members from trading prediction market contracts tied to their own elections, according to a Cointelegraph report published Tuesday, Oct. 6. The measure, titled the No Betting on Your Own Race Act, arrives with less than a month remaining before […]
Coverage Details
Total News Sources19
Leaning Left1Leaning Right1Center4Last UpdatedBias Distribution67% Center
Bias Distribution
- 67% of the sources are Center
67% Center
L 17%
C 67%
R 16%
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