Kalshi issues first-ever ‘insider trading’ bans to 3 candidates for betting on their own races 21%

By Lillian Mann0%

4/22/2026, 6:30:09 PM

BS Summary: This article contains 23 faulty reasoning types, including Negativity Bias, Framing Effect, and False Dilemma, with Appeal to Authority as the most egregious example at 25.5% saturation with 156 hits. Analysis detected 1,105 faulty-reasoning hits from 611 analyzed words, generating a BS Score of 35% and a BS Rank of 21% (16,842 of 21,198 articles). This article is better (less manipulative) than 79.40% of the article peer group.

Kalshi, online prediction market platforms allow people to place bets on wide-ranging subjects such as sports, finance, politics and currents events. 
(Photo Illustration by Scott Olson/Getty Images) 
OAN Staff Lillian Mann 
6:29 PM  Wednesday, April 22, 2026 
In a first-of-its-kind enforcement action, the prediction market Kalshi suspended three political candidates on Wednesday after an internal probe revealed that they had wagered on their own 2026 primary races. 
These suspensions and fines represent the most aggressive enforcement actions since prediction markets surged in popularity over the last year. 
According to CNN, these platforms allow users to wager on diverse outcomes ranging from sports and entertainment to global elections or even daily high temperatures. 
This crackdown reflects growing concerns that the rapidly expanding industry could undermine the integrity of the U.S. electoral process. 
Wednesday's crackdown represents an initial push to regulate prediction markets like Kalshi and its rival Polymarket, both of which are currently navigating how to operate without facilitating election manipulation. 
This regulatory friction mirrors growing anxiety among lawmakers, who warn that million-dollar stakes on political outcomes could “incentivize interference.” 
To address these risks, legislators have introduced at least two bills this year aimed at barring Commodity Futures Trading Commission (CFTC)-regulated companies from offering election-related contracts entirely. 
The candidates named in regulatory filings were Mark Moran (I-Va.), Matt Klein (D-Minn.) and Zeke Enriquez (R-Texas). 
Klein and Enriquez both cooperated with Kalshi's probes, according to the company. 
However, Moran was fined $6,229.30. 
Kalshi noted that Moran “traded in two markets related to his campaign. 
The first was a market on individuals who would run for public office in 2026. 
“This person placed a trade on himself in this market. 
Then, once the trader announced himself as a candidate for the Democratic Primary election for Virginia U.S. 
Senate, he again traded on his own candidacy.” 
Additionally, all three candidates “were flagged because of our newly released safeguards to block political candidates from trading on their own election,” Kalshi wrote in a statement. 
Robert DeNault  Kalshi's head of enforcement  referred to these cases as “political insider trading,” in a statement announcing the suspensions. 
“When a trader violates our exchange rules, they will be subject to exchange discipline,” DeNault said. 
“For more serious matters, we refer cases to the CFTC or DOJ for further investigation and prosecution, which didn't happen here.” 
“Regardless of the size of a trade, political candidates who can influence a market based on whether they stay in or out of a race violate our rules,” DeNault continued. 
“No matter how small the size of the trade, any trade that is found to have violated our exchange rules will be punished.” 
Representative Blake Moore (R-Utah) also wrote in a statement last month, while announcing his bipartisan bill, that “under-regulated prediction markets have exposed America to needless public safety and national security risks” by letting users trade on “sensitive matters,” emphasizing elections. 
Moran, however, claimed that his actions were a deliberate attempt to “get caught.” 
Writing on X, he argued that he traded $100 on his own outcome with the full expectation of being penalized, adding that he was never a serious contender for the Democrat nomination. 
“The attention it would create to highlight how this company is destroying young men,” he added. 
“And as Senator I will go after Kalshi and impose significant penalties on them  25%  a vice tax  to pay down our national debt.” 
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Article reasoning-pattern comparisonThis article: 9.3%Lillian Mann: 4.7%One America News Network: 4.6%Confirmation Bias9.3%This article: 0.0%Lillian Mann: 0.6%One America News Network: 2.4%Anchoring Bias0.0%This article: 4.1%Lillian Mann: 2.9%One America News Network: 3.9%Availability Heuristic4.1%This article: 0.0%Lillian Mann: 1.3%One America News Network: 1.1%Representativeness Heuristic0.0%This article: 0.0%Lillian Mann: 1.1%One America News Network: 0.9%Hindsight Bias0.0%This article: 0.0%Lillian Mann: 1.5%One America News Network: 2.9%Overconfidence Bias0.0%This article: 17.3%Lillian Mann: 8.6%One America News Network: 15.3%Framing Effect17.3%This article: 3.8%Lillian Mann: 0.2%One America News Network: 1.0%Loss Aversion3.8%This article: 0.0%Lillian Mann: 0.6%One America News Network: 1.1%Status Quo Bias0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Sunk Cost Effect0.0%This article: 4.4%Lillian Mann: 2.4%One America News Network: 4.3%Optimism Bias4.4%This article: 3.1%Lillian Mann: 1.1%One America News Network: 1.8%Pessimism Bias3.1%This article: 18.2%Lillian Mann: 8.8%One America News Network: 11.8%Negativity Bias18.2%This article: 10.8%Lillian Mann: 2.8%One America News Network: 3.2%Self-Serving Bias10.8%This article: 2.0%Lillian Mann: 1.5%One America News Network: 1.7%Fundamental Attribution Error2.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.2%Actor-Observer Bias0.0%This article: 0.0%Lillian Mann: 2.0%One America News Network: 3.9%In-Group Bias0.0%This article: 0.0%Lillian Mann: 0.8%One America News Network: 1.9%Out-Group Homogeneity Bias0.0%This article: 0.0%Lillian Mann: 3.3%One America News Network: 5.3%Halo Effect0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.4%Horn Effect0.0%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.0%Dunning-Kruger Effect0.0%This article: 3.3%Lillian Mann: 1.0%One America News Network: 2.0%Recency Bias3.3%This article: 0.0%Lillian Mann: 0.4%One America News Network: 0.7%Primacy Effect0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Blind-Spot Bias0.0%This article: 0.0%Lillian Mann: 1.2%One America News Network: 2.5%Ad Hominem0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.6%Straw Man0.0%This article: 25.5%Lillian Mann: 4.2%One America News Network: 6.3%Appeal to Authority25.5%This article: 11.9%Lillian Mann: 1.6%One America News Network: 1.8%False Dilemma11.9%This article: 8.8%Lillian Mann: 0.5%One America News Network: 1.1%Slippery Slope8.8%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.1%Circular Reasoning0.0%This article: 3.3%Lillian Mann: 4.2%One America News Network: 5.4%Hasty Generalization3.3%This article: 0.0%Lillian Mann: 0.4%One America News Network: 0.5%Red Herring0.0%This article: 0.0%Lillian Mann: 0.5%One America News Network: 1.1%Bandwagon0.0%This article: 11.6%Lillian Mann: 6.7%One America News Network: 10.0%Appeal to Emotion11.6%This article: 0.0%Lillian Mann: 1.3%One America News Network: 1.7%Begging the Question0.0%This article: 3.1%Lillian Mann: 2.5%One America News Network: 3.0%Post Hoc (False Cause)3.1%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.2%Tu Quoque0.0%This article: 0.0%Lillian Mann: 0.8%One America News Network: 0.6%Burden of Proof0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.2%Appeal to Nature0.0%This article: 0.0%Lillian Mann: 0.2%One America News Network: 0.3%Composition/Division0.0%This article: 7.4%Lillian Mann: 1.0%One America News Network: 1.6%Anecdotal7.4%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%No True Scotsman0.0%This article: 11.0%Lillian Mann: 1.8%One America News Network: 1.4%Ambiguity (Equivocation)11.0%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Middle Ground0.0%This article: 0.0%Lillian Mann: 0.7%One America News Network: 0.2%Personal Incredulity0.0%This article: 5.2%Lillian Mann: 0.2%One America News Network: 0.3%Special Pleading5.2%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.7%Genetic Fallacy0.0%This article: 1.6%Lillian Mann: 1.7%One America News Network: 1.7%Unattributed Quote1.6%This article: 2.5%Lillian Mann: 1.6%One America News Network: 1.5%Quote-first Misdirection2.5%This article: 9.0%Lillian Mann: 5.0%One America News Network: 5.2%Biased Writer Voice9.0%This article: 0.0%Lillian Mann: 0.8%One America News Network: 1.1%Indoctrination0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Lillian Mann: 2.9%One America News Network: 3.8%Politically Right Leaning Bias0.0%This article: 3.6%Lillian Mann: 1.5%One America News Network: 1.5%Attempt to Sell a Product or S…3.6%

611 words analyzed.

Speakers

6speakers59%attributed speech249writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 15 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageOAN Staff • 4 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageCNN • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageKalshi • 12 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageKalshi • 12 words • 0.0% coverageKalshi • 15 words • 0.0% coverageKalshi • 10 words • 100.0% coverageKalshi • 17 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageKalshi • 27 words • 0.0% coverageRobert DeNault • 22 words • 0.0% coverageRobert DeNault • 16 words • 0.0% coverageRobert DeNault • 21 words • 0.0% coverageRobert DeNault • 30 words • 0.0% coverageRobert DeNault • 23 words • 0.0% coverageBlake Moore • 40 words • 100.0% coverageMark Moran • 13 words • 0.0% coverageMark Moran • 32 words • 0.0% coverageMark Moran • 16 words • 0.0% coverageMark Moran • 27 words • 0.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverage
Selected voice

Blake Moore

100%flagged-word coverage
40 attributed words11% of attributed speech83% writer coverage
0%50.0%100.0%Biased Writer Voice+94.0 ptsWriter: 6.0%Blake Moore: 100.0%100.0%Attempt to Sell a Product -8.8 ptsWriter: 8.8%Blake Moore: 0.0%0.0%Quote-first Misdirection-6.0 ptsWriter: 6.0%Blake Moore: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.