A week doesn’t seem to go by without another news story about potential “insider trading” in prediction markets. Some examples are serious—like when a solider is alleged to have traded on classified information related to the capture of then-Venezuelan President Maduro. Some other examples are silly—like when a White House teleprompter operator is alleged to have traded on the words President Trump would say in a speech.
The sheer volume and kooky fact patterns of these cases have inspired the Senate to ban trading among members and staff, news organizations to prohibit their employees from betting on prediction markets, and officials at the Commodity Futures Trading Commission (CFTC) to chest thump that insider trading won’t be tolerated.
Though there’s been a flurry of activity to create the appearance of market integrity across prediction market platforms, the actual law governing these trades is murkier and full of gaps. The reason is that CFTC-related insider trading laws weren’t designed for exotic bets on everything from pop culture events to the World Series. Instead, they were designed for the agricultural and financial derivatives markets—a much different set of products with a much different set of needed market integrity guardrails. And even when conduct is covered by the law, the CFTC isn’t exactly well-equipped to police it.
Insider Trading in Prediction Markets: What the Law Says
When Congress rewrote financial laws in the Dodd-Frank Wall Street and Consumer Protection Act of 2010, it added a new section 6(c) of the Commodity Exchange Act (CEA) to prohibit fraud and manipulation “in connection with any swap” listed to trade on an entity registered with the CFTC.1 The inspiration for this provision was the movie Trading Places, where the plot hinged on an ill-gotten government report related to frozen orange juice concentrate and the traders who attempted to profit from it.
A year later, the CFTC implemented this provision of law by adopting Rule 180.1, which generally prohibits persons from trading on market moving information that they have a duty to protect; tipping or trading on information a person received by virtue of their employment; trading on information obtained by fraud or deception; or other forms of trading in which market participants take advantage of their customers by exploiting, front-running or improperly disclosing their data.
How CFTC Insider Trading Has Typically Differed from Stock Market Insider Trading
When people think of classic “insider trading” they’re generally thinking of securities markets and the relevant law and judicial precedent under the Securities and Exchange Commission’s (SEC) Rule 10b-5. Examples include CEO Jeffrey Skilling selling Enron shares before the company’s financial fraud came to light or Martha Stewart selling stock when her broker told her the FDA was about to reject a company’s drug application.
But the swaps market differs substantially from the stock market. For one, derivatives traders typically lack a fiduciary relationship with their counterparties. As an example, oil speculators facing head-to-head in the market don’t owe one another anything, so long as the information they’re trading on was acquired by legal means (i.e., one party didn’t nab top secret information from the Pentagon). In contrast, in the securities market, corporate insiders would be violating their duty to their shareholders if they trade on information before it’s public. The jump in the stock price due to the success of a drug trial or acquisition of a new patent must accrue to the entire market equally upon release of the information, not one corporate executive who can buy artificially low and sell high because they’re hoarding information.
Additionally, derivatives markets are traditionally different from the stock market in that some level of insider trading is kind of the point. For example, if a farmer knows this year’s corn crop will be a dud, she’d probably like to take out an insurance policy to guard against a poor harvest. Swaps—as originally contemplated in agricultural markets—act as an insurance policy of sorts, where participants with an insurable interest in a commodity trade with speculators and create a marketplace where price discovery occurs.
Enter Prediction Markets
The nuances and limits of the CFTC’s insider trading enforcement authorities generally worked fine in the 15 years after the passage of Dodd-Frank. The agency brought its first insider trading case in 2015 and a handful of other cases thereafter, all of which were related to employees trading from their personal accounts based on information they stole from their employer or, alternatively, brokers trading on information stolen from customers. These cases all generally fit the standard fact patterns envisioned by Dodd-Frank and the CFTC’s Rule 180.1.
But the explosion in prediction markets in the last 18 months has raised questions about the adequacy of the CFTC’s legal authorities. In other words, there are plenty of examples where the public would colloquially think of a prediction markets trading pattern as “insider trading,” but where it may not meet the definition under the CEA.
For example, the Wall Street Journal reported that Jeff Bezos’ stepson tipped his frat brothers off to his stepdad’s non-attendance at the Super Bowl, allowing those frat brothers to make money on prediction market Kalshi. That’s probably not “insider trading” according to CFTC Rule 180.1 (because his son and his friends aren’t violating a duty to anyone), though it may upset other traders and generally diminish confidence in the integrity of the market.
There are fuzzier examples, too. Take a California gubernatorial candidate that bet on his own election on Kalshi—and broadcast that bet via social media. While the CFTC said that the candidate “potentially” violated Rule 180.1, the agency declined to bring charges, instead heralding Kalshi’s own action to fine the candidate and kick him off their platform. While the CFTC didn’t explain their reasoning, the lack of prosecution likely underscores the ambiguity over whether “insider trading” applies if a person trades on information about their own intentions. As Bloomberg columnist Matt Levine notes, the rules around election candidate-related insider trading are “variable and unsettled.”
Even in the example of former politician George Santos—who traded on information about his own attendance at the State of the Union—the CFTC was only able to settle charges because Santos deceived the public about his intentions on social media. Had Santos merely bet on his own actions without misdirecting other bettors using deceptive social media posts, he likely could’ve escaped prosecution.
The Problems with Self-Policing
Because of the poor fit of CFTC insider trading law to the explosion in prediction markets, the agency has relied on self-policing as the mode to achieve market integrity. As the above example with Kalshi demonstrates, platforms themselves use the terms and conditions in their app to enforce market integrity.
But there are manifold problems with this do-it-yourself approach. First, prediction market platforms don’t have the force of law and can’t impose penalties that meaningful deter misconduct. And unlike the CFTC, which can bring civil enforcement actions to impose market-wide injunctions or lifetime trading bans, prediction markets can’t stop users from simply migrating to another platform. Kalshi barring someone from using its app does not protect the public if that person can simply turn around and do the same thing on Polymarket or another prediction market.
Second, policing these markets is hard. Realistically, how is a platform supposed to figure out if a trader is Bad Bunny’s brother’s barber who got a hot tip about his Superbowl halftime show?2 In the cases where platforms or the CFTC has caught insider trading, it’s typically because someone on social media identified the person or because the bettor was greedy or sloppy in using identifiable information. That type of luck is no way to run financial markets.
Third, when we rely on platforms to police themselves, there are important conflicts of interest. Compliance architecture is expensive. Policies that are too tight may hurt trading fee income. Big scandals coming to light might undermine confidence in the market. Platforms have mixed incentives to crack down too hard on manipulative trading.
Finally, even when insider trading is caught, it’s not like investors are made whole. In the stock market, investors can be remunerated if a corporate insider traded on confidential information. But in prediction markets, traders who were on the other side of the corrupted bet don’t get their money back, even if the prediction market kicks the trader off the platform or imposes penalties.
Is the CFTC Equipped?
As this piece demonstrates, the CFTC’s insider trading authorities are not fit for purpose for the ever-expanding prediction markets landscape. And even if the law was well-targeted, the CFTC doesn’t have nearly enough resources to do the job well. Since the current Administration took over, the agency has lost about a quarter of its staff, which has responsibilities far beyond prediction markets. In fact, the CFTC is supposed to be ensuring market-driven prices in everyday commodities like oil, wheat, cotton, natural gas and corn—not to mention the financial derivatives that contributed to the 2008 crash and additional crypto products, which Congress is currently contemplating. It is unfathomable to think that the agency has sufficient resources to track every wacky bet that prediction markets want to offer to their customers.
Conclusion
The most natural answer to this problem is for the CFTC to stick to its core mission and stop the spread of unregulated gambling outside of longstanding state and tribal authority. Short of that, lawmakers need to substantially rethink insider trading protections and the bandwidth of the agency—because as it stands, it’s inside out.
The framing here conflates two distinct mechanisms. Insider trading in equities works because corporate insiders possess *non-public* information about cash flows that the market hasn't priced. Prediction markets don't have that same information asymmetry structure. The signal quality argument from the JRFM banking study actually cuts the other way here.