As the war between prediction markets and the gambling industry rages on, two papers released from opposite sides of the aisle this month are perhaps indicative of how the regulatory process surrounding the controversial exchanges might unfold.
The dialogue was started by Elie Mishory, who previously served as Kalshi’s chief regulatory officer before becoming a senior advisor to Paul Atkins, chairman of the US Securities and Exchange Commission, earlier this year. Prior to Kalshi, Mishory was also an attorney for both the Commodity Futures Trading Commission and the Internal Revenue Service.
Mishory published a working paper this month titled “Information Asymmetry and Event Integrity in Prediction Markets: A Framework for the CFTC”, in which he addressed and codified the issues regarding insider trading and event manipulation on prediction markets. These issues have captured national headlines recently, including the arrest this week of a US soldier in connection to insider trades on the ouster of Venezuelan President Nicolas Maduro.
In Mishory’s view, “material non-public information” is a broad term that should be broken down into different categories from a regulatory perspective. Additionally, actual event manipulation, in which a participant affects the event being traded on, is a separate issue entirely and should be treated as such, he argued.
“The whole point of an event contract is to turn dispersed, unevenly distributed information about a future contingency into a tradable price,” Mishory wrote. “The question is therefore not whether asymmetry exists. The question is what sort of asymmetry the law should view as benign, what sort it should view as disclosable market risk and what sort it should condemn as abusive.”
Different types of informational advantage
When news of scandals and insider trading on prediction market breaks, Mishory said, the immediate instinct is to identify individuals connected to the underlying event and investigate them. But Mishory asserts this framing is incorrect, and instead regulators should establish a hierarchy of informational advantages. The first priority, he wrote, should be to “identify what informational advantages are improper and should be banned” and what should not.
“The right approach is to focus on the information itself,” Mishory asserted. “What kind of information is it? Was it stolen? Was it selectively shared because of a relationship? Is it simply the trader’s own non-public knowledge of facts that arise from the trader’s direct role in the underlying event or activity? Could it have been obtained through ordinary investigation, synthesis, observation, or skill? Those are the questions that matter.”
Through this lens, Mishory laid out four types of informational advantages that prediction market traders might employ, all of which he argued should be treated differently from a regulatory perspective:
- Use of non-public information that is stolen or used unlawfully
- Use of non-public own information, or information obtained directly by participants in the underlying event
- Use of non-public third-party information, or information obtained indirectly or through relationships to the underlying event
- Use of skill-based information that is gleaned through the trader’s own analysis or research
Good, bad and in-between
The unlawful use or stealing of non-public information should be clear-cut and expressly forbidden, Mishory argued. In these cases, individuals who are “monetising information that the trader had no legitimate right to appropriate for personal trading” are operating beyond the limits of acceptable market knowledge. Any exchange that allows such trading is rewarding crime and punishing lawful traders, the paper argues.
“Once trading on stolen information is tolerated, the market begins paying for disloyalty, breach, and informational theft,” Mishory wrote. “That is a straightforward market-integrity problem.”
Conversely, the use of skill-based information should be expressly allowed, Mishory argued, given that it was obtained legally through the trader’s own efforts.
The middle two categories – non-public own information and non-public third-party information – are where things become difficult. As Mishory argues, there is an inherent level of information asymmetry that cannot and should not be eradicated from trading. Financial markets and exchanges cannot require all participants to trade with identical information, as it would stifle price discovery and liquidity.
With regard to non-public third-party information, Mishory argued that it should be allowed, but only with proper disclosures. This is because the potential advantage for traders could be hard to identify but significant enough to warrant an unfair advantage. Essentially, the paper argues that traders should enter these markets at their own risk, knowing that other participants could have third-party information.
“The right disclosure is market-level disclosure,” Mishory wrote. “The exchange’s rules, contract terms, and customer-facing risk disclosures can make clear that certain markets may include participants trading on relationship-based, non-public informational access. That permits market participants to assess the character of the venue without imposing a universal reveal-your-edge rule.”
Direct knowledge permissible for trading?
Finally, Mishory asserts that non-public own information, gleaned from direct event particpants, should “generally be permitted without legal prohibition and without any special market-specific disclosure obligation beyond the ordinary disclosures that markets already make”. Here too he argues that direct participants trading with their own information has always been a feature of financial markets, on par with executives trading their company’s stock, for instance.
“The presence of traders with their own non-public information, including their own MNPI, is not an exotic distortion of a CFTC market,” Mishory wrote. “It is part of the architecture.”
All four of these informational categories are separate from actual event manipulation, which, in this debate, applies mainly to sports. Event manipulation erodes public market trust and should result in individual bans, but it should not be considered an informational problem, Mishory said.
He argued that “person-based prohibitions can be highly effective against outcome manipulation because the ability to distort the event often does attach to a category of participants. Players, coaches, referees, campaign officials, corporate decision-makers, and similar actors may pose special event-integrity risks because of what they can do, not merely because of what they know.
Using Mishory’s framework to analyse the Maduro scandal, the US special forces soldier Gannon Ken Van Dyke made the trades using non-public own information, which in itself would be legal. But given his ability to manipulate the event outcome by participating in the actual raid, he should have been prevented from trading, based on the proposed breakdown.
Russell: taxonomy does nothing for enforcement
After the release of Mishory’s paper, a response was published shortly afterward by Jon Russell, a longtime sports betting executive who previously worked for William Hill, Ladbrokes and Betway. Russell’s response, titled “From Taxonomy to Detection”, was “not a contradiction” of Mishory’s proposal.
Rather, Russell sought to analyse Mishory’s framework from an integrity and enforcement standpoint and apply it to real-world scenarios. Overall, he agreed with Mishory’s information taxonomy and the categories it establishes. But that framework hardly addresses the regulator’s job of identifying and stopping bad actors.
“The taxonomy tells you what you are looking for. It does not tell you how to find it,” Russell asserted.
Russell used examples to illustrate how “information seeps” into betting and trading markets from multiple sources. For instance, an employee on a game show might have non-public information about which contestant will be eliminated, but so might an employee from a third-party phone company that compiles the voting data, and neither are easily connectable to each other.
“The market sees only an evolving price and an anomalous order flow pattern,” Russell wrote. “Non-public status is not something the surveillance system can observe directly. It has to be inferred, imperfectly, from what the market should have known at the moment the bet was placed. Making that inference problem explicit is what any workable detection architecture has to start from. And it is the step that sits between Mishory’s framework and its enforcement.”
Prediction markets rehashing solved problems?
Much of Russell’s rebuttal centres around the idea that prediction markets are reinventing the wheel of regulated sports betting. The CFTC is embarking on a prediction market rulemaking process under the direction of Chairman Michael Selig. Selig largely deferred questions about prediction markets from House Ag Committee members last week, pointing to said rulemaking.
But might that process just be an exploration of ideas and concepts that regulated sports betting has already addressed and tested, especially with regard to event integrity? Selig is the CFTC’s lone sitting commissioner, and the agency is racing to staff up after a wave of departures from the previous administration.
There are questions as to whether this typically niche agency, which might gain additional duties if a crypto framework bill is passed, has the ability or capacity to properly surveil prediction markets on everything from finance and politics to sports and pop culture.
“Mishory has built the legal architecture. The detection infrastructure that makes it enforceable in practice – data feeds, alert logic, information-sharing agreements with event owners, investigative protocols – is the piece that is almost entirely absent from the current prediction market policy debate,” Russell wrote. “That infrastructure exists in regulated sports betting. It has been constructed over twenty years through exactly the kind of trial and error that prediction markets are now beginning.”
Jess Marquez
Jess has covered the global gaming industry since 2022. A native of Reno, Nevada, he’d like to note that it’s Ne-va-da, not Ne-VAH-da.
Original article: https://igamingbusiness.com/legal-compliance/regulation/prediction-market-research-papers/
