On April 1, 2026, Jamie Dimon publicly stated for the first time that JPMorgan Chase is considering offering clients prediction market services similar to Kalshi, Polymarket, moving this tool, long regarded as a “fringe play,” onto the agenda of leading Wall Street banks. The conflict is clear: one side represents traditional major banks, which are highly cautious and enveloped in compliance and reputational risks, while the other side, represented by Kalshi and Polymarket, dares to experiment in regulatory gray areas, and they are now facing each other head-on. Even more dramatically, Dimon explicitly excludes high-stakes areas in sports and politics, leaving ample room for market imagination and controversy while also giving regulators face. The question is posed to the reader: in the financial books of Wall Street, which bets qualify to be labeled as “investments,” and which can still only be classified as “gambling”?

Wall Street Giants Enter the Game: Renaming Prediction Markets

From Dimon's remarks, JPMorgan Chase is not simply looking to replicate the open betting models of Kalshi and Polymarket, but rather attempting to tailor a “prediction market-style tool” for institutions and high-net-worth clients. According to A/C sources, he emphasizes that part of these tools will be viewed as investment products based on “differences in professional judgment,” rather than entertainment betting tables, providing a new narrative for how traditional finance can digest emerging prediction markets.

Unlike Kalshi, which competes for compliance licenses with “event contracts,” and Polymarket, which openly offers various topics on-chain, JPMorgan Chase seems more focused on subtraction within the existing regulatory framework. Firstly, the scope of targets is narrowed—excluding sports and politics—focusing instead on constructing contracts around economic data, interest rate paths, macro policies, and financial variables, thus weakening public sentiment and opinion risks, and shaping the product as a “macro judgment + risk management” professional tool rather than a participatory guessing platform for everyone.

The binary distinction of “gambling vs investment” represents traditional finance's attempt to tame such new tools using an existing categorization framework. Some reports on English social media indicate that Dimon used similar phrasing in internal communications, but there is currently a lack of public materials that can be used to define precise standards. What can be confirmed is that in external narratives, he commits to strictly adhering to insider information regulations and emphasizes that such tools should fall under formal tracks of risk management and asset allocation, using familiar compliance language to advocate legitimacy for unfamiliar prediction markets.

Politics Locked Out: The Vacuum and Battle of Election Risks

In JPMorgan Chase's initial concept, sports and political events are directly excluded, effectively avoiding the two most contentious and high-traffic areas. For regulators, this is a “safe choice” that helps mitigate opinion risks; but for institutions actually needing to hedge complex risks, it leaves a significant gap in the most sensitive areas.

Former CFTC Commissioner Brian Quintenz publicly opposed the exclusion of political prediction markets, bluntly stating that “it is absurd to think that large institutions do not need to manage risks related to election outcomes.” Behind this statement lies an increasingly widening cognitive gap between regulatory practice and market reality: regulators prefer to avoid directly linking politics with price signals, while institutions understand that election outcomes can directly change tax policies, regulatory attitudes, fiscal expenditures, and even the structure of defense budgets.

For a long time, large institutions have been able to indirectly express their expectations and hedging needs regarding election results only through options, interest rate swaps, foreign exchange positions—for instance, betting on the probabilities of a “hawkish/dovish” government using the yield curve of government bonds or reflecting judgments on trade policy direction through currency fluctuations. However, such instruments struggle to provide “pure election risk exposure”, as political variables are diluted into a basket of macro factors, neither precise nor transparent.

Thus we see a stark contrast: on one side is Dimon’s type of self-censorship and contraction, trying to avoid touching politically sensitive areas; on the other side are former regulators like Quintenz, publicly demanding recognition of the legitimacy of “election risk hedging,” hoping political prediction markets will be viewed as serious risk management tools rather than public opinion bombs. This is not just a battle over product boundaries, but a contest over regulators' willingness to recognize real-world political risks and allow them to be priced transparently in financial instruments.

The Gray Area of Gambling and Investment: Information, Compliance, and Risk Control Language

The notion that “some bets based on information fall under the category of investment” is currently found only in secondary references from CBS-related interviews, representing a single source that lacks complete context and has not formed into operational classification standards. It resembles a concept hook grasped by public opinion, rather than Dimon or JPMorgan Chase's formal methodology, thus it must be made clear that this statement awaits further verification and clarification when used.

Nonetheless, this statement still hits upon the core gray area of prediction markets: What kind of information advantage qualifies as legitimate “research capability,” and what crosses the insider trading red line? In existing financial practices, research reports, public data, industry surveys, and professional experience constitute a “legitimate information basis,” giving institutional bets on stocks, interest rates, and credit derivatives legitimacy. However, when this information is used to participate in more direct “event outcome contracts,” it becomes challenging to distinguish it from illegal behavior based on non-public information.

To truly integrate prediction markets into the bank’s risk control system, the practical path will likely not be open to retail investors but will instead establish strict participant thresholds, trading limits, and target scopes, and legally package them as a form of OTC derivative risk management tool. This means that the product logic needs to converge toward “hedging portfolio risk exposure,” rather than “high-multiplicity short-term betting,” with pricing and risk control models included in the bank's comprehensive risk framework rather than existing as independent casino pools.

On the narrative level, traditional finance opts for a classic compliance rhetoric: continuously emphasizing “professional judgment differences,” “risk hedging,” “asset allocation optimization,” and avoiding direct expressions of “betting on the win/loss of a specific event.” Through renaming and functional positioning, it tries to find a legitimate identity for prediction markets within the regulatory gaps that does not shock compliance nerves while also meeting some institutional needs.

Crypto Natives as Pioneers: 24-Hour Prediction Machines in Market Lulls

While banks are still deliberating boundary definitions, crypto-native platforms have already provided another answer in practice. Platforms like Hyperliquid are handling significant macro and index-related trading needs during traditional market closures through various forms of perpetual contracts, effectively creating an “24-hour price discovery” battlefield (according to a single source).

On these platforms, traders are betting on variables that traditional prediction markets would typically focus on, such as the hawkish-dovish stance of the next interest rate decision, whether a geopolitical conflict in a certain region will escalate, or whether specific policies will be implemented. The difference lies in the fact that these bets are encoded into the price curves of BTC, ETH, or index derivatives, rather than expressed in explicit contract terms of “this event occurs/does not occur,” but functionally approach a part of prediction markets.

Inside viewpoints from Wall Street also show the flexibility of capital switching expression paths across different markets. According to a single source, some trading teams believe that the recent rebound in US stocks is more driven by short covering than a fundamental shift in geopolitical expectations—such judgments often manifest first in derivative markets with higher liquidity and less restriction on trading time, including crypto derivatives.

The divide between crypto and traditional is becoming evident here: the former relies on global contracts unrestricted by trading hours, providing immediate reactions and price signals when events occur; the latter attempts to reconstruct a “regulatable prediction market” within the confines of a closed banking system using fine thresholds, limits, and terms to incorporate the same risk judgments into compliance frameworks. The competition is shifting toward whose prices are more “realistic” and whose liquidity is more representative in the next stage.

Regulatory and Narrative Game: Who Defines the Future Boundaries of “Bets”?

The current discussions surrounding prediction markets essentially represent a tug-of-war of multiple forces. Bank executives like Dimon need to balance between the board, regulators, and public opinion, emphasizing compliance and reputational risks while avoiding any actions that could be interpreted as “encouraging gambling” or “manipulating political expectations.” Former regulatory officials like Brian Quintenz emphasize the real demand for hedging election risks from a practical standpoint, arguing that avoiding politics only pushes risk management into opaque OTC channels. Crypto platforms adopt the typical “jump on board first and sort it out later” strategy, occupying the high ground of price discovery within legal gray areas, forcing regulators to provide new interpretative spaces.

The stance of US regulation on prediction markets has been long fluctuating: on one hand, the CFTC remains highly alert towards political contracts, repeatedly limiting or halting related products; on the other, platforms like Kalshi continuously seek exemptions and legitimization through legal procedures. Now, when a large bank like JPMorgan Chase chooses to actively delineate “non-political” boundaries in product design, it essentially sends a signal to regulators through self-restraint: “We will not touch the most sensitive areas.”

Once institutional-level prediction markets are genuinely established, CFTC, SEC, and other regulators will be forced to directly address a question: should prediction contracts operating within institutional compliance systems be regarded as “financial products,” or as “gambling products” cloaked in a financial guise? If the boundaries are drawn too strictly, real hedging demands will continue to be pushed towards crypto and offshore markets; if the boundaries are drawn too loosely, the gray area may be systematically abused, becoming a new tool for manipulating expectations, money laundering, or avoiding disclosure obligations.

In this sense, when Wall Street begins to seriously discuss “prediction markets can be included in the risk control toolbox,” the true uncertainty lies not in technology, but in political and regulatory hesitation. What they need to decide is not just the fate of a new product, but how society will view the legitimacy of “betting on the future” over the next few decades, and who has the authority to express such bets through pricing.

From Underground Betting Tables to Bank Statements: The Next Stop for Prediction Markets

Looking back today, JPMorgan Chase's tentative move marks a turning point for prediction markets as they shift from marginal interests to mainstream visibility. Dimon chose the most conservative way to open: excluding sports and politics, limiting products to relatively “safe” areas like macroeconomics and financial variables and using risk control and asset allocation language to provide legitimacy. However, this self-imposed limitation also means that the current version of the bank's prediction market remains only a trimmed-down prototype.

The key variable that will determine how far it can go is not technology, but three invisible lines: whether regulators are willing to loosen restrictions on political prediction markets, allowing “election risk hedging” to exist in a controllable manner; whether the boundaries of insider information can be more clearly defined, to avoid completely conflating legitimate research and illegal trading in event contracts; and whether institutions can truly develop effective yet not overly complex hedging tools under compliance guardrails, rather than merely performing a superficial “engineering gesture.”

If traditional banks cannot meet these genuine risk management needs, capital will not pause— it will continue to seek more direct, higher-leverage, and less constrained betting paths through Hyperliquid and other crypto platforms, or various offshore derivatives. The result will be the marginalization of compliant markets, further shifting the focus of risk management and price discovery beyond regulatory oversight.

For readers, what is more important may not be the operational question of “whether to engage with prediction markets,” but the realization that prediction markets are evolving from the small bets of retail investors into a battlefield for the future pricing power jointly contested by Wall Street, regulators, and the crypto world. Whoever can more effectively write their judgments of the future into prices, whether within or outside the rules, will occupy a more advantageous position in the next phase of financial system reconstruction.

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