Prediction Market ETFs: A Foray into the Mainstream or Playing with Fire?

marsbit发布于2026-02-22更新于2026-02-22

文章摘要

Several major ETF issuers, including Bitwise Asset Management, GraniteShares, and Roundhill Investments, have recently filed applications with the U.S. SEC to launch prediction market ETFs. These ETFs are designed to track the outcomes of U.S. political events, such as the 2028 presidential election and the 2026 midterms, allowing investors to trade election probabilities through traditional brokerage accounts like Robinhood or Fidelity. Prediction markets aggregate crowd-sourced forecasts using real-money contracts, where prices reflect the market’s consensus probability of an event occurring. Platforms like Polymarket and Kalshi have demonstrated strong predictive accuracy in events like the 2024 U.S. election, often outperforming traditional polls due to their incentive-based structure. The proposed ETFs would track the price movements of these prediction market contracts, with share values fluctuating between $0 and $1. If the predicted event occurs, the corresponding “Yes” ETF would settle near $1; otherwise, it would approach $0. Unlike Bitcoin ETFs, which track asset prices, these are binary outcome products, more akin to options or insurance. If approved, these ETFs could bring prediction markets into mainstream finance, offering new tools for hedging and macro risk management. However, concerns remain about potential market manipulation, public perception influence, and regulatory approval, as the SEC may view them as gambling-like instruments. The move represents...

Original | Odaily Planet Daily (@OdailyChina)

Author | DingDang (@XiaMiPP)

Recently, ETF issuers Bitwise Asset Management and GraniteShares have filed applications with the U.S. Securities and Exchange Commission (SEC) for prediction market ETFs. Among them, Bitwise submitted six products under the "PredictionShares" brand, and GraniteShares quickly followed with a structurally similar proposal. A bit earlier, on February 13th, Roundhill Investments also submitted documents for a similar type of product.

The core of these ETFs is tracking the outcomes of U.S. political elections. They attempt to package the "probability of outcomes" of U.S. political elections into a financial product that can be traded directly in traditional securities accounts. Specifically, the underlying focus is on the 2028 presidential election (whether a Democrat or Republican wins) and the control of the Senate and House of Representatives in the 2026 midterm elections.

In other words, investors in the future might no longer need to go to the crypto world's Polymarket or register with the CFTC-regulated Kalshi; they could simply open their Robinhood or Fidelity account and, like buying a stock, bet on "who will win the White House."

Screenshot from @jason_chen998

What does this leap forward signify?

Why Do Prediction Markets Always Seem "One Step Ahead"?

The "foresight" of prediction markets regarding political events is hardly new.

A prediction market is a group of people using real money to express judgments. Participants buy and sell "Yes/No" contracts to express their confidence in an event occurring. The prices of these contracts fluctuate between $0 and $1, representing the market's consensus on the probability. For example, if you believe a candidate has a 70% chance of winning, you might buy a "Yes" contract for $0.70. If the event happens, the contract's worth rises to $1; otherwise, it becomes worthless.

This is a form of capital-weighted collective judgment. Unlike mere verbal expression, participants must bear the consequences of profit and loss for their judgments, as was vividly demonstrated in the 2024 U.S. election. At that time, trading volumes on Polymarket and Kalshi surged rapidly, with political contracts becoming the absolute mainstay. In the days leading up to the election, the cumulative trading volume on Polymarket for the single market "2024 Presidential Election Winner" was approximately $3.7 billion. Kalshi, a later entrant, won a key lawsuit against the CFTC in September 2024, allowing it to legally offer election-related contracts. By November, its monthly trading volume reached $127 million, with about 89% coming from politics and election markets.

More noteworthy is the signal the data itself conveyed. Weeks before the 2024 election, the probability of a Trump victory on Polymarket stabilized above 60%, while mainstream polls showed a tight race, sometimes even with a slight lead for Harris. The result? The prediction market seemed to "read" the election situation early.

This doesn't mean prediction markets are "magically accurate," but over multiple election cycles, they have indeed shown a strong ability to aggregate information. Research has found that, with sufficient liquidity and broad participation, the statistical performance of prediction markets often surpasses that of traditional poll samples. The older platform PredictIt has also been repeatedly regarded as an effective information aggregator. In contrast, traditional polls are susceptible to factors like sample bias and response bias.

The root of the difference lies in the incentive structure: polls express attitudes, prediction markets bear consequences. The former has no cost, the latter has clear profits and losses. This structural difference determines how information is processed.

Although prediction markets cooled down after the election—Polymarket's daily trading volume plummeted by about 84% after the results were announced—the number of prediction market projects grew rapidly entering 2025. By 2026, according to data from predictionindex.xyz, there are already 137 prediction market projects, with the leading player Polymarket's total trading volume exceeding $50 billion and monthly trading volume reaching $8 billion.

From a fringe experiment to a mainstream track, prediction markets are a far cry from what they used to be. Now, imagine if participation could be made easy through ETFs, this collective intelligence could more widely influence public perception of political events.

How ETFs Package Prediction Markets

So, how do these ETFs translate the玩法 (play/mechanism) of prediction markets to Wall Street?

What these issuers are essentially doing is translating the contract prices of prediction markets into a product structure understandable by the securities market. Dressed in the cloak of an ETF, it allows you to buy and sell through a正规 (formal/regular) brokerage account, but you're still betting on the outcome of a political event.

Taking the six ETFs submitted by Bitwise as an example, four directly target the 2028 presidential election (Democrat/Republican win), and the remaining two correspond to control of the Senate and House in the 2026 midterms. The structures from GraniteShares and Roundhill are largely similar. Simply put, these ETFs directly map the price performance of those binary event contracts on Kalshi or Polymarket into tradable ETF shares.

Mechanically, the share price of these ETFs will fluctuate between $0 and $1, like the contracts, reflecting the market's real-time consensus on the event probability. The funds will invest at least 80% of their assets in derivatives linked to these political events, such as contracts obtained from CFTC-approved exchanges like Kalshi, or use synthetic swaps to replicate the performance. The buying process is the same as buying a stock: through brokerage accounts like Robinhood or Fidelity, with expense ratios expected to be between 0.5% and 1%, and the trading venue is likely to be NYSE Arca.

At settlement, if the event occurs (e.g., a Democrat wins the presidency), the corresponding "Yes" ETF's value approaches $1; otherwise, it approaches $0. Bitwise's plan is that shortly after the event outcome is determined, the fund will liquidate and terminate, distributing the remaining assets pro-rata to holders; some products from GraniteShares and Roundhill are more "flexible," potentially allowing a "roll" into the next election cycle.

Compared to the Bitcoin ETFs we are familiar with, there is a clear distinction. Bitcoin ETFs like BlackRock's IBIT track the price of Bitcoin, with unlimited upside or downside potential, suitable as part of an asset allocation. Prediction market ETFs are more akin to binary probability bets, with a cap fixed at $1, similar to buying insurance or options—winner takes all, loser loses everything.

The question is, when probability becomes a tradable asset, is it still merely an information aggregation mechanism?

Mainstreaming, or Gamblification?

If these ETFs are approved, prediction markets will truly enter the mainstream financial view.

Currently, political prediction markets are still concentrated among crypto users or professional traders. Once ETFs are launched, the participation barrier for institutional capital and traditional investors will be significantly lowered. Companies might use them to hedge policy change risks, and portfolio managers might see them as macro risk management tools. Liquidity will be amplified, and price signals might become sharper.

But the problems on the other side are equally obvious. The 2024 election already proved that prediction market prices are cited by media, amplified on social platforms, and even influence public sentiment. When probability is packaged as "market consensus," it is easily interpreted as an objective trend. If the scale of capital further expands, could there be deliberate attempts to manipulate prices to influence public opinion? PredictIt was embroiled in legal disputes in its early days due to compliance issues; such concerns are not unfounded.

Regulation remains the biggest uncertainty. The SEC might worry this is essentially the "gamblification" of finance, increasing the risk of manipulation or moral hazard. The approval process might come with conditions, such as trading limits or additional disclosures. Currently, the CFTC allowing Kalshi to trade election futures is a positive signal, but the SEC's stance remains unclear.

Conclusion

From crypto-native markets to Wall Street ETFs, prediction markets are undergoing an identity transformation. However, before the regulatory framework is clear, the moves by issuers seem more like a probe—testing regulatory boundaries and the market's acceptance of "assetized probability."

相关问答

QWhat is the core function of the prediction market ETFs recently filed with the SEC by Bitwise and GraniteShares?

AThe core function of these ETFs is to track the outcome of U.S. political elections, specifically the probability of a particular party winning, and package it as a tradable financial product in traditional securities accounts.

QHow does the mechanism of a prediction market differ from a traditional opinion poll?

AThe key difference lies in the incentive structure. Opinion polls involve expressing an attitude with no cost, while prediction markets require participants to back their judgment with real money, directly linking their financial gain or loss to the outcome of the event.

QWhat is a significant potential risk associated with the mainstream adoption of prediction market ETFs mentioned in the article?

AA significant risk is the potential for 'gamblification' of finance and the possibility of market manipulation to influence public opinion, as the price signals from these large-scale markets could be misinterpreted as objective trends.

QHow do the proposed prediction market ETFs, like those from Bitwise, handle the settlement process after an election outcome is determined?

AFor Bitwise's ETFs, if the event occurs (e.g., a party wins), the corresponding 'Yes' ETF's value will be close to $1. The fund will then be quickly liquidated and terminated, distributing the remaining assets to shareholders proportionally.

QWhat major regulatory body's stance is still uncertain regarding the approval of these prediction market ETFs, according to the article?

AThe stance of the U.S. Securities and Exchange Commission (SEC) is still uncertain, as it may view these products as a form of gambling and have concerns about manipulation or moral hazard, despite the CFTC having already approved election contracts on platforms like Kalshi.

你可能也喜欢

OpenAI后训练工程师翁家翌,给Agentic AI提出了新范式假设

OpenAI工程师翁家翌提出名为“启发式学习”的新范式,探索AI通过自主编写和修改代码来提升能力,而非仅依赖训练神经网络参数。 在实验中,他让Codex在明确目标和反馈闭环中,为Atari Breakout等游戏编写纯Python策略代码,通过反复运行、查看日志与回放、定位失败并修改代码,最终使策略在Breakout中达到理论满分。这种“启发式学习”将经验沉淀为可阅读、修改和审计的软件系统,而非难以解释的神经网络权重。 文章对比了启发式学习与深度强化学习的差异:前者更新的是代码结构和规则,具备更好的可解释性、更高的样本效率,并能通过回归测试等方式缓解灾难性遗忘问题。在Atari57游戏的批量测试中,该方法在部分游戏上表现出接近或超越传统强化学习算法的效率,但在需要长程规划的复杂任务中仍存在局限。 该范式的潜在产业意义包括:为机器人控制等场景提供更轻量、可审计的解决方案;提升安全关键系统的可解释性与可维护性;以及为智能体产品提供能力沉淀和共享的新路径。然而,其实用性仍需在更复杂的真实场景中进一步验证。 翁家翌认为,未来更可能是神经网络(负责快速感知等)与启发式系统(负责规则、记忆与安全)结合的分工模式。这预示着AI发展的一条可能路径:在强大编码智能体的辅助下,部分经验可以重新转化为可读、可维护的软件工程资产。

marsbit19分钟前

OpenAI后训练工程师翁家翌,给Agentic AI提出了新范式假设

marsbit19分钟前

你的 Claude 今晚要做梦了,别打扰它

Anthropic在开发者大会上为AI智能体平台引入了“做梦”(Dreaming)功能,这实际上是一种基于历史运行日志的离线批处理与自我优化机制。AI智能体在完成复杂任务后,会利用闲置时间自动回顾大量操作记录,从中提炼有效模式(例如更优的操作路径),并固化为可共享的记忆,从而提升后续任务效率。 类似机制也出现在其他AI产品中,如Hermes Agent的“Curator”功能可自动将经验整理成“Skill”,OpenClaw的“做梦”流程则细分为浅睡、快速眼动和深睡三个阶段,通过多维度加权决定哪些信息应存入长期记忆。 该功能与“记忆”(Memory)技术紧密相关。当前AI能力的核心挑战之一是如何有效管理与利用不断增长的上下文信息。一方面,行业正通过技术创新(如Subquadratic公司宣称的1200万token超长上下文模型)试图扩大信息容量;另一方面,“做梦”这类功能旨在让AI在有限上下文窗口内,主动筛选、巩固重要信息,模仿人类睡眠中的记忆处理过程。 文章指出,科技公司频繁使用“思考”“记忆”“做梦”等拟人化术语来描述AI功能,这不仅是技术类比,更是一种营销策略和认知塑造。它模糊了机器与人的边界,影响用户对产品的感知与期待,并在无形中转移了技术缺陷的责任归属。本质上,AI的“做梦”仍是一种消耗计算资源的自动化数据处理,但其命名方式却让我们更倾向于将其视为拥有内在生命的数字实体。

marsbit20分钟前

你的 Claude 今晚要做梦了,别打扰它

marsbit20分钟前

交易

现货
合约
活动图片