The week ahead turns on Taiwan Semiconductor’s (TSM: NYSE) earnings. For all the noise around AI, geopolitics, and industrial policy, this is still the company that tells us what is actually shipping, what is stuck in labs, and what customers are quietly postponing. We’re here for it. And of course the huge JP Morgan Health Care conference will be in San Francisco — we’re looking for the intersection of AI in real companies and, of course some overhyped ones as well (yes, Tempus (TEM: NYSE) comes to mind.)

Check out the website, password free! It’s pretty: https://epistrophy.beehiiv.com 

As always, I’m focused on three things:
1) Technology-driven change;
2) the latest in innovation and startup trends, and;
3) stock fraud.

Companies Discussed

In This Note:

1. Companies Discussed

2. Who Knew Before the News?

   1. Does a Maduro bet prove blatant corruption in pred …

3. Tweet O’ The Week

4. Epistrophy In The News

5. 📆 of Epistrophy Events

6. Availability This Week

Who Knew Before the News?

Does a Maduro bet prove blatant corruption in prediction markets?

The trade hit the tape late Friday night. No press conference. No cable chyron. No presidential post. Yet the probability jumped.

Proof again that this is the Golden Age of Fraud.

On Polymarket, a contract asking whether Venezuelan President Nicolás Maduro would be out of power surged just before 10 p.m. ET, after sitting in the low single digits for weeks. One newly created account committed roughly $30,000 to the proposition. By Saturday morning, after Donald Trump announced Maduro’s capture, that position paid out $436,759.61.

For many Americans, the news felt sudden. For at least one trader, it was actionable.

The episode is not remarkable because someone made money. That happens every day. It is remarkable because the market moved before the announcement, with no public disclosure, no visible leak and no obvious path by which an ordinary participant could have known what was about to occur. The price was right. The process was not.

The same integrity problem has surfaced before in sports betting, most vividly during Super Bowl LV. In February 2021, streaker Yuri Andrade publicly claimed he had wagered roughly $50,000—spread across multiple accounts—on a proposition bet that a fan would run onto the field during the game, an outcome he then personally engineered. The wagers were reportedly placed on offshore sportsbook Bovada, which later moved to identify accounts linked to prior knowledge of the stunt, refund losing bets, and void or withhold payouts where insider involvement was suspected. The episode underscored a long-standing vulnerability in outcome-based wagering: when participants can influence—or pre-know—the outcome, enforcement relies largely on after-the-fact detection by platforms rather than clear, ex ante insider-trading prohibitions.

This is why prediction markets matter now. They are increasingly accurate. They are also increasingly detached from the regulatory logic that governs every other market where accuracy moves money.

As Robinhood Markets (HOOD: NASDAQ) pushes further into prediction markets, it has built a real-money event-contract ecosystem alongside its traditional equities, options, and crypto trading. In March 2025, Robinhood launched a Prediction Markets Hub in its app in partnership with KalshiEX LLC, a CFTC-regulated exchange, allowing U.S. customers to trade contracts on real-world outcomes such as the Federal Reserve’s target rate, sports results, and other binary events for cash payout.

By August 19, 2025, Robinhood expanded this offering to include pro and college football prediction contracts, enabling users to trade on NFL and Power-4 college game outcomes on the same interface where they trade stocks and options. Throughout late 2025, the firm has continued to broaden its slate of event contracts—sports, politics, economics, and entertainment—announcing plans in October 2025 to add 100+ additional markets across sports and political subjects.

Robinhood’s prediction markets are offered through Robinhood Derivatives, LLC and utilize Kalshi’s CFTC-regulated framework; the broker describes these as speculative event contracts rather than traditional bets. The rapid rollout of these products—and their integration into a mainstream brokerage app used by millions—has made prediction markets one of Robinhood’s fastest-growing business lines, even as regulators in some states challenge their legality under local gambling laws.This expansion exposes a regulatory tension. While these contracts often operate under commodities-style frameworks and are framed as tools for price discovery or hedging, they do not consistently carry the insider-trading prohibitions, disclosure requirements, or market-conduct rules that are foundational to securities regulation. Information advantages that would be illegal in equities or options markets may be permissible—or at least ambiguously treated—in prediction markets. The result is a structural asymmetry: economically meaningful markets trading on real-world outcomes, but without the full integrity regime that governs comparable financial instruments.

If prediction markets continue to grow inside mainstream brokerages—gaining liquidity, institutional participation, and influence over expectations—the separation between “betting” and “financial” markets may become untenable. At that point, convergence is likely. Either prediction markets will be pulled toward the regulatory standards of financial markets, or financial platforms will increasingly resemble lightly regulated wagering venues. In either case, the presence of firms like Robinhood suggests that prediction markets are no longer an academic curiosity. They are on a path to coalescing with traditional markets in form, scale and  consequence — forcing regulators to confront whether market-integrity rules should follow function rather than labels.

If you want, I can tighten this further to match a law-review tone, or sharpen the third paragraph into a more explicit normative claim about why convergence is inevitable.

The technical case for prediction markets is well established. Justin Wolfers and Eric Zitzewitz demonstrated more than two decades ago that market prices can be interpreted as probabilistic forecasts rather than mere wagers. In “Interpreting Prediction Market Prices as Probabilities” (2006), they showed that election markets routinely outperformed contemporaneous polling averages in forecast error, particularly late in election cycles, when incentives to incorporate marginal information were strongest.

Robin Hanson’s earlier work framed prediction markets as a general-purpose information aggregation technology rather than a niche political curiosity. In “Decision Markets” and related papers, Hanson argued that markets outperform committees precisely because they reward accuracy directly and punish error financially rather than reputationally.

Meta-analyses reinforce the point. A 2020 systematic review by Roberto Louis Forestal and co-authors examined dozens of empirical applications and found that prediction markets matched or exceeded alternative forecasting methods in a majority of cases, including elections, corporate sales forecasts and public health outcomes.

For investors the difference between regulated securities markets and prediction markets might not be obvious – especially when the interfaces look the same. But the contrast in investor protections is stark.

Accuracy, however, is not the same thing as fairness.

From a microstructure perspective, prediction markets resemble thin binary-options venues. Contracts pay a fixed amount if an event occurs and nothing if it does not. Prices represent implied probabilities, not claims on underlying cash flows. There is no requirement that traders disclose how they know what they know. There is no prohibition on trading with material nonpublic information. There is often no meaningful surveillance of who is trading, when or why.

In regulated securities markets, that combination would be intolerable.

The U.S. spent nearly a century constructing guardrails around the idea that markets should not reward access to hidden information at the expense of public participants. After the 1929 crash, Congress concluded that price discovery alone was insufficient. Investors needed protection from manipulation, selective disclosure and insiders trading with impunity. The Securities Exchange Act of 1934 embedded that judgment into law.

The academic literature explains why. In “The World Price of Insider Trading” (2002), Utpal Bhattacharya and Hazem Daouk showed that countries enforcing insider-trading laws experienced a statistically significant decline in the cost of equity capital. Investors demanded lower risk premia when they believed markets were not rigged.

Even so, informational advantage remains powerful. In “Estimating the Returns to Insider Trading” (1999), Leslie Jeng, Andrew Metrick and Richard Zeckhauser found that U.S. corporate insiders earned abnormal returns of roughly 0.40% per month despite existing restrictions, underscoring how valuable early access to information can be even in heavily regulated markets.

Prediction markets discard this framework almost entirely.

They do so deliberately. Advocates often argue that allowing insiders to trade improves price accuracy. That claim is largely correct. It is also beside the point. U.S. securities law does not prohibit insider trading because it produces wrong prices. It prohibits it because it produces unfair ones.

The Maduro trade illustrates the distinction with unusual clarity. The price on Polymarket was informative. It was also inaccessible. If a trader had advance knowledge of a covert operation or credible diplomatic signals unavailable to the public, the market rewarded that knowledge handsomely. Everyone else supplied liquidity.

This is textbook adverse selection.

George Akerlof’s “The Market for ‘Lemons’” (1970) is usually taught using used cars, but finance adopted the lesson decades ago. When participants suspect they are trading against better-informed counterparties, they withdraw. Liquidity erodes. Markets shrink or collapse. Regulation exists to prevent that unraveling.

Prediction markets invert the lesson. They rely on informed traders overwhelming uninformed ones. When un-corrupt traders lose they exit. Prices sharpen. From a forecasting standpoint, the system works. From a public-policy standpoint, it does not.

This is why prediction markets can appear uncannily prescient. They are efficient precisely because they are exclusionary.

Empirical evidence supports this interpretation. Studies of betting and prediction markets consistently show that a small number of informed accounts drive price discovery while uninformed participation subsidizes accuracy. Raphael Flepp and co-authors demonstrated that uninformed traders using “statistical noise” increase liquidity but reduce welfare by transferring wealth to informed participants.

Platform design reinforces the imbalance. Interfaces emphasize probabilities, realized gains and leaderboards while obscuring informational asymmetry. Legal and behavioral scholarship on “gamblification” documents how features borrowed from online gambling increase engagement without improving comprehension. Sharon Rabinovitz and Nizan Packin’s “All Bets Are On” (2025) details how prediction markets incorporate gambling mechanics while disclaiming gambling regulation, exposing users to risks they do not fully perceive.

The economic scale remains modest. Volumes on individual political contracts are trivial compared with equities or futures. But scale is not the relevant metric. Precedent is.

Prediction markets are being integrated into mainstream fintech products. They are marketed as informational tools rather than wagers. Their accuracy is used as a shield against scrutiny. Yet they lack every protection that makes modern financial markets socially sustainable.

The Maduro episode has already triggered political attention. Rep. Ritchie Torres is expected to introduce the Public Integrity in Financial Prediction Markets Act of 2026, which would restrict participation by federal officials and certain political insiders. The logic mirrors a century of securities regulation: if you help make the news, you should not be allowed to trade on it.

That may be only the beginning. Courts have narrowed agencies’ interpretive authority. Platforms are expanding faster than rules. The gap between what markets know and what the public is told is widening.

Prediction markets will continue to be right more often than polls. That is not in dispute. The question is whether society is willing to accept markets that are accurate because they are unequal.

U.S. financial history suggests the answer, eventually, is no. Markets are not judged solely by the quality of their prices. They are judged by who is allowed to profit from them.

The bottom line is simple. It is clearly possible – and entirely corrupt – to profit from major news events before they become news. The Maduro trade proved that again.

Tweet O’ The Week

Epistrophy In The News

On NewsNation (linked here), I joined Nicole Berlie to discuss my research into fraud risks and predictive markets, focusing on the suspicious trading that appeared to anticipate Nicolás Maduro’s ouster. We talked about timing, access, and why lightly regulated betting markets now raise questions that will feel familiar to anyone who has covered insider trading in financial markets.

I was also quoted in Yahoo! News (article here) in Laura Bratton’s reporting on renewed investor confidence in Intel, following claims that the company has shipped a functioning gate-all-around, back-powered 18A chip. The real issue is not the stock’s reaction, but whether Intel’s manufacturing roadmap is finally closing a credibility gap that has widened for most of the past decade.

📆 of Epistrophy Events

Availability This Week

I’ll be in our San Francisco office at the Ferry Building all week. It’s a good stretch for follow-ups, background conversations, and longer, off-the-record discussions that don’t fit neatly into a TV segment or a text.

Written reports are available to clients, with video summaries on YouTube, and of course our popular summaries of the summaries on Instagram, TikTok, and YouTube Shorts.