Major Wall Street banks are tightening employee rules for prediction markets as concerns grow over the use of confidential information on platforms such as Polymarket and Kalshi.
Summary
- Wall Street banks are restricting employee prediction-market trades as concerns about confidential information use increase.
- Goldman Sachs bars contracts tied to finance, politics, macroeconomics, geopolitics, and bank-specific events for staff.
- Federal cases and congressional probes are pushing platforms and employers toward tighter surveillance and compliance.
Goldman Sachs, Morgan Stanley, JPMorgan Chase and Bank of America have added or updated restrictions covering event contracts, according to a Reuters report. The policies aim to reduce insider trading and conflict-of-interest risks.
Goldman Sachs limits financial and political trades
Goldman Sachs has prohibited employees from trading prediction contracts linked to financial markets, political events and other subjects that could create a real or perceived conflict with the bank, its clients or the financial sector.
The policy reportedly covers macroeconomic data, elections, geopolitics and events involving Goldman Sachs. However, employees may continue trading contracts related to sports and entertainment. Repeated violations could lead to disciplinary action or the loss of profits from prohibited trades.
Morgan Stanley has also included prediction market rules in its employee code of conduct, although the bank has not disclosed the full scope of those restrictions.
Meanwhile, Bank of America recently gave employees clearer examples of banned activity. Its policy restricts contracts involving company-specific developments, macroeconomic data and financial services. JPMorgan’s existing rules prohibit staff from trading with confidential information, including through prediction markets.
Google case raises insider trading concerns
The policy changes follow a federal case involving Google software engineer Michele Spagnuolo. Prosecutors allege that he used confidential Google search data to earn more than $1.2 million on Polymarket.
According to the Department of Justice complaint, Spagnuolo allegedly accessed internal trend information before trading on markets connected to Google search results.
Prosecutors said he risked about $2.75 million through an account called “AlphaRaccoon” between October and December 2025. His trades allegedly generated $1.2 million after Google released the relevant information publicly.
The charges remain allegations, and Spagnuolo is presumed innocent unless proven guilty. However, the case showed how employees could use information that does not affect a company’s share price to profit from event contracts.
Polymarket and Kalshi face wider scrutiny
Lawmakers have also examined whether prediction platforms can detect users who trade with classified or nonpublic information.
Meanwhile, the House Oversight Committee requested records from Polymarket and Kalshi after reports of suspicious trades linked to military and political events.
The inquiry included allegations that a U.S. Army sergeant earned more than $409,000 by using classified information connected to a military operation involving former Venezuelan President Nicolás Maduro. Those claims also remain subject to court proceedings.
Meanwhile, Congress has considered restrictions on prediction market trading by government officials. The proposals seek to stop officials from wagering on political outcomes or public policy matters they could influence or learn about before the public.
Platforms strengthen market surveillance
Prediction market operators have responded by expanding compliance systems. Kalshi created an independent surveillance committee and partnered with Solidus Labs to monitor suspicious trades and possible manipulation.
Kalshi has also introduced employer disclosures for users trading in sensitive markets. Its systems assign risk scores to contracts involving corporate performance, national security and other subjects that may attract traders with private information.
Still, researchers disagree over how strict the rules should become. A recent study covered by crypto.news found that blanket insider trading bans could reduce prediction market accuracy by removing information from prices.
The study supported tougher enforcement when traders obtain information through leaks, stolen records or direct control over an outcome. However, it separated those cases from traders who gain an advantage through public research.
