As Utah battles Kalshi in court, Moore and Curtis look to regulate prediction markets in Congress

Utah's lawsuit against prediction-market platform Kalshi is proceeding in court while Reps. Blake Moore and John Curtis are pursuing bipartisan legislation to regulate prediction markets in Congress. These developments raise the prospect of new federal rules that constitute a sectoral regulatory risk for Kalshi and similar fintech platforms, but are unlikely to move broader markets.

Analysis

A move toward a clear federal framework for event/prediction markets structurally favors well-capitalized, regulated exchanges and clearinghouses that can absorb compliance costs and monetize new product flows through clearing, data, and market-making services. Expect incumbents to capture initial economics: transaction fees and data licensing are high-margin and scale with handle, so even modest reallocation of retail/speculative flow (single-digit percentages) could be a low-single-digit revenue lift for large exchanges within 12–24 months.

Second-order winners are liquidity providers, HFTs, and institutional brokers that plug into regulated rails: they will internalize order flow, arbitrage binary prices with options/volatility desks, and create inter-product spreads that broaden revenue per trade. Conversely, early-stage consumer apps and unregulated platforms face two simultaneous pressures — higher compliance costs and a flight of institutional counterparties — accelerating consolidation.

Key catalysts and timing: near-term legal rulings (weeks–months) set enforcement precedents; congressional language and jurisdictional clarity will take 6–18 months to crystallize; productization and meaningful trading volume migration are a 12–36 month story. Tail risks include a restrictive federal preemption or fragmented state patchwork that raises onboarding costs and could shut out smaller entrants entirely, or a high-profile market abuse episode that triggers an immediate clampdown.

The consensus frames this as a binary win/lose between regulators and startups; that misses the redistribution mechanics. A regulated outcome is more likely to enlarge the addressable market for institutional-grade derivatives (new volatility products, corporate-event hedges, data licensing) than to eliminate it — the short-term pain for challengers may seed longer-term revenue streams for incumbents and liquidity providers.

Key Decisions for Investors

• Long CME (CME) — buy shares with a 12–24 month horizon. Rationale: clearing/data/fee capture from regulated prediction product flows. Target +20% upside if adoption ramps; downside ~12–15% if regulatory outcomes ban or fragment markets. Tactical: size as a core risk-managed position (3–5% of equity sleeve) with a 12% stop-loss.
• Long ICE (ICE) or CBOE (CBOE) — overweight either exchange operator for 12–24 months. Rationale: cross-sell of data, listings, and clearing; incremental margins are high. Risk/reward ~2:1 (20%+ upside vs 10% downside). Prefer ICE if you want a larger mix of data/market infrastructure exposure.
• Long market-making/flow-capture names (VIRT) — 6–18 month horizon. Rationale: HFT/market makers arbitrage binary-event pricing into options/vol products; they scale cheaper than exchanges. Trade: buy VIRT stock or 9–12 month call spread to limit capital; target >25% upside with defined premium loss max.
• Pair trade — Long CME (or ICE) / Short Robinhood (HOOD) — 6–12 months. Rationale: incumbents win regulated flows and monetization; retail-first brokers face compliance friction and potential product restrictions. Structure via equal-dollar long CME + short HOOD (or buy HOOD 9–12 month puts) to get asymmetric payoff; aim for 2:1 reward:risk.
• Event hedge: buy 6–12 month puts on small-cap fintechs with heavy consumer/derivatives exposure if a restrictive federal ruling arrives. Rationale: fast downside if enforcement or state-level bans fragment liquidity. Keep position sized small — these are tail hedges (allocate <1–2% of portfolio).