Who is on the other side of a prediction market trade

The other side of a prediction market trade is rarely just another retail investor making a casual guess. While individual traders account for the vast majority of active exchange accounts, professional market makers, automated execution algorithms, and institutional participants supply much of the filled trade volume.

Both exchanges and regulators explicitly confirm that platforms function strictly as neutral matching engines rather than direct counterparties. Kalshi states that "you are always trading against another member of the platform, not the exchange itself".¹ The Commodity Futures Trading Commission (CFTC) confirms that regulated exchanges "do not take a side of the trade",² while Polymarket notes that prices "emerge from supply and demand as users trade with each other".³

When you execute an order, your counterparty typically falls into one of four primary participant categories:

• Professional market makers: Specialized firms paid by the exchange to keep buy and sell offers constantly available on screen.
• Informed and algorithmic traders: Quantitative traders, fast arbitrage bots that catch mispriced contracts, and early-information insiders.
• Traders closing or protecting positions: Early buyers cashing out profits, businesses buying risk insurance, and bots copying big market moves.
• System activity that looks like a trade: Artificial self-trading volume, small test orders posted by bots, and the clearinghouse that handles final payouts.

Firms that are paid to be on the other side.

• Market makers. Professional trading firms paid by exchanges to keep buy and sell offers continuously available. By standing on both sides of the market, they profit from the small gap between buying and selling prices while earning exchange incentives. Firms like Susquehanna⁴ and Wintermute⁵ supply liquidity, and designated market makers can receive lower trading fees and larger position limits.⁶
• The exchange's own trading affiliate. Some platforms operate an affiliated trading arm to supply contract liquidity. For example, Kalshi operates Kalshi Trading as a separate corporate entity to post offers on its own platform.⁷ Regulators monitor these setups closely, with proposed CFTC rules aiming to ensure exchange-affiliated orders fill last so regular traders get priority.⁸⁹
• Designated liquidity providers. Traders who sign formal agreements to guarantee active trading on specific events.¹⁰ Kalshi auctions designated provider slots for market series,¹¹ while Polymarket distributes daily cash rewards to users who post orders close to the current market price.¹²

Traders with an information or speed advantage.

• Skilled directional traders. Quantitative traders and specialized funds use statistical models to spot mispriced event contracts. These traders spend extensive time researching event mechanics and monitoring order flow, often relying on specialized intelligence platforms (like Chironus) to spot edge before the market updates. A Yale and London Business School study found that a small group of skilled accounts, comprising roughly 3% of users alongside market makers, captured over 30% of total gains on Polymarket.¹³ A separate Wall Street Journal analysis revealed that just 0.1% of accounts took 67% of total profits, while over 70% of total participants lost money.¹⁴
• Arbitrage bots. Automated software programs scan prediction platforms around the clock to exploit price differences between related markets. When event probabilities shift on external news feeds or connected exchanges, these algorithms can execute trades in seconds.¹⁵ Because automated systems continuously monitor order books, stale resting limit orders are frequently filled by fast execution software before manual traders can cancel them.
• Insiders. Participants who possess private material information place trades before public event probabilities can adjust. Examples include individuals with advance access to government speeches, regulatory announcements, or event results before live broadcasts. While exchange rules strictly bar individuals with early outcome knowledge or official influence,¹⁶ regulatory enforcement cases show that insider activity remains an ongoing risk in event contracts, such as mention markets.¹⁷

Traders rebalancing or exiting positions.

• Late sellers with real time event data. Traders often sell contracts near the end of an event to lock in profits or exit losing positions. In fast resolving contracts, a seller offering an order at a discount may already hold real time outcome knowledge before standard public feeds or exchange prices update. Buyers who take these prices risk trading against someone who already knows the result.
• Hedgers. Commercial entities and individual investors use event contracts as financial insurance to offset real world risks. Kalshi lists hedgers as participants who pay premiums for downside protection.¹⁸ Because hedgers focus on offsetting external risk rather than maximizing trade profit, they willingly pay market spreads, making them favorable counterparties for retail traders.
• Copy trading bots and followers. Automated programs monitor large wallet movements and immediately replicate major order flow. These automated systems execute trades seconds after a high volume participant enters a position, moving market prices before manual orders complete. Learn more about how these automated systems track large accounts in our guide to copy trading prediction markets.

Artificial volume and market infrastructure.

• Wash traders. Artificial trade volume occurs when single entities execute buy and sell orders against themselves to simulate active trading. A Columbia Business School study reported by CoinDesk revealed that nearly 25% of Polymarket historical volume originated from users rapidly buying and selling contracts with themselves.¹⁹ Wash trading inflates displayed platform activity, making a market appear busier than it actually is without representing a real counterparty.
• Price probers. Algorithmic systems place tiny test orders on thin order books to evaluate liquidity depth or hold spread boundaries open before canceling them. These orders appear briefly on screen, but they exist to gauge exchange mechanics rather than fill actual trades.