Every prediction market trade has a hidden cost beyond the exchange fee: the bid-ask spread. If a contract's best bid is 87 cents and the best ask is 91 cents, a trader buying at the ask and immediately selling at the bid would lose 4 cents per contract — before fees.
On Kalshi, spreads vary enormously across markets. High-profile political or macro-economic markets often have 1-2 cent spreads with deep order books. Obscure weather brackets or niche event markets can have 5-10 cent spreads with minimal volume.
A liquidity-first approach prioritizes execution quality over the perceived attractiveness of any individual trade thesis.
Consider a strategy with a 2% theoretical edge per trade. In a market with a 1-cent spread, the effective cost of entry and exit is roughly 1 cent. In a market with a 6-cent spread, the cost consumes the entire edge and then some.
This is not unique to prediction markets. Amihud and Mendelson (1986) demonstrated that across equity markets, assets with wider bid-ask spreads require higher gross returns to compensate — a phenomenon known as the illiquidity premium. The same principle applies to binary contracts.
Several observable metrics serve as proxies for market liquidity:
The academic field of market microstructure studies how trading mechanisms affect price formation, liquidity, and transaction costs. Several foundational concepts are directly relevant to prediction market trading:
Glosten and Milgrom (1985) showed that bid-ask spreads exist in part because market makers face the risk of trading against better-informed counterparties. In prediction markets, this means spreads tend to be wider in markets where information asymmetry is high — for instance, a weather market where some participants may have access to private meteorological models.
In thin markets, a single order can move the price significantly. Kyle (1985) formalized how informed trading affects prices as a function of market depth. On Kalshi, this means that in low-volume markets, the act of buying or selling can move the price against you, further eroding any theoretical edge.
Harris (2003) outlines the tradeoffs between market orders (immediate but costly) and limit orders (cheaper but uncertain). In liquid Kalshi markets, a limit order near the current price is likely to fill quickly. In illiquid markets, a limit order may sit unfilled while the market moves.
Research by Tetlock (2008) found that thin liquidity in prediction markets can distort prices away from true probabilities. This has a dual implication: illiquid markets may offer larger mispricings, but those mispricings are harder to exploit because of the execution costs involved.
Wolfers and Zitzewitz (2004) noted that prediction market accuracy tends to be highest in the most liquid markets, where informed traders can efficiently correct mispricings.
Prediction Pilot's Liquidity First template filters for the most actively traded markets with the tightest spreads on Kalshi.
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