Adverse Selection

#Definition

Adverse selection is a market phenomenon where one party's informational advantage leads to an imbalanced pool of participants, typically causing those with less information to face systematically worse outcomes. In prediction markets, adverse selection occurs when informed traders consistently take the profitable side of trades against less-informed participants.

This concept explains why market makers widen spreads and why retail traders often find themselves on the losing side of transactions with sophisticated counterparties.

#Why It Matters in Prediction Markets

Adverse selection directly impacts liquidity and trading costs in prediction markets. When market makers fear trading against informed participants, they protect themselves by widening bid-ask spreads, increasing costs for all traders.

In prediction markets like Polymarket—which saw over $9 billion in trading volume in 2024 with 314,000+ active traders—adverse selection dynamics intensify around major events. During the 2024 U.S. election, open interest peaked at$510 million, attracting both sophisticated traders with superior models and retail participants following headlines. The informed traders systematically profited from this information asymmetry.

Understanding adverse selection helps explain several market behaviors:

• Why spreads widen before major news events
• Why large orders often receive worse prices
• Why some markets remain illiquid despite apparent trading interest
• Why professional traders are selective about which markets they provide liquidity in

For individual traders, recognizing adverse selection dynamics prevents costly mistakes. If your orders fill instantly and completely on a large position, you should ask why the counterparty was so eager to take the other side.

#How It Works

Adverse selection in prediction markets follows a predictable pattern:

1. Information distribution: Some traders possess superior information about outcome probabilities
2. Self-selection: Informed traders selectively enter markets where they have an edge
3. Counterparty risk: Market makers and uninformed traders face a "winner's curse"—they're more likely to trade when they're wrong
4. Defensive response: Liquidity providers widen spreads or reduce size to compensate for losses to informed traders
5. Market impact: Overall liquidity decreases and trading costs increase

Numerical example:

A market maker offers to buy Yes shares at $0.48 and sell at$0.52 on a binary market. The true probability is unknown.

An informed trader knows the actual probability is 70%. They buy Yes shares at $0.52, expecting profit.

An uninformed trader randomly buys or sells. Half the time they buy at $0.52 when true value is below that; half the time they sell at$0.48 when true value is above that.

The market maker profits from uninformed traders (the spread) but loses to informed traders (mispricing). If informed traders dominate volume, the market maker loses money and must widen spreads to survive.

If the market maker widens to $0.40 bid /$0.60 ask, trading costs rise 150% for everyone.

#Quantifying Adverse Selection: Loss-Versus-Rebalancing (LVR)

In DeFi and AMM research, adverse selection costs are formally quantified as Loss-Versus-Rebalancing (LVR), pronounced "lever." LVR measures how much an AMM or liquidity provider underperforms compared to a strategy that could continuously rebalance at market prices.

Key findings from academic research:

• AMMs trade at "stale" prices that arbitrageurs exploit before the AMM updates
• On Uniswap V3, LPs in ETH/USDC pools lost approximately $100 million after fees—demonstrating that trading fees often don't compensate for adverse selection
• LVR is distinct from impermanent loss: IL compares to holding, while LVR compares to optimal rebalancing

Implication for prediction markets: The same dynamic applies. Market makers and passive liquidity providers systematically lose to traders with faster information or superior models. The "cost of correcting prices" is paid by those providing liquidity.

#Examples

Pre-announcement trading: Before a company earnings release, traders with advance knowledge (legal or otherwise) aggressively position in related prediction markets. Market makers, sensing unusual flow, widen spreads dramatically. Retail traders entering positions pay the elevated spread cost.

Expert-dominated markets: A prediction market on FDA drug approval attracts pharmaceutical industry insiders and biotech analysts. Casual traders betting on headlines consistently lose to those who understand clinical trial data. Eventually, only experts trade with each other, and retail participation disappears.

Geographic information asymmetry: Markets on local election outcomes attract residents with ground-level knowledge about campaign dynamics. Out-of-area traders relying on national media coverage systematically underperform. Liquidity providers in these markets demand wider spreads to compensate.

Time-sensitive events: During live events (elections, sports, trials), traders with faster information feeds exploit those with delayed data. A trader watching results on a 30-second delay faces severe adverse selection against those with real-time access.

#Risks and Common Mistakes

Ignoring the counterparty: Every trade has someone on the other side. If you don't know why they're willing to take your trade, you may be the uninformed party facing adverse selection.

Chasing immediate fills: Orders that fill instantly often indicate you're trading against the market's price expectations. Patient limit orders may achieve better prices.

Underestimating specialist knowledge: Prediction markets on technical topics (scientific outcomes, regulatory decisions, complex geopolitical events) attract domain experts. General knowledge rarely competes with specialized expertise.

Trading illiquid markets without edge: Thin markets have wide spreads precisely because liquidity providers fear adverse selection. Trading these markets without strong information advantages means paying high costs for likely-losing positions.

Assuming random counterparties: Unlike casino games where the house follows fixed rules, prediction market counterparties actively try to exploit information advantages. The game is adversarial.

#Practical Tips for Traders

• Use limit orders rather than market orders to avoid paying the full spread cost to potentially informed counterparties

• Be cautious when orders fill too easily—immediate fills on size often signal you're the less-informed party

• Trade closer to resolution only if you have time-sensitive information; otherwise, adverse selection intensifies as informed traders become more confident

• Specialize in markets where you have genuine expertise rather than spreading attention across domains where others have deeper knowledge

• Monitor spread widening as a warning signal—sudden spread expansion often indicates informed traders are active

• Consider the identity of likely counterparties before trading; markets attracting institutional or expert traders present higher adverse selection risk

• Size positions inversely to adverse selection risk—smaller positions in markets where you're likely outmatched informationally

#Related Terms

• Asymmetric Information
• Liquidity
• Slippage
• Market Maker
• Automated Market Maker (AMM)
• Order Book
• Bid-Ask Spread

#FAQ

#What is adverse selection in simple terms?

Adverse selection means you're more likely to trade when you're on the wrong side. In prediction markets, informed traders selectively enter trades where they have an edge, leaving uninformed traders holding losing positions more often than random chance would suggest.

#How does adverse selection differ from asymmetric information?

Asymmetric information describes the existence of knowledge imbalances between parties. Adverse selection is the consequence—the systematic disadvantage that results when better-informed parties selectively participate in transactions. Asymmetric information is the condition; adverse selection is the outcome.

#Can adverse selection be avoided entirely?

No, adverse selection is inherent to markets where participants have varying information quality. However, its impact can be managed by: trading only in markets where you have genuine information advantages, using limit orders to control execution prices, and avoiding markets dominated by specialist traders when you lack comparable expertise.

#Why do spreads widen when adverse selection risk increases?

Market makers must profit from uninformed traders to offset losses to informed traders. When adverse selection risk rises (more informed traders, higher stakes events), market makers widen spreads to maintain profitability. Wider spreads compensate for the increased likelihood that incoming orders come from traders with superior information.

#Is adverse selection worse in prediction markets than traditional financial markets?

Prediction markets can exhibit more severe adverse selection because they often attract domain experts with concentrated knowledge advantages. A pharmaceutical researcher trading FDA approval markets has a larger edge than most stock traders have in equity markets. Additionally, prediction markets' smaller size means individual informed traders have proportionally larger impact.