Published on: 31/07/2026

How Behavioral Biases Distort Pricing in Prediction Markets

Prediction markets are supposed to convert everyone’s private information into a single, honest price — a Yes contract trading at 68 cents is meant to mean roughly a 68% chance the event happens. In practice, the same mental shortcuts that distort horse-racing odds and stock portfolios show up in these contract prices too, and recent academic work on Kalshi and Polymarket puts real numbers on it. Understanding how behavioral biases affect prediction markets — as specific, recurring distortions rather than abstract psychology — is the difference between trading noise and trading edge.

Key takeaways

  • The favorite-longshot bias is the best-documented distortion. A 2026 analysis of 300,000+ Kalshi contracts found cheap longshots lose money on average while expensive near-certainties post small positive returns.
  • Herding shows up as price momentum with no matching news. A study of 2,500 election markets found 58% of Polymarket’s presidential contracts had negative day-to-day price correlation — spikes that reversed, a sign of overreaction, not information.
  • A handful of large orders can move implied probability by several points with nothing new having happened, because concentrated positions on thin contracts dominate the order book.
  • Disposition effect and anchoring distort individual traders more than the headline price — they explain holding losers to expiry and pricing news off your entry point instead of current fair value.
  • None of this makes prediction markets useless forecasters. It means prices are a noisy signal with known, partly tradeable distortions, not an oracle.

behavioral biases prediction markets

Why psychology still leaks into a “priced” market

Real money and continuous trading should, in theory, arbitrage away irrational prices — if a contract is mispriced, someone with better information trades against it until it corrects.

But arbitrage here is limited: capital is capped by what individual traders risk, and on pseudonymous platforms like Polymarket there’s no reputational cost to acting on a hunch.

  • Kalshi runs a CFTC-regulated order book;
  • Polymarket only brought a CFTC-regulated US entity online in mid-2025.

Neither structure removes the psychology, it just changes which biases dominate. New to the mechanics? Our prediction markets guide covers how Yes/No pricing works first.

Favorite-longshot bias: why longshots cost too much

This is the most robust finding in the entire betting and prediction-market literature, going back decades of horse-racing data. The mechanism: traders systematically overweight small probabilities and underweight large ones, so cheap, unlikely-to-hit contracts get bid up above their true win rate, while expensive, likely-to-hit contracts trade at a slight discount to theirs.

A 2026 study of Kalshi’s trade data (Bürgi, Deng and Whelan, via an academic analysis of over 300,000 contract outcomes) confirmed this in a modern, high-volume market, holding up across politics, entertainment and economic-data contracts alike. The paper also found a striking asymmetry between the two sides of a trade — takers (accepting the best quote) lost about 32% on average, while makers (posting the quote) lost only about 10%, because takers were effectively paying for the certainty of an immediate fill.

Example: A Kalshi “Yes” contract on a double-digit underdog trades at 4 cents. Buyers keep piling in because a win pays 24-to-1 — but the research says contracts priced at 4-5 cents historically resolve “Yes” well under that rate. The “No” side at 96 cents looks boring, but it’s the side with the better long-run return before fees.

Not every economist agrees this is “misperception” rather than a taste for lottery-like payoffs, so treat it as a documented pattern, not guaranteed arbitrage. It’s especially visible in sports prediction markets, where underdogs carry the same appeal as a longshot moneyline bet.

Overconfidence and miscalibration

Overconfidence is believing your own analysis beats the market’s price, especially with weeks left before resolution. The evidence here is more mixed than for the favorite-longshot bias. Academic work on the Iowa Electronic Markets — a small-stakes market run by the University of Iowa since the late 1980s — found no significant longshot bias, but did find a transitory overconfidence effect: mispricing at intermediate horizons that faded as contracts approached expiry and more information became public.

Example: A trader convinced their model has an edge two months out buys “Yes” on a championship-futures contract at 22 cents, insisting it’s really worth 35 — then holds through weeks of the market drifting to 15 as public information (injuries, form, schedule) catches up, refusing to update because they’re anchored to their own analysis rather than the market’s.

The honest takeaway: overconfidence mispricing looks real but is smaller and more temporary than the favorite-longshot bias, fading as more traders and information enter closer to resolution.

Herding and momentum-driven pricing

Herding is trading with the tape — following a price shift because others are trading, not because you have independent information. A 2025 study out of Vanderbilt University examined roughly 2,500 political markets and $2.5 billion in volume across Polymarket, Kalshi and PredictIt during the final five weeks of the 2024 US election. Setting aside disagreements over how to score “accuracy” (Kalshi’s own team argues calibration, not naive hit rate, is the right yardstick), the researchers found something harder to dispute: 58% of Polymarket’s national presidential markets showed negative serial correlation in daily prices — a spike on one day was typically reversed the next. Coverage of that research also found daily price changes for identical contracts across platforms were weakly or negatively correlated — a sign much of the volatility came from traders reacting to each other’s order flow, not real news.

Example: During the 2024 election, one pseudonymous trader (“the French Whale”) placed roughly $42 million across four accounts backing a Trump win, at one point holding over 20% of all “Yes” shares in a market where the largest opposing holder had only 7%. Smaller traders watching that concentration had every incentive to follow it — not because it proved anything about the election, but because a position that large visibly dominates the order flow.

Practically: a price move on thin volume, or one traceable to two or three large fills, is a herding signal, not new information.

Disposition effect: holding losers, selling winners early

The disposition effect — first documented in stock trading and later observed in Iowa Electronic Markets research — is holding losing positions too long hoping for a reversal, while closing winners too early to “lock in” the gain. Traders in that research were measurably less likely to close a position after it lost value than after it gained value, even with nothing about the underlying probability changed.

Example: A trader buys “Yes” on a CPI-print contract at 60 cents. Fresh data makes “No” look increasingly likely and the price drifts to 35, but the trader won’t close or hedge — “it’s not a real loss until I sell” — and rides it to zero. The same trader, holding a separate “Yes” bought at 20 cents that’s now worth 75, sells immediately to bank the win, missing a further run to 95. Same trader, same session, opposite mistake on each side.

The fix is mechanical, not psychological: decide your exit price before you enter, and treat a stop-loss and take-profit level as fixed instructions, not something to renegotiate mid-trade.

Understanding how behavioral biases affect prediction markets

Anchoring on the current price instead of new information

Anchoring is one of the most robust findings in behavioral economics — people over-rely on an initial reference point and adjust insufficiently from it, even when new information should move their estimate much further. Direct study of anchoring inside prediction markets specifically is thinner than the favorite-longshot literature, so this section leans on the well-established general mechanism rather than a market-specific dataset — but the pattern maps cleanly onto how these contracts trade.

Example: A trader buys “Yes” at 45 cents. Genuinely new information arrives that should push fair value to roughly 65. But anchored to “45 was my cost, and it’s already at 58, so I’m well up,” they sell at 58 — leaving the rest of the repricing on the table. The reverse happens too: a trader refuses to buy “Yes” at 70 even though new information justifies 85, because “yesterday it was 50, so 70 already feels expensive.”

The discipline is re-pricing from zero every time — asking “what would I pay for this contract with no position, seeing it for the first time right now” — rather than referencing your entry price or yesterday’s level.

Availability and recency bias

Availability bias is overweighting the most vivid, recent piece of information relative to the fuller base rate. In prediction markets this looks like a price spike triggered by one salient event — a debate soundbite, a single poll, a viral clip — that overshoots and partially unwinds once the reaction fades. This is functionally the same pattern the Vanderbilt researchers picked up as negative serial correlation: a jump that reverses is consistent with overreacting to whatever just happened rather than the full picture.

Example: A viral clip during a live debate spikes a “Yes” contract on a primary outcome from 12 cents to 28 within the hour, on volume that’s mostly reactive, not analytical. Over the next three days, as fuller context circulates, the price drifts back to around 16 — closer to before the clip, adjusted for what actually changed.

Availability bias and herding compound each other: the vivid headline creates the first move, and traders following the tape amplify it well past what the news justifies.

How to actually use this awareness when you trade

Knowing the names of these biases only matters if it changes what you do at the order screen:

  • Screen for extreme longshots deliberately. Contracts under roughly 10-15 cents carry the strongest documented overpricing — treat them as a likely value trap unless you have a specific edge.
  • Check trade size before following a move. A jump traceable to one or two large fills with no matching news is a herding signal — order size tells you who moved the price, not whether they’re right.
  • Re-price from zero, never from your entry. Ask what you’d pay today with no position; your original cost is sunk and irrelevant to fair value.
  • Pre-commit your exits before you enter. A stop-loss and take-profit set in advance defangs the disposition effect, which only works on you in the moment.
  • Give recency-driven spikes time to settle. If you trade a news jump immediately, size it small and expect some reversion.
  • Understand what automation can and can’t fix. Rules-based execution removes some in-the-moment decisions, but a poorly designed bot can just as easily automate a bias — see our guide to automated trading bots for prediction markets.

One structural note: contracts referencing ranges or multi-leg outcomes rather than a single Yes/No line add framing effects on top of everything above, since traders anchor to round-number thresholds — see our breakdown of exotic derivatives in prediction markets.

18+. Prediction markets involve real financial risk — contracts can expire worthless, and you should never trade with money you can’t afford to lose. If gambling or trading stops being fun, seek support from a problem-gambling helpline in your country.

Frequently asked questions

What is the favorite-longshot bias in prediction markets?

Cheap, low-probability contracts get overpriced relative to how often they resolve “Yes,” while expensive, high-probability contracts trade close to fair value. A 2026 analysis of 300,000+ Kalshi contracts confirmed the pattern across politics, entertainment and economic-data markets.

Are Kalshi and Polymarket prices unbiased probability estimates?

Not exactly. Prices are informative and improve near expiry, but carry a documented favorite-longshot bias and, on Polymarket especially, patterns consistent with herding. Treat them as a useful but biased signal, not a calibrated probability.

What is herding in a prediction market and why does it happen?

Herding is trading on the direction others are trading rather than on independent information. Large, visible positions and fast momentum are easier to react to than verify, especially on pseudonymous platforms where one trader can dominate a thin order book.

How does the disposition effect show up in a Yes/No contract?

A trader holds a losing “Yes” position to expiry hoping for a reversal instead of closing as news turns unfavorable, while closing a winning position early for a smaller gain than holding would have paid. Same pattern documented in stock trading, observed in prediction-market research back to the Iowa Electronic Markets.

What's the difference between anchoring and the disposition effect?

The disposition effect is about when you close a position, driven by whether you’re up or down against your entry. Anchoring is about how you value new information, pricing it off your entry point instead of re-assessing fair value from scratch.

 

Can awareness of these biases actually be traded profitably?

Partially. The favorite-longshot bias is documented enough that fading extreme longshots has a positive expected edge before fees, though thin liquidity eats into it. Herding and recency effects are harder to trade since timing the reversion isn’t straightforward — awareness here helps avoid mistakes more than it builds a standalone strategy.

Do all prediction markets show the same biases?

No. Some studies of the older, smaller-stakes Iowa Electronic Markets found no significant favorite-longshot bias at all, while high-volume modern platforms like Kalshi show it clearly. Market design and trader anonymity appear to change which biases dominate.

Read Also:

Copyright © 2026 JUSTGAMERS