Beginners to prediction markets are likely to make basic trading mistakes if they don’t fully understand the pricing mechanism they’re trading on. Dr. Harry Crane, a Rutgers University statistics professor who also studies prediction markets, helps explain market pricing and how to avoid common trading mistakes.
Crane’s 2024 presidential election model used prediction markets to forecast Trump’s victory with greater confidence than political polls and several prediction market platforms.
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On the eve of Election Day, Crane’s model gave Trump a 66.5% chance of victory, higher than Polymarket (63%), Kalshi (59%), and Betfair (61%). Manifold and Metaculus gave Trump about a 49% chance of winning, which Crane attributed to possible “herding,” a trading pattern in which other users bet based on popular predictions instead of prices near the market consensus.
With such large differences across platforms, it’s important to understand what goes into a prediction market price and how to interpret it. Crane spoke with Prediction News to explain the differences between prediction market platforms and how to tell a reliable price from a fuzzy one.
A prediction market price may claim to represent the “true” probability of an event occuring. However, a prediction market price is only accurate if the market is structured properly. In an inefficient election market for example, a 52% chance isn’t really 52%.
“If a market gives, let’s say, 52% to a candidate, well that could mean in a market that’s relatively efficient, that could mean that the real value is between 51 and 53 according to the market, whereas if it’s very inefficient it could mean anything from 40 to 60,” Crane said.
Prediction market prices aren’t just about attracting enough traders. It’s also about structural obstacles to a true price.
One of the most common obstacles is fees. Prediction market exchanges charge fees on winnings to operate the exchange profitably. Some markets also have maker and taker fees, which Kalshi’s sports contracts have so they can be listed on Robinhood.
PredictIt has 10% fees on winnings, because it’s an academic prediction market that limited the platform’s profitability to get clearance from the CFTC. Fees come in different sizes, and as a result, they can change the market’s prices in crucial ways.
On Election Day, PredictIt had Trump at 64% when Kalshi and Polymarket had him at 72%. Traders had to buy Trump at a lower price to sell it at a comparable price at commercial platforms. That pattern was consistent in the days and weeks leading to Election Day.
Kalshi and Polymarket have both surpassed 70% for Trump.
Will Trump fall back to 65% tonight? pic.twitter.com/19AirIciuy
— Prediction News (@PredictionNews_) November 6, 2024
As Crane explains, fee considerations will often skew your trading strategy in terms of buy or sell prices.
“You know that it [the true price] should be 50 cents,” Crane said. “You’re happy to sell it for 51, but on PredictIt, that’s not good enough because of all the fees, so you might have to sell it at 55 cents.”
Crane has called the fuzziness resulting from pricing issues the margin of inefficiency. It’s most commonly caused by platform fees and low trading volume. Understanding a platform’s margin of inefficiency will help traders avoid losses that sneak up on them.
He gives the example of a market with prices of 60/40, but whose true price is 50/50. A trader who pays 60 cents when the contract is really worth 50 cents is overpaying by 20%.
“You have to look at relative differences, not absolute differences,” Crane said. “That is a 20% difference in relative terms. So what that means then is if you’re actually that far off in your pricing, then you’re essentially losing 20% of your money.”
A 20% difference is a lot of money at scale. If a trader wants to buy 100 contracts, they could pay $10 less for 100 contracts if they traded at the true price instead of the inflated price.
Buying $100 worth of contracts makes the difference even more stark. Spending $100 on 50-cent contracts nets a trader 200 contracts. A $100 trade only gets a trader 167 contracts at 60 cents. That means if traders win, they miss out on $33 additional winnings they could have had if they had traded elsewhere.
| Contract Price | Price of 100 Contracts | $100 Worth of Contracts |
| 50 cents | $50 | 200 contracts |
| 60 cents | $60 | 167 contracts |
Extreme values carry even greater risks and rewards. If traders are deciding between a 1-cent and 2-cent contract, the price difference is 100%. Paying a dollar’s difference for 100 contracts may not seem important, but the relative difference is the one that matters.
The relative difference between prices becomes even more important at extreme values.
“It is important to be careful about those lower extreme value prices, especially in the 1%, 2% range, because those tend to be the least accurate by far for various reasons, one of which is that they’re just the hardest ones to really price. But also being wrong by a small amount, being wrong by a tenth of a cent actually can be pretty costly for people who are doing this at large scale.”
| Contract Price | Price of 100 Contracts | $100 Worth of Contracts |
| 1 cent | $1 | 10,000 contracts |
| 2 cents | $2 | 5,000 contracts |
A trader who spends $100 on 1-cent contracts would receive 10,000 contracts, twice the number as someone who spent $100 on 2-cent contracts.
The difference is greater if those traders win. The 2-cent trader would make $5,000 less than the 1-cent trader, a large difference for such cheap contracts.
One of the quickest ways to gauge a market’s efficiency is to look at a market’s trade volume. A market with high trade volume is more likely to have enough money and traders to converge on a price, even if individuals have extreme positions.
However, trade volume is only correlated with efficiency. It’s not the only factor to consider when deciding how seriously to take a prediction market price.
“If you had a market that, in theory, had no transaction fees whatsoever, no commissions whatsoever, and it was just operating commission-free, even if it was…a pretty low liquidity market, there’s no friction to [thinking], ‘if you see a good price there [then] why not bet it?’” Crane said.
Even small markets can attract the few traders knowledgeable enough about a topic to arrive at a price with a tight bid-ask spread. The bid-ask spread is the difference between what traders will buy and sell a contract for. When the spread is low, traders have come close to the market’s true price–fees aside.
Regardless of size, the ability to enter and exit a trade position is crucial. They need enough people trading for fun to offer and accept contracts, and they need professionals who have specific prices in mind to set prices to begin with.
“The recreational money are the ones who drive the market activity, but the professionals are the ones who drive the pricing,” Crane said. “But the professionals can’t really price a market if there’s no activity, because they’re not going to just trade against other professionals because there’s no money to be made.”
One of the biggest differences between a professional and an amateur trader is how specific a price a trader is willing to accept. Amateurs will accept a range of prices while professionals have a hard number in mind. That hard number is the one prediction market prices converge on.
It takes many moving parts to make a prediction market function properly. Different types of traders from skill level to ideological bias come together to create a prediction market price that can be as useful as its most passionate proponents claim. But inefficient markets will expectedly deviate from true pricing (and odds) for specific prediction market outcomes.
© 2024 Prediction News – All Rights Reserved.
© 2024 Prediction News – All Rights Reserved.
