Trump Wasnt The Only Winner On Election Night. Prediction Markets Also Won Over The Polls
Donald Trump claimed a dramatic victory over his rival Kamala Harris in the race to become the 47th President of the United States, but he wasnt the only winner on election night. Decentralized prediction markets like Polymarket, Kalshi, and PredictIt also emerged as clear winners in their battle with traditional pollsters.
In the final days before election night, pollsters claimed that the two candidates were neck-and-neck and that the result was too close to call. The famed statistician Nate Silver gave Harris a slight edge, saying she won 50,015% of the time across 80,000 simulations, though he also said that he had a gut feeling that Trump might just edge it.
However, the crypto-based prediction markets had a very different view. Polymarket put the odds solidly in favor of a Trump win, giving the newly crowned President-elect a 58% chance compared to just 42% for Harris. Meanwhile, Kaishis platform put Trumps chances at 57%. Those views proved to be a much more accurate picture of reality.
The rise of prediction markets
During the run-up to election night, the prediction markets received considerable attention from traditional media. Their odds were often reported alongside those of traditional polls, indicating a significant shift in the way public sentiment is gauged.
Prediction markets are platforms that enable people to bet on the outcome of future events, and they leverage the principle of wisdom of the crowd.
To participate on platforms like Polymarket, bettors buy shares in a potential outcome, and the price of those shares can be used as an indicator of the perceived probability of that outcome. It creates a real-time indicator of public sentiment; many believe it can indicate trends before they materialize.
Incentives Inform Collective wisdom
For crypto advocates, it wasnt at all surprising that the prediction markets were able to nail the election result.
The theory is that prediction markets, which encourage people to put money on the line when they express their opinions, provide a more realistic indicator of an outcome than any individual expert can come up with. Even if the people who bet on the outcomes are not particularly well-informed, no one wants to lose their money, and collective wisdom emerges from that aversion.
Those prediction market whales were more dialed in than the polling experts [that are] paid millions of dollars to predict elections, said Bitcoin entrepreneur Anthony Pompliano in a tweet on Wednesday.
Most bettors were probably not surprised either. Traditional betting markets have often been fairly accurate in predicting the results of U.S. elections, with one study showing that they correctly predicted 11 out of 15 election results between 1884 and 1940.
Theres a lot of academic research to support the accuracy of prediction markets, too. Harry Crane, a statistics professor and researcher at Rutgers University, published two peer-reviewed studies, in 2018 and 2020 that compared the accuracy of PredictIt against the statistics-based predictions of Silver across various Senate, House, and governor elections.
In both papers, Crane concluded that PredictIts betting market forecasts were more accurate than those of Silver.
According to Crane, the difference comes down to the methodology. He posits that polls ask people who they want to win, whereas prediction markets are more focused on who people think will win. In prediction markets, people dont express their preferences, but instead try to make an accurate prediction based on what they know, to try to earn a financial reward. It incentivizes them to think more logically, thus making them more accurate, Crane believes.
Not infallible
Of course, it has to be pointed out that prediction markets dont always get it right. In 2016, U.K. bookmakers odds were overwhelmingly in favor of Britains rejecting Brexit to stay in the European Union. That same year, PredictIt users were heavily in favor of Hilary Clinton, at one time giving her an 82% chance of prevailing over Trump in that years U.S. presidential election.
As it turned out, the smart money got it wrong. The U.K. voted for Brexit and Clinton suffered a resounding defeat, paving the way for Trumps first presidency.
In addition, there are valid concerns that the nature of prediction markets leaves them susceptible to potential manipulation. In a blog post, the hybrid exchange platform GRVT highlighted suspicions that a number of multi-million dollar bets on Polymarket, including a $30 million wager from a French crypto whale, were an attempt to manipulate the odds in their favor. Polymarket insisted that it had conducted an internal investigation and found no evidence of foul play, but the bets made headlines in mainstream publications like Fortune, which raised fears that decentralized platforms aiming to reflect public opinion may not always do so.
Moreover, critics say that the idea that the wisdom of the crowd is always accurate simply isnt true. As we saw with the COVID pandemic, herd mentality can result in distorted views. Another challenge for prediction markets is the overrepresentation of certain demographics, which may skew the odds. As GRVT pointed out, Polymarkets user base is overwhelmingly made up of young males who are fanatical supporters of crypto. Such a demographic may not mirror the public opinion of Americas broader public, as its widely known that most in the crypto community favored a Trump win, due to his apparent advocacy of digital assets.
Changing the future of forecasting
Despite these challenges, the result of the election makes it clear that prediction markets have a lot of potential. During the 2024 US election, platforms like Polymarket, Kalshi and PredictIt would constantly adjust their odds in real-time, in response to real-world events such as the assassination attempts on Trump, outpacing the traditional polls, which take time to collate their data.
This underscores the unique capacity of prediction markets to assimilate new information more rapidly and reflect any changes it causes in market sentiment.
Theres also the distinct possibility that prediction markets may improve over time. Ethereum co-founder Vitalik Buterin believes that the rise of artificial intelligence and machine learning will enable such platforms to access more accurate predictive analytics, making them even more valuable forecasting tools.
One technology that I expect will turbocharge info finance in the next decade is AI (whether LLMs or some future technology). This is because many of the most interesting applications of info finance are on micro questions: millions of mini-markets for decisions that individually have relatively low consequence, Buterin said in a recent blog post.
Lest we forget, these platforms are not just about people betting and trying to make a quick buck. Theyre about predicting outcomes, and they help to shape a more informed general public.
The U.S election was a strong validation of the usefulness of prediction markets, and even if they dont always get it right, its safe to speculate that theyre going to play a much bigger role in the future of forecasting.
Text source: ZyCrypto
