Prediction markets garnered attention in recent elections, calling results ahead of traditional polling by leveraging real-time updates that reflect the latest shifts in public sentiment and emerging information. While some expect these platforms to lose relevance after the election, their true utility extends far beyond this single event.
Skeptics of prediction markets often cite the case of the “French Whale”—a major trader on Polymarket who reportedly invested $28 million in Trump “win” contracts, noticeably shifting the market odds. While this high-volume trading undoubtedly affected odds, it does not equate to manipulation. These price shifts reflect the theory of market efficiency, whereby an asset’s value encapsulates all available information—a dynamic in which new data inherently moves the price.
Just as we make mundane predictions in our everyday lives (what time to leave the house to avoid traffic, how much coffee to drink to stay alert without jitteriness), prediction markets serve a similar purpose, translating available data into a singular probability that illuminates potential futures.
The value of prediction markets stems from their capacity to convert uncertainty and chaos into clear, actionable knowledge.
Forecasts are social good
“Just want to say that I moved from Kyiv to Lviv on Feb 13 /entirely/ thanks to this prediction thread and the Metaculus estimates…Thank you, everyone,” wrote an account under the username availablegreen on Metaculus, an online forecasting platform speculating on topics of global importance, in Feb. 2022, just days after Russia invaded Ukraine.
The Metaculus market “Will Russia invade Ukrainian territory before 2023?” projected a 50% chance of invasion a month prior, rising to 74% just a week before the invasion took on Feb. 24, 2022. This strong probability of invasion was produced by a collective pooling of insights from individuals responding to ongoing developments.
By contrast, expert forecasts in the media during this period were filled with cautious language, often hedging with phrases like “Russia could invade at any time” or “there’s a possibility of invasion,” which left the public with ambiguous views. The prediction market, however, synthesized diverse perspectives into a single, evolving probability, offering a clearer, data-driven forecast that adapted to new information.
Aswath Damodaran, NYU finance professor, explains why prediction markets outperform traditional forecasting methods. In his LinkedIn post, “The Wisdom (and Madness) of Crowds: Market Prices as Political Predictors,” Damodaran argues that prediction markets produce accurate probabilities because they reflect a crowd’s collective assessment, rather than relying on any single expert’s judgment. By aggregating a range of individual insights, prediction markets filter out personal biases, resulting in a data-driven probability that constantly adjusts as new information arises:
- Information aggregation: One of the almost magical aspects of well-functioning markets is how pieces of information possessed by individual traders about whatever is being traded get aggregated, delivering a composite price that is effectively a reflection of all of the information.
- Real time adjustments to news: While experts (rightfully) take their time to absorb new information and reflect that information in their assessments, markets do not have the luxury of waiting. Consequently, markets react in real time, often in the moment, to events as they unfold, and studies that look at that reaction find that they often not only beat experts to the punch but deliver better assessments.
- Law of large numbers: It is true that individual traders in a markets can make mistakes, often big ones, in their assessments of value, and can sometimes also let their preconceptions and biases drive their trading. To the extent that these mistakes and biases can lie on both sides, they will average out, allowing the “right’ price to emerge from several wrong judgments.
In essence, prediction markets are a “living” assessment tool, constantly evolving as information flows in and out. This fluidity, combined with the financial stakes that drive accuracy, creates a forecast that is balanced, real-time, and backed by collective insight.
By better approximating the truth, prediction markets’ value comes from offering society a more accurate probabilities that enable individuals and organizations to make well-informed decisions in times of uncertainty.
Stimulating the demand for accuracy
In an era flooded with information, the challenge isn’t access to data but rather knowing which predictions to trust. Anyone can forecast based on any piece of data, but without a mechanism to verify accuracy, many predictions remain speculative noise.
Traditional media and expert predictions leave people more confused than informed because they rarely quantify risks. In Superforecasting, Philip E. Tetlock criticizes traditional forecasting’s lack of accuracy checks. When experts speculate on major global events, they hedge with words like “might” and “could”.
Tetlock points out that accuracy in forecasting remains elusive, largely because the audience doesn’t demand evidence of accuracy. A prediction holds little value if it provides less to no clarity before it is stated.
In contrast, prediction markets turn probability into something real. When traders invest in a market, they have a stake in its accuracy. Thus, they would be more likely to calculate thoughtfully and weigh evidence.
With these markets, people who are consistently wrong lose money, while accurate forecasters profit. Public leaderboards and portfolios also provide a track record of winning and losing trades. These features raise the quality of predictions and make traders accountable.
The graph above illustrates the dynamics of supply and demand in the accuracy of predictions. If the value of a forecast—its accuracy—is shaped by supply and demand, then, before the advent of prediction markets, demand for precise forecasts was low.
With no mechanism to hold forecasters accountable or test accuracy rigorously, the market was flooded with low-quality predictions, where supply far exceeded demand, leading to a low overall value of forecasts.
Prediction markets increased the value of forecasts in two ways. First, prediction markets raised the demand for reliable forecasts, since traders who make incorrect predictions are penalized by losing money. When a forecast is verifiable, traders will choose the most accurate opinions to ensure their financial outcomes.
Second, prediction markets reduced the supply of low-quality predictions. Political pundits and online personalities, by theory, would be more careful in making predictions (the cost of making a prediction is higher), decreasing the supply. With this mechanism in place, prediction markets increase the supply of accurate forecasts.
With clear parameters, people can approach uncertain events with greater confidence, knowing that each probability reflects the collective input of all participants.
A new era of informed decision-making
The true value of prediction markets lies in their ability to turn collective insights into actionable knowledge. Prediction markets use a market-based system to aggregate probabilities, creating a mechanism that enhances human reasoning. By combining individual speculations into a single probability, they provide a reliable tool for individuals and organizations to make informed decisions.
Much like the stock market reflects corporate performance, event contracts capture public sentiment and forecast outcomes on political, economic, and cultural fronts.
However, to fully realize their potential, prediction markets must address a significant challenge: liquidity. The liquidity problem limits the accuracy of market-generated probabilities and fuels accusations of market manipulation, as low trading volumes make it easier for large participants to influence outcomes.
To overcome this predicament, prediction market platforms must expand their customer base and ensure that the contracts listed are mainstream, relevant, and clearly defined. Only by solving this fundamental liquidity problem can they evolve into a mature asset class.
As prediction markets continue to evolve and overcome these liquidity challenges, they hold the potential to reshape decision-making by improving human reasoning and offering clear, data-driven knowledge in a landscape often clouded by ambiguity.
