<\/p>\n
A few days before Christmas, Howard Qin was scrutinising weather forecasts on two laptops at home in Shanghai when he noticed prices of contracts for New York City snowfall creeping higher on the prediction market Kalshi. He checked the Times Square webcam for specks of white \u2013 affirmative.<\/p>\n
The recent Stanford math graduate and lifelong weather buff had spent about $200 on predictions that total NYC snowfall that month would top various thresholds \u2013 for example, more than 2 inches. Now the value of his shares was rising.<\/p>\n
There was just one wrinkle: Qin had a classical guitar recital to attend that night. \u201cI\u2019m not going to bail on my concert just to trade on this. That would be a little extreme,\u201d he recalls thinking. He sold and netted $327.79 \u2013 a 57% gain and his biggest win yet.<\/p>\n
That was 2024. Two years later, Qin, now 24, is still making small bets \u201cjust for fun,\u201d including on Central Park snowfall during January\u2019s megastorm (he won a few dollars). What\u2019s changed is the sheer number of people placing bets on weather markets \u2013 and the amount of money flowing through them. Trading volume for the January snowstorm topped $6 million on Kalshi, one of the largest weather contracts ever traded on the rapidly growing platform.<\/p>\n
That\u2019s still tiny compared with Kalshi bets on sports and elections. But weather trades are gaining traction, drawing in casual participants, weather experts and AI-driven weather-tech firms testing their wares.\u00a0As these markets grow, weather nerds and climate researchers are debating whether prediction markets can improve forecasts by aggregating knowledge \u2013 and, in turn, inform investment and policy \u2013 or whether they\u2019re simply zero-sum games in which uninformed gamblers make (or lose) a quick buck.<\/p>\n
Testing Weather Models<\/p>\n
Qin got into weather betting to test his meteorological knowledge. So he was excited when his managers at Windborne Systems \u2013 a weather-tech startup that launches balloons and uses the data to power an AI forecasting tool \u2013 encouraged him to use the company\u2019s models to make trades during his internship.<\/p>\n
\u201cIt\u2019s a great market to play around with to test out your predictive accuracy,\u201d said chief executive officer John Dean. He described the effort as \u201cdogfooding,\u201d a Silicon Valley term for using a company\u2019s own product to identify bugs and make improvements.<\/p>\n
The approach has had concrete payoffs. Through trading, Dean said he realised data from official weather stations \u2013 used by Kalshi and Polymarket to settle contracts \u2013 can be noisy. A sensor\u2019s temperature readings may spike in direct sun, making conditions seem hotter than they are. Windborne subsequently adjusted how it preprocesses its model-training data.<\/p>\n
There is also the straightforward motivation of money to be made.<\/p>\n
Established weather vendors sell their data \u2013 such as temperature, wind speed and cloud cover \u2013 to energy traders and hedge funds, who trade using that information. Marvin Gabler, co-founder and CEO of Swiss-based weather forecasting startup Jua, sees a way to unlock even more value: by trading on prediction markets with insights generated by his company\u2019s models.<\/p>\n
Last year, Gabler set up a separate investment vehicle, pooling funds from friends and family, and is trading on maximum-temperature contracts on Polymarket using Jua\u2019s forecasts. So far, \u201crelative returns are high,\u201d he said, though market volume and liquidity \u201care still too low for a well-sized fund.\u201d He declined to share further specifics.<\/p>\n
Better Forecasts?<\/p>\n
Not every person trading these markets is an expert in meteorology. In fact, one of the best performers on crypto-powered platform Polymarket is a total novice: A 23-year-old law student in Germany, who goes by the username Hans323 on the website, is currently the sixth-highest profit earner of all time on Polymarket\u2019s weather markets. His bets tracking daily temperatures across cities like London and New York have become a touchstone in prediction market circles, followed by eager eyes wanting to copy his strategies and turn a profit themselves.<\/p>\n
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Prediction markets may already be outperforming traditional weather forecasts, according to an analysis published last month by Patrick Brown, head of climate analytics at Interactive Brokers. He compared the implied forecasts of his firm\u2019s prediction markets to those from the US National Weather Service and found the former were more accurate. The key difference, Brown contended, was that prediction markets better \u201cincentivised human judgment.\u201d<\/p>\n
\u201cThere is a direct financial reward for being accurate and a direct financial penalty for being inaccurate. This creates a dual effect of attracting accurate individuals and systems into the market while deterring those who are inaccurate,\u201d Brown wrote.<\/p>\n
Still, some weather bettors are sceptical of the idea that their aggregated wagers produce any useful information. While Dean says weather prediction markets can reflect human intuition \u2013 unlike AI weather forecasts \u2013 Atte, a 40-something Finnish freelance software developer who is also among the top weather traders on Polymarket, describes his weather bets as \u201ccompletely worthless.\u201d That\u2019s despite his raking in some $33,000 on them\u00a0since October.<\/p>\n
\u201cI would prefer to be societally useful,\u201d said Atte, who goes by 1-800-LIQUIDITY on Polymarket and declined to share his full name because of safety concerns. He has sought jobs at AI startups that would put his coding chops to use, but for now he\u2019s applying them to weather markets, including writing software to automate trades.<\/p>\n<\/p>\n
As the climate veers further into uncharted territory \u2013 the last 11 years were the hottest on record and the warming may be accelerating \u2013 critics also worry that the gamification of weather could bring perverse consequences, including data manipulation and the sabotaging of weather stations.<\/p>\n
Other prediction markets may have already seen foul play. Late last year, a live map of the Russia-Ukraine war produced by a Washington, DC, think tank was mysteriously altered \u2013 long enough for Polymarket to resolve a bet that Russians had captured a city they hadn\u2019t. And in March, an Israeli reporter claimed Polymarket users tried to pressure him to change a story involving a missile strike outside Jerusalem.<\/p>\n
Polymarket did not respond to emailed questions.<\/p>\n
Science Markets<\/p>\n
Beyond the open-access platforms of Kalshi and Polymarket, scientists are building bespoke trading platforms that they hope will draw out societally useful information. There, weather experts with little or no betting or professional investing experience are stepping into betting pools designed to discover insurance-relevant risk \u2013 and infusing their slow-moving research with adrenaline.<\/p>\n
Mark Roulston is a PhD planetary scientist who spent a decade at investment firm Winton Group, incorporating weather and climate information into quantitative trading strategies. He developed a passion for prediction markets as a way to help institutions extract hidden information from diverse groups of researchers, who are invited to bet on their expertise using other people\u2019s money.<\/p>\n
That\u2019s the first and biggest difference between Roulston\u2019s effort and conventional markets: A sponsor supplies the betting money, rather than the losers funding the market. The sponsor always \u201closes,\u201d in the sense that it\u2019s giving away money without placing a bet. But it \u201cwins\u201d by cultivating the intelligence expressed by the participants, who keep whatever money they win.<\/p>\n
\u201cWe create markets not because we think, \u2018Oh, this is something lots of people will want to bet on,\u2019\u201d Roulston said. \u201cWe create a market because there\u2019s an end user \u2013 like a reinsurance company \u2013 that says \u2018We would like better predictions of this.\u2019\u201d He says this approach is closer to the vision for prediction markets advocated by economic luminaries such as Nobel Prize winner Kenneth Arrow.<\/p>\n
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Winton Group backed Roulston\u2019s first formal prediction effort in 2018, which aimed to forecast UK spring and summer temperature and precipitation.<\/p>\n
\u201cIt was very addictive, actually,\u201d said Kristian Strommen, a climate science and weather forecasting researcher at the University of Oxford. Making live forecasts is \u201cvery different from what you do in academia \u2013 writing a paper, and there\u2019s no immediate application of anything.\u201d<\/p>\n
That experience also taught participants about the mutable shape of expertise in a betting market. Strommen\u2019s team of half a dozen or so ended up placing second in the competition of 24 teams, behind a one-man operation.<\/p>\n
After registering solo, \u201cI actually ended up winning the competition, much to the annoyance of those guys,\u201d said Andrew McRae, at the time a fellow Oxford researcher.<\/p>\n
McRae, who still participates in Roulston\u2019s markets, now on a team with Strommen, said he won in part because his solitary position made him hyperconscious of how he was spending his time. He knew his colleagues\u2019 strength and combined size meant their model was probably more nuanced. So he looked for small bits of leverage, making improvements on the margins by writing programs that allowed him to trade faster than other teams and by betting on underpriced outcomes, rather than on likeliest outcomes.<\/p>\n
\u201cIt felt a lot more like a real financial market where there\u2019s lots of other participants on there, and the market is sort of correct,\u201d he said. \u201cAnd it\u2019s just like, I\u2019ve got a little bit of information on top of that.\u201d<\/p>\n
The Scor Foundation for Science, the philanthropic arm of a French reinsurer, announced in late 2024 it would support Roulston\u2019s group, known by its acronym, CRUCIAL, and housed at Lancaster University. Scor is interested in the approach because it can draw diverse insights out of researchers more efficiently than if it were to hire dozens of them at once, said Philippe Trainar, CEO of the Scor Foundation and chief economist of Scor SE.<\/p>\n
Roulston runs annual competitions on the number of Atlantic and Pacific cyclones and has four live markets to gauge when the next El Ni\u00f1o will hit \u2013 likely bringing record global temperatures with it.<\/p>\n
The complexity of climate change may make the topic unsuited for popular online prediction markets, said Madison Condon, an associate professor of law at Boston University, who writes about climate models and projections. There\u2019s definitely space for scientists to pool knowledge outside of complex Earth system models, she said, but markets may not be \u201cthe best way for synthesizing that domain knowledge or expert judgment.\u201d<\/p>\n
\u201cIt\u2019s not like a basketball game,\u201d she said. Projecting what climate change may do to hurricanes, or if the next El Ni\u00f1o is on its way, \u201cis just a different type of knowledge than polling the population would give you.\u201d<\/p>\n
Betting on the weather isn\u2019t new. The first weather derivatives began trading in the 1990s, and insurers have long scrutinized the costs of weather and climate risks to underwrite policies.<\/p>\n
But the market for offloading weather risk remains relatively small. Meanwhile, intensifying climate threats are exposing cracks in the insurance industry as extreme weather makes some places uninsurable.<\/p>\n
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Some industry veterans see prediction markets as a potential solution to the problem \u2013 or at least a disruption of the status quo.<\/p>\n
While weather derivatives are constrained by low liquidity and parametric insurance is costly, prediction markets open the floodgates to more traders, said Jim Huang, who spent a decade at the Chicago Mercantile Exchange working on product strategy and is now building a weather-focused prediction market platform called WeatherBook.<\/p>\n
That can help boost liquidity and improve the pricing of weather risks \u2013 \u201csomething that I don\u2019t think insurance or derivatives will be able to achieve, even if you give it more time,\u201d he said.<\/p>\n
\u2018Put Your Money Where Your Mouth Is\u2019<\/p>\n
One of the earlier generations of prediction markets can also trace its history back to weather predictions, though in this case, the bets were made in pursuit of a childhood fantasy: a snow day.<\/p>\n
Like all other 9- and 10-year-olds, Dean and John Aristotle Phillips spent the night before snowstorms in their North Haven, Connecticut, home, praying that school would be cancelled the next day. It was the mid-1960s. Then one day they realized they could do something more than pray: They could bet.<\/p>\n
\u201cIt was money,\u201d John Phillips said. The two sides were a snow day and a no-snow day. \u201cYou had to have the money to bet with. There was no credit, no margin.\u201d<\/p>\n
The brothers\u2019 snow day prediction market was the first partnership in entrepreneurial careers that soon led them to a snow-shovelling business, John\u2019s design of a nuclear bomb from public sources as a Princeton junior, work in election data and technology, and, in 2014, the launch of one of the first major online prediction markets, PredictIt.<\/p>\n
Once the boys\u2019 wintertime taunt, \u201cPut your money where your mouth is\u201d is now \u201ckind of the motto of PredictIt,\u201d Phillips said.<\/p>\n
The betting platform started as a nonprofit collaboration between Victoria University of Wellington, New Zealand, and the Phillips\u2019 data and consulting company, Aristotle International Inc. A US nonprofit called the Prediction Market Research Consortium Inc. took over the university\u2019s role last year, with Aristotle still running it day to day.<\/p>\n
For investors, betting itself can be an efficient way to deliver actionable policy information that complements normal due diligence, Phillips said. An investor considering putting money into a wind turbine firm, for example, must weigh factors spanning business, policy and geopolitics.<\/p>\n
\u201cThe real thing is, what\u2019s the government going to do in this regard?\u201d Phillips said. \u201cTo me, that\u2019s a really fertile area for these markets to contribute.\u201d<\/p>\n
\u00a9 2026 Bloomberg<\/p>\n
#Weather #prediction #markets #booming<\/p>\n","protected":false},"excerpt":{"rendered":"
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