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Prediction markets, where individuals bet on economic outcomes, are proving surprisingly accurate, sometimes outperforming professional forecasters. These markets offer insights into jobs reports, inflation, and interest rate decisions, prompting economists to take notice.
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- Prediction Markets Forecast Economic Indicators
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- Prediction markets, where individuals bet on economic outcomes, are proving surprisingly accurate, sometimes outperforming professional forecasters. These markets offer insights into jobs reports, inflation, and interest rate decisions, prompting economists to take notice.
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Prediction Markets Economics Forecasting Financial Markets Economic Indicators Inflation Federal Reserve Jobs Report
- Context Type
- Analysis
- AI Confidence Score
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1.000
- Context Details
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{ "tone": "analytical", "perspective": "neutral", "audience": "general", "credibility_indicators": [ "expert_quotes", "data_cited" ] }
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- Donato V. Pompo
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- February 11, 2026 at 5:34 PM
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{ "source_type": "extension", "content_hash": "e642f10104dced07a0854ac0a28e6f1556bd4010b51bc915bdb3a143a2d07e6b", "submitted_via": "chrome_extension", "extension_version": "1.0.18", "original_url": "https:\/\/www.nytimes.com\/2026\/02\/11\/business\/economy\/forecasts-prediction-markets-economy.html?campaign_id=60&emc=edit_na_20260211&instance_id=170916&nl=breaking-news®i_id=122976029&segment_id=215105&user_id=b25c5730c89e0c73f75709d8f1254337", "parsed_content": "U.S. EconomyJobs Report\u2018K-Shaped\u2019 EconomyDow Hits Record HighFed Holds Rates SteadyInflationEconomists at top banks and investment firms who command high salaries to divine the direction of the economy expected the latest jobs report on Wednesday to show that about 68,000 jobs were added last month.A crowd of anonymous online gamblers placing bets on Kalshi, a prediction site, expected to see 54,000 new jobs.The report ended up showing the U.S. economy had added 130,000 jobs at the start of the year. Both groups had missed the mark by a wide margin \u2014 and to similar degrees.Over the five years that Kalshi has existed, its thousands of gamblers have proved as accurate on average at predicting certain economic indicators as the highly trained forecasters, a working paper published last month by the National Bureau of Economic Research found. The crowd is also pretty good at predicting interest rate decisions from the Federal Reserve, and even better than the professionals at predicting the rate of inflation.\u201cGetting information from a large pool of people can be a remarkably good form of forecasting,\u201d said Jonathan Wright, an economics professor at Johns Hopkins University who co-wrote the paper.Thomas Simons, a U.S. economist with Jefferies, the investment firm, took notice when Kevin Warsh was leading in the prediction markets to be President Trump\u2019s nominee for chair of the Federal Reserve. Mr. Simons had dismissed the possibility because of Mr. Warsh\u2019s past advocacy for higher interest rates, rather than the lower rates that Mr. Trump prefers.\u201c\u2018How could it possibly be that he\u2019s at the head of this? It doesn\u2019t make any sense,\u2019\u201d Mr. Simons recalled thinking.But the markets turned out to be right, and he decided he shouldn\u2019t disregard the odds. Bettors, he realized, have one advantage: They don\u2019t have to make a prediction if they\u2019re not highly confident that they\u2019re right. Professional forecasters don\u2019t have a choice; even if the data are confusing and they don\u2019t have much conviction in the number, they guess.\u201cYou have to forecast these numbers every month even when you don\u2019t necessarily think you have some kind of edge,\u201d Mr. Simons said. \u201cSo it starts to make me feel like, if I go back to my priors on this, the people who have edge are the ones who are going to participate.\u201dAnother working paper, by economists at the London Business School and Yale University, found that Polymarket bettors as a whole forecast corporate earnings more accurately than the analysts who are paid to advise investors on whether to buy or sell.Theis Jensen, a Yale professor who worked on the paper, thinks the comparatively good performance by thousands of amateurs can be chalked up to incentives. Professional analysts may have conflicts of interest, such as their firm\u2019s trading commissions, which might rise in response to rosier forecasts. Analysts may also avoid publishing earnings forecasts that are out of the norm, which can lead to more embarrassment than sticking with the crowd.\u201cThe nice thing about prediction markets is that you have to put your money where your mouth is,\u201d Mr. Jensen said, \u201cand so that highly incentivizes you to state your true beliefs.\u201dOf course, that has been true for decades. The first online prediction markets emerged in the early 2000s. Sites like Intrade focused mostly on elections and the likelihood of other world events, and were generally found to be fairly accurate. In the 2010s, U.S. regulators cracked down, ruling that they were operating as illegal gambling platforms.But some platforms continued to operate in Europe, where political and economic contracts are a sideshow to enormous volumes of sports betting. The same is still true of Kalshi, which won a lawsuit allowing it to operate legally in 2024, and Polymarket, which is only sporadically accessible in the United States as lawsuits have blocked trading in many states.And yet betting volume even on nonsports questions has expanded at such a torrid pace that forecasters and analysts are taking notice. On any given day, more than $60 million is at stake on the platforms on political and economic questions \u2014 far more than the earlier platforms reached.Edward Ridgely runs Stand, a company that allows bettors to trade simultaneously on Kalshi and Polymarket and follow other large traders. He said many of his highest-volume customers worked in the same fields where they wagered. One user in Hong Kong buys and sells Nvidia stock in his day job and uses the tariff-related prediction market contracts as a hedge.\u201cIf the Trump tariffs escalate toward China or something, he can get out of his position and not get blown away,\u201d Mr. Ridgely said.He sees another piece of evidence that bettors specialize: Most of them aren\u2019t good at everything. \u201cYou can see that a lot of the traders who are really good at elections aren\u2019t very good at crypto. Or if you\u2019re really good at crypto, you\u2019re not very good at geopolitics,\u201d he said.Michael Feroli, chief U.S. economist of J.P. Morgan, has access to a deep well of expertise from the bank\u2019s political affairs staff, country specialists and equity researchers. But he still looks at the markets to get a more precise estimate.\u201cWhenever you talk to D.C. people, they\u2019ll say, \u2018Well, I think they\u2019ll get the budget done.\u2019 So, what\u2019s the probability?\u201d Mr. Feroli said. \u201cIt\u2019s a different language. Oftentimes you\u2019ve got to really push to get a quantitative answer.\u201dOn the quantitative questions that are his stock in trade, like forecasting changes to the Consumer Price Index and gross domestic product, Mr. Feroli suspects something else is going on: The betting markets are just following the experts. That could mean monitoring the Bloomberg consensus, reading research from the big investment houses or tracking the futures markets and investor expectations that groups like the Chicago Mercantile Exchange already aggregate.Tara Sinclair, an economist at George Washington University who studies forecasting, agrees that is likely. And therein lies a danger in prediction markets: If the crowd were to supplant professional forecasters, individual bettors would lose out.\u201cThey would be making the jobs of their contributors harder, because now they have individual sources of information to draw from,\u201d Ms. Sinclair said. \u201cIf they replace all of that, then they won\u2019t have those to also go to.\u201dMost forecasters aren\u2019t worried about that, because they do more than predict numbers. Every estimate comes with a detailed analysis of the factors underneath the headline number, which is what investors and companies need to figure out how to spend money.\u201cSurprises happen, and people want to know, \u2018What does this mean, what\u2019s going to happen, what\u2019s driving it?\u2019\u201d said Michael Pugliese, a U.S. economist with Wells Fargo. \u201cI think that\u2019s a lot of nuanced, important information that you\u2019d want to have when you are making decisions, as an operator in these markets.\u201dBut prediction markets could become an input for some complex forecasts, like those constructed by the Federal Reserve. Justin Wolfers, an economics professor at the University of Michigan who studied and wrote about the earlier iterations of prediction markets, has told Fed officials that they should take those markets into consideration. They have been hesitant, he said.\u201cThere\u2019s a deep problem, which is, if you were to do this, you democratize decision making,\u201d Mr. Wolfers said. \u201cRight now the senior economist has a ton of power. Their view goes.\u201dIt may also be true that neither individual experts nor a collective of thousands are the best at predicting the future. Over the past decade, a group called Good Judgment has developed a model of selecting people with good track records of figuring out what will happen. These \u201csuperforecasters\u201d are applied to longer-range questions of interest to paying clients. They work collaboratively, but ultimately cast their own votes.Warren Hatch, the organization\u2019s chief executive, thinks prediction markets complement his group\u2019s services because they focus on shorter-term questions and expand the use of probabilistic thinking.Now he is watching the emergence of another predictive force: artificial intelligence, which can synthesize large amounts of standardized information to come up with reasonably good estimates. But A.I. can have a tough time with questions that more have to do with humans and culture, and less to do with numbers and metrics.\u201cWhen the data is sparse and the environment is in flux, machines are backward looking by definition,\u201d Mr. Hatch said. \u201cAnd that\u2019s where I think the space for humans will remain.\u201dLydia DePillis reports on the American economy for The Times. She has been a journalist since 2009, and can be reached at lydia.depillis@nytimes.com.See more on: U.S. Politics, Donald TrumpRead 239 commentsShare full articleRelated ContentAdvertisementSKIP ADVERTISEMENT", "ai_headline": "Not specified", "ai_simplified_title": "Prediction Markets Forecast Economic Indicators", "ai_excerpt": "Prediction markets, where individuals bet on economic outcomes, are proving surprisingly accurate, sometimes outperforming professional forecasters. These markets offer insights into jobs reports, inflation, and interest rate decisions, prompting economists to take notice.", "ai_subject_tags": [ "Prediction Markets", "Economics", "Forecasting", "Financial Markets", "Economic Indicators", "Inflation", "Federal Reserve", "Jobs Report" ], "ai_context_type": "Analysis", "ai_context_details": { "tone": "analytical", "perspective": "neutral", "audience": "general", "credibility_indicators": [ "expert_quotes", "data_cited" ] }, "ai_source_vector": [ -0.0007182969, -0.017890846, -0.01370348, -0.074475676, 0.0034488018, -0.014397559, 0.002985366, -0.010753331, -0.01659717, -0.00917671, 0.015262519, -0.007118222, 0.03193176, -0.0002417948, 0.11736396, 0.01661214, 0.016367065, 0.00525635, 0.004837615, 0.030328497, -0.014657351, 0.0134910615, -0.0103484215, -0.008065222, 0.014025282, -0.011168716, 0.016520126, -0.010571657, 0.023740178, 0.006207357, -0.02359287, 0.001452403, -0.02240259, 0.026269263, 0.007947812, 0.024283178, -0.0062892805, 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<html lang="en" class="story nytapp-vi-article nytapp-vi-story story nytapp-vi-article " data-nyt-compute-assignment="fallback" xmlns:og="http://opengraphprotocol.org/schema/" data-rh="lang,class"><head> <meta charset="utf-8"> <title>Kalshi and Polymarket Create New Competition for Professional Economists - The New York Times</title> <meta data-rh="true" name="robots" content="noarchive, max-image-preview:large"><meta data-rh="true" name="description" content="Economists have noticed that betting markets like Kalshi and Polymarket are pretty good at predicting not just political events but economic data, too."><meta data-rh="true" property="twitter:url" content="https://www.nytimes.com/2026/02/11/business/economy/forecasts-prediction-markets-economy.html"><meta data-rh="true" property="twitter:title" content="Kalshi and Polymarket Create New Competition for Professional Economists"><meta data-rh="true" property="twitter:description" content="Economists have notice... - Parsed Content
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U.S. EconomyJobs ReportβK-Shapedβ EconomyDow Hits Record HighFed Holds Rates SteadyInflationEconomists at top banks and investment firms who command high salaries to divine the direction of the economy expected the latest jobs report on Wednesday to show that about 68,000 jobs were added last month.A crowd of anonymous online gamblers placing bets on Kalshi, a prediction site, expected to see 54,000 new jobs.The report ended up showing the U.S. economy had added 130,000 jobs at the start of the year. Both groups had missed the mark by a wide margin β and to similar degrees.Over the five years that Kalshi has existed, its thousands of gamblers have proved as accurate on average at predicting certain economic indicators as the highly trained forecasters, a working paper published last month by the National Bureau of Economic Research found. The crowd is also pretty good at predicting interest rate decisions from the Federal Reserve, and even better than the professionals at predicting th...
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Claims from this Source (38)
All claims extracted from this source document.
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π€ The author π News Article π·οΈ Economics , Statistics π a116481d-cc66-4155-9354-f406ae0ff699Simplified: Januaryβs jobs report was stronger than expected with 130000 positions added for month unemployment rate ticked down to 4.3 percent
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π€ The author π News Article π·οΈ Economics , Federal Reserve , Inflation π a1166095-e567-4434-a190-1154d6ee62a3Simplified: The crowd is good at predicting interest rate decisions from the Federal Reserve and better than professionals at predicting inflation rate
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π€ Jonathan Wright π News Article π·οΈ Economics , Forecasting π a1166096-141e-47cc-81c7-b0a304b23347Simplified: Getting information from a large pool of people can be a good form of forecasting Jonathan Wright said
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π€ The author π News Article π·οΈ Economics , Corporate Earnings , Forecasting π a1166096-41df-4c09-80eb-5a2a2b80e8beSimplified: Polymarket bettors forecast corporate earnings more accurately than analysts
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π€ The author π News Article π·οΈ Economics , Conflicts of Interest π a1166096-7240-4f5f-8f57-cfe1d0f3920cSimplified: Professional analysts may have conflicts of interest such as their firm's trading commissions
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π€ The author π News Article π·οΈ Economics , Earnings Forecasts π a1166096-9afe-4502-bb8a-1435576d271dSimplified: Analysts may avoid publishing earnings forecasts that are out of the norm
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π€ Mr. Jensen π News Article π·οΈ Economics , Prediction π a1166096-e9fb-438a-80b8-706278e1bb42Simplified: Prediction markets incentivize you to state your true beliefs
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π€ The author π News Article π·οΈ Economics , Elections , World Events π a1166097-9d33-4b94-ba3b-a598407567c3Simplified: Intrade focused on elections and world events and were fairly accurate
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π€ The author π News Article π·οΈ Regulation , Gambling π a1166098-04c9-4dad-bf8b-8691f2de33beSimplified: US regulators cracked down ruling that they were operating as illegal gambling platforms in 2010s
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π€ The author π News Article π·οΈ Regulation , Lawsuit π a1166098-614f-4f2a-b42f-ee901fa87e30Simplified: Kalshi won a lawsuit allowing it to operate legally in 2024
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π€ The author π News Article π·οΈ Economics , Forecasting π a116609a-04bf-484c-80e2-5717fa56b7f0Simplified: Betting markets are just following the experts on quantitative questions
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π€ The author π News Article π·οΈ Economics , Forecasting π a116609a-4f4b-4de8-b813-5ab56920b7cfSimplified: Tara Sinclair an economist at George Washington University agrees that is likely
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π€ Ms. Sinclair π News Article π·οΈ Finance , Prediction Markets π a116609a-800d-4746-93c0-98a5db6822f6Simplified: If crowd were to supplant professional forecasters individual bettors would lose out
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π€ Ms. Sinclair π News Article π·οΈ Finance , Prediction Markets π a116609a-af55-4b8b-a466-c438c95ccdf4Simplified: They would be making jobs of their contributors harder because they have individual sources of information to draw from
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π€ Ms. Sinclair π News Article π·οΈ Finance , Prediction Markets π a116609a-e22a-4d18-b06e-a3b91c0982cfSimplified: If they replace all of that they wonβt have those to also go to
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609b-0fab-4fa9-aaa9-71ac6dacc1c9Simplified: Most forecasters arenβt worried because they do more than predict numbers
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609b-3da4-47e6-8bab-171e63d732f3Simplified: Every estimate comes with detailed analysis of factors which investors and companies need to figure out how to spend money
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π€ Michael Pugliese π News Article π·οΈ Finance , Prediction Markets π a116609b-67ce-47df-9556-3cc017e7fbc3Simplified: Surprises happen people want to know what does this mean whatβs going to happen whatβs driving it
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π€ Michael Pugliese π News Article π·οΈ Finance , Prediction Markets π a116609b-977b-4aa9-84b5-e1bac2736ef6Simplified: Thatβs a lot of nuanced important information that youβd want to have when you are making decisions as an operator in these markets
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609b-e566-47d6-8bd0-e8102064351fSimplified: Justin Wolfers has told Fed officials that they should take those markets into consideration
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609c-242c-4b15-833a-f33c46aa4345Simplified: They have been hesitant
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π€ Mr. Wolfers π News Article π·οΈ Finance , Prediction Markets π a116609c-5348-4e64-bfbd-4c9666f445d4Simplified: If you were to do this you democratize decision making
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π€ Mr. Wolfers π News Article π·οΈ Finance , Prediction Markets π a116609c-87e8-4d04-b3ae-093ddfcc7c71Simplified: Right now senior economist has a ton of power
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Their view goes.0.900π€ Mr. Wolfers π News Article π·οΈ Finance , Prediction Markets π a116609c-e5b0-42e1-b366-3857608b4f38Simplified: Their view goes
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609d-40be-4ff0-933f-008e504225a6Simplified: Neither individual experts nor collective of thousands are best at predicting future
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609d-92d7-45be-a992-d9b85133c712Simplified: Over past decade Good Judgment has developed model of selecting people with good track records of figuring out what will happen
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π€ The author π News Article π·οΈ Finance , Prediction Markets π a116609d-fb3d-4d65-a848-23e6504e893cSimplified: They work collaboratively but ultimately cast their own votes
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π€ Warren Hatch π News Article π·οΈ Finance , Prediction Markets π a116609e-2a44-4b90-84a9-6883ba00835cSimplified: Warren Hatch thinks prediction markets complement his groupβs services because they focus on shorter-term questions and expand use of probabilistic th...
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π€ The author π News Article π·οΈ Finance , Prediction Markets , Artificial Intelligence π a116609e-5c29-43df-be7b-a69b87384d35Simplified: Artificial intelligence can synthesize large amounts of standardized information to come up with reasonably good estimates
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Simplified: Software and artificial intelligence may not be good at it
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When the data is sparse and the environment is in flux, machines are backward looking by definition.0.900π€ Mr. Hatch π News Article π·οΈ Finance , Prediction Markets , Artificial Intelligence π a116609e-ade1-409c-9dc8-bd1851a7c931Simplified: When data is sparse and environment is in flux machines are backward looking by definition
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π€ Mr. Hatch π News Article π·οΈ Finance , Prediction Markets , Artificial Intelligence π a116609e-daef-4d3d-aa08-f3a03c524166Simplified: Thatβs where space for humans will remain