Results for “prediction market”
315 found

New AI real money prediction markets just dropped

– Will ChatGPT remain free through April 30?:
– Will GPT-4 have 500b+ parameters?:
– Will OpenAI release GPT-4 by May 31?:
– Will Bing’s market share be >4% in February?:

Prediction Markets Should Be Legal

I submitted a public comment on Kalshi’s request to the CFTC to create a prediction market on which political party will be in control of each chamber of the U.S. Congress.

Political election markets have proven themselves to be a powerful tool for forecasting elections and are typically more accurate, timely and complete than alternative methods such as polls. These markets have been widely used by researchers to understand political behaviour, institutions and events. e.g. see the research summarized here

and an important application to understanding the costs of war here:

Political election markets are also useful to hedgers, traders and other market participants to help them predict and incorporate information about risks into asset prices.

Markets similar to political election markets have been used to predict other important events such as the prospects for war or scientific breakthroughs and have been adopted by firms to better estimate sales forecasts and other relevant events.

The United States has pioneered the use of these innovative markets and we should continue to lead in creating better means of aggreating information to improve the quality of decision making.

Are prediction markets going to make it this time around?

That is the topic of my latest Bloomberg column, here is one excerpt:

A skeptic might say that demand is limited because there are already so many good and highly informative markets in other assets. In 2009, for instance, was a market necessary to predict how well the iPhone was going to do? The share price of Apple might have served to perform a broadly similar function.

The question, then, is which prediction markets might prove most useful. Nobel Laureate economist Robert J. Shiller has promoted the idea of prediction markets in GDP, but most people face major risks at a more local, less aggregated level. One of the risks I face, for example, concerns the revenue of the university where I teach. This year enrollments rose slightly even though U.S. GDP fell sharply. So a GDP-based hedge probably is not very useful to me.

How about a prediction market in local real-estate prices, so that home buyers and real-estate magnates may hedge their purchases? Maybe, but then the question is whether enough professional traders would be attracted to such markets to keep them liquid. So-called binary options, particularly when the bet is on the price of a financial asset, often have remained unfairly priced or manipulated, and are viewed poorly by regulators.

For a prediction market to take off, it probably has to satisfy a few criteria: general enough to attract widespread interest; important enough to matter; and unusual enough not to be replicable by trading in existing assets. The outcomes also need to be sufficiently well-defined that contract settlement is not in dispute.

It remains to be seen how many new assets can meet all these standards.

Recommended, and the “hook” of the piece is the new attempt to jump-start prediction markets through the start-up Kalshi.

What to make of prediction markets this election season?

That is the topic of my latest Bloomberg column.  Mostly I am pro-prediction market, but my last two paragraphs contain the cautionary note:

Prediction markets have another potential flaw: They focus attention on clearly demarcated events that are easy to bet on, such as who will win an election or whether Rudy Giuliani will face federal charges. Sometimes these are important matters. Other times they are not.

There are more meaningful trends that are more difficult to measure, such whether Americans are feeling more lonely. These things certainly have an impact on politics, but they are not easy to bet on. Political prediction markets are undeniably useful and very often enlightening, but maybe they should come with a warning: Feel free to check the odds as often as you like, but do not let your obsession blind you to the larger issues at stake.

There is much more in the earlier parts of the piece.

If you love prediction markets you should love the art world

Think of art markets, and art collecting, as an ongoing debate over what is beautiful and also what is culturally important.  But unlike most debates, you have a very direct chance to “put your money where your mouth is,” namely by buying art (it is very difficult to sell art short, however).  In this regard, debates over artistic value may be among the most efficient debates in the world.  At least if you are persuaded by the basic virtues of prediction markets.  The prices of various art works really do aggregate information about their perceived values.

I have, however, noted a correlation, how necessary or contingent I am not sure.  The “white male nerd types” who are enamored of prediction markets tend to be especially skeptical of the market judgments of particular art works, most of all for conceptual and contemporary art.

In my view, discussions about the value of art, as they occur in the off-the-record, proprietary sphere, are indeed of high value and they deserve to be studied more closely.  Imagine a bunch of people competing to make “objects that are interesting but not interesting for reasons related to their practical value.”  And then we debate who has succeeded, or not.  And those debates reflect many broader social, political, and economic issues.  And it is all done with very real money on the line.  The money concerns not just the value of individual art works, but also the prestige and social capital value that arises from having assembled a prestigious and insightful collection.

Do stock markets respond to political prediction markets?

Analyses of the effects of election outcomes on the economy have been hampered by the problem that economic outcomes also influence elections . We sidestep these problems by analyzing movements in economic indicators caused by clearly exogenous changes in expectations about the likely winner during Election Day. Analyzing high frequency financial fluctuations following the release of flawed exit poll data on Election Day 2004, and then during the vote count, we find that markets anticipated higher equity prices, interest rates and oil prices and a stronger dollar under a Bush presidency than under Kerry. A similar Republican-Democrat differential was also observed for the 2000 Bush-Gore contest. Prediction market based analyses of all Presidential elections since 1880 also reveal a similar pattern of partisan impacts, suggesting that electing a Republican President raises equity valuations by 2-3 percent, and that since Reagan, Republican Presidents have tended to raise bond yields.
That is Snowberg, Wolfers, and Zitzewitz (pdf), via Adam Ozimek.  Here is the background context, relating to prediction markets today.

Corporate Prediction Markets Work Well

Prediction markets predict public events such as election outcomes better than do polls or other forecasting mechanisms. Internal corporate prediction markets in events such as sales forecasts, product launch times, and product feature demand have been less well studied. Internal corporate markets tend to have fewer participants than public markets and the participants often have strategic interests and biases. Thus, it has been an open question how well these markets operate.

Cowgill and Zitzewitz report on a number of different types of prediction markets run by Google, Ford and Firm X and although they find evidence for some biases they also find that corporate prediction markets also work better than alternative forecasting methods.

Despite large differences in market design, operation, participation, and incentives, we find that prediction market prices at our three companies are well calibrated to probabilities and improve upon alternative forecasting methods. Ford employs experts to forecast weekly vehicle sales, and we show that contemporaneous prediction market forecasts outperform the expert forecast, achieving a 25% lower mean-squared error (p = 0.104).

…The strong relative predictive performance of the Google and Ford markets is achieved despite several pricing inefficiencies. Google’s markets exhibit an optimism bias. Both Google and Ford’s markets exhibit a bias away from a naive prior (1/N, where N is the number of bins, for Google and prior sales for Ford). However, we find that these inefficiencies disappear by the end of the sample. Improvement over time is driven by two mechanisms: first, more experienced traders trade against the identified inefficiencies and earn higher returns, suggesting that traders become better calibrated with experience. Secondly, traders (of a given experience level) with higher past returns earn higher future returns, trade against identified inefficiencies, and trade more in the future. These results together suggest that traders differ in their skill levels, they learn about their ability over time, and self-selection causes the average skill level in the market to rise over time.

Addendum: It’s an interesting commentary on academic publishing that Marginal Revolution first covered this paper in a working version in 2008! An extended version was received by the Review of Economic Studies in 2010 which accepted a final version in 2014 and then published the paper in 2015.

Prediction markets for Chinese exam winners

Cameron Campbell writes to me:

There was indeed betting on the outcomes of the examinations, at least in Guangdong province in the 19th century.  At least one form of betting was on the surnames that would be represented in the pool of successful candidates. Such betting was quite widespread, so for example, there were publications dedicated to providing punters with background on exam takers.

It also seems that a Professor Haifeng Liu at Xiamen University last year gave a talk titled 闈姓賭博:清代廣東與澳門的科舉習俗, or “Examination hall surname gambling: Qing Guangdong and Macao examination customs.” (Cowen’s Second Law, though perhaps he still needs to write it up)

Here is my previous post on this topic.  Here is Campbell’s blog.  Campbell is still trying to find out whether the telegraph story cited in my earlier post can be verified, I thank him for his efforts, Robin Hanson will be happy.

Prediction markets in Jules Verne

From Around the World in Eighty Days:

Everybody knows that England is the world of betting men, who are of a higher class than mere gamblers; to bet is in the English temperament.  Not only the members of the Reform, but the general public, made heavy wagers for or against Phileas Fogg, who was set down in the betting books as if he were a race-horse.  Bonds were issued, and made their appearance on ‘Change; Phileas Fogg bonds” were offered at par or at a premium, and a great business was done in them.  But five days after the article in the bulletin of the Geographical Society appeared, the demand began to subside: “Phileas Fogg” declined.  They were offered by packages, at first of five, then of ten, until at last nobody would take less than twenty, fifty, a hundred!

Lord Albemarle, an elderly paralytic gentleman, was now the only advocate of Phileas Fogg left.

I should add that it is quite easy to read this novel as a critique of the “death of distance” view, and other forms of hyperbole about globalization.

Yet another case where prediction markets would come in handy

From the Financial Times (not Pravda):

Nikolai Vasiliev, a Crimean businessman, can hardly wait for his region to be annexed by Russia. It would “give us a new lease of life”, he says.

Mr Vasiliev is the general manager of AO Pnevmatika, a former state-owned engineering company that has struggled since the Soviet break-up. Now, he hopes, a bold future beckons in a newly minted Russian province.

“A huge market will be opened up to us,” he says. “We will have access to cheap Russian raw materials and low-priced gas and electricity. And the wages of our workers will rise to Russian levels.”

…Alexander Basov, head of the local chamber of commerce, echoes a widely held view that a Russian-ruled Crimea would garner more attention – and investment – from Moscow than it ever got from Kiev.

“Since independence, Ukraine has treated Crimea like an unloved stepchild, not a real son,” he says. “No big factory has been built here in the last 20 years. The only spending was on repairs to the road from Simferopol to the state dacha in Yalta.”

Yet on the other hand:

There are plenty of dissenting voices. One leading Simferopol businessman, who asked not to be named, said the impact of union with Russia on Crimea’s economy would be devastating, especially if the rest of the world refused to recognise it. “There will be no foreign investment in a place with such a dodgy legal status,” he says. “And the odds are that even Russians will not want to invest here.”

There is also concern that Crimea could not survive a total break from mainland Ukraine, the source of much of its water and electricity, with fears that if the peninsula votes to secede in a referendum planned for Sunday, Kiev could retaliate by switching off the lights or imposing an economic blockade. Already, Mr Vasiliev said, train links between Crimea and other parts of Ukraine had been cut or scaled back and online bank transfers from the Ukrainian Treasury shut down.

The huge bureaucratic headaches any change in Crimea’s status would cause are also worrying the business community. “I’ll have to get a new passport, re-register my business, my house,” said Ibrahim Zinedin, who trades in construction materials. “All that will take time and cost a lot.”

Loyal MR readers will not be surprised to read I would put my bets on the more negative scenario.  There is more here.

Are Prediction Markets Against the Public Interest?

Here is more on the CFTC’s attack on Intrade:

Why doesn’t Intrade just obey the complicated law and become a licensed exchange? They tried, but the CFTC won’t give them a license. When an established, licensed U.S. commodity exchange applied for permission to do what Intrade does, the CFTC turned them down, too.

Most importantly, in rejecting Nadex’s application to trade “political event derivatives contracts” the CFTC said this:

As a result of reviewing the complete record, the CFTC determined that the contracts involve gaming and are contrary to the public interest…

Thus the CFTC’s attack on Intrade is not about following or not following a particular regulation; it goes much deeper, the CFTC is arguing that all such markets are against the public interest.

Addendum: Kenneth J. Arrow, Robert Forsythe, Michael Gorham, Robert Hahn, Robin Hanson,
John O. Ledyard, Saul Levmore, Robert Litan, Paul Milgrom, Forrest D. Nelson,
George R. Neumann, Marco Ottaviani, Thomas C. Schelling, Robert J. Shiller,
Vernon L. Smith, Erik Snowberg, Cass R. Sunstein, Paul C. Tetlock, Philip E. Tetlock,
Hal R. Varian, Justin Wolfers, and Eric Zitzewitz disagree with the CFTC (among others).

Hansonian prediction market TV

Eric Crampton writes:

I love that we’re now getting punditry informed by market odds. Even better would be if the commentators disclosed the trades they were making consequent to their analysis rather than saying which way they would trade were they to trade!

There is now a whole prime-time TV show, in New Zealand, where pundits discuss various events through the market of prediction windows.  At the link you will find full clips of all the shows.  Bomber Bradbury hosts the programme.

A prediction market for climate outcomes?

From Shi-Ling Hsu:

This article proposes a way of introducing some organization and tractability in climate science, generating more widely credible evaluations of climate science, and imposing some discipline on the processing and interpretation of climate information. I propose a two-part policy instrument consisting of (1) a carbon tax that is indexed to a “basket” of climate outcomes, and (2) nested inside this carbon tax, a cap-and-trade system of emissions permits that can be redeemed in lieu of paying the carbon tax. The amount of the carbon tax in this proposal would be set each year on the basis of some objective, non-manipulable climate indices, such as temperature and mean sea level, and also on the number of certain climate events, such as hurricanes or droughts, that occurred in the previous year (or some moving average of previous years). In addition to setting a carbon tax rate each year, an auction would be held each year for tradeable permits to emit a ton of carbon dioxide in separate, specific, future years. That is, in the year 2012, a number of permits to emit in 2013 would be auctioned, as well as a number of permits to emit in 2014, in 2015, and so forth. In the year 2013, some more permits to emit in 2014 would be auctioned, as well as more permits to emit in 2015, 2016, and so forth.
The permits to emit in the future are essentially unitary exemptions from a future carbon tax: an emitter can either pay the carbon tax or surrender an emissions permit to emit in the specific vintage year. Because of this link between the carbon tax and the permit market, the trading price of the permits should reflect market expectations of what the carbon tax will be in the future, and concomitantly, expectations of future climate outcomes. The idea is to link the price of tradeable permits to future climate outcomes, so that a market is created in which accurate and credible information about future climate conditions are important inputs into the price of permits. The market for tradeable permits to emit in the future is essentially a prediction market for climate outcomes.

The rationale for this idea is clear, namely a desire to build consensus by getting agreement to a broader proposal ex ante.  Nonetheless I think of such fine-tuning as a misguided approach.  Is there such a good “basket” measure of climate outcomes with sufficiently low short-term volatility? (Or does the metric do econometrics on the higher-order polynomial?)  Should the tax be fine-tuned year-by-year when the lag times between energy inputs and climate outputs is thirty years or longer, possibly reaching up to one hundred years?  Still, I am happy to pass the idea along for consideration.

For the pointer I thank Chris Auld.

Manipulation of Prediction Markets

As many people suspected someone was manipulating Intrade to boost John McCain’s stock price:

An internal investigation by the popular online market Intrade has revealed that an investor’s purchases prompted “unusual” price swings that boosted the prediction that Sen. John McCain will become president.

Over the past several weeks, the investor has pushed hundreds of thousands of dollars into one of Intrade’s predictive markets for the presidential election, the company said.

This is big news but not for the reasons that most people think.  Although some manipulation is clearly possible in the short run, the manipulation was already suspected due to differences between Intrade and other prediction markets.  As a result,

According to Intrade bulletin boards and market histories, smaller investors swept in to take advantage of what they saw as price discrepancies caused by the market shifts – quickly returning the Obama and McCain futures prices to their previous value.

This resulted in losses for the investor and profits for the small investors who followed the patterns to take maximum advantage.

This supports Robin Hanson’s and Ryan Oprea’s finding that manipulation can improve (!) prediction markets – the reason is that manipulation offers informed investors a free lunch.  In a stock market, for example, when you buy (thinking the price will rise) someone else is selling (presumably thinking the price will fall) so if you do not have inside information you should not expect an above normal profit from your trade.  But a manipulator sells and buys based on reasons other than expectations and so offers other investors a greater than normal return.  The more manipulation, therefore, the greater the expected profit from betting according to rational expectations.

An even more important lesson is that prediction markets have truly arrived when people think they are worth manipulating.  Notice that the manipulator probably doesn’t care about changing the market prediction per se.  Instead, a manipulator willing to bet hundreds of thousands to change the prediction of a McCain win must think that the prediction will actually affect the outcome.  And if people think prediction markets are this important then can decision markets be far behind?

Hat tip to Paul Krugman.