tl;dr. Prediction markets give good forecasts because they attract “smart money” that will fix any gaps between current odds and best available information.
The “smart money” is in turn motivated by the profits they can take from “dumb money” coming from people who are participating in the market out of a desire for action or as a passive investment. The “dumb money” is itself reassured by presence of the “smart money” to keep prices roughly fair. At the limit of economic efficiency, the dumb money, by relying on public odds, can be almost as smart as the smart money.
One reason prediction markets have problems is the absence of sufficient dumb money to allow the smart money to overcome the vig.
Prediction markets are a way of aggregating knowledge and harnessing the wisdom of crowds.
To first order, just about any aggregation of forecasts will do better than individual guesses.
A simple method for forecasting some uncertain event (for example, the outcome of the upcoming presidential election) would be to randomly sample a bunch of random people, ask them to each give a forecast, and then average these forecasts. Research suggests that this direct approach works pretty well, at least for problems where the quantity being forecasted is in a clearly-defined range. (But don’t try this averaging method to estimate something that people are bad at guessing, and it won’t work so well; a classic example is total egg production in the United States in 1965, which came up in Alpert and Raiffa’s classic 1968 article, “A Progress Report on the Training of Probability Assessors,” which was reprinted in Kahneman, Slovic, and Tversky’s 1982 collection, Judgment Under Uncertainty; see also the activity on p.127-129 of our book Active Statistics. People aren’t so good at multiplying.)
You should be able to do better using some sort of weighted average, giving higher weights to guesses from people whose forecasts have a better track record, as for example in this classic 2004 paper from Prelec.
A betting market does the aggregation in a different way: Rather than the forecasts being averaged using some fixed rule, players in the market are financially motivated to offer bets based on their private knowledge. The idea is that this will keep the price—which can be treated as a sort of market forecast—at a reasonable value, and it will respond to new information at a rapid rate, as long as there are arbitrageurs with “smart money” who will jump in and achieve their expected gain by moving the line and taking bets when they see an opportunity.
This is related to the general point that the quality of the aggregate will depend on what information goes into the mix, as discussed in the final paragraph of this post.
It is well understood that, do their job of aggregating information, markets need smart money. Something I hadn’t realized until Josh Miller pointed it out to me the other day was that markets also need “dumb money.” The “smart money” bettors make their money by moving faster than the “dumb money.” To put it another way, the market gets it right because the sharks move in to rectify any mispricing. But the sharks need the minnows to feed on.
Here’s an elaboration of that point from Nick Whitaker and J. Zachary Mazlish.
They focus the question by asking, why are prediction markets not more popular in areas other than sports?
very few potential prediction markets are actually banned in the US. And yet most prediction markets that could legally exist do not exist, and the ones that do exist are not very popular. . . .
Even if one argued that the threat of regulation made these markets impossible in the US, this has problems explaining the lack of prediction markets in other countries where such regulation is not present, and seems unlikely to be introduced. Prediction markets, including election markets (as well as all sports betting), are completely legal in the United Kingdom, for example, and the country clearly has the financial institutions and market size to support them. Still, non-sport markets remain few and far between: Betfair, for example, offers markets on elections but only has a few dozen markets total and rarely offers markets on other topics like economics and science. In fact, even politics is relegated to a tab within sports. There are currently around twelve million pounds in play on the US presidential election – about the same as typically gets bet on a single cricket match. Entrepreneurs have not created ‘markets on everything’ even where it is legal to do so.
People bet on cricket! Who knew?
Whitaker and Mazlish “classify people who trade on markets into three groups”:
– Savers: who enter markets to build wealth. Prediction markets are not a natural savings device. They don’t attract money from pensions, 401(k)s, bank deposits, or brokerage accounts.
– Gamblers: who enter markets for thrills. Prediction markets are not a natural gambling device, due to various factors including their long time horizons and often esoteric topics. They rarely attract sports bettors, day traders, or r/WallStreetBets users.
– Sharps: who enter markets to profit from superior analysis. Without savers or gamblers, sharps who might enter the market to profit off superior analysis are not interested in participating. They also largely don’t need prediction markets to hedge their other positions.
They continue:
In our view, much of the volume that exists on financial markets comes from money that is not attempting to beat the market by correcting pricing errors (like an asset that is underpriced compared to its likely returns), but money that wants to be in a market for other reasons, like investing in companies that will deliver a long-run return (as savers do), or making a sports event more exciting (as gamblers do). . . . inelastic participants are often willing to pay a small premium for market access. But in doing so, investors or bettors of these kinds create a pool of surplus that smart participants try to obtain, which in turn drives prices toward efficiency. . . .
Markets become efficient when making them efficient is profitable. Large markets and markets where people will ‘pay’ expected return for access create those conditions. In our view, in prediction markets, no type of market participant – savers, gamblers, or sharps – is clamoring to be in the market, so there is no strong incentive pushing the market toward efficiency.
This brings them to the economic incentive:
There is one important reason that prediction markets are not used by savers, and probably never will be. Prediction markets, unlike most asset markets, are zero-sum – in fact they are negative-sum, once you factor in platform fees. And if your money is in a prediction market, it can’t be invested in equities, or be earning interest in the bank, either. Every winner of a prediction market necessitates an equal and opposite loser. Securities investors with diversified portfolios can expect positive returns in the long term, because they are giving up their money for others to use to create output and wealth, in exchange for a share of what they create. That’s why responsible people have their pensions in stocks and bonds, rather than a diversified portfolio of sportsbooks. Positive-sum savings vehicles are far, far superior to zero-sum ones, for the simple reason that they will grow your savings in the long run.
They argue that the only way to really make prediction markets work well is to subsidize them in some way. This might sound kinda goofy—the government or some private foundation stepping in to support a gambling platform—but, to the extent that a market provides the public good of fast and accurate forecasts, why not support it externally? There’s no need for the idea of a prediction market to be ideologically attached to a no-subsidy principle. Conversely, if someone is setting up a market, it’s not a bad idea to look into who’s in charge, as in the notorious case of the convicted terrorist who was running a terrorism prediction market.
Whitaker and Mazlish continue:
Without savers or gamblers, only sharps would remain. There are a few profiles of sharps who might seek value in prediction markets. Hobbyists, like politics nerds who want to capitalize on their knowledge, may constitute one group. Because insider trading is not prohibited in prediction markets, people with inside knowledge of some organization or event may want to trade on their information there. The hope of the prediction markets on everything vision is that true sharps would emerge in the form of hedge funds or other trading firms – professionals who would spend all their time investigating the probabilities of these events. . . . But since prediction markets lack savers – who flood security markets with capital and create profit opportunities – this never happens. Prediction markets are orders of magnitude smaller than other financial markets. . . . It’s hard to imagine how prediction markets would ever find the size and liquidity necessary to pay the salaries of top sharps without savers.
As most prediction markets also lack many of the features that attract gamblers, whom sharps would prefer to trade against, sharps are left with the unappealing prospect of trading only with one another. This is analogous to turning up to a poker table and discovering that all of the other competitors are poker champions. You would much rather have been at a table of drunk tourists.
Well put!
And this next bit is particularly relevant to election forecasting:
Markets are much less liquid when sharps trade only against sharps. As we’ve pointed out, the rewards for being right are smaller. But even beyond that, traders are more worried that they might be wrong when all of the other money is smart money. Why should they trust their model of the market probability over other sophisticated traders? . . . In practice, sharps would know they were mostly trading against sharps, but might still think they were better traders than their counterparties. But a sharp would usually understand they should be worried about their counterparty getting the better of them. The counterparty too assumes that they should also be worried, so both parties would be more hesitant to trade. And that’s not to mention platform fees, which would also take a cut.
They summarize:
We think that prediction markets as they exist are probably, at their best, similarly accurate to other high quality sources of information about the future, like the best forecasters, averages of forecasters like those found on Metaculus, and poll aggregators like 538. That is to say they do reasonably well, but are not authoritative or impossible for a highly motivated individual to beat.
That sounds about right. It’s related to the reasons I gave for not betting on the presidential election.
Subsidies?
As discussed above, one way to make prediction markets work better would be to subsidize them—an idea that I don’t think is as ridiculous or objectionable an idea as might sound at first. To the extent that a prediction market is a public good, it could make sense to subsidize it. There’s no need to be a market purist here, as just about everything is subsidized in some way or another—including people like me and various economists who post things for free on prediction markets; somebody’s paying for our time!
That said, Whitaker and Mazlish point out some practical challenges to the subsidy idea:
We haven’t seen many examples of this actually happening. . . . One way to subsidize a prediction market would be to get all those who are interested in gleaning information from the market to share the cost of the subsidy. . . . But how exactly to charge these users is difficult. Market prices tend to be public information. . . . Thus, a free rider problem emerges: many people who value the information a market provides cannot be charged. . . .
Subsidizing prediction markets likely is a relatively expensive way of aggregating information. . . . There is a simple reason for this: a subsidy needs to pay many market participants to create a crowd from which it could glean wisdom, whereas more conventional methods simply pay one group. Even if the wisdom of crowds derived from subsidized prediction markets performed better than individuals or teams, we worry that subsidizers would be unwilling to pay, as they might quickly run into diminishing marginal returns.
Indeed, if various poll aggregators are doing the job for free, and if market prices are reflecting some combination of polls and recent news, then not much value is being added by the market, except for a certain level of objectivity and whatever legitimacy is incurred by the feeling that somebody somewhere is betting real money on these numbers.
Whitaker and Mazlish add:
The final point is there are good alternatives to subsidizing prediction markets. Financial institutions have analysts; governments use intelligence agencies; companies use consultants; NGOs partner with economists and data scientists. Institutions employ these alternatives and virtually none employ subsidies.
Why would this be, if each of these groups can be beat when it comes to predicting the future? In many cases, individuals, firms, and governments do not just wish to know the probability of a future event. They would like to know the contingent probabilities around a cluster of events and actions and the reasoning behind those probabilities.
This is related to our point that a probability isn’t just a number; it’s part of a network of conditional statements.
They conclude:
We suspect that much demand for information about the future is satisfied by existing markets and firms. If it weren’t, wouldn’t private companies have taken up forecasting and prediction markets more quickly in the first place? That’s not to say that everyone has perfect information about the future. Instead, it’s that we suspect most people are paying for information that is as accurate as they need in a form that they can use. . . .
We are arguing against the view that were it not for pesky regulators, prediction markets for everything would be ubiquitous, and that those prediction markets would be the premier way to predict the future. On the contrary, the current size of the prediction market universe reflects market demand. Even if all regulatory hurdles were abolished, we do not expect that universe to dramatically expand.
Of course, we could be proved wrong. . . . But, in our view, prediction markets are held back by the lack of savers and gamblers . . .
Gambling
But . . . gambling is fun! Couldn’t recreational gamblers provide the “dumb money” needed for prediction markets to work?
Maybe so, but for elections, maybe not. First of all, any recreational gambler with access to the internet can see the poll aggregates, and it’s not clear that the “smart money” can do much more than that. To get some real “dumb money,” you’d want people betting just for fun, comparable to sports fans who will bet on the home team without looking at the odds, presumably relying on the accuracy of the market (the existence of “smart money”) to ensure that these odds are not major ripoffs.
But Whitaker and Mazlish argue that, in real life, most people only want to bet on events with very short time horizons:
Sports betting sites’ futures bets on longer-term outcomes are far less traded than bets on single games about to happen, even when the future event (like the winner of the Super Bowl) is far higher-profile than tonight’s game. For example, in late March, there was a mere £5,190 bet on the Wimbledon 2024 winner, but £227,421 was bet on the relatively unimportant, but in-play, Francesco Maestrelli vs. Pierre-Hugues Herbert match in tennis’s Napoli Cup. For reference, Wimbledon is the single biggest event in all of tennis, while no one ranked higher than 87th in the world is playing the Napoli Cup. Quick resolutions are so valued that live, in-game betting is becoming the most popular type of sports betting, despite the fact that the house tends to widen spreads on live bets, hurting bettors’ expected returns.
US presidential elections, surely the most well-known recurring political events on Earth, create a huge amount of buzz and theatrics, which fans closely follow. . . . Yet even in these cases, gamblers’ preference for quick resolution bites: 42 percent of the volume on the 2020 election was traded in the last week before the vote . . .
Very nice summary and I find it mostly convincing. However, there was one quoted sentence that I don’t agree with: “We suspect that much demand for information about the future is satisfied by existing markets and firms.” I believe that is far from the truth. Many people and organizations want information about future possibilities that no existing institutions have investigated – even a small local retailer has myriad uncertainties that they would like quantification about. Existing institutions only investigate issues big enough to concern them, and this leaves out many things. I suspect more things are not investigated than those that are.
I’m skeptical about prediction markets as forecasters for events like elections. When the results converge to something close to the actual outcome, the outcome will be in the very near future. What good is a forecast when the event is just about to happen? You want it to be reasonably accurate a long time before.
“…When the results converge to something close to the actual outcome, the outcome will be in the very near future. What good is a forecast when the event is just about to happen? …”
Sounds a lot like weather forecasts which people seem to find useful.
“We suspect that much demand for information about the future is satisfied by existing markets and firms. If it weren’t, wouldn’t private companies have taken up forecasting and prediction markets more quickly in the first place?”
But the authors already mentioned the free rider problem: prediction market forecasts are essentially public goods which are often underprovided because private companies can’t monetize them.
Andrew wrote, “People bet on cricket! Who knew?”
From Wikipedia:
https://en.wikipedia.org/wiki/Betting_controversies_in_cricket
As it happens, in the mid 1960s, I was paid by the Norwegian government regarding the betting outcomes of soccer matches in England that never took place. It is an involved tale that I have told often enough, including in this blog, so I will spare the reader the details. Suffice to say, the weather was so bad that the matches in England were cancelled for weeks on end, and the winners were determined by how close their submissions were to what “Norwegian experts” thought the (phantom, non-existent) outcome would be.
If you are puzzled by this Norwegian betting behavior, note that winters in Norway are long and the country is socialist and rich enough so that strange indulgences occur.
> the seller isn’t the actual company taking in what you pay for the stock and deploying it to grow their profitable operations, a potential ‘win / win’, but instead the seller is another investor
The quote you didn’t like followed the following remark: “Every winner of a prediction market necessitates an equal and opposite loser.”
“Securities investors with diversified portfolios can expect positive returns in the long term […]” refers to how investors on aggregate own a productive asset. Peter may be selling to Paul but they are exchanging the expectation of future cashflows (in form of dividends or share repurchases) coming from all the companies they jointly own.
The difference they point to is not about the seller’s and buyer’s motivations – it’s the difference between “positive-sum investments” and “zero-sum speculation”.
Carlos,
There are two issues here that seem to be conflated to try to make a distinction between buying stock on a stock exchange and buying a contract on a prediction market.
First is the question of whether either one is a ‘zero sum game’.
The other issue is whether buyers of stock are making a positive expectancy bet.
My initial comment is that the authors’ explanation for why an investor buying stock is a ‘positive sum game’ doesn’t make sense. Suppose that the seller is a short seller, then for every dollar of price appreciation and dividend paid that the buyer gains, the seller loses. That’s pretty close to the same mechanism that the authors use to characterize the prediction market as zero-sum, or ‘negative-sum’ when accounting for commissions (we’re not treating the exchange as part of the game). Is there another characterization of buying stock on a stock exchange as a multiplayer game that can be defended as a positive sum game? Maybe? But the authors certainly didn’t describe a mechanism for this that makes sense.
As for the other, separate issue, whether long term investors of stocks have positive expected returns, perhaps even exceeding the rate of inflation. Maybe? But does this have anything to do with what you paid for the stock being used by the company to fund its profitable operations? No.
Some people say it’s because companies can always increase the prices of their goods to compensate for inflation.
The question then is are sellers systematically just willing to sell stocks below their “actuarial value”? If so, why? If the argument is something about ‘risk premium’ then why does it make a difference that the asset is a ‘working asset’ as you call it?
> Suppose that the seller is a short seller,
I am sure that they would agree that “securities investors who sell short diversified portfolios can expect negative returns in the long term” just like they wrote that “securities investors with diversified portfolios can expect positive returns in the long term”. Someone having a (long) financial asset and someone else having the opposite (short) financial asset seems indeed like a zero-sum game.
Again, it may seem irrelevant to you but what they say is that equity investors in aggregate (all the investors who are or have been shareholders of each and every company) have given money to companies and have a reasonable expectation to get more money out from the pool of all the those companies – to be shared by all those shareholders.
(For individual investors it may take a long time to turn a meaningful profit if they buy at the wrong time and price. It may also never happen if all the companies in their not-diversified-enough portfolios go bankrupt or the objective conditions for a revolution are fulfilled and the capitalistic system collapses, etc.)
Prediction markets participants on aggregate are not getting money from anyone else! Shareholders on aggregate and bond holders on aggregate are getting money from companies on aggregate (who in turn are making money in aggregate by selling to their customers some product or service for more than what it costs them to manufacture or provide it).
(I’m not sure how these prediction markets work in terms of capital utilisation. In the “Alice sells a share to Bob for one dollar” example in aggregate they always have one share and one dollar. I don’t know if when they “bet” one dollar against each other on a future event they “get” at least the risk-free rate of return on the “tied” capital or not even that.)
“Securities investors with diversified portfolios can expect positive returns in the long term, because they are giving up their money for others to use to create output and wealth, in exchange for a share of what they create.”
This argument doesn’t make sense unless the investor is acquiring shares directly from the company, say in an IPO. An ‘investment’ via a purchase of shares on an exchange does not provide the company additional capital to use to expand or for R&D. Sure a liquid market for a company’s stock and stock price appreciation from stock exchange ‘investment’ could benefit the company if they choose to have a secondary offering or lower their cost of borrowing etc, but this seems like a fairly indirect connection between a securities investor’s investment and the company’s operations.
What I’m saying is: I don’t think this argument is what makes investing in stocks different than investing in prediction markets. It might not be a zero sum game… but how do we know? Couldn’t that the other investor selling shares to the securities investor on the NYSE be pricing their offer “fairly” according to some model that accounts for distribution of future prices for the stock?
Jyd:
I don’t have any special understanding of the theory or practice of the stock market . . . my general impression is that the experts say that if you invest your money in stocks and bonds, that over the long term the value of your portfolio will increase faster than inflation. So there is a financial reason to invest, without speculation being a motivation and without needing to be smarter than the herd. In contrast, if you put your money in sports betting or prediction markets, you will lose money on expectation (the vig).
Andrew,
My point was to address the misunderstanding that seems to be contained in the explanation of ‘why’ securities investors may expect positive returns in the long run. The statement seems to be attributing this to the investor giving the company funds to deploy in some way where they have an advantage. But when you buy stock on the NYSE the seller is rarely going to be the company itself but instead another investor.
So the question seems to be if it is such a well known fact that buying stocks on the stock market is a winning proposition, why is that other investor willing to sell to you? Is this ‘rule’ true at any price? What about the role of arbitrageurs?
Sure there are other reasons that might explain why stock market investing might beat out inflation, but the one given above doesn’t make sense to me.
A major reason people are willing to sell to you is that they need money to buy things. Everyday people don’t invest mainly with the goal of dying with the biggest pile. They have future expenses. Eventually those futures come for them and they need to sell and consume something with the money.
One reason someone might sell stocks is that they think US stocks in general will return say 8% this year, but they think this particular US stock will do worse. Another is that they decide they have too much of their assets in risky US stocks and want something that can’t lose half its value in a week and not recover for ten years. Its good practice to sell some of your investments which have been growing fastest or shrinking slowest to lock in your gains (or reduced losses) after the market shifts.
Buying stocks of profitable companies is a positive-sum game because those companies are making profit from capital, and owning the stock gives you a claim to a fraction of that profit, either in the form of dividends, or in rising share prices. Investing in a mine, or a startup, or an eCommerce giant that is not making a profit but promises that it will is a bit more speculative. And any one company may cease to be profitable, which is one reason why stock prices rise and fall so wildly.
The rhetorical question of ‘why is that other investor willing to sell to you’ was to emphasize that since the seller isn’t the actual company taking in what you pay for the stock and deploying it to grow their profitable operations, a potential ‘win / win’, but instead the seller is another investor: there isn’t that much of a distinction between a seller’s motivation to sell you stock and a seller’s motivation to sell you a contract in a prediction market. it may often be ‘I’m selling because i think it is overpriced’. But I guess in either market it could also be, ‘I’m selling cheaper than I think it is worth because I have expenses to cover or I found a better investment opportunity.’
[Obviously this reply belongs here]
> the seller isn’t the actual company taking in what you pay for the stock and deploying it to grow their profitable operations, a potential ‘win / win’, but instead the seller is another investor
The quote you didn’t like followed the following remark: “Every winner of a prediction market necessitates an equal and opposite loser.”
“Securities investors with diversified portfolios can expect positive returns in the long term […]” refers to how investors on aggregate own a productive asset. Peter may be selling to Paul but they are exchanging the expectation of future cashflows (in form of dividends or share repurchases) coming from all the companies they jointly own.
The difference they point to is not about the seller’s and buyer’s motivations – it’s the difference between “positive-sum investments” and “zero-sum speculation”.
“This argument doesn’t make sense unless the investor is acquiring shares directly from the company, say in an IPO. …”
The critical point is that you are acquiring a share in the company’s future profits. It doesn’t matter whether you acquire the share directly from the company or indirectly from someone else who already owns a share. If the company does well you should do well.
Why are you taking it as ‘given’ that the price the buyer pays for a stock or an index ETF is cheap relative to its “actuarial”value?
“Why are you taking it as ‘given’ that the price the buyer pays for a stock or an index ETF is cheap relative to its “actuarial”value?”
I’m not. The original question was whether stocks are zero sum. They pretty clearly are not. You can win in a stock investment without anyone else losing (or lose without anybody else winning). This is different from options which are essentially bets. To win your bet the person on the other side has to lose (and vice versa).
This doesn’t mean stock investments are guaranteed wins. Over any given period of time stock investors collectively will win or lose.
Perhaps a billionaire who prides himself on being the smartest guy in every trade could provide the subsidy. A company of ignorant plebs could use that money to play the market, which would attract the sharps, who can in their turn be outsmarted by the founder. Somewhat analogous to stocking a lake with fish.
My understanding is that it was not actually a terrorism prediction market.
This post completely omits hedging as a source of liquidity. If I have reason to believe my business will make money if Trump wins and lose money if Harris wins, I might bet some money on Harris so that I am guaranteed a return (and importantly this is done independently of who I actually think will win).
It’s not clear to me to what degree hedging makes sense in prediction markets compared to something like a traditional futures market where it is done all the time. But it is definitely something that should be mentioned when discussing liquidity.
Aaron:
In the article we link to, Whitaker and Mazlish discuss hedging:
You might disagree with them on this, but they do discuss it!
> We suspect there is simply very little demand for hedging events like whether a certain law gets passed; there is only demand for hedging the market outcomes those events affect, like what price the S&P 500 ends the month at. Hedging market outcomes already implicitly hedges for not just one event but all the events that could impact financial outcomes.
The passing of specific laws does have financial impacts on narrow sets of companies, as proven by expenditure on lobbying by sometimes concentrated industry bodies; to a first order approximation, if I spend X millions lobbying against some law, it means it might be also worth it to buy some amount of bets on it passing (at least in some cases). However that doesn’t happen as far as I know.
I think part of the problem faced by prediction markets is that the kinds of things a lot of people want to hedge against (trading with a loss in expectation) are things that happen with certain regularity, and we’ve built insurance for that (in a way, insurance is a regulated asymmetrical prediction market where only deep-pocket sellers and certain bets are allowed, because society has deemed hedging those important enough that we want to make sure there’s liquidity for that hedging and all bets will be honored).
Things not a lot of people want to hedge against (e.g. a law passing) are so specialized that there are probably relative information oligopolies on each issue; for them one-on-one consulting or bribes probably makes more sense than open markets. There’s a chicken-and-egg factor there as well, maybe.
These don’t cover all of the cases, but do contribute to narrow down the potential events for which prediction markets would work better than existing alternatives, at least without deliberate support policies. But then, we do already have the insurance industry as a deliberate way to provide hedging for all events deemed of systemic importance, and there isn’t likely to be public subsidies for things that aren’t; informational public goods as opposed to hedging aren’t directly provided by insurance, true, but we do have mixed private-public support for things like academic research and such – in a way, paying more experts to go public with their information by grants and such is not too dissimilar to subsidizing a prediction market so they trade there.
Nice post!
Disagree on the subsidy part though, totally not necessary. Kalshi is paying above 4% interest on the value of each portfolio, even if it’s tied up in contracts. This business is hugely profitable, if regulators permit the establishment of markets they will spring up by the dozen.
On cricket betting, here’s a list of folks suspended for throwing games for money, includes former captains of India and South Africa:
https://en.wikipedia.org/wiki/List_of_cricketers_banned_for_corruption
Interesting point about Kalshi paying interest, hard to tell if that is a temporary thing in order to attract participants. In Australia, where Betfair and other competitors have been operating political betting markets for years, they don’t subsidize, and the market for Australian political events are pretty thin.
“Whitaker and Mazlish “classify people who trade on markets into three groups”:”
There are other groups. Hedgers were already mentioned above. Another very important group is market makers (aka liquidity providers). In active markets they keep spreads small reducing trading costs.
I think the post underestimates the chilling effects of government hostility to prediction markets. Note even sports betting is heavily taxed. I would see what happens with neutrality before thinking about subsidies (which seem unlikely).
Some questions on prediction markets are the sort of thing that only excites the Internet nerds who love prediction markets, but questions like “who will be the next premier of Borduria?” or “will Syldavia land a little white dog on the moon by 1957?” are the kind of questions that lots of people have confident opinions on, like “who will win the World Cup?” Many of those opinions have a less sophisticated basis than “check a poll aggregator.”
So I don’t think its a good argument that too few ordinary people are interested in betting on the outcomes for sophisticated people to spend the time to figure out how to take the ordinary people’s money.
You could subsidize a prediction market by giving people a voucher which they have to spend on it.
> First of all, any recreational gambler with access to the internet can see the poll aggregates, and it’s not clear that the “smart money” can do much more than that.
In the same way that the financial market seemingly becomes nosier when there are zillions of divergent analyst coverage on the same stock from Goldman or Merrill, maybe another easier way to rescue the prediction market is to invite more Nate Silvers and create a forking path of poll aggregations.
One of the few pieces of advice my dad ever gave me was, “If you don’t know who the mark is at the card table, it’s you.” And my dad’s family made this quite evident—I knew I was the mark in any family card game (grandma ran all-weekend card games and all her kids, including my dad, just naturally counted cards in any game they played).
On another note, crowdsourcing (for training data in NLP) is the problem that got me into Bayesian stats.
What I don’t understand is why arbitrage doesn’t keep the prediction markets in synch. Isn’t there real money behind them? I learned about arbitrage and hedging in junior high (about 50 years ago) when my dad was a bookie—he explained how he couldn’t lose if he got two people to bet on the same game at different, opposing spreads, and how he’d have to go to Ohio to hedge his bets on Michigan games.
Bob:
Rajiv Sethi discusses the arbitrage thing in one of his posts that we’ve linked to. Here’s Sethi:
Glenn Loury had Rajiv Sehti on his podcast. It’s all interesting – but the discussion of prediction markets begins at around 33 minutes in.
https://open.spotify.com/episode/2N9dVvdROttIw7E4bTSxO1?si=TaQH21h9Sf-SmBIXn4TI-Q&t=1986
Joshua:
I’m so glad that Rajiv is getting some media exposure. He’s a thoughtful person with expertise in many areas.
Absolutely. I respect him a great deal. He’s been on Loury’s podcast before and enjoyed listening to him.
I used to have much more respect for Loury as well, but IMO, despite his obvious intellect, more recently he’s been caught up in “audience capture” mode of the podcast world. The discussion they had at the beginning of this pod (centered around Ta Nahasi Coates) was very interesting and reminded me of just how interesting Loury can be. Rajiv brings out the best in him. It’s very disappointing that Loury’s become mostly just another hot take guy.
BTW – one of the issues they discuss is arbitrage in the betting markets.