Prediction markets and the need for “dumb money” as well as “smart money”

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 . . .