Election prediction markets: What happens next?

Rajiv Sethi discusses a recent ruling by a U.S. regulatory agency to halt trading on election prediction markets. Here’s Rajiv:

As evidence for this assertion the agency cited some of my [Rajiv’s] writing [that] describe attempts at market manipulation, one for financial gain and the other seemingly for maintaining optimism about the prospects of a candidate.

However, I [Rajiv] feel that the agency is drawing the wrong conclusions from this work, and a proper understanding of it undermines rather than bolsters the case for prohibition.

Some people believe that attempts at manipulating prediction markets are doomed to failure—that such attempts can have no more than a modest and short-lived effect on prices before other traders see a significant profit opportunity and pounce. I do not subscribe to this view. But when there exist prediction markets that lie outside the reach of our regulators, such as crypto-based Polymarket or the British exchange Betfair, the best defense against market manipulation is not prohibition but greater competition and transparency.

That is, I favor allowing Kalshi to proceed with the listing of contracts that reference election outcomes, not because I dismiss concerns about market manipulation or election integrity, but because I take them very seriously. . . .

Visibility is sharpened by the existence of multiple competing markets, especially if they have limited participant overlap. Kalshi is a regulated exchange restricted to verified domestic accounts funded with cash. Polymarket is crypto-based, does not accept cash deposits from US residents, and lies largely outside the purview of our regulatory apparatus.

Rajiv continues:

Prediction markets are characterized by an inescapable paradox. If they are taken seriously as unbiased aggregators of distributed information, beliefs will shift when prices change, and incentives for manipulation will be significant. But if they are seen as vulnerable to manipulation and frequently biased, price changes will be largely ignored, and they will not be worth manipulating. This logic places bounds on the extent of manipulation in markets—it cannot be absent altogether, but cannot be so large as to undermine their credibility.

Another way of saying this is that there are three sorts of manipulations to be concerned about:

1. People manipulating the market to make money using classic schemes such as spreading false information and using this to pump-and-dump, or taking advantage of insider information.

2. People manipulating the market to affect voting behavior or the behavior of potential donors or endorsors. I doubt this would have much effect for major-party candidates in a general election, but it could be an issue in primary elections, where there’s a clear benefit to be perceived as being in the top tier.

3. People trying to throwing the election to win a bet—or something more subtle such as point shaving.

In his post, Rajiv talks about #1 and #2 but not #3. Related to his “prediction market paradox” is that #2 can happen if the amount of money in the markets is low compared to the stakes of the election, so that the prices are affordable to manipulate given the potential gain, whereas #3 can happen if the amount of money in the markets is high compared to the stakes of the election, so that it’s worth taking the risk to throw it. Is there a sweet spot where the markets are big enough so they can’t easily be manipulated but small enough that there’s no motivation to throw the election? I guess that for U.S. presidential elections, the answer would be yes. For some other elections, maybe not.

Rajiv is not saying that prediction markets should be unregulated, indeed he writes that derivative contracts in election markets “serve no legitimate purpose and open up rather obvious strategies for manipulation. And even if attempts at manipulation fail in the end, they still arouse suspicion and sow confusion. . . . It would be a good thing if they were discontinued.” And, even setting aside derivative contracts, even with straight-up election bets, concerns about market manipulation, insider trading, and point shaving are real. But these are issues with all markets: they’re reasons to regulate, not to ban. Or, to put it another way, regulations are about tradeoffs. There are political costs to gambling on elections, but, as Rajiv argues, there are political benefits too.

Rajiv also asks:

Can the forecasting accuracy of markets exceed that of statistical models?

My quick answer is, Yes, I think that markets can be more accurate than statistical forecasts! Statistical election forecasts are public, so market players can make use of that information for free. Indeed, as discussed in this recent post, I think the presence of different public forecasts (including ours at The Economist) does its part in stabilizing market behavior, at least for the national electoral college and popular vote.

For side bets such as individual states and tail probabilities (what’s the chance that Harris wins South Dakota?) or various trifecta-style bets (what’s the chance Trump wins states X, Y, and Z?), not so much, and I say this for two reasons. First, even the forecasts that do well at the headline numbers have problems in the tails (as with the Fivethirtyeight forecast and the Economist’s as well), and prediction markets also do weird things when the probabilities are near 0 or 1 (see here). As for conditional probabilities, which seem so tantalizingly available by juxtaposing prices on multiple bets . . . I think there might be something there, but it would take a bit of statistical modeling; given the noise on each price, if you try to put them together in a naive way, you’ve got nothing but trouble.

This is not to say that prediction markets are a bad thing; just that we should be understanding them empirically—or, I should say, scientifically, combining empirics and theory, since neither alone will do the job—, not just blindly following pro- or anti-market ideology. As Rajiv says, the markets are out there already, and we can learn from them.