[tech_ken]

One thing that really jumps out to me is the lack of a performance gap between the 90-day and 30-day resolution times. If 2-months of new information doesn't lead to materially improved forecast, then to me this seems to strongly reinforce the takeaway that these markets aren't really forecasting, so much as "the oracle is largely saying what other oracles already say, just updated faster." Am I misunderstanding the data here?

edit: I'm also going back to my bayesian theory days and would be super interested to see a deep dive into whether these markets are rationally updating their beliefs in time. My recollection is super vague here, but I recall something like non-transitive belief loops can lead to dutch-books (so like Johnny Punter things that Trump will win an election against Biden, Biden would win against Ross Perot, and Ross Perot would win against Trump). I'd like to know more about whether these kinds of issues are showing up in these markets?

[ddp26]

Author here. Great point, and I think this is due to what another commenter points out, that the questions are different.

The right test of this is to take the _same_ markets that run for 90+ days, and check accuracy 90 days out vs 30 days out. I've done this on other prediction market datasets, though not on Kalshi and Polymarket, and found that forecasts are in fact more accurate 30 days out.

I agree that if they weren't, that would be incredibly suspicious!

[crazygringo]

On the other hand, both 90 days off and 30 days off are both still a very long time before an event happens.

It's going to highly depend on the type of event. But it's not surprising to me that there will often not be much difference, because the main factors affecting the event might not really start to be known until just a few days in advance.

Which really makes me wonder about in which categories our prediction markets most useful at a timeframe of months, versus which categories are most useful at a timeframe of days.