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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, I have

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Speaker 2: to say, prediction markets have actually become a really significant

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Speaker 2: part of what I would call my news consumption.

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Speaker 3: I know you tweet about them a lot. In fact,

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Speaker 3: you have an ongoing joke that you keep repea day

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Speaker 3: that I would argue the novelty value has worn off.

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Speaker 3: But clearly I'm wrong because everyone on the platform finds

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Speaker 3: it very amusing.

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Speaker 2: Yeah, and other people have stolen my joke.

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Speaker 3: I've seen that. Yeah.

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Speaker 2: So the joke is that, you know, when people are

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Speaker 2: really confident about something and something's priced at seventy five

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Speaker 2: cents on the dollar, they're selling a dollar for seventy

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Speaker 2: five cents right now on polymarket. Is that when something

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Speaker 2: is fifty to fifty, I say, oh, they're selling dollars

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Speaker 2: for fifty cents, which of course sounds confident but can

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Speaker 2: mean in both directions. It's worked really well. I'm getting

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Speaker 2: a lot of mileage out that one. And you may

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Speaker 2: be bored of it now, but I think we're in

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Speaker 2: the valley of humor where if I'm still making the

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Speaker 2: joke a year from now, Tracy, You're gonna find it funny.

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Speaker 2: We shall see.

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Speaker 3: Okay, prediction markets, Okay, prediction markets are interesting. I have

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Speaker 3: a lot of questions about them. And first of all,

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Speaker 3: I feel like I should caveat this entire conversation with

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Speaker 3: an admission that I'm pretty sure I have made on

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Speaker 3: this podcast before. But I intuitively do not understand probabilities.

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Speaker 3: I'm kind of serious, Like I understand rationally, why if

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Speaker 3: you flip a coin five times, the probability of having

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Speaker 3: five instances of heads is not fifty percent, but if

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Speaker 3: you flip it once, it's fifty percent. But like, on

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Speaker 3: an intuitive basis, right, I don't really feel that way, right,

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Speaker 3: And then the other thing I would say when I

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Speaker 3: see prediction markets. So one of the things that happened

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Speaker 3: recently was people were going like, oh, prediction markets are

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Speaker 3: so smart and so interesting, because like on Predicted there

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Speaker 3: was a ten percent chance that Biden would drop out

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Speaker 3: of the race, and so people were going like, oh,

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Speaker 3: see the prediction market called it, and it was like, well,

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Speaker 3: a ten percent chance that was higher maybe than some

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Speaker 3: other platforms or some pundits expected but it happened, the

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Speaker 3: chance was one hundred percent. Like I just intuitively, I

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Speaker 3: don't get it. I don't get I don't get what

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Speaker 3: the percentage actually means.

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Speaker 2: Right, So, Tree, the way you think about the lottery,

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Speaker 2: for example, is that if you buy a lottery ticket,

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Speaker 2: your odds should be fifty to fifty because either you

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Speaker 2: win or you don't.

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Speaker 3: Right, therefore it should be binary.

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Speaker 2: No, So the way I think about them, and we'll

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Speaker 2: be talking very shortly to people who know a lot

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Speaker 2: more than me, is not that like I find conversations

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Speaker 2: about the rightness or the wrongness of prediction markets or

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Speaker 2: a given contract on a market to be sort of

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Speaker 2: the wrong question. It's more like, to me where I

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Speaker 2: find it valuable, It's like, Okay, a lot of people

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Speaker 2: think that Biden might drop out right, right, But is

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Speaker 2: that mean there's an eighty percent chance? Does that mean

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Speaker 2: there's a fifty five percent chance? And so what I

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Speaker 2: find interesting about prediction markets is not so much whether

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Speaker 2: going to be right or wrong, but that it seems

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Speaker 2: like they can put some sort of number on the

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Speaker 2: emerging conventional wisdom. Because look, even before the debate, some

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Speaker 2: people thought Biden still had a chance of dropping out,

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Speaker 2: and so it was interesting that the traders in those

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Speaker 2: markets assigned non negligible odds to that happening, and then

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Speaker 2: of course it all spiked after the debate and stuff

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Speaker 2: like that. So to me, you know, in thinking about

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Speaker 2: the election, we have polls, we have models, we have

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Speaker 2: prediction markets. Now we have punditry that appears on opinion

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Speaker 2: pages of various times. It's yet another tool to sort

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Speaker 2: of consume information and figure out where people's heads are at.

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Speaker 2: And I like it for that reason.

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Speaker 3: Yeah, okay, but I still don't get the value of

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Speaker 3: putting a specific number on it. Like you could just

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Speaker 3: have an arrow that goes up or down that shows momentum.

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Speaker 3: And I'm being slightly facetious here, but like you could

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Speaker 3: look at Google trends or something and you know, try

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Speaker 3: to gauge momentum that way. But okay, I have a

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Speaker 3: bunch of questions. I fully admit that this is something

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Speaker 3: that again on a sort of like instinctual level, I

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Speaker 3: do not quite understand. But we do, in fact have

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Speaker 3: the perfect guests to explain it all to us.

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Speaker 2: Yeah, yeah, I'm very excited. We are going to be

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Speaker 2: speaking with Nate Silver and Maria Kanakova. They're the co

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Speaker 2: hosts of the fairly brand new Risky Business podcast, which

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Speaker 2: is all about questions like this. They're both poker players

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Speaker 2: that are both recently played in the World Series of

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Speaker 2: Poker and have made a lot of money over the

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Speaker 2: years playing poker. They are people with probability in gambling

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Speaker 2: and odds and making decisions with money on the line,

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Speaker 2: both in their blood. Nate is the author of the

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Speaker 2: forthcoming book on the Edge, The Art of Risking Everything

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Speaker 2: in these also an advisor to Polymarket, which is one

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Speaker 2: of the big prediction markets that everyone has been quoting

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Speaker 2: these days. So a couple of great people to talk

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Speaker 2: to about things like betting on elections and how they

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Speaker 2: think about all this stuff. So Nave and Maria, thank

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Speaker 2: you guys both for coming on odd Laws.

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Speaker 4: Thank you so much. Joe and Tracy, Yeah.

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Speaker 5: Thanks for having us.

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Speaker 2: Absolutely so. When we look at all these markets and

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Speaker 2: poly markets, the hot one, but I forget the one

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Speaker 2: to his predict it was also really big. Do I

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Speaker 2: think they're running into some they have some regulatory issues.

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Speaker 2: Years ago, there used to be in trade. There is

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Speaker 2: the Iowa futures market, there's others. What is the value

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Speaker 2: when people look at all these numbers and say, like,

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Speaker 2: what is the value of markets like these?

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Speaker 4: I think it's the same value as any type of

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Speaker 4: market where you have price discovery from participants who have

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Speaker 4: a rational incentive to make smart trades and be closer

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Speaker 4: to whatever the value the market resolves to, you know,

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Speaker 4: I mean, Tracy asks some existential questions about the notion

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Speaker 4: of probability for things that are kind of seen as

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Speaker 4: one off events, But whether things are prinsically uncertain or

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Speaker 4: not is kind of above my paid grade, right. The

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Speaker 4: fact is that we have uncertain information. We're trying to

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Speaker 4: make judgement space and complete data and complete information, and

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Speaker 4: I think it's better to quantify things than to be

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Speaker 4: totally subjective and use weasel words and not have any

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Speaker 4: accountability for you know, a twenty percent chance versus a

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Speaker 4: forty percent chance for example.

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Speaker 5: Yeah, and I think that there is a huge difference

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Speaker 5: between the two, and Tracy your existential questions make a

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Speaker 5: lot of sense to me as a psychologist, because you know,

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Speaker 5: the human mind really does not like probabilistic thinking. You're

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Speaker 5: not the only one, and we do want to think

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Speaker 5: in absolutes, right, We do like zero and one hundred

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Speaker 5: those are easy numbers for us, whereas twenty three percent

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Speaker 5: not so much. But this idea that if we actually

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Speaker 5: put our money where our mouth is, it will help

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Speaker 5: us calibrate those probabilities better, help us calibrate our intuitive

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Speaker 5: sense of how confident we are in something, how much

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Speaker 5: we actually believe in it. That's I think why prediction

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Speaker 5: markets in all sorts of literature and all sorts of

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Speaker 5: areas have gained momentum because you think differently. I bet,

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Speaker 5: and I would put money on this tracy that if

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Speaker 5: I forced you to put money down on an opinion

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Speaker 5: or a thought, you would actually start homing in on

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Speaker 5: those instincts, on those decisions, and you would find that, Okay, well,

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Speaker 5: like you know, I'm probably less certain of this than

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Speaker 5: I thought I was, because I'm only willing to bet,

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Speaker 5: you know, ten dollars and not one hundred dollars. Asking

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Speaker 5: yourself questions like this, Actually putting down those bets really helps.

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Speaker 5: And what Nate was saying, when you have lots of people, right,

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Speaker 5: when you have hundreds and thousands of people doing this,

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Speaker 5: that's when the calibration becomes better. Because if we have

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Speaker 5: a thousand tracs, who might you know, not believe in

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Speaker 5: probabilities that much or not understand them, and yet they're

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Speaker 5: all putting their money down.

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Speaker 4: The idea is.

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Speaker 5: That in this particular case, the crowd actually knows something right,

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Speaker 5: there is some wisdom to the amount of money you're

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Speaker 5: putting down, because once again, actually betting with your wallet

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Speaker 5: so that it hurts, that's when you start to actually

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Speaker 5: question your beliefs.

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Speaker 3: Well, let me just say, off the bat, thank you

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Speaker 3: so much for trying to make me feel better. I

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Speaker 3: will just say, in my defense, I did do ap statistics,

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Speaker 3: and I did like I did, okay, probably because back

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Speaker 3: then you had to draw charts by hand, and I

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Speaker 3: was really into drawing like neat charts. So I think

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Speaker 3: I got points for that.

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Speaker 5: Okay.

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Speaker 3: Let me ask a non existential question, which is, if

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Speaker 3: I go on to polymarket or predict it, and let's

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Speaker 3: just say I see an event and the probability is

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Speaker 3: something like forty percent, where is that forty percent coming

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Speaker 3: from and what exactly is it telling me.

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Speaker 4: It's saying that in the long run, if you take

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Speaker 4: one hundred similar events some reference class, that the event

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Speaker 4: would occur about forty out of every one hundred times

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Speaker 4: and wouldn't occur the other sixty out of one hundred.

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Speaker 4: I think what bothers people is that they question whether

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Speaker 4: it's something like an election falls into an appropriate larger

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Speaker 4: subset of some random distribution of data. Right. That bugs people.

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Speaker 4: If you're playing poker, then there's one to fifty two

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Speaker 4: cards that might come next. It's technically not random, right,

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Speaker 4: the hand is actually dealt out once it's shuffled, but

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Speaker 4: de facto random, right. Or if you're watching a basketball game,

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Speaker 4: you have some sense that a free throw may or

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Speaker 4: may not go in, or may bounce off the back rim,

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Speaker 4: et cetera, et cetera. With elections, I mean, parties always

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Speaker 4: tell people that, oh, this is the most important election

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Speaker 4: of all time, but there are like a lot of

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Speaker 4: like fairly random contingencies in this election. Or example, to

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Speaker 4: use a somewhat pointed example, if Donald Trump had not

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Speaker 4: turned to look at a billboard of immigration data, then

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Speaker 4: the bullet that was fired at him might have done

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Speaker 4: much more damage than Grayze's ear, for example. Or in

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Speaker 4: two thousand, the butterfly ballot. This is going back a ways.

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Speaker 4: A ballot design issue in Palm Beach County Florida probably

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Speaker 4: costs Alt Gore the election and cause old people to

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Speaker 4: confusingly vote for Pat Buchanan instead. There are these kind

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Speaker 4: of near random circumstances even in elections. But even then,

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Speaker 4: you know, not everything is knowable or predictable. And if

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Speaker 4: you think it's locked in that Harris is going to win,

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Speaker 4: our Trump is going to win, then okay, well great,

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Speaker 4: maybe you're right, but you can make a lot of

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Speaker 4: money by betting on that at polymarket or predicted or

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Speaker 4: whatever else.

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Speaker 5: Yeah, I think one more time. You know, nothing in

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Speaker 5: life is certain, ever, no matter how much we want

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Speaker 5: it to be right, Like I might die in the

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Speaker 5: next minute. I hope I don't, but I'm right.

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Speaker 4: There's a non.

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Speaker 2: Zero, non zero exactly where my mind went when you

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Speaker 2: said nothing.

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Speaker 1: Right.

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Speaker 5: But it's so true. And if you actually think of

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Speaker 5: the election like a poker hand, except you know, think

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Speaker 5: about it in like multiverse terms, I'm going to go

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Speaker 5: back to existential terms, Tracey. You tried to get us

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Speaker 5: out of existential terms. I'm going to bring us right

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Speaker 5: back there. So imagine, you know, the multiverse experiment where

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Speaker 5: we have you know, a million possible versions of our

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Speaker 5: reality and in one of them. We had different ballots, right,

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Speaker 5: they weren't butterfly backs al Gore won the election, we

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Speaker 5: would probably not be here right now because you know,

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Speaker 5: it's funny that it's a butterfly. Yeah, butterfly ballot, butterfly effect. Right,

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Speaker 5: those tiny things do matter, but you do have to

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Speaker 5: realize that those probabilities still matter, right, like the fact

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Speaker 5: that Hillary Clinton lost and that Nate got a lot

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Speaker 5: for it. I defended Nate very publicly, as did a

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Speaker 5: lot of people, because his forecast was the single best one.

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Speaker 5: It gave her a good chance of winning, but it

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Speaker 5: gave Donald Trump the highest chance of winning. But people

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Speaker 5: don't like you know, you know, they don't understand that

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Speaker 5: over twenty percent is a hell of a lot of

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Speaker 5: a percent.

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Speaker 4: You know.

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Speaker 5: In my last book, I compared Nate's percentage just to poker,

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Speaker 5: and I said, the chances of Donald Trump winning, according

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Speaker 5: to Nate's prediction, was the same chance of you flopping

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Speaker 5: a pair in Texas. Hold them now, Think how often

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Speaker 5: you flop a pair right, that you pair up one

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Speaker 5: of the two cards that you're holding. That happens all

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Speaker 5: the time.

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Speaker 2: Never happens to me. I really just like sometimes I'm

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Speaker 2: sitting there at the poker table and I'm like, God,

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Speaker 2: just give me a set, and then I could double

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Speaker 2: up and walk away for the night. And it never

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Speaker 2: happens anyway. So I have two quick thoughts. One is

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Speaker 2: I always do find it funny when people talk about,

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Speaker 2: like our prediction markets good or not, because a point

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Speaker 2: that I've made many times in writing, et cetera, is

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Speaker 2: we actually do have a very popular liquid prediction market

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Speaker 2: that existed for a long time, which is the short

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Speaker 2: term treasury market, which is explicitly a bet on what

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Speaker 2: the you know, nine members or whatever of the FMC

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Speaker 2: are going to do over the next And that's literally

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Speaker 2: a prediction market. And no one questions whether that market works.

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Speaker 2: It does work, and it exists, and there's a lot

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Speaker 2: of money writing on it, and it literally is not

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Speaker 2: just metaphorically, it is a prediction market.

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Speaker 5: You know.

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Speaker 2: It occurs to me though, when we talk about, okay,

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Speaker 2: what is a forty percent return? This must be easily testable,

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Speaker 2: right Like if you take a thousand contracts that have

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Speaker 2: existed over the life of polymarket or the life of

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Speaker 2: predicted or intrade or whatever it was, and then say, like, okay,

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Speaker 2: one hundred days out from resolution. Did the ones that

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Speaker 2: say forty percent, did they resolve forty percent of the time? Like,

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Speaker 2: has that been tested? Like can we say that that

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Speaker 2: is the case that things with forty percent in the

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Speaker 2: prediction market over some timeframe do resolve forty percent towards

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Speaker 2: the direction.

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Speaker 4: There's been somewhat mixed literature on this. I do know.

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Speaker 4: I guess this is just a not so humble brag, right,

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Speaker 4: I do know historically that like the five thirty eight

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Speaker 4: now silver bullet and election forecasts have been better than

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Speaker 4: prediction markets, which is unusual. Usually publicly availble information is

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Speaker 4: not better than the market. However, they've been pretty well

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Speaker 4: calibrated according to most estimates. And also I think the

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Speaker 4: markets are maturing a lot where Look, I've talked to

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Speaker 4: a lot of different types of businesses, investment banks and

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Speaker 4: hedge funds and things like that, and they understand that

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Speaker 4: like political risk is market risk when you have this

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Speaker 4: big a difference between the two parties, that like whether

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Speaker 4: Donald Trump Orkamala Harris as the next president has a

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Speaker 4: lot of implication for every sector of the economy or

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Speaker 4: any trades you want to make. And so you have

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Speaker 4: seen more institutional attempts to either bet directly through pret

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Speaker 4: didiction markets or bet on proxies. And again I'm an

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Speaker 4: advisor to poly market, but their volumes are meaningfully much

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Speaker 4: higher than in the past, their structures are good, so

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Speaker 4: you don't pay as much of a tax in essence,

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Speaker 4: so I kind of think they've turned a corner from

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Speaker 4: being pretty good to maybe verging on very good.

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Speaker 5: Yeah, I think Nate, you just hit on a really

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Speaker 5: important point, which is volume. Right, prediction markets are only

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Speaker 5: as good as the traffic that they get. And so

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Speaker 5: for a long time, you know, there was no legal

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Speaker 5: prediction market in the United States, and so it's very

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Speaker 5: difficult when you're talking about betting on political events within

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Speaker 5: the United States to have a well calibrated market if

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Speaker 5: people within the United States can't actually bet on it. Right,

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Speaker 5: So I think that a lot of things are changing,

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Speaker 5: and hopefully we'll change for the better because volume is

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Speaker 5: essential in calibrating correctly, and you need people with expertise

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Speaker 5: doing it as well. But you also you just need

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Speaker 5: bodies to you need both of these things.

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Speaker 4: The ratio of smart money to dumb money is really important.

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Speaker 3: Yes.

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Speaker 4: In the Super Bowl, the literal actual chiefs versus forty

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Speaker 4: nine ers whatever. Super Bowl. There is so much dumb

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Speaker 4: money being bet on the Super Bowl that the sharp

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Speaker 4: money can't consume all of it, and so therefore you

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Speaker 4: often have positive expected value bets by fading the public

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Speaker 4: in the super Bowl. That's not true for a regular

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Speaker 4: season hornets Wizard's game in the NBA or something, but

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Speaker 4: for very very big events like a big UFC fight

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Speaker 4: or the super Bowl talk about in the book a

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Speaker 4: little bit, then those things potentially you have more dumb

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Speaker 4: money than sharp money. And so what you now have happening,

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Speaker 4: I think is like more of a professional class of

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Speaker 4: betters on politics, whether it is hedge funds or whether

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Speaker 4: it is just smart individuals who are betting on the

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Speaker 4: US election and elections around the world. You see some

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Speaker 4: sharp money coming into these markets.

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Speaker 3: So just on this idea of where the information and

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Speaker 3: the bets are actually coming from, talk to us a

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Speaker 3: little bit about the difference between a book maker versus

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Speaker 3: a prediction market, because my impression of a bookie is

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Speaker 3: like they kind of set the odds and then people

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Speaker 3: decide whether or not they want to place money on them,

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Speaker 3: and then you know they might calibrate those odds depending

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Speaker 3: on the response they are getting from people placing the bets.

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Speaker 3: But there's like a starting point that the bookies have,

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Speaker 3: and I guess it must be different for prediction markets.

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Speaker 3: Talk to us about like the difference in establishing those probabilities.

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Speaker 4: I mean, yeah, the classic act of bookmaking is where

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Speaker 4: you have some in essence prior. I guess it's an

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Speaker 4: advanced show, right, So like you have, you start out

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Speaker 4: with some number that you kind of probably comes out

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Speaker 4: of a model or some conversation. You have the initial

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Speaker 4: line that a sportsbook post on an NFL game is

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Speaker 4: actually probably often quite beatable. Right, It's just some like

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Speaker 4: nerd in a back room somewhere who's doing his best.

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Speaker 4: But then you look for action from sharp betters. Maybe

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Speaker 4: the book maker thinks that Maria is really good at

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Speaker 4: sports bidding, and I'm a total hack whale.

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Speaker 5: Definitely, definitely anyone listening that this is accurate.

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Speaker 4: So Maria bets on the Chiefs, she bets five K

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Speaker 4: on the Chiefs, and they'll move the line toward the

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Speaker 4: Chiefs in Maria's direction. Right, So now it's more extensive

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Speaker 4: for me about on the Chiefs. If I bet I

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Speaker 4: think I'm a whale. They to do about it, right,

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Speaker 4: because they think that's just dumb money they're trying to

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Speaker 4: make a profit from. So that's traditional book making, whereas

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Speaker 4: the prediction markets, it's all happening more organically, right, there's

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Speaker 4: no human at the switch who was deciding and evaluating

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Speaker 4: where to move their numbers.

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Speaker 5: I think that the other important thing is that you

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Speaker 5: I think suggested Nate, but didn't say explicitly when you're

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Speaker 5: talking about sports books, When you're talking about bookmaking, this

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Speaker 5: all happens pretty much manually, where people actually look to

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Speaker 5: see who's placing the bets, and then they actually adjust

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Speaker 5: the lines, right like there is a human adjusting the

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Speaker 5: line and deciding, okay, this is what we need to do,

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Speaker 5: and that might happen hundreds of times a day, maybe

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Speaker 5: even more, and the line gets better and better. In

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Speaker 5: prediction markets, those adjustments are just happening all the time.

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Speaker 4: It's the invisible hand, the same as like the stock market,

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Speaker 4: for example, YEP.

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Speaker 2: I could imagine that there are various types of betters

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Speaker 2: on prediction markets, and you broke it down into sharps

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Speaker 2: versus whales or whatever it is, or done money versus

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Speaker 2: smart money, but they're also different. Even within the category

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Speaker 2: of smart money, there are probably many different approaches. So

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Speaker 2: I could imagine that there are some people who are

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Speaker 2: very sophisticated in their political analysis, and they could say,

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Speaker 2: you know, this person is a better chance of winning,

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Speaker 2: you know, getting the vice presidential tap than someone else.

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Speaker 2: Someone else might just say, you know what, anytime the

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Speaker 2: model or the price diverges from the silver bulletin model,

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Speaker 2: I'm going to go either short or long that I imagine

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Speaker 2: that there are arbitrage traders who are just sort of

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Speaker 2: maybe looking at the price on one exchange versus the

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Speaker 2: price of another exchange and attempting to take advantage of discrepancies,

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Speaker 2: just like it exists in stock market. I assume maybe

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Speaker 2: there are momentum people. It's like this is hot right now,

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Speaker 2: even if I don't believe that. But talk to us

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Speaker 2: about some of the different strategies that the good betters

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Speaker 2: on prediction markets take.

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Speaker 4: I mean, I think you identified most of them, Joe.

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Speaker 4: And you know, one thing you learned being in the

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Speaker 4: gambling world, playing poker and so forth, is that if

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Speaker 4: there is an arbitrage to have or an edge to

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Speaker 4: be had, someone's going to hoover it up. Right. There

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Speaker 4: are actually people who go around and are professional slot

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Speaker 4: machine players, for example, because there are odd circumstances where

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Speaker 4: you might have a jackpod or a conditional probability where

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Speaker 4: playing a slot machine can don't do this at home,

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Speaker 4: do not do this at home, but there are cases

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Speaker 4: where race is going to do it. Yeah, Plus you

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Speaker 4: need to play a slot machine. So at the end

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Speaker 4: of the day, something like the vice presidential race. It's

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Speaker 4: inherently somewhat subjective, but if you have practice, I mean

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Speaker 4: as a poker player. Poker players develop uncanny intuitions for probabilities.

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Speaker 4: They'll say, I need thirty one percent to make this

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Speaker 4: call profitable on the river, and it was only getting

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Speaker 4: thirty percent and so I folded, right. I mean, they'll

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Speaker 4: literally get to like a one percent delta versus the

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Speaker 4: actual odds.

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Speaker 5: Are good poker players. You need an adjective in there.

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Speaker 4: Yeah. Yeah, but you learn it kind of. I mean,

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Speaker 4: it's just it's muscle memory at some point. Yeah.

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Speaker 5: Absolutely, And sometimes you don't even quite know what the

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Speaker 5: probabilities are. You just know that you have those odds

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Speaker 5: or you don't have those odds. You really do start

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Speaker 5: feeling them, So that is something that develops over time.

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Speaker 5: And I'm actually not sure though that it would develop

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Speaker 5: over time when you're doing something like prediction markets, right,

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Speaker 5: because slot machines are also something that is very experiential, right,

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Speaker 5: where you actually kind of are doing this and you're

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Speaker 5: trying it over and over and over. And for prediction markets,

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Speaker 5: I think you often just place lots of bets and

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Speaker 5: it's not like you're sitting there actually watching this and

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Speaker 5: experiencing it and learning from it. So I think you

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Speaker 5: already if you want to be a good better you

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Speaker 5: need to learn those intuitions from something like poker and

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Speaker 5: then apply them to the prediction markets. I don't think

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Speaker 5: it works the other way around, because the way that

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Speaker 5: the brain works, you need that experiences. You need to

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Speaker 5: sample those probabilities over and over and over. That's why

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Speaker 5: poker players know what one percent feels like, because they've

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Speaker 5: played tens of thousands, hundreds of thousands of hands and

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Speaker 5: they have seen the outcomes they've sampled correctly, right, so

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Speaker 5: they know exactly what that feels like. That doesn't happen

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Speaker 5: in prediction markets. So Nate can go into a prediction

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Speaker 5: market and he's going to crush, right, because he has

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Speaker 5: a good instinct for those probabilities. But someone who doesn't already,

437
00:21:26,640 --> 00:21:29,119
Speaker 5: I don't think that they're going to suddenly develop it

438
00:21:29,280 --> 00:21:31,440
Speaker 5: because they were betting in prediction markets.

439
00:21:31,960 --> 00:21:34,800
Speaker 3: Joe, I can say with one hundred percent confidence that

440
00:21:34,880 --> 00:21:38,000
Speaker 3: I have failed to apply my poker intuitions to my

441
00:21:38,160 --> 00:21:42,840
Speaker 3: slot machines playing strategy. That is definitely true. Okay, Wait,

442
00:21:43,000 --> 00:21:46,280
Speaker 3: so I'm looking at polymarket right now, and I should

443
00:21:46,280 --> 00:21:48,520
Speaker 3: look at this more because this is actually kind of fun.

444
00:21:49,320 --> 00:21:53,280
Speaker 3: How are the particular events that people are betting on?

445
00:21:53,640 --> 00:21:56,760
Speaker 3: How are those chosen or selected? Because right now I

446
00:21:56,800 --> 00:22:00,160
Speaker 3: am looking at will the US confirm that aliens exist

447
00:22:00,200 --> 00:22:03,760
Speaker 3: in twenty twenty four? What will Trump say in August?

448
00:22:03,920 --> 00:22:05,600
Speaker 3: I mean really, I feel like that could be anything?

449
00:22:06,080 --> 00:22:09,840
Speaker 3: And then will Taylor Swift get engaged in twenty twenty four?

450
00:22:09,920 --> 00:22:12,800
Speaker 3: So the big questions of our time? But how are

451
00:22:12,840 --> 00:22:15,960
Speaker 3: those sort of like why those particular questions? Is it

452
00:22:16,040 --> 00:22:18,159
Speaker 3: just if anyone wants to start a market in a

453
00:22:18,200 --> 00:22:19,040
Speaker 3: particular event.

454
00:22:19,640 --> 00:22:22,359
Speaker 4: Yeah, I'd say the polymarket guys who I know a

455
00:22:22,359 --> 00:22:25,960
Speaker 4: little bit are very attuned to finance, Twitter and politics,

456
00:22:25,960 --> 00:22:29,199
Speaker 4: Twitter and crypto Twitter sometimes they're kind of jokes and

457
00:22:29,280 --> 00:22:30,800
Speaker 4: kind of troll is. I mean, they're trying to have

458
00:22:30,960 --> 00:22:33,439
Speaker 4: some fun, right, and so you'll see some questions that

459
00:22:33,480 --> 00:22:35,200
Speaker 4: are a little bit memified. For sure.

460
00:22:35,440 --> 00:22:37,520
Speaker 5: I actually saw Nate and I'm not sure if this

461
00:22:37,600 --> 00:22:39,520
Speaker 5: was I don't remember if it was on polymarket or

462
00:22:39,560 --> 00:22:42,080
Speaker 5: one of the other prediction markets, but there was a

463
00:22:42,080 --> 00:22:45,639
Speaker 5: prediction market on our podcast including you know, number of

464
00:22:45,880 --> 00:22:48,719
Speaker 5: f bombs that Nate will drop within the first ten minutes.

465
00:22:49,040 --> 00:22:50,480
Speaker 5: You know, things like.

466
00:22:50,440 --> 00:22:52,840
Speaker 3: Wait, do you guys bet on that? Presumably you can

467
00:22:52,840 --> 00:22:53,760
Speaker 3: make a lot of money.

468
00:22:54,160 --> 00:22:56,600
Speaker 4: I mean some sites say that it's okay, right if

469
00:22:56,640 --> 00:22:59,679
Speaker 4: you talked so. The other the big free money prediction

470
00:22:59,720 --> 00:23:03,639
Speaker 4: market site is Manifold, which has a story dedicated community traders.

471
00:23:03,640 --> 00:23:06,400
Speaker 4: So even though as a polymarket advisor now I'd say

472
00:23:06,400 --> 00:23:08,240
Speaker 4: I think real money is a way to go, it's

473
00:23:08,240 --> 00:23:10,320
Speaker 4: pretty good. I certainly have a lot of respect for Manifold,

474
00:23:10,440 --> 00:23:13,320
Speaker 4: But yeah, a menifold is kind of radical transparency where

475
00:23:13,320 --> 00:23:15,840
Speaker 4: anyone can put up a market about anything and they

476
00:23:15,920 --> 00:23:18,320
Speaker 4: will even say yeah, as long as there's no complict

477
00:23:18,359 --> 00:23:22,240
Speaker 4: of interest, then inside information only makes the market smarter.

478
00:23:22,480 --> 00:23:24,000
Speaker 4: I'm a little bit worried about that. I mean, this

479
00:23:24,040 --> 00:23:26,800
Speaker 4: is an issue in sports markets too, where now you

480
00:23:26,920 --> 00:23:30,439
Speaker 4: have ESPN bets might offer a market on like the

481
00:23:30,520 --> 00:23:33,080
Speaker 4: NFL draft or something. Right, well, if you're an ESPN

482
00:23:33,200 --> 00:23:35,720
Speaker 4: NFL beat reporter, you might have a lot of knowledge

483
00:23:35,720 --> 00:23:37,719
Speaker 4: about what the Atlanta Falcons are going to do. And

484
00:23:37,760 --> 00:23:41,399
Speaker 4: for politics markets, I mean something moved the markets toward

485
00:23:41,760 --> 00:23:46,119
Speaker 4: Tim Walls Kamala Harris's VP pick aggressively about fifteen minutes

486
00:23:46,119 --> 00:23:49,800
Speaker 4: before that pick was confirmed. What is that? I don't know.

487
00:23:50,119 --> 00:23:52,320
Speaker 4: Maybe it's that there were SUVs as there seemed to

488
00:23:52,359 --> 00:23:54,840
Speaker 4: be outside of Tim Walls's residence in Minnesota where he

489
00:23:54,840 --> 00:23:58,520
Speaker 4: lives in Minnesota. But inside information is I mean from

490
00:23:58,520 --> 00:24:02,080
Speaker 4: a user's stand point, it's all incorporated, so it's useful,

491
00:24:02,080 --> 00:24:03,760
Speaker 4: But like you do wonder a little bit about like

492
00:24:04,080 --> 00:24:07,080
Speaker 4: are there ulterior motives for people who have you know,

493
00:24:07,160 --> 00:24:10,679
Speaker 4: inside knowledge about sports, politicst cetera, financial events to bet.

494
00:24:10,960 --> 00:24:13,560
Speaker 5: Yeah, and then you know there's a thin line. I'm

495
00:24:13,560 --> 00:24:15,760
Speaker 5: working right now on my next book, which is about cheating,

496
00:24:15,840 --> 00:24:18,200
Speaker 5: and there's a thin line when you can use inside

497
00:24:18,240 --> 00:24:21,199
Speaker 5: information where you know everything is fair game between that

498
00:24:21,359 --> 00:24:23,880
Speaker 5: and cheating, right and starting to throw games and starting

499
00:24:23,880 --> 00:24:27,560
Speaker 5: to actually do things where how you bet affects how

500
00:24:27,560 --> 00:24:30,960
Speaker 5: you act, and that's incredibly difficult to police. It's always

501
00:24:31,000 --> 00:24:33,240
Speaker 5: been difficult to police, and I think it's going to

502
00:24:33,280 --> 00:24:36,280
Speaker 5: become even more so. So I personally, and I'm not

503
00:24:36,359 --> 00:24:39,320
Speaker 5: involved with any prediction markets, I personally would like to

504
00:24:39,359 --> 00:24:42,320
Speaker 5: see there be some sort of penalties in it, not

505
00:24:42,400 --> 00:24:44,960
Speaker 5: being quite as open to that sort of thing, just

506
00:24:45,000 --> 00:24:47,840
Speaker 5: because I think that there is a huge possibility for

507
00:24:48,000 --> 00:24:52,800
Speaker 5: abuse and for things that actually end up sabotaging the

508
00:24:52,840 --> 00:24:56,199
Speaker 5: integrity of especially things like sports games, but politics, you know,

509
00:24:56,240 --> 00:24:57,840
Speaker 5: all sorts of contests.

510
00:24:57,680 --> 00:24:59,520
Speaker 2: Right, Tracy, and I I think it was last year

511
00:24:59,520 --> 00:25:04,880
Speaker 2: maybe we interviewed Rustin Benham, the head of the CFTC,

512
00:25:05,320 --> 00:25:08,240
Speaker 2: and they have not been friendly towards prediction markets for

513
00:25:08,480 --> 00:25:11,359
Speaker 2: some of these specific reasons which you there are probably

514
00:25:11,440 --> 00:25:14,880
Speaker 2: good reasons why you don't want manipulation of actual political

515
00:25:14,880 --> 00:25:18,440
Speaker 2: events to have money on the line. And actually, while

516
00:25:18,480 --> 00:25:21,760
Speaker 2: we're on this topic specifically, since you're here, Nate, my

517
00:25:21,880 --> 00:25:27,240
Speaker 2: understanding is that polymarket is not legally available to people

518
00:25:27,280 --> 00:25:31,400
Speaker 2: in the US, but as many people know, VPNs exist,

519
00:25:31,520 --> 00:25:34,399
Speaker 2: and because it uses stable coins to fund it, there

520
00:25:34,400 --> 00:25:36,879
Speaker 2: are ways around it. Is that an accurate characterization of

521
00:25:36,920 --> 00:25:37,280
Speaker 2: its state.

522
00:25:37,760 --> 00:25:39,639
Speaker 4: We look. Since I am in an especial capacity for them,

523
00:25:39,640 --> 00:25:41,280
Speaker 4: I las say is that you are not allowed to

524
00:25:41,280 --> 00:25:43,320
Speaker 4: bet on polymoticket if you're in the United States. Got it?

525
00:25:43,359 --> 00:25:45,879
Speaker 2: Okay, we know what's going on here. You know, you

526
00:25:45,960 --> 00:25:50,000
Speaker 2: mentioned prediction markets on podcasts actually there and you mentioned

527
00:25:50,080 --> 00:25:52,119
Speaker 2: manifold at one point, Tracy, I don't know if you

528
00:25:52,240 --> 00:25:54,720
Speaker 2: saw it. This fellow who's like it used to be

529
00:25:54,760 --> 00:25:58,200
Speaker 2: a pro magic the gathering player in gamblings, Vimashwitz, he

530
00:25:58,320 --> 00:26:01,840
Speaker 2: put up a contract and it's said, well's Vimashevitz get

531
00:26:01,880 --> 00:26:04,520
Speaker 2: invited onto the Odd Lots podcast this year, and I

532
00:26:04,600 --> 00:26:06,480
Speaker 2: thought about going, Oh, I was like, I just got

533
00:26:06,480 --> 00:26:08,320
Speaker 2: money on that. I'm going to take all my I'm

534
00:26:08,359 --> 00:26:11,919
Speaker 2: gonna take all my fake Manifold currency go along and

535
00:26:11,960 --> 00:26:14,399
Speaker 2: then invite them on the I didn't do that, but

536
00:26:14,680 --> 00:26:16,879
Speaker 2: it is very amusing to think of the various ways

537
00:26:16,920 --> 00:26:18,159
Speaker 2: you could very easily get.

538
00:26:18,000 --> 00:26:21,120
Speaker 3: Thank you for protecting the integrity of the podcast.

539
00:26:20,920 --> 00:26:21,439
Speaker 2: And the market.

540
00:26:21,560 --> 00:26:24,680
Speaker 3: Yes, and the market the most the price discovery mechanism

541
00:26:24,720 --> 00:26:26,000
Speaker 3: of prediction markets. Thank you.

542
00:26:26,200 --> 00:26:29,000
Speaker 2: But I want to get back to actually get deeper

543
00:26:29,119 --> 00:26:32,600
Speaker 2: into this question of like you know, on polymarket, there's

544
00:26:32,600 --> 00:26:35,000
Speaker 2: a leader board or there's a top volume this week,

545
00:26:35,040 --> 00:26:36,639
Speaker 2: and I think you can see leader boards just kind

546
00:26:36,640 --> 00:26:38,520
Speaker 2: of open so you can see talk to us more

547
00:26:38,680 --> 00:26:42,440
Speaker 2: about like what good traders are doing and why some

548
00:26:42,520 --> 00:26:44,520
Speaker 2: people seem to be better at this than others.

549
00:26:45,280 --> 00:26:47,119
Speaker 4: I think good traders are good at kind of quick

550
00:26:47,160 --> 00:26:51,000
Speaker 4: slice intuition and working with limited information. And you know

551
00:26:51,040 --> 00:26:53,679
Speaker 4: where's that skill come from. I mean it's partly the

552
00:26:53,720 --> 00:26:58,160
Speaker 4: combination of being both analytical and really competitive. Right. These

553
00:26:58,200 --> 00:27:01,840
Speaker 4: are not people who were just in the abstract building

554
00:27:01,960 --> 00:27:04,720
Speaker 4: academic models, but they're people who have to use the

555
00:27:04,920 --> 00:27:07,080
Speaker 4: cliche of another author, they have skin in the game.

556
00:27:07,359 --> 00:27:09,120
Speaker 4: They have a lot of practice, and they really want

557
00:27:09,160 --> 00:27:12,639
Speaker 4: to win, which is why gamifying these things and having

558
00:27:12,720 --> 00:27:16,399
Speaker 4: leaderboards and having discussion forums for people to explain the

559
00:27:16,480 --> 00:27:19,560
Speaker 4: rationales behind their bets. I mean, these are really really

560
00:27:19,600 --> 00:27:22,000
Speaker 4: competitive people, which is true for the other fields that

561
00:27:22,080 --> 00:27:24,439
Speaker 4: Marie and I indulge in, and the cream rises to

562
00:27:24,480 --> 00:27:25,920
Speaker 4: the top over the long run, I suppose.

563
00:27:41,640 --> 00:27:44,679
Speaker 3: So one thing I wanted to ask is we've obviously

564
00:27:44,920 --> 00:27:48,679
Speaker 3: been focused on prediction markets, but if I want to

565
00:27:48,800 --> 00:27:53,280
Speaker 3: express an opinion or bet money on something like who's

566
00:27:53,359 --> 00:27:56,240
Speaker 3: going to win the US election. Putting a bet on

567
00:27:56,320 --> 00:27:59,159
Speaker 3: poly market or something like that is not my only

568
00:28:00,680 --> 00:28:04,760
Speaker 3: So we know that Trump has that spack DJT. I'm

569
00:28:04,800 --> 00:28:09,479
Speaker 3: looking at the share price right now. It's spiked, you know,

570
00:28:09,640 --> 00:28:14,080
Speaker 3: in sort of early July, I guess, post the assassination attempt,

571
00:28:14,400 --> 00:28:17,359
Speaker 3: but it's come back down dramatically. And there are also

572
00:28:17,440 --> 00:28:19,159
Speaker 3: things like I mean, I think at this point there

573
00:28:19,160 --> 00:28:21,960
Speaker 3: are a number of them, but Trump related coins and

574
00:28:22,040 --> 00:28:24,280
Speaker 3: tokens and things like that. So I could just buy

575
00:28:24,320 --> 00:28:26,439
Speaker 3: one of those if I think that Trump is going

576
00:28:26,520 --> 00:28:29,520
Speaker 3: to win in November. How are you seeing people sort

577
00:28:29,560 --> 00:28:35,119
Speaker 3: of evaluate the opportunity from prediction markets versus things like,

578
00:28:35,840 --> 00:28:38,080
Speaker 3: you know, a Trump spack or Trump token.

579
00:28:38,640 --> 00:28:40,560
Speaker 4: I mean, look, if I were talking to the CFTC,

580
00:28:40,680 --> 00:28:43,200
Speaker 4: then one thing I'd say is that because there are

581
00:28:43,240 --> 00:28:47,160
Speaker 4: proxies for all these things anyway, that certain sectors of

582
00:28:47,160 --> 00:28:49,640
Speaker 4: the economy might be better off with the Harris versus

583
00:28:49,680 --> 00:28:53,640
Speaker 4: Trump administration, or interest rates treatments might be different. Or

584
00:28:53,760 --> 00:28:56,080
Speaker 4: Bitcoin itself, yeah, which is sort of.

585
00:28:56,000 --> 00:28:59,320
Speaker 3: Become yeah, which has become like a Trump administration become.

586
00:28:59,160 --> 00:29:02,960
Speaker 2: Kind of a Trump given how suddenly bitcoin friendly has become.

587
00:29:02,800 --> 00:29:05,760
Speaker 4: Yeah, Yeah, So you know, why have people bet on

588
00:29:05,800 --> 00:29:09,760
Speaker 4: these noisy proxies when they're trying to hedge political risk,

589
00:29:09,800 --> 00:29:11,680
Speaker 4: which is also economic risk. So that's me as I

590
00:29:11,680 --> 00:29:14,120
Speaker 4: guess now, as a paid spokes and for prediction market

591
00:29:14,120 --> 00:29:16,880
Speaker 4: company talking to the CFTC and saying you and we're

592
00:29:16,920 --> 00:29:18,960
Speaker 4: all making you all have to figure out this risk anyways,

593
00:29:19,000 --> 00:29:21,040
Speaker 4: So why not have prediction markets instead of having to

594
00:29:21,080 --> 00:29:23,680
Speaker 4: bet on DJT coins or whatever.

595
00:29:23,920 --> 00:29:26,240
Speaker 5: Yeah. I think a really important point is that this

596
00:29:26,280 --> 00:29:28,560
Speaker 5: is what traders do all the time, right, This is

597
00:29:28,640 --> 00:29:32,240
Speaker 5: the job of a professional trader is to buy and

598
00:29:32,360 --> 00:29:34,840
Speaker 5: sell things and trade things based on what they think

599
00:29:34,920 --> 00:29:37,920
Speaker 5: is going to happen, especially if you're a macro trader. Right,

600
00:29:37,960 --> 00:29:40,600
Speaker 5: there are people so Nate and I this week on

601
00:29:40,640 --> 00:29:44,040
Speaker 5: the podcast had Bill Perkins, who is a very famous

602
00:29:44,160 --> 00:29:48,000
Speaker 5: energy trader who made millions of dollars trading on predicting

603
00:29:48,080 --> 00:29:51,320
Speaker 5: correctly what's going to happen and to oil future prices too,

604
00:29:51,560 --> 00:29:56,160
Speaker 5: to energy prices depending on certain global events. What was

605
00:29:56,200 --> 00:29:58,240
Speaker 5: he doing. He was betting on the likelihood that those

606
00:29:58,280 --> 00:30:00,520
Speaker 5: events were going to happen, right, he thought that the

607
00:30:00,560 --> 00:30:02,800
Speaker 5: market was off, and then he was able to make

608
00:30:02,840 --> 00:30:06,640
Speaker 5: those trades accordingly and So if you think about it

609
00:30:06,680 --> 00:30:09,640
Speaker 5: in those terms, I mean, we have prediction markets. That's

610
00:30:09,680 --> 00:30:12,320
Speaker 5: what the stock market is, that's what all of these

611
00:30:12,320 --> 00:30:16,719
Speaker 5: different commodities markets are. You're constantly, constantly betting on the

612
00:30:16,760 --> 00:30:19,680
Speaker 5: future and betting on these things that will happen. And

613
00:30:19,720 --> 00:30:22,960
Speaker 5: the best macro traders out there, the people who trade events,

614
00:30:23,000 --> 00:30:25,600
Speaker 5: are the ones who have those intuitions and who are

615
00:30:25,760 --> 00:30:28,719
Speaker 5: able to figure out, Okay, what do I do and

616
00:30:28,760 --> 00:30:30,000
Speaker 5: how do I pull the trigger?

617
00:30:30,160 --> 00:30:30,320
Speaker 4: Right?

618
00:30:30,360 --> 00:30:32,000
Speaker 5: These are also people who have to have a high

619
00:30:32,080 --> 00:30:34,840
Speaker 5: risk tolerance and who need to understand risk taking. For

620
00:30:34,880 --> 00:30:37,320
Speaker 5: someone like Bill Perkins, he's also a poker player, right,

621
00:30:37,360 --> 00:30:39,400
Speaker 5: So a lot of times, once again, these things go

622
00:30:39,480 --> 00:30:40,080
Speaker 5: hand in hand.

623
00:30:40,560 --> 00:30:43,520
Speaker 2: I remember Tracy when I really was like into poker,

624
00:30:43,560 --> 00:30:45,560
Speaker 2: and I used to read these like biographies of poker

625
00:30:45,600 --> 00:30:48,840
Speaker 2: players and reading about like how much some of them

626
00:30:48,920 --> 00:30:51,880
Speaker 2: just like love to bet on literally everything was really fascinating.

627
00:30:51,920 --> 00:30:54,360
Speaker 2: Like they would like two guys would see like a

628
00:30:54,400 --> 00:30:56,920
Speaker 2: fly on the table and they're like, oh, I'll bet that.

629
00:30:56,480 --> 00:30:58,960
Speaker 2: I'll go that's right, one thousand dollars that that fly

630
00:30:59,080 --> 00:31:01,240
Speaker 2: is going to take off the next ten seconds from

631
00:31:01,280 --> 00:31:03,560
Speaker 2: the felt or something like that, And I get the impression,

632
00:31:04,040 --> 00:31:06,240
Speaker 2: and it sort of speaks to what Maria was saying

633
00:31:06,280 --> 00:31:10,880
Speaker 2: about building up then intuition, like over thousands and thousands

634
00:31:10,880 --> 00:31:13,640
Speaker 2: of events. It almost read to me like the great

635
00:31:13,640 --> 00:31:17,320
Speaker 2: poker players see probability on something like the way we

636
00:31:17,400 --> 00:31:19,840
Speaker 2: see a color of something. It's like that I see

637
00:31:19,840 --> 00:31:23,360
Speaker 2: your sunglasses on the table. They're green. Someone else looks

638
00:31:23,360 --> 00:31:26,920
Speaker 2: at that sunglass and say, I see there's some chance

639
00:31:27,000 --> 00:31:28,800
Speaker 2: that Tracy's gonna pick up the sunglasses.

640
00:31:29,040 --> 00:31:31,040
Speaker 3: But the probability that they're gonna break or the.

641
00:31:30,960 --> 00:31:33,320
Speaker 2: Products here they're gonna self combust, and it's just like

642
00:31:33,440 --> 00:31:35,960
Speaker 2: becomes the way they see the world. We have a

643
00:31:36,000 --> 00:31:38,920
Speaker 2: few minutes left, Nate, what's gonna happen in the election?

644
00:31:39,480 --> 00:31:41,680
Speaker 4: So we are actually pretty close to prediction markets. The

645
00:31:41,720 --> 00:31:44,280
Speaker 4: Silver Bullets and forecast as of this morning has Harris

646
00:31:44,320 --> 00:31:46,200
Speaker 4: with a fifty three percent chance and Trump at forty

647
00:31:46,200 --> 00:31:48,600
Speaker 4: seven percent. If you want to round that down to

648
00:31:48,640 --> 00:31:50,960
Speaker 4: fifty to fifty, a poker player would say, it's not

649
00:31:51,040 --> 00:31:53,120
Speaker 4: quite the same, but fifty to fifty more or less.

650
00:31:53,320 --> 00:31:55,120
Speaker 4: I think the question is like whether she so she

651
00:31:55,200 --> 00:31:58,560
Speaker 4: clearly has momentum. Now in the polls, we can debate

652
00:31:58,560 --> 00:32:00,840
Speaker 4: what momentum means exactly whether that implies when me and

653
00:32:00,840 --> 00:32:03,479
Speaker 4: will continue. But there are a couple of downsides for Harris.

654
00:32:03,520 --> 00:32:06,880
Speaker 4: One is that the electoral college likely still favors Trump.

655
00:32:06,960 --> 00:32:09,080
Speaker 4: It's a very very close popular vote, and to us

656
00:32:09,080 --> 00:32:11,680
Speaker 4: that she could be at something of a high water mark.

657
00:32:11,840 --> 00:32:15,640
Speaker 4: There is enthusiasm among Democrats right now that verge is

658
00:32:15,680 --> 00:32:18,800
Speaker 4: almost up to being like rapidly excited for Kamala Harris,

659
00:32:19,040 --> 00:32:21,840
Speaker 4: but we still have three months to go. Cannot be sustained.

660
00:32:22,200 --> 00:32:23,000
Speaker 4: We shall see.

661
00:32:23,240 --> 00:32:27,680
Speaker 2: And then, you know, looking across the different markets, you know,

662
00:32:27,760 --> 00:32:30,080
Speaker 2: as you mentioned, I'm looking at poly market right now.

663
00:32:30,160 --> 00:32:33,520
Speaker 2: It's forty nine forty nine percent Kamalas, So they're a

664
00:32:33,560 --> 00:32:36,720
Speaker 2: little bit tighter than what you haven't like so far.

665
00:32:37,240 --> 00:32:39,160
Speaker 2: I don't know, are there things that you're watching for

666
00:32:39,240 --> 00:32:41,720
Speaker 2: in the markets themselves. You know, we did an episode

667
00:32:41,800 --> 00:32:44,240
Speaker 2: on sports gambling with the Sports Better and he was

668
00:32:44,280 --> 00:32:47,320
Speaker 2: talking about, you know, sometimes there is edge in very

669
00:32:47,400 --> 00:32:49,760
Speaker 2: niche markets. So he was a tennis he liked to

670
00:32:49,760 --> 00:32:51,920
Speaker 2: bet on tennis. But he was saying, you know, there's

671
00:32:51,960 --> 00:32:54,560
Speaker 2: often opportunities where you can like bet like well so

672
00:32:54,600 --> 00:32:57,520
Speaker 2: and so we'll win in two sets or three sets

673
00:32:57,560 --> 00:33:00,120
Speaker 2: instead of four or whatever it is. Other In youre

674
00:33:00,160 --> 00:33:03,080
Speaker 2: sing things that you're seeing on the markets, whether it's

675
00:33:03,120 --> 00:33:06,600
Speaker 2: polymarket or others that seem off or on or interesting

676
00:33:06,840 --> 00:33:10,680
Speaker 2: or things that we should watch. Perhaps besides that headline

677
00:33:10,760 --> 00:33:12,800
Speaker 2: Harris versus Trump contract.

678
00:33:12,640 --> 00:33:14,400
Speaker 4: I mean you can bet on individual states. You can

679
00:33:14,440 --> 00:33:17,000
Speaker 4: bet on things like margins of victory and individual states.

680
00:33:17,000 --> 00:33:18,640
Speaker 4: And you know, if you have a model like mine,

681
00:33:18,640 --> 00:33:21,560
Speaker 4: then you have lots of opinions about that that you

682
00:33:21,600 --> 00:33:24,440
Speaker 4: can bet on potentially, However, the volumes tend to be

683
00:33:24,880 --> 00:33:27,160
Speaker 4: a lot smaller. There's also kind of a more macro question,

684
00:33:27,200 --> 00:33:30,840
Speaker 4: which is in recent elections, the markets have been a

685
00:33:30,840 --> 00:33:34,000
Speaker 4: little bit Republican leaning relative to what the models say.

686
00:33:34,560 --> 00:33:37,840
Speaker 4: Partly that's because I think the demographics of who trades

687
00:33:37,960 --> 00:33:40,680
Speaker 4: on these markets, right, they are a little bit crypto pilled,

688
00:33:41,120 --> 00:33:43,760
Speaker 4: very literally, so in cas it's looking like polymarket, So

689
00:33:43,920 --> 00:33:46,000
Speaker 4: that affects things a little bit. But also, you know,

690
00:33:46,400 --> 00:33:49,760
Speaker 4: poles have been off by a little bit in favor,

691
00:33:50,080 --> 00:33:52,120
Speaker 4: you know, the low bald Trump in twenty sixteen and

692
00:33:52,120 --> 00:33:55,040
Speaker 4: twenty twenty, not in twenty twenty two. But Trump wasn't

693
00:33:55,080 --> 00:33:56,800
Speaker 4: on the ballot. So one way to read the delta

694
00:33:56,920 --> 00:34:00,640
Speaker 4: between my forecast in the markets is that the market

695
00:34:00,680 --> 00:34:04,720
Speaker 4: is pricing in just a little tiny bit of Trump

696
00:34:04,760 --> 00:34:08,080
Speaker 4: beating his poles, which you know, might not be entirely irrational.

697
00:34:08,760 --> 00:34:11,759
Speaker 3: I'm glad you brought this up because the wisdom that

698
00:34:11,960 --> 00:34:16,000
Speaker 3: is being derived from this particular crowd, Like the crowd

699
00:34:16,160 --> 00:34:22,600
Speaker 3: is technically non American, but definitely you know, mostly male

700
00:34:22,760 --> 00:34:26,040
Speaker 3: from what we know, and maybe a little Republican leaning

701
00:34:26,200 --> 00:34:30,839
Speaker 3: as you just mentioned. Are there like efforts underway or

702
00:34:30,920 --> 00:34:35,480
Speaker 3: is there a desirability to broaden out the number of

703
00:34:35,520 --> 00:34:38,680
Speaker 3: people who are using this in order to maybe like

704
00:34:39,080 --> 00:34:41,680
Speaker 3: up the size of the crowd and make sure that

705
00:34:41,719 --> 00:34:45,320
Speaker 3: you're getting the best sample that you could possibly get.

706
00:34:45,440 --> 00:34:48,319
Speaker 3: And again, if you look at the official polls, the

707
00:34:48,360 --> 00:34:52,640
Speaker 3: poles always attempt to get a slice of the American

708
00:34:52,680 --> 00:34:56,239
Speaker 3: population that is reflective of its actual makeup. That's not

709
00:34:56,400 --> 00:34:59,800
Speaker 3: really happening on these prediction markets. So is there anything

710
00:34:59,800 --> 00:35:02,160
Speaker 3: that you could do about that or is it a concern?

711
00:35:02,719 --> 00:35:05,080
Speaker 5: I think that in an ideal world, of course, we

712
00:35:05,120 --> 00:35:08,200
Speaker 5: would take all of these things into consideration and you

713
00:35:08,239 --> 00:35:12,399
Speaker 5: would have a more representative sample of betters. But we're

714
00:35:12,440 --> 00:35:14,640
Speaker 5: not living in an ideal world, and like we're not

715
00:35:15,760 --> 00:35:19,080
Speaker 5: I can't affect the regulation, but I do think that

716
00:35:19,840 --> 00:35:24,880
Speaker 5: it's important to get a sort of sample that is

717
00:35:24,960 --> 00:35:27,920
Speaker 5: representative of the people who are going to be voting, right,

718
00:35:27,960 --> 00:35:31,560
Speaker 5: So it's not just representative of the population, but who's

719
00:35:31,600 --> 00:35:34,040
Speaker 5: actually going to be going to the polls, who's going

720
00:35:34,120 --> 00:35:38,320
Speaker 5: to be casting the ballots. Those are important questions to ask,

721
00:35:38,760 --> 00:35:41,880
Speaker 5: and sometimes the fact that things are skewed also reflects

722
00:35:41,920 --> 00:35:44,840
Speaker 5: like who's actually going to be walking? Who are we

723
00:35:44,960 --> 00:35:47,319
Speaker 5: actually going to be asking? So I think there's so

724
00:35:47,400 --> 00:35:50,279
Speaker 5: many different parts of this question. And yes, Nate, as

725
00:35:50,320 --> 00:35:52,400
Speaker 5: you correctly point out, I am worried about bias, and

726
00:35:52,440 --> 00:35:55,160
Speaker 5: I am worried about all of these different things, but

727
00:35:55,239 --> 00:35:57,680
Speaker 5: some of them are just like a fact of existence

728
00:35:57,880 --> 00:36:00,319
Speaker 5: and we need to account for them. But we need

729
00:36:00,320 --> 00:36:02,880
Speaker 5: to realize that, Okay, like if we try to make

730
00:36:02,920 --> 00:36:05,759
Speaker 5: it representative, we might actually be skewing things if the

731
00:36:05,800 --> 00:36:08,280
Speaker 5: vote isn't going to be representative. If that makes sense.

732
00:36:08,719 --> 00:36:11,359
Speaker 2: Yeah, Actually that leads me to one more question, which

733
00:36:11,400 --> 00:36:13,640
Speaker 2: is that just thinking about the election itself, setting aside

734
00:36:13,760 --> 00:36:17,200
Speaker 2: betting markets, So there is reason to think that maybe

735
00:36:17,280 --> 00:36:22,319
Speaker 2: in twenty sixteen, perhaps the polls weren't capturing all of trump'support,

736
00:36:22,400 --> 00:36:24,319
Speaker 2: and I guess it's sort of played out like that,

737
00:36:24,400 --> 00:36:27,280
Speaker 2: but right now it actually seems to cut in both directions.

738
00:36:27,600 --> 00:36:31,600
Speaker 2: So there is a view that okay, polls aren't good anymore,

739
00:36:31,880 --> 00:36:33,839
Speaker 2: and that people don't pick up the phone and they've

740
00:36:33,880 --> 00:36:37,160
Speaker 2: become much more expensive and less efficient, and that there

741
00:36:37,200 --> 00:36:38,879
Speaker 2: could be a skew in the type of person who

742
00:36:38,880 --> 00:36:41,400
Speaker 2: answers the phone. On the other hand, one thing that

743
00:36:41,440 --> 00:36:46,480
Speaker 2: we've seen, particularly emerging in midyear elections is that Democrats

744
00:36:46,600 --> 00:36:48,520
Speaker 2: just like they love to turn out to vote, They

745
00:36:48,560 --> 00:36:51,279
Speaker 2: loved the vote in any random election. And there is

746
00:36:51,360 --> 00:36:54,840
Speaker 2: the perception, probably accurate, that many Trump voters are you know,

747
00:36:55,239 --> 00:36:57,800
Speaker 2: less engaged and they might support Trump, but whether that

748
00:36:57,920 --> 00:37:03,520
Speaker 2: support actually translates into showing up, registering to vote, finding

749
00:37:03,520 --> 00:37:06,800
Speaker 2: the polling location, going there, and voting is another question.

750
00:37:07,200 --> 00:37:09,399
Speaker 2: How are you thinking about like some of these sort

751
00:37:09,440 --> 00:37:13,400
Speaker 2: of risks right now in twenty twenty four, with some

752
00:37:13,520 --> 00:37:16,160
Speaker 2: of the quality of data, and you know, ways it

753
00:37:16,160 --> 00:37:17,279
Speaker 2: could be biased right now.

754
00:37:17,560 --> 00:37:20,120
Speaker 4: Yeah. Look, the model more or less assumes that although

755
00:37:20,160 --> 00:37:23,000
Speaker 4: polls can and probably will be biased some degree, that

756
00:37:23,040 --> 00:37:26,520
Speaker 4: the direction of the bias is unpredictable, which I Joe

757
00:37:26,640 --> 00:37:30,600
Speaker 4: kind of see as a spinoff from like efficient market hypothesis.

758
00:37:31,160 --> 00:37:33,279
Speaker 4: The markets aren't always right, they do silly things. But

759
00:37:33,360 --> 00:37:34,880
Speaker 4: if you think you know better than the market what

760
00:37:35,040 --> 00:37:37,400
Speaker 4: Nvidia should be priced at or something, then then you

761
00:37:37,400 --> 00:37:40,399
Speaker 4: can make a killing trading options or whatever else. Look,

762
00:37:40,600 --> 00:37:43,640
Speaker 4: pollsters have an incentive to get the right answer. And

763
00:37:43,680 --> 00:37:45,960
Speaker 4: we've also moved away from kind of like a purist

764
00:37:46,080 --> 00:37:48,920
Speaker 4: world where you call everybody in the phone book at

765
00:37:49,000 --> 00:37:51,000
Speaker 4: random and say, can I speak to the person with

766
00:37:51,040 --> 00:37:53,799
Speaker 4: the next birthday? And they always answer. We moved away

767
00:37:53,840 --> 00:37:57,279
Speaker 4: from that to where like poles are basically models, so

768
00:37:57,360 --> 00:38:00,359
Speaker 4: my model is kind of like aggregating models to one

769
00:38:00,400 --> 00:38:03,719
Speaker 4: big meta model. And so therefore I think the market

770
00:38:03,760 --> 00:38:07,000
Speaker 4: incentives are pretty good, right, And when polls are biased

771
00:38:07,200 --> 00:38:10,839
Speaker 4: toward Democrats, then Republican leading polsters tend to get more

772
00:38:10,880 --> 00:38:13,080
Speaker 4: work the next cycle. So it's a little bit it's

773
00:38:13,120 --> 00:38:16,080
Speaker 4: fairly self correcting. I think, actually that's a.

774
00:38:16,080 --> 00:38:18,080
Speaker 5: Very good way of looking at it. So I hope

775
00:38:18,120 --> 00:38:18,400
Speaker 5: you're right.

776
00:38:18,480 --> 00:38:22,000
Speaker 2: Night, Nate and Maria, thank you so much both of

777
00:38:22,040 --> 00:38:25,200
Speaker 2: you for coming on the podcast. Good luck and congratulations

778
00:38:25,239 --> 00:38:27,719
Speaker 2: on the launch of the new podcast. Maybe a little

779
00:38:27,719 --> 00:38:30,439
Speaker 2: competition who knows, But NO really appreciate having you both

780
00:38:30,480 --> 00:38:33,400
Speaker 2: on odd lots to talk about something that's very timely

781
00:38:33,480 --> 00:38:34,120
Speaker 2: and exciting.

782
00:38:34,239 --> 00:38:36,560
Speaker 5: Thank you so much. It's been a pleasure.

783
00:38:49,120 --> 00:38:51,520
Speaker 2: Tracy. That was a lot of fun. I'm a terrible gambler.

784
00:38:51,560 --> 00:38:54,799
Speaker 2: I'm really bad, but I still like it. I don't

785
00:38:54,840 --> 00:38:57,280
Speaker 2: like losing money, but I still like in my mind,

786
00:38:57,520 --> 00:39:00,000
Speaker 2: I wish I were good, and I like talking about it.

787
00:39:00,320 --> 00:39:00,520
Speaker 5: Well.

788
00:39:00,560 --> 00:39:03,880
Speaker 3: I thought Maria's point about when you actually have to

789
00:39:03,880 --> 00:39:07,200
Speaker 3: put money behind, like your thought process about who is

790
00:39:07,239 --> 00:39:10,319
Speaker 3: going to win the election, it sort of sharpens your

791
00:39:10,360 --> 00:39:13,520
Speaker 3: reasoning and your thinking. I thought that was interesting. By

792
00:39:13,520 --> 00:39:16,719
Speaker 3: the way, Joe, I have good news for you and

793
00:39:16,800 --> 00:39:20,840
Speaker 3: I have bad news. Oh okay, So the bad news

794
00:39:21,000 --> 00:39:24,879
Speaker 3: is there's a question on poly market, that is, will

795
00:39:24,960 --> 00:39:28,840
Speaker 3: America bans in in twenty twenty four. The good news

796
00:39:28,960 --> 00:39:32,080
Speaker 3: is that the current probability is seven percent, So I

797
00:39:32,080 --> 00:39:34,000
Speaker 3: think you're okay, that's all right.

798
00:39:34,080 --> 00:39:36,840
Speaker 2: I have Zalt instead. There's one hundred other ones that,

799
00:39:36,960 --> 00:39:39,359
Speaker 2: as we learned in a recent episode, will probably get

800
00:39:39,400 --> 00:39:43,319
Speaker 2: through border security regardless of the regulatory environment. But No,

801
00:39:43,520 --> 00:39:45,279
Speaker 2: I thought it was fun. And look, it gets back

802
00:39:45,280 --> 00:39:49,120
Speaker 2: to two things that I still basically think which is

803
00:39:49,120 --> 00:39:51,960
Speaker 2: that A in the form of regular markets, prediction markets

804
00:39:51,960 --> 00:39:55,400
Speaker 2: have worked for a long time, and B they're just

805
00:39:55,520 --> 00:39:59,400
Speaker 2: from my perspective, I don't participate in betting markets, but

806
00:39:59,760 --> 00:40:02,520
Speaker 2: I find it interesting is like, what is the conventional

807
00:40:02,520 --> 00:40:05,240
Speaker 2: wisdom right now on what's going to happen where people's

808
00:40:05,280 --> 00:40:08,640
Speaker 2: heads at. And as a news consumer, I find them

809
00:40:08,760 --> 00:40:11,960
Speaker 2: additive in a way that just say tweets like I

810
00:40:11,960 --> 00:40:14,600
Speaker 2: think Kamala is gonna win or has the momentum. It's

811
00:40:14,760 --> 00:40:16,640
Speaker 2: it's something helpful. On top of that, it's.

812
00:40:16,600 --> 00:40:21,160
Speaker 3: Definitely something to talk about in the media, and that

813
00:40:21,239 --> 00:40:24,560
Speaker 3: is true. I do think not to go all existential again,

814
00:40:24,600 --> 00:40:27,759
Speaker 3: but I do think it opens up really interesting questions

815
00:40:27,880 --> 00:40:31,439
Speaker 3: about the nature and the purpose of market. So Nate

816
00:40:31,600 --> 00:40:34,080
Speaker 3: talked in the beginning about, well, this is a price

817
00:40:34,160 --> 00:40:37,880
Speaker 3: discovery vehicle, and you know, I can see that. But

818
00:40:38,400 --> 00:40:41,680
Speaker 3: I guess if you were a market traditionalist, you know

819
00:40:41,719 --> 00:40:44,239
Speaker 3: there might be people out there who think that markets

820
00:40:44,280 --> 00:40:48,640
Speaker 3: are about efficiently allocating capital, and so the question is like,

821
00:40:48,680 --> 00:40:53,720
Speaker 3: what are you really allocating capital too in this particular instance.

822
00:40:53,800 --> 00:40:56,359
Speaker 3: But then I guess the reverse argument would be like, well,

823
00:40:56,360 --> 00:40:59,759
Speaker 3: there is value in making a correct prediction and so

824
00:40:59,840 --> 00:41:01,680
Speaker 3: you are rewarding that behavior.

825
00:41:01,920 --> 00:41:03,440
Speaker 2: So I think that's a great point. And I think

826
00:41:03,480 --> 00:41:06,080
Speaker 2: I would say two things from my perspective, which is

827
00:41:06,160 --> 00:41:09,279
Speaker 2: Ny pointed out, for some of these bets, there is

828
00:41:09,520 --> 00:41:13,000
Speaker 2: real economic issue at stake. So if you are interested

829
00:41:13,160 --> 00:41:15,760
Speaker 2: in the future of electric vehicles or oil or whatever

830
00:41:15,800 --> 00:41:18,320
Speaker 2: it is, then it might make a very big difference

831
00:41:18,480 --> 00:41:21,239
Speaker 2: who wins the election. In other words, like you like

832
00:41:21,400 --> 00:41:24,080
Speaker 2: you already have skin in the game and this becomes

833
00:41:24,080 --> 00:41:26,399
Speaker 2: an instrument to hedging, or I'm like a little bit

834
00:41:26,400 --> 00:41:29,040
Speaker 2: more skeptical as like, Okay, so here is one of

835
00:41:29,080 --> 00:41:31,759
Speaker 2: the things I see, what will Trump say during his

836
00:41:31,840 --> 00:41:34,600
Speaker 2: interview with Elon Musk. There's a seventy two percent he

837
00:41:34,600 --> 00:41:36,960
Speaker 2: says make America great again. There's a forty nine percent

838
00:41:37,120 --> 00:41:39,879
Speaker 2: chance he mentions crypto. There's a fifty seven percent chance

839
00:41:39,880 --> 00:41:44,000
Speaker 2: you mentioned censorship, et cetera. Like for a market like that,

840
00:41:44,000 --> 00:41:47,960
Speaker 2: that seems like pure speculation because there are very few

841
00:41:48,600 --> 00:41:53,279
Speaker 2: actual outside exogynists crypto. But yeah, no, that's I take

842
00:41:53,280 --> 00:41:55,759
Speaker 2: the point. Well, like, so okay, like okay, So for

843
00:41:55,920 --> 00:41:58,439
Speaker 2: maybe a better one, will Taylor Swift get engaged right

844
00:41:58,960 --> 00:41:59,759
Speaker 2: or you know.

845
00:42:00,200 --> 00:42:03,319
Speaker 3: The number here that might that might actually mean something

846
00:42:03,360 --> 00:42:04,400
Speaker 3: for inflation.

847
00:42:04,320 --> 00:42:06,600
Speaker 2: Or the marriage market, right, because a lot of people

848
00:42:06,640 --> 00:42:09,200
Speaker 2: think that if Taylor Swift gets engaged and a bunch

849
00:42:09,239 --> 00:42:11,160
Speaker 2: of people are like, it's time for us to tie

850
00:42:11,160 --> 00:42:12,440
Speaker 2: the knot and sort of do the same thing.

851
00:42:12,480 --> 00:42:14,400
Speaker 3: Maybe we should just do this for a while and

852
00:42:14,600 --> 00:42:16,680
Speaker 3: just play a game where we go through all of

853
00:42:16,719 --> 00:42:20,080
Speaker 3: these particular events and questions, come up with the real

854
00:42:20,080 --> 00:42:21,520
Speaker 3: world economic it come up.

855
00:42:21,480 --> 00:42:24,520
Speaker 2: With a je there's one. The number of Elon Musk

856
00:42:24,560 --> 00:42:28,719
Speaker 2: tweets between August second August nine, currently between eighty and

857
00:42:28,760 --> 00:42:31,000
Speaker 2: eighty nine is at one hundred percent, So people, well

858
00:42:31,440 --> 00:42:32,280
Speaker 2: to keep tweeting.

859
00:42:32,480 --> 00:42:35,800
Speaker 3: Maybe Elon must tweets have an impact on the presidential election.

860
00:42:35,880 --> 00:42:38,280
Speaker 3: There has an impact on a bunch of real world stuff.

861
00:42:38,520 --> 00:42:41,600
Speaker 2: Five percent that there's a doge coin etf in twenty

862
00:42:41,640 --> 00:42:44,040
Speaker 2: twenty four. That seems a little high to me, because

863
00:42:44,280 --> 00:42:46,600
Speaker 2: but you know, we don't even have anyway. We could

864
00:42:46,680 --> 00:42:49,000
Speaker 2: go on and on. It's very fun. It's just another

865
00:42:49,000 --> 00:42:50,560
Speaker 2: way for me to consume the news.

866
00:42:51,400 --> 00:42:54,440
Speaker 3: All right, Well, I'm sure we'll probably end up doing

867
00:42:54,480 --> 00:42:57,719
Speaker 3: more prediction market episodes in the future, but for now,

868
00:42:57,760 --> 00:42:58,560
Speaker 3: shall we leave it there.

869
00:42:58,640 --> 00:42:59,399
Speaker 2: Let's leave it there.

870
00:42:59,560 --> 00:43:02,360
Speaker 3: This has been another episode of the Odd Lots podcast.

871
00:43:02,440 --> 00:43:05,320
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

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Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.

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00:43:08,480 --> 00:43:11,200
Speaker 2: Follow our guest Nate Silver, He's at Nate Silver five

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Speaker 2: thirty eight. Maria Kanakova, She's at Mconnakova. Follow our producers

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Speaker 2: Carmen Rodriguez at Kerman armand Dashil Bennett at deshbot and

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Speaker 2: Killbrooks at Kilbrooks. And thank you to our producer Moses Onam.

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Speaker 2: And for more Oddlots content, go to Bloomberg dot com

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Speaker 2: slash odd lots, where of transcripts, a blog and a

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Speaker 2: newsletter and you can chat about all of these topics

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Speaker 2: in our discord. In fact, there's a very active channel

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Speaker 2: where people are talking about this stuff all the time,

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Speaker 2: in the elections channel and the pundit dunk take channel

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Speaker 2: where they people drop bad predictions or make fun of people.

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Speaker 2: Very entertaining subchannel in there might be my favorite. Check

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Speaker 2: out our discord, Discord dot gg, slash odlots.

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Speaker 3: And if you enjoy all thoughts, if you like it,

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Speaker 3: when we talk about prediction markets and the nature of

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Speaker 3: with Apple Podcasts. In order to do that, just find

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Speaker 3: the Bloomberg channel on Apple Podcasts and follow the instructions there.

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Speaker 3: Thanks for listening.