The Derivative - From Oxford Research to a $70M Fund: How ORCA Predicts Weekly Markets

Episode Date: April 9, 2026

In this episode, Jeff Malec sits down with Vuk Vukovic and Scott Alford of Oraclum Capital (ORCA) to explore how an academic project on elections turned into a $70M hedge fund powered by crowd predictions. Vuk explains how he and his co-founders, coming from economics, physics, and computer science backgrounds, built a survey-based system that originally nailed events like Brexit and the 2016 and 2020 U.S. elections, then adapted the same framework to financial markets. Scott breaks down how ORCA combines wisdom of crowds, network analysis, and machine learning to identify the best retail predictors each week and turn their aggregated views into directional options trades on the S&P and Nasdaq. They discuss incentives for participants, how they filter noise, why independence and diverse networks matter more than “experts,” the limits of traditional polling, and the rise, and risks, of retail trading and prediction markets. The conversation also touches on political polarization, elite networks, and what it really takes to build a differentiated strategy in today’s markets. SEND IT!Chapters:00:00-01:34=Intro01:35-12:38= Origins of ORCA: From Broken Polls to a Crowd-Powered Market Prediction Engine12:39-21:01= Why Traditional Polls Fail and How Academic Research (and Grants) Really Work21:02-35:35= Inside ORCA’s Signal: Paying Predictors, Mapping Networks, and Turning Weekly Surveys into Option Trades35:36-49:49= Timing the Crowd: Weekly Signals, Zero-Dated Options, and How ORCA Differs from Prediction Markets49:50-1:01:03= Hot Streaks, Crypto Crowds, and Why True Wisdom of Crowds Needs Independent Thinkers1:01:04-01:20:36= Retail Traders, Polarization, and Building Better Predictors: How ORCA Sees the Future of MarketsFrom the Episode: Youtube: Predict Market Moves by Oraclum https://www.youtube.com/@predictmarketmovesYoutube: https://www.youtube.com/@vuk_vukovic_author/videosPersonal website: https://www.vukvukovic.org/Follow along with Vuk , Scott and ORCA on LinkedIn, you can find Vuk on X @wolf_vukovic and ORCA @OraclumCapital as well - be sure to check out oraclumcapital.com for more information!Don't forget to subscribe to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sign-up for our blog digest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.rcmalternatives.com/disclaimer⁠⁠⁠⁠

Welcome to the derivative by RCM Alternatives. Send it. Hello there. Welcome back. I got a haircut. It's Masters Thursday. There's a two-week ceasefire in Iran. All seems to be good in the world.

We're brought to you by RCM Alternatives. As always, head on over to RCMaltz.com slash white papers. Check out all the new content we've been putting out. This is good episode. A couple of guys doing something I'd never heard of before saying, hold my beard at Kalshi and Polymark. using predictions in a very unique way, running a contest. And then whoever does well in those contests, kind of follow those predictions for a while.

So super unique, haven't heard people do this, brings up tons of questions. Who wants to do these contests? How do they get incented to do them? Are there any persistence in all this? So we get into all that and more. Send it. All right, everybody. We're here with Scott and Fouke. Did I get that right, Vuk? Yes, you did. And the last name?

Vukovych. Vukovitch. Love it. So I'll tell you, so Bukh means wolf, basically. Wolf Wolf, Wolf. Yeah, so Wolf son of Wolf. Wolf. That's what nationality is that.

Croatian, Croatian original. Nice. He's the Vuk of Wall Street. Let's not go that far. And Scott, you're just boring old, old Scott, sorry? Yeah, yeah, I mean, yeah. Scott Alford is Yeah

Nothing too crazy Nationality Can you compete? Yeah it's British British All right Well thank you guys for coming on Where are you at today?

New York New York, yes And I'm in Raleigh, North Carolina That's right I was just in New York last weekend Did a quick trip Visit a friend for their 50th birthday Was lovely, the sun was out

Yeah And then it goes from like very warm to very cold in the space of a few days. It's in the Newark Spring, right? I want to do, we took my teenage daughter down to Canal Street for all the fake purses and whatnot. And then the cops would kind of drive by and they'd gather it all up and sprint into dark alleys and corners.

So I would want to see a documentary on that of like, okay, where's that stuff actually coming from? Who's the kingpin? How does it make its way down to those lower level guys selling it on the street? There's a market for everything, especially in this city. Market for everything. It's great.

So anyway, I wanted to have you guys on. We met down in Miami, right, at Eye Connections. How was your eye connections? You guys found that valuable? You like it? Yeah, absolutely. So for us, that's been our cornerstone fundraising event. So we started doing that one two years ago, right, Scott?

In New York. We went to New York, same for Miami. We do it every year. And I think we raised about 20 million involved from those events. It's been useful for us, definitely. There you go. So you've only been since it's been in the conference center? In Miami?

Yeah, we've been the last two years. So it was in the conference. Where was it before? It was all at the Fountain Blue. It was much nicer. It feels more conference-y now that it's all in the conference center. Like there, you got outside a bit more. You got to do the lunches and the dinners outside.

Okay. The first one that we did. Go ahead, Scott. It's a lot of people, right? Like that. The first one was what 8, 8,000 or something like that they told us Yeah, it's too many people. Some might say but good Scott what were you saying? I was just saying the first one that we went to was actually the salt collaboration in New York and that's where we had some of our biggest tickets in fact and built some of the The best relationships that was when they had a partnership with Anthony Skarrett-Mucci and company

and we met some amazing allocators from that event and that's what led us to to continue the relationship with eye connections. So we've done Singapore, we've done Miami, and have continued on that circuit because it's been fantastic. And then the last New York event we actually went to, we were told basically, hey, we want Vuk to present. And it turned out they did a secret emerging manager competition. And we won a prize.

Yeah, we won't a prize because Vook's a great presenter. Well, I thought you were going to say without us, but no. Okay, he was there. He presented at the emerging. Love it. All right. Well, no free ads. We'll stop talking about them.

We met down there, and you guys are doing something I had never heard of before and haven't seen in the space. I wanted to have you come on and tell everybody that I want to say the crazy stuff you're doing, but it's not crazy, the interesting stuff that you're doing. So who wants to jump on that grenade and take it from a 30,000-foot view of what you guys are trying to do, what you're doing? Sure, let me do it. Let me kind of start from the beginning. So believe it or not, the whole thing started as an academic project, essentially, back in.

in what, 2014-15. It was myself, my two partners. So all three of us were science and academics. I was doing a PhD at Oxford in Econ. And my two colleagues, they have PhDs in physics and computer science, right? You were all in London? No, no. So I was in Oxford, and they were in, so one is in Croatia, one in Singapore.

But the creation one, he has his PhD from the States. He was later at the Princeton IAS Institute. He's the physicist, and then the computer scientist, he was in Croatia and then later in Singapore. And our whole idea was to try to find, because back then we were, we kind of met each other at a conference, and we were talking about polling and how polls are being worse and worse and unreliable anymore because of, you know, standard statistical properties no longer being applied. So we wanted to use, and all three of us were doing somewhat research adjacent in network theory, network science, from different perspectives, obviously.

And so we decided, let's see if you can use networks to improve, essentially. But it kind of morphed from one thing led to another, and we developed this methodology that looks at, it takes the combination of wisdom of crowds. So it's not just like with election specifically who you're going to vote for, but it's who you think is going to win, right? So it's about the neighbor method, right, that was used in this last election by this French trader for a Polly market that made a huge battle.

that we basically used that methodology back in 2014. So we did that, so the wisdom of prows and adding a kind of a complimenting with network analysis to get a much more, much better, much more accurate insight into what the actual outcome might be. We applied this for Brexit and Trump in 2016 and got them both really, really accurate, at least with the Brexit prediction was correctly in a single percentage point, margin of error. The swing states for the Trump-pillar election were called all the swing states within the single percentage point monitoring they're all correct. So those for Hillary for Hillary, those for Trump and for Trump.

And then, you know, we just, so our initial goal was to write a paper, like an epidemic paper to publish it in like science or nature or somewhere, but we said no, right? We're not going to publish it. We're going to try to monetize it instead. So we opened the market research company in the UK and did, this is during the time when I was at Oxford. That's why it was in the UK and we did mostly like market research in elections. And then after the Biden election in 2020, we, we did, we did, we did the time when we, we did the

which we also got really accurate all the swing states once again then we decided to see if this works on the markets right and so in 2021 I started trading this on my own I took $20,000 of my own money we built our own well app 40s is it basically a survey-based approach we'll explain how it works and invited people in and basically making bets on these on these predictions just the best and I was doing it very openly I reopened the substand newsletter and And every week I was posting, you know, the signal and what I was buying. And then the end of the week, a screenshot of how it went, right? Did I make money or lose money?

And this developed credibility over time. So we have steps that started growing. People started coming in. And after about a year and a half, people started saying, you know, you should start running money. This is when Scott kind of joined us. We knew each other from before. So he was, so one of my Oxford Scholarships, Scott, actually, was in charge of allocating that scholarship to me.

That's how we met each other. We stayed in touch ever since. And, yeah, he joined us to kind of help us start the fund in the U.S. And we opened the fund at the end of 22, started trading February 23. And we started with $700,000, like with virtually nothing in the AOM, build up to 70 million in two and a half years, essentially just based on good performance, based on things like high connections and how expanding, expanding allocations and getting people on board. And now, you know, in the meantime, I moved to New York.

and I'm here to essentially scale it, scale of this business. So it's, you know, for an academic project to a hedge fund and a lot of things happening along the way, but that's the, that's the kind of the bird's idea. Love it. Scott, can you put that in a sentence for us? Oracle does. Oracle harnesses behavioral psychology and uses human intelligence and artificial intelligence to predict weekly markets. Good job. Well, well done. And what struck me when we met down in Miami, you were, right?

My brain goes right to like, oh, you're doing wisdom of the crowds and doing that kind of stuff. And what you kind of mentioned, right, you'd push back on that and say no, because we're asking what they think the other people will do. Right? Is that the biggest unlock sort of? Or is that just my simple brain, that was my unlock? No, no, absolutely. And this comes directly from the papers by Kahn Mterski, right, the behavioral psychologists. it's called this. So the logic is if you give people that type of meta question where you're asking them to put yourself in other people's shoes, right? So it's like, for example, if I ask me in your state who's going to win an election, so you might have an idea. But when I ask you, what do you, what do other people around you think who's going to do it? Then you start, you know, we force people to spend 45 to 60 seconds more on a question, really start thinking about it. And this kind of switches you from this automated system one thinking, it's a system two taking, the kind of interesting.

logic, right? And this, we saw this experimentally because we were doing this with students. We were, you know, treatment control group asking different types of questions. And you can literally see the improvement and predictions when you give them a second, when you give people that second questions because it forces them to come back to the first one and reexamined. Yeah, and system one, just to clear, for anyone who hasn't heard of it, system one, it's kind of that reactionary thinking, right? It's your split second, your lizard, brain kind of approach to thinking about the world around you. And that system too is the more deliberate, logical,

thoughtful, measured way of thinking and engaging with the world. And what we're trying to do through the survey is tap into that second system and get people to think deliberately about the world around them. And we see that it produces much better results when we're able to do that. So they go this way in terms of elections, right? If I ask you, you know, who's we in your state? Let's say you're very partisan, right? You're very strong Democrat.

Republican, whatever. And you say, oh, you know, Democrats are winning my state. But then, but what about other? And I ask you, what do you think other people say? Would they say that Democrats are winning the state? And you start thinking, you know, maybe, maybe not. So maybe I adjust the vote share that the party is going to get. So that that's the type of idea that we're trying to get to, right? Again, no, what we saw in these surveys, no one single individual is always right. But as a group, they tend to be very accurate. And that's kind of the wisdom of crowds element. Makes me think of, right, haven't there been several surveys over the years of how good at you

are at driving? People are like, I'm an above average driver, right? Like 70% or above average? You're like, well, that's impossible. So, but if you ask them, how good are you're, everyone on your block or your neighbors at driving? They'd probably be closer to 50. It's the same thing with sense of humor, right?

Everyone thinks they have an above average sense of humor. Yeah. Everyone thinks they're above average driver. I know I am, but, you know. I know I am. Right. Exactly. There we go. But everyone else is not.

So you popped in my brain before. Were you in college, you had like a Nate Silver poster in your room? Or is he God or is he a villain? What's your Nate Silver take? The God is more Talib. More Nassim Thaler than Nate Silver, to be honest. Because it's better statistics, I would say. Nate Silver is, so what we wanted to do, our initial idea,

when we did like the polling thing that I mentioned in when we were back in Croatia, was let's do a Nate Silver thing, right? But let's do an improvement on that. Because he was doing, so what made silver did and it was great, it was the aggregation of polls, right? So he, and they had that own methodology of, okay, so these pollsters are more likely to be accurate based on past performance, based on how they evaluate their models, based on weighting methodologies, whatever. And then he would rank them based on this and put different weights on different poles. Our idea was that most polls are going to get it wrong just because the statistical properties that we have now are not the same as, you know, in the 19s and the 80s and the 70s, when these poles were actually. in fact accurate, much more accurate than the now, and the errors are much lower.

The reason is response bias, right? People are no longer responding to surveys. First of all, there's no telephone surveys anymore, at least not to that extent. And online servers are very biased. They can always be biased towards the younger, more educated urban populations. And this causes problems, and that means that you have depending much more as a pollster on models. So if your models are wrong, if your assumptions of your models are wrong, the more wrong they

are the bigger your errors. which means is that when you read a poll, you know, the plus minus 3% error is not really accurate. You should think of it as, you know, plus minus 7 or 8% when you look at a poll on average. And I, you know, obviously, I'm not going to say this, it's bad for business, but that's some of the problems that we noticed. So our initial idea was, you know, let's improve poll. Let's, you know, be better pollsters. But there's, you know, it's, it was an okay business, but it was, you know, it was

linear and it was spinning the bills but it wasn't really making that much of a big deal especially because you know there's the established industry and we're fighting against them as someone who's small it's yeah it's it's difficult so we saw this application of markets you know we saw an opportunity and the logic was let's test it for like a year two years and you know if nothing it just it doesn't work it doesn't work we we go back to doing what we're doing but if it does work then we might have a potentially fantastic idea and here we are and you don't wake up as a little be like, I want to be a pollster when I grew up, right? Exactly.

Maybe, or did you? I don't know. But, and to me, polls have been dead for a lot. Like, I don't think I've ever taken a poll of who I'm going to vote for. Like, I've never gotten a call for sure. Or I got one and hit end, right? I didn't answer it. Exactly.

That's the problem. Yeah. Usually, like, before, when you were calling, the response rate used to be 40 to 50%. So if you needed a sample of 1,000, you call 2,000 or 2,500. thousand people. Now the response rate has been, I think, like, 9%. Some of them I read at a private business review, even like three or four percent, which means if you want to sample a thousand, you need to call tens of thousands of people. And that's costly. And, you know, and it's ineffective and, you know, it doesn't help the bottom line. So you have to do models

to try to approximate this. That's where the errors happen. You think they'll go more to basically, well, I mean, the problems are responders, right? Yeah, exactly. I mean, not how they're measuring. What you said, are you willing to do it? Are you willing to do it? No, right? And a lot of people are like you. They're not going to need to do. That's the thing.

So the way that Vuk and I met was actually through the university. And I was doing a lot of university investments. And this was a classic problem that was felt across the graduate students that I was working with and the grant programs that I was working with where a lot of them were in M-Turk. And to do the kinds of analysis they wanted to do, whether they were doing economic analysis or political science analysis was they need to get very large sample size. But to do that, if you're a grad student, that's extremely expensive. And then once you actually get the data, there's so much noise in it, there's so much bias. And you're trying to build your,

you're trying to build your thesis that you're defending in front of your committee to get a job in the academy, the thing you've dreamed of your whole life. And so if your sample is not good or you're not able to pull out a signal from the noise, that's really expensive and really frustrating process. And the pollsters have to do it, but they're doing it real time around events that are anchored to a specific window. It creates some real challenges for pollsters today.

And what do you mean you were allocating the university's money? So you were in charge of grant programs or you were on the investment? I was not in a university. I was allocating two universities. So I was a grant maker. a grant maker. Tell me more about that real quick. I never heard of that. Yeah. Yeah. So what we were doing was we were strategically. So I'll use Vuk as the example because with whose money. So Vuk was researching elite networks. And he was looking at how elite networks are a driver of inequality. And that

happened to be a topic that the group that I was working with in terms of university investments was interested in investing in future research around those questions of economic freedom and equality. And so we made an investment in Vuk's research, right, that went directly to Oxford. And that allowed him to do some of the research that he was doing around elite networks. We were doing that both in terms of, yes, grad students, but we were also doing it along university centers. So whole centers that were looking at different pockets of research. So I was working in foreign policy research and geopolitics and economic research. And essentially, it's the same thing that you might think about of any kind of investment, right?

We're in the investment industry. You have certain kinds of ROI that you're looking for, certain kinds of measurement, and it's just a matter of instead of thinking about it in terms of returns, you're thinking about it in terms of either impact or the different areas that you want to foster more research. But whose money was? It was the university's money? So, I mean, the money came from a... whole network of donors that were interested in making investments at the university level. So,

you know, some of them are alumni of the university, but they want, they would go through a foundation a lot of times because they wanted more strategic management. If you're, let's say you're a wealthy individual and you want to make sure that you're getting the ROI, you could either buy and build a team yourself to do it, or you could outsource it to somebody who's been doing it for years and years. And so that's what we essentially do. You see that in the hedge fund space a lot, right? Like we're partnered with XYZ University and we, fund their research and help them do this and we get good talent out. Exactly.

It made me go off on a tangent of, it was driving me crazy early. And when Trump was going after all these universities and like, they get X from the government. Like, we're not just giving it to them, right? Aren't they funding a research project? We're getting stuff back as the, as the U.S. taxpayer. So we won't go off on that tangent. But would you agree with that? That's the point, right?

Like, hey, we're giving this money and we expect something back. We're not just sending it. It's not a donation. Yeah, and I think this is one of the things that the public doesn't think about not all universities have the same mission, right? There are public benefit universities, and the kinds of research that they're doing is supposed to be for the public benefit. For a lot of them, the state universities or the A&Ms, right, they're focused on different groups. So like A&Ms, you might be focusing on farming and engineering. You're actually thinking about the application of that research to advancing the public interest.

So from that standpoint, those are really, really important, right, areas of research and investment. So, yeah, you're right. It's not just a, here's a blank check, spend it on whatever you like. It's, hey, we are trying to improve in these specific ways as a society and give us the resources and the research that we need to be able to do that. Love it. But my flip side of the area, it might be like, yeah, but then you get that money and then you can pull out the talent like Vuk out of there. That's not fair. But it's for another podcast.

I was going to pull out anyway. Let's circle back to the fund and the investment strategy. So you're putting out, you're getting these predictions, but it's like dig into how that actually works because it still breaks my brain of like who, like we're talking about the polling. Who wants to sign up for that? Why do they want to sign up for that? What do they get out of it? So money.

Very simple. Cash. Yeah. So we give cash. Cash prices. Every quarter we give $8,000. to the top 30, so the first got $4,800,000, and then 1,000, and so on and so forth. And then at the end of the quarter, 3% of what we make, our GP banks, we also distribute

to the top 40 participants. So this is how you keep engagement, right? You want to keep it consistent, and you want to reward it to both precision and consistency, essentially. And these are mostly retail traders, people that are following markets, people that are interested in markets, and, you know, we've been kind of curating them for a while now. And yes, but the way that the signaling process works, so we have this survey app organizes a competition where people compete,

there's a leaderboard, and then there's this prize distributions, and they give us very simple answers. And there's no, so we don't ask any type of sociographical questions. A lot of them are anonymous, right? And it's fine. What we care about is their responses, that's the most important, but also their networks. It's where they are positioned.

When I say they network, so when people come in, they give us consent to just see who their followers are on Twitter, and LinkedIn, right? So that's what we care about. Again, no, I don't care about the name or whatever profile pictures or something irrelevant. We only want to see who you're connected to them. We can connect you to other people who already might be in the survey because we already have a big database there.

And the point there is because you want to use that to recognize clusters. So the wisdom of crowns element, what we just talked about earlier, like who do the others think are going to vote for? Or in our case where the market is going to end up, like where is the S&P going to be by the end of Friday? The network element is also important because it gives you clusters. It's a think of some people are necessarily, you know, permabairs or permaboles or exposed to those type of opinions, right? So the perma bears in your survey, they're going to be right. Well, they were right lately over the past few weeks during the Iraq War, but they were, you know, usually they're not going to be right. So you're trying to figure out the people that are kind of in a more heterogeneous, diversified groups, right?

So we're exposed to different types of opinions. It was similar with elections, right? You have right-wing bubbles, left-wing bubbles, and those people are less likely to be accurate. You want people that are in between, right? Some of your friends are left-wing, some of them, or right-wing, some of the percentress. This gives you a higher probability of being right.

Again, it doesn't necessarily make you right, but it gives you a higher probability. The same thing with markets, the same logic. We want you to be exposed to different types of opinions, and this gives you a higher probability, which determines your weight in the survey. So think about it this way, right? sentiment is formed in groups, right?

So, yes, we each have our own opinion, but sentiment is formed in groups. Bias is also formed in groups. So part of our edge is that we're able to identify that bias, right? Help people correct for their own bias and then see based on their network who's more likely to be themselves biased. And that is what allows us to see who is more likely to be a good predictor than not. Correct. Yeah.

Do you tell them, do you alert them that they're biased? bias? No, let them go. No, so, I mean, they can see their performance on leaderboard, right? So if you're performing well, then there's a probability that you're less biased, right? But it doesn't necessarily have to be so, especially over time. That's why our third element here is you observe this performance over time. So think of it like in sports. You have hot streaks and so some people are good at some, okay, let's say someone is good at bear market, someone is good for ball market, someone is good for like sideways markets. So during a hot streak, so what happens after

after a hot streak, mean reversion, right? So you're waiting people up during a hot streak, so to speak, and then you're waiting them down when the mean reversion happens. And that's why we're only looking at the top performers in our survey. We're only looking at the top 100 performers and sourcing the signal from them.

Everything else, the other 3,000 or whatever, or how many we tend to have, is mostly noise in a year-week. So you only focus on the really the top, top 100, and that's where you source the signal from. But Jeff, one of the things that underlies that question that I think is so important as well is who is the nature of the person in the survey and what motivates

them right so we talked about cash cash is king right but we also know that a lot of the participants they're data junkies they are market junkies they follow the market the s&p the Dow the NASDAQ and they themselves want to know how they can improve right they want to understand their own data so yeah cash is a big motivator for them being able to have bragging rights to the leaderboard but also many of them enjoy the process of actually learning which of these indices am I really good at it? Am I really good at the S&P, but I need to improve at the NASDAQ? You can take that all kinds of different ways, but the kind of avatar is somebody who follows market as a retail trader. Many of them are data junkies, but they're all independent

observers. They all have their own perspective that they bring to the market, and not all of them are going to be good all at the same time. So the goal is that, and that's where this machine learning and AI component comes in, where we are able to identify and look at their track record, in different market regimes and over time, and that helps factor in as well. So you've got a component that is wisdom of crowds, very important. You've got a network analysis component, and then you've got this third component,

which is track record over time. And those three together are what really generate the signal where we're able to see sentiment and track what retail traders are thinking on a weekly basis. I'd take that group that thinks they should trade the Dow and just put it in a waste basket over here. I don't know about the Dow. We make fun of one of my friends.

He's like, the Dow was up 120 today. We're like, nobody quotes the Dow anymore. Like, come on, get with the times. Exactly. Why, like, if it's just those top guys, why did the, you kind of just said it, but like those bottom guys want to stay in because they're part of the game and they want to keep going? Like, but you would, like, how much turnover is there? You get people dropping out all the time?

Yeah, very high. But that's the thing is. And you typically see heightened participation at the beginning of the quarter. And then it kind of wrinkles down, obviously. Because if you're not going to be, if your probability of participating in a prize, being a top 30 drops, then you're going to have less of an incentive to keep going. And that's normal. It's expected.

But then at the every quarter of the game restarts, right? So it's a new quarter, so maybe new opportunities. And we do tend to see this in terms of the leaderboard. You know, it's never the same people. Some people tend to, you know, come back. But like on the first three or five spots, there's always a variation. The other thing that I think is really important, we have quarterly prizes, absolutely, but we also have a yearly prize where we take 3% of our funds take,

and that incentivizes people not only to be accurate, but to be consistent and to stay in. So we even see some people that may not win prizes during the year in the quarter, but they stick with it through the whole year, and they end up being one of the top 40 predictors and winning a pretty substantial prize at the end of the year. I got an idea for you guys. If you want some free consulting on the calls, do have the top, the bottom three or five get rewards, right? And then you could fade that or you could add to your signal.

Like, these guys are predicting how bad it. So if they start to suck, like, then they can go for really sucking for the rest of that quarter. It would take out their yearly price. But if they're like, okay, I've sucked so bad, I'm going to keep doing what I'm doing. And it's so bad that it's going to be at the bottom. Right. If you can correctly be wrong every time, that's just as valuable as correctly being right every time. That's true.

I was going to say, wasn't that in the U.S. military academy, they used to, there was a period which they did that even before the civil war where there were some people that tried to get the test perfect, but there were other people that were like, I can't do that. So they would go forgetting every single question on the entrance exam wrong. Wrong. Yeah. Which means that you know. And they're a standout either way. Exactly. Exactly.

But if you get every question wrong, you basically know that the answer. right and you're deliberately doing it that's the point yeah exactly so we used to have a platform called i system or it's still out there i systems but it's and we should talk offline if we could put that into the mix for you guys but right it's people create these they put them on the platform then other people can invest in them and then we were using some of those in a in a hedge fund we ran and for we had we ran that idea for about a year but the transaction cost got to us like the bottom performers were bottom performers not just because they were were wrong, but because they traded a lot or they did whatever. So it was we...

That is the thing. Yeah, exactly. Exactly. We hear, you know, it's an interesting idea we have to check through basically saying we should find ourselves a bunch of Jim Kramer's at the end, right? And just fade. Yeah. The fade. Yeah. I love it. And then do you measure or do you care, right? If, say they're all just, the top 10 guys are just bought and hold S&P for the quarter. Like, is their alpha? Like, is their measurement by their alpha or just, their raw performance. Just basically the raw performance of what they're telling us in the service.

They're responses and how close you work. So the question is give us the values that there's like a slider and give us the value of where the S&P is going to be by the end of the week, right? And then the closer you are, the better. So it's measured by a brier score, being that that's the difference between what the actual outcome is and what you predicted. And then the smaller the difference, the more accurate you are. Right.

So it's not their really trading performance whatsoever. It's just their prediction performance. They might be on their own. Like, yeah, I wouldn't know that. But for us, it's just about extracting essentially that part of the signal and then trading it themselves. And when we say the signal, it's a directional signal, right, whether the market's going to go up or down. And I think that's really important. But it is notable that as a group, they're remarkably accurate on where the market does end up as well.

There is a level of a degree of accuracy there as well. Not just the direction of the market, but how close where the market will actually end up. And so you, the model, the trading model itself, the survey goes out and what comes back of by, like, when does it go out? When are you looking to trade by? Give me all those details. It goes out every Tuesday, 8 a.m. Easter. And it's open for 24 hours. It closes on a Wednesday, 8 a.m. Eastern.

So before the market opens, we would get the signal. Then we'll typically wait for about half an hour hour. It depends. this is always something that's refined with back tests like when is the optimal entry when should we go in is it like 10 10 30 11 and then we place our positions on Wednesday and hold them and go Friday and there's again a variety of ways of how we trade this but when I say we place position so we would get a number essentially a monitor value and that would be the value of let's say the S&P and then if it's higher than it's

you know it's Wednesday open then you go for calls if it's lowered and you go for posts essentially so you try to bet direction you're in the market. When I say, yeah, calls or puts, this is what we trade. So we take only 2% of our nav, of our portfolio, and buy either calls or puts on that on the week. So we buy the weekly options. We typically buy one to two strikes out the money for the two-day expiry, and then we have a bunch of ways of, you know, so if it starts going in the money, so if you're making

money, you're raising stops, trading stop loss strategy, in order to kind of capture these profits if the market starts going against you. But if it's out of money, then you cap it at a 75% loss. So the last 25% you take and then you're out of the position. So that's how it works. So there's no concept of this was a super strong signal? Like 95% of the sample said it's going to be. We found that, yeah, that's a good point. I'll give you an example actually of one of those types of signals, but we unfortunately didn't react it that way. So no, we didn't see that that kind of the the confidence of the signal that we should kind of vary position sizing based on it. We did see that it works.

We vary position sizing based on ball, the price of balls, so the price of options, right? If options are more expensive, then we would size a little bit smaller, and we would capture profits more quickly. If options are cheaper, then we can size higher and we can wait for the realization of these profits to really happen. So I'll give it an instance. So typically we buy stuff between $2 and $3, the option we trade, spy contracts, we buy on $2 or $3. And that's kind of a, let's say, cheap to medium price, which means that, you know, your weak can, you know, these options, if they really move in your direction, they can go from $2 to $6.

And that's a great trade, right? But if you're buying them at $6, then the probability of them going from $6 to $18, you know, something cataclysmic has happened or like a powerful short squeeze on the other hand. So it doesn't happen. But going back to your question, we didn't have a situation where there was only once that happened that every single person in the survey was there. negative, right? And that was, believe it or not, April last year, April 25, Liberation Day, April 1st, right? And so we remember that day, so yeah, we got surveyed and we kind of reduced our position sizing because markets were very range bound back then. So we went with a smaller position. But I do remember on the day, so we bought the puts in the morning and markets

were trending up that day, right? The whole day they were trending up. So we were losing, 30% of our premium. By the time the announcement came and Trump came after the market closed, you know, the next day everything drops the next day we more than overcompensated these losses we ended up with like four and a half percent but it was clearly an example of hindsight you should have done more I thought you were going to go the other way and say like that was a clear opposite signal like if everyone's on one side of the boat boat's going to tip over yeah true but for us it's always they listen to the signal right the signal has been for us correct you know over 60 percent of time so we always tend to listen to the signal but it's been interesting in terms of like

the magnitude, yeah, so everyone was saying literally expecting a market decline because of the impact of tariffs. And that was fun. And it was, you know, interesting, we didn't exploit this to the full extent, right? If it was a bit of full allocation, that returned within the old digits. You have to capture everything you're doing it between Wednesday and Friday close. So, like, what's the, have you looked at that, right? What's this last month?

A lot of stuff's happened from Friday to Monday open, right? Exactly. Exactly. So if you had like rolling contest or something, I'm sure you've looked at all of that. We have a quant team that looks at all this. They're very rigorous in saying, hey, how can we apply the signal? And they test. You know, there's a limit on what you can test obviously. But they test all of these different points in time of, hey, what if we did? And that's how we introduced zero days into our trading. Because when we originally started, we were doing two day, Wednesday to Friday. But they found, hey, the signals. actually strong even through time. So if we can capture these moves and know when to time getting into the market with zero days, we can take advantage of the signal in some other meaningful ways.

Because sometimes the move goes against you before it goes for you. Exactly. Also, another thing that we introduced as of this year, January 1st, 26th was using the signal, the moving average is a signal. So every week we get a prediction. And you can basically do a nice little moving average

to see how this signal is doing. And based on this moving average, we're replacing. So we used to have 2% in options, 8% was a kind of cash buffer, and 90% was bots, US treasury bills, T bills, one year duration. But this year, we decided to allocate the 90% into long positions on the S&P and the NASDAQ based on the moving average of our signal. The 90% is it? Yeah, the rest of it.

Yeah, essentially the rest of the portfolio. And this basically, however, with hedges, they're a role every Friday. So we would buy puts every Friday and enroll in next Friday. And this was very useful in a couple of weeks in the first quarter when you had, for example, yeah, news would happen over the weekend. Markets would drop by Monday and our puts would more than overcompensate the losses on the longs. It's also been a helpful part of the portfolio and making a difference, making a net positive, even though these long positions are losing, but you have these puts that are helping.

But at one point, so what we have is when the signal drops below, it's, it's, let's say 100 period moving average, then we take out the long position. So for us, the signal, the moving average of the signal tells you either you should be long or in cash, right? There's no short positions from the signal. And that's a different program or the original program? It's the same. So it's the same signal, but it's just you look at that because now we have a lot of data,

now you can look at the moving average of that data. So are still kind of the money maker, the main thing is still the survey itself and the options that we buy every Wednesday. So that's the differentiator. Oh, 9010 made me, 92 made me think that's not the case. But you're saying like on a nominal basis, but on the... No, no, it is. It is because I'll tell you.

So in our first quarter, we ended up plus nine. 7% of that came from the options and 2% came from the net longs, which is also driven by the put options, but not the signal pro options, but just the regular protection. The majority of the returns has been through the Bayzon approach, which is the one that's derived from the survey. Since inception. Got it. I'll ignore the 90% number. That scared me.

What's next? So in the survey, so they have a slider. So I'm saying, okay, I think the S&P is going to be, let me pull it up. I think the S&P is going to be 7,000. Okay. Very, very optimistic. So it'll start with asking, do you think the market will be higher or lower, right? You click the button and then it will say, where do you think that the market's going to end up?

and we give you the chart, right? So you can actually see it. And then it's a sliding scale. And then you ask, hey, what would others that you see on social media and your social network would think is going to happen? You give them, right, a sliding scale. And what you see is through that whole process of getting people to think about it deliberately, they self-correct for their own bias.

They say, wait, maybe I should readjust my original guess, right? And they think through and they, and we can see that tinkering. in the actual survey. And that matters. That ends up mattering. Because you're measuring, we're measuring the timing that it takes for you to do a survey. This is very important for us, for example, to eliminate bots. It's bots.

So there's two ways. Bots would typically come in and they solve the survey in like five seconds or they have no friends. So on social media, they're not connected to anyone. So you can very easily kind of distinguish out. Okay, so this is a fake account. It could be not an actual body. It can be a human bot, but if it's not connected to anyone, if it's outside the network,

and it gives you an idea that it's not via health. And then, but would you even care? Like if a bot was coming in and doing really well, who cares? Typically, yes, exactly. So we had, so I haven't seen any kind of bought infestation with the market survey. We did have them with elections, obviously, but it was very easy to kind of just discourage them. But you're absolutely right.

So we always get that question of what if someone wants to manipulate you. Sure, they can pick and try. But in order to manipulate us, they have to be. be very helpful for about three to four months for them, you know, getting to our top 100. And then we're actually, you know, using these surveys because they have to help us before they can hinder us in a sense. And even if they start hindering, it's going to mess up two weeks, right, the most. And yeah, not a big deal.

Yeah. So let's move to prediction market, right? So you're not, you're careful not to call yourself prediction people or what are you saying? No, I mean, yeah, kind of prediction survey because like it's attracts people. But it's not, you know, it's for me, it's statistics more than the predictions, right? But it is kind of organized in that way to kind of, it is like a prediction competition. Right. So people are competing based on their predictions using whatever they might use.

So, yeah, there is an inherent prediction on it. But like the super growth of polymarkets and calcium and all this. And right, how do you guys view that? Is that competing? Is it adding to the signal? Do you use it all? What's your take on the prediction market? We don't use it.

I mean, one of them I, you know, I spoke before. We kind of exchanged data before like two, three years ago. Unfortunately, it wasn't that helpful for us. And we were talking about us trading unlike these platforms, like taking our prediction trading on the platform. The problem is the upside is limited, right? Whereas what we do with options, it's the opposite. Our upside is unlimited and the downside is limited, right?

So I can only lose the premium that I can never physically lose more than that if I'm super wrong, they cannot physically lose more than that. But the upside is unlimited, essentially. In theory, unlimited, but there's a high upside. Whereas with prediction markets, it's not the case. So we haven't used it directly, but what helps us, I think, is the whole gamification of that prediction element. I think we benefit from it, right?

Because people now are more engaged in this, and by extension, they can be more engaged into things like what we do. And so I don't consider the competition at all. and, you know, I consider them to be fun and interesting. Yeah. But if you were looking at a prediction, 80% chance of S&P this level, and it's showing 40% like there's a difference

or they're matched up. Again, so we had that data before, and it wasn't that helpful, to be honest. The signal wasn't bad on here. So, anyway, not sounding like I'm trying to be scared someone, but like our signal was better, essentially. And I think we have one shared compelling assumption that is true with Calci and Poly Markets.

Actually, the New York Times wrote an article about this very recently, which is that they were looking at betters, especially on Calci, and how accurate they were predicting the FOMC meetings and the insights that they had being betters and how a group of betters could independently, like, consistently predict what was going to happen to markets after the FOMC meeting and how they were beating Wall Street experts. And I think that's one of the key insights that we've thought a lot about

is that retail traders in the same way have a different opinion, a different perspective that they bring to markets and a different view than just experts who might live in a bubble, right? That are just talking about all these same ideas and they're talking about it within an institutional mindset and an institutional framework.

They might be conflicted. They might be talking their book. Exactly. And one of the things that's neat about betters as well is they have to put their money where their mouth is. They have an incentive, they have a financial incentive to strategically think about the decision that they make because there's a reward.

There's a profit for it. But if you look, right, sports betting famously, like usually the most money on an NFL game is wrong, right? Absolutely. If the crowd was right at every horse race, the favorite horse would win every time. So sports betting world is full of counter examples. So that's interesting to me of why is it different in this space.

important that what you just brought up, right? It's not that the crowd is always smarter. It's that what we're trying to do is not just take an aggregate of the whole crowd. We're trying to identify amongst the crowd who are those better observers, those better individuals that are able to have an edge because they're being strategic about the way. Yeah, and at a certain point in time. Absolutely. Which is all that level too, like, okay, you're probably going to be, this crowd's probably

going to be wrong, but their view on what the rest of the crowd is potentially right. Exactly. Also, it's about the type of event that you're predicting. So what we're asking questions about, like, elections or markets is an outcome that you, well, you can't affect it, but other people are affecting it. With sports or like weather, you can't affect it. So if you, you know, say, yeah, I want this team to win. It's my favorite team. Whether you want it to win or not, that's not really affect how the players are going to play. You can use some of things like, you know, past performance. These guys are on a class streak.

Their most important player of the other team is injured, stuff like that, right? Information like that to help you out. And like professional betters do that. But there's a different edge from that, right? What we use is, yes, one person's vote is not going to affect an election, but if you have a good idea, and this is our whole hypothesis, some people might have a good idea of what everyone else in their neighborhood is going to do. Right.

Same thing with markets. I might have, you know, my position is not going to change anything in the market, but if I know that other traders are probably going to go in this direction, that's what I think. That's a completely different thing because these are events that are affected by the people's actions. And you're participating in that is affecting it. It's not making a difference, but it's, you have a good guesstimate of what might happen. Whereas sporting events and weather, weather is, good to say, exogenous, right?

You might say it's going to rain or not. It doesn't depend on your opinion or the actions of millions. of others. And is it so I keep thinking back to horse racing because you can see on the odds board of like what the other people are actually doing. So is it what the other people are actually doing or what this group thinks they're going to be doing? Yeah but but even right versus what the thinking doesn't have to line up with what they're actually going to do the other people. That's one thing and the other thing is like it doesn't affect the outcome right. So if a lot of people are thinking that this horse might win whether or not it's going to

win depends on the horse depends on the other horse is not. on the actions of millions of us can be playing that game, but it's not going to affect it. And if millions of us are playing a game where we're voting or where we're placing bets or investing, then, yeah, we might have a better idea of these outcomes. And timing does matter in these kinds of games as well, right? So we know with sports betting, a lot of the bets that come in,

they come in 24 hours before. And actually some of the betting periods that are the closer to the event, you bet, a lot of those are the worst bets, right? Because you actually, if you make bets a few days out, you're much more likely to have the kinds of odds than you are when you get close to the event. We're doing kind of the same thing with markets, right? Because if we ask people to predict the Friday close, you know, Thursday, it's not going to be as profitable of, oh, we're asking people to take that stake on, you know,

Tuesday and Wednesday for close on Friday. And before also too long, for example. It's either too short or too long, so you have to kind of get it just right. And even the Tuesday to Wednesday period gave me pause because I feel like I would wait till the last second, an hour before the Wednesday or 10 minutes before to see where the overnight was and see what all is going on. But you're saying like, well, sometimes that helps people, sometimes it doesn't. They most do.

We open it on the Tuesdays to keep people enough time, but most of them wait until the final hour, literally after the final hours. So Wednesday, exactly what you said, observing the futures, like how the, the, day before played how Tuesday played out and you observe the futures and then you make the info on guess. And I refer to that New York Times article, right? That New York Times article said, hey, this group is good at making bets 24 hours out from the FOMC meeting, right? It was actually really important because they kind of cherry picked a group, right, of timing because the closer got to the event didn't work. If it was too far out, it didn't work. So the timing does matter

when you're asking people about these predictions as well. You ask you can if you can. Yes, you can. Yes, please. I'm going to do it. I've got to free up my Wednesday mornings. It's a survey.orgon.com. UK.

So that's the website of the survey and it's open, as we said, like every Tuesday. And that's because that original company was in UK? Because, yes, the UK company that has the, because our IP that we developed is originally with the UK company. so our UK company owns the American company that runs the fund. And they're still doing, like, polling and consulting and all that or no, no, no. It's just the shell company right now. Like the owner, it's just the owner and they hold the IP.

Wrote down here, Hot Streak. We mentioned a couple times as a statistician. Would you consider yourself statisticians? Definitely, yeah. Like there's been papers, right, of Hot Streaks aren't a real thing in sports? Yeah, yeah, yeah. Do you disagree? Do you agree?

I mean, it's not a real thing in the sense of, So it is real in a sense that you can use it to kind of adjust the weight. So for us, it's been very helpful because you can use it to adjust weights. I know the papers that you mentioned, the sports papers that refer to this. But for us, it's like, I use the analogy, but it's different in a sense that so you're observing someone's trading performance, right? And you're saying to these people are more relevant now at this point in time. It doesn't necessarily mean that it will be relevant in the future, but the weighting has to be adjusted for this, right?

So if you're coming off of a very good performance, then your weight is higher and then until it's no longer that anymore, right? The reason we did this is because we noticed this huge heterogeneity. So this wasn't part of the initial design. This came later, right? For us, it's always everything like that. Our whole trading strategy, our whole, you know, the signal, everything came from trial. It's observing what works and what doesn't and then adapting. And so the signal thing is now pretty much consistent with these three things that we mentioned.

The trading strategy always basically adapts. One of the things we'll often talk about as well is I'll use the analogy of what Phil Tetlock did with super forecasters. But what we're doing isn't super forecasters, right? He had a very specific concept that he laid out in the Good Judgment Project where he was trying to find people that really are better forecasters. But really, when we're looking at a crowd, we're trying to understand people that in a specific point in time, are better at making forecasts or in certain kinds of market conditions. But our idea isn't, oh, these best observers are always best. They're best in a certain point of time, and that can evolve.

So as a group, they're remarkably accurate, even though as individuals, most of them are 50, 50, or even worse. Yeah. I disagree with that paper. I believe in hot streaks in sports. I think it, like, I can see both sides of it. Like, statistically, they're going to make 20 shots in a row just based on a sampling. But also, it just makes sense that you've made a few. You're freer.

Your arm moves more better, right? Yeah, you're more confident. Like, just. And it works anyway, remember, like, so when you're not, you know, scoring and then you lose confidence, it's the same thing, right? Because people get kind of into these holes. Yeah, like in golf, I had just double bogey, like, three holes. There's no way I'm going to get, like, four cars in a row after that. I'm a mush.

Exactly. I don't really get this golf language, but yeah. You're not going to. No, unfortunately. Not yet. Not yet. People tell me I have to start playing. Yeah.

That's where the deals happen. Do you think it would work? This would drive me, like, two questions here. One, do you think it would work on a pod shop model? The podchaps are sort of doing a similar way, right? They have 50 groups and they're waiting them based on their performance over time. And if you lose too much, you're out. If you make your back in.

So totally different. I don't like that question. I'll skip it. but it made me think of that of they're somewhat doing the same thing, but they're not asking them for predictions. They're just, they're measuring them. So I guess that would be my question. They're trying to build a portfolio or a pseudo portfolio through.

Right, versus you're trying to arrive at a single prediction. A single good. Exactly. Um, so the real question was, how do you not tinker constantly with this? Or are you tankering constantly? Like, what's the research project look like? Like, I've had 30 ideas just in our short talking about. You should do this.

And what about doing it on oil? And what about doing it on this? Yeah, we went through these ideas like back before, back in, you know, 21, 22, 23. We did, we did use to do, not that you mentioned, we used to do oil, we used to do FX, we used to do Bitcoin. Bitcoin was an interesting one. For Bitcoin, literally every week we would get up predictions. There was no. They're all maxis.

There was no variation. I think it was a bubble, unfortunately. We tapped into. There was no variation. Like every week, it was just up, up, up. I mean, as well, just buy it every week. So there's no. Could you see that in your beautiful node?

graph of like basically there's no nodes it's all just yeah it's a cluster yeah it was typically like a bit like a blastered unfortunately but you know that's fine um and we did like single range stocks we did yeah i mentioned commodities the problem is in order to get accurate predictions for those things you need to get a good group of those predictions so for example if i want to do crypto i need to really dig into the crypto groups where i can get people that are not by not like bitcoin and maximalists, I need to get people that are more realistic about it. With commodities, I would have to get commodity trainers. And it's, you know, it's getting retail traders to watch the S&B is, it's not easy, to be

honest. It's really niche, but it's relatively okay to get. Getting commodity traders is going to be professionals. So they're not going to be motivated by a $1,400 prize in the survey. They might be when we're bigger, you know. We've got a network of like a thousand farmers we work with. That would be. That would be interesting, for example.

Prime for that. Yeah. Exactly. And they're like on Twitter in their in in their combine harvesting and looking at Twitter and trade that would be great That would be because then so the the whole point here is you're not one of those people's like it to be right all the time Right, but you're trying to figure out that as a group they will be that's the whole point So we're trying to take them as a group, right, you know averaging them out and getting that action The accurate signal out there essentially that's that's the that's the logic so yes that will that will definitely be helpful again. It's not not as easy to get to these people and or to motivate them

But I think, like, as we grow, as we can offer bigger prices, we can offer more money, I think this can happen. So, for example, our biggest kind of, there's typically a comparison that people use for us is the Numerite tournaments. Numer.A.I. So this is also a group that came from Renaissance. I don't know. It's got Numer. Numer. Numer.

A.I. Numeri. And they organized this before we started. But they did it differently. They had, they were, it's like a data science term. So they have this huge data set that they're giving out for free to all these people that are participating. The people are being rewarded by the cryptocurrency that they minted. So you're basically your good performance is like a mine of your cryptocurrency.

And there's a secondary market. I think it's quite a big liquid market. So you can easily exchange it for cash. And that's been the system that they be using. But it's, you know, asking people to give us predictions of this. For them, it's using like data science to operate. and teams or whatever, individuals, to use whatever data they can find, the number of guys given, and then get a prediction, accurate beat, or like an accurate

trade. So I think they're giving them actual trades. They don't, they basically, what do you think about this trade or whatever? Yeah. So they have to take a dataset and then formulate a trading opinion. I think that's how it works. And then the folks at the hedge fund just place goes best. And then you track performance. I don't know how performance has been going lately. I have to check, but you know, I like the idea. I think it was great. And that's kind of the closest to what we do, essentially. So we could do some of these things with like oils and commodity.

The challenge is, this is where the wisdom of crowds aspect is really important. They need to be independent, right? Because otherwise you just end up tapping into the thought process and the logic of a bubble. And if you're doing that, you don't have an edge. So what you want is... There's another group, Scott. What's the name of that group? They also does this with professional.

They pay like professional analysts. that keep forgetting their name. So, I mean, there are a number of groups that do this kind of. There's one specific fund that also pays analysts across the board and then gets their opinions and then, you know, uses that basically plays their trades. It's also something similar. But what Scott said, for us, the most important thing is diversification of opinion, desexualization.

So I'll give you a good example. Let's say there's a hundred of us in the room. And I ask someone, you know, what's where the market is going to end up? And you, Jeff, you speak first, and you say, you send it. thousand by the end of the month, right? And you're likely to prime a lot of opinions your way. So you're already correlated people, right? So when you do that, these people, what we have, they're all focused on work.

The alpha is going to, it's, everyone's going to come to their sense. Exactly. And this way, because they're decentralized, they don't know who each other is, then they're uncorelated to each other. They don't affect each other's predictions or all opinions, right? And they're all across to us in the U.S., Europe, Eastern Europe, East Asia, South Africa, a few. across the world. And it's not out in the public.

They don't have to defend their persona or whatever. Exactly. There were a lot of those funds that were like, we're going to scrape Twitter and trade sentiment and all AI and automated. I haven't seen any of those funds do well. Exactly. And a lot of them are anonymous and they prefer that this anonymity. Sometimes they like covered themselves because they want to break.

Well, when they want to get paid, right? So that's when we know who they are. So that's how we know some of them are retail traders. I don't know anything about it, but when we pay the prizes, then we typically ask them, and they tell or they tell us, you know, I'm a retail trader. Sovo Scott, this guy who just won this Norwegian person, the Norwegian guy who just won. It's a great example. Sharing with Jeff, what did you tell you?

Yeah, so reached out because when we give the prizes, we let them know, we notify them, and they had sent us a response that said, thank you for doing the survey. Love to be a part of it. They were like, look, basically, I want to use this as my resume. builder to say, I want to go and do this for a hedge fund and basically be a part of the finance industry, building on this experience that I've had with you. And I think that's an amazing story for us to tell. Yeah, go for it. Hedge fund. That was the way he said. It's fantastic. You could maybe, yeah, give them physical awards. Give them a certificate. Put it on your resume.

What's the name all about? Some Latin seer. So Orca is short for Oracleon Capital with first two letters. Orca. And then the analogy of the killer whale is something that's, but I love the Orcas when I was wearing up, one of my shared with movies, but I was a kid was free willy. There he can.

Yeah. So Orca is kind of, you know, embedded. But Oracle is the name of our British company. We found that Oracle is Latin word for prediction. So our company is called Oracle Intelligence and it was Latin word for prediction. And then, you know, the spinoff of that is the work on capital, which Orcaub It's short. And then give a little flip-roo.

We're academics. We're nerds, so why not lead into it? And to be honest, it looks cool. The logo looks cool. And the whole whale thing works. It's like whales in the markets are moved markets. So we hope to become that killer whale one day. But it's funny.

The pod, right? The pod of killer. Yeah, you're anti-whale, right? You're like getting it from the... We want to build our whale. We want to grow to be a whale. But maybe you're like tracking the sardines or we're whatever, and then the killer whales coming to follow that.

Exactly. Love it, guys. What else? Anything else we need? I guess the only last thought that I was mentioning to Vuk is that I think part of the thesis that we think about is retail traders are only going to be more important in terms of the market. Like post-COVID, we've seen such a growth from the emergence of Robin Hood and the platforms

that they have to the zero-day trading. And I think they're going to be a much bigger part of the. the conversation in the market in the years moving forward. And with that, I think that our thesis is only going to be more interesting, more compelling, and more important as more retail traders move into the space. And they're looking for ways to improve themselves, right? So even if you look at the world around us, there's so many people that are giving out stock tips. But this isn't that. Like, it's not that at all. It's actually saying, hey, take an opportunity, take a bet on yourself to learn the S&P, the NASDAQ, see where you're good, see where you could improve.

And here's a free platform to do it. And guess what? If you turn out to be good, you could win cash prizes, absolutely. But we're creating a market where retail traders can think about these kinds of predictions, and they don't have to spend their own money on prediction markets to be able to do it. They can access it. They can be a part of it, but not have to spend money on it. That's a good one.

Hey, you like prediction markets. Predict away. Here you go. And that's, yeah, I think we're in the, I think there's a dark side to that too. We're not to get dark at the end of the pot here, but I think sometimes we've given too much to the retail trader, especially young people who don't know. And they like, oh, I think the market's going up, buy call and they get confetti on the screen, right? Robin Hood got in some trouble for some of this kind of stuff.

Like if it's too gamified and people are losing actual real money that they perhaps don't have, right? or if they're spending their net worth buying like GameStop calls and whatnot. So yeah, I see both sides. I think this is like the best it's ever been to invest your own money, making your own decisions. But that's, you've got a loaded gun at the same time. So it's like. So let me give one quick story as well that I think is really important.

I was during COVID, right, we all had weird things that happened during COVID. I jumped into teaching some economics and psychology classes at a local high school, private high school. And this is something that students always wanted to talk to me about. They wanted to talk about crypto. They wanted to talk about trading. Online or you were in person?

Sorry. Say again? Like you, like during COVID, you did this online or you were in person? I taught in person. In person. Believe it or not. It didn't exist in the Carolinas.

Yeah. But actually, it was in D.C. So I taught right outside of D.C. in Fairfax. But students were constantly, they wanted to know. They wanted to learn. And I was trying to push them towards healthier platforms, healthier habits.

Because so many students, they're interested. They want to learn. They want to grow. But there's also so many bad habits that they could pick up along the way. And one of the things that we want to do is encourage people to, as retail traders, as young retail traders or people that are newer to markets to be able to learn, to have the opportunity to learn and to get the growth that comes along with following markets and the benefits, because I think all of us think there are benefits to people following markets

and being attached to their investments and they not just be passive, but doing so in a way that is not gamifying problematic behavior, which is the temptation. And we want to avoid that. This shot my brain to my kids are in high school and they do these investing contests. And they're like, can you help me with this? I'm like, no, this is stupid. Like you've got, they want you to pick stocks. And, like, that are going to do the best in by the end of the semester, like, eight weeks or whatever, right?

So it's like, so you're not really learning anything about investing, about long-term wealth building, about all the good stuff of investing. You're just, and maybe like, okay, if you want to be a trader, maybe this is useful. Like, that this is an investing, how to learn about investing is totally counter to me, right? Because you're going to just going to have to pick the highest beta, like most volatile stuff and hope you get it right. and like the smart move would be like, okay, have it vol-weighted or something, like have some different thoughts. So anyway, I don't know if you guys have any thoughts of that of like, how do you switch, right? I can almost see like, hey, this is a better tool there in the classroom of like, we're going

to predict where the market's going to be each week. So you can learn what moved the market that week. So I taught history. I taught economics. I taught psychology. And part of the thought process was getting students to engage with, hey, when you're thinking about markets, it's really important to understand the world around. you. It's not about, you know, it's about creating the kinds of habits of being an informed and

thoughtful person who can engage with the world with rigor, who can test their own hypotheses. And there's a psychology to it that's really important as well, right? There's a positive psychology that you want to invest in markets. You want to find better ways to do it and point them in the right direction because there are so many people out there who will try to give stock tips or there's so much bad content on social media, whether you're talking TikTok or YouTube, or I don't want to call anybody out, put anybody on blast. Or right there on CNBC.

We already called one of them out. I almost said it. Wasn't there an anti-Kramer ETF? It is. It was closed? It's closed? No, it did close. Yeah, yeah.

It was run by Matthew Tuttle. But it was a good idea. But, well, part of the reason that it was so challenging is that this is the other thing. kids watch and kids ask me a lot about politicians that were doing insider trading that came up all the time with students and like how can we have this in our world and society and it's a hey this is why it's important to be informed right politicians are trading on the insider information but guess what there's a lot of information of you just being an informed citizen somebody who engages with the news

thoughtfully engages with different perspectives and listens to people whether they're right or left, whether they are bulls or bears. The key insight of what we do as a fund is, if you want to be better, diversify your network. Talk to people who think differently than you do. Look at different sources, engage with others who think differently, because the best predictors in our survey tend to be the people who have diversified networks

and are willing to put their own thought process under the microscope, test it, and make it better. How do we, right, bring, me to like, I don't know if you guys have stats on that from polling, but we've become more clustered. I think you said that close to the beginning, right? Like as a society and politically more clustered, where to use God of like the future should be less clustered or your brain's going to work better, you're going to have better decision-making abilities, less clustered. So I don't, I don't know if that's in your guy's purview to fix society as well as these. I'd love to.

Maybe. So that's, so part of mine, um, was Scott mentioned initially, but Part of my research in OXW, it was elite networks, which I got published as a book by Oxford Street Press. My brain went to eyes wide shut, the Tom Cruise movie, Elite networks of having the crazy parties. Was it like that? I wasn't looking at that. There's no data for that, unfortunately.

There's no data. The Epstein files do provide some interesting data, but that was unfortunately later. But I did a little, kind of a brief analysis of their network. And it's very interesting. I didn't have it on my Twitter. But anyways, my whole logic is looking at the connections between politics and the corporate world and how this, you know, their connection. So people who used to work together or went to the same school, et cetera. And people that are connected to politics, you know, as CEOs, they have much higher salaries compared to those who don't within the same company, right?

Which basically explains the kind of inequality differential, especially in the top income distributions. That was the whole thesis. And I wrote this book about it. And so at the end of it talks about these things of how do you, so for me, the most important element here is, so polarization is an outcome of something else, right? All of these social ills that we have are products of something, right? For me, it's always been the,

this is what I find the problem is the concentration of power, the concentration of political power and corporate power. Whenever that becomes concentrated, you have negative social outcomes in terms of inequality, in terms of polarization, in terms of other, a bunch of other issues. So the first step to removing that is reducing political power. And then there's elements how you should do that with greater conspiracy, with open media and free media, with other things like they can be implemented in terms of like constraining

politicians directly, turning them more into, let's say, simply reducing the scope of complexity that they have to handle and reducing the scope of decision making, decentralizing it on a more community level. These are deep things that we can obviously talk about more, but I would say that, you know, engaging in a community level and reducing power are the most important things that you can do to tackle some of these issues. And you've found that polarization leads to greater power or vice versa. It's an outcome off, right?

So it's, you know, if it's in your... But part of me thinks it's like they're tied together. Like once you become more polarized, you can kind of get more power because you have more... It's a reinforcing mechanism, right? So it's, you know, one being forced is the other. Absolutely. I don't dwell too much into polarization in the book, but it's also about, so I do look at, for example, should the divide in U.S. politics, for example, has mostly been, I think you

might have noticed this, it's been urban rural more than anything else. Like, yeah, the red states and the blue states, but look at within each state, cities are all blue and everything else in the country is red. In New York, the state of New York or state of California, cities are blue and everything else is red in Texas or like still yeah it's it's the same thing so you have this very strong divide and there was a great book by angus deetan and in case all the deaths of despair how these people in like the midwestern parts of the country and in these more rural communities literally dying there being uh you know there's several elements affecting there one of them is the drug abuse

especially particularly like things like crystal meth and alcohol abuse and it's specifically targeting the white male kind of rural populations that have been, you know, basically portrayed as losers of this technological transition and losers of the globalization. And they're much more likely to feel like they're not winning it all in life. And that, you know, everything around them needs a radical shift, which is why they're more prone to go, going for radical solutions. There is some really interesting work. And I think the implications of the underlying idea that we have is that viewpoint diversity is

important and addressing, especially in addressing polarization. There's some really great, there's a trilogy of books written by Bob Talese. He's a philosopher and he looks at these questions, but one of the things he finds is that the impacts of our political polarization and something he calls belief polarization is also really critical. He separates those out because one of the things he talks about in belief polarization that makes things so problematic and is so important with echo chambers is that it's not just that you're only listening to that bubble. It's that in belief polarization, you have a group of individuals that independently of one another are less polarized, but when you put them together and you cluster them in community and they're

regularly in contact with one another, they actually become more extreme as a group. And I think that's one of the things that we want to encourage people to break out of that mindset of even if they're individually more independent, we also need to create spaces where we're not just with our intellectual tribe, where we're reinforcing belief. polarization that's so toxic. And there's some great resources also by groups like more in common. There's a ton of really amazing resources to think about this. But I think if I was to say the underlying idea of the hedge fund is that polarization does not make you a better predictor. And what you actually want to be is an independent thinker and independent observer.

Bringing that nobody thinks of polarization or bubble think in the financial sphere, or you guys are, but right I haven't heard it before you guys yeah and but I've seen it actually before in these pod shops some groups say hey we're having a weekly call with all our managers and they discuss what's happening in the world others like deliberately silo them and say I don't want you infecting right talking with the other groups and infecting them with you know they're great traders and if they hear like oh I don't know about the strata hormoose might open tomorrow maybe now they're a little going to do something different than they were going to do before which is interesting your so your thought it's more valuable to keep them siloed.

Yeah. More valuable in a sense of, you know, extracting value in that sense, right? Trying to figure out what actually might happen. So we'll leave it. We're taping this on a Friday morning, 3rd, April 3rd. Good Friday. What was good Friday? Thank you.

What was the prediction Wednesday? It was long. Long. It was wrong already? It was wrong already? or it's so far as wrong? Yeah, but the markets are closed today. So it was, yeah.

Look at me. It was good. It was good. We were going well on Wednesday. And then there was that Trump press conference on Thursday. It kind of spooked everything. And then it fell down. And when you're trading on, eventually markets ended up flag.

If you noticed, like on Friday. But you still lost, we still lost premium just because. So it was not a big loss. It was small loss. But you still lost premium because, you know, markets eventually, when they went, you go down. and your option expiring the same day, the case basically kills you, and then you lose most of it. And then even though it came back before the end, there was no, there was that, I think there was

on the news that Iran is considering something with Oman on the protocol on opening the straight or whatever. So it came back, ended up flat, which was a very interesting day. So 30th, they ended up as VIX is down to 25, markets were flat, and oil prices were up 12%, which makes you think. I've been noticing this too of like, what's happening? that. I think we're past peak panic on markets with respect to the oil prices. We're not still probably peak oil price panic, but we could be, it could be, we'll see. If the VIX is not reacting, if the markets are not reacting, but oil prices are still going up. So, yeah, on the other hand, you know, if oil prices keep going up, it's the trigger for inflation, for sure. And,

you know, we've been battling inflation since 2021, right? And it's still here, to be honest, right? The price levels, prices are still elevated, right, compared to where they were. And people remember this. So prices were still elevated. Inflation is lower, but a little bit of optic in the inflation keeps the high prices, again, growing at higher rates. And we don't need that, which means that the central banks have to start. The hiking instead of cutting and that's a whole different problem. I'm with you.

You know too much. You have to give it to the... I try. All right. Thanks, guys. We'll leave it there. Thank you, Jeff. We'll put all your good stuff.

How do people sign up for all that, the websites, in the show notes for everybody. Perfect. And Vuk, you do a lot on YouTube still or just the substack? Yeah, so Vogue YouTube, Twitter, Twitter, right, LinkedIn, wherever. What's your handle, Vuk? I'm sorry, so Twitter is both, Walk underscore Vukovic. Again, Wolf, right? And on LinkedIn, it's just Vukukovic and on Orkong, what's our channel called?

I'm sorry, on YouTube. Product Market Moves. predict market moves about our own. That's the channel on YouTube. And then what are you doing on that quickly? Just talking through what was predicted or no, or just general stuff? On the YouTube channel, we're doing this whole thing because I have all this kind of lessons

from trading. I've been trading options since 2018, essentially so long before the fund. And I have all these lessons that I've come late because I'm a type of person who scars or less. Yeah, scars definitely. I won and lost a lot of money twice before the fund, like before everything. And without that episode, I never would have, you know, the fun never would have been a success. No way, right?

We would have gone under for sure by now. But yes, you know, these things build character. These lessons are useful. So I'm kind of sharing some of these lessons on like YouTube and Twitter, hopefully getting people engaged. I love it. Which feeds into then, hey, also be a predictor. Exactly. So, yeah, it's a way of kind of expanding the thought funnel and inviting people in.

Like, you know, see what we have. It's something that's interesting. Yeah, you might as well enjoy. Here's my last unsolicited idea for you guys. Go ahead. Give them swag, right? So if you're in the top X, you get T-shirt hat, and then they're at the bar, whatever, talking, trading with the guy,

and they're like, what's that? Like, oh, I'm in this predictor thing. Yes, yes, I would definitely. Yeah. We are giving swag to our investors, but I should start doing it to predictors. You absolutely right, 100%. Scott, write it down. Jeff, it's a great idea.

We need to do more of it. And maybe that's where, hey, you know, if you participate throughout the whole year or, hey you do a certain number of weeks we give other kinds of prizes some cash some related other things we have very nice baseball caps we have a lot of things right

oh yeah a cruise food gloves a cruise to see the orcas yeah it'd be amazing but then all our traders would meet each other and they would no longer be independent so what do we do we got to get them all on little boats to see all the

orcas yeah kayaks you're not allowed to talk to anything I'll say it to cruise for myself for now. All right. Love it. Thanks, guys. Thank you, Joel.

Okay, that's it for the pod. Thanks to RCM for sponsoring. Thanks to Jeff Berger for producing. Thanks to Vuk and Scott for coming on. We'll be back next week. Maybe I got to go to Puerto Rico for a conference. We're going to record that panel actually that I'm doing down there. And we'll put it out on the podcast here.

So maybe next week, maybe the following week, depending how that turnaround goes. But in the meantime, peace. You've been listening to The Deriviviv. Links from this episode will be in the episode description of this channel. Follow us on Twitter at RCMaltz and visit our website to read our blog or subscribe to our newsletter at RCMaltz.com. If you liked our show, introduce a friend and show them how to subscribe. And be sure to leave comments.

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