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!

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From the Episode: 

Youtube: Predict Market Moves by Oraclum https://www.youtube.com/@predictmarketmoves

Youtube: https://www.youtube.com/@vuk_vukovic_author/videos

Personal website: https://www.vukvukovic.org/

 

Check out the complete Transcript from this week’s podcast below:

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

Jeff Malec  00:09

Welcome to The Derivative by RCM Alternatives Send it.

 

Jeff Malec  00:19

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 RCMalts.com/whitepapers. Check out all the new content we’ve been putting out. This is good episode. Couple of guys doing something I never heard of before, saying hold my beer to Cal sheet and poly markets, 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 this there any persistence in all this? So we get into all that and more. Send it.

 

Jeff Malec  01:12

All right, everybody. We’re here with Scott and Vuk Did I get that right? Vuk, yes, you did. And the last name Vukovic. Vuk Vukovic, love it.

 

Vuk Vukovic  01:23

so I’ll tell you. So, book means Wolf, basically. So that Wolf, Wolf, yeah, so Wolf, son of wolf.

 

Jeff Malec  01:29

Wolf, son of wolf. Yes, that’s what nationality is that creation, creation, original, nice.

 

Scott Alford  01:36

He’s the book of Wall Street

 

Jeff Malec  01:40

that far. And Scott, you’re just boring old, old Scott, sorry, yeah, I mean yeah,

 

Scott Alford  01:47

Scott Alford is, yeah, nationality.

 

Jeff Malec  01:52

Can you compete? Yeah, it’s British, British. All right. Well, thank you guys for coming on. Where are you at today? New York.

 

Scott Alford  02:01

New York, yes, and I’m in Raleigh, North Carolina,

 

Jeff Malec  02:04

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,

 

Vuk Vukovic  02:13

yeah, and then, and then it goes from like, very warm to very cold. Space of a few days, New

 

Jeff Malec  02:20

York 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 gather it all up and sprint into dark alleys and corners. So I want to, 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? That was a market. There’s a

 

Vuk Vukovic  02:46

market for everything, especially in this city, for everything.

 

Jeff Malec  02:49

It’s crazy. So anyway, wanted to have you guys on we met down in Miami, right at eye connections. How is your eye connections? You guys found that valuable? You like it?

 

Vuk Vukovic  02:59

Yeah, absolutely. So for us, that’s been our cornerstone fundraising event. So we started doing that two years ago. Right Scott in New York, went to New York, Singapore, Miami. We do it every year, and I think we raised about 20 million in total from those from those events. So it’s been, it’s been useful for us.

 

Jeff Malec  03:17

Definitely. There you go. But so you’ve only been since it’s been in the Conference Center

 

Vuk Vukovic  03:23

in Miami, yeah, within the last two years. So it was in the conference center. Where was it before it was

 

Jeff Malec  03:28

all at the Fontainebleau. It was much nicer, like, it feels more conferencey Now that it’s on the conference center. Like, there you you got outside a bit more. You got to do the lunches and the dinners outside. Okay, so the first one that we did,

 

Vuk Vukovic  03:44

it’s a lot of people, right, like that. First one was, what, 8000 or something like that. But they told us,

 

Jeff Malec  03:50

yeah, it’s too many people, some might say, but good Scott, what were you saying? I was just saying.

 

Scott Alford  03:56

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 best relationships. That was when they had a partnership with Anthony Scaramucci and company, and we met some amazing allocators from that event, and that’s what led us to continue the relationship with I connections. So we’ve done Singapore, we’ve done Miami, and have continued on that circuit, because it’s, it’s been fantastic. And then the last New York event we actually went to, we had, we were told, basically, hey, we want vouch to present. And it turned out they did a secret emerging manager competition and prize. Yeah, we won a prize because Luke’s

 

Jeff Malec  04:37

a great presenter with, I thought you were gonna 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. So 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. 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

 

Vuk Vukovic  05:07

you’re doing? Sure, let me. Let me do it. Let me kind of start from beginning for So, believe it or not, the whole thing started as an academic project, essentially back in what 2014, 15. It was myself, my two partners. So all three of us were sciences academics. I was doing a PhD at Oxford in econ, and my two colleagues, they have PhDs in physics and computer science, right?

 

Jeff Malec  05:29

You were all in London? No, no.

 

Vuk Vukovic  05:30

So I was in, I was in Oxford, and they were in so there, one is in Croatia, one in Singapore, but the creation one had, he has his PhD from the States. He was later at the Princeton is 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 in adjacent in network theory, in network science, from different perspectives, obviously. And so we decided, let’s see if we 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 the takes, the combination of wisdom of crowds. So it’s not just like with elections, specifically what you know who you’re going to vote for, but it’s who you think is going to win, right? So it’s about so the neighbor method, right? That was used in this last election by this French trader for poly market that made a made a huge bet on that. We basically use that methodology back in 2014 so we did that so the wisdom of crowds 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, might might be. We applied this for Brexit and Trump in 2016 and got them both really, really accurately. So the Brexit prediction was correct with a single percentage point margin, where the swing states for for the Trump Hillary election were called all the swing states within a single percentage point margin that are all correct. So those were Hillary for Hillary, those for Trump, for Trump. And then, you know, we this, we So our initial goal was to write a paper, like 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 about market research and elections. And then after the Biden election in 2020 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 app for these is it basically survey based approach. We’ll explain how it works, and invited people in and basically making bets on these on these predictions, just to test it. And I was doing it very openly. We opened the sub stack newsletter, 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 substack started growing. People started coming in. And after about a year and a half, people started saying, you know, you should start running Maya. This is when Scott kind of joined us. We knew each other from before, from so he was so one of my Oxford scholarships, Scott actually was, 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. Help us start the fund and in the US, and we opened the fund at the end of 22 started trading February 23 and we started with $700,000 like with virtually in AUM, build that up to 70 million in two and a half years, essentially just based on good performance, based on things like high connections and spreading, expanding on allocations, and getting people on board. And now, you know, in the meantime, I moved to New York, and then I’m here to essentially scale it, scale of this business. So it’s, you know, people from academic project to a hedge fund, and a lot of things happening along the way. But that’s, that’s the story. That’s the kind of the bird’s eye view.

 

Jeff Malec  09:21

Love it. Scott, can you put that in a sentence for us? Oracle um, does Oracle?

 

Scott Alford  09:27

Oracle um harnesses behavioral psychology and uses human intelligence and artificial intelligence to predict weekly markets.

 

Jeff Malec  09:38

Good job. Well done. And what? 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 own? Luck?

 

Vuk Vukovic  10:00

No, absolutely. And this comes directly from the papers by Carman Turkey, right, the behavioral psychologist, psychologist. 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 in your state who’s going to win an election, so you might have an idea, but if what I asked you, what do you other What do you 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 system two, taking the current risky 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 in predictions when you give them a second when you give people that second question, because it comes forces them to come back to the first one and re examine it. Yeah, in

 

Scott Alford  10:56

system one, or just, just to clear for anyone who hasn’t heard of it, system one is 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 two 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.

 

Vuk Vukovic  11:29

So think of it this way, in terms of elections, right? If I ask you, you know, who’s winning your state, let’s say you’re very partisan, right? You’re very strong, Democrat or Republican, whatever, and you say, oh, no, Democrats are winning my state. But then what about other Can I ask you, what do you think other people would say? Would they say that Democrats are winning 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’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 elements.

 

Jeff Malec  12:05

It makes me think of right? Having there been several surveys over the years of how good are you at driving? People like, I’m an above average driver, right? Like, 70% or above average. Like, well, that’s impossible. So it’s like that. If you ask them, how good are your everyone on your buck, or your neighbors are driving, they’d probably be closer to 5050,

 

Vuk Vukovic  12:24

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,

 

Jeff Malec  12:33

I know I am, right?

 

Vuk Vukovic  12:35

There we go. But everyone else is not. You it.

 

Jeff Malec  12:47

So you popped in my brain before. Were you in college, you had like a Nate Silver poster in your room. Or is he? No? Is he God? Or is he a villain? What? What’s your Nate Silver take

 

Vuk Vukovic  12:57

the God is more talent born to see fellow like because it better is the best. 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, and 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 Nate Silver did, and it was great. It was the aggregation of polls, right? So he and they had their own methodology of Okay, so these polls, pollsters are more likely to be accurate based on best 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 polls. 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, the 90s and the 80s and the 70s, when these polls were actually, in fact, accurate, much more accurate than the now, and the errors were 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 like 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 that when you read a poll, the plus minus 3% error is not really accurate. You should think of it as plus minus seven or 8% when you look at a poll on average, and, you know, obviously they’re 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 polling. Let’s, you know, be better pollsters. But there’s, you know, it’s, 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 fighting against them. As someone who’s small, it’s, yeah, it’s difficult. So we saw this application of markets, and we saw an opportunity. And the logic was, let’s test it for, like, a year, two years, and you know, if nothing changes. Doesn’t work. Doesn’t work, we go back to doing what we’re doing, but if it does work, then you might have a potentially fantastic idea. And here

 

Jeff Malec  15:07

we are, and you don’t wake up as a little kid and be like, I want to be a pollster when I grow up. Right exactly, maybe, or did you? 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 did

 

Vuk Vukovic  15:28

answer it. So exactly that’s the problem, right? Yeah, okay. Now, usually, like before, when you bring me, they were calling, the response rate used to be 40 to 50% so if you needed a sample of 1000 you call 2000 or two and a half 1000 people. Now the response rate is when I think, like 9% some of them I read in our business review, even like three or 4% which means if you want to sample 1000 you need to call 10s of 1000s of people. And that’s costly, and, you know, and it’s ineffective, and you know, it doesn’t, doesn’t help the bottom line. So you have to do models to try to approximate this. That’s where the errors happen.

 

Jeff Malec  16:01

You think they’ll go more to basically, well, I mean the problems of responders, right? So I mean, not how they’re

 

Vuk Vukovic  16:08

measuring, just, are you willing to do it? No, right? And a lot of people are like you. They’re not willing to do that’s, that’s the thing. So the way that

 

Scott Alford  16:16

Luke and I’m, the way that book 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 MTurk, and to do the kinds of analysis they wanted to do, whether they were doing economic analysis or political science analysis, was they needed 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 for pollsters

 

Jeff Malec  17:20

today, and how is 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

 

Scott Alford  17:29

a university. I was allocating two universities. So I was a grant

 

Jeff Malec  17:32

maker, a grant maker. Tell me more about that real quick. I never

 

Scott Alford  17:37

heard of that. Yeah, yeah. So what we were doing was we were strategically so I’ll use Vuk as the example, because 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 vooke’s research right that went to directly to Oxford, and that allowed him to do some of the some of the research that 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 different areas that you want to foster more research.

 

Jeff Malec  18:55

But whose money was? It was the university’s money.

 

Scott Alford  18:58

So I mean, the money coming 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, 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 why you

 

Jeff Malec  19:27

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, what, we’re not just giving it to them, right? Aren’t they funding a research project? They’re, we’re getting stuff back as the as the US taxpayer. So we’ll go off on that tangent. But would you agree with that? That’s the point, right? Like, hey, we’re, we’re giving this money and we expect something back. We’re not just, it’s. And it’s not a donation,

 

Scott Alford  20:01

yes, and I think this is one of the things that the public doesn’t think about. Not all universities are 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 and M’s, right, they’re focused on different groups. So like a and M’s, 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.

 

Jeff Malec  20:51

Love it. But am I flip side of there? Might be like, Yeah, but then you get that money, then you can pull out the talent, like Luke out of there. That’s not fair. But for

 

Vuk Vukovic  20:58

another package I was I was gonna pull out anyway. So

 

Jeff Malec  21:11

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

 

Vuk Vukovic  21:27

get out of it? So, very simple. Cash, yeah. So we give cash, cash prizes. Every quarter. We give $8,000 to the top 30, so the first gun, 1400 and then 1000 and so on and so forth. And then at the end of the quarter, 3% of what we make are GP banks. We also distribute to the top 40 participants. So this is how you keep engagement, right? You wanted to keep it consistent, and you want to reward it for 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, we’ve been, we’ve been kind of curating them for a while now. And yes, but, 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 price distributions, and they give us very simple answers, and there’s no so we don’t ask any type of socio demographic questions. It’s a lot of them are anonymous, right? And it’s fine, what we care about is their responses. That’s the most important part. But also their networks. It’s where they are positioned. When I say the 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 other I don’t care about the name or whatever profile pictures or something irrelevant. We only want to see who you’re connected to so 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 crowds element we just talked about earlier, like, who the other thing, who do the others think are going to vote for? Or, in our case, where the market is going to end up, like, where’s 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 thing because some people are necessarily, you know, perma bears or permeables, 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 will, you know, usually they’re going to be right. So you’re trying to figure out people that are kind of in more heterogeneous, diversified groups, right? So we’ve exposed different types of opinions. It was similar with elections, right? You have, you know, 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 are right wing, some of them are centrist. This gives you a higher probability of being right again. Doesn’t necessarily make you right, but it gives you a higher probability. The same thing with markets, same object, we want you to be exposed to different types of opinions, and this gives you a higher probability, which determines your weight in the in the survey.

 

Scott Alford  24:00

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.

 

Jeff Malec  24:28

Yeah, do you tell them? Do you alert them that they’re biased? No, let them go. No. I mean, they can see that

 

Vuk Vukovic  24:34

performance on leaderboard, right? So if you’re performing well, then there’s a higher probability that you’re less biased, right? But doesn’t necessarily have to be, so especially over time. That’s why, when you look, 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 bull market, someone is good for like, sideways markets. So. During a hot streak. So what happens after a hot streak? Mean reversion, right? So you’re weighting people up during a hot streak, but so to speak, and then weighting 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 3000 or whatever, how many we tend to have, is mostly noise in a given week. So you only focus on the on the really the top, top 100 and that’s where you source the signal from.

 

Scott Alford  25:29

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 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? Am I really good at the s, p, but I need to improve in the NASDAQ. Or 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.

 

Jeff Malec  26:54

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, but we make fun of one of my friends. He quote. He’s like, the Dow was up 120 today. We’re like, nobody quotes the Dow anymore. Like, come on, get get with the times. I 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 you would like, how much turnover is there? You get people dropping out all the time?

 

Vuk Vukovic  27:27

Yeah, very high. But that’s thing. You typically see heightened participation at the beginning of the quarter, and then it kind of dwindles down, obviously, because if you’re not going to be if your probability of participating in the prize being a top 30 drops, then you’re going to have less of an incentive to keep going. But, and that’s normal, it’s expected. But then at the every quarter, 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 in the first three or five spots, there’s always a variation. The other

 

Scott Alford  28:04

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, and 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.

 

Jeff Malec  28:29

I got an idea. I have an idea for you guys, if you want some free consulting on the call to 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, and you if you can correctly be wrong every time, that’s just as valuable as correctly being right every that’s true.

 

Scott Alford  29:04

I was gonna say, wasn’t that? In the US Military Academy, they used to there was a period which they did that even before the 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 for getting every single question on the entrance exam, wrong, wrong.

 

Jeff Malec  29:22

Yeah, which means that you’re a standout either way, exactly. But if you

 

Vuk Vukovic  29:26

get every question wrong, you basically know the answer right, and you’re deliberately doing it, that’s the point.

 

Jeff Malec  29:32

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 we had, we ran that idea for about a year, but the transaction costs got to us. Like the bottom performers were bottom performers, not just because they were wrong. But because they traded a lot, or they did whatever, so it was we, that is the thing,

 

Vuk Vukovic  30:05

yeah, exactly, exactly we hear. Is an interesting idea. We have to check your base saying we should find ourselves a bunch of Jim Cravers at the end, right?

 

Jeff Malec  30:12

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,

 

Vuk Vukovic  30:31

just basically the raw performance of what they’re telling us in the service their 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 where the S and P is going to be by the end of the week, right? And then the closer you are, the you know, the better. So it’s measured by a prior score being that that’s the difference between what the actual outcome is and what you predicted, right? And then the smaller the difference, the more accurate you are, right?

 

Jeff Malec  30:58

So it’s not their really trading performance whatsoever, just their prediction performance, they might be

 

Vuk Vukovic  31:03

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.

 

Scott Alford  31:11

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 direction of the market, but how close where the market will actually end up.

 

Jeff Malec  31:32

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.

 

Vuk Vukovic  31:43

It goes out every every Tuesday, 8am Eastern, and it’s open for 24 hours. So it closes on Wednesday, 8am Eastern. So before the market opens, we would get the signal, then we would typically wait for about a half an hour. Hour depends. This is always something that’s refined with back tests, like, when’s the optimal entry? When should we go in? Is it like 1010, 3011, and then we place our positions on Wednesday and hold them until Friday. And there’s, again, a variety of ways of how we trade this, but when I say we place positions, so we would get a number essentially amount to the value, and that will be the value of, let’s say the s, p, and then if it’s higher than what it’s It’s Wednesday open, then you go for calls, if it’s lower, then you go for puts, essentially. So you try the better actually on the market. When I say, Yeah, calls or puts, this is what we trade. So we take only 2% of our of our nav, of our portfolio, and buy either calls or puts on that on the week. So we buy the weekly options we would typically buy one to two strikes out the money for the two day expiry, and then we have a bunch of ways of 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 the money, then you cap it at a 75% loss. So the last 25% you take, and then you’re out of the position.

 

Jeff Malec  33:04

So there’s, there’s no concept of this was a super strong signal, like 95% of the sample said it’s going to be we found

 

Vuk Vukovic  33:12

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 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 all 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 you So typically, we buy stuff at, you know, between two and $3 the option we trade spire spike contracts, we buy on two or $3 so, and that’s a kind of a, let’s say cheap to medium price, which means that, you know your week can, you know these options, if they really move in your direction, they can go from two to $6 and that’s a great trade, right? But if you’re buying them at $6 then the probability of them going from six 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 to 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 bear Schneider, right? And that was, believe it or not, April last year, April 25 Deliberation Day, April 1, right? And so we remember that day. So yeah, we got the survey, and we kind of reduced our position sizing because markets were very range bound back then. So we went with 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 trading up. So we were losing like 30, 40% of our premium by the time the announcement came, and Trump came after the market closed, he announced the tariffs. The next day, everything drops the next day. We more than overcompensated these losses. We ended that week up with like four and a half percent. But it was clearly an example of. Of hindsight,

 

Jeff Malec  35:01

you should have thought you’re gonna 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 gonna tip over.

 

Vuk Vukovic  35:09

Yeah, true, but no. But for us, it’s been listen to the signal, right? The signal has been for us, correct, you know, over 60% of the 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, it was, you know, interesting. We didn’t exploit this to the full extent, right? If it was a bit of full allocation, that return would have been double digits.

 

Jeff Malec  35:42

You so 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? If, what this last month, a lot of stuff’s happened from Friday to Monday open, right? Exactly. So if you had, like, rolling contests or something, I’m sure you’ve looked at all of that. We have a quant

 

Scott Alford  36:05

team that looks at all this. They are. 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 signal is 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.

 

Vuk Vukovic  36:43

Also, another thing that we introduced as of this year, January 1, 26 was using the kind of the signal, the moving average of the signal. So every week, you know, we get a prediction, and you can basically, kind of do a nice little moving average to see how this signal is doing. And based on this moving average, we’re just, we’re replacing. So we used to have 2% in options, 8% was a kind of cash buffer, and 90% was bonds, us, treasury bills, T bills, one year duration. But this year, we decided to allocate the 90% into long positions on the S and P and the Nasdaq based on the moving average of our signal, right? So the

 

Jeff Malec  37:18

90% isn’t it? Yeah, the rest of it,

 

Vuk Vukovic  37:21

yeah, essentially, the rest of the portfolio. And this basically, however, with hedges that are rolled 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. Market 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 boots 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, 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,

 

Jeff Malec  38:09

and that’s a different program or the original program.

 

Vuk Vukovic  38:13

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 movie 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.

 

Jeff Malec  38:28

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,

 

Vuk Vukovic  38:35

no, it is, because, like, 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 put options, but just the regular protection.

 

Scott Alford  38:50

The majority of the returns has been through the bay zone approach, which is the one that’s derived from the survey. Since inception, got it.

 

Jeff Malec  38:56

I’ll like, I’ll ignore the 90% number that scared me. What’s next? So, what 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 7000 okay, very,

 

Scott Alford  39:14

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 in 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, they think through and they and we even see that tinkering you can in the actual survey and that matters. That ends up mattering

 

Vuk Vukovic  39:57

because, 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, there’s two ways bots will 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, okay, so these, this is a fake account. It could be not an actual bot. It’d be a human bot. But if it’s not connected to anyone, if it’s outside the network, and it gives you, gives you an idea that it’s not vehicles

 

Jeff Malec  40:27

and then, but would you even care, like, if a bot was coming in and doing really well? But typically, yes, exactly.

 

Vuk Vukovic  40:33

So we had so I haven’t seen any kind of bot infestation with with this, 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 if that, we always get that question of, What if someone wants to manipulate? You sure, they can, they can, they can try. But in order to manipulate us, they still, they have to be very careful for about three to four months for them to, you know, get into our top 100 and then we 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 when it’s up two weeks, right? The most,

 

Jeff Malec  41:08

not a big deal. Yeah, so let’s move to prediction market, right? So you’re not, you’re careful not to call yourselves prediction people, or what are you saying?

 

Vuk Vukovic  41:18

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 than 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, that there is an inherent, inherent prediction.

 

Jeff Malec  41:41

But, like, the super growth of poly markets and Cal she 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, what’s your take on the prediction market?

 

Vuk Vukovic  41:53

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 on like 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 downside is limited, right? So I can only lose the premium that I buy. Can never physically lose more than that. If I’m super wrong, I can physically lose more than that. But the upside is unlimited, essentially, in theory, unlimited, but there’s, there’s a high upside, whereas with with prediction markets, it’s not the case. So we haven’t, we haven’t used it directly. But what helps us, I think, is the whole gamification of that prediction element. I think it 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. I’m, you know, I consider them to be fun and interesting.

 

Jeff Malec  42:53

Yeah. But have you ever looked of like we’re we’re seeing a prediction 80% chance of SMP this level, and it’s showing 40% like there’s a difference, or they’re matched up again.

 

Vuk Vukovic  43:04

So we had that data before, and it wasn’t that helpful. To be honest, the signal wasn’t that clear. So not not sounding like I’m trying to be spirit someone, but like our signal was better, essentially.

 

Scott Alford  43:15

And I think we have one shared, compelling assumption that is true with Cal shee and poly markets that actually the New York Times wrote an article about this very recently, which is that they were looking at betters, especially on Cal she and how accurate they were at 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 with within an institutional mindset and an institutional framework.

 

Jeff Malec  44:05

Be conflicted. They might be talking their book. I might be exactly

 

Scott Alford  44:09

and one of the things that 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 there’s a reward, there’s a profit for it.

 

Jeff Malec  44:25

But if you look right, sports betting famously, like usually, the most money on an NFL game is wrong, right? And absolutely, if the if the crowd was right at every horse race, the favorite horse would win every time. Sports betting world is is full of counter examples. So it’s that’s interesting to me, like, why in this space important

 

Scott Alford  44:47

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, 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, yeah, and at a certain point in time,

 

Jeff Malec  45:10

absolutely, but which is all that level too, of like, okay, you’re probably going to be you’re this crowd’s probably gonna be wrong, but their view on what the rest of the crowd is potentially Right, exactly.

 

Vuk Vukovic  45:21

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, right? So if you say, Yeah, I want this team to win, it’s my favorite team. Whether you want it to win or not does not really affect how the players are going to play. You can use some 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 letters 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 the election. But if you, 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, or 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 in the weather, weather is exogenous, right? You might say it rain or not. Doesn’t depend on your opinion or the actions of millions of others.

 

Jeff Malec  46:45

And is it so I’m 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 even right versus the thinking doesn’t have to line up with what they’re actually going to do the

 

Vuk Vukovic  47:01

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, you know, placing bets or investing, then, yeah, we might have a better idea of these outcomes.

 

Scott Alford  47:31

And timing does matter in these kinds of, 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

 

Vuk Vukovic  48:09

for the weekend before. Also too long. For example, it’s too short or too long, so you have to kind of get it

 

Jeff Malec  48:14

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. They mostly 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.

 

Vuk Vukovic  48:31

They. Mostly, we open it on the Tuesdays. We give people enough time, but most of them wait until the final hour, literally after the final hours of Wednesday. Exactly what you said observing the future is how the old day before play how Tuesday played out, and you observe the futures, and then you make a informed guess.

 

Scott Alford  48:45

And I refer to that New York 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.

 

Vuk Vukovic  49:11

Yeah, Tim, if you can come in, yes, we can.

 

Jeff Malec  49:13

Yeah, please join and do it. I gotta free up my Wednesday mornings. It’s a survey.org.com

 

Vuk Vukovic  49:20

UK, so that’s the website of the survey, and it’s open, as we said, like every Tuesday.

 

Jeff Malec  49:24

And that’s because that original company was in UK. Because, yeah, it’s

 

Vuk Vukovic  49:29

the UK company that 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,

 

Jeff Malec  49:38

and they’re still doing, like, polling the shell

 

Vuk Vukovic  49:42

company right now, like the owner. It’s just the owner, and they hold the IP.

 

Jeff Malec  49:58

Wrote down here. Hot streak we mentioned. A couple times as this statistician, would you consider yourself? Statisticians, definitely, definitely, like, there’s been papers right? Of hot streaks aren’t a real thing in sports. Yeah, yeah. Do you disagree? Do you agree? I mean,

 

Vuk Vukovic  50:13

it’s not a real thing in the sense of, so it is real in a sense that you can, 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 that the sport sports papers that refer to this, but for us, it’s like, I use the analogy, but it’s, it’s, it’s different in a sense, though. So you’re observing someone’s trading performance, right? And you’re saying, so these people are more relevant now at this point in time, doesn’t necessarily mean that they will be relevant in the future, but the weighting has to be adjusted for this, right? So if you’re 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’s like that, our whole trading strategy, our whole, you know, development signal, everything came from trial Effort, Right? Observing what works and what doesn’t, and then adapting. And it’s also the signal thing is now pretty much consistent with these three things that we mentioned. The trading strategy always basically adapts.

 

Scott Alford  51:17

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 at a certain point in time, and that can evolve. So as a group, they’re remarkably accurate, even though, as individuals, most of them are 5050, or even worse.

 

Jeff Malec  52:00

Yeah, I disagree with that paper. I believe in hot streaks in sports, I think it like, 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? You’re more Yeah, you’re more confident. Like, just, it works anyway.

 

Vuk Vukovic  52:20

Remember, like, so when you’re not, you know, scoring, and then you lose confidence. It’s the same thing, right? People get kind of into these, these holes, yeah, like in

 

Jeff Malec  52:29

golf, I just double bogey, like three holes. There’s no way I’m gonna get like, four cars in a row after that. I’m mush Exactly. Tell me, tell me, it’s golf language.

 

Vuk Vukovic  52:39

But yeah, you’re not golfer. No, unfortunately, not yet, not yet. Told me, I have to, I have to start playing.

 

Jeff Malec  52:44

But 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? Right? The pod shops are sorted doing similar way, right? They have 50 groups, and they’re weighting them based on their performance over time. And if you lose too much, you’re out, if you make you’re back in. So, totally different. So I don’t like that question. I’ll skip it, but it made my me think of that of their somewhat doing the same thing, but they’re not asking them for predictions. They’re just, they’re measuring them. So I

 

Scott Alford  53:12

guess that would be my trying to build a portfolio, or a pseudo portfolio through right?

 

Jeff Malec  53:17

Versus you’re trying to arrive at at a single prediction? Yeah? Addiction. So the real question was, how do you not Tinker constantly with this? Or are you tinkering constantly, like, what’s the research project look like? Like, I’ve had 30 ideas just in our short talk. Absolutely, you should do this. And what about doing it on oil? And what about doing it on this?

 

Vuk Vukovic  53:37

Yeah, we went through these ideas, like, back and forth, back, you know, 2122 23 we did, we did. Used to do that. You mentioned it. We used to do oil. We used to do FX, Bitcoin. Bitcoin was an interesting one for Bitcoin. Literally, every week we would get up predictions.

 

Jeff Malec  53:53

There was no they’re all Maxi is.

 

Vuk Vukovic  53:55

There was no variation. I think it was, it was a bubble. Unfortunately, we tapped into there was no variation, like every week it was just up, up, up. Might as well just buy it every week. Since there’s no could you see that in your

 

Jeff Malec  54:05

beautiful node graph? Like, basically there’s no nodes. It’s all just,

 

Vuk Vukovic  54:09

yeah, it’s a cluster. Yeah, it was typically, like a Bitcoin cluster, unfortunately. But yeah, that’s fine. 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 maximalists. I need to get like people that are more realistic about it. With commodities, I would have to get commodity traders. And it’s, you know, it’s getting retail traders that watch the S, P is, it’s not easy, to be honest, it’s very niche, but it’s but it’s relatively okay to get getting commodity traders. These tend 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,

 

Jeff Malec  54:54

you know, our we’ve got a network of, like, 1000 farmers we work with. That would be, that

 

Vuk Vukovic  54:58

would be interesting for. Example,

 

Jeff Malec  55:01

exactly. And they’re like, on Twitter, in their in in their combine, harvesting and looking at Twitter and

 

Vuk Vukovic  55:07

trading, that will be great. That’ll be because then, so the whole point here is, you’re not one of those people who’s likely 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 you’re trying to take them as a group, but Right? You know, averaging them out and getting that action, the accurate signal out there, essentially, that’s the, that’s the logic. So yes, that would, 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, as we grow as we can offer bigger prizes, 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 numerai tournaments, numer.ai so this is also a group that came from Renaissance,

 

Jeff Malec  55:51

I don’t

 

Vuk Vukovic  55:53

know, numer.ai numeri. And they organized this before we got started. But they did it differently. They had, they were, it’s like a data science tournament. 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 your good performance is like a mining 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 that’s been the system that they’ve been using. But it is, you know, asking people to give us prediction of this for them. For them, it’s using, like, data science operators and teams or whatever individuals to use whatever data they can find the number of guys given and then get a prediction accurate video, or, like, an accurate trade. So I think they’re giving them actual, actual trades. They don’t they basically, they say,

 

Jeff Malec  56:44

What do you think about this trade? Or whatever?

 

Vuk Vukovic  56:46

Yeah, so they so they have to take a data set and then formulate a trading opinion. I think that’s how it works. And then the folks at the hedge fund just place those best. And then you track performance. I don’t know how performance has been going lately. You 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,

 

Scott Alford  57:07

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? There’s another

 

Vuk Vukovic  57:24

group, Scott, what’s the name of that group that also does this with professional that pay, like professional analysts that keep

 

Scott Alford  57:31

forgetting their names. So, I mean, there are, there are a number of groups

 

Vuk Vukovic  57:35

that there’s one specific fund that also, like pays analysts across the board and then gets their opinions and then 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, decentralization. So I’ll give you a good example. Let’s say there’s 100 of us in the room, and I asked someone, you know what’s where the market is going to end up. And you Jeff, you speak first, and you say 7000 by the end of the month, right? And you’re likely to prime a lot of opinions your way, so you’re already correlating people, right? When you do that, and these people, what we have, they’re all focus group problem.

 

Jeff Malec  58:11

The alpha is gonna Exactly. Everyone’s gonna come to their sense Exactly.

 

Vuk Vukovic  58:15

And this way, because they’re decentralized, they don’t know who each other is, then they’re uncorrelated to each other. They don’t affect each other’s predictions or opinions, right? And they’re all across the US, Europe, Eastern Europe, East Asia, South Africa, few across the

 

Jeff Malec  58:30

world, and it’s not out in the public. They don’t have to defend their persona or whatever, like the Twitter, right? There were a lot of those funds that were like, We’re gonna scrape Twitter and trade sentiment and all AI and automated, as I haven’t seen any of those

 

Vuk Vukovic  58:44

funds Exactly. And a lot of them are anonymous, and they prefer that this anonymity. Sometimes they cover themselves because they want burger isolated well, when

 

Jeff Malec  58:53

they want to get paid, right?

 

Vuk Vukovic  58:54

Yeah. So that’s when we know who they are. So that’s how we know how some of them are retail traders. I don’t know anything about them, 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. So it was got this, this guy who just won, this Norwegian person, what is a great example sharing with Jeff,

 

Scott Alford  59:13

what did you tell you? Yeah, so, so reached out, because when we give the prizes, we let them know, we notify them, and they 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, I’ve had with you, and I think that’s an amazing story for us to tell hedge fund.

 

Vuk Vukovic  59:44

That was we said, it’s fantastic.

 

Jeff Malec  59:47

Maybe you could, maybe, yeah, give them physical awards, give them a certificate, put it on your resume. What’s the name all about? Or some Latin seer.

 

Vuk Vukovic  59:59

So. Or stands for so Orca is short for Oracle capital with first two letters, or the analogy of the killer whale is something that’s very I love the orcas when I was growing up, one of my favorite movies, but I was a kid who’s Free Willy. So Orca is kind of, you know, embedded. But Oracle is the name of our British company. We found that Oracle is a lot longer for prediction. So our company is called Oracle intelligence, and it was Latin word for prediction. And then, you know, the spin off of that is Oracle capital, which woke up,

 

Jeff Malec  1:00:29

is short and then gave a little little flip through. Or academics

 

Scott Alford  1:00:33

were nerds, so why not lead into it? To be

 

Vuk Vukovic  1:00:37

honest, it looks cool. The logo looks cool, and the Whole Whale thing works. It’s like, you know, whales in the markets are, you move markets. So we hopefully hope to become that killer whale one day.

 

Scott Alford  1:00:47

But it’s funny, right?

 

Jeff Malec  1:00:48

The pod of killer Yeah, you’re anti whale, right? You’re, you’re like, that’s getting it from the we

 

Vuk Vukovic  1:00:53

want to build our whale, we want to grow to be. But maybe

 

Jeff Malec  1:00:56

you’re, like, tracking the sardines or whatever, and then the killer whales coming to follow that exactly.

 

Jeff Malec  1:01:13

Love it guys. What else? Anything else we need?

 

Scott Alford  1:01:15

I guess the only last thought that that I was mentioning to book 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 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 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 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 to spend money on it.

 

Jeff Malec  1:02:33

That’s a good one. Hey, you like prediction markets? Predict away. Here you go. Yeah, and that, yeah, I think we’re in the I think there’s a dark side to that, too, not to get dark at the end of the pot here, but it 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 by 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 buying, they’re spending their net worth buying, like, GameStop calls and whatnot. So, yeah, I’d 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,

 

Scott Alford  1:03:16

let me get 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, and we were in

 

Jeff Malec  1:03:37

person, sorry. Second, like you feel like during covid, you did this online, or you were in person,

 

Scott Alford  1:03:42

taught in person, in person, believe it or not, that’s

 

Jeff Malec  1:03:47

Carolinas, yeah.

 

Scott Alford  1:03:48

But actually I did it. It was in DC, so I taught right outside of DC, in Fairfax. But students were constantly they wanted to 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.

 

Jeff Malec  1:04:44

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, the that this is an investing how to learn about investing is, is totally counter to me, right? Because you’re going to 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 that. 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 gonna predict where the market’s gonna be each week, so you can learn what moved the market that week.

 

Scott Alford  1:05:43

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 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

 

Jeff Malec  1:06:32

there on CNBC, we already called one of them out. Yeah, I almost said it, but wasn’t there an anti Kramer ETF?

 

Vuk Vukovic  1:06:41

It’s closed?

 

Scott Alford  1:06:42

No, it did close. Yeah, Matthew Tuttle, but Okay, okay, it was a, I mean, 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 asked 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.

 

Jeff Malec  1:07:52

How do we right? Brings 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 is 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 guys purview to fix society as well as these

 

Vuk Vukovic  1:08:19

Well, I’d love to maybe so. So that’s so part of mine was Scott mentioned initially, like part of my research at Oxford was elite networks, which we tried got published as a book by Oxford.

 

Jeff Malec  1:08:29

Is to press my brain went to Eyes Wide Shut the Tom Cruise movie. Elite networks of having the crazy parties.

 

Vuk Vukovic  1:08:37

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 their connection. So people used to work together or went to the same school, etc, and people that are connected to politics as CEOs tend to 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 pieces, and I wrote this book about it, and so that the end of it talks about these things, of how you so, for me, the most important element here is so polarization is an outcome of something else, right? All of these, all these social ill that we have are products of something right. For me, it’s always been the this is what I find the problem is the problem is the concentration of power, the concentration of political power and corporate power. When that, 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 transparency, with, you know, open. Media and Phoebe, with other things like that can be implemented, like, in terms of, like, constraining politicians, directly turning them more into, let’s say, simply reducing the scope of complexity 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 these issues.

 

Jeff Malec  1:10:29

And you’ve found that polarization leads to greater power, or vice versa.

 

Vuk Vukovic  1:10:35

It’s an outcome off, right? So it’s, you know, if it’s in your part of me

 

Jeff Malec  1:10:38

thinks it’s, 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,

 

Vuk Vukovic  1:10:45

yes, of course, it’s a reinforcing mechanism, right? So it’s, you know what? One reinforces the other. Absolutely. I don’t dwell too much into polarization in the book, but it’s, it’s also about so I do look at, for example, sugar divide in US politics, for example, it has mostly been, I think you might have noticed this is urban, rural more than anything else, like you have, yeah, the United 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, state of New York, or state of California, cities are blue and everything else is red in Texas. Or, like, still, yeah, it’s the same thing. So you have this very strong divide. And there was a great book by Anna Dieter, and case called the deaths of despair, how these people in, like, the Midwestern parts of the country, in these small rural communities, literally dying there being, you know, there’s several elements affecting there. One of them is the drug abuse, specifically, like things like crystal meth and alcohol abuse, and it’s specifically targeting the white, the white male kind of rural populations that have been 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 at all in life, and that everything around them needs a radical shift, which is why they’re more prone to going to radical solutions.

 

Scott Alford  1:12:04

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 Talis. He’s a philosopher, and he looks at these 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. And he separates those out, because one of the things he talks about in belief polarization, that 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 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 that

 

Jeff Malec  1:13:33

bringing that nobody thinks of polarization or bubble think in the financial sphere or you guys are, but right? 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. If they hear like, oh, I don’t know about the Strait of Hormuz might open tomorrow, maybe now they’re a little gonna do something different than they were gonna do before, which is interesting. Your so your thought would be like, it’s more valuable to keep them siloed, yeah, more

 

Vuk Vukovic  1:14:12

valuable in a sense of extracting value, in that sense, trying to figure out what actually might happen.

 

Jeff Malec  1:14:21

So we’ll leave it. We’re taping this on a Friday morning. Third, April, 3. Good Friday. What? What was Good Friday? Thank you. What was the prediction?

 

Vuk Vukovic  1:14:32

Wednesday? It was long, long and was wrong.

 

Jeff Malec  1:14:36

Long, wrong already, or it’s so far as wrong.

 

Vuk Vukovic  1:14:38

Yeah, the markets are closed today, so it

 

1:14:40

was, yeah, look at me. It was good.

 

Vuk Vukovic  1:14:43

It was good. We’re 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, then markets ended up flat, 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 they went, when they go down, and your option is firing the same day that 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 some something with Omaha on the protocol on opening the straight or whatever. So it came back and ended up flat, which was a very interesting day. So thirdly, ended up as VIX is down to 25 markets were flat and oil prices were up 12% which makes, yeah,

 

Jeff Malec  1:15:34

it gets you noticing this too.

 

Vuk Vukovic  1:15:36

Of like, 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, could be, we’ll see. You know, 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 badly. We’ve been battling inflation since 2021 right? And it’s still here, to be honest, right? The price level prices are still elevated, right? Compared to where they were, and people remember this. So prices are still elevated. Inflation is lower, but a little bit of uptick in 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 hiking instead of cutting. And that is a whole different problem.

 

Jeff Malec  1:16:28

I’m with you. You know, too much to give it to the Yeah, I try. All right, thanks, guys. We’ll leave it there. Thank you, Joe. We’ll put all your good stuff. How do people sign up for all that? The websites in the show notes for everybody. And book, you do a lot on YouTube still, or just the sub

 

Vuk Vukovic  1:16:44

stack, yeah. So YouTube, X, Twitter, right, LinkedIn, wherever.

 

Scott Alford  1:16:50

So what’s your handle?

 

Vuk Vukovic  1:16:52

Luke, sorry. So Twitter is both wolf underscore book, of which, again, walks right and on LinkedIn is just a hook of each and on. Or what’s our channel called, I’m sorry, on market moves predict market moves by Oracle. Yeah, that’s the channel on YouTube.

 

Jeff Malec  1:17:07

And then what are you doing on that quickly, just talking through what was predicted

 

Vuk Vukovic  1:17:13

or no, or just 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 lessons that I’ve committed because I’m the type of person who scars, or less scars. I want to lost a lot of money twice before the fund, like before everything. And without that episode, I never would have. You know, the fund never would have been a success. And no way, right? We would have gone under for sure by now. But yes, you know this. 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.

 

Jeff Malec  1:17:51

I love it, which feeds into then, hey, also be a predictor, exactly.

 

Vuk Vukovic  1:17:55

So yeah, it’s a way of, kind of spanning the top funnel, inviting people in. Like, you know, see what we have. It’s something that’s interesting. Yeah, might as well enjoy.

 

Jeff Malec  1:18:04

Here’s my, my last unsolicited idea for you guys. You 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, like, what’s that? Like, oh, I’m in this predictor thing.

 

Vuk Vukovic  1:18:17

Yes, yes. I would definitely, yeah. We are giving swag to our investors, but it should start doing it to predictors. You absolutely right.

 

Scott Alford  1:18:24

100% Scott, write it down. Jeff, it’s a great idea. We need to do more of it. And maybe, maybe that’s where, hey, you know if you, 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 we

 

Vuk Vukovic  1:18:39

have shirts. We have very nice baseball caps. We have a lot of things, right?

 

Jeff Malec  1:18:44

Oh, yeah, cruise on cruise to see the orcas.

 

Scott Alford  1:18:48

Yeah? That’d be amazing. But then all our traders would meet each other, and they would

 

Jeff Malec  1:18:55

infect each other’s brain. We got to get

 

Scott Alford  1:18:56

them all on little boats to see all the orcas.

 

Jeff Malec  1:18:59

Yeah? Kayaks

 

Vuk Vukovic  1:19:02

for myself for now. So,

 

Jeff Malec  1:19:05

All right, love it. Thanks, guys. Okay, that’s it for the pod. Thanks to RCM for sponsoring, thanks to Jeff Burger for producing, thanks to Vuk and Scott for coming on. We’ll be back next week. Maybe I got to go 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.

This transcript was compiled automatically via Otter.AI and as such may include typos and errors the artificial intelligence did not pick up correctly.